Policy and Action Standard

Transcript

1 Policy and Action Standard An accounting and reporting standard for estimating the greenhouse gas effects of policies and actions

2 World Resources Institute team David Rich Pankaj Bhatia Jared Finnegan Kelly Levin Apurba Mitra Advisory committee Samuel Tumiwa Asian Development Bank Ajay Mathur Bureau of Energy Efficiency, India Mary Nichols California Air Resources Board Center for Clean Air Policy Ned Helme Andrei Bourrouet Costa Rican Institute of Electricity Robert Owen-Jones Department of Climate Change and Energy Efficiency, Australia Brian Mantlana Department of Environmental Affairs, South Africa Ecofys Niklas Höhne Ethiopia Environmental Protection Authority Dessalegne Mesfin European Commission Jürgen Lefevere Jamshyd N. Godrej Godrej & Boyce Mfg. Co. Ltd., India Jennifer Layke Johnson Controls John Kornerup Bang Maersk Group Karen Suassuna Ministry of Environment, Brazil Alexa Kleysteuber Ministry of Environment, Chile Ministry of Environment, Japan Yuji Mizuno Ministry of Environment and Sustainable Development, Colombia Andrea García-Guerrero Zou Ji National Development and Reform Commission, China New York City Mayor’s Office of Long-Term Planning and Sustainability Jonathan Dickinson Organisation for Economic Co-operation and Development (OECD) Jane Ellis Kersten-Karl Barth Siemens Suzana Kahn Ribeiro State of Rio de Janeiro Michael Lazarus Stockholm Environment Institute – U.S. Chaiwat Munchareon Thailand Greenhouse Gas Management Organization Teng Fei Tsinghua University Neta Meidáv United Kingdom Department of Energy and Climate Change Katia Simeonova United Nations Climate Change Secretariat Yamil Bonduki United Nations Development Programme (UNDP) Maurice LeFranc United States Environmental Protection Agency Xueman Wang World Bank Thierry Berthoud World Business Council for Sustainable Development (WBCSD)

3 Table of Contents Background, concepts, and principles 4 1. Introduction 14 Objectives of Estimating the GHG Effects of Policies and Actions 2. 18 3. Overview of Steps, Key Concepts, and Requirements 30 4. Accounting and Reporting Principles ssessment s teps g a gH Define policy/action 34 5. Defining the Policy or Action Identify effects 6. 48 Identifying Effects and Mapping the Causal Chain Identify effects Defining the GHG Assessment Boundary 7. 60 Estimate effects 8. 72 Estimating Baseline Emissions Estimate effects Estimating GHG Effects Ex-Ante 94 9. Estimate effects Monitoring Performance over Time 110 10. Estimate effects 11. 120 Estimating GHG Effects Ex-Post Estimate effects Assessing Uncertainty 12 . 134 Verify Verification 142 13. Report Reporting 150 14. appendices 156 A. Guidance on Collecting Data 160 B. Guidance on Assessing Policy Interactions C. Examples of Non-GHG Effects 167 168 Cost-Effectiveness and Cost-Benefit Analysis D. 173 Abbreviations and Acronyms 175 Glossary 181 References Contributors 184 1

4 Detailed Table of Contents Define policy/action 1 i ntroduction 4 He p 5 34 ction olicy or a efining t 5 d Purpose of this standard 1.1 36 Select the policy or action to be assessed 5.1 1.2 6 How the standard was developed 6 36 5.2 Define the policy or action to be assessed 1.3 Intended users Applicability of the standard 1.4 Decide whether to assess an individual 6 5.3 40 7 Scope of the standard 1.5 policy/action or a package of policies/actions 1.6 46 When to use the standard 5.4 Choose ex- ante or ex- post assessment 7 1.7 Considerations for implementing 8 the standard Identify effects Relationship to GHG inventories 8 1.8 6 identifying e Relationship to The GHG Protocol ffects and 1.9 48 10 mapping t ain ausal cH for Project Accounting He c Relationship to the GHG Protocol 6.1 Identify potential GHG effects 1.10 50 10 Mitigation Goal Standard of the policy or action 11 6.2 1.11 Sector- specific guidance Identify source/sink categories 11 Calculation models and tools 1.12 and greenhouse gases associated 1.13 Cost- 53 benefit analysis 12 effectiveness or cost- with the GHG effects 6.3 Map the causal chain 1.14 Estimating non-GHG effects or co-benefits 12 55 1.15 12 Terminology: shall, should, and may 12 Limitations 1.16 He g Hg 7 d efining t 60 assessment Boundary 2 oB jectives of e 7.1 Assess the significance of potential stimating 62 GHG effects Hg e tHe g ffects of p olicies 14 7.2 Determine which GHG effects, source/sink and a ctions categories, and greenhouse gases to include ey 64 3 o verview of s teps, k in the GHG assessment boundary 7.3 Define the GHG assessment period 18 70 equirements concepts, and r 3.1 19 Overview of steps 3.2 19 Key concepts Estimate effects 3.3 Example of following the steps 8 e stimating Baseline e 25 72 missions in the standard 8.1 74 Key concepts 27 Requirements in the standard 3.4 8.2 Determine sequence of steps for estimating 74 the GHG effects of the policy or action 4 a ccounting and Choose type of baseline comparison 77 8.3 30 rinciples reporting p 8.4 Estimating baseline emissions 78 using the scenario method 8.5 Estimating baseline emissions and GHG effects using the comparison group 91 method (for ex- post assessment only) 8.6 Aggregate baseline emissions 93 across all source/sink categories Policy and Action Standard 2

5 Detailed Table of Contents 134 uncertainty ssessing a 12 12.1 Introduction to uncertainty assessment 136 Types of uncertainty 136 12.2 Range of approaches 12.3 137 138 12.4 Sensitivity analysis 12.5 Qualitative uncertainty analysis 140 12.6 Quantitative uncertainty analysis 141 Verify erification 142 13 v 143 Introduction 13.1 144 13.2 Benefits of verification 94 9 e stimating g Hg e ffects e x- ante 9.1 Define the most likely policy scenario 96 144 Key concepts 13.3 96 Identify parameters to be estimated 9.2 13.4 Subject matter relevant to 99 9.3 146 Select a desired level of accuracy Policy and Action Standard the 9.4 146 13.5 Types of verification Estimate policy scenario values 99 146 Levels of assurance 13.6 for parameters 9.5 Estimate policy scenario emissions 105 13.7 Competencies of verifiers 147 13.8 Verification process 147 9.6 Estimate the GHG effect of the policy 105 or action Report m performance onitoring 10 r 150 14 eporting 110 ime over t 14.1 Required information 151 10.1 Define key performance indicators 112 14.2 154 Optional information 10.2 Define parameters needed for 114 post assessment ex- 116 10.3 Define the policy monitoring period 155 a ppendices 116 10.4 Create a monitoring plan Guidance on Collecting Data A 156 117 Monitor the parameters over time 10.5 B Guidance on Assessing Policy Interactions 160 C Examples of Non-GHG Effects 167 168 D Cost-Effectiveness and Cost-Benefit Analysis 120 post x- ffects e Hg e stimating g 11 e Update baseline emissions or 11.1 122 ex- ante assessment (if applicable) 173 Abbreviations and Acronyms 122 11.2 Select an ex- post assessment method Select a desired level of accuracy 175 123 11.3 Glossary 126 Estimate policy scenario emissions 11.4 References 181 11.5 Estimate the GHG effect 184 126 Contributors of the policy or action 11.6 Additional steps to inform decision 130 making (optional) 3

6 1 Introduction

7 reenhouse gas (GHG) emissions are driving climate change and its impacts around the world. According to climate scientists, global greenhouse gas G emissions must be cut by as much as 72 percent below 2010 levels by 2050 to have a likely chance of limiting the increase in global mean temperature to 2 degrees preindustrial levels (IPCC 2014). Every degree increase in Celsius above increasingly unpredictable and dangerous impacts for will produce temperature people and ecosystems. As a result, there is an urgent need to accelerate efforts to reduce GHG emissions. National and subnational governments, financial institutions, This standard helps answer the following questions: and private sector organizations are planning and What effect is a given policy or action likely to have • implementing a variety of policies and actions to reduce on GHG emissions in the future? GHG emissions. As they do so, they are seeking to assess • Is a given policy or action on track and delivering and communicate the effects of policies and actions expected results? both before adoption to inform the on GHG emissions— What effect has a given policy or action had on • design of policies and actions and after implementation to GHG emissions? understand whether the intended effects were achieved. The standard was developed with the following objectives in mind: Purpose of this standard 1.1 • To help users assess the GHG effects of specific policies Policy and Action Standard provides The GHG Protocol and actions in an accurate, consistent, transparent, a standardized approach for estimating and reporting the complete, and relevant way change in GHG emissions and removals resulting from • To help policymakers and other decision makers policies and actions. develop effective strategies for managing and reducing GHG emissions through a better understanding of the emissions impacts of policies and actions 5

8 To support consistent and transparent public reporting of • governmental financial institutions, research institutions, non- 1 emissions impacts and policy effectiveness organizations, and businesses. Throughout this standard, the • To create more international consistency and term “user” refers to the entity implementing the standard. transparency in the way the GHG effects of policies The following examples show how different types of users and actions are estimated can use the standard: Estimate the GHG effects of planned • governments: 1.2 How the standard was developed policies and actions to inform decision making, monitor This standard was developed by the Greenhouse progress of implemented policies and actions, and Gas Protocol (GHG Protocol). The GHG Protocol is a retrospectively evaluate GHG effects to learn from multistakeholder partnership of businesses, NGOs, experience. donor agencies and financial institutions: Estimate • governments, academic institutions, and others convened the GHG effects of finance provided, such as grants or by the World Resources Institute (WRI) and the World loans to support GHG reductions and low emissions Business Council for Sustainable Development (WBCSD). 2 development strategies. Launched in 1998, the mission of the GHG Protocol Estimate GHG effects of private sector Businesses: • is to develop internationally accepted GHG accounting actions larger than individual projects, such as company- and reporting standards and tools, and to promote wide energy efficiency programs implemented by their adoption in order to achieve a low emissions electric utilities; voluntary commitments; implementation economy worldwide. All GHG Protocol standards and of new technologies, processes, or practices; or private guidance are available at www.ghgprotocol.org. 3 sector financing and investment. In June 2012, WRI launched a two- year process to develop Estimate the s: research institutions and ngo • member Advisory the Policy and Action Standard . A 30- GHG effects of any of the above types of policies or Committee provided strategic direction throughout the actions to assess performance or provide support to Policy and Action Standard process. The first draft of the decision makers. was developed in 2012 by two Technical Working Groups consisting of over 50 members, then reviewed by members of a Review Group, including during three stakeholder 1.4 Applicability of the standard workshops. In 2013, the second draft was pilot tested In this standard, “policies” and “actions” refer to on 27 policies and actions in 20 countries and cities interventions taken or mandated by a government, across a range of sectors to determine how the standard institution, or other entity, and may include laws, worked in practice. Pilot countries included Bangladesh, directives, and decrees; regulations and standards; Belgium, Chile, China, Colombia, Costa Rica, Germany, taxes, charges, subsidies, and incentives; information India, Indonesia, Israel, Japan, Mexico, South Africa, South instruments; voluntary agreements; implementation Korea, Tunisia, the United Kingdom, and the United of new technologies, processes, or practices; and States. The standard was revised based on pilot testing public or private sector financing and investment. feedback and circulated for public comment in July 2014. The terms “policy” and “action” may refer to interventions making continuum, at various stages along a policy- 1.3 Intended users level from (1) broad strategies or plans that define high- This standard is intended for a wide range of organizations objectives or desired outcomes (such as increasing energy and institutions. The primary intended users are analysts efficiency by 20 percent by 2020); to (2) specific policy and policymakers assessing government policies and instruments to carry out a strategy or achieve desired actions at any level, including national, state, provincial, or outcomes (such as an energy efficiency standard for municipal. Other intended users include donor agencies and appliances); to (3) the implementation of technologies, Policy and Action Standard 6

9 CHAPTER 1 Introduction processes, or practices (sometimes called “measures”) Scope of the standard 1.5 This standard includes steps related to estimation of GHG that result from policy instruments (such as the effects, as well as specific steps on monitoring, reporting, replacement of old appliances with more efficient ones). and verification. It details a general process that users This standard is primarily designed to assess specific should follow when conducting an assessment, but it does policy instruments and the implementation of not prescribe specific calculation methodologies, tools, or technologies, processes, or practices (at a scale larger data sources. than an individual project). Users that intend to assess The standard includes both requirements and guidance. The the effects of broad strategies or plans, such as low requirements represent the accounting and reporting steps that emissions development plans or strategies framed users must follow if they choose to implement the standard in terms of desired outcomes, should first define the and wish to report that their assessment is in conformance individual policy instruments or technologies, processes, with it. Users may choose to implement the standard in part or practices that will be implemented to achieve the rather than in full. However, users must follow all applicable strategy or plan. Broad strategies or plans can be difficult accounting and reporting requirements in order for the to assess since the level of detail needed to estimate assessment to be in conformance with the standard. GHG effects may not be available without further specificity, and different policies or actions used to achieve 5 It does not provide guidance The standard is policy- neutral. the same goal could have different GHG effects. on what type of policy or action to implement but only how to estimate the emissions effects associated with its The standard is applicable to policies and actions: implementation. • At any level of government (national, subnational, municipal) in all countries and regions ante assessment— The standard covers both ex- the • In any sector (such as agriculture, forestry, and other estimation of expected future GHG effects of a policy or land use [AFOLU]; energy supply; industry; residential action— the estimation of historical and ex- post assessment— and commercial buildings; transportation; or waste) GHG effects of a policy or action. sector policy instruments (such as as well as cross- emissions trading programs or carbon taxes) Intended to mitigate GHG emissions or intended to When to use the standard 1.6 • The standard may be used at multiple points in time achieve objectives unrelated or contrary to climate 6 design and implementation throughout a policy change mitigation (but that have an effect, either process, including: positive or negative, on GHG emissions) That are planned, adopted, or implemented, or are • To estimate Before policy implementation: • extensions, modifications, or eliminations of existing expected future effects of a policy or action (through policies or actions ante assessment) ex- To estimate during policy implementation: • This standard may be useful for estimating the GHG achieved effects to date, ongoing performance of key effects of nationally appropriate mitigation actions performance indicators, and expected future effects of (NAMAs) that are framed as policies or programs, as a policy or action well as policies and measures under the United Nations 4 after policy implementation: To estimate what • Framework Convention on Climate Change (UNFCCC). effects have occurred as a result of a policy or action Users should follow project- level methodologies such (through ex- post assessment) (2005) for GHG Protocol for Project Accounting The as actions at the level of an individual mitigation project. Depending on individual objectives and when the standard Section 1.9 provides more information on projects. is applied, users may implement the steps related to ex- ante assessment, ex- post assessment, or both. The most comprehensive approach is to apply the standard first 7

10 1.8 Relationship to GHG inventories before implementation, annually (or regularly) during policy National, subnational, and company/organizational GHG implementation, and again after implementation. Users inventories are critical for tracking changes in overall GHG ante assessment only may skip Chapters carrying out an ex- emissions at a national, subnational, or organizational level. 10 and 11. Users carrying out an ex- post assessment only GHG inventories are also needed to identify and prioritize may skip Chapter 9. mitigation opportunities. All jurisdictions and organizations Figure 1.1 outlines a sequence of steps to monitor and should develop a GHG inventory as a first step to managing assess GHG effects at multiple stages in a policy design GHG emissions, following established standards such and implementation process. In this example, the process Guidelines for National Greenhouse Gas as the IPCC is iterative, whereby policy development is informed by Inventories (2006) for national governments, the WRI/C40/ previous experience. Figure 1.1 is an example only. Not all Global Protocol for Community- ICLEI Scale Greenhouse steps may be relevant to all users. (2014) (along with the IPCC Gas Emission Inventories Guidelines ) for cities and subnational jurisdictions, or the GHG Protocol Corporate Accounting and Reporting 1.7 Considerations for (2004) for companies and organizations. Standard implementing the standard Before using the standard, users should consider establishing However, changes in GHG inventories over time do not a working group of experts and stakeholders with relevant explain why emissions have grown or declined over time or and diverse skills and expertise. The time and human reveal the effects of individual policies or actions. Emissions resources required to implement the standard depends on may change as a result of a variety of factors, such as a variety of factors, including the complexity of the policy a combination of many different policies that increase or action being assessed, the scope of the assessment, the and decrease emissions, as well as a range of non- policy extent of data collection needed and whether relevant data factors (for example, changes in economic activity, energy has already been collected, whether analysis related to the prices, or weather). By attributing changes in emissions policy or action has previously been done, and the desired to specific policies and actions, this standard can inform level of accuracy and completeness needed to meet the policy selection and design and enable an understanding user’s objectives. of policy effectiveness. Policy/action accounting should be figure 1.1 assessing g Hg effects throughout a policy design and implementation process addressed Hg inventory develop g by the standard not addressed define policy assess g Hg effect by the standard of policies ex-post objectives and identify ( c h . 11) potential policies assess g Hg effect monitor progress during of policies ex-ante policy implementation (ch. 9) (ch. 10) select and implement policies Policy and Action Standard 8

11 CHAPTER 1 Introduction carried out as a complement to developing and updating policies and actions. Common methods can improve a GHG inventory on a regular basis. See Table 1.1 for a comparability between the GHG assessment for a policy comparison of GHG inventory and policy/action accounting. or action and the GHG inventory, even if the effect of individual policies and actions may not be visible in the To the extent possible, users should apply the same GHG inventory. See Figure 1.2 for an illustration of the basic calculation methods, such as those provided difference between inventory accounting, on the one Guidelines for National Greenhouse Gas in the IPCC hand, and policy and action accounting, on the other. Inventories , to calculate source- or sector- level emissions for both GHG inventories and GHG assessments of table 1.1 comparison of g Hg inventory and policy/action accounting type of accounting purpose limitations Comprehensive accounting of a • jurisdiction’s or organization’s GHG • May not explain why emissions change emissions impact on the atmosphere over time gHg inventory • Provides information on the sources of Does not reveal the effects of individual • accounting emissions and trends over time policies Necessary to track overall progress toward • GHG reduction goals • Not a comprehensive accounting of total emissions; overall emissions may increase • Attributes changes in emissions to specific policy/action even if individual policies and actions are policies and actions accounting reducing emissions (compared to a Informs policy design and evaluation • baseline scenario) figure 1.2 comparison of inventory accounting and policy/action accounting inventory accounting methodology p olicy/action accounting methodology Hg actual g reductions relative to y ear 1 emissions Hg estimated g Baseline scenario emissions effect relative to baseline scenario policy scenario emissions gHg emissions gHg emissions year 1 year 2 year 2 year 1 9

12 1.10 Relationship to the GHG Protocol 1.9 Relationship to The GHG Mitigation Goal Standard Protocol for Project Accounting is based on an accounting Policy and Action Standard Policy and Action Standard The GHG Protocol The and GHG Protocol Mitigation Goal Standard framework and a sequence of steps similar to those of (2014) are relevant to policies and goals undertaken by governments and are Project The GHG Protocol for Project Accounting (or intended to support evaluating and reporting progress Protocol ). Both involve estimating changes in GHG emissions from the implementation of an action relative toward GHG mitigation objectives. The two standards were developed simultaneously as part of the same standard to a baseline scenario that represents what would have development process in order to ensure harmonization of happened in the absence of that action (as illustrated in Figure 1.2). However, they apply to different scales: in overlapping topics, where they exist, such as development of baseline scenarios, uncertainty assessment, verification should be used for small- Project Protocol general, the procedures, and accounting and reporting principles. scale interventions, such as those occurring at a single site, while this standard should be used for interventions at a The user’s objectives should drive the use of one or 7 Table 1.2 illustrates the differences in their broader scale. both of the standards. The Policy and Action Standard applicability, objectives, and methodological approach. enables users to estimate the expected change in such as projects of the Some types of interventions— emissions and removals resulting from specific policies same type implemented at multiple sites, infrastructure Mitigation Goal Standard enables users and actions. The programs, or implementation of new technologies, to evaluate and report overall progress toward national practices, or processes— may blur the line between or subnational GHG reduction goals (see Table 1.3). projects and policies. In situations where multiple While each standard can be implemented independently, standards are applicable, users should consider their the standards can also be used together. For example, level methodologies objectives. For example, project- Mitigation Goal Standard to users can apply the are typically designed for crediting or offsetting. understand the level of GHG reductions needed to meet a given GHG mitigation goal and then use the Policy and Action Standard to estimate the GHG effects of selected policies and actions to determine if they are collectively sufficient to meet the goal. Conversely, users can first apply Policy and Action Standard and the Project Protocol table 1.2 comparison of the standard differences in approach objectives applicability Focused Provides detailed guidance on project-specific Individual mitigation projects, primarily on Project baselines, including addressing additionality such as an individual solar crediting or Protocol of projects photovoltaic installation offsetting Policies and actions at a larger Provides guidance on estimating interactions Intended to scale than an individual project, between policies or actions; defining a baseline support broader such as renewable energy Policy scenario at a larger scale than a project; and objectives policies at the sectoral or and Action identifying and estimating various indirect (described in jurisdiction level; Standard effects at a broader scale, such as international Chapter 2) policies and actions that increase leakage of emissions or decrease emissions Policy and Action Standard 10

13 CHAPTER 1 Introduction Policy and Action Standard to estimate expected GHG the This standard can be used in tandem with models by reductions from various mitigation policies and actions to providing an overarching framework to guide the GHG understand the range of possible GHG reductions, then assessment process, including defining the scope of the to set a mitigation goal Mitigation Goal Standard use the assessment and making deliberate assumptions and and track and report progress. transparently reporting those assumptions. The standard may also be useful to inform model development. Use of models in the absence of a standard may result in a Sector- specific guidance 1.11 lack of consistency and transparency regarding methods This standard provides a general framework of principles, and assumptions. concepts, and procedures applicable to all sectors and types of policies and actions. To complement this general standard, sector- specific guidance and examples for five AFOLU, energy supply, residential and commercial sectors— buildings, transportation, and waste— are available at standard. www.ghgprotocol.org/policy- and- action- Calculation models and tools 1.12 The standard details a general process that users should follow when estimating the GHG effects of policies and actions, but it does not prescribe specific calculation methodologies or tools that should be used. Users should supplement the standard with models, calculation tools, spreadsheets, or other methods to carry out calculations. To help users apply the standard, the GHG Protocol website provides a list of calculation tools and resources relevant to estimating the effects of policies and actions (available action- at www.ghgprotocol.org/policy- standard). The and- GHG Protocol website also provides GHG calculation tools that allow users to calculate GHG emissions from specific tools). sources (available at www.ghgprotocol.org/calculation- Mitigation Goal Standard and the Policy and Action Standard table 1.3 comparison of the standard description How to estimate the greenhouse gas effects of policies and actions. Types of policies and actions include Policy and Action regulations and standards; taxes and charges; subsidies and incentives; information instruments; voluntary Standard agreements; and implementation of new technologies, processes, or practices. How to assess and report overall progress toward national, subnational, and sectoral GHG reduction goals. Types of mitigation goals include GHG reductions from a base year, reductions to a fixed level Mitigation Goal of emissions (such as carbon neutrality), reductions in emissions intensity, and GHG reductions from a Standard baseline scenario. 11

14 1.15 effectiveness or 1.13 Terminology: Cost- cost- benefit analysis shall, should, and may This standard uses precise language to indicate which This standard estimates the change in GHG emissions and removals caused by a policy or action, in tonnes of provisions of the standard are requirements, which e. GHG estimates can be combined with information CO are recommendations, and which are permissible or 2 effectiveness on costs and used as part of a cost- allowable options that users may choose to follow. The benefit analysis. Appendix D provides analysis or cost- is used throughout this standard to indicate term “shall” effectiveness guidance on using the results in a cost- what is required in order for a GHG assessment to be in benefit analysis, or multicriteria analysis. analysis, cost- is “should” conformance with the standard. The term used to indicate a recommendation, but not a requirement. “may” The term is used to indicate an option that is Estimating non-GHG 1.14 permissible or allowable. The term “required” is used in the effects or co-benefits guidance to refer to requirements in the standard. “Needs,” This standard may be used to assess the broader “can,” and “cannot” are used to provide guidance on environmental, social, and economic impacts of a implementing a requirement or to indicate when an action policy or action, rather than GHG effects only. The basic is or is not possible. procedures outlined in this standard are applicable, GHG effects most clearly linked to especially for non- Limitations 1.16 GHG emissions in terms of data needs, such as energy using results that are sufficiently accurate for use, waste generation, or local air pollution. For example, the stated objectives: This standard incorporates a estimating GHG reductions from promotion of public offs range of approaches to allow users to manage trade- transit requires information on how many passengers between the accuracy of the assessment and available no longer travel by private vehicle, which is needed to time, resources, and capacity, in the context of individual calculate fuel savings and GHG reductions. The same objectives (described further in Chapter 3). Depending information can be used to estimate money saved by not on the methods used, the results of the assessment may purchasing that fuel, and reduced emissions of local air or may not be sufficiently accurate for effective decision level ozone, pollutants, such as particulate matter, ground- , and NO . making. Several challenges involved in estimating the SO x 2 GHG effects of policies and actions— such as the need GHG effects should follow Users that estimate non- to estimate effects relative to a counterfactual baseline the steps in each chapter for each non- GHG effect scenario and estimating interactions between related of interest. When doing so, users should supplement policies— can result in high uncertainty. Understanding this standard with additional estimation methods the uncertainty of the results (described in Chapter 12) GHG effect. and data sources related to each non- can help identify where more effort is needed to gather Additional methods and data will be necessary to accurate data, and ensure that the uncertainty of the results assess impacts less related to GHG emissions, such as is communicated appropriately. Given the uncertainties, public health impacts or broader economic impacts, the results of the assessment should be interpreted such as changes in GDP or jobs. Non- GHG effects may as “estimates” of the effect of policies and actions. also be described qualitatively rather than estimated. GHG Appendix C provides examples of various non- comparing results: Users should exercise caution effects that may be estimated along with GHG effects. when comparing the results of GHG assessments. Differences in reported emissions impacts may be a result world of differences in methodology rather than real- Policy and Action Standard 12

15 Introduction CHAPTER 1 differences. Additional measures are necessary to enable programmatic decisions about eligibility of credit- generating valid comparisons, such as consistency in the timeframe of activities; and registries and procedures to ensure that the assessments, the types of effects included in the GHG each emission reduction is counted toward no more assessment boundary, baseline assumptions, calculation than one goal or compliance obligation. For guidance methodologies, methods for assessing policy interactions, level GHG reductions to generate on quantifying project- and data sources. Additional consistency can be provided credits, see . GHG Protocol for Project Accounting The through GHG reporting programs or more detailed sector- specific guidance (see Section 1.11). To understand whether endnotes comparisons are valid, all methodologies and data sources 1. Where this standard refers to policy effectiveness, it is limited used must be transparently reported. Comparable results to effectiveness in reducing GHG emissions, as well as achieving can best be achieved if GHG assessments are undertaken GHG effects that users choose to or improving any specific non- by the same entity in order to ensure consistency of include in the assessment, rather than a broader definition of methodology between assessments. For more information policy effectiveness. on comparability, see Chapter 4. 2. The standard does not provide a methodology for allocating GHG reductions among various donors or financial institutions. Users should also exercise caution aggregating results: 3. Companies may find some of the concepts and guidance useful to when aggregating the results of GHG assessments for estimate the GHG effects of private sector actions, but may need different policies or actions. GHG effects should not to adapt concepts to the business context or supplement with be directly aggregated across policies or actions if they additional methodologies. Companies seeking to quantify GHG affect the same emissions sources or sinks and potential The reductions associated with mitigation projects should refer to interactions exist between them that have not been GHG Protocol for Project Accounting . Under the UNFCCC, NAMAs are undertaken “by developing country 4. accounted for. In such a case, the sum would either over Parties in the context of sustainable development, supported or underestimate the GHG effects resulting from the and enabled by technology, financing and capacity building, in combination of policies. For example, users should not a measurable, reportable and verifiable manner.” To quantify aggregate the effects of a local energy efficiency policy and GHG reductions from NAMAs framed as individual projects, see a national energy efficiency policy in the same country, since The GHG Protocol for Project Accounting . For NAMAs framed the combined effect of the two policies is likely not equal to level GHG reduction goals, see the GHG Protocol as jurisdiction- the sum of the individual effects, as a result of overlapping . Mitigation Goal Standard sources. (Chapter 5 provides more information on policy “Policy- 5. neutral” means the methodology is generic and applicable interactions.) Results should also not be aggregated across to any policy type, rather than designed for any specific policy policies if the methodologies, assumptions, and data instruments, programs, or policy framework. sources are not comparable or if the baseline scenarios Where the word “policy” is used, it is used as shorthand to refer to 6. developed for each policy were not developed to enable both policies and actions. accurate aggregation (further described in Appendix B). Project Protocol Users following the should also refer to two sector- 7. specific guidelines as applicable: the GHG Protocol Guidelines Hg reductions: potential crediting of g The results from for Quantifying GHG Reductions from Grid- Connected Electricity using this standard are not sufficient to support crediting The Land Use, Land- (2007) and Use Change, and Projects of GHG reductions from policies or actions for sale in the (2006). Both are Forestry Guidance for GHG Project Accounting carbon market. Additional specifications would be necessary, available at www.ghgprotocol.org. Users may also consider other such as more detailed, sector- specific calculation methods project- level methodologies, such as those developed under the to lead to more consistent and comparable results; greater Clean Development Mechanism (CDM), available at http://cdm. unfccc.int/methodologies. emphasis on the principle of conservativeness (described in Chapter 4) and provisions to ensure additionality; 13

16 Objectives of Estimating the 2 GHG Effects of Policies and Actions

17 ssessing the GHG effects of policies and actions is a key step toward developing effective GHG reduction strategies and reducing emissions. GHG assessment based decision making A Quantitative evidence- supports by enabling policymakers and stakeholders to understand the relationship between policies and actions and expected or achieved changes in GHG emissions. during or after policy implementation: A GHG assessment should begin by defining the objectives • Understand whether policies and actions are effective in of the assessment. Examples of objectives for assessing delivering the intended results the GHG effects of a policy or action are listed below. • Inform and improve policy implementation Before policy implementation: • Decide whether to continue current activities or • Choose among policy options based on their expected implement additional policies Learn from experience and share best practices • GHG effects • Improve the design of policies by understanding the • Evaluate the contribution of policies and actions GHG effects of different design choices toward broader GHG reduction goals Understand potential GHG reductions from policy Ensure that policies and actions are cost- effective and • • that limited resources are invested efficiently options to inform GHG reduction goals • • Report on expected future GHG effects of policies and Report on the GHG effects of policies and actions over time (for domestic or international purposes) actions being considered or implemented (for domestic • Meet funder requirements to report GHG reductions or international purposes) Attract and facilitate financial support for mitigation • from mitigation actions actions by estimating potential GHG reductions 15 15

18 Users should estimate the GHG effects of policies and Box 2.1 objectives of assessing the g Hg effects of the actions with a sufficient level of accuracy and completenes ampaign city of c own’s electricity saving c ape t s to meet the stated objectives of the assessment. The level of accuracy and completeness needed may vary The City of Cape Town, South Africa, launched an by objective. electricity- saving information campaign in 2009. The campaign is designed to educate consumers and As mentioned in Chapter 1, the assessment may be businesses and encourage a range of behavior- changing designed to assess non- GHG effects of policies and actions actions (such as installing solar water heaters) that would to meet a wider range of objectives. The assessment result in electricity savings and save consumers money. may also incorporate information on costs to facilitate an understanding of cost- effectiveness. The city decided that it needed to monitor and evaluate the results of the campaign, including its GHG emission GHG assessments may be carried out on policies and effects. The Energy Research Centre at the University actions that have objectives unrelated or contrary to of Cape Town worked with the City of Cape Town and climate change mitigation, including those that increase prepared recommendations on how to carry out such GHG emissions. Policymakers and analysts may choose to an assessment. assess the GHG effects of all major policies and actions to understand or minimize GHG increases, not only to assess The city’s specific objectives were the following: 1 GHG mitigation policies. • Determine whether the campaign was a justifiable report the objective(s) and the intended Users shall use of financial and human resources (on the basis of audience(s) of the GHG assessment. Possible audiences reduced electricity consumption and associated GHG may include policymakers, the general public, NGOs, emission reductions) companies, funders, financial institutions, analysts, research • Inform how future elements of the campaign could be institutions, and the UNFCCC. designed to increase its effectiveness • Understand the drivers behind changes in electricity Box 2.1 provides a case study of defining the objectives of consumption and behavior and the impact of the an assessment. campaign in driving such changes Understand city performance in meeting electricity • reduction targets and GHG targets emissions • Report on emissions reductions, since CO 2 reporting is part of the city’s electricity and financial savings reporting • Communicate the benefits of the campaign to stakeholders Provide accurate data to feed into the South African • National Climate Change Response Database, which is part of the national climate change monitoring and evaluation system endnote mitigation policy, 1. For an example of applying the standard to a non- see Box 8.3 in Chapter 8. Policy and Action Standard 16

19 CHAPTER 2 Objectives of Estimating the GHG Effects of Policies and Actions 17

20 Overview of Steps, Key 3 Concepts, and Requirements

21 his chapter provides an overview of the steps involved in policy and action accounting and reporting, an introduction to key concepts, an example T of following the steps in the standard, and a checklist of accounting requirements. 3 .1 Overview of steps implementation of new technologies, processes, or This standard is organized according to the steps a user practices; and public or private sector financing and follows in accounting for and reporting changes in GHG investment; among others. emissions from a policy or action. See Figure 3.1 for an “Policies” and “actions” are treated equivalently in overview of steps in the standard. Depending on when all steps in the standard, so no further distinction the standard is applied, users may skip Chapters 9, 10, is made between what constitutes a policy versus or 11. For example, if the standard is applied before a policy an action. However, users may choose to define is implemented, users may skip Chapters 10 and 11. “policies” as distinct from “actions” depending on their objectives and context. For example, policies could be defined as instruments (such as regulations, taxes, Key concepts 3.2 subsidies, and information instruments) that enable or This section describes several key concepts used incentivize concrete actions to be implemented (such in this standard. 1 as replacement of technology or changes in behavior). “Actions” may also be defined more broadly. Section 3.2.1 policies and actions 1.4 provides more information on the relationship “Policies” and “actions” refer to interventions taken or between broad strategies or plans, policy instruments, mandated by a government, institution, or other entity and the implementation of technologies, processes, or and may include laws, directives, and decrees; regulations practices. Users may assess either an individual policy/ and standards; taxes, charges, subsidies, and incentives; 2 action or a package of related policies/actions. information instruments; voluntary agreements; 19

22 figure 3.1 overview of steps chapter overall steps detailed steps Define the policy or action to be assessed; 5 define policy/action choose ex-ante or ex-post assessment Identify all potential GHG effects of the policy or action; 6 include them in a map of the causal chain identify effects Define the GHG assessment boundary around significant effects; 7 identify the sources/sinks in the boundary Estimate baseline emissions for all affected sources/sinks 8 included in the boundary Ex-ante assessment: Estimate policy scenario emissions for affected 9 sources/sinks; subtract baseline emissions to estimate GHG effect estimate effects Identify key performance indicators; 10 monitor performance over time Ex-post assessment: Estimate policy scenario emissions for affected 11 sources/sinks; subtract baseline emissions to estimate GHG effect Assess uncertainty (relevant to Chapters 8, 9, 10, and 11) 12 13 Verify results (optional) verify report Report results and methodology used 14 Policy and Action Standard 20

23 CHAPTER 3 Overview of Steps, Key Concepts, and Requirements gHg assessment 3.2.2 energy savings). Chapter 6 provides more information This standard uses the term “GHG assessment” to GHG effects. For additional on GHG effects and non- refer to the estimation of changes in GHG emissions GHG effects, see Appendix C. examples of non- resulting from a policy or action. In other contexts, “GHG gHg assessment boundary 3.2.4 ante GHG appraisal” is sometimes used to describe ex- The GHG assessment boundary defines the scope of assessment and “GHG evaluation” is used to describe the assessment in terms of the range of GHG effects ex- post GHG assessment. This standard uses “GHG GHG effects, if relevant) that are included (and non- assessment” to refer to both cases. in the GHG assessment. This standard encourages a gHg effects gHg effects and non- 3.2.3 comprehensive assessment that includes the full range of GHG effects are changes in GHG emissions or removals effects considered to be significant. Chapter 7 provides that result from a policy or action. Emissions are guidance on defining the GHG assessment boundary. releases of greenhouse gases into the atmosphere, gHg assessment period 3.2.5 while removals are removals of GHG emissions from The GHG assessment period is the time period over the atmosphere through sequestration or absorption. which GHG effects resulting from the policy or action GHG effects are changes in environmental, social, Non- are assessed. The GHG assessment period may differ or economic conditions other than GHG emissions or from the policy implementation period— the time period climate change mitigation that result from the policy and should during which the policy or action is in effect— or action. For example, a home insulation subsidy may be as comprehensive as possible to capture the full range lead to both GHG effects (reduced GHG emissions from of effects based on when they are expected to occur. GHG effects reduced home energy use) as well as non- Chapter 7 provides more information on defining the GHG (increased household disposable income resulting from assessment period. 21

24 attributing changes in emissions 3.2.6 See Equation 3.1 for the basic equation for estimating the to policies and actions GHG effect of a policy or action. This standard is designed to support users in attributing changes Attributing changes in emissions to specific policies and in GHG emissions and removals to a specific policy or action actions is distinct from tracking changes in overall emissions (or package of policies or actions) to understand how effective through a GHG inventory, which does not explain why various policies are in reducing emissions. Attributing changes emissions have changed. Attributing changes in emissions in emissions to specific policies and actions can be difficult, to policies is also distinct from tracking trends in key since GHG emissions can change as a result of a variety of performance indicators. Monitoring trends in indicators factors, including (1) the policy or action being assessed; (2) can demonstrate changes in the targeted outcomes of the other policies or actions that directly or indirectly affect the policy or action, which is helpful to understand whether same emissions sources; and (3) various external drivers a policy or action is on track and being implemented as that affect emissions, such as changes in economic activity, planned but does not explain why the changes in indicators population, energy prices, weather, autonomous technological are occurring or demonstrate the effectiveness of a improvements, or structural shifts in the economy. policy. To meet certain objectives, tracking performance For example, a city may implement a GHG mitigation policy indicators may be sufficient. (Chapter 10 provides guidance in the electricity sector and then observe that energy- related on monitoring performance indicators over time.) emissions in the following year have declined. However, the Baseline scenario and policy scenario 3.2.7 fact that emissions have decreased does not mean that the Estimating the change in GHG emissions resulting from policy has caused the decrease in emissions. A correlation a given policy or action requires a reference case, or between a policy being implemented and emissions baseline scenario, against which the change is estimated. decreasing is not sufficient to establish causation. In actuality, The baseline scenario represents the events or conditions emissions may have declined because an economic downturn most likely to occur in the absence of the policy or reduced demand for electricity, not because the policy has action being assessed. The baseline scenario is not a been successful. Further analysis is required to understand why historical reference point but is instead an assumption emissions have changed. about conditions that would exist over the policy To estimate a change in emissions resulting from a policy or implementation period if the policy or action assessed action, users follow three basic steps: were not implemented. The baseline scenario depends on assumptions related to other policies or actions that 1. Define the baseline scenario and estimate baseline are also implemented, as well as various external drivers scenario emissions (Chapter 8) and market forces that affect emissions, such as changes Define the policy scenario and estimate policy scenario 2. in economic activity, population, energy prices, weather, emissions (Chapter 9 or 11) autonomous technological improvements, and structural Subtract baseline scenario emissions from policy 3. shifts in the economy. Chapter 8 provides guidance on scenario emissions to estimate the GHG effect of the developing the baseline scenario. policy or action (Chapter 9 or 11) equation 3.1 estimating the g Hg effect of a policy or action Total net change in GHG emissions resulting from the policy or action (t CO e) = 2 Total net policy scenario emissions (t CO e) – Total net baseline scenario emissions (t CO e) 2 2 “Net” refers to the aggregation of emissions and removals. “Total” refers to the aggregation of emissions and removals across all Note: sources and sinks included in the GHG assessment boundary. Policy and Action Standard 22

25 Overview of Steps, Key Concepts, and Requirements CHAPTER 3 In contrast to the baseline scenario, the policy scenario ante and Figure 3.2 illustrates the relationship between ex- represents the events or conditions most likely to occur in post assessment. In the figure, a policy comes into effect ex- the presence of the policy or action being assessed. The in 2010. A user carries out an ex- ante assessment in 2010 policy scenario is the same as the baseline scenario except to estimate the expected future GHG effects of the policy that it includes the policy or action (or package of policies/ through 2020 by defining an ex- ante baseline scenario and actions) being assessed. The difference between the policy ante policy scenario. The difference between the ex- an ex- scenario and the baseline scenario represents the effect of ante policy scenario and the ex- ante baseline scenario is the the policy or action. Chapters 9 and 11 provide guidance on ante). In 2015, the estimated GHG effect of the policy (ex- developing the policy scenario, either ex- ante or ex- post. user carries out an ex- post assessment of the same policy to estimate the historical GHG effects of the policy to date, by ex- ante and ex- post assessment 3.2.8 observing actual emissions over the policy implementation A GHG assessment is classified as either ex- ante or ex- post that is, the ex- and defining a post policy scenario— period— depending on whether it is prospective (forward- looking) or revised ex- post baseline scenario. The difference between retrospective (backward- looking): the ex- post baseline post policy scenario and the ex- post). scenario is the estimated GHG effect of the policy (ex- • ex- ante assessment: The process of estimating expected future GHG effects of a policy or action If conditions unrelated to the policy or action unexpectedly • ex- post assessment: The process of estimating change between 2010 and 2015, the ex- post baseline historical GHG effects of a policy or action ante baseline scenario. For scenario will differ from the ex- example, the ex- post and ex- ante baseline scenarios will ante assessment can be carried out before or during Ex- differ if observed fuel prices or rates of economic growth post assessment can be policy implementation, while ex- differ from ex- ante forecasts made in 2010, or if significant carried out either during or after policy implementation. new policies are introduced. The ex- post policy scenario ante assessment, an ex- post Users may carry out an ex- may differ from the ex- ante policy scenario for the same assessment, or both, depending on objectives. In general, reasons, or if the policy is less effective in practice than it ante and effective GHG management involves both ex- post assessment. ex- figure 3.2 ex-ante and ex-post assessment e) 2 ex-ante baseline scenario mt co gHg effect of gHg effect of policy/action policy/action ex-post baseline scenario Historical (ex-ante) (ex-post) ex-post policy scenario gHg (observed emissions) emissions Hg emissions* ( ex-ante policy scenario net g 2020 2 015 2 010 * Net GHG emissions from sources and sinks in the GHG assessment boundary. Note: 23

26 ante and ex- was assumed to be. In such cases, the ex- choosing the desired level 3.2.10 post of accuracy and completeness among estimates of the policy’s GHG effect will differ. a range of methodological options ante assessment, the baseline scenario and policy In an ex- In many cases, users will confront a choice in the scenario are both hypothetical or forecasted, rather than methodological options available to estimate changes observed. In an ex- post assessment, only the baseline in emissions. Often the methodological options present scenario is hypothetical, since the ex- post policy scenario off between accuracy or completeness, on one a trade- can be observed. hand, and the cost of implementation, on the other. In such cases, this standard allows for a range of methods up and top- Bottom- down approaches 3.2.9 with varying levels of accuracy and completeness, rather Multiple types of data and estimation methods can be than a single method. used to estimate the GHG effects of policies and actions, down approaches. up and top- including both bottom- Users should determine the desired level of accuracy and completeness of the GHG assessment based on a range up and top- down data Bottom- of factors, including the following: Bottom- • up data are measured, monitored, or collected Objectives of the assessment, intended uses of the • (for example, using a measuring device such as a fuel results, and the level of accuracy and completeness meter) at the source, facility, entity, or project level. required to meet stated objectives Examples include energy used at a facility (by fuel type) • Relative significance of the policy or action being assessed and production output. Data availability • level statistics collected at the • Top- down data are macro- Capacity, resources, and time available to carry out the • jurisdiction or sector level. Examples include national assessment energy use, population, GDP, and fuel prices. In some Users should estimate the GHG effects of policies and cases, top- up down data are aggregated from bottom- actions with a sufficient level of accuracy and completeness data sources. to meet the stated objectives. More rigorous methods Bottom- up and top- down methods enable a wider set of uses than less rigorous methods. The Bottom- up methods (such as engineering models) • results of a comprehensive and accurate assessment can be calculate or model the change in GHG emissions for used to meet the widest range of applications, since users each source, project, or entity affected by the policy or and stakeholders can generally have high confidence that action, then aggregate across all sources, projects, or the results represent an accurate and complete estimate entities to determine the total change in GHG emissions. of the GHG effects of a given policy or action. In general, down methods (such as econometric models or Top- • more rigorous approaches should be applied to policies and regression analysis) use statistical methods to calculate actions that are most significant in terms of expected GHG or model changes in GHG emissions and can be applied impact or are otherwise most relevant to decision makers down data. to either bottom- up or top- and stakeholders. Both bottom- up and top- down data and methods are In contrast, less rigorous approaches may be used to valuable for different purposes. Hybrid approaches that roughly estimate the GHG effect of a policy or action, up and top- combine elements of both bottom- down requiring fewer resources to implement than a more approaches may also be used. The GHG Protocol website accurate and complete assessment. However, the results provides a list of calculation tools and resources relevant of simplified approaches should be limited to a smaller to estimating the effects of policies and actions (available range of applications and objectives for which a lower level action- at www.ghgprotocol.org/policy- and- standard). of accuracy and completeness is sufficient, such as certain Policy and Action Standard 24

27 Overview of Steps, Key Concepts, and Requirements CHAPTER 3 internal planning or reporting purposes where indicative emissions (further described in Chapter 8). Users may estimates of GHG effects are acceptable. Users should also group related policies or actions together and assess exercise caution in using the results from a simplified them as a package (further described in Chapter 5). assessment to claim that a specific policy or action results in If double counting between policies is suspected, GHG specific GHG reductions, without further understanding the reductions from overlapping policies and actions should associated uncertainty. Users may consider implementing not be aggregated to determine total emissions or simplified approaches in the short term and more rigorous reductions in a given jurisdiction or geographic region. approaches in the longer term. When reporting results, users should acknowledge any Subsequent chapters provide tables outlining a range of potential overlaps and possible double counting with other methodological options, including Chapter 8 for estimating policies and actions to ensure transparency and avoid baseline emissions, Chapter 9 for estimating GHG misinterpretation of data. Where applicable, coordination effects ex- ante, Chapter 11 for estimating GHG effects of GHG accounting for policies and actions by a single post, and Chapter 12 for assessing uncertainty. ex- agency within a jurisdiction can also help reduce potential for double counting (for example, by specifying the 3.2.11 policy interactions same methodology and identifying potential overlaps). An individual policy or action may interact with other If GHG reductions take on a monetary value or receive policies and actions to produce total effects that differ credit in a GHG trading or crediting program, users should from the sum of the individual effects of each individual take additional measures to avoid double counting or policy. Policies and actions can interact in either overlapping double claiming of credits, including specifying whether or reinforcing ways or can be independent of each the reductions are claimed by the implementing other. Potential interactions should be considered at jurisdiction or are sold to another jurisdiction; specifying multiple points during the GHG assessment, including exclusive ownership of reductions through contractual when deciding whether to assess an individual policy agreements; and recording all transactions in domestic or action or a package of related policies and actions. or international registries, such as an international For more information, see Chapter 5. Guidance on transaction log. For guidance on avoiding double counting assessing policy interactions is provided in Appendix B. of transferable emission units such as offset credits across jurisdictional boundaries, refer to the GHG Protocol avoiding double counting 3.2.12 Mitigation Goal Standard . Hg reductions of g Multiple actors in society may implement similar or overlapping policies or actions and each may claim GHG 3.3 Example of following reductions resulting from their policies or actions. GHG the steps in the standard accounting for policies and actions is intended to support Table 3.1 provides an example of following the various the simultaneous action of multiple entities to reduce a subsidy for steps in the standard for an illustrative policy— emissions throughout society. However, users should home insulation. In practice, a GHG assessment following avoid double counting of emission reductions. Users can this standard would be more comprehensive. Subsequent minimize the potential for double counting by using more chapters provide more detail using the same policy example accurate and complete methods described in subsequent to illustrate the various steps throughout the standard. chapters. In particular, users should develop a baseline scenario that includes all other implemented (and adopted, if applicable) policies, actions, and GHG mitigation projects in the baseline scenario that have a significant effect on 25

28 table 3.1 example of carrying out the various steps in the standard for an illustrative policy chapter simplified example for an illustrative subsidy for home insulation The objectives are: (1) to inform the design of a government subsidy for home insulation before chapter 2: implementation; and (2) to track and report on the policy’s effectiveness during implementation. objectives chapter 5: The policy to be assessed is a government subsidy for home insulation. An individual policy is defining the assessed, rather than a package of related policies. policy or action The subsidy aims to incentivize consumers to purchase and install more insulation, which is expected chapter 6: to reduce natural gas and electricity use in homes, thereby reducing GHG emissions. The energy identifying effects savings is also expected to result in consumers having more disposable income, leading to the and mapping the consumption of more goods and services, thereby increasing emissions. (Figure 6.6 in Chapter 6 causal chain illustrates the causal chain.) The reductions in CO emissions from reduced natural gas use and reduced electricity use are expected chapter 7: 2 to be significant, so they are included in the GHG assessment boundary. The increase in emissions from defining the increased production of goods and services is expected to be insignificant based on initial estimates, so it gHg assessment is excluded from the boundary. (Box 7.3 provides more detail on the GHG assessment boundary.) Boundary The baseline scenario is assumed to be the continuation of historical residential energy consumption trends, dependent on projected changes in household income and current rates of home insulation, absent the subsidy. To estimate baseline emissions from natural gas use, the emissions estimation method is assumed to be: Baseline emissions for household natural gas combustion (t CO e/year) = historical natural gas use chapter 8: 2 (MMBtu/year) × (1+ % change in GDP) × baseline emission factor (t CO e/MMBtu) estimating 2 Baseline The estimated values of the parameters in this equation are assumed to be: emissions • Average annual historical natural gas use (1,000,000 MMBtu/year) • Average annual change in GDP (2%) • Baseline emission factor (0.2 t CO e/MMBtu) 2 Baseline emissions in a given year are calculated as: 1,000,000 MMBtu/year ×1.02 x × 0.2 t CO e/ 2 MMBtu = 204,000 t CO e/year 2 To estimate policy scenario emissions, the same emissions estimation method is used, but the assumed parameter values in the policy scenario are different. The emissions estimation method is: Policy scenario emissions for household natural gas combustion (t CO e) = policy scenario 2 natural gas use (MMBtu/year) × policy scenario emission factor (t CO e/MMBtu) 2 Policy scenario natural gas use is estimated to be 910,000 MMBtu/year, based on the assumption that 30% of households will install insulation as a result of the subsidy and that insulation will chapter 9: reduce household natural gas use by 30%, so the policy will lead to a 9% reduction (0.3 × 0.3) in Hg estimating g residential natural gas use. The policy scenario emission factor is assumed to be the same as in the effects ex-ante baseline scenario (0.2 t CO e/MMBtu), since the policy does not affect the emissions intensity of 2 natural gas. Policy scenario emissions in a given year are calculated as: 910,000 MMBtu/year × 0.2 t CO e/ 2 MMBtu = 182,000 t CO e/year. 2 The GHG effect of the policy in the same year is estimated ex-ante to be a reduction of 22,000 t CO e/year (policy scenario emissions of 182,000 – baseline emissions of 204,000). 2 Policy and Action Standard 26

29 Overview of Steps, Key Concepts, and Requirements CHAPTER 3 table 3.1 example of carrying out the various steps in the standard for an illustrative policy (continued) simplified example for an illustrative subsidy for home insulation chapter Key performance indicators are identified, including the number of homes that have applied for the chapter 10: subsidy. Monitoring reveals that only 20% of homes have applied for the subsidy, so the total GHG monitoring reduction is likely to be lower than estimated ex-ante. Data needed for ex-post assessment are also performance collected, including GDP and a representative sample of residential energy use. over time The parameter values in the baseline calculation are updated with actual data for the identified baseline drivers—that is, actual rather than predicted GDP data. Similarly, for the policy scenario calculations, the parameter value for energy use is based on observed energy use and data on the actual number of homes that installed insulation, rather than forecasted estimates. GDP grew at 3% rather than 2% over the period, while the emissions estimation method and the values of other parameters remained the same. chapter 11: Ex-post baseline emissions are calculated as: 1,000,000 MMBtu x 1.03 × 0.2 t CO e/MMBtu = 2 Hg estimating g 206,000 t CO e/year (rather than 204,000 t CO e/year as estimated ex-ante). 2 2 effects ex- post Residential energy use decreased by 6% rather than 9%, so ex-post policy scenario emissions are calculated to be: 940,000 MMBtu × 0.2 t CO e/year (rather than e/MMBtu = 188,000 t CO 2 2 182,000 t CO e/year as estimated ex-ante). 2 The GHG effect of the policy is estimated ex-post to be a reduction of 18,000 t CO e/year (policy 2 scenario emissions of 188,000 – baseline emissions of 206,000). The estimated reduction ex-post is less than the 22,000 t CO e reduction estimated ex-ante. 2 Uncertainty is assessed in both qualitative and quantitative terms and sensitivity analyses are carried out chapter 12: to identify which parameters are most sensitive to changes in assumptions. The uncertainty range is assessing estimated to be a GHG reduction of 18,000 t CO e/year +/- 6,000 t CO e/year. uncertainty 2 2 The results of the GHG assessment are verified by an accredited third-party verifier. chapter 13: Limited assurance is attained. verification chapter 14: The results and the methodology are reported, following the reporting requirements in Chapter 14. reporting 3.4 Requirements in the standard As noted in Chapter 1, the term “shall” is used throughout Subsequent chapters include accounting and reporting the standard to indicate requirements. “Should” is used requirements to help users develop a GHG assessment that to indicate a recommendation but not a requirement, represents a true and fair account of the GHG effects of while “may” is used to indicate an option that is a policy or action. Table 3.2 provides a summary checklist permissible or allowable. Table 3.2 compiles all the “shall” of the accounting requirements included in the standard. statements related to accounting, while “shall” statements A box at the beginning of each chapter also summarizes related to reporting are compiled in Chapter 14. the accounting requirements in each chapter. Chapter 14 provides a summary checklist of reporting requirements. 27

30 table 3.2 checklist of accounting requirements chapter accounting requirement chapter 4: GHG accounting and reporting shall be based on the principles of relevance, completeness, • accounting eporting and r consistency, transparency, and accuracy. principles chapter 5: defining Clearly define the policy or action (or package of policies/actions) that is assessed. • olicy the p ction or a • Identify all potential GHG effects of the policy or action. chapter 6: Separately identify and categorize in-jurisdiction effects and out-of-jurisdiction effects, if relevant and • identifying feasible. effects and • Identify all source/sink categories and greenhouse gases associated with the GHG effects of the policy mapping the or action. hain causal c • Develop a map of the causal chain. chapter 7: • Include all significant GHG effects, source/sink categories, and greenhouse gases in the GHG defining assessment boundary. the g Hg Define the GHG assessment period based on the GHG effects included in the GHG assessment • assessment boundary. Boundary If applying the scenario method: Define a baseline scenario that represents the conditions most likely to occur in the absence of the • policy or action for each source or sink category included in the GHG assessment boundary. Estimate baseline emissions and removals over the GHG assessment period for each source/sink • category and greenhouse gas included in the GHG assessment boundary. chapter 8: Apply global warming potential (GWP) values provided by the IPCC based on a 100-year time horizon. • estimating If applying the comparison group method: Baseline • Identify an equivalent comparison group for each source or sink category included in the GHG emissions assessment boundary. • Estimate emissions and removals from the comparison group and the policy group over the GHG assessment period for each source/sink category and greenhouse gas included in the GHG assessment boundary. Apply GWP values provided by the IPCC based on a 100-year time horizon. • If carrying out an ex-ante assessment: • Define a policy scenario that represents the conditions most likely to occur in the presence of the policy or action for each source or sink category included in the GHG assessment boundary. chapter 9: Estimate policy scenario emissions and removals over the GHG assessment period for each source/ • estimating sink category and greenhouse gas included in the GHG assessment boundary, based on the GHG gHg e ffects effects included in the boundary. ex- ante • Apply the same GWP values used to estimate baseline emissions. • Estimate the GHG effect of the policy or action by subtracting baseline emissions from policy scenario emissions for each source/sink category included in the GHG assessment boundary. Policy and Action Standard 28

31 CHAPTER 3 Overview of Steps, Key Concepts, and Requirements table 3.2 checklist of accounting requirements (continued) chapter accounting requirement If monitoring performance over time: chapter 10: Define the key performance indicators that will be used to track performance of the policy or action • monitoring over time (and parameters for ex-post assessment, if relevant). performance Create a plan for monitoring key performance indicators (and parameters for ex-post assessment, if relevant). • ime over t Monitor each of the parameters over time, in accordance with the monitoring plan. • If carrying out an ex-post assessment: Estimate policy scenario emissions and removals over the GHG assessment period from • chapter 11: each source/sink category and greenhouse gas included in the GHG assessment boundary. estimating ffects gHg e • Apply the same GWP values used to estimate baseline emissions. ex- post Estimate the GHG effect of the policy or action by subtracting baseline emissions from policy scenario • emissions for each source/sink category included in the GHG assessment boundary. chapter 12: Assess the uncertainty of the results of the GHG assessment, either quantitatively or qualitatively. • assessing • Conduct a sensitivity analysis for key parameters and assumptions in the assessment. uncertainty chapter 14: • See Chapter 14 for a list of reporting requirements. reporting endnotes 1. Concrete actions are sometimes called “measures.” 2. In most steps throughout the standard, the term “policy or action” is used to refer to either case, since the basic approach is the same. In project accounting, users typically calculate “GHG reductions” 3. as the difference between baseline emissions and project emissions. Equation 3.1 is used in this standard because it enables calculation of changes in emissions (whether positive or negative), rather than GHG reductions, to be consistent with the overall methodology. Negative results indicate GHG reductions achieved by the policy or action, while positive results indicate an increase in GHG emissions resulting from the policy or action. 29

32 Accounting and 4 Reporting Principles

33 enerally accepted GHG accounting principles are intended to underpin and guide GHG accounting and reporting to ensure that the reported GHG assessment represents a true and fair account of changes in GHG emissions G resulting from a policy or action. The fi ve principles described below are intended to guide users in estimating and reporting changes in GHG emissions, especially where the standard provides flexibility. checklist of accounting requirements section accounting requirements • GHG accounting and reporting shall be based on the principles of relevance, chapter 4: accounting completeness, consistency, transparency, and accuracy. and r eporting principles GHG accounting and reporting be based on the shall Include all significant GHG effects, completeness: following five principles: sources, and sinks in the GHG assessment boundary. Disclose and justify any specific exclusions. relevance: Ensure the GHG assessment appropriately reflects the GHG effects of the policy or action and serves consistency: Use consistent accounting approaches, data the decision- both making needs of users and stakeholders— collection methods, and calculation methods to allow for internal and external to the reporting entity. Users should meaningful performance tracking over time. Transparently apply the principle of relevance when selecting the desired document any changes to the data, GHG assessment level of accuracy and completeness among a range of boundary, methods, or any other relevant factors in the 1 methodological options. Applying the principle of relevance time series. depends on the objectives of the assessment (Chapter 2). 31

34 transparency: Provide clear and complete information for internal and external reviewers to assess the credibility and reliability of the results. Disclose all relevant methods, data sources, calculations, assumptions, and uncertainties. Disclose the processes, procedures, and limitations of the GHG assessment in a clear, factual, neutral, and understandable manner through an audit trail with clear documentation. The information should be sufficient to enable a party external to the GHG assessment process to derive the same results if provided with the same source data. Ensure that the estimated change in GHG accuracy: emissions and removals is systematically neither over nor under actual values, as far as can be judged, and that uncertainties are reduced as far as practicable. Achieve sufficient accuracy to enable users and stakeholders to make appropriate and informed decisions with reasonable confidence as to the integrity of the reported information. Accuracy should be pursued as far as possible, but once uncertainty can no longer be calculation methodologies, methods for assessing policy practically reduced, conservative estimates should be interactions, and data sources. Additional consistency can used. Box 4.1 provides guidance on conservativeness. be provided through GHG reporting programs or more In addition, users should follow the principle of specific guidance. To understand whether detailed sector- comparability if relevant to the assessment objectives. comparisons are valid, all methodologies, assumptions, and data sources used must be transparently reported. comparability (optional): Ensure common methodologies, data sources, assumptions, and reporting formats such that the estimated change in GHG emissions and removals resulting from multiple policies or actions guidance can be compared. The principle of comparability should In practice, users may encounter trade- offs between be applied if the objective is for a single entity to assess principles when developing a GHG assessment. For and compare multiple policies or actions using the same example, a user may find that achieving the most complete methodology. If the objective is to compare the results assessment requires using less accurate data for a portion of of independent assessments of policies carried out the assessment, which would compromise overall accuracy. by different entities, users should exercise caution in Conversely, achieving the most accurate assessment may comparing the results of policy assessments based on require excluding sources or effects with low accuracy, this standard. Differences in reported emissions impacts compromising overall completeness. Users should balance may be a result of differences in methodology rather than trade- offs between principles depending on their objectives. real- world differences. Additional measures are necessary Over time, as the accuracy and completeness of data to enable valid comparisons, such as consistency in the off between these accounting principles increases, the trade- timeframe of the assessments, the types of effects included will likely diminish. in the GHG assessment boundary, baseline assumptions, Policy and Action Standard 32

35 Accounting and Reporting Principles CHAPTER 4 endnote Box 4.1 conservativeness 1. For additional guidance on ensuring consistency, see IPCC 2006: Vol. 1, Chap. 5, “Time Series Consistency.” Conservative values and assumptions are those more likely to overestimate GHG emissions or underestimate GHG reductions resulting from a policy or action. Users should consider conservativeness in addition to accuracy when uncertainty can no longer be practically reduced, when a range of possible values or probabilities exists (for example, when developing baseline scenarios), or when uncertainty is high. Whether to use conservative estimates and how conservative to be depends on the objectives and the intended use of the results. The principle of relevance can help guide what approach to use and how conservative to be. For some objectives, accuracy should be prioritized over conservativeness in order to obtain unbiased results. Conservativeness should not be used as a substitute for collecting accurate data where data exist and can be collected, or as a justification for not improving data collection systems to collect more accurate data. Users should apply sensitivity analysis when uncertainty is high to understand the range of possible outcomes using both more conservative and less conservative assumptions. Chapter 12 provides guidance on uncertainty and sensitivity analysis. 33

36 5 Defining the Policy or Action

37 Define policy/action n order to estimate the GHG effects of a policy or action, users first need to define and provide a detailed description of the policy or action that will be assessed, decide whether to assess an individual policy or action or a package I whether ante or out an ex- to carry and choose or actions, policies of related ex- post assessment. figure 5.1 overview of steps in the chapter decide whether to select the policy clearly define the choose ex-ante assess an individual or ex-post policy or action to or action to policy/action or a assessment be assessed be assessed package of policies/ (section 5.4) (section 5.1) (section 5.2) section 5.3) actions ( checklist of accounting requirements section accounting requirements • Clearly define the policy or action (or package of policies/actions) that is define the policy or action to be assessed assessed. (section 5.2) Reporting requirements are listed in Chapter 14. Note: 35

38 5 .1 Select the policy or Define the policy or 5.2 action to be assessed action to be assessed A complete and accurate definition and description of Table 5.1 presents general types of policies and actions that may be assessed. Some types of policies and actions the policy or action is necessary to effectively carry out subsequent steps in the assessment process and to are more difficult to assess than others, since the causal transparently report the results. relationship between implementation of the policy and its GHG effects may be less direct. For example, information Users clearly define the policy or action (or package shall instruments and research, development, and deployment of policies/actions) that is assessed. Table 5.2 provides (RD&D) policies may have less direct and measurable a checklist of information that should be provided. At a effects than regulations and standards. While the standard report the required information in shall minimum, users can be applied to any policy type, subsequent chapters Table 5.2. The optional information in Table 5.2 may be may pose data collection and estimation challenges relevant depending on the context. that hinder a complete and credible assessment. Users that assess a package of policies/actions should apply Table 5.2 either to the package as a whole or separately to each policy/action within the package. Users that assess a modification of an existing policy or action, rather than a new policy or action, may define the policy to be assessed as either the modification of the policy or the policy as a whole, depending on the objectives. Policy and Action Standard 36

39 CHAPTER 5 Defining the Policy or Action ypes of policies and actions table 5.1 t type of policy or action description Regulations or standards that specify abatement technologies (technology standard) regulations and or minimum requirements for energy consumption, pollution output, or other activities standards (performance standard). They typically include penalties for noncompliance. A levy imposed on each unit of activity by a source, such as a fuel tax, carbon tax, traffic taxes and charges congestion charge, or import or export tax. Direct payments, tax reductions, price supports or the equivalent thereof from a government subsidies and incentives to an entity for implementing a practice or performing a specified action. Define policy/action A program that establishes a limit on aggregate emissions from specified sources, requires sources to hold permits, allowances, or other units equal to their actual emissions, and allows emissions trading permits to be traded among sources. These programs may be referred to as emissions trading programs systems (ETS) or cap-and-trade programs. An agreement, commitment, or measure undertaken voluntarily by public or private sector actors, either unilaterally or jointly in a negotiated agreement. Some voluntary agreements voluntary agreements or include rewards or penalties associated with participating in the agreement or achieving measures the commitments. Requirements for public disclosure of information. These include labeling programs, emissions reporting programs, rating and certification systems, benchmarking, and information or information instruments education campaigns aimed at changing behavior by increasing awareness. Policies aimed at supporting technological advancement, through direct government funding or research, development, investment, or facilitation of investment, in technology research, development, demonstration, and deployment ( rd&d ) and deployment activities. policies Policies requiring that specific attributes (such as GHG emissions) are considered as part of public procurement public procurement processes. policies Provision of (or granting a government permit for) infrastructure, such as roads, water, urban infrastructure programs services, and high speed rail. implementation of new Implementation of new technologies, processes, or practices at a broad scale (for example, technologies, processes, those that reduce emissions compared to existing technologies, processes, or practices). or practices Public or private sector grants or loans (for example, those supporting development strategies financing and or policies). investment Source: Adapted from IPCC 2007. 37

40 table 5.2 checklist of information to describe the policy or action assessed example explanation information required information the title of the policy Policy or action name home insulation Federal subsidy for or action The type of policy or action, such as those presented in Table 5.1, or Subsidy other categories of policies or actions type of policy or action that may be more relevant The specific intervention(s) carried out as part of the description of specific Subsidy of $200 per household policy or action interventions Whether the policy or action is planned, adopted, the status of the policy Implemented or implemented or action The date the policy or action comes into effect (not 2 010 date of implementation the date that any supporting legislation is enacted) If applicable, the date the policy or action ceases, such as the date a tax is no longer levied or the end date of date of completion 2020 an incentive scheme with a limited duration (not the (if applicable) date that the policy/action no longer has an impact on GHG emissions) Which entity or entities implement(s) the policy or implementing entity or Department of Energy of City X action, including the role of various local, subnational, entities national, international, or any other entities The intended effects(s) or benefit(s) the policy or objective(s) of the Reduction in residential energy use action intends to achieve (for example, the purpose policy or action stated in the legislation or regulation) The jurisdiction or geographic area where the policy or action is implemented or enforced, which may be City of X geographic coverage more limited than all the jurisdictions where the policy or action has an impact Residential energy use (energy sector, Which sectors, subsectors, and source/sink primary sectors, IPCC category 1A4b, residential), categories are targeted, using sectors and subsectors subsectors, and grid-connected electricity generation from the most recent IPCC Guidelines for National emission source/sink (energy sector, IPCC category 1A1ai, Greenhouse Gas Inventories or other sector categories targeted electricity generation) classifications If applicable, which greenhouse gases the policy or greenhouse gases action aims to control, which may be more limited CO , N O , CH targeted 2 4 2 than the set of greenhouse gases that the policy or (if applicable) action affects Policy and Action Standard 38

41 CHAPTER 5 Defining the Policy or Action table 5.2 checklist of information to describe the policy or action assessed (continued) example explanation information Natural gas tax, information campaign to educate residents on Other policies or actions that may interact with the other related policies the financial benefits of installing policy or action assessed or actions insulation optional information If relevant and available, the total emissions and The residential energy use sector removals from the sources and sinks targeted; intended level of Define policy/action currently emits 1,000,000 t CO the target amount of emissions to be reduced or e 2 mitigation to be removals to be enhanced as a result of the policy or annually. The subsidy aims to reduce achieved and/or target action, both annually and cumulatively over the life emissions by 20% to result in level of other indicators of the policy or action (or by a stated date); and/or annual emissions of 800,000 t CO e 2 (if applicable) the target level of key indicators (such as the number by 2020. of homes to be insulated) title of establishing The name(s) of legislation or regulations authorizing legislation, regulations, or establishing the policy or action (or other founding Energy Policy Act (2005) or other founding documents if there is no legislative basis) documents Data are collected monthly on number of energy audits carried out, References to any monitoring, reporting, and monitoring, reporting, total subsidies provided, and amount verification procedures associated with implementing and verification of insulation installed; for more the policy or action procedures information, see website. Audits to ensure installation is Any enforcement or compliance procedures, enforcement installed; for more information, such as penalties for noncompliance mechanisms see website Information to allow practitioners and other interested parties to access any guidance documents reference to relevant N/A related to the policy or action (for example, through guidance documents websites) Broader context for understanding the policy or See website for a full list of the broader context/ action, such as other policies or actions that the Department of Energy programs and significance of the policy/action replaces, or the political context of targets to reduce energy use. policy or action the policy/action Any anticipated benefits other than GHG gHg outline of non- Increase in household disposable mitigation, such as energy security, improved air effects or co-benefits of income resulting from energy savings quality, health benefits, or increased jobs, and the policy or action any relevant target indicators Any other relevant information N/A other relevant information 39

42 5.3 Decide whether to assess an together, that differ from the sum of the individual effects individual policy/action or a had they been implemented separately. Policies or actions package of policies/actions may interact if they affect the same source(s) or sink(s). If multiple policies or actions are being developed or For example, national and subnational policies in the same implemented in the same timeframe, users may assess sector are likely to interact, since they likely affect the same the policies or actions either individually or together as source(s). Two policies implemented at the same level 1 When making this decision, users should a package. for example, a carbon tax that reduces may also interact— consider the assessment objectives, feasibility, and the the GHG intensity of the electricity grid and an energy degree of interaction between the policies and actions efficiency policy that reduces demand for electricity. under consideration. Policies or actions do not interact if they do not affect the same source(s) or sink(s), either directly or indirectly. In subsequent chapters, users follow the same general steps and requirements, whether they choose to assess Policies or actions that interact with each other can be an individual policy or action or a package of related overlapping, reinforcing, or overlapping and reinforcing. policies or actions. Depending on the choice, the GHG Table 5.3 provides an overview of four possible relationships effect estimated in later chapters will either apply to the between policies and actions. individual policy or action assessed or to the package of Figure 5.2 illustrates independent, overlapping, and policies and actions assessed. reinforcing policies, as well as policies that may have both shall Users report whether the assessment applies to an overlapping and reinforcing effects. In the figure, Policy X e when implemented individual policy/action or a package of related policies/ reduces emissions by 100 tonnes CO 2 on its own and Policy Y reduces emissions by 60 tonnes report which shall actions. If a package is assessed, users e when implemented on its own. Effect O represents CO individual policies and actions are included in the package. 2 an overlapping effect, while Effect R represents a reinforcing overview of policy interactions effect. See Box 5.1 for an example that illustrates the various Multiple policies or actions can either be independent of possible relationships and the importance of considering each other or interact with each other. Policies or actions interactions when estimating GHG effects. interact if they produce total effects, when implemented Policy and Action Standard 40

43 Defining the Policy or Action CHAPTER 5 ypes of relationships between policies and actions table 5.3 t type description Multiple policies do not interact with each other. The combined effect of implementing the policies together independent is equal to the sum of the individual effects of implementing them separately. Multiple policies interact, and the combined effect of implementing the policies together is less than the sum of the individual effects of implementing them separately. This includes policies that have the same or complementary goals (such as national and subnational energy efficiency standards), as well as policies overlapping that have different or opposing goals (such as a fuel tax and a fuel subsidy). The latter are sometimes referred to as counteracting policies. Define policy/action Multiple policies interact, and the combined effect of implementing the policies together is greater than the reinforcing sum of the individual effects of implementing them separately. Multiple policies interact, and have both overlapping and reinforcing interactions. The combined effect of overlapping implementing the policies together may be greater than or less than the sum of the individual effects of and reinforcing implementing them separately. Source: Adapted from Boonekamp 2006. figure 5.2 t ypes of relationships between policies and actions overlapping independent policy x o= policy y policy x policy y e e 100 t co 60 t co 20t 100 t 60 t 2 2 Combined effect = X + Y Combined effect < X + Y Combined effect = 100 + 60 = 160 t CO Combined effect = 100 + 60 - 20 = 140 t CO e e 2 2 reinforcing overlapping and reinforcing policy x policy y policy x o= policy y 60 t 60 t 20t 100 t 100 t R = 40 t R = 40 t Combined effect > X + Y Combined effect may be > or < X + Y Combined effect = 100 + 60 + 40 = 200 t CO e e Combined effect = 100 + 60 - 20 + 40 = 180 t CO 2 2 Effect O represents an overlapping effect. Effect R represents a reinforcing effect. Note: 41

44 Box 5.1 example of interacting policies and actions A city government implements a subsidy program for home if the information campaign were in place). or were in place insulation as well as an information campaign to educate Suppose that 5,000 households would install insulation if residents on the financial benefits of installing insulation. either one of the policies were in place. In this case, only Both policies are intended to reduce household energy use 25,000 households would install home insulation, resulting e/year, rather than and emissions. If the subsidy were implemented on its own, in total GHG reductions of 50,000 t CO 2 e/year (see Scenario D). 60,000 t CO 20,000 households would install home insulation, reducing 2 emissions by a total of 40,000 t CO e/year (see Scenario A). 2 Conversely, the combination of policies may reinforce each If the information campaign were implemented on its own, other if some households would only install insulation if 10,000 households would install home insulation, reducing both the subsidy and the information campaign were in emissions by a total of 20,000 t CO e/year (see Scenario B). 2 place (rather than either on its own). Suppose an additional 20,000 households would respond only to the presence of The two policies would be independent if one set of households responds to the subsidy, while a separate set both policies. In this case, 50,000 households would install home insulation (the 20,000 households from Scenario A, of households responds to the information campaign. In the 10,000 households in Scenario B, plus an additional this case, 30,000 households would install home insulation and the total GHG reduction from both policies being 20,000 households that would only respond to the presence of both policies), resulting in total GHG reductions implemented would be 60,000 t CO e/year (see Scenario C). 2 of 100,000 t CO e/year (see Scenario E). In practice, 2 However, the policies would overlap if some households there may be both overlapping and reinforcing effects the subsidy either would install insulation in either scenario (if (see Scenario F). number of households scenario that install insulation total g Hg reduction 20,000 40,000 t CO A. Subsidy alone is introduced e/year 2 e/year B. Information campaign alone is introduced 10,000 20,000 t CO 2 C. Independent case: Both the subsidy and information 30,000 campaign are introduced. Separate sets of 60,000 t CO e/year 2 households respond to each policy. D. Overlapping case: Both the subsidy and information campaign are introduced. Some households would 50,000 t CO 25,000 e/year 2 policy were in place. install insulation if either E. Reinforcing case: Both the subsidy and information campaign are introduced. Some households would 50,000 100,000 t CO e/year 2 only install insulation if both policies were in place. F. Overlapping and reinforcing case: Both the subsidy and information campaign are introduced. Some households would install insulation if either policy 90,000 t CO 45,000 e/year 2 were in place, while other households would only install insulation if both policies were in place. Policy and Action Standard 42

45 Defining the Policy or Action CHAPTER 5 5.3 guidance For more guidance on characterizing policy interactions, refer to the policy interaction matrix in Appendix B. To decide whether to assess an individual policy/ action or a package of policies/actions, users should: Table 5.4 provides examples of identifying interactions among policies or actions. • Step 1: Characterize the type and degree of interaction between the policies or actions under consideration step 2: a pply criteria to determine Step 2: Apply criteria to determine whether to assess an • whether to assess an individual package/ individual policy/action or a package of policies/actions action or a package of policies/actions If policy interactions exist, there can be advantages and haracterize the type and degree step 1: c disadvantages to assessing the interacting policies and of interaction between the policies actions individually or as a package (see Table 5.5). To or actions under consideration Define policy/action help decide, users should apply the criteria in Table 5.6. Potentially interacting policies and actions can be identified by identifying the targeted emission source(s) or sink(s), In some cases, certain criteria may suggest assessing an then identifying other policies and actions that target the individual policy/action, while other criteria suggest assessing same source(s) or sink(s). Once these are identified, users a package. Users should exercise judgment based on the should assess the relationship between the policies/actions specific circumstances of the assessment. For example, (independent, overlapping, or reinforcing) and the degree of related policies may have significant interactions (suggesting interaction (major, moderate, or minor). The assessment of a package), but it may not be feasible to model the whole interaction should be based on expert judgment, published package (suggesting an individual assessment). In this studies of similar combinations of policies/actions, or case, a user may undertake an assessment of an individual consultations with relevant experts. The assessment should policy (since a package is not feasible), but acknowledge also be qualitative, since a quantitative assessment would in a disclaimer that any subsequent aggregation of require many of the steps needed for a full assessment of both the results from individual assessments would be the individual policy/action and the package of policies/actions. inaccurate given the interactions between the policies. examples of identifying policies/actions that target the same emission source and characterizing the type table 5.4 and degree of interaction policy or action type of other policies/actions targeting degree of targeted emission assessed interaction interaction the same source(s) or sink(s) source(s) or sink(s) Energy tax Overlapping Moderate example 1: Household space subsidy heating for home Information instruments Reinforcing Moderate insulation Energy efficiency standards Moderate Overlapping example 2: Energy use in appliance refrigerators Reinforcing Moderate Subsidies for new appliances energy labels Fuel taxes Overlapping Minor example 3: Emissions of Minor Overlapping Biofuel subsidies fuel economy new car fleet regulation Rebates for efficient cars Minor Overlapping 43

46 table 5.5 advantages and disadvantages of assessing policies/actions individually or as a package disadvantages advantages approach • The estimated GHG effects Shows the effectiveness of individual policies/actions, which • from assessments of decision makers may require to make decisions about which assessing individual policies cannot be individual policies/actions to support policies/ straightforwardly summed May be simpler than assessing a package in some cases, since • actions to determine total GHG the causal chain and range of impacts for a package may be individually effects, if interactions are not significantly more complex accounted for Captures the interactions between policies/actions in the package • assessing and better reflects the total GHG effects of the package • Does not show the policies/ effectiveness of individual • May be simpler than undertaking individual assessments in some actions as a policies/actions cases, since it avoids the need to disaggregate the effects of package individual policies/actions table 5.6 criteria for determining whether to assess policies/actions individually or as a package questions guidance criteria Do the end-users of the assessment results want to know the impact If “Yes” then undertake an objectives and of individual policies/actions, for example, to inform choices on which individual assessment use of results individual policies/actions to implement or continue supporting? Are there significant (major or moderate) interactions between the If “Yes” then consider assessing significant identified policies/actions, either overlapping or reinforcing, that will a package of policies/actions interactions be difficult to estimate if policies/actions are assessed individually? Will the assessment be manageable if a package of policies/actions is If “No” then undertake an assessed? Is data available for the package of policies/actions? individual assessment feasibility For ex-post assessments, is it possible to disaggregate the observed If “No” then consider assessing impacts of interacting policies/actions? a package of policies/actions Users may also conduct assessments for both individual If users choose to assess both an individual policy/action packages of policies/actions. Doing and policies/actions and a package of policies/actions that includes the so will yield more information than conducting only individual policy/action assessed, users should define one option or the other. Undertaking both individual each assessment separately and treat each as a discrete assessments and assessments for combinations of policies application of this standard in order to avoid confusion of user requires information should be considered if the end- the results. on both, resources are available to undertake multiple Box 5.2 provides a case study of deciding whether to assess analyses, and undertaking both is practically feasible. a package of policies. Policy and Action Standard 44

47 CHAPTER 5 Defining the Policy or Action Box 5.2 deciding whether to assess a package of policies for china’s industrial energy efficiency policies The Institute for Global Environmental Strategies (IGES) carried were also affected by three other policies: (1) the Ten Key Projects energy efficiency program, (2) a value- out an ex- added tax post assessment of China’s energy efficiency (EE) reduction for utilizing waste heat and pressure, and (3) Year Plan policies in the industry sector during the 11th Five- differentiated electricity pricing. Since all four policies were (2006–10). The objective was to evaluate to what extent the implemented during the same time period and by the same energy savings achieved by the industry sector during the 11th set of entities, the policies likely interacted with each other. Year Plan could be attributed to the implementation of EE Five- If assessed individually, the sum of energy savings from the policies as opposed to other factors. policies would likely not accurately represent the total effect The first critical step in the assessment was to decide whether on energy conservation. IGES therefore decided to evaluate Define policy/action to assess policies individually or as a package. IGES initially the four EE policies as a package. set out to assess the Top 1000 Enterprises program, which is The assessment found that the EE policies collectively one of the most significant EE policies in China. The program affects roughly 1,000 of the largest enterprises in nine energy- achieved energy savings of 316 Mtce (9.2 EJ), accounting for 58 percent of the industry sector’s total energy savings from consuming industries and aimed to achieve energy savings of 2006 to 2010. External factors such as economic activity, 100 Mtce (2.9 EJ) during the 11th Five- Year Plan. energy prices, autonomous technology improvements, and However, examining other related EE policies revealed that structural shifts in the economy accounted for the remainder the enterprises involved in the Top 1000 Enterprises program of the change in sectoral energy use. 45

48 Choose ex- ante or 5.4 5.4 guidance ex- post assessment ante or ex- Choosing between ex- After defining the policy or action (or package of post assessment depends on the status of the policy or action. If the policy or action policies or actions) to be assessed, the next step is to is planned or adopted, but not yet implemented, then the ante assessment, an choose whether to carry out an ex- assessment will be ex- ante and ex- post assessment, or a combined ex- ex- post ante by definition. Alternatively, if ante and ex- assessment. For descriptions of ex- the policy has been implemented, then the assessment post ante, ex- can be ex- assessments, see Section 3.2. post, or a combination of ex- ante and ex- post. In this case, users should carry out an ex- post ante, Users shall report whether the assessment is ex- assessment if the objective is to estimate the effects of ante and ex- post. ex- post, or a combination of ex- the policy or action to date; an ex- ante assessment if the 2 objective is to estimate the expected effects in the future; or a combined ex- ante and ex- post assessment to estimate both the past and future effects of the policy or action. Box 5.3 provides a case study of carrying out a combined post assessment. ex- ante and ex- Policy and Action Standard 46

49 CHAPTER 5 Defining the Policy or Action ante and ex- Box 5.3 combined ex- post assessment of Belgium’s federal tax reduction for roof insulation the years 2013–20. The ex-post assessment shows the ECONOTEC and VITO, on behalf of the Belgian Federal Public contribution to the federal and national commitments in the Service, Health, Food Chain Safety, and Environment, carried out a combined ex-ante and an ex-post assessment of the federal framework of the Kyoto Protocol, while the latter helps assess to what extent existing policies will be sufficient to meet tax reduction for roof insulation investments by households in future targets. In the future, ex-post assessments will also Belgium (ECONOTEC and VITO 2014). The objective was to evaluate the emissions reduction generated as part of enable the government to evaluate whether implementation is on track. a follow-up of the implementation of the Belgian National Climate Plan and the European Union climate policy for 2020. Figure 5.3 presents the results of the combined ex-post and ex-ante assessment. The assessment includes uncertainty The assessment was undertaken in 2013. The ex-post Define policy/action ranges for each year, which were obtained using a Monte assessment covered the Kyoto Protocol first commitment period (2008–12), while the ex-ante assessment covered Carlo simulation method (further described in Chapter 12). figure 5.3 ex-post and ex-ante assessment results 5000 ex-post assessment ex-ante assessment 4000 e) 2 3000 2000 (ktonne co emissions reductions 1000 2 014 2 015 2 016 2 017 2 018 2 019 2020 2008 2009 2 010 2 011 2 012 2 013 likely minimum maximum endnotes 1. Policies or actions that are implemented earlier in time than the policy or action being assessed should be included in the baseline scenario for the policy or action being assessed. For more information, see Chapter 8. 2. An ex- ante assessment may include historical data if the policy or action is already implemented, but it is still an ex- ante rather than an ex- post assessment if the objective is to estimate future effects of the policy or action. 47

50 Identifying Effects and 6 Mapping the Causal Chain

51 Identify effects n order to estimate GHG effects of the policy or action, users have to first what the effects are. This chapter explains how to identify all understand I potential GHG effects of the policy or action and include them in a map of the causal chain. A subset of effects identified in this chapter will then be included in the GHG assessment boundary in Chapter 7. figure 6.1 overview of steps in identifying effects and mapping the causal chain identify potential identify all sources/sinks g Hg effects of the and greenhouse gases map the causal chain Hg policy or action associated with the g (section 6.3) (section 6.1) effects ( section 6.2) The three steps in this chapter are closely interrelated. Users may carry out the steps in parallel or in any sequence. Note: checklist of accounting requirements section accounting requirements • Identify all potential GHG effects of the policy or action. identify potential g Hg effects of the policy or • Separately identify and categorize in-jurisdiction effects action ( section 6.1) and out-of-jurisdiction effects, if relevant and feasible. Identify all source/sink categories and greenhouse gases • identify all sources/sinks and greenhouse gases associated with the GHG effects of the policy or action. section 6.2) associated with the g Hg effects ( • Develop a map of the causal chain. section 6.3) map the causal chain ( Note: Reporting requirements are listed in Chapter 14. 49

52 6.1 guidance 6 .1 Identify potential GHG effects of the policy or action In order to identify the GHG effects of the policy or action, identify and report all potential GHG effects shall Users it is useful to first consider how the policy or action is of the policy or action. GHG effects include both increases activities and inputs implemented by identifying the relevant as well as increases and and decreases in GHG emissions— associated with implementing the policy or action. See decreases in GHG removals— that result from the policy or Table 6.1 for definitions and examples. Understanding ), action. Greenhouse gases include carbon dioxide (CO 2 inputs and activities is a means to understanding which ), nitrous oxide (N O), hydrofluorocarbons methane (CH 4 2 effects are expected to occur, since inputs are necessary ), (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF 6 for activities to occur, and activities are necessary for separately shall ). Users and nitrogen trifluoride (NF 3 GHG effects to occur (see Figure 6.2). Users should then jurisdiction effects and out- identify and categorize in- identify all intermediate effects of the policy or action jurisdiction effects, if relevant and feasible. of- that may lead to GHG effects. Users should ensure that less obvious effects, which may be potentially significant, are not omitted from the assessment. Users may also identify relevant of the policy or action. non- GHG effects table 6.1 summary of inputs, activities, and effects examples for a home definitions indicator types insulation subsidy program Resources that go into implementing a policy or action, Money needed to implement inputs such as financing the subsidy program Administrative activities involved in implementing the Energy audits, provision policy or action (undertaken by the authority or entity that activities of subsidies implements the policy or action), such as permitting, licensing, procurement, or compliance and enforcement Consumers purchase and install Changes in behavior, technology, processes, or practices that intermediate insulation, home natural gas and result from the policy or action effects electricity use are reduced Reduced CO Changes in greenhouse gas emissions by sources or removals , and N , CH O 2 2 4 by sinks that result from the intermediate effects of the policy emissions from reduced natural gHg effects or action gas and electricity use Changes in relevant environmental, social, or economic Increase in disposable income conditions other than GHG emissions or climate change gHg effects non- due to energy savings mitigation that result from the policy or action Source: Adapted from W. K. Kellogg Foundation 2004. In other frameworks, intermediate effects are called “outcomes” and GHG effects and non-GHG effects are called “impacts.” In this example Notes: (used throughout the standard), homes are heated by natural gas and electricity; in reality, homes may be heated by oil, coal, or other fuels. Policy and Action Standard 50

53 CHAPTER 6 Identifying Effects and Mapping the Causal Chain 1 elationship of inputs, activities, intermediate effects, g Hg effects, and non- gHg effects figure 6.2 r gHg effects intermediate activities inputs effects gHg non- effects Identify effects term effects: • types of effects short- Effects that are and long- To ensure a complete GHG assessment, users should both nearer and more distant in time, based on identify as many potential GHG effects as possible. Many the amount of time between implementation of effects of the policy or action may not be immediately the policy and the effect. Users should define the apparent, and many GHG effects (whether GHG increasing distinction between “short term” and “long term” or GHG decreasing) may be far removed from the direct based on the individual assessment (for example, or immediate effects of the policy or action. Policies and 5 years or 10 years). Some effects may also be actions can lead to effects beyond the sector or country temporary, while others are permanent. intended and unintended effects: • Effects that are where they are implemented, to a variety of unexpected both intentional and unintentional, based on the original lasting impacts. or unintended consequences, and to long- objectives of the policy or action. Unintended effects For example, RD&D policies may spur technological may include a variety of effects, such as rebound effects development over a long time period. (marginal increases in energy- using activities or behavior Users should consider the following types of effects: 2 effects resulting from energy efficiency improvements); of- in- jurisdiction and out- • jurisdiction effects: in sectors other than the targeted sector (such as leakage Effects that occur inside the geopolitical boundary between sectors); effects on members of society not over which the implementing entity has authority, targeted by the policy or action (sometimes called non- such as a city boundary or national boundary, as participant spillover effects); effects on behavior once a well as effects that occur outside of the geopolitical policy is announced but before it is implemented (such jurisdiction effects are called of- boundary. Out- as early action); or lack of compliance or enforcement. or multiplier effects if they reduce spillover effects Unintended effects may increase or decrease emissions. likely, possible, and unlikely effects: All potential • emissions outside the jurisdictional boundary, and effects, regardless of how likely they are to occur. leakage if they increase emissions outside the Effects gHg increasing and decreasing effects: • jurisdictional boundary. Jurisdictional boundaries that both increase and decrease GHG emissions from may not be relevant for all GHG assessments (for sources and removals of GHGs by sinks. example, for private sector actions). See Table 6.2 for examples of the various types of effects for an illustrative policy. 51

54 table 6.2 illustrative example of various effects for a united states vehicle fuel efficiency standard type of effect examples of effects Fuel consumption and tailpipe emissions per mile driven are reduced. intended effect • • Some consumers drive further distances, since improved vehicle fuel efficiency decreases the cost of driving per kilometer, thereby reducing some of the emissions benefits. This is called a rebound effect. unintended effect • Emissions from the U.S. electricity generation sector increase as a result of more electric vehicles being sold. • Automakers in the U.S. produce and sell more efficient cars, which reduces gasoline consumption in-jurisdiction effect in the United States. Because of the U.S. regulation, Canada adopts a similar vehicle fuel efficiency regulation, leading • to reduced emissions from cars in Canada. This is a spillover effect. out-of-jurisdiction • U.S. automakers might sell old models to countries without similar standards, which could increase effect emissions in other countries (leakage). • U.S. automakers produce more efficient vehicles, using the same basic technology (cars fueled by short-term effect gasoline and diesel). • U.S. automakers develop new vehicle technologies that reduce emissions even further, such as long-term effect zero emissions vehicles. The above lists of types of effects are intended to guide the Users should also consider potential GHG effects in terms of development of a comprehensive list of potential effects. the following: They are not intended to be prescriptive or exhaustive. technology effects: • Design or deployment of Not all types of effects listed may be relevant to the policy new technologies or action under consideration, and not all relevant effects Development of new infrastructure • infrastructure effects: may be listed. The various types of effects are also not consumer behavior and practices: Changes in • mutually exclusive. Each effect will be a combination of the purchasing decisions or other practices characteristics listed above. For example, a single effect may Business behavior and practices: Changes in • of- term, unintended, possible, and jurisdiction, long- be out- manufacturing decisions or other practices cycle GHG increasing and may involve market effects, life- market effects: • Changes in supply and demand, effects, and trade effects. changes in prices, or changes in market structure or market share resulting from the policy or action While users should identify a long list of potential effects life- • cycle effects: Changes in upstream and in this step, not all potential effects need to be included downstream activities, such as extraction and production in the GHG assessment boundary in Chapter 7. of energy and materials, or effects in sectors not Hg effects methods for identifying g targeted by the policy or action • macroeconomic effects: Various approaches may be used to identify Changes in macroeconomic potential effects, such as the following: conditions, such as GDP, income, employment, or structural changes in economic sectors • Literature review of prior assessments of similar policies • trade effects: Changes in imports and exports, and circumstances such as leakage Policy and Action Standard 52

55 CHAPTER 6 Identifying Effects and Mapping the Causal Chain Consultations, surveys, or panels with relevant experts • since estimation of baseline emissions and policy scenario and stakeholders emissions (in Chapters 8, 9, and 11) occurs at the level of • Review of regulations, statutory authorities, development individual source/sink categories and greenhouse gases. plans, regulatory impact analyses, environmental impact assessments, or economic studies 6.2 guidance specific guidance or methodologies Sector- • • Expert judgment Sources are processes or activities that release GHGs into the atmosphere. Sinks are processes or activities that increase jurisdiction separate tracking of in- storage or removals of GHGs from the atmosphere. The IPCC and out- of- jurisdiction effects provides Guidelines for National Greenhouse Gas Inventories 3 By separately identifying and categorizing in- jurisdiction and definitions of source/sink categories that may be used. out- of- jurisdiction effects, users can more accurately link Identify effects In addition to the greenhouse gases covered by the the GHG effects of the policy or action to the relevant , N , CH O, HFCs, UNFCCC and the Kyoto Protocol (CO 4 2 2 jurisdiction’s GHG inventory and any jurisdiction- level ), users may identify additional , and NF PFCs, SF 6 3 of- GHG mitigation goals (since out- jurisdiction GHG gases that are identified by the IPCC or covered by the effects do not contribute to GHG mitigation goals that 4 If additional gases are included in Montreal Protocol. apply only to emission sources within the jurisdictional the assessment, users should report the results with and boundary). Separate categorization also creates without additional gases included. transparency around any potential double counting of Table 6.3 provides examples of source/sink categories and out- jurisdiction effects between jurisdictions. of- greenhouse gases. In certain cases, a single effect may affect both in- jurisdiction jurisdiction emissions. In this case, separate and out- of- defining source/sink categories tracking may not be feasible. Alternatively, users may choose Users may define sources and sinks either as individual to apportion the effect between in- jurisdiction emissions and sources and sinks (such as fossil fuel combustion in specific jurisdiction emissions based on assumptions. out- of- power plants) or as aggregated categories of sources and sinks (such as all fossil fuel combustion in all power plants identifying non- gHg effects connected to an electric grid). The decision of whether to GHG effects of the policy Users may also identify any non- identify individual sources/sinks or categories of sources/ or action that are relevant to the assessment, which may sinks depends on the policy or action assessed, the types of include the following: data collected and monitored, and the estimation methods • Environmental effects, such as improved air quality or up data used. Individual sources correspond to bottom- water quality down data. and aggregated sources correspond to top- • Social effects, such as improved health or quality of life When defining affected sources and sinks, users should • Economic effects, such as increased employment, consider defining the sources and sinks narrowly around the income, or GDP specific processes or activities affected by the policy or action. See Appendix C for additional examples of non- GHG effects. This helps ensure that processes or activities not affected by the policy or action are not unnecessarily estimated in later steps. Using the example of a home insulation subsidy, users may 6.2 Identify source/sink categories define a source as “residential natural gas combustion for and greenhouse gases associated space heating” (for the whole residential sector) or may with the GHG effects define the source more narrowly as “residential natural gas Users shall identify and report a list of all source/sink combustion for space heating in homes that receive the categories and greenhouse gases associated with the subsidy.” Likewise, users may define a source as “fossil fuel GHG effects of the policy or action. This step is necessary 53

56 table 6.3 examples of sources/sinks and greenhouse gases examples of emitting relevant description source category greenhouse gases equipment or entity Power plants, industrial Combustion of fuels to stationary fossil fuel facilities, boilers, furnaces, CO , N O , CH 2 4 2 generate energy combustion turbines Trucks, trains, airplanes, mobile fossil fuel CO Combustion of fuels , N , CH O 2 4 2 ships, cars, buses combustion Chemical or physical Industrial facilities CO cement manufacture 2 processes Chemical or physical Industrial facilities CO , PFCs aluminum production 2 processes Fugitive emissions from natural gas transmission and Pipelines CH , CO natural gas systems 4 2 distribution systems Degradation or CH Landfills landfills 4 decomposition of waste electrical transmission Fugitive emissions Electricity T&D systems SF 6 and distribution Fugitive emissions from Refrigeration and air refrigeration and air HFCs conditioning equipment equipment conditioning equipment Biological processes, agricultural soil CO Agricultural soils , N O 2 2 emissions from fertilizer use management Forest degradation, forests and other CO Forests, vegetation, soils O , CH , N 2 4 2 deforestation land use examples of equipment relevant sink category description or entity greenhouse gases Removal and storage of CO 2 Forests, vegetation, soils CO Biological processes 2 through photosynthesis Industrial facilities, carbon capture and power plants, geological CO Removal and storage of CO 2 2 storage formations Policy and Action Standard 54

57 CHAPTER 6 Identifying Effects and Mapping the Causal Chain connected power plants” (for the whole combustion in grid- Section 6.1 and the sources/sinks and greenhouse gases electricity generation sector) or may define the source more identified in Section 6.2. narrowly as “fossil fuel combustion in grid- connected power Users assessing a package of policies and actions may plants for supplying electricity to the homes that receive either (1) develop a single causal chain for the package the subsidy.” How best to define the source depends on as a whole or (2) develop separate causal chains for the estimation methods and data that will be used. each policy or action included in the package. Either approach is likely to help identify overlaps and interactions between the policies and actions included in the package, 6.3 Map the causal chain which may be useful in subsequent estimation steps. A causal chain is a conceptual diagram tracing the process by which the policy or action leads to GHG effects through a series of interlinked logical and sequential stages of cause- Identify effects 6.3 guidance and- effect relationships. Mapping the causal chain can help identify effects not previously identified. It also helps scope the user and decision makers understand in visual terms At a minimum, the causal chain should include all how the policy or action leads to changes in emissions, intermediate effects and GHG effects that have been which can serve as a useful tool to enhance policy design, identified. Since the various categories of effects outlined in improve understanding of policy effectiveness, and Section 6.1 are not mutually exclusive, users should be sure communicate the effects of the policy to stakeholders. not to include the same effect in the causal chain twice. Users develop and report a causal chain for the shall Figure 6.3 provides a generic example of a causal chain policy or action assessed, based on the effects identified in that includes intermediate effects and GHG effects. figure 6.3 generic example of mapping g Hg effects by stage x stage fourth stage third stage second stage first stage fourth stage third stage effect effect GHG effect third second stage first stage stage effect effect effect GHG effect policy or second stage third stage fourth first stage x stage effect action effect effect effect stage effect GHG effect second first stage p olicy or action stage effect effect GHG effect intermediate effect third gHg effect second stage stage effect effect GHG effect 55

58 figure 6.4 generic example of mapping inputs, activities, and effects by stage second stage first stage x stage intermediate GHG effect effect intermediate intermediate GHG effect activity effect effect intermediate intermediate policy or GHG effect activity inputs effect action effect intermediate GHG effect effect Users may include inputs and activities in the causal chain of the causal chain through a series of cause- effect and- as steps toward identification of effects. See Figure 6.4 for that is, a series of intermediate effects— relationships— a generic example that includes inputs and activities along a change in GHG emissions until it leads to a GHG effect— with intermediate effects and GHG effects. Users should or removals occurring at a source or sink. For example, a GHG effects along with GHG effects in the include non- change in electricity use (an intermediate effect) should causal map, if relevant. be followed through the causal chain until it reaches a change in fuel combustion to generate grid- connected The causal chain represents the changes expected to electricity (a GHG effect occurring at a source). occur as a result of the policy or action. Implicitly, these changes are relative to a baseline scenario that represents In some cases, multiple branches of effects lead to distinct the conditions most likely to occur in the absence of the sources or sinks. In other cases, two or more branches of policy or action. Users may refine the causal chain after effects lead to the same source or sink (if the policy or action more clearly defining the baseline scenario in Chapter 8. has two or more effects on the same source or sink). See Users may also choose to develop two separate causal Figure 6.5 for an example where two distinct effects (emissions chains— one representing the baseline scenario and one per kilometer traveled decrease and consumers drive more) representing the policy scenario— rather than a single causal lead to the same source (tailpipe emissions from cars). chain representing the policy scenario. completeness Users should separately indicate which GHG effects in the The causal chain should be as comprehensive as possible, of- causal chain are in- jurisdiction effects and which are out- rather than limited by geographic or temporal boundaries. jurisdiction effects, if relevant and feasible. To make the mapping step more practical, users should only include those branches of the causal chain that are stages reasonably expected to lead to changes in GHG emissions To develop the causal chain, users should identify the or removals. Users do not need to identify effects or proximate (first stage) effects of the policy or action. branches that are unrelated to changes in GHG emissions Each first stage effect represents a distinct “branch” of or removals. Where feasibility is a concern, users may the causal chain. Users should then extend each branch Policy and Action Standard 56

59 CHAPTER 6 Identifying Effects and Mapping the Causal Chain summarize the GHG effect for each branch without mapping and sin ks, and affected greenhouse gases for the same each intermediate effect for each stage separately. policy example. See Figure 6.6 for an illustrative causal chain for a subsidy See Box 6.1 for a case study of developing a causal for home insulation. Table 6.4 provides an example of chain for Belgium’s offshore wind promotion program. developing a list of potential GHG effects, affected sources figure 6.5 example of multiple effects leading to the same source (for an illustrative vehicle fuel efficiency regulation) fourth stage second stage third stage first stage reduced emissions Identify effects per kilometer tailpipe emissions traveled decrease from cars vehicle fuel consumers Businesses purchase more produce more efficiency efficient cars efficient cars regulation marginal consumers increase drive more in tailpipe p olicy or action emissions from cars intermediate effect gHg effect figure 6.6 example of a causal chain for an illustrative subsidy for home insulation fifth stage first stage fourth stage third stage second stage reduced reduced reduced emissions reduced emissions from electricity from electricity demand for coal mining generation generation electricity and natural reduced reduced reduced gas to heat consumers emissions emissions from emissions from homes purchase and natural gas from natural home natural install insulation gas systems systems gas use subsidy increased increased increase in increased for home production demand for disposable emissions from insulation of goods & goods & income due manufacturing services services to savings increased p olicy or action Businesses emissions produce more from insulation intermediate effect insulation manufacturing gHg effect 57

60 table 6.4 Hg effects, affected sources and sinks, and affected greenhouse example of developing a list of potential g gases for a home insulation subsidy program affected affected potential g Hg effect affected sources greenhouse gases sinks Combustion of fuels to generate grid- reduced emissions from electricity CO N/A , N O , CH 2 4 2 connected electricity for use in homes generation N/A CH Coal mines reduced emissions from coal mining 4 reduced emissions from natural gas N/A Natural gas systems CO , CH 2 4 systems (from reduced electricity use) Residential natural gas combustion reduced emissions from home natural CO N/A , CH , N O 4 2 2 (space heating) gas use (space heating) reduced emissions from natural gas , CH N/A CO Natural gas systems 2 4 systems (from reduced natural gas use) O , CH , N N/A CO Manufacturing processes increased emissions from manufacturing 2 4 2 increased emissions from insulation Insulation manufacturing processes N/A CO , CH O, HFCs , N 4 2 2 manufacturing Box 6.1 developing a causal chain for Belgium’s offshore wind energy promotion program VITO, on behalf of the Belgian Federal Public Service, Health, assumed that without offshore wind, the same amount Food Chain Safety, and Environment, carried out a combined of electricity would have been generated by a combined ex-post and ex-ante assessment of a package of policies taken by cycle gas turbine power station, which could be from an existing power plant or a new installation. the Belgian federal government to promote the development of offshore wind energy. These policies include a green certificate Changes in emissions from macroeconomic effects 3. scheme that offers financial support to offshore wind turbine resulting from the green certificate scheme, which will operators for each megawatt of electricity generated. The increase electricity prices for industry, the commercial objective of the assessment was to estimate the GHG effects sector, and households and thus affect electricity (both in-jurisdiction and out-of-jurisdiction) of the program. consumption. The first step was to identify and map all the sources and The causal chain (see Figure 6.7) proved to be a useful sinks affected by the program. Three categories of affected tool to identify all the sources and sinks affected by the sources and sinks were identified: policy, beyond the boundaries normally used in impact assessments. Although not all of the effects were included Increased GHG emissions resulting from the construction, 1. installation, and connection to the grid of the offshore in the GHG assessment boundary and estimated at a later stage, mapping the causal chain was an insightful way to wind turbines. illustrate that policies can have significant upstream and Avoided emissions from electricity generation relative to 2. downstream effects as well as in-jurisdiction and out-of- a baseline scenario without offshore wind energy. It is jurisdiction effects. Policy and Action Standard 58

61 CHAPTER 6 Identifying Effects and Mapping the Causal Chain Box 6.1 developing a causal chain for Belgium’s offshore wind energy promotion program (continued) ausal chain of Belgium’s offshore wind energy promotion program figure 6.7 c third stage fourth stage second stage sixth stage first stage fifth stage reduced reduced emissions power from existing increased generation power plants emissions increased by existing from emisions in reduced increased power installation other sectors Identify effects price of fossil offshore plants increased (leakage) fuels wind demand for offshore installation offshore wind increased avoided wind and energy power emissions increased mfg of promotion generation from new reduced emissions offshore wind program reduced by offshore power plants additions from mfg turbines emissions wind of new from mfg of power reduced mfg new power plants of power p olicy or action plants plants intermediate effect reduced increased reduced gHg effect emissions electricity consumer from reduced price demand demand endnotes GHG effects may also lead to GHG effects, such as an 1. Some non- increase in disposable income from home insulation leading to more consumption and therefore more emissions (as illustrated in Figure 6.6). 2. For example, households using more space heating in winter as a result of energy efficiency improvements that allow for higher indoor temperatures at lower costs. 3. See IPCC 2006: Vol. 1, Chap. 8, Sec. 8.5, “Classification and Definition of Categories.” 4. Additional gases include chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), halogenated ethers, nitrogen methane volatile organic compounds (NMVOC), ), non- oxide (NO X ), and ammonia (NH ). carbon monoxide (CO), sulfur dioxide (SO 3 2 For more information, see IPCC 2006: Vol. 1, Chap. 8, Sec. 8.2.2, “Gases Included.” Users may also separately estimate the effects of the policy or action on black carbon as long as the results are not aggregated with other GHGs included in the assessment. 59

62 Defining the GHG 7 Assessment Boundary

63 Identify effects n this chapter, users define the GHG assessment boundary by determining which identified in Chapter 6 are significant. GHG effects potential The GHG assessment boundary defines the scope of the assessment in terms of the range of GHG I and sinks, and greenhouse gases included in the assessment. This effects, sources the time period over which GHG chapter also defines the GHG assessment period— effects resulting from the policy or action are assessed. figure 7.1 overview of steps for defining the g Hg assessment boundary determine which g Hg effects, assess the significance source/sink categories, and define the g Hg of potential greenhouse gases are included in assessment period gHg effects Hg assessment boundary the g (section 7.3) (section 7.1) (section 7.2) checklist of accounting requirements section accounting requirements determine which g Hg effects, source/sink categories, Include all significant GHG effects, source/sink categories, • and greenhouse gases to include in the g Hg and greenhouse gases in the GHG assessment boundary. section 7.2) assessment boundary ( • Define the GHG assessment period based on the GHG define the g Hg assessment period ( section 7.3) effects included in the GHG assessment boundary. Reporting requirements are listed in Chapter 14. Note: 61

64 Assess the significance 7.1 effect occurring in the future as a result of the policy or of potential GHG effects action. For ex- post assessments, this involves assessing the The primary step in defining the GHG assessment likelihood that the effect occurred in the past as a result boundary is to assess each of the potential GHG effects of the policy or action. (Certain effects may have occurred identified in the causal chain to determine which are during the GHG assessment period for reasons unrelated significant and should therefore be included in the to the policy or action being assessed.) In cases where the GHG assessment boundary. Any type of effect may be likelihood is unknown or cannot be estimated, the effect jurisdiction and out- significant, including in- jurisdiction of- should be classified as “possible.” effects and short- term effects. term and long- The likelihood should be based on evidence to the extent possible, such as published literature, prior experience, modeling results, risk management methods, consultation 7.1 guidance with experts and stakeholders, or other methods. If relevant In order to identify significant effects, users should assess evidence does not exist, expert judgment should be used. each potential GHG effect in terms of both: step 2: e stimate the relative • The likelihood that each GHG effect will occur (Step 1); magnitude of each g Hg effect and Next, users should categorize the relative magnitude • The relative magnitude of each GHG effect (Step 2). of each GHG effect as major, moderate, or minor. This involves approximating the change in GHG emissions stimate the likelihood step 1: e and removals resulting from each GHG effect. The Hg effect will occur that each g relative magnitude of each effect depends on the size For each potential effect identified in Chapter 6, users of the source/sink category affected and the magnitude should estimate the likelihood that it will occur by classifying of change expected to result from each source/ each effect according to the options in Table 7.1. For ex- ante sink category. The size of the source/sink category assessments, this involves predicting the likelihood of the affected may be estimated based on jurisdictional GHG inventories or other sources. table 7.1 assessing likelihood of g Hg effects likelihood description Reason to believe the effect will happen (or did happen) as a result of the policy. very likely (For example, a probability in the range of 90–100%.) Reason to believe the effect will probably happen (or probably happened) as a result of the policy. likely (For example, a probability in the range of 66–90%.) Reason to believe the effect may or may not happen (or may or may not have happened) as a result of the policy. About as likely as not. (For example, a probability in the range of 33–66%.) Cases where possible the likelihood is unknown or cannot be determined should be considered possible. Reason to believe the effect probably will not happen (or probably did not happen) as a result of the unlikely policy. (For example, a probability in the range of 10–33%.) Reason to believe the effect will not happen (or did not happen) as a result of the policy. (For example, very unlikely a probability in the range of 0–10%.) Adapted from IPCC 2010. Source: Policy and Action Standard 62

65 CHAPTER 7 Defining the GHG Assessment Boundary Box 7.1 estimating relative magnitude Users do not need to accurately calculate GHG effects in this step, but the relative magnitude should be categorized as based on absolute values major, moderate, or minor based on evidence to the extent possible. Evidence may include results from previous studies The absolute value of a number is the non- negative and literature, prior experience, emission factor databases value of that number without regard to its sign. For (national or international), life- cycle databases or studies example, the absolute value of 5 is 5, and the absolute (for out- of- jurisdiction effects), consultation with experts and value of -5 is also 5. When estimating the relative stakeholders, or other methods. If evidence does not exist, magnitude of effects, users should compare effects expert judgment should be used. Users should consider based on their absolute value. For example, assume the size of the groups (such as businesses or consumers) that one effect increases emissions by 1,000 t CO e, 2 expected to take action as a result of the policy, if relevant. e, a second effect reduces emissions by 2,000 t CO 2 Users may estimate changes in relevant activity data (such Identify effects e. and a third effect enhances removals by 3,000 t CO 2 as changes in vehicle kilometers traveled or electricity To compare each effect, users should estimate the consumption), rather than changes in emissions. To follow a total change in emissions in absolute value terms, as more rigorous approach, users may estimate each potential follows: |1,000 t CO e| + |– 2000 t CO e| + |– 3,000 t 2 2 GHG effect by using simplified calculation methods. e| = 6,000 t CO CO e. The relative magnitude of each 2 2 effect should be compared to other effects in relation The relative magnitude of each GHG effect should be estimated to the total change in absolute value terms. In this based on the absolute value of the total change in GHG example, the first effect represents one- sixth of the emissions and removals associated with the various effects, total estimated change, the second effect represents taking into account both increases and decreases in GHG two- sixths (or one- third) of the estimated change, and emissions and removals. For more information, see Box 7.1. half) the third effect represents three- sixths (or one- Table 7.2 provides percentage figures as a rule of thumb to of the total estimated change in emissions in absolute help identify whether an effect is major, moderate, or minor. value terms. The percentage figures represent the estimated relative magnitude of the GHG effect being considered (in absolute value terms), relative to the estimated total change in GHG emissions and removals resulting from the policy or action (in absolute value terms). Users may choose to use different percentage thresholds than those presented in Table 7.2. table 7.2 assessing relative magnitude of g Hg effects relative approximate relative magnitude description magnitude (rule of thumb) The effect significantly influences the effectiveness of the policy or action. The change in GHG emissions or removals is likely to be > 10% major significant in size. The effect influences the effectiveness of the policy or action. The 1%–10% moderate change in GHG emissions or removals could be significant in size. The effect is inconsequential to the effectiveness of the policy or action. < 1% minor The change in GHG emissions or removals is insignificant in size. 63

66 gwp assessing relative magnitude ) values Box 7.2 selecting global warming potential ( separately by source/sink category Depending on how a GHG effect is defined, it may GWP values convert emissions data for non- CO GHGs 2 affect one source/sink category or multiple source/ into units of carbon dioxide equivalent (CO e). GWP 2 sink categories. If it affects more than one source/ values describe the radiative forcing impact (or degree sink category, the relative magnitude of the GHG effect of harm to the atmosphere) of one unit of a given GHG should be assessed separately by source/sink category, relative to one unit of carbon dioxide. since not all sources/sinks affected may be significant The IPCC provides GWP values for 20- year, year, 100- and some may therefore be excluded. The relative year time horizons. In most cases, users should and 500- magnitude depends on both the size of the source/sink year GWP values to estimate the relative use 100- estimated based on national emission factors, category— magnitude of GHG effects. Twenty- year GWP values jurisdictional GHG inventories, or other sources— and may be used to focus on short- term climate drivers, the magnitude of change expected to result from each and should be used if the policy or action assessed is source/sink category. specifically designed to reduce emissions of short- lived greenhouse gases, such as methane. Users should report assessing relative magnitude separately by gas the GWP values and time horizon used to determine If a GHG effect affects more than one greenhouse gas, year GWP significance. Regardless of whether 20- the relative magnitude of the GHG effect should be year GWP values are used to determine values or 100- assessed separately for each gas. Doing so can enable significance, users are required to estimate GHG effects the exclusion of certain gases, since not all greenhouse using 100- year GWP values in Chapters 8, 9, and 11. gases related to a given effect may be significant. For example, if an insulation subsidy reduces natural gas For purposes of determining significance, users should combustion, the relative magnitude of each affected apply the most recent GWP values published by the O) should be separately , CH , and N greenhouse gas (CO 2 2 4 IPCC. In Chapters 8, 9, and 11, users may either apply emissions may be expected assessed. The change in CO 2 (1) the IPCC GWP values agreed to by the UNFCCC, or O emissions may be to be major, but the change in N 2 (2) the most recent GWP values published by the IPCC. O may be excluded expected to be minor. In this case, N 2 from the assessment. The relative magnitude depends on both the relative contribution of the greenhouse estimated based on national emission factors, gas— 7. 2 Determine which GHG effects, jurisdictional GHG inventories, or other sources— as well source/sink categories, and as the magnitude of change expected to result from greenhouse gases to include in each gas. Table 7.3 provides an example of assessing the GHG assessment boundary the significance of GHG effects separately by gas. include all significant GHG effects, source/ shall Users gases CO Assessing the relative magnitude of non- 2 sink categories, and greenhouse gases in the GHG , N ) requires global O, HFCs, PFCs, SF , and NF (CH 3 6 2 4 assessment boundary. Users may define significance warming potential (GWP) values. See Box 7.2 for guidance based on the context and objectives of the assessment. on selecting GWP values when determining significance. In general, users should consider all GHG effects to be significant (and therefore included in the GHG assessment boundary) unless they are estimated to be either minor in size or expected to be unlikely or very unlikely to occur (see Figure 7.2). Users may consider unlikely effects that Policy and Action Standard 64

67 CHAPTER 7 Defining the GHG Assessment Boundary figure 7.2 r ecommended approach for determining significance based on likelihood and magnitude magnitude likelihood moderate major minor very likely should include likely possible Identify effects unlikely may exclude very unlikely Note: The area shaded green corresponds to significant GHG effects. are moderate or major to be significant, depending on the in GHG emissions resulting from the policy or action and context and objectives. making needs of users of the that it serves the decision- assessment report. Users shall report the approach used to determine the significance of GHG effects. Users should exercise caution in excluding any significant effects from the assessment. Exclusions are likely to lead to disclosing and justifying exclusions misleading and biased results and not accurately represent Users should strive for completeness, but accounting for all the change in emissions resulting from the policy or action. significant effects may not be feasible in all cases. Excluding Where possible, instead of excluding significant effects effects may be necessary in certain cases based on altogether, users should: limitations related to: Use simplified or less rigorous estimation methods to • • Measurability or data availability approximate the magnitude of the effect; or 1 Use proxy data to fill data gaps. • Relevance to policy objectives and context (such as • the requirements of the applicable program, project, disclose and justify any exclusions of shall Users or agreement) GHG effects, source/sink categories, or greenhouse User resources and capacity • gases from the GHG assessment boundary. Users may exclude GHG effects from the assessment, provided that any exclusion is disclosed and justified. Users 7.2 guidance should follow the principles of relevance, completeness, accuracy, consistency, and transparency when deciding Box 7.3 provides an example of selecting GHG effects whether to exclude any GHG effects, and should not for inclusion in the GHG assessment boundary based exclude any GHG effects that would compromise the on an estimation of likelihood and relative magnitude. relevance of the GHG assessment. Users should ensure that the GHG assessment appropriately reflects the changes 65

68 Hg assessment boundary for a home insulation subsidy program Box 7.3 illustrative example of defining the g Chapter 6 includes an illustrative example of a causal chain for a home insulation subsidy program (Figure 6.6). Figure 7.3 and Table 7.3 illustrate how to assess each effect in terms of expected likelihood and relative magnitude to determine which effects to include in the GHG assessment boundary. In Figure 7.3, stars indicate GHG effects included in the boundary. Hg effect to determine which effects to include in the g Hg figure 7.3 example of assessing each g assessment boundary first stage third stage fifth stage fourth stage second stage reduced reduced reduced emissions emissions from electricity from electricity coal mining generation reduced generation demand for likely, major possible, electricity minor and natural reduced reduced consumers reduced gas to heat emissions from emissions from purchase and emissions from homes natural gas natural gas install home natural systems systems insulation gas use possible, possible, very likely, minor minor major subsidy for home increased increase in increased insulation increased production disposable demand for emissions from of goods & income due to goods & manufacturing services savings services possible, increased minor Businesses emissions produce more p olicy or action from insulation insulation manufacturing intermediate effect possible, moderate gHg effect Stars indicate GHG effects included in the boundary. Note: table 7.3 example of assessing each g Hg effect separately by gas to determine which g Hg effects and greenhouse gases to include in the g Hg assessment boundary gHg effect relative magnitude included? likelihood reduced emissions from electricity generation Major Likely included co 2 Likely Minor Excluded cH 4 Excluded Minor Likely o n 2 Policy and Action Standard 66

69 CHAPTER 7 Defining the GHG Assessment Boundary Box 7.3 illustrative example of defining the g Hg assessment boundary for a home insulation subsidy program (continued) Hg effect separately by gas to determine which g example of assessing each g table 7.3 Hg effects and greenhouse gases to include in the g Hg assessment boundary (continued) Included? gHg effect likelihood relative magnitude Reduced emissions from coal mining Minor Possible Excluded cH 4 Reduced emissions from natural gas systems (from reduced electricity use) Identify effects Excluded Minor Possible co 2 Possible Minor Excluded cH 4 reduced emissions from home natural gas use (space heating) Major Very likely included co 2 Very likely Minor Excluded cH 4 Minor Excluded Very likely o n 2 reduced emissions from natural gas systems (from reduced natural gas use) Possible Minor Excluded co 2 Excluded Possible Minor cH 4 increased emissions from manufacturing of goods and services Possible Minor Excluded co 2 Possible Excluded Minor cH 4 Possible Minor Excluded o n 2 increased emissions from insulation manufacturing Possible Moderate included co 2 Possible Minor Excluded cH 4 Minor Excluded Possible o n 2 Moderate Possible included Hfcs Finally, the significant GHG effects, source/sink categories, and greenhouse gases are included in the GHG assessment boundary (see Table 7.4). 67

70 Hg assessment boundary for a home insulation subsidy program Box 7.3 illustrative example of defining the g (continued) example of developing a list of g table 7.4 Hg effects, source/sink categories, and greenhouse gases Hg assessment boundary included in the g gHg effect included sources sinks greenhouse gases Fossil fuel combustion in reduced emissions from electricity N/A CO 2 grid-connected power plants generation Residential natural gas reduced emissions from home N/A CO 2 combustion (space heating) natural gas use (space heating) Insulation manufacturing increased emissions from insulation CO N/A , HFCs 2 processes manufacturing reevaluating significance impacts in nonlinear systems. If more accurate estimation through an iterative process leads to significant differences in the estimated magnitude The application of the significance criteria may be an of GHG effects, a reevaluation of significance in this iterative process. The estimation of the GHG effects chapter may be necessary. in Chapters 8, 9, and 11 may result in changes to the Box 7.4 provides a case study of defining the GHG expected magnitude or likelihood of effects. For example, assessment boundary. small or unlikely effects can result in large unforeseen Box 7.4 defining the g unisian nama for energy conservation in the building sector Hg assessment boundary for the t The National Agency for Energy Conservation (ANME) of To define the GHG assessment boundary for the NAMA, Tunisia, Alcor, and Ecofys carried out an ex- ante assessment each potential GHG effect (identified in the causal chain) of the nationally appropriate mitigation action (NAMA) was assessed in terms of both its likelihood of occurring and its estimated emissions impact (using initial calculation for energy conservation in the building sector in Tunisia. methods). Effects were included in the assessment boundary The NAMA includes a solar program for commercial and unless they were found to be either minor or very unlikely. residential buildings (including solar water heaters and solar Table 7.5 presents the results for the solar water heater photovoltaic energy) and a thermal insulation program for existing and new residential buildings. The objective of program and the thermal insulation program. Defining the the assessment was to estimate and report the expected boundary around significant effects helped focus efforts GHG emission reductions in order to attract and facilitate on the most significant impacts, while ensuring that no international support for the NAMA. significant effects of the NAMA were excluded. Policy and Action Standard 68

71 Defining the GHG Assessment Boundary CHAPTER 7 Box 7.4 defining the g Hg assessment boundary for the t unisian nama for energy conservation in the building sector (continued) Hg effects to include in the g unisian Hg assessment boundary for the t example of identifying the g table 7.5 energy conservation nama estimated relative included in relative magnitude (in assessment likelihood gHg effect magnitude absolute value terms) boundary? solar water heater program Identify effects reduced g Hg emissions as a result Major 70% Very likely included of reduced residential lpg use reduced g Hg emissions as a Very likely Major 27% included result of reduced residential natural gas use Hg emissions reduced fugitive g Excluded Minor 0.3% Likely as a result of reduced gas transport (minor) and storage increased emissions as a result Excluded Very unlikely Moderate 2% of increased demand for goods (very unlikely) and services increased emissions as a result of Excluded Likely Minor 1% increased transport activity by solar (minor) water heater service providers thermal insulation program reduced g Hg emissions as a result of reduced combustion in Major 14 % Very likely included conventional power plants for a household building reduced g Hg emissions as a 84% Major Very likely included result of reduced residential natural gas use reduced fugitive g Hg emissions Excluded Likely Minor 1% due to reduced gas transport (minor) and storage Excluded increased g Hg emissions due to (very unlikely, Very unlikely Minor 1% increased demand for goods minor) and services 69

72 7. 3 Define the GHG assessment period term and long- term In the steps outlined above, both short- effects are included in the GHG assessment boundary if determined to be significant. Users shall define and the time period over report the GHG assessment period— which GHG effects resulting from the policy or action are based on the time horizon of the GHG effects assessed— included in the GHG assessment boundary. 7.3 guidance ante GHG assessment period (forward- looking) The ex- term effect included in the is determined by the longest- GHG assessment boundary. The GHG assessment period may be longer than the policy implementation period— the time period during which the policy or action is in effect— and should be as comprehensive as possible to capture the full range of significant effects based on when they are expected to occur. post GHG assessment period (backward- looking) The ex- should cover the period between the date the policy or action is implemented and the date of the assessment. The GHG assessment period for a combined ex- ante and ex- post assessment should consist of both an ex- ante GHG assessment period and an ex- post GHG assessment period. In addition, users may separately estimate and report GHG effects over any other time periods that are relevant. For example, if the GHG assessment period is 2015–40, a user may separately estimate and report GHG effects over the periods 2015–20, 2015–30, and 2015–40. endnote For guidance on filling data gaps, see IPCC 2006: Vol. 1. 1, Chap. 2, “Approaches to Data Collection.” Policy and Action Standard 70

73 CHAPTER 7 Defining the GHG Assessment Boundary Identify effects 71

74 8 Estimating Baseline Emissions

75 stimating the effect of a policy or action requires a reference case, or scenario, against which GHG effects are estimated. The baseline baseline Estimate effects E scenario represents what would have happened in the absence of the policy step, assessed. baseline emissions is a critical estimating or action being Properly since it has a direct and significant impact on the estimated GHG effect of the policy or action. In this chapter, users estimate baseline scenario emissions for the set of sources and sinks included in the GHG assessment boundary. figure 8.1 overview of steps for estimating baseline emissions review key estimate baseline aggregate baseline choose type emissions using scenario section concepts ( of baseline emissions across method ( section 8.4) 8.1) and determine comparison all sources/sinks sequence of steps or comparison group (section 8.6) (section 8.3) (section 8.2) section 8.5) method ( checklist of accounting requirements section accounting requirements For users applying the scenario method: estimate • Define a baseline scenario that represents the conditions most likely to occur in the absence of the baseline policy or action for each source or sink category included in the GHG assessment boundary. emissions using • Estimate baseline emissions and removals over the GHG assessment period for each source/sink the scenario category and greenhouse gas included in the GHG assessment boundary. method • Apply GWP values provided by the IPCC based on a 100-year time horizon. (section 8.4) For users applying the comparison group method: estimate • Identify an equivalent comparison group for each source or sink category included in the GHG assessment baseline boundary. emissions using • Estimate emissions and removals from the comparison group and the policy group over the GHG assessment the comparison period for each source/sink category and greenhouse gas included in the GHG assessment boundary. group method • Apply GWP values provided by the IPCC based on a 100-year time horizon. (section 8.5) Reporting requirements are listed in Chapter 14. Note: 73

76 Key concepts 8 .1 The methods described in this chapter apply to both To estimate the change in GHG emissions resulting from post baseline scenarios. See Figure 8.2 for ante and ex- ex- a given policy or action, users define two scenarios: a diagram illustrating both types of baseline scenarios. , which represents the events or The • baseline scenario This standard is not based on the concept of additionality as conditions most likely to occur in the absence of the based accounting. See Box 8.1 commonly defined in project- policy or action (or package of policies and actions) for more information. being assessed; and • , which represents the events or The policy scenario 8.2 Determine sequence of steps conditions most likely to occur in the presence of the for estimating the GHG effects policy or action (or package of policies and actions) of the policy or action being assessed. To estimate a change in emissions resulting from a policy or The baseline scenario depends on assumptions related action, users follow four basic steps (see Figure 8.3). These to key emissions drivers over the GHG assessment ante assessment) steps cover both Chapters 8 and 9 (for ex- period. Drivers include other policies or actions and Chapters 8 and 11 (for ex- post assessment). that have been implemented or adopted, as well Users may first estimate baseline emissions (described in policy drivers, such as economic conditions, as non- this chapter) before estimating policy scenario emissions, energy prices, and technological development. ante (Chapter 9) or ex- either ex- post (Chapter 11). In When estimating baseline emissions, users should at a this case, users should proceed first with Chapter 8 minimum estimate all sources and sinks expected to change and then subsequently with Chapter 9 or 11. between the baseline scenario and the policy scenario. Alternatively, users may first estimate policy scenario Users do not need to calculate emissions from sources and emissions before estimating baseline scenario sinks that remain constant between the baseline scenario emissions, or may implement the two steps in parallel and the policy scenario, since they do not contribute to the rather than in sequence (for example, if necessitated change in emissions resulting from the policy or action. by certain models), as long as both steps are carried ante or ex- Baseline scenarios can be determined ex- post. out and separately reported (if feasible based on the ante baseline scenario looking baseline An ex- is a forward- method used). In these cases, users should implement scenario, typically established prior to implementation Chapters 8 and 9 jointly (for ex- ante assessment) or of the policy or action, which is based on forecasts Chapters 8 and 11 jointly (for ex- post assessment). of emissions drivers (such as projected changes in In certain cases, users may calculate the GHG effect of the population, economic activity, or other drivers that affect policy or action directly, without explicitly defining separate ante baseline emissions), in addition to historical data. Ex- baseline and policy scenarios. In this case, users should ante assessment in Chapter 9. scenarios are used for ex- still use the guidance provided in Chapters 8 and 9 is a backward- An ex- looking baseline post baseline scenario ante assessment) or Chapters 8 and 11 (for (for ex- scenario established during or after implementation of the ex- post assessment). For more information, see Box 8.2. post baseline scenarios should include policy or action. Ex- Users may apply different sequences of steps for ante forecasts of emissions drivers, if an updates to the ex- different categories of sources/sinks and then aggregate post baseline ante assessment was first undertaken. Ex- ex- the GHG effects across source/sink categories to post assessment in Chapter 11. scenarios are used for ex- estimate the total GHG effect of the policy or action. Policy and Action Standard 74

77 CHAPTER 8 Estimating Baseline Emissions figure 8.2 ex-ante and ex-post assessment e) 2 ex-ante baseline scenario mt co gHg effect of gHg effect of policy/action ex-post baseline scenario policy/action Historical (ex-ante) (ex-post) ex-post policy scenario gHg (observed emissions) emissions Hg emissions* ( ex-ante policy scenario net g Estimate effects 2020 2 015 2 010 Note: * Net GHG emissions from sources and sinks in the GHG assessment boundary. Box 8.1 additionality incentives generated by an offset crediting program. A This standard is designed to determine whether a policy or action results in GHG effects that are additional to project is additional if it would not have been implemented 1 This standard does not in the absence of such incentives. what would have happened in the absence of the policy address additionality in this sense, because the objective or action, since GHG effects are estimated relative to a baseline scenario that represents what would have most is not to determine whether a policy or action would have likely happened in the absence of the policy or action. For been implemented in the absence of a particular financing example, if emissions under the baseline scenario and or support mechanism. the policy scenario are the same, the policy does not lead If GHG reductions achieved by policies or actions are to GHG effects that are additional to what would have credited by programs, those programs may impose happened otherwise. additionality requirements or tests beyond the scope of this standard to determine whether the policy or action would The concept of additionality in project- based accounting often concerns whether a GHG mitigation project would have been implemented without receiving the additional finance or incentives generated by the program. have been implemented in the absence of financing or ypical steps in estimating the g Hg effect of the policy or action figure 8.3 t for each source/sink aggregate g estimate baseline estimate policy Hg category, subtract scenario emissions effects across source/ emissions for baseline from policy for each source/sink each source/sink sink categories to scenario emissions to category in the g Hg Hg estimate total g Hg category in the g Hg effect estimate the g assessment chapter 9 assessment boundary effect ( of the policy or action (chapter 9 for ex-ante or boundary for ex-ante or chapter (chapter 9 for ex-ante or (chapter 8) chapter 11 for ex-post) 11 for ex-post) chapter 11 for ex-post) 75

78 alculating the g Box 8.2 c Hg effect directly (further described in Section 8.4). Users should also apply In certain cases, users may apply a simplified method to conservative assumptions and correct for free rider effects, calculate the GHG effect of the policy or action directly, policy interactions, or other factors not otherwise considered without separately estimating baseline emissions and policy scenario emissions. One example is the deemed estimates (further described in Section 8.4). method (also called the “deemed savings” or “unit savings” The deemed estimates method may be more practical approach), where the change in emissions is estimated for example, where it is not feasible to in certain cases— directly by collecting data on the number of actions taken as estimate separate scenarios, where a lower level of accuracy a result of the policy (such as the number of buildings that and completeness is sufficient to meet stated objectives, install insulation) and applying default values that represent or for less significant source/sink categories. Users should the estimated change in GHG emissions or other relevant exercise caution in using the deemed estimate method, since parameter per action taken, relative to a baseline (such as it involves establishing implicit baseline and policy scenario the average reduction in energy use per building that installs assumptions, which are reflected in the default “estimated insulation relative to buildings without insulation or relative to change in GHG emissions per action taken” value. Users buildings with a different type of insulation). Default values should be explicit about baseline scenario and policy scenario may be derived from previously estimated effects of similar assumptions by following all applicable reporting requirements policies or actions. Figure 8.4 outlines the steps involved in in Chapters 8, 9, and 11. The primary method outlined carrying out the deemed estimates method. in Chapters 8, 9, and 11 is the most comprehensive and In order to estimate baseline emissions and removals in transparent approach to developing explicit baseline scenario Equation 8.2, users should define the most likely baseline and policy scenario assumptions. scenario by considering various drivers (both existing Users may use the deemed estimates method for some policies and non- policy drivers) that would affect emissions source/sink categories affected by the policy or action and in the absence of the policy or action being assessed use the primary method for other source/sink categories, then aggregate them (in Section 8.6). figure 8.4 steps in carrying out the deemed estimates method Hg effects aggregate g estimate change multiply to estimate across source/sink estimate number the g Hg effect Hg emissions in g categories to estimate of actions taken per action taken (see equation 8.2) total g Hg effect equation 8.2 c alculating g Hg effect using the deemed estimates method change in emissions and removals = number of actions taken as a result of the policy × (policy scenario emissions and removals for each affected unit, source, or sink – baseline emissions and removals for each affected unit, source, or sink) Policy and Action Standard 76

79 Estimating Baseline Emissions CHAPTER 8 Choose type of baseline comparison 8.3 group exists and the type of policy or action. To reliably post Estimating the GHG effects of a policy or action ex- and credibly implement a comparison group method, involves a comparison of the outcome of the policy actors affected by the policy (the policy group) and actors or action with an estimate of what would most likely not affected by the policy (the comparison group or have happened in the absence of that policy or action. control group) must be otherwise equivalent. Under ideal This comparison can be done in one of two ways: experimental conditions, the two groups would be randomly assigned to ensure that any differences between the A comparison of a baseline scenario • scenario method: groups are a result of the policy, rather than any underlying with a policy scenario for the same group or region systematic differences or biases. If random assignment is • comparison group method: A comparison of one not possible, other methods can be used to avoid “selection group or region affected by the policy or action with bias” and ensure valid comparisons (further described an equivalent group or region not affected by the policy in Section 8.5). If an appropriate comparison group is Estimate effects or action not available, the scenario method should be used. Ex- ante assessments can only use the scenario method. The comparison group method may be feasible for policies post assessments can either use the scenario method Ex- or actions implemented in one subnational jurisdiction or the comparison group method. Figure 8.5 provides a but not in a similar neighboring jurisdiction (assuming that decision tree for choosing between the two methods. the subnational jurisdictions are otherwise equivalent). The comparison group method may not be feasible for broad policies and actions applied to all relevant actors in 8.3 guidance a sector or jurisdiction, such as regulations and standards, taxes or charges, or emissions trading programs, since no determining whether the comparison comparison group would exist. group method is feasible and appropriate post assessment only) (for ex- Users may use a combination of both approaches by using Whether to choose the scenario method or comparison the comparison group method for one subset of source/ group method for ex- post assessment depends on several sink categories and the scenario method for another factors, including whether an equivalent comparison subset, then aggregating the results (in Section 8.6). figure 8.5 decision tree for choosing the type of baseline comparison is the assessment ex-ante or ex-post? ex-post ex-ante is the comparison group method use scenario method feasible and appropriate? ye s no use either the scenario method or use scenario method comparison group method 77

80 Users should only use a combination of methods if it yields more accurate and complete results than would be obtained by using one method consistently for all source/ sink categories. In some cases, data obtained from a comparison group can also be used to update or calibrate specific parameters in what is otherwise an ex- post baseline scenario developed using the scenario method. Users implementing the scenario method should proceed with Section 8.4. Users implementing the comparison group method should proceed to Section 8.5. Estimating baseline emissions 8.4 using the scenario method This section provides guidance on estimating baseline emissions using the scenario method. It is applicable to all ex- post assessments that use the ante assessments and to ex- scenario method. See Figure 8.6 for an overview of steps. Drivers that affect emissions are divided into two types: define the most likely baseline scenario • other policies or actions: Policies, actions, and 8.4.1 The first step in applying the scenario method is to define other than the policy or action being assessed— projects— the baseline scenario. For each source or sink category that are expected to affect the emissions sources and shall included in the GHG assessment boundary, users sinks included in the GHG assessment boundary policy drivers: Other conditions such as non- • define a baseline scenario that represents the conditions socioeconomic factors and market forces that are most likely to occur in the absence of the policy or action. expected to affect the emissions sources and sinks The most likely baseline scenario depends on drivers that included in the GHG assessment boundary would affect emissions in the absence of the policy or action Users shall report a description of the baseline being assessed. Identifying key drivers and determining a description of the events or conditions most scenario— reasonable assumptions about their “most likely” values in likely to occur in the absence of the policy or action being the absence of the policy being assessed have a significant and justification for why it is considered to be assessed— impact on baseline emissions, and consequently on the the most likely scenario. eventual estimate of the GHG effect of the policy or action. figure 8.6 overview of steps for estimating baseline emissions using the scenario method define emissions estimate estimate estimation define the baseline baseline select a method(s) most likely emissions for values for desired level and parameters baseline each source/ each of accuracy needed to calculate scenario sink category parameter (section 8.4.2) baseline emissions (section 8.4.1) (section 8.4.4) (section 8.4.5) (section 8.4.3) Policy and Action Standard 78

81 CHAPTER 8 Estimating Baseline Emissions Estimate effects including other policies or actions report a list of policies, actions, and projects Users shall In addition to the policy or action being assessed, there are included in the baseline scenario and disclose and justify likely to be other policies, actions, or projects that affect any implemented or adopted policies, actions, or projects the sources and sinks included in the GHG assessment with a potentially significant effect on GHG emissions boundary. These may include regulations and standards, that are excluded from the baseline scenario. If planned taxes and charges, subsidies and incentives, emissions policies are included in the baseline scenario, users trading programs, voluntary agreements, information shall report that the baseline scenario includes planned instruments, Clean Development Mechanism (CDM) policies and report which planned policies are included. projects, or voluntary market offset projects. (For more report a list of non- shall policy drivers included in Users examples of policies and actions, see Table 5.1.) the baseline scenario and disclose and justify any relevant Users should include all other policies, actions, and projects policy drivers excluded from the baseline scenario. non- in the baseline scenario that: • Have a significant effect on GHG emissions (increasing 8.4.1 guidance or decreasing) from the sources or sinks included in the Users should identify plausible baseline options and then GHG assessment boundary; and Are implemented or adopted at the time the assessment • choose the option that is considered to be the most likely is carried out (for ex- ante assessment) or are to occur in the absence of the policy or action. Possible implemented during the GHG assessment period (for options include: post assessment). ex- The continuation of current technologies, practices, • or conditions Discrete baseline alternatives, practices, technologies, • cost alternative practice or scenarios (such as the least- or technology), identified using environmental, financial, economic, or behavioral analysis or modeling A performance standard or benchmark indicative of • baseline trends 79

82 See Table 8.1 for definitions of implemented, adopted, Users should establish a significance threshold (such as the ante assessment, and planned policies and actions. For ex- thresholds in Table 7.2) or other criteria to determine which adopted policies should be included in the baseline scenario policies, actions, and projects are significant. if they are likely to be implemented and if there is enough For other policies or actions that are included, users should information to estimate the effects of the policy. Users may determine whether they are designed to operate indefinitely optionally include planned policies in the baseline scenario or are limited in duration. Users should assume that policies ante assessment, for example if the objective is to for ex- or actions will operate indefinitely unless an end date is assess the effect of one planned policy relative to other explicitly stated. planned policies. See Table 8.2 for examples of other policies or actions that may be included. table 8.1 definitions of implemented, adopted, and planned policies and actions policy or action status definition Policies and actions that are currently in effect, as evidenced by one or more of the following: (a) relevant legislation or regulation is in force; (b) one or more voluntary agreements have implemented been established and are in force; (c) financial resources have been allocated; (d) human resources have been mobilized. Policies and actions for which an official government decision has been made and there is a clear commitment to proceed with implementation, but that have not yet begun to be adopted implemented (for example, a law has been passed, but regulations to implement the law have not yet been established or are not being enforced). Policy/action options that are under discussion and have a realistic chance of being adopted planned and implemented in the future, but that have not yet been adopted. UNFCCC 2000. Source: table 8.2 examples of other policies or actions that may be included in a baseline scenario examples of policies or actions examples of other policies or actions being assessed that may be included in the baseline scenario Feed-in tariffs, production tax credits or renewable incentives, renewable energy renewable portfolio standard certificate markets, utility regulations and interconnect fees, rate structures Fuel taxes; tolls on bridges, tunnels, highways subsidies for public transit Mandatory landfill diversion rates, regulations covering waste combustion, inclusion landfill gas management of landfill gas management activities as offset mechanisms in voluntary or mandatory carbon markets, regulations for landfill gas management National agricultural policies, conservation program subsidies sustainable agriculture policy Voluntary/mandatory carbon markets, forest management policies, land-use policies afforestation/reforestation policy Policy and Action Standard 80

83 CHAPTER 8 Estimating Baseline Emissions including non- policy drivers policy drivers by policy type. See Table 8.4 for examples of non- policy drivers include a wide range of exogenous Non- when free rider effects Users should also identify potential factors such as socioeconomic factors and market forces identifying the most likely baseline scenario. The free rider that may cause changes in emissions but are not a result effect refers to participants in a policy or program who of the policy or action assessed. Users should consider the would have implemented the technologies, practices, policy drivers outlined in Table 8.3. types of non- or processes associated with the policy or program in 2 For example, the Users should identify non- policy drivers based on literature the absence of the policy or program. baseline scenario for an insulation subsidy should consider reviews of similar assessments and policies, consultations that a fraction of consumers receiving the subsidy may have with relevant experts and stakeholders, expert judgment, installed the same insulation even without the subsidy. modeling results, or other methods. policy drivers in the baseline Users should include all non- Estimate effects defining a range of baseline scenario options scenario that are not caused by the policy or action being To the extent possible, users should identify the single assessed (i.e., that are exogenous to the assessment), baseline scenario that is considered most likely. In certain and that are expected to result in a significant change in cases, multiple baseline options may seem equally likely. calculated emissions between the baseline scenario and In such cases, users may report a range of results based policy scenario. In ex- ante assessments, users do not need on multiple alternative baseline scenarios. Users should to include drivers that are expected to remain the same conduct sensitivity analysis to see how the results vary under both the policy scenario and baseline scenario. depending on the selection of baseline options. (For more Users should establish a significance threshold (such as the information on sensitivity analysis, see Chapter 12). See thresholds in Table 7.2) or other criteria to determine which Box 8.3 for a case study of choosing the baseline scenario. non- policy drivers are significant. table 8.3 examples of non-policy drivers examples of non-policy drivers specific examples GDP, household income economic activity population National population, city population energy prices Prices of natural gas, petroleum products, coal, biofuels, electricity other relevant prices Commodity prices costs Costs of various technologies weather Heating degree days, cooling degree days autonomous technological Ongoing decarbonization of economic sectors, energy efficiency improvements, long- improvement over time term trends in the carbon- or energy-intensity of the economy Structural changes in economic sectors, shifts from industry to service sector jobs, structural effects shifts of industrial production between countries Changes in preferences for types of vehicles, household size, commuting practices consumer preferences 81

84 table 8.4 examples of non-policy drivers that may be included in a baseline scenario examples of non-policy drivers examples of policies or actions Load forecast, fuel prices by fuel type, renewable technology prices, transmission renewable portfolio standard and distribution accessibility, grid storage capacity, biomass supply, population, GDP Fuel prices, population, cost of transit alternatives, convenience of transit subsidies for public transit alternatives, socioeconomic status of commuters, GDP Landfill tipping fees, value of recycled commodities, waste collection and transport landfill gas management costs, availability of land area for new landfills, population, GDP Agricultural productivity, cropland expansion rate, mixed farming and improved sustainable agriculture policy agroforestry practices, fertilizer and seed prices, population, GDP Value of forest products (fiber or timber), suitability of lands to support forest afforestation/reforestation policy growth, demand for production of food, population, GDP choosing the baseline scenario for the k eystone xl pipeline Box 8.3 The Stockholm Environment Institute (SEI) carried out an and (3) half of the oil would go to market and be consumed ante assessment of the proposed Keystone XL pipeline, ex- (a middle- ground option). Given lack of better information and the different perspectives in the literature, each was which would deliver oil from Canada’s oil sands to the Gulf of Mexico. In 2013, the U.S. government made its approval of considered to be equally likely. the pipeline contingent in part on whether the pipeline would The assessment found that based on the choice of baseline not result in a net increase in greenhouse gas emissions. The scenario, the pipeline could either increase global emissions by objective of the assessment was to inform that decision by e annually, decrease global emissions by 0.3 Mt CO e, 93 Mt CO 2 2 estimating the net global GHG effect of the pipeline, including or increase emissions by some amount in between. jurisdiction and out- both in- of- jurisdiction effects. ante assessment The assessment shows the limitations of ex- The most critical step in the assessment was the if there is no way to identify the most likely baseline scenario, determination of the most likely baseline scenario: What since the results of the assessment hinge on the selection would most likely happen to the oil from the Canadian oil of the most likely baseline scenario. It also shows the sands if the pipeline to the Gulf of Mexico were not built? importance of defining and reporting alternative baseline SEI defined three illustrative baseline scenarios to represent scenarios when uncertainty is high, and conducting sensitivity the range of possibilities if the pipeline were not built: (1) analyses to understand the range of possible results given the uncertainties. (For more information on uncertainty and none of the oil to be carried by Keystone XL would otherwise sensitivity analysis, see Chapter 12.) make it to global oil markets and be consumed; (2) all of the oil would otherwise make it to market and be consumed; Policy and Action Standard 82

85 CHAPTER 8 Estimating Baseline Emissions 8.4.2 select a desired level of accuracy define the emissions estimation 8.4.3 method(s) and parameters needed A range of methods and data can be used to estimate to calculate baseline emissions baseline emissions using the scenario method. Table 8.5 For each source/sink category and greenhouse gas outlines a range of methodological options. included in the GHG assessment boundary, users Users should select a desired level of accuracy based on the should first identify a method (such as an equation, objectives of the assessment, the level of accuracy needed algorithm, or model) for estimating baseline emissions to meet stated objectives, data availability, and capacity/ or removals from that source, then identify the resources. In general, users should follow the most accurate parameters (such as activity data and emission factors) approach that is feasible. needed to estimate emissions using the method. More complex methods often yield more accurate results report the methodology used to estimate shall Users than simpler methods, but not in all cases. Similarly, more Estimate effects baseline emissions, including the emissions estimation source- specific data often yield more accurate results than method(s) (including any models) used. For models default data, but not in all cases. Users should choose without clear documentation, this may require the methods and data that yield the most accurate results within user to extract and simplify key sections of model a given context, based on the methodological and data documentation so the methodology is accessible to options available. relevant stakeholders. table 8.5 range of methodological options for estimating baseline emissions using the scenario method assumptions source of data about non-policy emissions other policies for drivers and drivers and or actions drivers estimation level of parameters parameters method accuracy included included Lower accuracy methods lower Most assumed to (such as Tier 1 International be static or linear Few significant Few significant methods in the default values extrapolations of policies drivers IPCC Guidelines historical trends for National GHG Inventories ) National Intermediate Most significant Most significant Combination average values policies drivers accuracy methods Most assumed to Higher accuracy be dynamic and Jurisdiction- or methods estimated based All significant All significant source-specific (such as Tier 3 on detailed drivers policies data methods in the modeling or Higher ) Guidelines IPCC equations 83

86 Users apply GWP values provided by the IPCC shall For certain types of policies or actions, simple equations year time horizon. Users may use either based on a 100- may not be sufficient to represent the complexity (1) the IPCC GWP values agreed to by the UNFCCC or necessary to accurately estimate baseline or policy scenario (2) the most recent GWP values published by the IPCC. emissions. Detailed modeling approaches may be needed shall report the GWP values used. Users may Users to estimate the effects of certain policies or actions separately estimate and report GHG effects using 20- year (such as an emissions trading program). Detailed models year GWP values. GWP values, in addition to using 100- may also be appropriate when the emissions estimation method includes multiple interacting parameters. The GHG Protocol website provides a list of calculation 8.4.3 guidance tools and resources relevant to estimating the effects of policies and actions (available at www.ghgprotocol.org/ defining the emissions estimation method(s) standard). action- policy- and- The typical method of estimating emissions from a source or sink category, whether baseline scenario emissions identifying parameters in the emissions or policy scenario emissions, is to multiply activity data estimation method(s) by an emission factor. Users should refer to the most Users should identify all parameters required to estimate for National Greenhouse Gas recent IPCC Guidelines baseline emissions using the emissions estimation Inventories for GHG estimation methods and equations method(s) for each source and sink. Parameters are for various sectors and sources/sinks. Users should select variables such as activity data and emission factors methods consistent with the desired level of accuracy. that make up the emissions estimation equations or The same emissions estimation method(s) should be algorithm. The identified parameters will guide the user used to estimate baseline emissions (in this chapter) and in understanding what data needs to be collected to policy scenario emissions (either in Chapter 9 or 11). estimate baseline emissions. A variety of equations, algorithms, and models may Activity data be used to estimate baseline emissions, including: Activity data is a quantitative measure of a level of activity up methods (such as engineering models), top- • Bottom- that results in GHG emissions. Activity data is multiplied by down methods (such as econometric models, regression an emission factor to derive the GHG emissions associated analysis, or computable general equilibrium models), with a process or an operation. Examples of activity data and hybrid methods that combine elements of bottom- are provided in Table 8.6. down methods up and top- Simple equations (such as simple extrapolation) • Emission factors and complex models (such as simulation models or An emission factor is a factor that converts activity data into integrated assessment models) GHG emissions data. Emission factors may be expressed in e emitted per liter terms of energy output (such as kg CO down methods typically model economic relationships Top- 2 e of diesel consumed) or physical output (such as kg CO and often rely on more aggregated data sets, whereas 2 emitted per tonne of steel or cement produced). Table 8.6 bottom- up approaches typically use disaggregated source provides examples of emission factors. or sink data. Hybrid models attempt to combine the up modeling by linking down and bottom- advantages of top- See Box 8.4 for an example of identifying emissions the two types of approaches. For more information, see estimation methods and parameters. See Appendix A for Section 3.2. Users may use existing methods or models that guidance on collecting data. are relevant to the affected sources/sinks or may develop new methods or models (if no relevant and appropriate methods or models exist). Policy and Action Standard 84

87 Estimating Baseline Emissions CHAPTER 8 table 8.6 examples of activity data and emission factors examples of activity data examples of emission factors emitted per liter of fuel consumed kg CO liters of fuel consumed 2 emitted per kWh of electricity consumed kg CO kilowatt-hours of electricity consumed 2 kg PFC emitted per kg of material consumed kilograms of material consumed emitted per kilometer traveled t CO kilometers of distance traveled 2 emitted per hour of time operated kg SF Hours of time operated 6 Estimate effects O emitted per square meter of area g N square meters of area occupied 2 emitted per kg of waste generated g CH kilograms of waste generated 4 example of identifying emissions estimation methods and parameters for a home insulation subsidy program Box 8.4 Box 7.3 in Chapter 7 outlines three emission sources that are affected by a home insulation subsidy program and need to be estimated. One of the sources is residential natural gas combustion. The following equation is an example of an emissions estimation method for this source. gHg emissions from residential natural gas combustion (t co e) = 2 [natural gas used for space heating (Btu) + natural gas used for water heating (Btu) + e/Btu) natural gas used for cooking (Btu)] × natural gas emission factor (t CO 2 The parameters in the emissions estimation method are natural gas used for space heating, natural gas used for water heating, natural gas used for cooking, and natural gas emission factor. Since the policy only affects space heating in particular, users may narrow the equation and parameters to focus only on the specific process or activity affected by the policy, as follows: gHg emissions from residential natural gas combustion related to space heating (t co e) = 2 e/Btu) natural gas used for space heating (Btu) × natural gas emission factor (t CO 2 In this case, the parameters in the emissions estimation method are natural gas used for space heating and natural gas emission factor. In practice, the choice between these two emissions estimation methods may depend on data availability. 85

88 estimate baseline values 8.4.4 option 1: using baseline values for each parameter from published data sources Once parameters are identified, the next step is to In some cases, existing data sources of sufficient quality estimate the values of each parameter under the baseline may be available to determine values for baseline that is, the most likely values for each parameter scenario— parameters. Potential data sources of historical or if the policy or action is not implemented— over the GHG projected data include peer- reviewed scientific literature, assessment period. government statistics, reports published by international institutions (such as the IEA, IPCC, World Bank, FAO, etc.), shall report the following: Users and economic and engineering analyses and models. The baseline values for key parameters in the baseline • to- Users should use high- date, and peer- quality, up- emissions estimation method(s) reviewed data from recognized, credible sources if available. • The methodology and assumptions used to estimate When selecting data sources, users should apply the data baseline values for key parameters, including whether quality indicators in Table 8.8 as a guide to obtaining the each parameter is assumed to be static or dynamic highest quality data available. Users should select data and assumptions regarding other policies/actions and that is the most representative in terms of technology, non- policy drivers that affect each parameter time, and geography; most complete; and most reliable. All sources of data used for key parameters, including • activity data, emission factors, and assumptions option 2: developing new baseline values • Any potential interactions with other policies and In some cases, no published baseline data and assumptions actions and whether and how policy interactions will be available for historical or projected data, or the were estimated existing data may be incomplete, of poor quality, or in justify the choice of whether to develop shall Users need of supplementation or further disaggregation. Users new baseline data and assumptions or to use published should develop new baseline data and assumptions baseline data and assumptions. Users that are not able to when no relevant data are available that supports the justify why the document and report a data source shall level of accuracy needed to meet the stated objectives. source is not reported. To develop new baseline values, users should: Figure 8.7 illustrates the concept of estimating baseline 1. Collect historical data for the parameter emissions by estimating baseline values for each parameter, 2. Identify other policies/actions and non- policy drivers based on underlying drivers. that affect each parameter Table 8.7 provides an example of reporting parameter Estimate baseline values for each parameter, based on 3. values and assumptions. assumptions for each driver Collect historical data for the parameters For each parameter, users should collect historical data 8.4.4 guidance going back to the earliest date for which data of sufficient To estimate baseline values for each parameter, users accuracy, completeness, consistency, and reliability is should first decide whether to develop new baseline values available. Users should collect data with as high a frequency or use baseline values from published data sources. Users as is available and relevant, where multiple sources of data should use conservative assumptions to define baseline exist. For example, monthly data should be preferred over values when uncertainty is high or a range of possible quarterly data, and quarterly data should be preferred over values exist. Conservative values and assumptions are those annual data. more likely to underestimate GHG emissions in the baseline scenario. Conservative values should be used to avoid overestimation of emission reductions. Policy and Action Standard 86

89 CHAPTER 8 Estimating Baseline Emissions figure 8.7 estimating baseline emissions by estimating baseline values for each parameter emissions estimation emissions estimation emissions estimation emissions estimation method d method a method c method B other policies or actions parameters parameters parameters parameters parameters parameters parameters parameters non-policy drivers emissions from emissions from emissions from emissions from source a source c source B source d Estimate effects Baseline emissions table 8.7 example of reporting parameter values and assumptions used to estimate baseline emissions for a home insulation subsidy Baseline value(s) applied over the data sources parameter methodology and assumptions to estimate value(s) gHg assessment period Historical data • Average annual natural gas used for space heating over the previous 10 years is 1,250,000 MMBtu/year • The trend over the past 10 years has been constant (after normalization for variation in heating degree days and cooling degree days) rather than increasing or decreasing Implemented and adopted policies included in the baseline scenario: • Federal energy efficiency standards (expected to reduce National energy natural gas use by 10% in the baseline scenario) statistical agency; natural • Federal energy tax (expected to reduce natural gas use by peer-reviewed 1,000,000 MMBtu/ gas used 7.5% in the baseline scenario, taking into account overlaps literature: year from 2010–25 for space with the federal energy efficiency standards) Author (Year). heating Non-policy drivers included in the baseline scenario: Title. Publication. • Natural gas prices are projected to increase by 20% (expected to reduce natural gas use by 2% in the baseline scenario based on price elasticity of natural gas) • Free rider effect: 10% of households that receive the subsidy are expected to install insulation even if they did not receive the subsidy (expected to reduce natural gas use by 3% in the baseline scenario, given 30% expected reduction in energy use per home insulated) Expected to remain constant at historical levels since no policies natural 55 kg CO National energy are implemented or adopted to reduce the GHG intensity of e/MMBtu gas 2 statistical agency natural gas. Non-policy drivers (such as GDP and energy prices) from 2010–25 emission are not expected to affect this parameter. factor 87

90 table 8.8 data quality indicators indicator description The degree to which the data set reflects the relevant technology(ies). technological representativeness The degree to which the data set reflects the relevant time period. temporal representativeness The degree to which the data set reflects the relevant geographic geographical representativeness location (such as the country, city, or site). The degree to which the data are statistically representative of the relevant activity. Completeness includes the percentage of locations for which data are available and completeness used out of the total number that relate to a specific activity. Completeness also addresses seasonal and other normal fluctuations in data. The degree to which the sources, data collection methods, and verification procedures used to obtain the data are dependable. Data should represent the most reliability likely value of the parameter over the GHG assessment period. Source: Adapted from Weidema and Wesnaes 1996. Identify other policies/actions and non- policy that are included in the baseline scenario. As described drivers that affect each parameter in Chapter 5, policies or actions included in the baseline If users choose to develop new baseline values, the second scenario may interact with each other in overlapping step is to identify key drivers of the emission sources and or reinforcing ways— especially if they affect the same sinks being estimated. Drivers that affect emissions are parameter(s) in the emissions estimation method(s). If divided into two types: (1) other policies or actions, and multiple policies included in the baseline scenario are likely policy drivers. See Section 8.4.1 for guidance on (2) non- to interact, users should estimate the policy interactions identifying and including other policies/actions and non- when estimating baseline parameter values. Users should policy drivers in the baseline scenario. estimate the total net effect of all policies included in the baseline scenario on each parameter. Some models used Estimate baseline values for each parameter to estimate baseline emissions may automatically calculate by making assumptions for each driver interactions between policies. Appendix B provides further Once key drivers have been identified, the next step is guidance on estimating policy interactions. to develop assumptions regarding the change in each Users should estimate baseline values for each driver over the GHG assessment period under the parameter and specify how each parameter is expected baseline scenario (assuming the policy or action is not to change over time in the baseline scenario, taking implemented). Assumptions should represent the most into account the historical data collected for each likely scenario for each driver, based on evidence, such as parameter and the assumptions for each driver over the reviewed literature, government statistics, or expert peer- GHG assessment period. Similar types of estimation judgment. If a variety of assumptions are available from equations, algorithms, and models outlined in Step 2 reliable sources, or assumptions are highly uncertain, users may be used to estimate baseline values of individual should use conservative assumptions that are more likely parameters. For example, users may apply regression to underestimate GHG emissions in the baseline scenario. analysis, simple extrapolation, or various models to The baseline value for each parameter depends on the forecast the baseline value of a parameter in the future effects of the implemented or adopted policies or actions based on assumptions for key drivers. Policy and Action Standard 88

91 CHAPTER 8 Estimating Baseline Emissions Each parameter in the baseline scenario (such as activity sensitivity analysis For either Option 1 or Option 2, users should conduct data or an emission factor) may be assumed to be either static or dynamic over the GHG assessment period. Static sensitivity analysis around key parameters to determine parameters are those assumed to stay constant over time, bound the range of likely values based on upper- while dynamic parameters are assumed to change over and lower- bound assumptions. Sensitivity analysis 3 See Figure 8.8 for an illustration of static and dynamic involves varying the parameters (or combinations time. parameters. Dynamic parameters can be assumed to of parameters) to understand the sensitivity of the change at a linear or nonlinear rate over time. See Figure 8.9 overall results to changes in those parameters. Users for different trends parameters can take over time. Dynamic should prioritize data collection efforts to obtain models that allow for conditions to change throughout the more accurate assumptions for those parameters that GHG assessment period are typically the most accurate are highly sensitive to changes in assumptions— for and should be used where relevant and feasible. A linear example, where a small change in assumptions leads Estimate effects extrapolation of historical trends may be used if there to a large change in estimated GHG effects. (For more are justifiable reasons to assume that historical trends information on sensitivity analysis, see Chapter 12.) would continue in the baseline scenario during the GHG assessment period. figure 8.8 illustration of static and dynamic parameters Baseline emission rate time time static emission rate dynamic emission rate ypes of parameter changes over time figure 8.9 t non-linear linear increase/ constant value decrease parameter value time time time time 89

92 8.4.5 estimate baseline emissions Users shall estimate baseline emissions and removals over for each source/sink category the GHG assessment period for each source/sink category The final step is to estimate baseline emissions by and greenhouse gas included in the GHG assessment using the emissions estimation method identified in boundary. Any sources, sinks, or greenhouse gases in the Section 8.4.3 and the baseline values for each parameter GHG assessment boundary that have not been estimated identified in Section 8.4.4. shall be disclosed, justified, and described qualitatively. Box 8.5 provides a case study of calculating baseline emissions for a policy. alculating baseline emissions for t unisia’s prosol elec program Box 8.5 c The National Agency for Energy Conservation (ANME) Data were needed on: (1) the electricity consumption of together with the Deutsche Gesellschaft für of Tunisia— residential and commercial buildings; and (2) the electrical Internationale Zusammenarbeit (GIZ) GmbH, with support mix used in power plants (by power plant type, such as carried out a combined ex- post natural gas and coal, taking into account grid losses). Baseline from ALCOR Consulting— values for each parameter were derived from statistical reports ante assessment of the PROSOL Elec program in and ex- Tunisia. PROSOL Elec is a renewable energy support program, of the National Electricity and Gas Utility and studies on the launched by ANME in 2010, that aims to promote and support assessment and development of the energy sector in Tunisia. the installation of photovoltaic (PV) systems in residential and These data sources took into account the development of key commercial buildings with low- drivers (such as economic activity, population, energy prices, voltage grid connections. The objectives of the assessment were to assess the program’s and technical costs) and other implemented policies. progress to date and to estimate the program’s future To calculate baseline emissions, the electricity production of contribution to mitigation at the national level. the different power plant types was divided by the efficiency of each plant type to calculate the quantity of gas and fuel The GHG assessment boundary included three significant effects that needed to be estimated: (1) reduction of GHG consumed in each plant. The quantity of consumed gas or fuel was multiplied by national emission factors to calculate emissions resulting from reduced combustion in conventional the total emissions from combustion. Fugitive emissions from power plants; (2) reduction of fugitive GHG emissions gas transport and storage were calculated by multiplying the resulting from reduced gas transport and storage; and quantity of gas consumed with the default emission factor (3) increased GHG emissions resulting from increased production of PV systems (an out- derived from the IPCC. Emissions of methane and nitrous oxide jurisdiction effect). of- were multiplied by their global warming potential (GWP) values For the first effect, the primary source affected by the program to calculate emissions in units of carbon dioxide equivalent. is the production of electricity by conventional power plants for consumption in the residential and commercial buildings The following equation was used to calculate baseline emissions from natural gas combustion in conventional CO sector. To calculate baseline emissions for this source, the 2 power plants in 2010: emissions estimation method and parameters were identified. {Electricity consumption in residential and commercial buildings in 2010 [5,039 GWh] / (1— transmission and distribution losses factor for 2010 [13.5%] ) x natural gas share in energy mix for electricity generation in 2010 [99%]} / (average gas power plants efficiency in 2010 [35%] × conversion factor GWh - >Tj [3.6]) x national emission factor for natural gas in 2010 [56,000 kg/Tj] = emissions from natural gas combustion in 2010 [3,321,895,214 kg = 3,321,895 t] CO 2 Policy and Action Standard 90

93 CHAPTER 8 Estimating Baseline Emissions Estimating baseline emissions identify the policy group 8.5 and comparison group and GHG effects using the comparison group method The first step is to identify the policy group (the group (for ex- post assessment only) or region affected by the policy) and the comparison As outlined in Section 8.3, users may use the comparison group or control group (an equivalent group or region group method to define the baseline scenario when not affected by the policy). The policy groups and post assessment. The comparison carrying out an ex- comparison groups may be groups of people, facilities, group method cannot be used for ex- ante assessments, companies, jurisdictions, sectors, or other relevant groups. since comparative data for the comparison group and The policy group and the comparison group should be policy group during policy implementation cannot be equivalent in all respects except for the existence of the observed prior to policy implementation. policy for the policy group and absence of the policy for Estimate effects The comparison group method involves comparing the comparison group. The most robust way to ensure one group or region affected by a policy or action with two groups are equivalent is to implement a randomized an equivalent group or region that is not affected by experiment— for example, by randomly assigning one that policy or action. Users following the comparison subset of entities to participate in a program and randomly group method identify an equivalent comparison shall assigning the other subset to not participate in the program. group for each source or sink category in the GHG To be equivalent means the comparison group should assessment boundary. be the same or similar to the policy group in terms of: Users applying the comparison group method shall geography: for example, facilities in the same city, • estimate emissions and removals from the comparison subnational region, or country group and the policy group over the GHG assessment • time: for example, facilities built within the same period for each source/sink category and greenhouse time period gas included in the GHG assessment boundary. Users for example, facilities using the technology: • shall apply GWP values provided by the IPCC based on a same technology year time horizon. Any sources, sinks, or greenhouse 100- • other policies or actions: for example, facilities gases in the GHG assessment boundary that have subject to the same set of policies and regulations, shall be disclosed, justified, and not been estimated except for the policy or action being assessed described qualitatively. policy drivers: for example, facilities subject to • non- the same external trends, such as the same changes in economic activity, population, weather, and energy prices 8.5 guidance When identifying a potential comparison group, users should See Figure 8.10 for an overview of key steps. This section collect data from both the policy group and the comparison includes a final step of estimating the GHG effect of the group before the policy or action is implemented to policy or action, in addition to estimating baseline emissions. determine whether the groups are equivalent. Users figure 8.10 overview of steps for using the comparison group method estimate emissions identify the policy collect data from from both groups and the policy group and group and comparison Hg effect estimate the g comparison group group of the policy or action 91

94 should ensure that the entities in the comparison group be attributed to the existence of the policy, rather than to are not directly or indirectly affected by the policy. other factors. If the groups are similar but not equivalent, statistical In most cases, differences are expected to exist between methods can be used to control for certain factors that the groups. If material differences exist that may affect the differ between the groups (described further below). If outcome, users should use statistical methods to control the groups are not sufficiently equivalent, the comparison for variables other than the policy that differ between the group method will yield misleading results, so users should non- equivalent groups. Such methods are intended to help follow the scenario method instead (see Section 8.4). address the “selection bias” and isolate the effect of the policy being assessed. See Box 8.6 for examples of methods collect data from the policy group that may be used. and comparison group post, For additional guidance on estimating GHG effects ex- Users should collect data from both the policy group refer to Chapter 11. and the comparison group for all the parameters (such as activity data and emission factors) included in the Box 8.6 statistical methods for estimating emissions estimation methods. (Section 8.4.3 provides gHg effects and controlling for factors guidance on selecting an emissions estimation method.) that differ between groups Users should collect data from both groups at multiple points in time to account for changes in emissions and involves including data for each regression analysis various drivers that occur over time. At a minimum, users relevant driver that may differ between the groups (such should collect data from both groups before and after the as economic activity, population, energy prices, and policy or action is implemented (in the policy group), so weather) as explanatory variables in a regression model, that the two groups can be compared during both the as well as proxies for other relevant policies that may differ policy period and the policy implementation period. pre- between the two groups (other than the policy being up data may be used. To collect down or bottom- Either top- assessed). If the expanded regression model shows a bottom- up data, representative sampling may be used to statistically significant effect of the policy being assessed, collect data from a large number of individual sources or then the policy can be assumed to have an effect on the facilities. If so, appropriate statistical sampling procedures policy group, relative to the comparison group. should be used, and the sample size should be large difference- compare two difference methods in- enough to draw valid statistical conclusions. Chapter 10 and groups over two periods of time: a first period in which Appendix A provide additional guidance on collecting data. neither the policy group nor the comparison group estimate emissions from both groups and implements a given policy and a second period in which estimate the g Hg effect of the policy or action the policy group implements the policy and the comparison After data are collected, users should estimate baseline group does not. This method estimates the difference - B1 between the groups prior to policy implementation (A1 emissions (from the comparison group) and policy scenario = X); the difference between the two groups after policy emissions (from the policy group). In rare cases where the implementation (A2 - B2 = Y); and the difference between policy group and comparison group are equivalent, the the two differences (Y - X) as a measure of the change outcomes of each group in terms of emissions over time attributable to the policy. test) can be compared directly. A statistical test (such as a t- should be employed to ensure that the difference in values are statistical approaches for making matching methods cannot be attributed to chance. If the difference between two groups (a policy group and a comparison group) more the two groups is statistically significant, the difference can equivalent, when random assignment is not possible. Policy and Action Standard 92

95 Estimating Baseline Emissions CHAPTER 8 Aggregate baseline emissions 8.6 across all source/sink categories The final step is to aggregate estimated baseline emissions across all categories of sources and sinks included in the GHG assessment boundary to estimate total baseline emissions, if feasible based on the method used. This may involve aggregating baseline emissions across sources and sinks calculated using the scenario method and/or the comparison group method. When aggregating across sources and sinks, users should address any possible overlaps or interactions between sources and sinks to avoid or underestimation of total baseline emissions. over- Estimate effects Users shall report total annual and cumulative baseline scenario emissions and removals over the GHG assessment period, if feasible based on the method used. Users should separately estimate in- jurisdiction baseline emissions/ removals and out- of- jurisdiction baseline emissions/ removals, if relevant and feasible. See Table 8.9 for an example of calculating and aggregating baseline emissions. table 8.9 example of calculating and aggregating baseline emissions for a home insulation subsidy gHg effect included in the Baseline emissions affected sources gHg assessment boundary Fossil fuel combustion in 50,000 t CO e reduced emissions from electricity use 2 grid-connected power plants reduced emissions from home natural gas use Residential natural gas combustion 20,000 t CO e 2 (space heating) e 5,000 t CO Insulation manufacturing processes increased emissions from insulation production 2 e 75,000 t co total baseline emissions 2 The table provides data for one year in the GHG assessment period. Note: endnotes 1. The UNFCCC defines additionality under the Clean Development 3. These terms are sometimes used differently in the context based accounting (such as CDM), where the term Mechanism (CDM) as follows: “A CDM project activity is additional of project- “dynamic baseline” refers to a baseline scenario that is changed if anthropogenic emissions of greenhouse gases by sources are reduced below those that would have occurred in the absence of or updated ex- post during or after the project implementation period. This standard uses the terms to refer not to updating a the registered CDM project activity.” 2. Adapted from Kushler, Nowak, and Witte 2014. baseline scenario over time but instead to parameter values that are assumed to change over time. 93

96 9 Estimating GHG Effects Ex- Ante

97 his chapter future GHG effects of the expected how to estimate describes (ex- In this chapter, users estimate the policy or action ante assessment). policy scenario emissions for the sources and sinks included in the GHG T The GHG effect of the policy or action is estimated by assessment boundary. subtracting baseline emissions (as determined in Chapter 8) from policy scenario Estimate effects emissions (as determined in this chapter). Users that choose only to estimate GHG post may skip this chapter and proceed to Chapter 10. effects ex- figure 9.1 overview of steps for estimating g Hg effects ex-ante define the most identify parameters select a desired likely policy scenario to be estimated level of accuracy (section 9.2) (section 9.3) (section 9.1) estimate policy scenario Hg effect estimate the g estimate policy scenario values for parameters of the policy or action emissions ( section 9.5) (section 9.4) (section 9.6) checklist of accounting requirements (for users carrying out ex-ante assessment) accounting requirements section • Define a policy scenario that represents the conditions most likely to occur in the define the most likely policy presence of the policy or action for each source or sink category included in the GHG scenario ( section 9.1) assessment boundary. Estimate policy scenario emissions and removals over the GHG assessment period • for each source/sink category and greenhouse gas included in the GHG assessment estimate policy scenario boundary, based on the GHG effects included in the boundary. emissions ( section 9.5) • Apply the same GWP values used to estimate baseline emissions. Estimate the GHG effect of the policy or action by subtracting baseline emissions • estimate the g Hg effect of the from policy scenario emissions for each source/sink category included in the GHG section 9.6) policy or action ( assessment boundary. Reporting requirements are listed in Chapter 14. Note: 95

98 Define the most likely 9.1 Users do not need to calculate emissions from sources and policy scenario sinks that remain constant between the baseline scenario ante assessment may estimate Users carrying out an ex- and the policy scenario, since they do not contribute to the ex- ante policy scenario emissions either before or after change in emissions resulting from the policy or action. estimating ex- ante baseline emissions. See Section 8.2 in Chapter 8 for more information on the sequence of steps. Identify parameters 9.2 Chapter 8 outlines two approaches to defining the baseline to be estimated scenario: the scenario method and the comparison The same emissions estimation method(s) used group method. Only the scenario method is relevant to to estimate baseline emissions should also be ante assessment. This chapter assumes the user has ex- used to estimate policy scenario emissions from estimated baseline emissions using the scenario method. each source or sink. Consistency ensures that the estimated change in emissions reflects underlying represents the events or conditions The policy scenario differences between the two scenarios, rather than most likely to occur in the presence of the policy or differences in estimation methodology. For more action (or package of policies or actions) being assessed. information on emissions estimation methods The policy scenario is the same as the baseline scenario and parameters, see Chapter 8, Section 8.4. except that it includes the policy or action (or package of policies/actions) being assessed. Policy scenario To estimate policy scenario emissions, users should are an estimate of GHG emissions and removals emissions first identify all the parameters (such as activity data associated with the policy scenario. See Figure 9.2 for and emission factors) in the emissions estimation ante. an illustration of estimating GHG effects ex- method(s) that are affected by the policy or action. These parameters need to be estimated in the policy For each source or sink category included in the GHG scenario. Parameters that are not affected by the policy assessment boundary, users shall define a policy or action do not need to be estimated because the scenario that represents the conditions most likely to values remain constant between the baseline scenario occur in the presence of the policy or action. Users and the policy scenario. To identify affected parameters, should identify various policy scenario options and users should consider each GHG effect included in then choose the one considered to be the most likely the GHG assessment boundary (see Figure 9.3). to occur in the presence of the policy or action. Users shall report a description of the policy scenario. figure 9.2 estimating g Hg effects ex-ante e) 2 ex-ante baseline scenario mt co gHg effect of policy/action Historical (ex-ante) gHg emissions Hg emissions* ( ex-ante policy scenario net g 2 015 2020 2 010 * Net GHG emissions from sources and sinks in the GHG assessment boundary. Note: Policy and Action Standard 96

99 Ante Estimating GHG Effects Ex- CHAPTER 9 figure 9.3 identifying parameters affected by the policy or action emissions estimation emissions estimation emissions estimation emissions estimation method B method c method d method a gHg effect 1 gHg effect 2 gHg effect 3 parameters parameters parameters parameters parameters parameters parameters parameters Estimate effects emissions from emissions from removals by emissions from source c sink d source a source B policy scenario emissions Stars indicate parameters affected by the policy or action. Note: 9.2 guidance identifying parameters affected by the policy or action In some cases, it may be straightforward to determine which parameters are affected by the policy or action. See Box 9.1 for an example. In other cases it may be difficult to determine whether a parameter is affected. In such cases users may apply the significance methodology outlined in Chapter 7 to determine the likelihood of each parameter being affected and the relative magnitude of the expected impact. For parameters unlikely or very unlikely to be affected by the policy or action— or where the expected impact is expected to be minor— baseline values may be used in the policy scenario, under the assumption that the parameter remains constant between the baseline scenario and the policy scenario. 97

100 Box 9.1 example of identifying parameters and determining which are affected by the policy or action assessed (for a home insulation subsidy) Box 8.4 in Chapter 8 defines an emissions estimation method and parameters needed to estimate baseline emissions for residential natural gas combustion, one of three sources affected by the subsidy. To estimate policy scenario emissions from this source, the same emissions estimation method and parameters are used to estimate policy scenario emissions, as follows: gHg emissions from residential natural gas combustion (t co e) = 2 [natural gas used for space heating (Btu) + natural gas used for water heating (Btu) + natural gas used for cooking (Btu)] × natural gas emission factor (t CO e/Btu) 2 The parameters in the emissions estimation method are: A. natural gas used for space heating B. natural gas used for water heating C. natural gas used for cooking D. natural gas emission factor The next step is to identify which parameters are affected by the home insulation subsidy and which are not. Parameter A (natural gas used for space heating) is affected by the policy (since insulation reduces energy demand for space heating), so the policy scenario value for this parameter is expected to differ from the baseline scenario value. However, parameters B, C, and D are not affected by the policy (since insulation does not reduce energy demand for water heating or cooking), so the policy scenario values for these parameters are expected to stay the same as in the baseline scenario. The difference in emissions between the policy scenario and baseline scenario for this source (residential natural gas use) will result from the change in parameter A (natural gas used for space heating). Alternatively, since the policy only affects space heating in particular, users may narrow the equation and parameters to focus only on the specific process or activity affected by the policy, as follows: gHg emissions from residential natural gas combustion related to space heating (t co e) = 2 natural gas used for space heating (Btu) × natural gas emission factor (t CO e/Btu) 2 The parameters in the emissions estimation method are: A. natural gas used for space heating natural gas emission factor B. In this case, the difference in emissions between the policy scenario and baseline scenario for this source (residential natural gas use) will also result from the change in parameter A (natural gas used for space heating) only. Policy and Action Standard 98

101 CHAPTER 9 Ante Estimating GHG Effects Ex- for parameters not affected by the policy or Select a desired level of accuracy • 9.3 Users may use a range of methods and data to estimate action: For these parameters, the parameter value policy scenario emissions. Table 9.1 outlines a range of is not expected to differ between the policy scenario methodological options that may be used. Users should and baseline scenario. The baseline value for that select a desired level of accuracy based on the objectives parameter (estimated in Chapter 8) should also be of the assessment, the level of accuracy needed to meet used as the policy scenario value for that parameter (in stated objectives, data availability, and capacity/resources. this chapter). All drivers and assumptions estimated in In general, users should follow the most accurate approach the baseline scenario should be the same in the policy that is feasible. scenario except for those drivers and assumptions that are affected by the policy or action being assessed. report the methodology used to estimate policy shall Users • for parameters affected by the policy or scenario emissions, including the emissions estimation Estimate effects For these parameters, the parameter value action: method(s) (including any models) used. is expected to differ between the policy scenario and baseline scenario. Users should follow the same Estimate policy scenario 9.4 general steps described in Section 8.4 but should values for parameters for each parameter estimate the policy scenario value The approach to estimating policy scenario values for each rather than the baseline scenario value for each parameter depends on whether the parameter is expected parameter. This requires developing assumptions to be affected by the policy or action. about how the policy or action is expected to affect each parameter over the GHG assessment period. table 9.1 range of methodological options for estimating policy scenario emissions interactions with assumptions about policies included in level of parameters in the data emissions policy scenario sources the baseline scenario estimation method accuracy Lower accuracy methods Most assumed to lower (such as Tier 1 methods Few interacting be static or linear International in the IPCC Guidelines policies assessed extrapolations of default values for National Greenhouse historical trends s) Gas Inventorie Intermediate Most interacting National Combination accuracy methods average values policies assessed Most assumed to Higher accuracy methods be dynamic and Jurisdiction- All interacting (such as Tier 3 methods estimated based on or source- Higher policies assessed ) in the IPCC Guidelines detailed modeling specific data or equations 99

102 • shall report the following: Barriers to policy implementation or effectiveness Users Policy interactions • • The policy scenario values for key parameters in the Sensitivity of parameters to assumptions • emissions estimation method(s) • The methodologies and assumptions used to estimate To the extent relevant, users should also consider the policy scenario values for key parameters, including whether following additional factors: each parameter is assumed to be static or dynamic Non- • policy drivers included in the baseline scenario (see • All sources of data for key parameters, including activity Chapter 8), which should be the same between the policy data, emission factors, GWP values, and assumptions scenario and baseline scenario if they are not affected by Any potential interactions with other policies and actions • the policy assessed, but should be different between the and whether and how policy interactions were estimated two scenarios if they are affected by the policy Learning curves (economic patterns related to new • If users are not able to report a data source, users shall product development and deployment) justify why the source is not reported. Economies of scale • Technology penetration or adoption rates (the pace of • adoption by targeted actors, which may be slow initially then 9.4 guidance accelerate as products become more socially accepted) estimating policy scenario values for Depending on the assessment, users may not need to parameters affected by the policy or action consider each of these factors. In practice, users may also Users should estimate the change in the parameter over be limited by the following considerations: time based on what is considered to be the most likely Type of policy or action (which may require • scenario for each parameter, based on evidence, such as consideration of certain factors but not others) peer- reviewed literature, modeling or simulation exercises, Emissions estimation method (for example, simplified • government statistics, or expert judgment. Existing literature approaches may be limited to linear approximations) or methodologies may not be similar enough to use directly. • Data availability (which may limit the number of factors Users may need to make adjustments to results found in that can be considered) literature to adapt to the assumptions made in the baseline Objectives of the assessment (which may require a • scenario and other elements of the assessment. Users may more or less complete and accurate assessment) need to apply new methods, models, and assumptions not • Available resources to conduct the assessment previously used in the baseline methodology to estimate the expected change in each parameter as a result of the GHG 1 Historical trends and expected values effects of the policy or action. in the baseline scenario Each parameter may be assumed to be static or dynamic over Historical data informs the expected future values of the GHG assessment period, and dynamic parameters can each parameter, in both the baseline scenario and the change at a linear or nonlinear rate. In many cases, dynamic policy scenario. Understanding the historical values of the models that allow for conditions to change throughout the parameter as well as the expected values in the baseline GHG assessment period are expected to be most accurate, scenario are both useful when estimating policy scenario so they should be used where relevant and feasible. values. For more information on historical data, see To estimate policy scenario values for each parameter Section 8.4.4. affected by the policy or action, users should consider a timing of effects variety of factors (described in more detail below), such as: Policy scenario values over time depend on the timing of • Historical trends and expected values expected effects. There may be a delay between when the in the baseline scenario policy or action is implemented and when effects begin to Timing of effects • Policy and Action Standard 100

103 Estimating GHG Effects Ex- CHAPTER 9 Ante occur. Effects may also occur before policy implementation In addition to estimating and reporting the full effects of begins because of early action taken in anticipation of the the policy or action over the GHG assessment period, policy or action. users may separately estimate and report GHG effects over any other time periods that are relevant. For Users should consider whether the policy or action is designed example, if the GHG assessment period is 2015–40, to operate indefinitely or is limited in duration (defined in users may separately estimate and report GHG effects Chapter 5). Users should assume that a policy or action will over the periods 2015–20, 2015–30, and 2015–40. operate indefinitely unless an end date is explicitly embedded in the design of the policy or action, despite inherent Barriers to policy implementation or effectiveness uncertainty over whether it will eventually be discontinued. If The policy scenario values should represent the values the policy or action is limited in duration, the GHG assessment most likely to occur in the presence of the policy or period may include some GHG effects that occur during the action, which depend on assumptions related to policy Estimate effects policy implementation period and some GHG effects that implementation and effectiveness. Depending on what occur after the policy implementation period. is considered most likely in an individual context, users Users should also consider whether and how the should either (1) estimate the maximum effects of the implementation of the policy or action is expected to policy or action if full implementation and enforcement is change over the GHG assessment period. Examples most likely or (2) discount the maximum effects based on include tax instruments where the tax rate increases expected limitations in policy implementation, enforcement, over time, performance standards where the level or effectiveness that would prevent the policy or action 2 Users should apply of stringency increases over time, or regulations or from achieving its maximum potential. conservative assumptions if there is uncertainty about emissions trading programs with multiple distinct phases. the extent of policy implementation and effectiveness. 101

104 policy interactions values and policy scenario emissions. Users should The policy or action assessed may interact with implemented estimate the total net effect of all policies included in the or adopted policies and actions included in the baseline baseline scenario on each parameter in the emissions scenario. To accurately estimate policy scenario parameter estimation methods. For guidance on assessing policy values, policy scenario emissions, and the GHG effects of the interactions, see Appendix B. policy or action, users should determine whether the policy or action assessed interacts with any policies included in the sensitivity of parameters to assumptions baseline scenario (either in reinforcing or overlapping ways). Users should use sensitivity analysis to understand the range of possible values of various parameters and If there are no interactions with other policies or actions determine which scenario is most likely. Users should included in the baseline scenario, the policy or action also understand the range of uncertainty associated with assessed will have the full range of effects expected. If the various parameters. For more information on assessing policy or action assessed has a reinforcing effect with policies uncertainty and sensitivity analysis, see Chapter 12. in the baseline scenario, the policy or action assessed will have a greater range of positive effects than expected. See Table 9.2 for an example of reporting parameter values However, if the policy or action overlaps with policies in and assumptions. the baseline scenario, the positive effect of the policy or Users may refer to model documentation that explains action will be reduced. In an extreme case where the the methodologies and algorithms embedded in a model, policy or action assessed overlaps completely with policies whether the model was subjected to peer review, and why included in the baseline scenario, the policy or action would the selected model was chosen for use in the assessment. have no GHG effects relative to the baseline scenario. See Box 9.2 for a case study of developing assumptions for If interactions with policies included in the baseline scenario a baseline scenario and policy scenario. exist, users should estimate the magnitude of the policy interactions when estimating policy scenario parameter table 9.2 example of reporting parameter values and assumptions used to estimate ex-ante policy scenario emissions for a home insulation subsidy policy scenario value(s) applied methodology and assumptions data source(s) over the g Hg assessment period parameter to estimate value(s) Values calculated based on 30% Peer-reviewed anticipated uptake of the insulation subsidy natural literature: starting in 2015 and remaining constant 1,000,000 MMBtu/year from 2010–14; gas used Author (Year). through 2025; and 30% energy use 910,000 MMBtu/year from 2015–25 for space Title. Publication. reduction per home with insulation (based heating on previous studies of similar policies) natural National energy Same value as in baseline scenario since gas 55 kg CO e/MMBtu (constant) 2 statistical agency the policy does not affect this parameter emission factor Policy and Action Standard 102

105 Ante Estimating GHG Effects Ex- CHAPTER 9 Box 9.2 developing assumptions for the baseline scenario and policy scenario for the german r enewable energy act Institut e.V. carried out an ex- ante assessment of the Öko- term development of renewables in study on the long- Germany (DLR, Fraunhofer IWES, and IFNE 2012). All Renewable Energy Act (EEG) in Germany. The main purpose renewable sources are expected to increase in the policy of the policy is to promote renewable electricity generation. The EEG involves mandatory connection of renewable scenario, with wind power increasing the most dramatically. electricity generators to the power grid, preferential access of Figure 9.5 presents the policy scenario assumptions for renewable electricity (over fossil and nuclear electricity), and renewable electricity generation (excluding wind in order to show the same scale as Figure 9.4). Figure 9.6 presents in tariffs for renewable electricity generation. feed- the policy scenario assumptions for renewable electricity For the baseline scenario, it was assumed there would be generation (including wind), with a different scale. no further increase in renewable electricity absent the EEG, Estimate effects The difference in electricity generation between the baseline except for photovoltaic (PV) electricity. For PV electricity, it and policy scenarios represents the effect of the policy. The was assumed that electricity generation would remain at overall annual policy effect amounts to 95 Mt CO the 2010 level through 2020. After 2020, it was assumed in 2020 2 in 2050 (see Figure 9.7). To calculate the and 138 Mt CO that world market prices will come down considerably so 2 GHG effect of the policy, it was assumed that in the absence that PV will be cost- effective and therefore will be installed of the EEG, the additional electricity would have been even without feed- in tariffs. Figure 9.4 presents the baseline scenario assumptions for renewable electricity generation. produced by the fossil generation mix. The assumed fossil /kWh generation mix (746 g CO /kWh in 2020 and 519 g CO 2 2 The policy scenario represents the development of additional in 2050) was taken from recent modeling exercises for the renewable electricity generation under the EEG. The policy German Ministry of the Environment, Nature Conservation, scenario was estimated using assumptions from a research and Nuclear Safety. figure 9.4 Baseline scenario assumptions for renewable electricity generation 70 Hydro 60 wind 50 pv 40 Biomass 30 geothermal 20 10 electricity generation (twh) 0 2040 2045 2050 2035 2 010 2 015 2025 2020 2030 10 3

106 Box 9.2 developing assumptions for the baseline scenario and policy scenario for the german r enewable energy act (continued) figure 9.5 p olicy scenario assumptions for renewable electricity generation (excluding wind) 70 60 Hydro 50 pv 40 Biomass 30 geothermal 20 10 electricity generation (twh) 0 2025 2030 2040 2045 2050 2 010 2020 2 015 2035 olicy scenario assumptions for renewable electricity generation (including wind) figure 9.6 p 300 Hydro 250 wind 200 pv 150 Biomass 10 0 geothermal 50 electricity generation (twh) 0 2020 2 015 2045 2035 2 010 2050 2040 2030 2025 figure 9.7 the estimated g Hg effect of the policy, 2010–50 16 0 500 - 14 0 policy effect ( 400 - 120 - 10 0 300 - 80 mt co 200 - 60 - 40 2 10 0 ) - 20 electricity generation (twh) 0 0 2 015 2020 2025 2030 2040 2045 2050 2 010 2035 r enewable energy - mt co gHg effect of policy ( ) r enewable energy - 2 policy scenario (twh) Baseline scenario (twh) Policy and Action Standard 104

107 CHAPTER 9 Estimating GHG Effects Ex- Ante 9.5 Estimate policy scenario emissions Estimate baseline emissions from each source/sink 1. shall Users estimate policy scenario emissions and category (Chapter 8) removals over the GHG assessment period for each source/ Estimate policy scenario emissions for each source/sink 2. sink category and greenhouse gas included in the GHG category assessment boundary, based on the GHG effects included 3. For each source/sink category, subtract baseline shall in the boundary. Users apply the same GWP values emissions from policy scenario emissions to estimate used to estimate baseline emissions. Any sources, sinks, the GHG effect of the policy or action for each source/ greenhouse gases, or GHG effects in the GHG assessment sink category be disclosed, boundary that have not been estimated shall 4. Aggregate GHG effects across all source/sink categories to justified, and described qualitatively. estimate total GHG effect of the policy or action After estimating policy scenario emissions for each source Alternatively, users may follow these steps: Estimate effects and sink, users should aggregate policy scenario emissions 1. Estimate baseline emissions from each source/sink across all categories of sources and sinks included in the category (Chapter 8) GHG assessment boundary to estimate total policy scenario 2. Aggregate baseline emissions across all source/sink emissions, if feasible based on the method used. When categories to estimate total baseline emissions (Chapter 8) aggregating across sources and sinks, users should address 3. Estimate policy scenario emissions for each source/sink any possible overlaps or interactions between sources category or underestimation of total policy and sinks to avoid over- 4. Aggregate policy scenario emissions across all source/ scenario emissions. sink categories to estimate total policy scenario shall report total annual and cumulative policy Users emissions scenario emissions and removals over the GHG assessment 5. Subtract total baseline emissions from total policy period, if feasible based on the method used. scenario emissions to estimate the total GHG effect of the policy or action Both approaches yield the same result. See Table 9.3 9.6 Estimate the GHG effect for an example. In this example, a user has two options: of the policy or action shall Finally, users estimate the GHG effect of the policy • Estimate total policy scenario emissions (70,000 t CO e) 2 e), and total baseline emissions (75,000 t CO or action by subtracting baseline emissions from policy 2 then subtract the two to estimate the total change scenario emissions for each source/sink category included e); or (–5,000 t CO in the GHG assessment boundary (see Equation 9.1). 2 • Estimate the GHG effect for each source/sink category Users should estimate the GHG effect for each source/ e), then e, +1,000 t CO e, –4,000 t CO (–2,000 t CO 2 2 2 sink category separately, by following these steps: sum across source/sink categories to estimate the total e). change (–5,000 t CO 2 equation 9.1 estimating the g Hg effect of the policy or action total net change in g Hg emissions resulting from the policy or action (t co e) = 2 e) – Total net baseline scenario emissions (t CO e) Total net policy scenario emissions (t CO 2 2 Note: “Net” refers to the aggregation of emissions and removals. “Total” refers to the aggregation of emissions and removals across all sources and sinks included in the GHG assessment boundary. 10 5

108 Hg effect of a home insulation subsidy table 9.3 example of estimating the g affected Baseline gHg effect policy scenario emissions sources change emissions included Fossil fuel combustion reduced emissions 48,000 t CO in grid-connected 50,000 t CO e e -2,000 t co e 2 2 2 from electricity use power plants reduced emissions Residential natural gas 16,000 t CO e 20,000 t CO e -4,000 t co e from home natural 2 2 2 combustion gas use Insulation increased emissions manufacturing 6,000 t CO e e 5,000 t CO +1,000 t co from insulation e 2 2 2 processes production total emissions / 70,000 t co total change in e -5,000 t CO e 75,000 t co e 2 2 2 emissions The table provides data for one year in the GHG assessment period. Note: shall report the estimated total net change in GHG Users emissions and removals resulting from the policy/action or package of policies/actions, in tonnes of carbon dioxide equivalent, both annually and cumulatively over the GHG assessment period. shall Users report the total in- jurisdiction GHG effects (the total net change in GHG emissions and removals that occurs within the implementing jurisdiction’s geopolitical boundary), of- jurisdiction GHG effects (the net separately from total out- change in GHG emissions and removals that occurs outside of the jurisdiction’s geopolitical boundary), if relevant and feasible. Users should separately estimate and report the change in GHG emissions/removals resulting from each individual GHG effect included in the GHG assessment boundary, 3 Users may also separately where relevant and feasible. report by type of effect, by source or sink, or by category of source or sink. Users should report the GHG effect of the policy or action as a range of likely values, rather than as a single estimate, when uncertainty is high (for example, because of uncertain baseline assumptions or uncertain policy interactions). See Chapter 12 for guidance on uncertainty and sensitivity analysis. Policy and Action Standard 106

109 CHAPTER 9 Ante Estimating GHG Effects Ex- 9.6.1 separate reporting based on category (very likely, likely, possible, unlikely, very unlikely) likelihood and probability (optional) where relevant and feasible. Each GHG effect of the policy or action included in the Where likelihood is difficult to estimate, users may report assessment may vary in the likelihood that it will actually a range of values for a given effect based on sensitivity occur. In Chapter 7, users categorize potential effects analysis around key parameters (further described in based on whether they are very likely, likely, possible, Chapter 12). Users may additionally incorporate probability unlikely, or very unlikely to occur. Depending on how into the estimation of ex- ante policy scenario emissions, ante the GHG assessment boundary is defined, the ex- based on the likelihood that each effect will occur. For assessment may include effects that are possible, more information, see Box 9.3. unlikely, or very unlikely to occur as a result of the policy or action assessed. See Box 9.4 for a case study of calculating the GHG effect of a policy ex- ante. Estimate effects If unlikely or very unlikely effects are included in the assessment, users should report the estimated GHG effects resulting from those effects separately from the results based on very likely, likely, and possible effects. Users should separately report effects by each likelihood Box 9.3 estimating policy scenario emissions based on likelihood of effects occurring the actual outcome will either be 0 t CO e, or 10,000 t In addition to reporting unlikely and very unlikely effects 2 separately, users may choose to estimate policy scenario CO e, depending on whether the possible effect happens 2 or does not happen. Nevertheless, a probability- emissions and GHG effects of the policy or action by adjusted estimating a probability- adjusted sum. In this approach, all estimate is useful to approximate the expected outcome, effects are included and weighted by their probability. Under e or 10,000 t CO rather than assuming either 0 t CO e 2 2 when the probability of either outcome is only 50 percent. the most robust approach, users may develop a Monte Users following this approach should clearly disclose that the Carlo simulation in which a range of outcomes is predicted adjusted estimate and report results represent a probability- based on the magnitude and probability of the individual the probability values used. effects. As a simpler approach, users may multiply each estimated GHG effect by its expected probability to calculate adjusted estimate (or expected value) for each a probability- table 9.4 default probability values effect. If probabilities are unknown, users should use the default probability values in Table 9.4 based on the qualitative default probability value likelihood likelihood that each effect will occur. For example, if a potential effect is considered “possible” and it would reduce 100% very likely e, the probability- adjusted estimate emissions by 10,000 t CO 2 e × (or expected value) for that effect would be 10,000 t CO 75% likely 2 50% = 5,000 t CO e. Users of this approach should disclose 2 50% possible the individual effects and their assumed probabilities. Users and stakeholders should be aware that this approach 25% unlikely may yield a predicted outcome that will not actually happen. 0% very unlikely adjusted In the example above, the estimated probability- e will not actually occur. Instead, estimate of 5,000 t CO 2 107

110 Box 9.4 c Hg effect ex- unisia’s prosol elec program alculating the g ante of t PROSOL Elec is a renewable energy support program, Tunisia made by ANME. The specific energy production is an launched by the National Agency for Energy Conservation empirical value based on annual on- site measurements of (ANME) of Tunisia in 2010, that aims to promote and support 20 percent of all new installed PV systems in Tunisia. This the installation of photovoltaic (PV) systems in residential value is not expected to change significantly in the future. and commercial buildings with low- voltage grid connections. The following equation was used to estimate electricity ante assessment was to estimate the The objective of the ex- production from PV systems in 2020. For information on the program’s future contribution to mitigation at the national level. calculation of baseline emissions, see Box 8.5. The estimated GHG effect is the difference between policy scenario ante policy scenario emissions from one of the To estimate ex- the production of electricity by conventional emissions and baseline emissions. affected sources— power plants for consumption in the residential and Installed PV capacity in Tunisia [184,000 kWp] × commercial buildings sector— the same emissions estimation specific energy production of PV systems in Tunisia method used to estimate baseline emissions (in Box 8.5) [1,600 kWh/kWp] = Electric energy produced by was applied, but one parameter value was changed. The PV systems [294,400,000 kWh = 294 GWh] consumption of electricity in buildings was reduced by the amount of electric energy expected to be produced by future Baseline electricity consumption in residential and photovoltaic systems expected to be installed. The electricity commercial buildings in 2020 = [8,390 GWh] produced by PV systems was calculated by multiplying Policy scenario electricity consumption in residential and the amount of kWp installed PV capacity by the specific commercial buildings in 2020 = [8,390 GWh - 294 GWh] production of the PV systems in Tunisia. = 8,096 GWh The number and capacity of PV systems expected to be See Figure 9.8 for a graph of the program’s estimated installed over the period 2014–30 were derived from a GHG effect. strategic study on the development of renewable energies in figure 9.8 estimated g Hg effect of the program, 2010–30 7,000,000 6,000,000 5,000,000 e 2 4,000,000 t co p olicy scenario emissions 3,000,000 Baseline emissions 2,000,000 1,000,000 2 014 2 010 2 016 2 018 2020 2022 2024 2026 2028 2030 2 012 Policy and Action Standard 108

111 CHAPTER 9 Ante Estimating GHG Effects Ex- endnotes New methods should not be used to estimate total emissions from 1. source/sink categories, since the emissions estimation method used to estimate baseline emissions should also be used to estimate policy scenario emissions. 2. Barua, Fransen, and Wood 2014 provides a framework for considering factors that may influence effective policy implementation in more detail. 3. An individual effect can be separately estimated and reported if it influences distinct sources/sinks within the GHG assessment boundary that are not influenced by the other effects being estimated. In this case, the change in emissions/removals from the source/sink is equal to the change resulting from that GHG effect. Estimate effects If multiple effects influence the same source/sink, the combined effect can be estimated, but not the individual effects. 10 9

112 10 Monitoring Performance over Time

113 Estimate effects his chapter provides guidance on monitoring the performance of a policy or action during the policy implementation period and on collecting data to estimate GHG monitoring that estimate GHG effects ex- effects post. Users T ex- ante without performance may skip this chapter and proceed to Chapter 12. figure 10.1 overview of steps for monitoring performance over time define define key define policy create a monitor parameters performance parameters monitoring monitoring indicators for ex-post plan period over time assessment (section 10.1) (section 10.4) (section 10.5) (section 10.3) (section 10.2) checklist of accounting requirements (for users monitoring performance) section accounting requirements Define the key performance indicators that will be used to track performance of the • define key performance policy or action over time. indicators ( section 10.1) define parameters for ex-post For users planning to carry out an ex-post assessment: Define the parameters necessary • section 10.2) assessment ( to estimate ex-post policy scenario emissions and ex-post baseline scenario emissions. create a monitoring plan • Create a plan for monitoring key performance indicators (and parameters for ex-post (section 10.4) assessment, if relevant). monitor parameters over time Monitor each of the parameters over time in accordance with the monitoring plan. • (section 10.5) Reporting requirements are listed in Chapter 14. Note: 111

114 Monitoring performance during the policy implementation post The monitoring plan should be informed by the ex- period serves two related functions: estimation method that will be used in order to ensure that the proper data are collected (see Chapter 11). • to monitor implementation progress: Monitor trends in key performance indicators to understand For additional guidance on collecting data, see Appendix A. whether the policy or action is on track and being implemented as planned indicators key performance Define 10 .1 • to estimate g Hg effects: Collect the data needed for shall Users that monitor performance define the post assessment of GHG effects ex- key performance indicators that will be used to Users may monitor data to fulfill one or both functions, track performance of the policy or action over time. are depending on objectives. Key performance indicators Where relevant, users should define key performance metrics that indicate the performance of a policy or action, , and indicators in terms of the relevant inputs , activities such as tracking changes in targeted outcomes. Parameter associated with the policy or action. intermediate effects is a broader term meaning any type of data (such as activity Table 10.1 provides definitions and examples of each data or emission factors) needed to estimate emissions. type of indicator. Inputs and activities are most relevant for monitoring policy or action implementation , while Monitoring key performance indicators is generally less GHG effects are most intermediate effects and non- onerous than estimating GHG effects and can provide . Indicators effects relevant for monitoring policy or action a low- cost way of understanding policy effectiveness can be either absolute (such as the number of homes by tracking trends in key indicators. If progress is not on e/km). Users based (such as g CO insulated) or intensity- track, monitoring can inform corrective action. However, 2 may also define indicators to track non- GHG effects . monitoring indicators is not sufficient to estimate the effect post, users need to of a policy. To estimate GHG effects ex- report the key performance indicators selected shall Users collect data on a broader range of parameters, which should and the rationale for their selection. be monitored during the policy implementation period. The selection of the indicators should be tailored to the Where possible, users should develop the monitoring plan policy or action in question, based on the type of policy or during the policy design phase (before implementation), action, the requirements of stakeholders, the availability of rather than after the policy has been designed and existing data, and the cost of collecting new data. implemented. Doing so ensures that the data needed Tables 10.2 and 10.3 provide examples of activity and to assess the effectiveness of the policy are collected. 1 intermediate effect indicators. Policy and Action Standard 112

115 CHAPTER 10 Monitoring Performance over Time table 10.1 t ypes of key performance indicators for monitoring performance indicator examples for a home types insulation subsidy program definitions Money spent to implement Resources that go into implementing a policy or action, such inputs the subsidy program as financing Administrative activities involved in implementing the Number of energy audits carried out, policy or action (undertaken by the authority or entity that activities total subsidies provided implements the policy or action), such as permitting, licensing, procurement, or compliance and enforcement Estimate effects Amount of insulation purchased and installed by consumers, fraction of homes Changes in behavior, technology, processes, or practices that intermediate that have insulation, amount of natural result from the policy or action effects gas and electricity consumed in homes Reduced CO Changes in greenhouse gas emissions by sources or removals , and N , CH O 2 2 4 by sinks that result from the intermediate effects of the policy emissions from reduced natural gas gHg effects or action and electricity use Changes in relevant environmental, social, or economic Household disposable income from conditions other than GHG emissions or climate change gHg non- energy savings mitigation that result from the policy or action effects (see Appendix C for examples) Source: Adapted from W. K. Kellogg Foundation 2004. Notes: GHG effects are typically not monitored directly but instead are estimated based on changes in various other parameters. In other frameworks, intermediate effects are called “outcomes” and GHG effects and non-GHG effects are called “impacts.” table 10.2 examples of activity indicators for various policies examples of activity indicators examples of policies Quantity of long-term contracts with renewable energy power generators established, renewable portfolio standard number of renewable energy certificates (RECs) issued Number of emission certificates issued per year, number of vehicle manufacturers fuel economy standard from which information on cars sold is collected by the government Amount of subsidies issued subsidy for home insulation Number of appliance standards and reporting templates published, number of energy efficiency standards appliance manufacturers from which information on sold appliances is collected for appliances Number of retrofit projects procured (for example, number of contractors selected for government buildings installation through open bidding process) retrofit program Adapted from Barua, Fransen, and Wood 2014. Source: 113

116 table 10.3 examples of intermediate effect indicators for various policies examples of policies examples of intermediate effect indicators Total electricity generation by source (such as wind, solar, coal, natural gas) renewable portfolio standard Passenger-kilometers traveled by mode (such as subway, bus, train, private car, public transit policies taxi, bicycle) Tonnes of waste sent to landfills, tonnes of waste sent to recycling facilities, waste management regulation tonnes of waste sent to incineration facilities Tonnes of methane captured and flared or used landfill gas management incentive Soil carbon content, tonnes of synthetic fertilizers applied, crop yields sustainable agriculture policies Area of forest replanted by type afforestation/reforestation policies Number of renewable lamps sold, market share of renewable lamps, volume of grants for replacing kerosene lamps kerosene used for domestic lighting with renewable lamps Number of buildings retrofitted, energy use per building subsidy for building retrofits information campaign to encourage Household energy use (sample of households or average use) home energy conservation 10.2 Define parameters needed post assessment vary by The parameters needed for ex- for ex- post assessment type of policy or action and sector. For selected examples, post assessment Users planning to carry out an ex- shall see Table 10.4. post policy define the parameters necessary to estimate ex- down data up and top- Bottom- post baseline scenario emissions. scenario emissions and ex- Both bottom- up and top- down data may be used, and either post Users should first define the methods needed for ex- may be most appropriate depending on the type of policy assessment in order to identify the parameters that should or action, sector, quantification methods used, and data be monitored. See Chapter 11 for a description of various up and availability. See Section 3.2 for definitions of bottom- up and top- bottom- down estimation methods. The selection top- down data. of methods and identification of data sources is an iterative process, since the availability of data informs the selection up data may be most appropriate for sectors with Bottom- of methods, and the selection of methods defines the data a relatively small, finite set of emitting sources (such as that need to be collected. There may be overlap between power generation or cement production), where bottom- post assessment and intermediate parameters needed for ex- up data collection at the facility level is feasible. Top- down effect indicators used for monitoring performance. data may be most appropriate for sectors with a large up data number of diffuse emitting sources, where bottom- If relevant, users should monitor the parameters in the ex- ante down data are more collection is not feasible or where top- baseline estimation method defined in Chapter 8, including accurate and complete. policy drivers, data related to other policies and actions and non- to determine the extent to which the original assumptions in Table 10.5 provides examples of both types of data. the baseline scenario remain valid or need to be recalculated. Policy and Action Standard 114

117 CHAPTER 10 Monitoring Performance over Time table 10.4 examples of parameters to be monitored by policy/action type selected examples of parameters to be monitored examples of policies • Electricity use (annual, direct metering) energy efficiency program in the • Emission factor from grid electricity commercial buildings sector • Gross floor area of building units • Solar panels produced each year • Capacity of solar power installed solar power incentives • Electricity generated from solar power Number of electric vehicles (quarterly) • Estimate effects • Passenger figures (monthly) electric vehicle subsidy • Vehicle-kilometers traveled (monthly) • Facility-level monitoring of emissions data from covered facilities emissions trading system Surveys of a representative sample of households to collect data such as: • information campaign to awareness of the campaign, actions taken as a result of the campaign, household encourage energy savings size, household income, and household energy use over time in the residential sector table 10.5 examples of bottom-up and top-down data by sector sector examples of bottom-up data examples of top-down data Total fuel sold in a city, by fuel type • Distance traveled (vehicle-kilometers traveled) • by transport mode and vehicle type • Example data source: city statistics • Percentage of trips taken every year by each mode of transportation, length of each trip by transportation mode, number of trips taken by mode per year • Example data source: annual household surveys and/or transportation models Quantity of waste collected by type, quantity Method of disposal (incineration, landfill) • • of recyclables collected by type, quantity of • Landfill: tonnage by depths of landfill compost collected, gross quantity of municipal • Incineration: incineration rate by type waste solid waste, waste diversion rate of waste Example data source: waste management • • Location of disposal sites companies (private) or agencies (public) • Example data source: city statistics Aggregate fuel and electricity consumed • residential and • Building-level energy use by fuel/energy type by all buildings in a city, by fuel/energy type commercial • Example data source: annual building surveys or • Example data source: city statistics from city buildings reporting requirements utilities or energy agencies 115

118 10.3 10.4 Create a monitoring plan Define the policy Users shall monitoring period create a plan for monitoring key performance The policy implementation period is the time period post assessment, if indicators (and parameters for ex- during which the policy or action is in effect (defined relevant). A monitoring plan is important to ensure that in Chapter 5). The is the time GHG assessment period the necessary data are collected and analyzed. Where period over which GHG effects resulting from the possible, users should develop the monitoring plan during policy or action are assessed (defined in Chapter 7). the policy design phase (before implementation), rather than after the policy has been designed and implemented. The policy monitoring period is the time period over which the policy or action is monitored. At a minimum, For each of the key performance indicators or parameters, users the policy monitoring period should include the should describe the following elements in a monitoring plan: policy implementation period, but where possible it Measurement or data collection methods • should also include pre- policy monitoring of relevant Sources of data (either existing data sources or additional • activities prior to the implementation of the policy data collected specifically to monitor indicators) and post- policy monitoring of relevant activities after • Monitoring frequency the policy implementation period. In general, the • Units of measure longer the time series of data that is collected, the • Whether data are measured, modeled, calculated, or more robust the assessment will be. See Box 10.1 estimated; level of uncertainty in any measurements or for an example of a policy monitoring period. estimates; how this uncertainty will be accounted for • Sampling procedures (if applicable) • Whether data are verified, and if so, verification procedures used Box 10.1 example of policy monitoring period for a biofuels policy A biofuels policy is implemented over the 10- year period policy trends prior to collect baseline data and monitor pre- to 2010. It continues through the policy implementation 2010–19. The GHG assessment period (ex- ante) continues until 15 years after the policy implementation period ends period and ends in 2024 in order to monitor any post- lasting GHG effects resulting from land- to account for long- policy effects between 2020 and 2024. Figure 10.2 use change. The policy monitoring period begins in 2005 illustrates the various periods. figure 10.2 example of policy implementation period, policy monitoring period, and g Hg assessment period years 2005–09 2010 –14 2015–19 2020–24 2025–29 2030–34 policy implementation period policy monitoring period gHg assessment period (ex-ante) Policy and Action Standard 116

119 Monitoring Performance over Time CHAPTER 10 data refers to data derived from quantitative models, such as models representing emissions processes from landfills or livestock. Calculated data refers more specifically to data calculated by multiplying activity data by an emission factor. Estimated data (in the context of monitoring) refers to proxy data or other data sources used to fill data gaps in the absence of more accurate or representative data sources. up monitoring methods may involve collecting data Bottom- from representative samples of individual facilities or other sources, rather than from all affected facilities or sources. Estimate effects frequency of monitoring Users may monitor indicators at various frequencies, such as monthly, quarterly, or annually. In general, users should collect data with as high a frequency as is feasible and appropriate in the context of objectives. The appropriate frequency of monitoring should be determined based on the needs of decision makers and stakeholders, following • Entity(ies) or person(s) responsible for monitoring activities the principle of relevance, and may depend on the type and roles and responsibilities of relevant personnel of indicators and data availability. For example, data on Competencies required and any training needed to • inputs are typically available immediately following policy ensure personnel have necessary skills implementation. In contrast, data on the outputs and • Methods for generating, storing, collating, and reporting outcomes of the policy or action may not be realized data on monitored parameters for some time after implementation. It may therefore be • Databases, tools, or software systems to be used for necessary to monitor some indicators over different time collecting and managing periods than for others. Procedures for internal auditing, quality assurance (QA), • See Box 10.2 for a case study of developing a monitoring plan. and quality control (QC) • Record keeping and internal documentation procedures needed for QA/QC, including length of 10.5 Monitor the parameters over time time data will be archived monitor each of the parameters over shall Users • Any other relevant information time in accordance with the monitoring plan. Users The accuracy of measurement or data collection approaches report the performance of the policy or action shall depends on the instruments used, the quality of data over time, as measured by the key performance collected, and the rigor of the quality control measures. indicators, and whether the performance of the policy Users report the sources of data used. Users should shall or action is on track relative to expectations. also report any calculation assumptions and uncertainties If monitoring indicates that the assumptions used in the related to the data. See Appendix A for guidance on data ante assessment are no longer valid, users should ex- collection and Chapter 12 for guidance on uncertainty. document the differences and take the monitoring ante estimates results into account when updating the ex- measurement or data collection methods or when estimating GHG effects ex- post. Users shall Data may be measured, modeled, calculated, or estimated. report whether the assumptions on key parameters Measured data refers to direct measurement, such as within the ex- ante assessment remain valid. directly measuring emissions from a smokestack. Modeled 117

120 Box 10.2 developing a monitoring plan for the t unisian nama for energy conservation in the building sector The National Agency for Energy Conservation (ANME) of GHG effects, since related to both GHG effects and non- the NAMA is intended to achieve both GHG and various Tunisia, Alcor, and Ecofys carried out an ex- ante assessment of the nationally appropriate mitigation action (NAMA) for sustainable development benefits, such as creation of skilled energy conservation in the building sector in Tunisia. The jobs and companies in the energy technology sector, reduced NAMA includes a solar program for commercial and residential household expenditure for energy, and reduced fossil fuel subsidies for the Tunisian government. including solar water heaters (SWH) and solar buildings— and a thermal insulation program photovoltaic (PV) energy— Monitoring will be used to track the performance of the NAMA for existing and new residential buildings. The objective of on a regular basis, to inform corrective actions if needed, and to the assessment was to estimate and report the expected post. The NAMA includes assess the impacts of the NAMA ex- GHG emission reductions in order to attract and facilitate provisions to strengthen monitoring capacity to implement the international support for the NAMA. monitoring plan, such as improving information management systems, establishing new electronic information systems, A monitoring plan was included as part of the NAMA design. The plan identifies key performance indicators, data sources, improving data collection and coordination, and developing monitoring frequency, and the entities responsible for data procedures for sampled on- site verifications, internal auditing, collection. Table 10.6 provides examples of information quality assurance, and quality control. contained in the monitoring plan. The plan includes indicators table 10.6 examples of information contained in the t unisia energy conservation nama monitoring plan measured, calculated, indicator or parameter monitoring or estimated (and responsible frequency (and unit) source of data uncertainty) entity gHg impact of thermal insulation Number of houses ANME information Measured insulated and insulated ANME system (to be Annual (Low uncertainty) area by type (roof, wall, created) 2 glazing) and m Collected by energy counsellors; For existing dwellings: feed into ANME historical annual electricity Measured Annual Energy bills information and primary thermal energy (Low uncertainty) 2 system through consumption (kWh/m ) electronic application file Collected by For new dwellings: Sampled metering ANME control annual electricity and Measured for 50 on 50 new officers to build primary thermal energy dwellings and estimated Annual dwellings and survey 2 a baseline consumption (kWh/m for the rest verification ) of to assess energy scenario for (Medium uncertainty) dwellings that do not apply profile (baseline) new dwellings to the program Policy and Action Standard 118

121 CHAPTER 10 Monitoring Performance over Time unisian nama for energy conservation in the building sector (continued) Box 10.2 developing a monitoring plan for the t table 10.6 examples of information contained in the t unisia energy conservation nama monitoring plan (continued) measured, calculated, indicator or parameter monitoring responsible or estimated (and source of data frequency (and unit) uncertainty) entity gHg impact of thermal insulation (continued) Sampled metering on Control officers 100 new and existing carry out on-site For new and existing Measured for 100 dwellings and survey Estimate effects verification; feed dwellings: final electricity dwellings and estimated to assess energy information into Annual savings and primary for the rest profiles’ changes Promo-isol+ thermal energy savings (Medium uncertainty) (including possible 2 information (kWh/m ) rebound effect) after system first year of operation Energy intensity of buildings: annual electricity and ANME information primary thermal energy To be determined ANME Every 5 years system consumption (kWh/year) unisia energy conservation nama monitoring plan table 10.6 examples of information contained in the t 2 per m and per dwellings job creation ANME accreditation Number of employees in Measured system and new and existing companies ANME Annual (Low uncertainty) human resources that provide energy services department for buildings creation of new companies ANME accreditation Number of new Measured system and companies created to ANME Annual (Low uncertainty) human resources provide energy services department for buildings saved energy costs for end users and saved energy subsidies for the t unisian government (Energy savings by GHG ex-post source from GHG ex-post Measured and assessment and assessment) × (Energy calculated ANME Annual ANME sources on prices for electricity, (Low uncertainty) energy prices and natural gas, LPG, subsidies kerosene, wood, charcoal) endnote Barua, Fransen, and Wood 2014 provides additional guidance on 1. selecting input and activity indicators. 119

122 11 Estimating GHG Effects Ex- Post

123 his chapter that have occurred the GHG effects how to estimate describes or action In this chapter, post assessment). as a result users of the policy (ex- the GHG effect of the policy or action by comparing observed policy T estimate Estimate effects (based on monitored data) to ex- post baseline scenario emissions scenario emissions 8). The GHG effect or action (ex- post) is estimated by of the policy (described in Chapter subtracting baseline emissions from policy scenario emissions. Users that choose only ante may skip this chapter and proceed to Chapter 12. to estimate GHG effects ex- figure 11.1 overview of steps for estimating g Hg effects ex-post update baseline emissions or select a desired select an ex-post ex-ante assessment assessment method level of accuracy (if applicable) (section 11.3) (section 11.2) (section 11.1) additional steps Hg effect estimate the g to inform decision estimate policy scenario of the policy or action making (optional) section 11.4) emissions ( (section 11.5) (section 11.6) checklist of accounting requirements (for users carrying out ex-post assessment) section accounting requirements Estimate policy scenario emissions and removals over the GHG assessment period • for each source/sink category and greenhouse gas included in the GHG assessment estimate policy scenario boundary. section 11.4) emissions ( Apply the same GWP values used to estimate baseline emissions. • Estimate the GHG effect of the policy or action by subtracting baseline emissions • estimate the g Hg effect of the from policy scenario emissions for each source/sink category included in the GHG policy or action ( section 11.5) assessment boundary. Reporting requirements are listed in Chapter 14. Note: 121

124 Update baseline emissions or 11.1 The baseline scenario should also be recalculated to include ante assessment (if applicable) ex- policy drivers based on their observed updates to all non- Figure 11.2 provides an illustration of estimating GHG effects values over the GHG assessment period, as well as possible post. In contrast to ex- ex- ante policy scenario emissions, policy free rider effects. See Table 8.3 for a list of non- which are forecasted based on assumptions, ex- post policy drivers that should be considered in the baseline scenario scenario emissions are observed based on data collected that is, if they are if they are exogenous to the assessment— during the time the policy or action was implemented. not affected by the policy or action being assessed. Users Users carrying out an ex- post assessment may either do not need to calculate emissions from sources and sinks post policy scenario emissions before or after estimate ex- that remain constant between the baseline scenario and post baseline emissions. See Section 8.2 in estimating ex- the policy scenario, since they do not contribute to the Chapter 8 for more information on the sequence of steps. change in emissions resulting from the policy or action. If an ex- ante assessment for the policy or action was carried out prior to the ex- post assessment, the same method figure 11.2 ex-post assessment may be used by replacing the forecasted parameter values (ex- ante) with observed parameter values (ex- post) in e) 2 the ex- post estimation. Alternatively users may apply a different methodology than was used in the ex- ante mt co assessment. Users should choose the approach that yields ex-post baseline scenario ante and ex- the most accurate results. If both an ex- post gHg effect assessment are carried out for the same policy or action of policy/ Historical ex-post policy scenario at different points in time, each assessment will likely yield action gHg (observed emissions) different estimates of the GHG effects of the policy, since (ex-post) emissions Hg emissions* ( the observed (ex- post) parameter values will likely differ from assumptions forecasted in the ex- ante scenario. net g 2 015 2 010 Note: * From sources and sinks in the GHG assessment boundary. 11. 2 Select an ex- post assessment method This section provides a list of ex- post assessment methods Baseline emissions (as described in Chapter 8) should that users may use to estimate the GHG effects of a policy post assessment is be recalculated every time an ex- post estimation methods are classified post. Ex- or action ex- post baseline scenario should include undertaken. The ex- into two bottom- down methods. For up methods and top- all other policies or actions with a significant effect on down methods and data, up and top- definitions of bottom- emissions that were implemented both (1) prior to the see Section 3.2. Both top- down and bottom- up methods implementation of the policy or action being assessed and can be carried out under either the scenario method or (2) after the implementation of the policy/action being the comparison group method (described in Chapter 8). assessed but prior to the ex- post GHG assessment. Any down, bottom- up, or Users should select either top- interactions between the policy or action being assessed integrated top- down/bottom- up methods based on a and the policies or actions included in the baseline scenario combination of factors, such as: should be taken into account. For guidance on assessing • Data availability, including the type, quantity, quality, and shall policy interactions, see Appendix B. Users report any resolution of data available (which may dictate the use potential interactions with other policies and actions and down data) up or top- of either bottom- whether and how policy interactions were estimated. Policy and Action Standard 122

125 Estimating GHG Effects Ex- CHAPTER 11 Post • Type of policy and sector (which may determine In general, the emissions estimation method used to up or top- down data and methods are whether bottom- estimate baseline emissions for each source/sink included in more relevant and accurate) the GHG assessment boundary should be used to estimate • Number of interacting policies and actions (typically top- policy scenario emissions for each source/sink. However, down methods are more appropriate when there are a in specific cases highlighted in Table 11.1, this may not be large number of interacting policies) necessary. For example, if direct monitoring of emissions • Number of actors influenced by the policy (typically top- is used to measure GHG emissions in the policy scenario, down methods are more appropriate when there are a consistency with the baseline emissions estimation method large of number of affected actors) (based on forecasted activity data) is not necessary. Capacity, resources, and level of expertise available to • carry out the methods 11. 3 Select a desired level of accuracy Estimate effects Table 11.1 lists a variety of ex- post assessment methods Table 11.2 outlines a range of methodological options that may be used. The list is not exhaustive, and users may that may be used for ex- post assessment. When selecting classify methods differently depending on the individual methods to estimate GHG effects ex- post, users should context. Users may also use a combination of approaches consider objectives, the level of accuracy needed to meet listed in Table 11.1. stated objectives, the availability and quality of relevant data, the accessibility of methods, and capacity/resources for the assessment. 12 3

126 .1 ex-post assessment methods table 11 description method Bottom-up methods Parameter values in the policy scenario are determined through data collected from affected participants, sources, or other affected actors. Data collection methods may include direct collection of data from monitoring of emissions (such as continuous emissions monitoring systems), monitoring of affected participants/ parameters (such as metering of energy consumption), collecting expenditure or billing data sources/other affected (such as purchase records), or sampling methods. Activity data are combined with emission actors factors to estimate policy scenario emissions. Parameter values in the policy scenario are estimated using engineering models that represent the emissions or parameter values that would result from the use of a particular equipment, building, vehicle, or other unit, based on assumptions about how the unit is used. engineering estimates Uncertainty may arise if the way a unit is used in practice differs from the manufacturing design specifications. The change in parameter values or emissions (rather than the policy scenario value of parameters or emissions) is estimated using previously estimated effects of similar policies or actions. This involves collecting data on the number of actions taken (such as the number of building that install insulation) and applying default values for the estimated change in GHG emissions or other relevant parameter per action taken (such as the average reduction in energy use per building that installs insulation). The deemed estimate may be based on published studies, equipment specifications, surveys, or other methods. Deemed estimates are used as a lower-cost method for policies or actions that are homogenous across policy deemed estimates contexts, such that deemed estimates from other contexts are representative of the policy or action being assessed. Deemed estimates can be complemented by sampling the affected participants or sources to determine whether the deemed estimates are sufficiently accurate and representative. In this approach, the change is estimated directly, without subtracting baseline scenario emissions from policy scenario emissions. Baseline emissions may be estimated as a subsequent step by adding/subtracting the deemed estimates from observed policy scenario emissions. methods that can be bottom-up or top-down depending on the context Parameter values in the policy scenario are estimated using stock models, market statistics, and/ or surveys to measure diffusion, uptake, or stock turnover. This is typically used for equipment, vehicles, or other units that are consumed or purchased over time. When conducting a stock stock modeling modeling analysis, users should consider whether the uptake or purchasing indicators measure replacement of equipment (and the type of equipment that is being replaced) or whether the total usage of units is increasing. Parameter values in the policy scenario are estimated using indicators that reflect the share of specific equipment or changes in activities in the market, often for end-use consumption that results in GHG emissions. In contrast to stock modeling, users may have limited data on the stock of new equipment or other units in the asessment boundary, but may have data on indicators of diffusion indicators use. If indicators are monitored and there are no other drivers, this method is bottom up. Users may also conduct a regression analysis to identify the effect of the policy, in which case the method is considered top down. Policy and Action Standard 124

127 Estimating GHG Effects Ex- Post CHAPTER 11 table 11 .1 ex-post assessment methods (continued) method description top-down methods Parameter values in the policy scenario are estimated using sector or subsector activity changes. In this case, the user may have limited or no information on end use or stock statistics, but may have information on changes in relevant indicators for a sector (such as tranportation or monitoring of indicators buildings) or subsector (such as space heating in buildings). Policy scenario parameter values should be compared to baseline parameter values to estimate the change. The change in parameter values and/or emissions (rather than the policy scenario value of Estimate effects parameters or emissions) is estimated by using econometric models, regression analysis, extended modeling such as input/output analysis with price elasticities, or computable general equilibrium models. These types of models may be most appropriate for fiscal policies, such as taxes or subsidies. Economic models may specify that a dependent variable (GHG emissions or economic modeling energy use) is a function of various independent variables, such as the policy being assessed, other policies, and various non-policy drivers, such as prices, price elasticities of fuels, economic activity, weather, and population. By doing so, models can control for various factors that affect emissions other than the policy assessed. Source: Adapted from Eichhammer et al. 2008. .2 range of methodological options for ex-post assessment table 11 interactions with policies included in the baseline scenario sources of data emissions estimation method level of accuracy Lower accuracy methods l ower Few significant interacting (such as Tier 1 methods in the International default values policies assessed IPCC Guidelines for National ) Greenhouse Gas Inventories Most significant interacting National average values Intermediate accuracy methods policies assessed Higher accuracy methods (such All significant interacting Jurisdiction- or source- as Tier 3 methods in the IPCC policies assessed specific data Guidelines ) Higher Adapted from AEA et al. 2009. Source: 12 5

128 11. 4 Estimate the GHG effect Estimate policy scenario emissions 11. 5 Some ex- of the policy or action post assessment methods outlined in Table 11.1 estimate the GHG effect of the policy or shall Users lead to an estimate of policy scenario emissions, while action by subtracting baseline emissions from policy others lead directly to an estimate of the GHG effect of the scenario emissions for each source/sink category included policy or action. If feasible based on the method used, users in the GHG assessment boundary. See Equation 11.1. shall estimate policy scenario emissions and removals over the GHG assessment period for each source/sink category Users should estimate the GHG effect for each source/ and greenhouse gas included in the GHG assessment sink category separately, by following these steps: post boundary. To do so, users should apply the ex- Estimate baseline emissions from each source/sink 1. assessment method from Section 11.2 with data collected category (Chapter 8) in Chapter 10. 2. Estimate policy scenario emissions for each source/sink Users should assess whether the effects identified in category the causal chain (Chapter 6) actually occurred. This may For each source/sink category, subtract baseline 3. include assessing the degree of policy implementation emissions from policy scenario emissions to estimate to ensure that the policy or action was implemented as the GHG effect of the policy or action for each source/ planned, including assessing the extent of enforcement and sink category noncompliance, if relevant. Aggregate GHG effects across all source/sink categories 4. to estimate total GHG effect of the policy or action Users should then update the effects identified in the causal chain based on observed data before estimating each GHG Alternatively, users may follow these steps: effect. To estimate certain effects— such as spillover effects Estimate baseline emissions from each source/sink 1. or rebound effects— users may find it useful to conduct category (Chapter 8) surveys with consumers or businesses affected by the policy 2. Aggregate baseline emissions across all source/sink or action, or use results from similar policy assessments, if categories to estimate total baseline emissions (Chapter 8) the conditions are similar enough for valid comparisons. 3. Estimate policy scenario emissions for each source/ apply the same GWP values used to estimate shall Users sink category baseline emissions. Any sources, sinks, or greenhouse 4. Aggregate policy scenario emissions across all source/ gases in the GHG assessment boundary that have sink categories to estimate total policy scenario emissions not been estimated shall be disclosed, justified, and 5. Subtract total baseline emissions from total policy described qualitatively. scenario emissions to estimate the total GHG effect of the policy or action report the following: shall Users Total annual and cumulative policy scenario emissions • Both approaches yield the same result. See Table 9.3 for and removals over the GHG assessment period, if an example. feasible based on the method used Users shall report the estimated total net change in GHG • The methodology used to estimate policy scenario emissions and removals resulting from the policy/action emissions, including the emissions estimation method(s) or package of policies/actions, in tonnes of carbon dioxide (including any models) used equivalent, both annually and cumulatively over the GHG All sources of data for key parameters, including activity • assessment period. data, emission factors, GWP values, and assumptions Users shall report the total in- jurisdiction GHG effects shall If users are not able to report a data source, users (the total net change in GHG emissions and removals that justify why the source is not reported. occurs within the implementing jurisdiction’s geopolitical of- jurisdiction GHG boundary), separately from total out- Policy and Action Standard 126

129 Estimating GHG Effects Ex- Post CHAPTER 11 Estimate effects equation 11.1 estimating the g Hg effect of the policy or action e) = total net change in g Hg emissions resulting from the policy or action (t co 2 Total net policy scenario emissions (t CO e)* e) – Total net baseline scenario emissions (t CO 2 2 Notes: * Taking into account policy interactions. “Net” refers to the aggregation of emissions and removals. “Total” refers to the aggregation of emissions and removals across all sources and sinks included in the GHG assessment boundary. effects (the net change in GHG emissions and removals Users should report the GHG effect of the policy or that occurs outside of the jurisdiction’s geopolitical action as a range of likely values, rather than as a single boundary), if relevant and feasible. estimate, when uncertainty is high (for example, because of uncertain baseline assumptions or uncertain policy Users should separately estimate and report the change interactions). See Chapter 12 for guidance on uncertainty in GHG emissions/removals resulting from each individual and sensitivity analysis. GHG effect included in the GHG assessment boundary, 1 Users may also separately where relevant and feasible. See Box 11.1 for a case study of calculating the GHG effect report by type of effect, by source or sink, or by category of post and Box 11.2 for a case study comparing of a policy ex- source or sink. ante results. post and ex- ex- 127

130 Hg effect ex- unisia’s prosol elec program c alculating the g post for t B ox 11.1 The National Agency for Energy Conservation (ANME) from the PROSOL Elec program subsidies and interrelated bank credits. The specific energy production is an empirical of Tunisia— together with the Deutsche Gesellschaft für site measurements of 20 percent value based on annual on- Internationale Zusammenarbeit (GIZ) GmbH, with support of all new installed PV systems in Tunisia. carried out a combined ex- from ALCOR Consulting— post ante assessment of the PROSOL Elec program and ex- The following equation was used to calculate electricity in Tunisia. PROSOL Elec is a renewable energy support produced by PV systems in 2010. For details on the program, launched by ANME in 2010, that aims to promote calculation of baseline emissions, see Box 8.5. The and support the installation of photovoltaic (PV) systems in estimated GHG effect is the difference between policy voltage grid residential and commercial buildings with low- scenario emissions and baseline emissions. post assessment was to connections. The objective of the ex- Installed PV capacity in Tunisia [145 kWp] × specific energy assess the program’s progress to date. production of PV systems in Tunisia [1,600 kWh/kWp] = Estimating ex- post policy scenario emissions from one of the electric energy produced by PV systems [232,000 kWh affected sources— production of electricity by conventional = 0.23 GWh] power plants for consumption in the residential and Baseline electricity consumption in residential and applied the same equation commercial buildings sector— commercial buildings in 2010 = [5,039 GWh] used to estimate baseline emissions (in Box 8.5), except that the consumption of electricity in buildings was reduced Policy scenario electricity consumption in residential and by the electric energy produced by photovoltaic systems commercial buildings in 2010 = [5,039 GWh – 0.23 GWh] already installed. The electricity produced by PV systems is = 5,039 GWh installed PV calculated by multiplying the amount of kWp- Since the policy was launched in 2010, the impact of the capacity by the specific production of PV systems in Tunisia. policy to date in 2010 is relatively small, but the impact of the The number and capacity of PV systems installed and policy is designed to increase each year from 2010 to 2020. operational were derived from an ANME database. The See Figure 9.8 for a graph of the estimated GHG effect of the database is a complete and reliable data source, as every PV program over the period 2010–30. installation has to be registered in this database to benefit Policy and Action Standard 128

131 CHAPTER 11 Post Estimating GHG Effects Ex- Box 11.2 comparison of ex- post and ex- ante results for energy efficiency policies in the south african mining sector Mining activities (and the resulting emissions) also declined In 2005, the South African government published the dramatically as a result of the global recession. As a result, Energy Efficiency Accord, which calls for an energy demand ex- post baseline scenario emissions in 2013 were the same reduction of 15 percent in the mining sector by 2015 as the expected emissions under the ex- ante policy scenario (relative to the projected mining sector energy use in 2015). for 2013. Promethium Carbon carried out an ex- post assessment of the energy efficiency policies implemented in the South Figure 11.3 highlights the importance of developing a African mining sector to comply with the Energy Efficiency post baseline scenario. If the ex- ante baseline credible ex- Accord in order to determine their effectiveness and to post scenario had been used instead of developing an ex- estimate the resulting change in GHG emissions and Estimate effects post assessment would have baseline scenario, the ex- removals. As part of the assessment, the ex- post results shown that initiatives implemented as a result of the accord from 2013 were compared to the ex- ante estimated reduced GHG emissions in the mining sector by 2.62 Mt CO e 2 reductions from 2006. (relative to the ex- ante baseline scenario). In fact, these ante baseline scenario was established from The ex- initiatives only reduced emissions by 1.09 Mt CO e (relative 2 government literature, which stated that in 2005, energy post baseline scenario). Without the ex- to the ex- post demand and resulting GHG emissions in the mining sector baseline scenario, an assessment would have shown that were expected to rise at an annual rate of 2.8 percent. the mining sector was on track to meet the policy without However, as seen in the ex- post baseline scenario, energy having to implement any GHG reducing initiatives, when in demand and GHG emissions in the mining sector were fact additional activities were needed. extremely variable over the period from 2006 to 2013. figure 11.3 comparison of ex-post and ex-ante results from a policy 13.50 ex-ante baseline scenario 13.00 ex-ante net change in g Hg 12.50 emissions e) 2 resulting from 12.00 ex-post baseline policy in 2013 scenario mt co e = 1.53 mt co 11. 50 2 11. 0 0 ex-post net Hg change in g 10.50 ex-ante policy scenario emissions Hg emissions ( resulting from 10.00 policy in 2013 ex-post policy net g scenario = 1.09 mt co e 2 9.50 2 012 2 013 2006 2007 2008 2009 2 010 2 011 12 9

132 non- normalized results and shall report the normalization Additional steps to inform 11. 6 methods used. Non- normalized results be reported so shall decision making (optional) that the ex- post GHG assessment reflects actual changes in In addition to estimating the GHG effect of the policy or action, emissions and removals over the GHG assessment period. users may take additional steps to help inform decision making. These include: For example, the effectiveness of a building insulation program in reducing emissions from home heating depends • Normalizing results on weather conditions. If one year in the GHG assessment Harmonizing top- • up assessments down and bottom- period is warmer than another year, the GHG effect of the Comparing the GHG effects of policies to the GHG inventory • policy in the warm year is reduced compared to a colder year • Applying decomposition analysis because less heating energy is needed in the warmer year. post assessments • Combining ex- ante and ex- In this case, emissions from home heating decline in both Each step is explained below. the baseline scenario and the policy scenario. Users may normalize the results by estimating the GHG effect that would normalizing results 11. 6 .1 have been achieved under average weather conditions, rather Users may separately normalize data, depending on the than actual weather conditions, in order to determine the user’s objectives. Normalization is a process to make GHG effect achieved “in principle” as a result of the insulation conditions from different time periods comparable. It may program, isolated from statistical fluctuations in weather. be useful if the objective is to compare policy effectiveness In addition to weather conditions, data for a building insulation by removing fluctuations not influenced by the policy or program could also be normalized for changes in occupancy action, such as weather variations. If data are normalized, levels, hours of operation for commercial buildings, or the separately report normalized results from shall users Policy and Action Standard 130

133 Estimating GHG Effects Ex- CHAPTER 11 Post impacts of economic or business cycles, if such changes 11. 6 . 3 comparing the g Hg effects of policies and actions to the g Hg inventory occur during the policy implementation period. If feasible, users should also compare the results of the See Box 11.3 for an example of normalizing results. ex- post GHG assessment to the annual GHG emissions inventory for the relevant jurisdiction(s) or organization(s) down and Harmonizing top- 11. 6 . 2 to understand any differences in the reported GHG bottom- up assessments effects based on a GHG assessment (as a result of the Both top- up methods have down methods and bottom- policy or action) and the changes in GHG emissions that limitations. Typically, only either a top- up down or bottom- are reflected in the inventory (as a result of the policy or assessment is carried out. However, it is possible to carry action as well as many other factors). A comparison can out both methods in parallel. If both methods are used, also be a useful quality control measure to evaluate the up and top- down users should harmonize the bottom- reliability of the GHG assessment. This is typically only Estimate effects assessments to the extent possible to compare and possible with top- down indicators or a combination of control for the differences between the methods. Users bottom- up and top- down methods. However, the effect shall report a description of differences between results of individual policies and actions may not be visible in up methods (if applicable). from top- down and bottom- the GHG inventory, especially if a policy or action avoids emissions relative to a baseline scenario but does not lead to absolute reductions in emissions. See Section 1.8 for more information on the relationship with GHG inventories. Box 11.3 example of normalizing results for a german space heating policy Figure 11.4 shows the impact of normalizing for weather conditions for an example from a German space heating policy. The figure emissions per dwelling evolves rather uniformly over the time period (see blue line), but in shows that the average decrease in CO 2 individual years the change in CO emissions per dwelling can vary significantly as a result of weather variations (see green line). 2 figure 11.4 normalization with respect to weather conditions 4.0 3.5 3.0 2.5 2.0 per dwelling 1. 5 2 1. 0 t co 0.5 0.0 2 012 1996 1998 2000 2002 2004 2006 2008 2 010 1994 co emissions from space heating per dwelling 2 emissions from space heating with climatic corrections co 2 Source: Odyssee- Mure 2014. 131

134 11. 6 . 4 decomposition analysis individually tracked as follows: Distance traveled (km) × Users may apply a decomposition analysis, where relevant, fuel efficiency (liters of fuel consumed per km) × GHG e per liter). to understand the various factors that lead to changes intensity of fuels (t CO 2 in overall GHG emissions (as demonstrated in a sectoral Figure 11.5 provides an example of understanding the or jurisdictional GHG inventory) over time. Through changes in residential energy consumption that result decomposition analysis, a policy assessment can feed into from the policies assessed rather than from other factors. a broader assessment of changes in emissions in a sector Energy use for heating in the European Union increased or jurisdiction. over the period 1990–2004 (shown in gray), despite the policies implemented during the period (shown Decomposition analysis is a method to subdivide emissions in blue). In the baseline scenario, energy use would into individual drivers, which can be individually tracked have increased even more as a result of various non- to understand why emissions change over time. For policy drivers (shown in teal). But the policies reduced example, residential energy use can be divided into its energy use (shown in blue) compared to the baseline constituent parameters as follows: Number of houses 2 per house) × energy down and bottom- scenario. The comparison of top- × average size of houses (m 2 ) × GHG intensity of energy efficiency (Btu per m up methods resulted in an unexplained difference (in e per Btu). Similarly, transportation emissions (t CO orange), which may result from uncertainties in some 2 can be disaggregated into parameters that can be of the assumptions or from data quality limitations. figure 11.5 example of decomposition analysis for residential energy consumption in the european union from 1990–2004 1990: final energy use for heating ( eu15) increase in number of dwellings shift from multi- to single-family homes increase in home size increase in home temperatures in winter improved building regulations subsidies for retrofits subsidies for boiler substitution promotion of solar collectors other policies unexplained difference eu15) 2004: final energy use for heating ( 0 50 100 150 200 250 change in energy use ( mtoe) Source: Adapted from EMEEES 2009. Policy and Action Standard 132

135 Estimating GHG Effects Ex- Post CHAPTER 11 endnote 11.6.5 combining ex- ante An individual effect can be separately estimated and reported if 1. and ex- post assessments it influences distinct sources/sinks within the GHG assessment In addition to the monitoring of performance indicators boundary that are not influenced by the other effects being ante and ex- post monitoring described in Chapter 10, ex- estimated. In this case, the change in emissions/removals from may be combined in a “rolling monitoring” approach. the source/sink is equal to the change resulting from that GHG ante Under this approach, the projection provided by the ex- effect. If multiple effects influence the same source/sink, the assessment is continuously overwritten with the results from combined effect can be estimated but not the individual effects. post assessment, which allows for a comparison of the ex- original expectations and the final result. By combining ex- post data, rolling monitoring can demonstrate ante and ex- the GHG reductions that have been initiated up to a certain ante assessment); the GHG reductions date (through ex- Estimate effects that have been achieved up to a certain date (through ex- post assessment); and the GHG reductions that have been ante estimates. post) compared to the ex- achieved (ex- 13 3

136 Assessing Uncertainty 12

137 Estimate effects his chapter provides an overview of concepts and procedures for evaluating sources of uncertainty in a GHG assessment, as well as guidance on is relevant to estimating baseline analysis. sensitivity T This chapter emissions ante (Chapter 9), monitoring performance (Chapter 8), estimating GHG effects ex- post (Chapter 11). over time (Chapter 10), and estimating GHG effects ex- figure 12.1 overview of steps in the chapter review introduction qualitative quantitative (section 12.1), types of sensitivity uncertainty uncertainty uncertainty ( section 12.2), analysis analysis analysis and range of approaches (section 12.4) (section 12.5) (section 12.6) (section 12.3) checklist of accounting requirements in this chapter accounting requirements section • Assess the uncertainty of the results of the GHG assessment, either quantitatively introduction to uncertainty or qualitatively. assessment ( section 12.1) • Conduct a sensitivity analysis for key parameters and assumptions in the sensitivity analysis ( section 12.4) assessment. Reporting requirements are listed in Chapter 14. Note: 13 5

138 Introduction to uncertainty 12 .1 of values. Users should also use an appropriate number assessment of significant figures depending on the uncertainty of the Understanding uncertainty can be crucial for properly results, to avoid overstating the precision of the results. interpreting GHG assessment results. Uncertainty Users should make a thorough yet practical effort to assessment refers to a systematic procedure to quantify communicate key sources of uncertainty in the results. and/or qualify the sources of uncertainty in a GHG If feasible, users should present both qualitative and assessment. Identifying and documenting sources of quantitative uncertainty information in the report. Users uncertainty can help users improve assessment quality should also describe their efforts to reduce uncertainty and increase the level of confidence in the results. in future revisions of the assessment, if applicable. Users should identify and track key uncertainty sources throughout the assessment process. Identifying, assessing, Uncertainty can be reported in many ways, including and managing uncertainty is most effective when done qualitative descriptions of uncertainty sources and during, rather than after, the assessment process. quantitative representations, such as error bars, histograms, and probability density functions. Users should provide as assess the uncertainty of the results of the Users shall complete a disclosure of uncertainty information as possible. GHG assessment, either quantitatively or qualitatively. Users may choose a qualitative and/or quantitative approach to uncertainty assessment. Quantitative uncertainty 12 . 2 Types of uncertainty assessment can provide more robust results than Uncertainty is divided into three categories: parameter qualitative assessment and help users better prioritize data uncertainty, scenario uncertainty, and model uncertainty. improvement efforts on the sources that contribute most The categories are not mutually exclusive, but they can to uncertainty. Reporting quantitative uncertainty estimates be evaluated and reported in different ways. Table 12.1 also gives greater clarity and transparency to stakeholders. summarizes each type of uncertainty. Understanding uncertainty can help users understand parameter uncertainty whether to apply conservative assumptions. As explained Parameter uncertainty may arise from measurement errors, in Chapter 4, accuracy should be pursued as far as inaccurate approximation, or the way the data was modeled possible, but once uncertainty can no longer be practically to fit the conditions of the activity. If parameter uncertainty reduced, conservative estimates should be used. can be determined, it can typically be represented as a reporting uncertainty probability distribution of possible values that include the Reporting information about uncertainty helps users chosen value used in the assessment. Individual parameter and stakeholders assess the accuracy and uncertainty uncertainties can be combined to provide a quantitative of the reported results, to inform how the information measure of the uncertainty of the assessment results, which shall should be used. Users report a quantitative may be represented in the form of a probability distribution. estimate or qualitative description of the uncertainty of scenario uncertainty the results, as well as the range of results from sensitivity Scenario uncertainty is created when multiple analysis for key parameters and assumptions. methodological choices are available, such as the selection Users should report the range of possible outcomes based of baseline assumptions. The use of a standard reduces on different parameter values (representing upper and scenario uncertainty by constraining choices users may make lower bounds of plausible values) to indicate the level in their methodology. To identify the influence of these of uncertainty. When uncertainty is high, users should choices on the results, users should undertake a sensitivity consider reporting a range of values rather than a single analysis for key parameters (described in Section 12.4). value. See Figure 5.3 for an example of reporting a range Policy and Action Standard 136

139 Assessing Uncertainty CHAPTER 12 Range of approaches model uncertainty 12 . 3 Various approaches are available to assess uncertainty, Simplifying the real world into a numeric model always introduces some inaccuracies. For example, models can including qualitative and quantitative approaches. Table 12.2 outlines a range of approaches for assessing uncertainty. introduce uncertainty when used for extrapolation— Users should select an approach based on the objectives that is, application of the model beyond the domain for which model predictions are known to be valid. Users of the assessment, the level of accuracy needed to meet should acknowledge model uncertainties and state model stated objectives, data availability, and capacity/resources. limitations qualitatively. If feasible, users may estimate model Depending on the methods used and data availability, users uncertainty by comparing model results with independent may not be able to quantify the uncertainty of all parameters in data for purposes of verification; comparing the projections the emissions estimation method(s) or quantify the uncertainty of alternative models; using expert judgment regarding the of the total estimated change in GHG emissions and removals. magnitude of model uncertainty; or other approaches. Estimate effects Users should quantify the uncertainty for all parameters for which it is feasible. For cases where quantitative uncertainty is table 12.1 t ypes of uncertainties types of uncertainty description possible sources of uncertainty Uncertainty regarding whether a parameter • Activity data value used in the assessment accurately • Emission factors parameter uncertainty represents the true value of a parameter • Global warming potential (GWP) values • Methodological choices • Selection of baseline scenario and Variation in calculated emissions due to estimation of baseline emissions scenario uncertainty methodological choices Selection of policy scenario and estimation • of policy scenario emission Limitations in the ability of modeling approaches, equations, or algorithms to • Model limitations model uncertainty reflect the real world table 12.2 range of approaches for assessing uncertainty extent of sensitivity method of assessing parameters and assumptions assessed analysis uncertainty level of rigor for uncertainty Few key parameters and assumptions Few key parameters and l ower Qualitative assessed assumptions analyzed Many key parameters and Quantitative: Single Many key parameters and assumptions assumptions analyzed parameter uncertainty assessed All key parameters and Quantitative: Propagated All key parameters and assumptions Higher assessed parameter uncertainty assumptions analyzed 137

140 not possible to calculate, uncertainty should be assessed and described qualitatively. In addition to estimating or describing uncertainty, users should conduct sensitivity analyses for key parameters, which is less data- intensive than and time- quantitative uncertainty assessment. report the method or approach used to Users shall assess uncertainty. Sensitivity analysis 12 .4 Sensitivity analysis is a useful tool to understand differences resulting from methodological choices and assumptions variations on the overall results. Users should consider and to explore model sensitivities to inputs. A sensitivity reasonable variations in parameter values. Not all parameters analysis involves varying the parameters (or combinations need to be subjected to both negative and positive of parameters) to understand the sensitivity of the overall variations of the same magnitude, but they should be varied results to changes in those parameters. based on what is considered reasonable. Past trends may Users shall conduct a sensitivity analysis for key parameters be a guide to determine the reasonable range. As a general and assumptions in the assessment. Key parameters are rule, variations in the sensitivity analysis should at least cover those that are highly variable or most likely to significantly a range of +10 percent and -10 percent (unless this range is impact assessment results. Users should identify these not deemed reasonable under the specific circumstances). parameters in Chapters 8, 9, and 11. See Box 12.1 for a case study of carrying out a To conduct a sensitivity analysis, users should adjust the sensitivity analysis. value of key parameters to determine the impact of such Box 12.1 sensitivity analysis for chile’s program for minimum efficiency p erformance standards for residential lighting The Climate Change Office of Chile’s Ministry of Environment— together with the Energy Efficiency Department of the Ministry of carried out an ex- ante assessment (according to the Policy and Action Standard ) of the Program for Minimum Efficiency Energy— Performance Standards for residential lighting (MEPS). MEPS is a national policy that intends to gradually eliminate incandescent light bulbs from the market and reduce energy consumption from residential lighting. Through the assessment, the policy was emissions by 247,000 t CO e per year (on average), or 1,730,000 t CO e on a cumulative basis, over the estimated to reduce CO 2 2 2 period 2014–20. the percentage of households that replace One key parameter in the assessment is the estimated replacement rate— incandescent light bulbs with efficient lamps each year. The analysts made assumptions about the replacement rate each year based on a combination of national statistics and expert judgment. Table 12.3 presents the assumed values for the replacement rate over the GHG assessment period. table 12.3 estimated values for replacement rate used for policy scenario estimation 2014 2015 2016 2017 2018 2019 2020 94% 95% 0% 37% 64% 74% 84% Policy and Action Standard 138

141 CHAPTER 12 Assessing Uncertainty erformance standards sensitivity analysis for chile’s program for minimum efficiency p Box 12.1 for residential lighting (continued) The assumed replacement rate was expected to be a high source of uncertainty in the assessment. As a result, conservative assumptions were used and a sensitivity analysis was carried out to define overall sensitivity of estimates of emissions impact to variations in the assumed replacement rate. The analysis also included three other key parameters: number of houses; hours of daily lamp use; and grid emission factors. For each parameter, a range of likely values was defined. For the replacement rate, it was assumed that the value could be as high as 150 percent, or as low as 50 percent of the assumed value in a given year (see Table 12.4). Hg assessment period (2014–20): table 12.4 sensitivity analysis for ex ante results over g Estimate effects activity data variation considered activity data variation assessed sensitivity grid emission Hours of replacement Housing units scenarios lamp use factor rate 0% 0% 0% 0% primary scenario +50% +15% +50% +20% alternative scenario 1 -15% -50% -20% -50% alternative scenario 2 Table 12.5 shows the sensitivity of the overall results to the variation in each key parameter. In the case of the replacement rate, the variation can lead to estimated GHG reductions as high as 2,037,000 t CO e or as low as 1,080,000 t CO e. 2 2 table 12.5 sensitivity analysis for ex ante results over the period of assessment (2014–20): cumulative results for different scenarios gHg emission variation (t co e) 2 replacement Hours of grid emission sensitivity Housing units factor rate lamp use scenarios -1,730,000 -1,730,000 -1,730,000 -1,730,000 primary scenario -2,037,000 -1,823,000 -2,595,000 alternative scenario 1 -1,989,000 -865,000 -1,080,000 -1,553,000 -1,470,000 alternative scenario 2 The results confirm that the assessment is highly sensitive to assumptions about the replacement rate, and also highly sensitive to assumptions about hours of lamp use. Chile can use these results to prioritize future data collection efforts to reduce uncertainty of future assessments and improve understanding of how consumers are likely to respond to the program. 13 9

142 1 12.5 Qualitative uncertainty analysis The degree of agreement is a measure of the consensus To qualitatively assess uncertainty, users should or consistency across available sources for a parameter characterize the level of confidence of the results based value or result. The degree of agreement can be defined on (1) the quantity and quality of evidence and (2) in terms of high, medium, or low. As a rule of thumb, high the degree of agreement of the evidence. The level of agreement means that all sources had the same conclusion; confidence is a metric that can be expressed qualitatively medium agreement means that some sources had the to express certainty in the validity of a parameter value same conclusion; and low agreement means that most or result. (The qualitative confidence level described of the sources had different conclusions. This step may in this section is distinct from statistical confidence not be applicable if there is only one source available. and should not be interpreted in statistical terms.) A level of confidence provides a qualitative synthesis of When characterizing parameter uncertainty, evidence the user’s judgment about the result, integrating both refers to the sources available for determining a parameter the evaluation of evidence and the degree of agreement value. Evidence should be assessed with regard to both in one metric. Figure 12.2 depicts summary statements the quantity and quality of evidence and can be defined in for evidence and agreement and their relationship with overall terms of being robust, medium, or limited. Evidence confidence, where confidence increases as evidence should be considered robust when there is a large quantity and agreement increase. The level of confidence can be quality evidence. Evidence should be considered of high- considered very high, high, medium, low, and very low. In medium when there is a medium quantity of medium- the best case (high confidence), the evidence found should quality evidence. Evidence should be considered limited . be sourced from multiple credible, independent institutions when there is a small quantity of low- quality evidence. Presentation of findings with “low” and “very low” High- quality evidence adheres to principles of research confidence should be reserved for areas of major concern, quality evidence shows deficiencies in adhering quality. Low- and the reasons for their presentation should be explained. to principles of research quality. Medium- quality evidence The confidence level of individual parameters, models, 2 is a mix of high- quality and low- quality evidence. and scenarios should be aggregated to provide a level of confidence for the overall assessment, if feasible. figure 12.2 summary statements for evidence and agreement and their relationship with confidence High High agreement High agreement High agreement confidence scale limited evidence robust evidence medium evidence medium agreement medium agreement medium agreement limited evidence medium evidence robust evidence agreement low agreement low agreement low agreement robust evidence limited evidence medium evidence low evidence (type, amount, quality, consistency) Adapted from IPCC 2010. Source: Policy and Action Standard 140

143 Assessing Uncertainty CHAPTER 12 • Quantitative uncertainty analysis monte c arlo simulation: A form of random sampling 12 .6 Quantitative uncertainty analysis should be undertaken used for uncertainty analysis that shows the range of where feasible to characterize the uncertainty of key likely results based on the range of values for each parameters. Estimates of uncertainty should be made for parameter and probabilities associated with each individual parameters (single parameter uncertainty), then value. In order to perform Monte Carlo simulation, aggregated to source and sink categories as well as to the input parameters must be specified as uncertainty assessment as a whole (propagated parameter uncertainty). distributions. The input parameters are varied at random Propagated parameter uncertainty is the combined effect but restricted by the given uncertainty distribution of each parameter’s uncertainty on the total result. for each parameter. Repeated calculations produce a distribution of the predicted output values, reflecting Users should collect appropriate information to estimate the combined uncertainty of the various parameters. specific /sink- overall uncertainty as well as source- Estimate effects estimates of uncertainty at a specified confidence level further references (preferably 95%). Since it may not be practical to measure For guidance on the methods outlined in this section, see uncertainty of every source or sink category in a single the references below. way, various methods for quantifying uncertainty may Ecoinvent. 2013. Chap. 10, “Uncertainty.” In • Overview be used. Users should use the best available estimates, and Methodology: Data Quality Guideline for the which may be a combination of measured data, published . Accessible at http:// Ecoinvent Database, Version 3 information, model outputs, and expert judgment. files. www.ecoinvent.org/support/documents- and- Approaches of quantifying single parameter uncertainty Good Practice Guidance and Uncertainty IPCC. 2000. • include the following: Management in National Greenhouse Gas Inventories . Measured uncertainty approach (represented by • nggip.iges.or.jp/public/ Accessible at http://www.ipcc- standard deviations) gp/english. Default uncertainty estimates for specific activities or Guidelines for • IPCC. 2006. Chap. 3, “Uncertainties.” In • . Vol. 1. National Greenhouse Gas Inventories parameters (from IPCC 2006 or other literature) Probability distributions from commercial databases • World Resources Institute (WRI) and World Business • • Uncertainty factors for parameters reported in literature Council for Sustainable Development (WBCSD). 2003. • Pedigree matrix approach (based on qualitative data Aggregating Statistical Parameter Uncertainty in GHG quality indicators) Accessible at http:// Inventories: Calculation Worksheets. and lower- • Survey of experts to generate upper- bound www.ghgprotocol.org. WRI/WBCSD. 2003. “GHG Protocol Guidance on • estimates Expert judgment (based on as much data as available) • Uncertainty Assessment in GHG Inventories and • Other approaches Calculating Statistical Parameter Uncertainty.” Accessible at http://www.ghgprotocol.org. Once the uncertainties of single parameters have been • WRI/WBCSD. 2011. “Quantitative Inventory Uncertainty.” estimated, they may be combined to provide uncertainty Accessible at http://www.ghgprotocol.org. estimates for the entire assessment. Approaches to combine WRI/WBCSD. 2011. Uncertainty Assessment Template • uncertainties include the following: for Product GHG Inventories . Accessible at http://www. An analytical method • error propagation equations: ghgprotocol.org. used to combine the uncertainty associated with individual parameters from a single scenario. Equations endnotes involve estimates of the mean and standard deviation 1. This section is adapted from IPCC 2010. of each input. 2. Adapted from DFID 2014. 141

144 13 Verification

145 Verify his chapter provides guidance on verification. While verification is not a requirement of this standard, verifying the results of the GHG assessment is useful for providing the implementing entity and relevant T stakeholders with confidence in the results. Users that choose not to verify the results may skip this chapter. Introduction 13 .1 Assurance is the level of confidence that the information Policy and Action according to the requirements of the reported is relevant, complete, accurate, consistent, . It is relevant to users planning for verification or Standard transparent, and without material misstatements. Verification considering whether to do so. is the process for assessing the level of assurance. To Assurance can be provided for both ex- post ante and ex- provide assurance, verifiers follow a documented rigorous assessments, by either validating or verifying the change in and systematic verification process for the assessment of GHG emissions, respectively. The terminology differs, but the reported information against agreed criteria, for example the approach in both cases is essentially the same. the requirements of a regulation, a standard, a program, or • ante estimates validation: Provides assurance of ex- good practice guidance. before or during the implementation of a policy or action The verification process evaluates whether the post estimates • verification: Provides assurance of ex- requirements of the standard have been met, whether during or after the implementation of a policy or action the GHG accounting and reporting principles have For the purposes of this standard, the term “verification” been followed, and whether reasonable methods and is used to include both verification and validation. assumptions have been applied. Verification should be a cooperative, iterative process that provides feedback, Users should decide whether and what type of verification allowing users to improve accounting practices. to pursue depending on individual objectives. To meet some objectives (such as external reporting or attracting This chapter provides an overview of the process for finance), verification may be required or beneficial, while to providing assurance that the reported GHG effect of a policy or action has been estimated and reported 14 3

146 • Increased confidence in the reported progress of a meet other objectives (such as internal decision making) policy or action in meeting its expected outcome during verification may not be necessary. implementation report whether the GHG assessment results shall Users • Increased confidence in the reported performance and were verified and, if so, the type of verification (first party or effectiveness of a policy or action after implementation third party), the relevant competencies of the verifier(s), and and in its relative contribution toward meeting a broader the opinion issued by the verifier. GHG reduction goal • Enhanced internal accounting and reporting practices Verification is related to quality assurance (QA) and (such as data collection, estimation methods, and quality control (QC). Users should use any combination of internal reporting systems), and facilitation of learning verification, QA, and QC, depending on individual objectives and knowledge transfer within the organization or and circumstances. For additional guidance on QA/QC jurisdiction Guidelines for National and verification, see the IPCC • Improved efficiency in planning or implementing further Greenhouse Gas Inventories (2006), Vol. 1, Chap. 6, mitigation policies and actions “Quality Assurance/Quality Control and Verification ” . Increased confidence in the results reported by • other entities using the Policy and Action Standard , Benefits of verification 13. 2 promoting a credible representation of the relative Obtaining assurance is valuable for reporting entities and efforts undertaken by different entities participating in a others who make decisions informed by the estimated GHG collective goal • Greater stakeholder trust in the reported results effects of a policy or action. Users should have the results of the GHG assessment verified where feasible. Verification can provide a variety of benefits, including the following: Key concepts 13. 3 Increased confidence in the reported information • Table 13.1 includes definitions of key concepts related to as a basis for GHG mitigation strategies before assurance and verification. implementation of the policy or action Policy and Action Standard 144

147 CHAPTER 13 Verification k ey concepts table 13.1 description and examples concept A statement by the reporting entity on the results of a policy or action. The assertion is presented to the verifier performing assurance. • Example of an assertion: “The estimated greenhouse gas effect of the policy relative to the most assertion likely baseline scenario is a reduction of 2 million tonnes of CO e. The change is calculated in 2 conformity with the GHG Protocol Policy and Action Standard , supplemented by our entity-specific policies and methodologies described in the policy assessment report.” An assessment report, completed by the user, documents all required accounting steps and reporting assessment requirements. report Verify The results of the verification of the reporting entity’s assertion regarding the estimated change in GHG assurance emissions resulting from the policy or action. If the verifier determines that a conclusion cannot be opinion expressed, the opinion should cite the reason. See Table 13.3 for examples of assurance opinions. Standards or requirements used by verifiers, which determine how the assurance process and the verification steps are performed to be able to formulate an assurance opinion. assurance • Examples: ISO 14064-3 Specification with Guidance for the Validation and Verification of Greenhouse standards Gas Assertions ; UNFCCC Clean Development Mechanism Validation and Verification Standard. Data sources, estimation methods and documentation used to calculate changes in emissions and that support the subject matter of the reporting entity’s assertion. Evidence should be sufficient in quantity and appropriate in quality. • Examples: Physical observations on the implementation of the policy or action; interview with evidence the planning, implementing, and enforcing authorities; documents prepared by an independent party and/or the reporting entity, such as policy evaluation reports; internal audit reports on the performance of the policy or action. Central to a verifier’s activities is the assessment of the risks of material discrepancies in the change in GHG emissions reported by the user. Discrepancies are differences between reported information by the user and information that could result from the proper application of the Policy and Action Standard’s requirements and guidance. A material discrepancy, or materiality, occurs when individual or aggregate materiality errors, omissions, and misrepresentations have an impact on the estimated change in GHG emissions significant enough that it could influence the user’s decisions. A materiality threshold is the quantitative level of material discrepancy above which an assertion is considered in nonconformity with a standard, regulation, or benchmark. against which the reported policy or Requirements and guidance of the Policy and Action Standard Policy and Action action results will be evaluated. riteria Standard c • Example: Table 3.2 in Chapter 3, which summarizes the requirements of the standard. The GHG assessment results and supporting information included in the assessment report. The type of subject matter verification performed will determine which subject matter(s) should be assessed. See Section 13.4. The process that results in an assurance opinion on whether an assertion is in conformity with the Policy verification requirements. and Action Standard’s 14 5

148 13.4 Subject matter relevant to the Types of verification 13. 5 Either first- Policy and Action Standard party verifiers may be used (see Table or third- The GHG assessment results are the ultimate subject party verifiers should follow and third- 13.2). Both first- matter assessed in the assurance process. To verify similar procedures and processes. Third- party verification that these results represent a true and fair account of is likely to increase the credibility of the reported policy or the change in GHG emissions and removals resulting party verification action results to external stakeholders. First- from a policy or action in conformity with the Policy can also provide confidence in the reliability of those results, , the verifier assesses whether all and Action Standard and it can be a worthwhile learning experience prior to the requirements of the standard are met. Each step in party verification. Verification could also commissioning third- the standard constitutes a subject matter. The verifier be done by a partner organization or by the party receiving needs to check that the information reported meets the the data, rather than by an internal or independent party. requirements and that the methods and assumptions party verification offers a higher degree Inherently, third- used are reasonable. A list of the main steps, or subject of objectivity and independence. Typical threats to matters, involved in the estimation of GHG effects independence may include allegiance to an employing required by the standard is included below. See Table 3.2 entity, pending renewal of funding for a policy or action in Chapter 3 for the full list. based on reported performance, promotion of an entity • The causal chain and list of all potential effects official conditional on performance, or political pressure considered in the assessment and other conflicts of interest between the reporting The definition of the GHG assessment boundary around • entity and the verifier. These threats should be assessed significant effects throughout the verification process. Entities receiving first- • The baseline methodology and assumptions party verification should report how potential conflicts of The ex- • ante and/or ex- post assessment methodology interest were avoided during the verification process. and assumptions The treatment of policy interactions • Levels of assurance 13.6 The data collection and monitoring of the policy or • The level of assurance refers to the degree of confidence action effects over time The assessment of uncertainty • that stakeholders can have in the reported GHG assessment • The assessment report results. There are two levels of assurance: limited and reasonable. The thoroughness with which the assurance evidence is obtained is less rigorous in limited assurance. Limited assurance provides a “negative opinion” that no table 13.2 t ypes of verification type of verification description Internal verification performed by independent person(s) from within the reporting entity. first-party • Example: person(s) from a different department in an organization not involved in the process of verification planning, implementing and reporting on a policy or action. Assurance performed by person(s) from an independent entity. third-party • Examples: independent accounting, engineering or policy analysis organization; accredited third-party verification verification body Policy and Action Standard 146

149 CHAPTER 13 Verification errors were detected. Reasonable assurance provides a • Ability to assess internal information systems for gathering and reporting data, including quality “positive opinion” that all assertions are valid. Table 13.3 gives examples of limited and reasonable assurance control procedures Credibility, independence, and the professional • opinions. The level of assurance requested by the user skepticism required to challenge data, methods, and will determine the rigor of the verification process and the other information amount of evidence required. The highest level of assurance that can be provided is a reasonable level of assurance. Absolute assurance is typically not provided since it is not 13.8 Verification process feasible to test 100 percent of the inputs to the assessment. Many elements have to be considered as part of the systematic process for providing assurance that an assertion 13.7 Competencies of verifiers of a reported change in GHG emissions is in conformity Verify Selecting a competent verifier is important to give with the . The following sections Policy and Action Standard the assurance opinion credibility. A competent describe the main elements of the verification process, verifier has the following characteristics: assuming that the entity has already selected a suitable type and a level of assurance and identified a competent verifier. • Assurance and verification experience Knowledge of, and experience in, GHG assessment • 13.8.1 timing of the verification process for policies and actions, including baseline and policy The timing of verification depends on the subject matter scenario development and needs of the entity. For example, verification can be • Knowledge of the reporting entity’s activities performed before the implementation of a policy or action • Technical expertise to determine whether any technical when the user, as part of its planning activities, wants to or methodological decisions could have a material obtain confidence that a policy or action is likely to achieve impact on the estimated effect of the policy or action ante. Alternatively, assurance its expected GHG effect ex- • Ability to assess the emission sources and sinks can be performed before an entity’s public release of included in the GHG assessment boundary, the an interim or final report to provide a progress update selected modeling approach and assumptions, as well and inform a potential course adjustment, or it can offer as the magnitude of potential errors, omissions, and conclusions on the final performance and effectiveness misrepresentations of a policy or action through ex- post assessment. This evels of assurance table 13.3 l assurance nature of opinion opinion negative opinion • Example: “Based on our verification, we are not aware of any material modifications that should be made to the entity’s assertion that the policy’s change in GHG emissions from the baseline scenario limited assurance is a reduction of 2 million tonnes of CO GHG Protocol Policy and e and is in conformity with the 2 ” Action Standard. positive opinion reasonable • Example: “In our opinion the reporting entity’s assertion that the policy’s change in GHG emissions assurance from the baseline scenario is a reduction of 2 million tonnes CO e is fairly stated, in all material 2 ” respects, and is in conformity with the GHG Protocol Policy and Action Standard. 147

150 allows for any material issues to be corrected before the and magnitude of potential errors, omissions, and release of the assurance opinion (or revised opinion) and misrepresentations in the GHG assertion. The the assertion of a change in GHG emissions. The work assurance plan is structured around the assurance should be initiated long enough before the planned date standards. It identifies the level and objectives of the of implementation of the policy or action, or the release assurance, the criteria and scope (subject matter and date of the assessment report, so that the verification materials to be verified), the materiality threshold, is useful in improving the estimation of the change in and the activities and schedule the verifier plans to GHG emissions, when necessary. The time required for implement to assess the GHG assertion against the verification depends on the nature and complexity of standard’s principles and requirements. the subject matter and the level of assurance selected. 2. i dentifying data, methods, and assumptions : This step requires identifying GHG emissions from the preparing for verification 13.8.2 sources and sinks included in the baseline and policy Preparing for verification is a matter of ensuring that the scenario, as well as the assumptions and methods evidence the verifier needs is easily accessible. The type used for estimating the change in GHG emissions. If of evidence and documentation requested by the verifier applicable, the internal controls and systems of the depends on the subject matter, the type of policy or action entity relevant to the policy or action are also identified, considered, and the type and level of assurance being such as quality control and quality assurance activities sought. Maintaining documentation of the GHG assessment and internal audits. process through the use of a data management plan is helpful for ensuring that the assurance evidence is available. 3. verification : This step requires carrying out the verification activities as planned in the schedule. The Prior to initiating verification, the reporting entity main steps in a schedule include the collection and should ensure that the following are prepared analysis of evidence as well as the appraisal of the and made available to the verifier: evidence against the standard’s criteria. The verification • The entity’s written assertion on the estimated change process generally includes the following steps: • Determining whether the requirements in the in GHG emissions and removals resulting from the standard are correctly interpreted by the user and policy or action The completed assessment report and a referenced • whether the assessment is in conformance with the description of the tools and methods used requirements Access to sufficient and appropriate evidence (such • • Assessing the relevance, completeness, consistency, transparency, and accuracy of the data/information as baseline data, decisions and supporting rationales, interim reports, internal evaluations and performance provided, as well as the reliability and credibility of reports, and peer reviews). data sources Where multiple methodological choices, equations, • steps of verification 13.8. 3 or parameters are available to the user, determining The systematic process of verification, whether performed whether adequate justification for the selected party verifier who provides limited or by a first- or third- choice has been provided Checking whether all the assumptions and data • reasonable assurance, features several steps that are used are clearly disclosed along with references and common to all approaches. sources as well as whether justifications are provided : Planning involves the lanning and scoping 1. p (where required) that are reasonable and supported prioritization of effort by the verifier toward the data, by evidence methods, and information most likely to affect the Identifying issues that require further elaboration, • reported change in GHG emissions from a policy research, or analysis or action. In practice the verifier assesses the risks Policy and Action Standard 148

151 CHAPTER 13 Verification 4. a ssessing materiality : This consists in determining if the verification findings support the entity’s assertion on the change in GHG emissions from its policy or action. Depending on the level of assurance and materiality threshold agreed, the verifier assesses if the information reported by the entity is in conformity with the standard’s criteria or if there is any material discrepancy in the information reported. : The 5. f orming and reporting an assurance opinion verifier next forms an assurance opinion, the nature of which depends on the level of assurance agreed (see Verify Table 13.3). As part of their opinion, verifiers should report the following: • A description of the studied policy or action • A reference to the reporting entity’s assertion included in the GHG assessment report • A description of the assurance process Policy and Action Standard’ s principles • A list of the and requirements • A description of the reporting entity’s and verifier’s responsibilities Whether the verification was performed by a first or • third party • The verification standard used to perform the verification, for example ISO 14064–3: Specification To complete these steps, verifications should consider the with Guidance for the Validation and Verification of following activities: Greenhouse Gas Assertions How any potential conflicts of interest were avoided • Interviewing relevant stakeholders and experts • Reviewing relevant documents, including available • party assurance in the case of first- • A summary of the work performed assessment reports or studies of other similar • The level of assurance achieved (limited or policies or actions Cross- checking information provided by the • reasonable) or a statement as to why an opinion assessment entity with independent sources other cannot be expressed The materiality threshold, if set • than those used (for example, through independent Any additional details regarding the verifier’s • research) Site visits to observe monitoring systems and take • conclusion, including details on any discrepancies sample measurements (if applicable), preferably noted or issues encountered in performing the focusing on issues deemed material verification Other standard auditing techniques and procedures • • Practical modifications to help rectify any discrepancies 14 9

152 14 Reporting

153 Report requirements explaining what information his chapter provides reporting shall be publicly reported in order for a GHG assessment report to be Policy and Action Standard . This T in conformance with the GHG Protocol chapter also lists optional reporting information that users should report, if relevant. A sample reporting template is available at www.ghgprotocol.org/ action- standard. policy- and- • Whether the GHG assessment is an ex- 14 .1 Required information ante assessment, report the following information about the shall Users post assessment, or a combined ex- an ex- ante and GHG assessment and the estimated change in GHG post assessment ex- The GHG assessment period • emissions and removals resulting from the policy or action: The estimated total net change in GHG emissions and • • The title of the policy or action (or package of policies/ removals resulting from the policy/action or package actions) assessed of policies/actions (i.e., the difference between the Whether the assessment applies to an individual policy/ • baseline scenario and the policy scenario), in tonnes action or a package of policies/actions, and if a package, of carbon dioxide equivalent, both annually and which individual policies and actions are included in the cumulatively over the GHG assessment period package Total in- • jurisdiction GHG effects (the total net change • The objective(s) and the intended audience of the GHG in GHG emissions and removals that occurs within assessment the implementing jurisdiction’s geopolitical boundary), • The year the assessment was developed separately from total out- of- jurisdiction GHG effects (the Whether the reported assessment is an update of a • net change in GHG emissions and removals that occurs previous assessment, and if so, links to any previous outside of the jurisdiction’s geopolitical boundary), if assessments relevant and feasible 151

154 shall report the following information about the Users estimating Baseline emissions policy or action assessed and the methodology used to (chapter 8) A description of the baseline scenario (i.e., a description • estimate changes in GHG emissions and removals resulting of the events or conditions most likely to occur in the from the policy or action: absence of the policy or action) and justification for why olicy or action defining the p it is considered the most likely scenario Total annual and cumulative baseline scenario emissions • (chapter 5) • The status of the policy or action (planned, adopted, or and removals over the GHG assessment period, if implemented), the date of implementation, and the date feasible based on the method used The methodology and assumptions used to estimate • of completion (if applicable) • The implementing entity or entities baseline emissions, including the emissions estimation • The objective(s) of the policy or action method(s) (including any models) used The type of policy or action • Justification for the choice of whether to develop new • • A description of the specific interventions included in baseline assumptions and data or to use published the policy or action baseline assumptions and data The geographic coverage; the primary sectors, A list of policies, actions, and projects included in the • • baseline scenario subsectors, and emission source/sink categories targeted; • Any implemented or adopted policies, actions, or and the greenhouse gases targeted (if applicable) • Other related policies or actions that may interact with projects excluded from the baseline scenario, with the policy or action assessed justification for their exclusion Whether the baseline scenario includes any planned • identifying effects and mapping the c ausal chain policies and if so, which planned policies are included A list of non- • policy drivers included in the baseline (chapter 6) • A list of all potential GHG effects of the policy/action that scenario policy drivers excluded from the • Any relevant non- were considered in the assessment • A list of all source/sink categories and greenhouse gases baseline scenario, with justification for their exclusion The baseline values for key parameters (such as activity • associated with the GHG effects of the policy or action • A causal chain data, emission factors, and GWP values) in the baseline emissions estimation method(s) defining the g Hg assessment Boundary The methodology and assumptions used to estimate • (chapter 7) baseline values for key parameters, including whether Any potential GHG effects, source/sink categories, or • each parameter is assumed to be static or dynamic, and greenhouse gases excluded from the GHG assessment assumptions regarding other policies/actions and non- boundary, with justification for their exclusion policy drivers that affect each parameter • All sources of data used for key parameters, including • The approach used to determine the significance of GHG effects activity data, emission factors, GWP values, and assumptions • Any potential interactions with other policies and actions and whether and how policy interactions were estimated • Any sources, sinks, or greenhouse gases in the GHG assessment boundary that have not been estimated in the baseline scenario, with justification, and a qualitative description of those sources, sinks, or gases Policy and Action Standard 152

155 CHAPTER 14 Reporting post Hg effects ex- ante estimating g Hg effects ex- estimating g (chapter 9) (chapter 11) • Total annual and cumulative policy scenario emissions A description of the policy scenario (i.e., a description • and removals over the GHG assessment period, if of the events or conditions most likely to occur in the presence of the policy or action) feasible based on the method used • The methodology and assumptions used to estimate • Total annual and cumulative policy scenario emissions and removals over the GHG assessment period, policy scenario emissions, including the emissions estimation method(s) (including any models) used if feasible based on the method used • The methodology and assumptions used to estimate • All sources of data for key parameters, including activity data, emission factors, GWP values, and assumptions policy scenario emissions, including the emissions • Any potential interactions with other policies and actions estimation method(s) (including any models) used • The policy scenario values for key parameters (such as and whether and how policy interactions were estimated Report • If data are normalized, the normalized results separately activity data, emission factors, and GWP values) in the reported from the non- normalized results, and the emissions estimation method(s) • The methodology and assumptions used to estimate normalization methods used • Description of differences between results from top- policy scenario values for key parameters, including down and bottom- up methods (if applicable) whether each parameter is assumed to be static Any sources, sinks, or greenhouse gases in the GHG • or dynamic • All sources of data used for key parameters, including assessment boundary that have not been estimated in activity data, emission factors, GWP values, and the policy scenario, with justification, and a qualitative assumptions description of the change to those sources, sinks, or gases • Any potential interactions with other policies and actions and whether and how policy interactions were estimated assessing uncertainty Any sources, sinks, greenhouse gases, or GHG effects • (chapter 12) A quantitative estimate or qualitative description of the • in the GHG assessment boundary that have not been uncertainty of the results estimated in the policy scenario, with justification, and The range of results from sensitivity analysis for key • a qualitative description of the change to those sources, parameters and assumptions sinks, or gases • The method or approach used to assess uncertainty monitoring p erformance over time verification (chapter 10) (chapter 13) • The key performance indicators selected and the • Whether the GHG assessment results were verified, and rationale for their selection • The sources of indicator data if so, the type of verification (first party or third party), • The performance of the policy or action over time, the relevant competencies of the verifier(s), and the as measured by the key performance indicators, and opinion issued by the verifier whether the performance of the policy or action is on track relative to expectations • Whether the assumptions on key parameters within the ante assessment remain valid ex- 15 3

156 Optional information 14. 2 Users should report, where relevant: • Any possible double counting of GHG reductions by • The net change in GHG emissions and the net change in other parties that may claim GHG reductions from GHG removals, separately reported in tonnes of carbon dioxide equivalent the same policies or actions, and any practices or • Net changes in GHG emissions and removals, reported precautions used to avoid double counting GHG effects of the policy or A description of non- • separately by individual greenhouse gas Net changes in GHG emissions and removals, reported • action, estimates of non- GHG effects of the policy or separately by individual effect, by type of effect (i.e., action, and the methodologies used to estimate non- jurisdiction intended effects, unintended effects, in- GHG effects • Cost and/or cost- effectiveness of the policy or action effects, out- term effects, of- jurisdiction effects, short- and the methodologies used to quantify costs term effects), or by source or sink category and long- adjusted estimate (or expected value) A probability- Any limitations in the assessment not described elsewhere • • • Other relevant information of the net changes in GHG emissions and removals resulting from the policy or action, with disclosure that the results represent a probability- adjusted estimate • A range of likely values for the net change in GHG emissions and removals, rather than a single estimate, when uncertainty is high (for example, because of uncertain baseline assumptions or uncertain policy interactions) • Net changes in GHG emissions and removals resulting from likely effects, separately reported from net changes in GHG emissions and removals resulting from unlikely effects • Net changes in GHG emissions and removals, separately reported by likelihood category (very likely, likely, possible, unlikely, very unlikely) • Annual or cumulative GHG effects over time periods other than the GHG assessment period • Trends in key performance indicators used to monitor performance, such as the change in key performance indicators since the last reporting period • The GHG inventory of the jurisdiction or organization implementing the policy or action Historical GHG emissions of the jurisdiction or • organization implementing the policy or action • GHG mitigation goal(s) of the jurisdiction or organization implementing the policy or action • The contribution of the assessed policy or action toward the jurisdiction’s or organization’s GHG mitigation goal • Any potential overlaps with other policies and actions Policy and Action Standard 154

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158 Appendix A Guidance on Collecting Data his appendix provides general guidance on data collection and is relevant to Chapters 8, 9, 10, and 11. More specific information on the data required for specific steps in the GHG assessment is provided in Chapters 8, 9, 10, and 11. T Developing a GHG assessment of a policy or action is for those effects determined to be most significant when intensive process. The quality of the GHG typically a data- defining the GHG assessment boundary (see Chapter 7). assessment depends on the quality of the data used to develop it. Users should collect data of sufficient quality A.2 Select data to ensure that the GHG assessment appropriately reflects After prioritizing GHG effects, users should select data actual changes in GHG emissions and removals resulting based on the objectives of the assessment and the making from the policy or action and serves the decision- level of accuracy needed to meet those objectives, needs of users, both internal and external to the reporting data availability, and the quality of available data. entity. See Figure A.1 for an overview of the process for collecting data. GHG emissions calculation methods require a variety of parameters, including activity data and emission factors (see Chapters 8–11). For each parameter A .1 Prioritize data collection efforts needed to estimate effects of policies or actions, Users should prioritize data collection efforts on the GHG users may use either primary data or secondary data. effects expected to have the most significant impact on total See Table A.1 for descriptions of each data type. results. In general, users should collect higher quality data .1 iterative process for collecting data figure a prioritize data improve data quality over collection select data collect data fill data gaps time efforts table a .1 primary and secondary data type of data description Data collected from specific sources or sinks affected by the policy or action (for example, fuel use primary data measured at a specific facility) Data that is not collected from specific sources or sinks affected by the policy or action (for example, secondary data data from published databases, government statistics, literature studies, and industry associations) Policy and Action Standard 156

159 Appendix A Guidance on Collecting Data post assessment For example, if a user is carrying out an ex- by sampling a subset of homes affected by the program to of a home insulation subsidy using the deemed estimate verify whether the actual energy savings are similar to the approach, the user may collect data on the number estimated savings based on secondary data. If sampling is of homes insulated (primary data) and multiply that not possible, users should select secondary data based on number by energy savings per home to determine total data quality indicators (see Table 8.8). energy savings. The estimated savings per home can Users may use any combination of primary and be based on either primary data (measured changes in secondary data. In general, users should collect high- energy use for each home or a representative sample priority effects. In some quality primary data for high- of homes) or secondary data (average estimates of cases, primary data may not be available or may be of energy savings based on similar, previous studies). lower quality than the available secondary data for a Primary data is most relevant to monitoring performance during given activity (for example, if data are collected using post assessment policy implementation (Chapter 10) and ex- unreliable measurement methods). In some cases, top- (Chapter 11), but may also be relevant when developing down secondary data may be more reliable, accurate, and baseline scenarios and ex- ante policy scenarios derived based complete than bottom- up primary data (for example, for on historical data, which may be primary or secondary. policies and actions with national scope where national statistics are accurate and complete). Both types of data Primary data may be obtained through meter readings, have advantages and disadvantages (see Table A.2). purchase records, utility bills, engineering models, direct monitoring, mass balance, stoichiometry, or other methods for Users should select data that are the most representative obtaining data from specific sources and sinks affected by the in terms of technology, time, and geography; most policy or action. complete; and most reliable (see Table 8.8). When uncertainty exists, users should choose conservative When using secondary data sources, users should prioritize values. Users are required to document and report all databases and publications that are internationally recognized, sources of data used, including activity data, emission provided by national governments, or peer- reviewed. Any factors, GWP values, and assumptions (see Chapter 14). secondary data used should be representative of the policy or action being assessed. For the example described above, the representativeness of secondary data can be determined table a .2 advantages and disadvantages of primary data and secondary data type of data advantages disadvantages • May be costly • Provides better representation of the policy’s specific effects • May be difficult to verify primary data • Enables more accurate assessment of policy effectiveness the quality of primary data Enables estimation when primary data is unavailable or • of insufficient quality • Data may not be representative of the policy • Can be useful for estimating GHG effects for minor sources or action’s specific effects or effects secondary data Can be more cost-effective and easier to collect May limit the ability to • • accurately quantify and • Can be used to estimate the relative magnitude of various effects assess policy effectiveness (for example, when defining the GHG assessment boundary in Chapter 7) and prioritize efforts in primary data collection 157

160 Collect data A.3 See Table A.4 for a description of various data collection Data collection should be viewed in the context of procedures. the overall policy assessment process. Data may be collecting data on emission factors collected before a policy or action is implemented, during Emission factors can be global, national, subnational, or implementation, and after implementation (if applicable). specific. Users should choose emission factors that source- See Table A.3 for an example for a hypothetical insulation are the most geographically, temporally, and technologically subsidy program. representative of the activity being estimated. The precise data that will need to be collected depends Users may use either marginal emission factors or average on the policy in question, the stage in the process (such emission factors. Users should choose emission factors that as defining the baseline or estimating GHG effects ex- are most appropriate and representative for the individual post), and the method being followed. It is also useful context. When estimating the GHG effect resulting from to consider the data required across all steps in the a change in electricity consumption or generation, users standard. By understanding the data required for each should apply marginal emission factors, which are generally step, users can best ensure a consistent approach more accurate than average emission factors. Unlike to data collection and make the best use of existing data sources and data collection mechanisms. table a .3 examples of data to be collected by stage stage purpose examples of data to be collected Amount and type of insulation installed prior to Informs the baseline scenario pre-policy the policy Amount and type of insulation installed during policy Indicates ongoing performance of policy each year of policy implementation implementation Amount and type of insulation installed over Informs the estimate of the policy impact ex-post post-policy lifetime of the policy .4 data collection procedures table a procedures description The processes that have been followed to compile the data should be clearly described. This may include a data compilation description of how the data is compiled, who has compiled the data, and where the data is stored. The steps taken to further process the data should be clearly described. This should include details of any modifications or corrections that have been made to the data, including the cleaning of data sets, data processing the removal of outliers and any other adjustments. These changes should be documented, along with a brief justification for any key decisions. For key data sources or data sets, users should provide a judgment on the overall quality of the analysis. This quality assurance / may require a subjective assessment, but the aim is to provide an indication of the overall quality of the data quality control and the main uncertainties. Established QA/QC procedures should be clearly followed. Policy and Action Standard 158

161 average emission factors (which represent aggregated total Fill data gaps A.4 If data of sufficient quality are not available, proxy data emissions associated with producing electricity from all may be used to fill data gaps. Proxy data are data from sources of supply divided by the total amount of electricity), in for the given a similar activity that is used as a stand- marginal emission factors reflect the emissions profile of activity, such as similar data from other geographic a select subset of electricity generation facilities based on regions. Proxy data used in the assessment should be their role in the dispatch order of the system. If appropriate strongly correlated with the relevant parameter. Use of marginal emission factors are not available in a given region, proxy data should be reported and justified as part of the average emission factors should be used. description of data sources used (see Chapter 14). For Sources of emission factors include the following: additional guidance on filling data gaps, see IPCC 2006: • IPCC, Guidelines for National Greenhouse Gas Inventories Volume 1, Chapter 2, “Approaches to Data Collection.” (2006) 1 IPCC, Emission Factor Database • • A.5 specific emission factors from national Country- Improve data quality over time Collecting data, assessing data quality, and improving data inventories, reports, and guidelines Emission factors contained in the GHG Protocol • quality is an iterative process. Over time, users should 2 calculation tools and guidance replace lower quality data with higher quality data as it and the GHG Protocol for Project Accounting The • becomes available. related GHG Protocol Guidelines for Quantifying GHG Reductions from Grid- Connected Electricity Projects endnotes (if applicable) 1. Available at www.ipcc- nggip.iges.or.jp/EFDB/. CDM databases and the CDM “Tool to Calculate the • 2. Available at www.ghgprotocol.org. 3 (if applicable) Emission Factor for an Electricity System” 3. Available at http://cdm.unfccc.int/methodologies/ v2.pdf/. PAmethodologies/tools/am- tool–07- 15 9

162 Appendix B Guidance on Assessing Policy Interactions his appendix provides guidance on assessing policy interactions when estimating the GHG effects of policies and actions. This appendix is relevant T to multiple chapters in the standard where policy interactions may arise, including Chapters 5, 8, 9, and 11. An individual policy or action may interact with other chapter 8: Estimating baseline emissions when the 2. policies and actions to produce total effects that differ baseline scenario consists of multiple interacting policies from the sum of the individual effects of each individual chapter 9: 3. ante policy scenario emissions Estimating ex- policy. Policies and actions can interact in either overlapping when the policy or action assessed interacts with or reinforcing ways or can be independent of each policies included in the baseline scenario other. For more background information and examples hapter 11: 4. c Estimating GHG effects ex- post when the of policy interactions, see Chapter 5, Section 5.3. policy or action assessed interacts with policies included in the baseline scenario Interactions can occur between policies included in the optional step: Allocating GHG effects to individual 5. baseline scenario, between policies included in the policies within a policy package, when policies within the baseline scenario and the policy or action being assessed, package interact (ex- post) ante or ex- or within a set of policies or actions that is assessed as a ptional step: 6. Aggregating GHG effects across multiple o package. Understanding policy interactions is an important post) policies or packages of policies (ex- ante or ex- step toward accurately estimating the GHG effects of a policy or action. Guidance on each case is provided below. Policies may interact with each other if they affect the same parameter(s) in the emissions estimation method(s) Case 1: Deciding whether to assess an for a source or sink in the GHG assessment boundary. For individual or package of policies/actions example, for the source “residential natural gas emissions,” See Chapter 5, Section 5.3. the emissions estimation method may be: “GHG emissions e) = natural gas use (MMBtu) × emission factor (t (t CO 2 e/MMBtu).” In this case, “natural gas use” is a parameter. CO 2 Case 2: Estimating baseline emissions (For more information on emissions estimation methods when the baseline scenario and parameters, see Chapter 8, Section 8.4.) Multiple consists of interacting policies policies may affect the same parameter either directly (such As described in Chapter 8, Section 8.4, the baseline as a natural gas tax and a natural gas subsidy that both affect scenario should include all currently implemented or natural gas use) or indirectly (such as two policies that have adopted policies and actions that have a significant effect systemic effects on a broader economic, environmental, or on GHG emissions. The policies and actions included in the social system). baseline scenario may interact with each other. If the policies are likely to interact with each other, users should estimate Policy interactions should be considered or addressed in the the policy interactions when estimating baseline emissions, following cases: by taking into account the net impact of all the policies 1. chapter 5: Deciding whether to assess an individual included in the baseline scenario on all the emissions policy/action or a package of policies/actions sources/sinks included in the GHG assessment boundary. Policy and Action Standard 160

163 Guidance on Assessing Policy Interactions Appendix B To do this, users should follow three steps for each source/ Finally, users should narrow the list of interactions sink in the GHG assessment boundary: to those that are either overlapping or reinforcing and either moderate or major. Uncertain interactions evelop a list of policies influencing parameter 1. d should also be retained in the list. This set represents First, identify all parameters in the emissions values: (potentially) significant interactions that should be estimation method(s) for each source/sink (see Section estimated in Step 3. 8.4). For each parameter, develop a list of policies included in the baseline scenario that may influence the figure B.1 generic example of a policy interaction parameter value. matrix for one parameter evelop a policy interaction matrix : Next, develop 2. d p olicy n policy 3 policy 2 policy 1 policies a policy interaction matrix for each parameter influenced by multiple policies. A policy interaction matrix is a policy 1 n/a visual way to understand the interactions between + + policy 2 n/a combinations of policies identified in Step 1. See Figure B.1 for a generic example of a policy interaction matrix policy 3 + + + n/a - and Figure B.2 for an illustrative example for a specific parameter. A separate matrix should be developed for n/a u 0 - - - policy n each relevant parameter. For each matrix, each axis Source: Adapted from Boonekamp and Faberi 2012. of the matrix should contain all policies in the list of policies (identified in Step 1), such that each cell in the key: matrix represents a pair of potentially interacting policies. Independent 0 - - - major/- minor interaction Overlapping - moderate/- For each combination of policies in the matrix, users +++ major/++ moderate/+ minor interaction Reinforcing should make a qualitative determination of whether the U Uncertain net interaction of policies is likely to be independent, Significant interactions to be estimated (highlighted in teal) overlapping, or reinforcing with respect to the parameter. (For descriptions and examples of each type of 3. e stimate the combined effects of interacting interaction, see Section 5.3). Any combination of policies Users should estimate policies on each parameter: can have both overlapping and reinforcing effects, and the collective, combined effect of all significant determining whether the net effect is overlapping or interacting policies (that are either moderate or reinforcing may require detailed analysis (see Box 5.1 in major and either reinforcing or overlapping) on each Chapter 5). parameter in the emissions estimation method for For each combination of policies in the matrix, users should the source/sink. Some models selected to estimate also categorize the general magnitude of interaction into emissions in Chapters 8, 9, or 11 may automatically three categories: major, moderate, or minor. The assessment calculate interactions between policies. If using simpler should be based on expert judgment, published studies of models where interaction effects are not calculated similar combinations of policies/actions, or consultations with automatically, users should manually estimate and relevant experts. If users cannot determine the type and/ incorporate the interaction effects between policies on or magnitude of the interaction, the interaction should be the various parameters. Users may also need to carry categorized as “uncertain.” out surveys of affected actors, such as consumers or businesses, to understand whether the actors made a Based on the combination of interaction type decision to implement a particular action based on one (independent, overlapping, reinforcing) and magnitude policy, another policy, the combination of both policies, estimate (major, moderate, minor), users should fill out the matrix using the symbols in the key in Figure B.1. 161

164 figure B.2 illustrative example of a policy interaction matrix for a specific parameter: natural gas used in space heating insulation natural energy energy efficiency policies standards labeling gas tax subsidy n/a insulation subsidy natural gas tax n/a - - + + energy labeling - n/a energy efficiency - - - - - - n/a standards key: Independent 0 Overlapping - - - major/- - moderate/- minor interaction Reinforcing +++ major/++ moderate/+ minor interaction U Uncertain Significant interactions to be estimated (highlighted in teal) or neither policy. In some cases the necessary data may the policy/action will be less than if it were assessed not be available and expert judgment may be necessary. without considering interactions with baseline policies. Conversely, if the interaction effect between the policy/ action being assessed and the policies included in the Estimating ex- ante policy scenario Case 3: baseline scenario is net reinforcing, then the net GHG effect emissions when the policy or action of the policy/action will be more than if it were assessed assessed interacts with policies without considering interactions with baseline policies. included in the baseline scenario As described in Chapter 9, Section 9.4, the policy or See Box B.1 for a case study of assessing policy interactions. action assessed may interact with the policies included in the baseline scenario. Users should estimate policy Case 4: post Estimating GHG effects ex- scenario emissions taking into account the net impact when the policy or action assessed of any significant interactions between the policy/action interacts with policies included assessed (or package of policies/actions assessed) and in the baseline scenario the various policies included in the baseline scenario. To As mentioned in Chapter 11, the policy or action (or package do so, users should follow the same three steps outlined post may interact with of policies/actions) being assessed ex- for Case 2 above, with the addition of including the policy/ policies included in the baseline scenario. Any interaction action (or package of policies/actions) being assessed effects (either reinforcing or overlapping effects) between in the list of policies in the policy interaction matrix. policies included in the baseline scenario and the policy or The incremental effect of the policy/action being action being assessed are attributed to the policy or action assessed relative to other policies/actions included in the being assessed. This results from the methodology itself baseline scenario is attributed to the policy/action being because the baseline scenario includes other implemented assessed. If the interaction between the policy/action policies but not the policy or action assessed (and being assessed and the policies included in the baseline hence does not include any interactions between other scenario is net overlapping, then the net GHG effect of Policy and Action Standard 162

165 Guidance on Assessing Policy Interactions Appendix B Box B.1 assessing policy interactions for an air quality management plan in colombia The Clean Air Institute (CAI) carried out an ex-ante assessment of each policy in order to understand whether each was effective and should continue to be supported and implemented. The the Air Quality Management Plan of the Area Metropolitana del authority was also interested in the total impact of both policies Valle de Aburra (AMVA) in Antioquia, Colombia. The objectives when implemented together to understand how effective the were to evaluate the GHG impact of the transportation measures policies would be when implemented together. Assessing policy in the plan and inform the development of an integrated interactions was necessary to accomplish these objectives. environmental strategy for sustainable urban mobility in the AMVA. The assessment was performed with the Long-Range Energy To help understand the interactions, CAI developed a table Alternatives Planning System (LEAP), an energy model, using (Table B.1) to identify the types of vehicles affected by each information from the latest emissions inventory developed locally. policy. The policies affected two common sources: cars and motorcycles. The assessment of the interaction focused on The plan consists of two transportation policies: (1) regulations to improve vehicle technologies; and (2) incentives to reduce these two common sources. CAI then estimated emissions under four scenarios: the baseline scenario where neither trips from private cars and motorcycles and increase trips by bicycle, walking, and public transportation. Both policies affect policy is implemented, a policy scenario where only Policy 1 is implemented, a policy scenario where only Policy 2 is emissions from urban transport—the first by improving vehicle implemented, and a policy scenario where both policies are technology and the second by shifting toward less-emitting modes of transport. As a result, interactions between the two implemented together. See Figure B.3. CAI found that the combined effect of the two policies together was similar to the policies were considered likely. sum of the individual effects of the two policies had they been CAI assessed the policies both individually and as a package. The implemented on their own. metropolitan authority was interested in the individual impact of table B.1 t ypes of vehicles affected by each policy policies Buses Bus rapid transit trucks motorcycles cars taxis policy 1: improve x x x x vehicle technology policy 2: incentives x x x to reduce trips figure B.3 estimating emissions and policy interactions for the air quality management plan 5.0 4.5 emissions 2 4.0 co (million tonnes) 3.5 2020 2 016 2 017 2 018 2 019 2 012 2 0 21 2022 2023 2 011 2 013 2 014 2 015 p olicy 1+2 Baseline p olicy 2 p olicy 1 16 3

166 Box B.2 example of estimating policy interactions ex- post implemented policies and the policy or action assessed). In contrast, the (observed) policy scenario includes other implemented policies as well as the policy or action Two policies are in effect: (1) an energy labeling program assessed (and hence includes interactions between other for appliances and (2) an information campaign that makes implemented policies and the policy or action assessed). the labels known to users. The policy being assessed is When baseline emissions are subtracted from policy the information campaign. The energy labeling program is scenario emissions, the interaction effects are automatically included in the baseline scenario. The (observed) policy attributed to the policy or action assessed. For an example scenario emissions reflect the combined effect of both post, see Box B.2. of estimating policy interactions ex- policies— that is, what types of appliances consumers actually but do not reveal whether the purchases were purchased— Attributing interaction effects to the policy or action a result of the labeling program, the information campaign, assessed yields an accurate estimate of the effects of the both policies taken together, or neither policy. policy in the specific context and policy environment in which it was implemented. Users should exercise caution In the observed policy scenario (with both policies in effect), in generalizing the results to other jurisdictions, since the e. related emissions decreased by 2,000 t CO appliance- 2 results will be misleading if applied to another jurisdiction To estimate the effect of the information campaign, users with a different combination of policies in effect. The results should estimate baseline emissions based on the scenario will only be meaningful in the jurisdiction where the policy in which the labeling program existed but the information was implemented. campaign did not. The relative effect of each policy can be estimated through survey methods in which a sample of consumers is asked whether their appliance purchasing Case 5: Allocating GHG effects between decisions were influenced by the labeling program, the individual policies within a policy information campaign, both policies together, or neither policy. package, when policies within the package interact (ex- ante or ex- post) Assume that the survey finds that the labeling program alone would have reduced emissions by 1,500 t CO Users that assess a package of policies/actions may want e 2 and the information campaign alone would have reduced to determine the individual effects of each policy or action e. Therefore, in the baseline emissions by 200 t CO within that package. If any overlapping or reinforcing 2 scenario, a GHG reduction of 1,500 t CO e would have interactions may exist between those policies and actions, 2 occurred as a result of the implementation of the labeling users should not allocate the total GHG effect among the program alone. The GHG effect of the information various policies in the package. Instead, to determine the campaign is the difference between policy scenario relative effect of each policy with respect to the others, emissions and baseline emissions (500 t CO e). The users should carry out new assessments of each policy 2 estimated GHG effect of 500 t CO e is greater than the individually, rather than as a package, by estimating the 2 200 t CO e that the policy would have achieved on its GHG effects of each policy or action separately, assuming 2 own, because the interaction effect (a reinforcing effect of the other policies were not implemented. To estimate the 300 t CO e) is attributed to the information campaign. relative effect of each policy, assuming all the policies will 2 be implemented, users should carry out new assessments of each policy individually and include all other policies in the baseline scenario. If the policies are completely independent of each other, the sum of the GHG effects of the policies within the package individually would be the same as the GHG effects from the combination of policies. Policy and Action Standard 164

167 Guidance on Assessing Policy Interactions Appendix B Aggregating GHG effects Case 6: To aggregate GHG effects across two or more interacting across policies or actions policies, users should consider assessing the policies as GHG effects should not be directly aggregated across a package to estimate the total net effect of all of the policies or actions if any overlapping or reinforcing policies, rather than assessing them individually and interactions between the policies being aggregated have then summing the results. For ex- post assessments, if not been accounted for. In this case, the sum would either assessing a policy package is not possible, users should overestimate or underestimate the GHG effects resulting estimate the GHG effects of each policy using a different from the combination of policies. approach than the approach explained in Chapter 11. This alternative approach is explained in Box B.3. Users not In general, GHG effects may be directly aggregated across post should follow the guidance in aggregating results ex- policies or actions if: Chapter 11 rather than following the approach in Box B.3. They are independent of each other (for example, • because they do not affect the same sources or sinks) or the interactions between them have been accounted for; The methods, assumptions, and data sources are • otherwise comparable; and The baseline scenario for each policy being aggregated • includes only policies implemented before that policy was implemented. 16 5

168 Hg effects ex- post to enable aggregation across policies Box B.3 approach for estimating g The approach in Chapter 11 does not enable valid aggregation Figure B.4 illustrates a situation in which aggregation of GHG effects across policies may be valid. Policy A, Policy B, and across policies ex- post because the baseline scenario for each Policy C are three policies implemented sequentially: Policy policy includes all other policies that are implemented during A is implemented in 2002, Policy B is implemented in 2004, the GHG assessment period. If each policy includes all the other policies in its baseline scenario, aggregation of results would lead and Policy C is implemented in 2006. All three policies affect to double counting of interactions between the policies, and total the same set of sources. In any monitoring period, the sum of individual GHG effects from A, B, and C will equal the estimated results would be different from the actual combined combined GHG effects from A, B, and C if: effect of all policies being implemented together. post, the baseline scenario for The baseline scenario for Policy A includes neither Policy B • To aggregate results ex- nor Policy C; each policy being aggregated should include only policies The baseline scenario for Policy B includes Policy A but • implemented before that policy was implemented (rather not Policy C; than including all policies that were implemented at the • The baseline scenario for Policy C includes both Policy A time the assessment was carried out). The baseline scenario for each policy being aggregated should exclude other and Policy B; and policies being aggregated that were introduced later in time. • The emissions estimation method used for each scenario If this approach is applied consistently to all policies being is the same. aggregated, ex- post assessments of multiple policies may be aggregated to estimate the total GHG effects (assuming the methodologies are comparable). figure B.4 aggregating g Hg effects across policies scenario without p olicy a , B, or c e) scenario with p olicy a 2 gHg effect (policy a ) mt co scenario with p olicy a and B gHg effect gHg effect (policy a+B+c ) (policy B) , B, and c olicy a scenario with p Hg emissions ( gHg effect (policy c ) net g 2 012 2002 2006 2004 Policy and Action Standard 166

169 Appendix C Examples of Non- GHG Effects his standard is designed to inform policy development through estimation of GHG effects. In practice, policymakers will decide which policies to implement T and how to evaluate their effectiveness within a broader context that also takes into account various impacts in addition to greenhouse gas emissions. Non- GHG effects are any effects of a policy or action other Chapter 8) from policy scenario values for the non- GHG than changes in GHG emissions and may include a wide effect (in Chapters 9 or 11). Indicators related to non- range of social, economic, and environmental impacts. GHG effects may be monitored over time (Chapter 10), as GHG effects that may be Table C.1 provides a list of non- illustrated in Box 10.2. Quantification methods and data relevant depending on the objectives of a given assessment. GHG effect. For example, sources will vary by type of non- to estimate macroeconomic effects such as effects on GDP, GHG effects may be identified alongside GHG Any non- employment, or trade, users should use computable general effects while developing the causal chain (Chapter 6) and equilibrium or other economic models. Users may choose included in the GHG assessment boundary (Chapter 7). The to identify and qualitatively describe the non- GHG effects of GHG effects of policies and actions may be estimated non- a policy or action, rather than quantitatively estimating them. by subtracting baseline values for the non- GHG effect (in table c .1 examples of non- gHg effects examples of non- category gHg effects • Toxic chemical/pollutants Air quality and air pollution (such as particular • Biodiversity/wildlife loss • matter, ozone, carbon monoxide (CO), sulfur • Loss or degradation of ecosystem services dioxide (SO ), nitrogen oxides (NO ), lead, and x 2 environmental Deforestation and forest degradation • mercury) effects Loss of top soil • Water quality, water pollution, and water scarcity • • Loss or degradation of natural resources • Ozone depletion Energy use • Waste • • Road safety • Public health Walkability • • Quality of life social effects Access to energy, thermal comfort, fuel poverty • • Gender equality • Stakeholder participation in policy-making processes • Traffic congestion • Household income • Employment and job creation Poverty reduction • • Productivity (such as agricultural yield) • New business/investment opportunities Prices of goods and services (such as decreased • economic Energy security/independence • energy prices) effects • Imports and exports Cost savings (such as decreased fuel costs) • • Inflation Overall economic activity (such as GDP) • • Budget surplus/deficit 167

170 Appe ndix D Benefit Analysis Cost- Effectiveness and Cos t- his standard is designed to help inform policy development through estimation of GHG effects (see Chapter 2). In practice, policymakers will T decide which policies to implement and how to evaluate their effectiveness within a broader context that also takes into account costs and a wider set of benefits. D.2 Process for carrying out This appendix describes various cost analysis and decision cost- effectiveness analysis effectiveness support methods that can be used: cost- benefit analysis and cost- benefit analysis (CBA), and multicriteria analysis (CEA), cost- Table D.2 describes the process for carrying out CEA analysis (MCA). These methods allow policymakers and and CBA. Some steps are common to both methods. analysts to evaluate and compare various options before ante) implementation (to inform policy development ex- The results of CEA can be presented using GHG abatement or outcomes after implementation (to track performance cost curves (sometimes called marginal abatement cost ex- post), not only in terms of their GHG effects but also curves, or MAC curves). This step may be useful in ex- ante through a broader assessment of benefits and costs. assessment to inform decision making in terms of selecting policies and actions among a set of policy options. A GHG By following the steps in this standard, users determine the abatement cost curve presents the cost and GHG reduction total net change in GHG emissions and removals caused (or “abatement”) potential of various mitigation options by a policy or action. This GHG effect represents the relative to a baseline scenario. GHG abatement cost curves effectiveness of the policy in reducing emissions, which can be presented as either a histogram or a curve. In either is a fundamental input into any CEA, CBA, or MCA related case, the following information is represented graphically to to GHG emissions. After implementing this standard to help policymakers prioritize mitigation options based on cost: estimate the total net GHG effect of a policy or action, users can optionally apply CEA, CBA, or MCA to the same policy • e) of each The GHG reduction potential (in t CO 2 or action. mitigation option (as estimated ex- ante by using the Policy and Action Standard ) The cost per tonne of CO e reduced for each mitigation • 2 D.1 Comparison of methods option and the total cost of each mitigation option effectiveness analysis, cost- Cost- benefit analysis, and The GHG reduction potential (in t CO e) and total cost • 2 multicriteria analysis can be useful tools for policy across all mitigation options evaluation. CEA compares the effectiveness of a policy to For further guidance on CBA and GHG abatement cost its costs and therefore requires two parameters: a measure curves, see the references below. of effectiveness and a measure of costs. CBA compares the benefits of a policy to its costs, and therefore requires two (or more) parameters: measure(s) of benefits and measure(s) of costs. Multicriteria analysis (MCA) compares alternative policy options, given multiple objectives (such as various environmental, social, and economic objectives), but, unlike CBA, it does not require that all benefits be quantified in monetary terms. See Table D.1 for a summary of the three approaches. Policy and Action Standard 168

171 Appendix D Cost-Effectiveness and Cost-Benefit Analysis table d .1 summary of methods purpose advantages disadvantages method Does not consider Simple method to compare To compare policy options to determine wider benefits of the policy effectiveness based on which is most effective in achieving cost- policy/action other than GHG emissions reduced per a single desired outcome for a given effectiveness a single measure of unit of money spent level of cost (such as GHG reduced per ) cea analysis ( effectiveness (such as Useful when benefits cannot dollar), or which option achieves a given GHG reduction) be calculated or are uncertain objective for the least cost Difficult to monetize To compare policy options to determine Assesses broader benefits non-economic which has the greatest net benefit to of a policy beyond a single benefits and society (the difference between their measure of effectiveness cost-benefit determine appropriate total social benefits and total social (which may include analysis ( cBa) discount rates; can costs); or to analyze a single policy or environmental, social, and underestimate non- action to determine whether its total economic benefits) economic benefits benefits to society exceed its costs Incorporates a wide set of Does not allow variables; does not require To compare policy options and multicriteria comparison of costs and subjective assumptions determine the most preferred option, analysis benefits using a single about how to monetize non- given multiple objectives (mca) unit of measure economic benefits .2 overview of steps in cost-effectiveness analysis and cost-benefit analysis table d step cost-effectiveness analysis cost-benefit analysis 1. identify scope of the analysis CEA and CBA involve assessment of the impact of the policy on society as a whole. Users should include all members of the relevant society, such as in a country or a city, in the analysis. Users should define a time period that is sufficient to capture significant costs and benefits of the program, which should be consistent with the GHG assessment period defined in Chapter 7. 2. identify and estimate costs identify and estimate costs and benefits The next step is to identify costs (and benefits) over the selected time period. Costs may include only the costs of implementing the policy (such as financial expenditures for policy implementation and compliance or costs of installing technology), or may also include the broader costs to other members of society (such as increased prices for goods and services or decreases in economic activity and income), as well as cost reductions associated with policies (such as reduced energy costs from increased energy efficiency). CBA should include a wide range of social, economic, and environmental costs and benefits. See Appendix C for potential non-GHG costs and benefits that may be included. Even though all costs and benefits cannot be known for certain, users should make a reasonable effort to identify and estimate those that are most significant. 16 9

172 table d .2 overview of steps in cost-effectiveness analysis and cost-benefit analysis (continued) cost-effectiveness analysis cost-benefit analysis step quantify and monetize benefits 3. quantify effectiveness CBA involves quantifying a broader set of benefits and then assigning Effectiveness is a measure of the quantifiable a monetary value as a proxy to represent benefits for social and outcome central to the program’s objectives. environmental impacts that may not have an explicit economic CEA typically involves only one measure of or monetary value. CBA is dependent on the assumption that the effectiveness. In this standard, it is assumed value of non-economic impacts can be represented by the value that the single measure of effectiveness is that individuals are willing to pay to preserve or avoid damages. the total net change in GHG emissions and However, some benefits may be intangible, uncertain, subjective, or removals resulting from the policy or action controversial to monetize. See Box D.1 for information on monetizing (as quantified by applying this standard). the benefits of avoiding climate change impacts. calculate present values for costs (and benefits) 4. In economic theory, monetary impacts in the future are worth less to individuals than resources available today, since individuals can earn a return on investment on money they possess today, which they forego when receiving the same amount of money in the future. Thus, both CEA and CBA typically convert monetary values to their present value (or their equivalent value at the beginning of the policy or action) by using a discount rate. For GHG-related analyses, users should use a social discount rate, which reflects a society’s relative valuation of today’s well-being versus well-being in the future. Social discount rates can vary widely (for example, from 0% to over 10%), depending on how they address equity concerns with respect to future generations, among other considerations not accounted for in national interest rates or typical discount rates. (For more information on social discount rates, refer to ADB 2007). Present value is calculated as follows. V Y PV = t (1+r) Where PV = present value, V = Value in a particular year, r = discount rate, and t =number of years from present. Y 5. calculate net present value calculate cost-effectiveness CEA results in a ratio of costs to effectiveness, Once present values for costs and benefits are calculated, the result of the CBA is represented as the net present value (NPV) of all as follows: benefits and costs, representing the net social benefit: cost effectiveness = PV(c) = NPV = PV(B) – PV(C) effectiveness n ▒ C n n t ▒ B ∑ ▒ C t t t ∑ NPV = ∑ (1+r) t =0 t t (1+r) (1+r) t =0 t =0 e net reduction in t CO 2 B = benefits, C = costs, t = year, n = analysis period C = costs, t = year, n = analysis period Policy and Action Standard 170

173 Appendix D Cost-Effectiveness and Cost-Benefit Analysis analysis Box d .1 monetizing the benefits of avoiding D.3 Multicriteria Multicriteria analysis is a method for comparing alternative climate change impacts policy options and determining the most preferred option, given multiple objectives, such as various environmental, The social cost of carbon (SCC) is a concept used to social, and economic objectives. Indicators related to each monetize climate change impacts. The SCC reflects objective can be measured in various units, including the marginal benefits that society gains by avoiding an monetary or non- monetary units, and can be quantitative e emitted, expressed in the form of additional ton of CO 2 or qualitative. For example, various environmental and annual monetized costs. The SCC often includes changes social objectives may be measured using non- monetary in agriculture, human health, property, and ecosystem indicators, while economic costs and benefits may be ser vices in its derivation. While it is a useful concept, measured using monetary indicators. MCA involves uncertainty about the timing and severity of climate change establishing a given set of options, a set of criteria for impacts and significant regional variation pose a challenge comparing the options, and a method for ranking the for quantifying damages from climate change. The timing options. MCA can be especially useful when significant of potential catastrophes in the future to be fed into a CBA environmental and social impacts exist and cannot readily can be difficult to determine, and the choice of a discount be assigned monetary values. A CEA and/or CBA can rate for SCC calculations results in widely ranging estimates. also feed into the process of conducting an MCA. For The use of SCC in a CBA can be valuable for decision further guidance on MCA, see the following references. making as long as uncertainties are acknowledged. 171

174 D.4 Further references benefit analysis and cost- Related to cost- effectiveness analysis: Related to GHG abatement cost curves: • Energy Research Centre, the Netherlands. 2010. • Asian Development Bank. 2007. “Theory and Practice “Marginal Abatement Cost (MAC) Curve.” Accessible at in the Choice of Social Discount Rate for Cost- http://www.ecn.nl/docs/library/report/2011/o11017.pdf. Benefit Analysis: A Survey.” Economics and Research Food and Agriculture Organization of the United Nations. • Department Working Paper, Series No. 94. Accessible 2012. “Using Marginal Abatement Cost Curves to Realize at http://www.adb.org/sites/default/files/pub/2007/ the Economic Appraisal of Climate Smart Agriculture WP094.pdf. Cellini, Stephanie R., and James E. Kee. 2010. “Cost- • Policy Options.” Accessible at http://www.fao.org/docs/ Handbook of Benefit Analysis.” In Effectiveness and Cost- act_MACC_116EN.pdf. up/easypol/906/ex- • Kesicki, Fabian. 2011. “Marginal Abatement Cost Curves Practical Program Evaluation . 3rd ed. Edited by Joseph Based vs. Model- Derived Expert- for Policy Making— S. Wholey, Harry P. Hatry, and Kathryn E. Newcomer. Curves.” London: UCL Energy Institute. Accessible at Bass. San Francisco: Jossey- Department for Energy and Climate Change, United • http://www.homepages.ucl.ac.uk/~ucft347/Kesicki_ Kingdom. 2013. “Valuation of Energy Use and Greenhouse MACC.pdf. • McKinsey & Company. 2009. Pathways to a Low- Gas (GHG) Emissions: Supplementary Guidance to the Carbon Economy: Version 2 of the Global Greenhouse HM Treasury Green Book on Appraisal and Evaluation in . Accessible at http://www. Gas Abatement Cost Curve Central Government.” Available at https://www.gov.uk/ vice/sustainability/latest_ mckinsey.com/client_ser and- use- of- energy- government/publications/valuation- thinking/greenhouse_gas_abatement_cost_curves. gas- appraisal. for- emissions- greenhouse- The Green Book: HM Treasury, United Kingdom. 2014. • Related to multicriteria analysis: Appraisal and Evaluation in Central Government. Department for Communities and Local Government, • Accessible at https://www.gov.uk/government/ United Kingdom. 2009. “Multi- criteria Analysis: A in- evaluation- publications/the- appraisal- book- green- and- Manual.” Accessible at https://www.gov.uk/government/ central- governent. • Interagency Working Group on Social Cost of Carbon, manual- publications/multi- criteria- analysis- making- for- United States. 2010. “Technical Support Document: policy. government- Department for Environment, Food, and Rural Affairs, • Social Cost of Carbon for Regulatory Impact Analysis United Kingdom. 2003. “Use of Multi- criteria Analysis in under Executive Order 12866.” Accessible at http:// Air Quality Policy: A Report.” Accessible at http://www. tsd.pdf. www.epa.gov/oms/climate/regulations/scc- World Bank. 2008. Social Discount Rates for Nine Latin • defra.gov.uk/environment/airquality/mcda/index.htm. . Washington, DC: World Bank. American Countries Accessible at http://elibrary.worldbank.org/content/ workingpaper/10.1596/1813–9450 –4639. World Bank. 2014. “Real Interest Rates.” Accessible at • http://data.worldbank.org/indicator/FR.INR.RINR. Policy and Action Standard 172

175 Abbreviations and Acronyms afolu agriculture, forestry, and other land use International Energy Agency iea Area Metropolitana del Valle de amva Institute for Global Environmental Strategies iges Aburra (Antioquia, Colombia) Intergovernmental Panel on Climate Change ipcc National Agency for Energy anme kilogram kg Conservation (Tunisia) kilometer km Bau business as usual hour kw h kilowatt- British thermal unit Btu kwp kilowatt- peak cai Clean Air Institute leap Long- Range Energy Alternatives benefit analysis cost- cBa Planning System Clean Development Mechanism cdm leds low emissions development strategy effectiveness analysis cost- cea liquefied petroleum gas lpg methane cH 4 land use, land- lulucf use change, and forestry carbon monoxide co marginal abatement cost mac carbon dioxide co 2 multicriteria analysis mca e carbon dioxide equivalent co 2 mmBtu 1 million Btu ee energy efficiency municipal solid waste msw Renewable Energy Act (Germany) eeg million tonnes mt ej exajoule mtce million tonnes of coal equivalent ets emissions trading system e million tonnes of carbon dioxide equivalent mt co 2 Food and Agriculture Organization fao nationally appropriate mitigation action nama of the United Nations nitrogen trifluoride nf 3 g grams governmental organization non- ngo gross domestic product gdp ammonia nH 3 gHg greenhouse gas non- nmvoc methane volatile organic compound giZ Gesellschaft für Internationale nitrogen oxide no Zusammenarbeit (German Corporation x for International Cooperation) nitrous oxide o n 2 gwp global warming potential oecd Organisation for Economic Co- operation and Development hydrochlorofluorocarbon Hcfc pfc perfluorocarbon hydrofluorocarbon Hfc 173

176 pv photovoltaic solar water heater sw H qa quality assurance t tonne (metric ton) quality control qc transmission and distribution d t& research, development, and deployment rd& d undp United Nations Development Programme renewable energy certificate rec unfccc United Nations Framework Convention on Climate Change social cost of carbon scc World Business Council for wBcsd Stockholm Environment Institute sei Sustainable Development sulfur hexafluoride sf 6 World Resources Institute wri sulfur dioxide so 2 Policy and Action Standard 174

177 Glossary absolute value negative value of a number without regard to its sign. For example, the absolute The non- value of 5 is 5, and the absolute value of -5 is also 5. action See “policy or action.” When used as a type of indicator, the administrative activities involved in implementing activities the policy or action (undertaken by the authority or entity that implements the policy or action), such as permitting, licensing, procurement, or compliance and enforcement. Examples include energy audits and provision of subsidies. activity data A quantitative measure of a level of activity that results in GHG emissions. Activity data is multiplied by an emissions factor to derive the GHG emissions associated with a hours of electricity process or an operation. Examples of activity data include kilowatt- used, quantity of fuel used, output of a process, hours equipment is operated, distance traveled, and floor area of a building. adopted policies and actions Policies and actions for which an official government decision has been made and there is a clear commitment to proceed with implementation but that have not yet been implemented. Baseline emissions An estimate of GHG emissions, removals, or storage associated with a baseline scenario. Baseline scenario A reference case that represents the events or conditions most likely to occur in the absence of the policy or action (or package of policies or actions) being assessed. Baseline value The value of a parameter in the baseline scenario. Black carbon A climate forcing agent formed through the incomplete combustion of fossil fuels, biofuel, and biomass. Bottom- up data Data that are measured, monitored, or collected (for example, using a measuring device such as a fuel meter) at the source, facility, entity, or project level. Methods (such as engineering models) that calculate or model the change in GHG Bottom- up methods emissions for each source, project, or entity, then aggregate across all sources, projects, or entities to determine the total change in GHG emissions. calculated data Data calculated by multiplying activity data by an emission factor. For example, calculating emissions by multiplying natural gas consumption data by a natural gas emission factor. causal chain A conceptual diagram tracing the process by which the policy or action leads to GHG effects through a series of interlinked logical and sequential stages of cause- and- effect relationships. The universal unit of measurement to indicate the global warming potential (GWP) of e) equivalent ( co co 2 2 each greenhouse gas, expressed in terms of the GWP of one unit of carbon dioxide. It is used to evaluate different greenhouse gases against a common basis. 175

178 drivers Socioeconomic or other conditions or other policies/actions that influence the level of emissions or removals. For example, economic growth is a driver of increased energy consumption. Drivers that affect emissions activities are divided into two types other policies or actions and non- policy drivers. A descriptor for a parameter (such as an emission factor) that changes over time. dynamic Changes that result from a policy or action. See intermediate effects, GHG effects, and effects non- GHG effects. e emission factor A factor that converts activity data into GHG emissions data. For example, kg CO 2 emitted per liter of fuel consumed. The release of greenhouse gases into the atmosphere. emissions emissions estimation method An equation, algorithm, or model that quantitatively estimates GHG emissions. For example, a simple emissions estimation method is the following equation: GHG emissions = emission factor × activity data. An emissions estimation method is comprised of parameters. estimated data In the context of monitoring, proxy data or other data sources used to fill data gaps in the absence of more accurate or representative data sources. ex- ante assessment The process of estimating expected future GHG effects of policies and actions. ex- ante baseline scenario A forward- looking baseline scenario, typically established prior to implementation of the policy or action, based on forecasts of external drivers (such as projected changes in population, economic activity, or other drivers that affect emissions), in addition to historical data. expert judgment A carefully considered, well- documented qualitative or quantitative judgment made in the absence of unequivocal observational evidence by a person or persons who have a demonstrable expertise in the given field (IPCC 2006). ex- post assessment The process of estimating historical GHG effects of policies and actions. ex- post baseline scenario A backward- looking baseline scenario that is established during or after implementation of the policy or action. free rider effect Participants in a policy or program who would have implemented the technologies, practices, or processes associated with the policy or program in the absence of the policy or program. gHg See greenhouse gas. gHg assessment The estimation of changes in GHG emissions and removals resulting from a policy or action, either ex- ante or ex- post. gHg assessment boundary The scope of the assessment in terms of the range of GHG effects (and non- GHG effects, if relevant), sources and sinks, and greenhouse gases that are included in the assessment. The time period over which GHG effects resulting from the policy or action are assessed. gHg assessment period Policy and Action Standard 176

179 Glossary gHg effects Changes in GHG emissions by sources and removals by sinks that result from a policy or action. A factor describing the radiative forcing impact (degree of harm to the atmosphere) global warming potential . (gwp ) of one unit of a given GHG relative to one unit of CO 2 For the purposes of this standard, GHGs are the seven gases covered by the UNFCCC: greenhouse gas ( gHg) ), methane (CH ), nitrous oxide (N O), hydrofluorocarbons (HFCs), carbon dioxide (CO 4 2 2 ), and nitrogen trifluoride (NF ). perfluorocarbons (PFCs), sulfur hexafluoride (SF 3 6 Policies and actions that are currently in effect, as evidenced by one or more of the implemented policies following: (a) relevant legislation or regulation is in force, (b) one or more voluntary and actions agreements have been established and are in force, (c) financial resources have been allocated, or (d) human resources have been mobilized. Policies that do not interact with each other, such that the combined effect of independent policies implementing the policies together is equal to the sum of the individual effects of implementing them separately. indicator See key performance indicator. in- jurisdiction effects Effects that occur inside the geopolitical boundary over which the implementing entity has authority, such as a city boundary or national boundary. inputs Resources that go into implementing a policy or action, such as financing. Effects that are intentional based on the original objectives of the policy or action. intended effects Policies that produce total effects, when implemented together, that differ from the sum interacting policies of the individual effects had they been implemented separately. Changes in behavior, technology, processes, or practices that result from a policy or action. intermediate effects The geographic area within which an entity’s (such as a government’s) authority is exercised. jurisdiction key performance indicator A metric that indicates the performance of a policy or action, such as tracking changes in targeted outcomes. For example, the quantity of wind power generated in a country may be used as an indicator for a production tax credit for wind power. leakage An increase in emissions outside the jurisdictional boundary that results from a policy or action implemented within that jurisdiction. cycle effects Changes in upstream and downstream activities, such as extraction and production of life- energy and materials, or effects in sectors not targeted by the policy, resulting from the policy or action. long- term effects Effects that are more distant in time, based on the amount of time between implementation of the policy and the effect. macroeconomic effects Changes in macroeconomic conditions— such as GDP, income, employment, or structural changes in economic sectors— resulting from the policy or action. Changes in supply and demand or changes in prices resulting from the policy or action. market effects 177

180 measured data Direct measurement, such as directly measuring emissions from a smokestack. model uncertainty Uncertainty resulting from limitations in the ability of modeling approaches, equations, or algorithms to reflect the real world. modeled data Data derived from quantitative models, such as models representing emissions processes from landfills or livestock. Hg emissions The aggregation of GHG emissions (positive emissions) and removals (negative emissions). net g gHg effects Changes in environmental, social, or economic conditions other than GHG emissions non- or climate change mitigation that result from a policy or action, such as changes in economic activity, employment, public health, air quality, and energy security. non- policy drivers Conditions other than policies and actions, such as socioeconomic factors and market forces, that are expected to affect the emissions sources and sinks included in the GHG assessment boundary. For example, energy prices and weather are non- policy drivers that affect demand for air conditioning or heating. normalization A process to make conditions from different time periods comparable, which may be used to compare policy effectiveness by removing fluctuations not influenced by the policy or action, such as weather variations. other policies or actions Policies, actions, and projects— other than the policy or action being assessed— that are expected to affect the emissions sources and sinks included in the GHG assessment boundary. Policy and Action Standard 178

181 Glossary of- out- jurisdiction effects Effects that occur outside the geopolitical boundary over which the implementing entity has authority, such as a city boundary or national boundary. overlapping policies Policies that interact with each other and that, when implemented together, have a combined effect less than the sum of their individual effects when implemented separately. This includes both policies that have the same or complementary goals (such as national and subnational energy efficiency standards for appliances), as well as policies that have different or opposing goals (such as a fuel tax and a fuel subsidy). The latter are sometimes referred to as counteracting policies. parameter A variable such as activity data or an emission factor that is part of an emissions estimation method. For example, “emissions per kWh of electricity” and “quantity of e/kWh of electricity electricity supplied” are both parameters in the equation “0.5 kg CO 2 e.” × 100 kWh of electricity supplied = 50 kg CO 2 parameter uncertainty Uncertainty regarding whether a parameter value used in the assessment accurately represents the true value of a parameter. The value of a parameter. For example, 0.5 is a parameter value for the parameter parameter value “emissions per kWh of electricity.” peer- reviewed Literature (such as articles, studies, or evaluations) that has been subject to independent evaluation by experts in the same field prior to publication. planned policies and actions Policy or action options that are under discussion and have a realistic chance of being adopted and implemented in the future but that have not yet been adopted or implemented. policy or action An intervention taken or mandated by a government, institution, or other entity, which may include laws, regulations, and standards; taxes, charges, subsidies, and incentives; information instruments; voluntary agreements; implementation of new technologies, processes, or practices; and public or private sector financing and investment, among others. policy implementation period The time period during which the policy or action is in effect. policy monitoring period The time over which the policy is monitored. This may include pre- policy monitoring and post- policy monitoring in addition to monitoring during the policy implementation period. policy scenario A scenario that represents the events or conditions most likely to occur in the presence of the policy or action (or package of policies or actions) being assessed. The policy scenario is the same as the baseline scenario except that it includes the policy or action (or package of policies/actions) being assessed. policy scenario emissions An estimate of GHG emissions and removals associated with the policy scenario. propagated parameter The combined effect of each parameter’s uncertainty on the total result. uncertainty proxy data Data from a similar process or activity that are used as a stand- in for the given process or activity. 179

182 rebound effect Marginal increases in energy- using activities or behavior resulting from energy efficiency improvements. regression analysis A statistical method for estimating the relationships among variables (in particular, the relationship between a dependent variable and one or more independent variables). Policies that interact with each other and that, when implemented together, have a combined reinforcing policies effect greater than the sum of their individual effects when implemented separately. Removal of GHG emissions from the atmosphere through sequestration or absorption, removal is absorbed by biogenic materials during photosynthesis. such as when CO 2 scenario uncertainty Variation in calculated emissions resulting from methodological choices, such as selection of baseline scenarios. sensitivity analysis A method to understand differences resulting from methodological choices and assumptions and to explore model sensitivities to inputs. The method involves varying the parameters to understand the sensitivity of the overall results to changes in those parameters. short- Effects that are nearer in time, based on the amount of time between implementation term effects of the policy and the effect. sink Any process, activity, or mechanism that increases storage or removals of greenhouse gases from the atmosphere. source Any process, activity, or mechanism that releases a greenhouse gas into the atmosphere. spillover effect Out- of- jurisdiction effects that reduce emissions outside the jurisdictional boundary, or effects that amplify the result but are not directly driven by the policy or action being assessed (also called multiplier effects). A descriptor for a parameter (such as an emission factor) that does not change over time. static down data top- Macro- level statistics collected at the jurisdiction or sector level, such as energy use, population, GDP, or fuel prices. top- down methods Methods (such as econometric models or regression analysis) that use statistical methods to calculate or model changes in GHG emissions. Changes in imports and exports resulting from the policy or action. trade effects uncertainty 1. Quantitative definition: Measurement that characterizes the dispersion of values that could reasonably be attributed to a parameter. 2. Qualitative definition: A general term that refers to the lack of certainty in data and methodology choices, such as the application of non- representative factors or methods, incomplete data on sources and sinks, or lack of transparency. unintended effects Effects that are unintentional based on the original objectives of the policy or action. Unintended effects may include a variety of effects, such as rebound effects, lack of compliance or enforcement, effects on behavior once a policy is announced but before it is implemented, and effects on members of society not targeted by the policy or action. Policy and Action Standard 180

183 References AEA, Ecofys, Fraunhofer ISI, and ICCS. 2009. Quantification Department for Environment, Food, and Rural Affairs, United of the Effects on Greenhouse Gas Emissions of Policies Kingdom (DEFRA). 2003. “Use of Multi- criteria Analysis . Reference: ENV.C.1/SER/2007/0019. and Measures in Air Quality Policy: A Report.” Accessible at http://www. Accessible at http://ec.europa.eu/clima/policies/ defra.gov.uk/environment/airquality/mcda/index.htm. package/docs/ghgpams_report_180110_en.pdf. Department for International Development, United Asian Development Bank (ADB). 2007. “Theory and Practice Kingdom (DFID). 2014. “Assessing the Strength of in the Choice of Social Discount Rate for Cost- Benefit Evidence.” Accessible at http://www.homepages. Analysis: A Survey.” Economics and Research Department ucl.ac.uk/~ucft347/Kesicki_MACC.pdf. Working Paper, Series No. 94. Accessible at http://www. DLR, Fraunhofer IWES, and IFNE. 2012. “Long- adb.org/sites/default/files/pub/2007/WP094.pdf. Term Scenarios and Strategies for the Deployment Barua, Priya, Taryn Fransen, and Davida Wood. 2014. of Renewable Energies in Germany in View of “Climate Policy Implementation Tracking Framework.” WRI European and Global Developments.” Accessible Working Paper. Washington, DC: World Resources Institute. at http://www.dlr.de/dlr/Portaldata/1/Resources/ Accessible at http://www.openclimatenetwork.org. documents/2012_1/leitstudie2011_kurz_en_bf.pdf. Boonekamp, P. 2006. “Actual Interaction Effects between Ecoinvent. 2013. “Uncertainty.” Chapter 10 of “Overview Policy Measures for Energy Efficiency: A Qualitative and Methodology: Data Quality Guideline for the Matrix Method and Quantitative Simulation Results Ecoinvent Database Version 3.” Accessible at http:// Energy for Households.” 31, no. 14: 2848–73. files. and- www.ecoinvent.org/support/documents- Boonekamp, P., and S. Faberi. 2012. “Interaction ECONOTEC and VITO. 2014. Evaluation of the Impact of between Policy Measures— Analysis Tool in the MURE Policy Instruments and Measures Implemented in the MURE Database.” Report in the frame of the Odyssee- Context of the Federal Climate Policy . Study commissioned project. Accessible at www.odyssee- indicators.org. vice, Health, Food Chain Safety, by Belgian Federal Public Ser and Environment. Brussels and Mol: ECONOTEC and VITO. Cellini, Stephanie R., and James E. Kee. 2010. “Cost- Effectiveness and Cost- Handbook Benefit Analysis.” In Eichhammer, Wolfgang, et al. 2008. “Distinction of . 3rd ed. Edited of Practical Program Evaluation Energy Efficiency Improvement Measures by Type by Joseph S. Wholey, Harry P. Hatry, and Kathryn of Appropriate Evaluation Method.” Accessible at E. Newcomer. San Francisco: Jossey- Bass. http://www.evaluate- energy- savings.eu/emeees/ downloads/EMEEES_WP3_Report_Final.pdf. Department for Communities and Local Government, United Kingdom. 2009. “Multi- criteria Analysis: A Manual.” Energy Research Centre, the Netherlands (ECN). 2010. Accessible at https://www.gov.uk/government/publications/ “Marginal Abatement Cost (MAC) Curve.” Accessible at analysis- policy. government- making- for- manual- criteria- multi- http://www.ecn.nl/docs/library/report/2011/o11017.pdf. Department for Energy and Climate Change, United Evaluation and Monitoring for the EU Directive on Energy Kingdom (DECC). 2013. “Valuation of Energy Use and Use Efficiency and Energy (EMEEES). 2009. “Evaluation End- Greenhouse Gas (GHG) Emissions: Supplementary and Monitoring for the EU Directive on Energy End- Use Guidance to the HM Treasury Green Book on Appraisal and vices.” Accessible at http:// Efficiency and Energy Ser Evaluation in Central Government.” Available at https:// energy- www.evaluate- savings.eu/emeees/en/home. www.gov.uk/government/publications/valuation- of- appraisal. for- emissions- gas- greenhouse- and- use- energy- 181

184 Food and Agriculture Organization of the United Nations Intergovernmental Panel on Climate Change (IPCC). 2000. (FAO). 2012. “Using Marginal Abatement Cost Curves Good Practice Guidance and Uncertainty Management to Realize the Economic Appraisal of Climate Smart . Accessible at in National Greenhouse Gas Inventories Agriculture Policy Options.” Accessible at http://www.fao. http://www.ipcc- nggip.iges.or.jp/public/gp/english. org/docs/up/easypol/906/ex- act_MACC_116EN.pdf. IPCC. 2006. Guidelines for National Greenhouse GHG Protocol . 2004. Washington, Corporate Standard Gas Inventories . Accessible at http://www. DC: World Resources Institute and World Business ipcc- nggip.iges.or.jp/public/2006gl. Council for Sustainable Development. Accessible at http:// IPCC (Gupta, S., D. Tirpak, N. Burger, J. Gupta, N. Höhne, standard. www.ghgprotocol.org/standards/corporate- A. Boncheva, G. Kanoan, C. Kolstad, J. A. Kruger, A. The GHG Protocol for Project Accounting . 2005. Michaelowa, S. Murase, J. Pershing, T. Saijo, and A. Washington, DC: World Resources Institute and World Sari). 2007. “Policies, Instruments, and Co- operative Business Council for Sustainable Development. Accessible Climate Change 2007: Mitigation of Arrangements.” In protocol. at http://www.ghgprotocol.org/standards/project- . Climate Change Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental GHG Protocol Scale Global Protocol for Community- Panel on Climate Change. Edited by B. Metz, O. Davidson, Greenhouse Gas Emission Inventories (GPC). 2014. P. Bosch, R. Dave, and L. Meyer. Cambridge: Cambridge Washington, DC: World Resources Institute, C40 Cities University Press. Accessible at http://www.ipcc.ch/pdf/ Climate Leadership Group, and ICLEI. Accessible at report/ar4/wg3/ar4- wg3- chapter13.pdf. assessment- accounting. http://www.ghgprotocol.org/city- IPCC. 2010. “Guidance Note for Lead Authors of the Guidelines for Quantifying GHG Reductions GHG Protocol IPCC Fifth Assessment Report on Consistent Treatment . 2007. Washington, from Grid- Connected Electricity Projects of Uncertainties.” Accessible at http://www.ipcc.ch/pdf/ DC: World Resources Institute and World Business guidance- material/uncertainty- supporting- note.pdf. Council for Sustainable Development. Accessible at http:// www.ghgprotocol.org/standards/project- protocol. Climate Change IPCC. 2014. “Technical Summary.” In 2014: Mitigation of Climate Change. Contribution of GHG Protocol Land Use, Land- Use Change, and Forestry Working Group III to the Fifth Assessment Report of the . 2006. Washington, Guidance for GHG Project Accounting Intergovernmental Panel on Climate Change. Edited by DC: World Resources Institute. Accessible at http:// Ottmar Edenhofer, R. Pichs-Madruga, Y. Sokona, S. Kadner, protocol. www.ghgprotocol.org/standards/project- J. Minx, and S. Brunner. Cambridge: Cambridge University GHG Protocol Mitigation Goal Standard . 2014. Washington, Press. Accessible at http://www.ipcc.ch/report/ar5/wg3. DC: World Resources Institute. Accessible at http:// International Organization for Standardization (ISO). goal- www.ghgprotocol.org/mitigation- standard. ISO 14064–3: Greenhouse Gases— 2006. Part 3: The Green Book: HM Treasury, United Kingdom. 2014. Specification with Guidance for the Validation and Appraisal and Evaluation in Central Government. Accessible . Geneva: ISO. Verification of Greenhouse Gas Assertions at https://www.gov.uk/government/publications/the- green- Kesicki, Fabian. 2011. “Marginal Abatement Cost Curves for and- evaluation- in- central- book- governent. appraisal- Policy Making— Derived Curves.” Based vs. Model- Expert- Interagency Working Group on Social Cost of Carbon, London: UCL Energy Institute. Accessible at http://www. United States. 2010. “Technical Support Document: homepages.ucl.ac.uk/~ucft347/Kesicki_MACC.pdf. Social Cost of Carbon for Regulatory Impact Analysis under Executive Order 12866.” Accessible at http:// tsd.pdf. www.epa.gov/oms/climate/regulations/scc- Policy and Action Standard 182

185 References Kushler, Martin, Seth Nowak, and Patti Witte. 2014. W. K. Kellogg Foundation. 2004. “Logic Model Examining the Net Savings Issue: A National Survey of Development Guide.” Accessible at http://www. State Policies and Practices in the Evaluation of Ratepayer- smartgivers.org/uploads/logicmodelguidepdf.pdf. Funded Energy Efficiency Programs . Washington, DC: Social Discount Rates for Nine World Bank. 2008. Efficient Economy. American Council for an Energy- Latin American Countries . Washington, DC: World Pathways to a Low- McKinsey & Company. 2009. Bank. Accessible at http://elibrary.worldbank.org/ Carbon Economy: Version 2 of the Global Greenhouse content/workingpaper/10.1596/1813–9450–4639. . Accessible at http://www. Gas Abatement Cost Curve World Bank. 2014. “Real Interest Rates.” Accessible at mckinsey.com/client_ser vice/sustainability/latest_ http://data.worldbank.org/indicator/FR.INR.RINR. thinking/greenhouse_gas_abatement_cost_curves. World Resources Institute (WRI) and World Business Mure. 2014. “Database on Energy Efficiency Odyssee- Council for Sustainable Development (WBCSD). 2003. Indicators.” Accessible at http://www.indicators. “GHG Protocol Guidance on Uncertainty Assessment in database.html. mure.eu/energy- odyssee- efficiency- GHG Inventories and Calculating Statistical Parameter United Nations Framework Convention on Climate Uncertainty.” Accessible at http://www.ghgprotocol.org. Change (UNFCCC). 2000. “UNFCCC Guidelines on Aggregating Statistical Parameter WRI/WBCSD. 2003. Reporting and Review.” FCCC/CP/1999/7. Accessible Uncertainty in GHG Inventories: Calculation Worksheets. at http://unfccc.int/files/national_reports/annex_i_ Accessible at http://www.ghgprotocol.org. natcom/_guidelines_for_ai_nat_comm/application/ pdf/01_unfccc_reporting_guidelines_pg_80–100.pdf. WRI/WBCSD. 2011. “Quantitative Inventory Uncertainty.” Accessible at http://www.ghgprotocol.org. Weidema, B. P., and M. S. Wesnaes. 1996. “Data Quality Management for Life Cycle Inventories: An WRI/WBCSD. 2011. Uncertainty Assessment Template Journal Example of Using Data Quality Indicators.” for Product GHG Inventories . Accessible at http://www. 4, no. 3–4: 167–74. of Cleaner Production ghgprotocol.org. 18 3

186 Contributors Technical Working Group members: Chapter leads Michael Van Brunt, Covanta Energy AEA Daniel Forster, Ricardo- Marion Vieweg, Current Future Matthew Brander, University of Edinburgh Wolfgang Eichhammer, Fraunhofer ISI Gary Kleiman, World Bank Technical Working Group members Jannick Schmidt, Aalborg University Clare Breidenich, Independent Sanjay Mande, ABPS Infrastructure Advisory Pvt. Ltd. Raihan Uddin Ahmed, Infrastructure Jette Findsen, Abt Associates Development Company Limited Robert Dornau, Carbonflow Martial Bernoux, Institut de Recherche William Wills, CentroClima, Federal pour le Développement University of Rio de Janeiro Colin Hughes, Institute for Transportation Jeff Deason, Climate Policy Initiative and Development Policy Karen Laughlin, Climate Policy Initiative Jacob Mason, Institute for Transportation Stacey Davis, Center for Clean Air Policy and Development Policy Sara Moarif, Center for Climate and Energy Solutions (C2ES) Ramiro Alberto Rios, Institute for Transportation Stephen Roe, The Center for Climate Strategies and Development Policy Jen McGraw, Center for Neighborhood Technology Manpreet Singh, KPMG Doug Huxley, CH2M HILL Julia Kalloz, LMI Joanne Green, Clean Air Institute Jenny Mager, Ministry of Environment, Chile Juan Pablo Orjuela, Clean Air Institute Gregory Briner, Organisation for Economic Derik Broekhoff, Climate Action Reserve operation and Development (OECD) Co- Peggy Kellen, The Climate Registry Akira Shibata, Osaka Gas Co., Ltd. Tim Kelly, Conservation Council SA AEA James Harries, Ricardo- Jacob Krog Soebygaard, Danish Energy Agency David Lerpiniere, Ricardo- AEA Jessica Allen, Department of Climate Change Michael Young, Scottish Government and Energy Efficiency, Australia Patric Reiff, Siemens Ken Xie, Department of Climate Change Walter Figueiredo De Simoni, State of Rio de Janeiro, Brazil and Energy Efficiency, Australia Nick Hall, TecMarket Works Todd Krieger, DuPont Matt Sommerville, Tetra Tech Julia Larkin, Ecofys Justin Felt, Thomson Reuters Point Carbon Cynthia Menéndez, EMBARQ Mexico Sylvie Marchand, United Nations Climate Change Secretariat Hilda Martínez, EMBARQ Mexico Jennifer Leisch, United States Agency for Louis Bockel, Food and Agriculture International Development (USAID) Organization of the United Nations Kate Larsen, United States Department of State Manu Maudgal, Deutsche Gesellschaft für Matt Clouse, United States Environmental Protection Agency Internationale Zusammenarbeit (GIZ) GmbH Jon Sottong, United States Environmental Protection Agency Joe Indvik, ICF International Ian Williams, University of Southampton Diana Pape, ICF International Jerry Seager, Verified Carbon Standard Rachel Steele, ICF International Taryn Fransen, World Resources Institute Policy and Action Standard 184

187 Contributors Pilot testers Mohamed H. Belhaouane, ALCOR Consulting Luis Roberto Chacón, EMA Imed Thabet, ALCOR Consulting Anthony Dane, Energy Research Centre, vice, Health, Claire Collin, Belgian Federal Public Ser University of Cape Town Food Chain Safety and Environment Leah Davis, Greater London Authority Magdalena Fandiño, Clean Air Institute Michael Doust, Greater London Authority Joanne Green, Clean Air Institute Yuqing Ariel Yu, IGES Juan Pablo Orjuela, Clean Air Institute Raihan Uddin Ahmed, Infrastructure Jonas Bleckmann, Deutsche Gesellschaft für Development Company Limited Internationale Zusammenarbeit (GIZ) GmbH Sandip Keswani, KPMG Torsten Greis, Deutsche Gesellschaft für Manpreet Singh, KPMG Internationale Zusammenarbeit (GIZ) GmbH Vishal Bhavsar, Mahindra World City Developers Ltd. Ramiro Barrios, DNV GL Hernán Sepúlveda, Ministry of Energy, Chile Elizabeth Sara Ramírez, DNV GL Andrés Pirazzoli, Ministry of Environment, Chile Nadine Braun, Ecofys Meike Sophie Siemens, Ministry of Environment, Chile Caroline de Vit, Ecofys Ronnie Cohen, Ministry of Environmental Protection, Israel Francis Altdorfer, ECONOTEC Gil Proaktor, Ministry of Environmental Protection, Israel Aurélie Draguet, ECONOTEC 18 5

188 Pilot testers (continued) Mouna Besbes, National Agency for Energy Conservation of Tunisia/UNDP Zied Ferjani, National Agency for Energy Conservation of Tunisia Ralph Harthan, Oeko- Institut e.V. María Luz Farah, POCH Ambiental S.A. Ignacio Rebolledo, POCH Ambiental S.A. Harmke Immink, Promethium Carbon Miriam Lev- On, Samuel Neaman Institute, Technion, Haifa, Israel Perry Lev- On, Samuel Neaman Institute, Technion, Haifa, Israel Soffia Alarcón Díaz, Secretary of Environment and Natural Resources (SEMARNAT), Mexico Carlos Vázquez, Secretary of Environment and Natural Resources (SEMARNAT), Mexico Peter Erickson, Stockholm Environment Institute— U.S. Michael Lazarus, Stockholm Environment Institute— U.S. Toshiko Chiba, Tokyo Metropolitan Government Yu Wang, Tsinghua University Diana Marcela Quinceno, Universidad Pontificia Bolivariana María Victoria Toro, Universidad Pontificia Bolivariana Tom Dauwe, VITO Ranping Song, World Resources Institute Jingjing Zhu, World Resources Institute Kemen Austin, World Resources Institute/Duke University Reviewers Stefanie Giese- Bogdan, 3M Nimisha Pandey, The Energy and Fabio Peyer, Amcor Ltd. Resources Institute (TERI), India Gerald Rebitzer, Amcor Ltd. Xiang Gao, Energy Research Institute, NDRC, China Eros Artuso, AS Management & Consulting Sàrl Zhu Songli, Energy Research Institute, NDRC, China Michael Doust, C40 Cities Climate Leadership Group Carolina Dubeux, Federal University of Rio de Janeiro Ryan McCarthy, California Air Resources Board Emily Castro Prieto, GIZ Mexico Courtney Smith, California Air Resources Board Miriam Faulwetter, GIZ Mexico Liao Cuiping, Chinese Academy of Sciences Arturo Bernal Márquez, Green to Go, Colombia Leilani L. Cortes, Climate Change Commission, Philippines Wei Zeng, Hubei University of Technology Sebastian Wienges, Deutsche Gesellschaft für Scott Williamson, Jack Faucett Associates Internationale Zusammenarbeit (GIZ) GmbH Siriluk Chiakakorn, King Mongkut’s University Cameron, ECN Lachlan of Technology, Thonburi, Thailand Luis Roberto Chacón, EMA Koji Ina, Ministry of Economy, Trade, and Industry, Japan Policy and Action Standard 186

189 Contributors Reviewers (continued) Abdelrhani Boucham, Ministry of Environment, Morocco Sameer Akbar, World Bank Brad Upton, NCASI Martina Bosi, World Bank Takayoshi Sonoda, Nippon Kaiji Kentei Quality Assurance Ltd. Klaus Oppermann, World Bank Einar Telnes, Norad Rama Reddy, World Bank Kazuyoshi Sasaki, Overseas Environmental Suphachol Suphachalasai, World Bank Cooperation Center, Japan Nate Aden, World Resources Institute Mariela Ramos, POCH Ambiental S.A. Juan- Carlos Altamirano, World Resources Institute Gareth Phillips, Sindicatum Sustainable Resources Aileen Carrigan, World Resources Institute Peng Li, SinoCarbon Innovation & Investment Co., Ltd. Wee Kean Fong, World Resources Institute Ingo Puhl, South Pole Group Kevin Kennedy, World Resources Institute Kimberly Todd, United Nations Development Anjali Mahendra, World Resources Institute Programme (UNDP) Kristin Meek, World Resources Institute Samir Tantawi, UNDP LECB Project Manager, Egypt Jennifer Morgan, World Resources Institute Massamba Thioye, United Nations Michael Obeiter, World Resources Institute Climate Change Secretariat Janet Ranganathan, World Resources Institute Alexia Kelly, United States Department of State Ranping Song, World Resources Institute Alban Fournier, VALUE2020 Davida Wood, World Resources Institute Kai- Uwe Schmidt, Verified Carbon Standard Timon Wehnert, Wuppertal Institute Funders This standard development process was generously Global Environment Facility (GEF); Gold Fields Limited; the supported by the German Federal Ministry for the Greater London Authority; Harmony Gold Mining Company Environment, Nature Conservation, Building, and Nuclear American Development Bank; the Israel Limited; the Inter- Safety based on a decision of the German Bundestag. Ministry of Environmental Protection; Kumba Iron Ore Additional support was provided by the Ministry of Foreign Limited; Low Emission Capacity Building Project (European Affairs of the Netherlands, Siemens AG, and the United Commission, Government of Australia, Government of Kingdom Department of Energy and Climate Change. Germany); PPC Limited; the Strategic Climate Institutions Programme (SCIP); the Tokyo Metropolitan Government; WRI would also like to thank the following funders the United Nations Development Programme (UNDP); for supporting the pilot testing of the standard: Alcoa the United States Agency for International Development vice, Health, Food Foundation; Belgian Federal Public Ser (USAID); UPS Foundation; and the World Bank. Chain Safety, and Environment; Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH; German Federal Ministry for the Environment, Nature Conservation, Building, and Nuclear Safety (BMUB); the 187

190 dedication disclaimer Policy and Action Standard is designed This standard is dedicated to Andrei Bourrouet, a member of The GHG Protocol to promote best practice GHG accounting and reporting. It the Advisory Committee, who passed away in 2013. Andrei was the environmental representative from the Costa Rican has been developed through an inclusive multistakeholder process involving experts from businesses, non- Institute of Electricity, and formerly the Viceminister of Energy and Environmental Management at the Costa Rican governmental organizations (NGOs), governments, and others convened by the World Resources Institute (WRI). Ministry of Environment, Energy, and Telecommunications. Policy and Action Andrei devoted his career to furthering climate change While WRI encourages use of the by all relevant organizations, the preparation and Standard policymaking in Costa Rica and internationally. publication of reports or program specifications based fully or partially on this standard is the full responsibility of those producing them. Neither WRI nor other individuals who contributed to this standard assume responsibility for any consequences or damages resulting directly or indirectly from its use in the preparation of reports or program specifications or the use of reported data based on the standard. Policy and Action Standard 188

191 about the w orld r nstitute esources i WRI is a global research organization that works closely with leaders to turn big ideas into action to sustain a healthy — the foundation of economic opportunity environment and human well- being. our challenge Natural resources are at the foundation of economic being. But today, we are opportunity and human well- depleting Earth’s resources at rates that are not sustainable, endangering economies and people’s lives. People depend on clean water, fertile land, healthy forests, and a stable climate. Livable cities and clean energy are essential for a FPO sustainable planet. We must address these urgent, global challenges this decade. Printed on Chorus Art Silk, an FSC-certified paper with 30% pcw recycled content and with inks that are of soy content. our vision Stock photography: Shutterstock.com We envision an equitable and prosperous planet driven by the wise management of natural resources. We aspire to Design: Alston Taggart, Studio Red Design create a world where the actions of government, business, ISBN 978-1-56973-840-5 and communities combine to eliminate poverty and sustain Printed in USA people. the natural environment for all Copyright 2014 World Resources Institute. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivative Works 3.0 License. To view a copy of the license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/

192 The Greenhouse Gas Protocol provides the foundation for sustainable climate strategies. GHG Protocol standards are the most widely used accounting tools to measure, manage and report greenhouse gas emissions. www.wri.org www.ghgprotocol.org

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