A Retrospective Analysis of the Benefits and Impacts of U.S. Renewable Portfolio Standards


1 A Retrospective Analysis of the Benefits and Impacts of U.S. Renewable Portfolio Standards

2 A Retrospective Analysis of the Benefits and Impacts of U.S. Renewable Portfolio Standards 1 1 2 Ryan Wiser , , Galen Barbose , Jenny Heeter 2 2 1 Trieu Mai , Lori Bird , Mark Bolinger , 2 2 , Garvin Heath , Alberta Carpenter 2 2 David Keyser , Jordan Macknick , 1 1 , and Dev Millstein Andrew Mills 1 Lawrence Berkeley National Laboratory 2 National Renewable Energy Laboratory National Laboratory is a Department of Energy Lawrence Berkeley Office of Science lab managed by University of California. NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for ble Energy, LLC. Sustaina This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. Technical Report -65005 TP -6A20 January 2016 (NREL) and Contract Nos . DE -AC36 -08GO28308 -05CH11231 (LBNL) DE -AC02

3 NOTICE This report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately d rights. Reference herein to any specific commercial product, process, or service by trade name, owne trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. This report is being disseminated by the Department of Energy. As such, the document was prepared in compliance with Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 554) and information quality guidelines issued by the Department of Energy. Though this report (Public Law 106- does not constitute “influential” information, as that term is defined in DOE’s information quality guidelines or the Office of Management and Budget’s Information Quality Bulletin for Peer Review (Bulletin), the report was cation. reviewed both internally and externally prior to publi This report is available at no cost from the National Renewable Energy www.nrel.gov/publications . Laboratory (NREL) at ech Connect SciT Available electronically at http:/www.osti.gov/scitech Available for a processing fee to U.S. Department of Energy and its contractors, in paper, from: U.S. Department of Energy Office of Scientific and Technical Information P.O. Box 62 Oak Ridge, TN 37831- 0062 http://www.osti.gov OSTI Phone: 865.576.8401 Fax: 865.576.5728 Email: [email protected] Available for sale to the public, in paper, from: U.S. Department of Commerce National Technical Information Service 5301 Shawnee Road Alexandr ia , VA 22312 NTIS http://www.ntis.gov Phone: 800.553.6847 or 703.605.6000 Fax: 703.605.6900 Email: [email protected] Printed on paper that contains recycled content .

4 ITATION C Please cite as f ollows: Wiser, R., G. Barbose, J. Heeter, T. Mai, L. Bird, M. Bolinger, A. Carpenter, G. Heath, D. . 6 etrospective Analysis of the A R Keyser, J. Macknick, A. Mills, and D. Millstein. 201 Benefits and Impacts of U.S. Renewable Portfolio Standards. Lawrence Berkeley National oratory. N Lab oratory and Nation al Renewable Energy Lab REL/TP-6A20-65005. http://www.nrel.gov/docs/fy16osti/65005.pdf. iii

5 P REFACE report in a series that es the costs, benefits, and other impacts of state renewable This is one explor , both retrospectively and prospectively . The terminology applied in this series portfolio standards (RPS) does not align precisely with the traditional concepts of costs and benefits, but rather is a function of how RPS programs have often been evaluated in practice. In particular, this analysis series evaluates RPS programs in terms of the following: RPS com • represent the incremental cost of meeting RPS compliance obligations, from pliance costs the perspective of the utility or other load- serving entity , compared to the costs that would have been borne in the absence of the RPS . RPS compliance costs if the renewable electricity may be negative, . used for RPS compliance is less expensive to the utility than the alternatives • Benefits , as analyzed in this report series, consist specifically of environmental benefits that accrue to society at large, rat her than to individual utilities . In theory, such benefits may be negative, representing net environmental cost s, if the renewable electricity used for RPS compliance leads to more harmful environmental impacts than it avoids . • Other impacts , in the form of resource transfers from one market participant or segment to another , are also evaluated. These other impacts may also entail net costs or benefits to society at large, but our analyses focus only on the gross impacts, not the net cost or benefit . rst report The fi , A Survey of State -Level Cost and Benefit Estimates of Renewable Portfolio Standards , published in 2014, comprehensively summarized historical RPS compliance costs , drawing in part on . The study the estimates developed by utilities and state regulatory agencies also reviewed analyses of broader societal benefits and impacts of several states’ RPS policies , typically conducted for or by the or RPS administrator s in th ose state s. H owever, regulatory agencies the small number of such studies, and their widely varying methods and scopes, ultimately limited the ability to compare benefits across states or to generalize beyond the specific studies performed. That limitation set the stage for the present study. This report, the second in the series, analyzes historical benefits and impacts of , in all state RPS policies aggregate , employing a consistent and well -vetted set of methods and data sets. The analysis focuses on three specific benefits: greenhouse gas emissions, air pollution, and water use . It also analyzes three other impacts: gross job additions, wholesale electricity market price suppression, and natural gas price suppression. These are an important subset, but by no means a comprehensive set , of all po ssible effects associated with RPS polic ies. These benefits and impacts are also subject to many uncertainties, which are described and, to the extent possible, quantified within the report. The present report is intended to help policymakers, RPS admini strators, and other decision- makers gauge the potential significance of a number of key benefits and impacts from state RPS programs. By noting limitations, caveats, and uncertainties in these results, the report also seeks to highlight important methodological considerations to evaluating RPS benefits and impacts. This report does not, however, provide a complete picture, and comparable information on both the costs and benefits, as well as other impacts, are ultimately needed to inform decision- making. To that end, a third report in this series is planned for the coming year to evaluate the future costs, benefits, and other impacts of state RPS policies , and possible revisions . under both current policies iv

6 A CKNOWLEDGMENTS The authors would like to thank the U.S. Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy’s (EERE) Strategic Programs Office for primary funding support for this analysis. In particular, the authors are grateful to Steve Capanna of the Strategic Programs Office for his support of this project. The authors would also like to thank the following individuals for their thoughtful review: Doug Arent, Joint Institute for Strategic Energy Analysis; Howard Bernstein, Massachusetts Department of Energy Resources; Kevin DeGroat, Antares Group; Jeff Deyette, Union of Concerned Scientists; Warren Leon, Clean Energy Group; Tom Stanton, National Regulatory Research Institute ; Jaquelin Cochran, Jeffrey Logan, David Mooney, and Robin Newmark of the National Renewable Energy Laboratory (NREL) ; and Caitlin Callaghan , Steve Capanna, Eric Hsieh, and Lawrence Mansueti of DOE. We also wish to thank Karin Haas of NREL f or editorial support , Jenny Melius of NREL for GIS support , and Stuart Cohen for research support . v

7 A CRONYMS AVERT AVoided Emissions and geneRation Tool British thermal unit Btu Clean Air Interstate Rule CAIR combined cycle gas turbine CCGT CO carbon dioxide 2 CO carbon dioxide -equivalent e 2 Clean Power Plan CPP concentrating solar power CSP CT combustion turbine DOE U.S. Department of Energy EIA U.S. Energy Information Administration -Atlantic region EMW Great Lakes/Mid U.S. Environmental Protection Agency EPA FTE full -time equivalent GDP gross domestic product GHG greenhouse gas I-O model input -output model IWG Interag ency Working Group JEDI Jobs and Economic Development Impacts model kilowatt kW kWh hour kilowatt- Lawrence Berkeley National Laboratory LBNL landfill gas LFG serving entity LSE load- megawatt MW megawatt -hour MWh NEMS National Energy Modeling System NO nitrogen oxides x NREL National Renewable Energy Laboratory O&M operations and maintenance PM particulate matter PV photovoltaic RE renewable energy REC renewable energy certificate renewable portfolio standard RPS regional transmission organization RTO SCC social cost of carbon SO sulfur dioxide 2 TW terawatt TWh terawatt -hour VSL value of statistical life W watt vi

8 E XECUTIVE UMMARY S State renewable portfolio standards (RPS) currently exist in 29 states and Washington, D.C. Most of these policies, enacted larg 1990s and 2000s, will reach their terminal targets within the next ely during the late decade. As states consider extending, eliminating, or otherwise revising existing RPS programs , or developing new ones, increasing attention is being paid to the costs, benefits, and other impacts of these . policies A prior study (Heeter et al. 2014) and subsequent update (Barbose et al. 2015) in this report series found over the 2010–2013 period were generally equivalent to less than 2% of that RPS compliance costs varied substantially , with the net cost to utilities and other average statewide retail electricity rates, but 1 serving entities ranging from -0.4 to 4.8¢ per kilowatt of renewable electricity (kWh -RE) . load- -hour In aggregate, total RPS compliance costs approximately $1 billion per year , on average, over the represented Th e prior study also summarized RPS benefits studies published by individual state 2010–2013 period. regulatory agencies; however, the small number of such studies, and their widely varying methods and scopes, ultimately limited any compar across states or generaliz ation beyond the specific studies isons performed. This dearth of comparable analyses prompted the need for a broader evaluation of RPS program benefits and impacts, relying on a st andardized methodology and scope. The present report follows directly from the preceding study, and is intended to provide the first national - level assessment of the potential benefits and impacts of state RPS programs, using an established and uniform set of methodologies and robust data sets. The analysis focuses specifically on “new” renewable electricity (RE) resources built after RPS enactment and used to meet RPS compliance obligations in 2013— the most recent year for which the necessary data were available. Based on data compiled from regulatory filings and other sources, 98 terawatt -hours (TWh) of new RE generation was used to meet RPS obligations in 2013, representing 2.4% of total U.S. neration in that year. Over the electricity ge 2014, an average of roughly capacity was course of 2013– 5,600 megawatts (MW) per year of new RE 2 built to service RPS requirements. EPA’s Avoided Emissions and geneRation Tool (AVERT) model to estimate displaced fossil Using estimate that new RE used for RPS compliance in 2013 resulted in a 3.6% reduction in generation, we total fossil fuel generation. Based on outputs estimated with AVERT and several additional analysis tools, ed with reductions in greenhouse gas (GHG) emissions, air we evaluate potential societal benefits associat pollution emissions, and water use. We also assess the impacts—describing below the distinction between “impacts” and “benefits”— of state RPS policies on gross jobs and economic development, wholesa le These benefits and impacts are quantified in both physical and electricity prices, and natural gas prices. monetary terms where possible. We focus on the aggregate benefits and impacts of all state RPS compliance obligations , presenting results national ly and, where data and methods allow , by region and state; we do not, however, analyze the effects of individual state RPS programs. Based on the methods described in the full report, new RE resources used to meet RPS compliance yielde benefits (summarized also in Figure ES -1): obligations in 2013 d the following GHG Emissions and Climate Change Damage Reductions: • -cycle GHG emissions were L ife reduced by 59 million metric tons of carbon dioxide -equivalent ( CO e) in 2013, which translates 2 1 RPS compliance costs represent the incremental cost to the utility or other load -serving entity with RPS obligations, net of avoided conventional generation costs. Negative RPS compliance costs may occur when renewable electricity procured to meet RPS obli gations is less expensive than avoided conventional generation. 2 For the purpose of capacity additions, we focus on the average over the 2013 through 2014 time period, because some portion of capacity additions completed in 2014 were under construction in 2013; averaging capacity additions over a multi -year period also smooths out some of the volatility in annual renewable capacity build rates. vii

9 into $2.2 billion of global benefits when applying a “central value” ($37/metric ton of CO ) for 2 equivalent to 2.2¢ per kilowatt -hour (kWh) of the social cost of carbon. These global benefits are (kWh RE -RE) used to meet 2013 RPS compliance. Benefits estimates span $0.7 billion to new cost carbon estimates $6.3 billion ( 0.7 to 6.4¢/kWh of -RE) across the full range of social considered. National emissions Air Pollution Emissions and Human Health and Environmental Benefits: • of sulfur dioxide ( SO NO ), and particulate matter 2.5 ( PM ) were reduced ), nitrogen oxides ( 2 x 2.5 by 77,400, 43,900, and 4,800 metric tons in 2013, respectively. We estimate that these —using a range of approaches —produced health and environmental benefits equal to reductions nefits are equivalent to 5.3¢/kWh of new RE $5.2 billion, on average. These be 2013 RPS used for compliance. Across the full range of approaches considered, health and environmental benefits RE). The largest health benefits accrue to the span $2.6 billion to $9.9 billion (2.6 to 10.1¢/kWh- eastern half of the country, especially in the Mid -Atlantic, Great Lakes, Northeast, and Texas. Water Use Reduction: were reduced by • National water withdrawals and consumption in 2013 830 billion gallons and 27 billion gallons, respectively, equivalent to savings of 8,420 gallons of -hour (MWh) of new RE used for 2013 withdrawal and 270 gallons of consumption per megawatt 3 . RPS compliance These reductions amount to 2% of both total 2013 power sector water withdrawals and consumption. Water use reductions var y seasonally and come predominantly from freshwater sources, with reductions varying regionally due to geographic differences in power plant fuel types and cooling system configurations. The largest withdrawal and consumption reductions were in California and Texas, respectively, demonstrating the benefits can have in water -stressed regions. RPS policies In addition to the set of societal benefits described above, we estimate three other impacts associated with new RE used to meet RPS compliance obligations in 2013. We distinguish these i mpacts from societal costs and benefits , because . the direction of impact (positive or negative) varies by market participant might thus best be considered resource transfers, and the present study does not assess These impacts , economy- ir net effects. Given that context, we estimate that new RE resources used to meet RPS the wide compliance obligations in 2013 produced the following impacts : Gross Jobs and Economic Development: Renewable generation used to meet 2013 RPS • compliance obligations, along with average annual RPS capacity additions in 2013 and -related based gross jobs in 2013 and drove 2014, supported nearly 200,000 U.S.- over $20 billion in gross , primarily based on NREL’s Jobs and Economic Development Impacts domestic product (GDP) (JEDI) suite of models jobs are related to ongoing . More than 30,000 of these gross domestic operations and maintenance ( O&M ), while 170,000 gross jobs are related to construction activity. Solar photovoltaic ( PV ) installations account for the majority of construction jobs, while established wind plants account for the majority of O&M jobs. California had the most significant compliance obligations, and renewable capacity expansion and generation associated with RPS associated onsite RE jobs than any other state. thus had more of the Wholesale Electricity Price Reductions : Renewable generation used to meet 2013 RPS • compliance obligations potentially shifted the supply curve for electric power, reducing wholesale electricity prices and yielding an estimated $0.0 to $1.2 billion in savings to electricity consumers across the United States . These consumer savi ngs are equivalent to 0.0 to 1.2¢/kWh of new RE used to meet 2013 RPS compliance . The wide range of estimates reflects bounding assump tions 3 Withdrawals are defined as the amount of water removed or diverted from a water source for use, while consumpti on refers to the amount of water that is evaporated, transpired, incorporated into products or crops, or otherwise removed from the immedi ate water environment. viii

10 about how the effects of renewable generation on wholesale spot market prices decline over time and the extent to which consumers are exposed to those prices. Natural Gas Price Reductions: Renewable generation used to meet 2013 RPS compliance • obligations reduced natural gas demand by an estimated 0.42 quads (422 million MMBtu) . This reduction lowered average natural gas prices by an estimated 5¢ to 14¢ /MMBtu in 2013, resulting in consumer savings ranging from $1.3 billion to $3.7 billion. These consumer savings are equivalent to 1.3 ¢ to 3.7¢/kWh of new RE used to meet 2013 RPS compliance. T he range in estimates reflects bounding assumptions about when RPS natural gas -induced reductions in dem and begin to affect prices . Figure ES -1. Benefits and impacts of new RE used to meet 2013 RPS compliance Our analysis includes a number of limitations and caveats. First, it relies on a common standardized set of states, but individual states may have used or could use more -refined approaches methods applied to all , for example, more advanced modeling tools could be used to more precisely estimate wholesale market price suppression impacts . Second, while we use consistent methods and ro bust data sets, our assessments rely on models and methodologies that are sensitive to multiple parameters ; accordingly, w e qualify the , our study results where appropriate sources of uncertainty not explicitly quantified. Third and highlight work distinguishes between the potential benefits and impacts of RPS programs. As noted previously, impacts are best considered resource transfers, benefiting some stakeholders at a cost to others, though ix

11 such impacts might still be RPS programs. Fourth, our analysis considers an relevant to evaluating state important subset of —but not all —potential benefits and impacts; for example, we do not quantify land use and wildlife impacts. Fifth , this analysis does not formally compare the benefits and impacts of RPS programs to their costs. Although prior analysis (Heeter et al. 2014) focused specifically on RPS compliance costs, a dditional research is ultimately needed to evaluate the full set of RPS cost s on a consistent basis across states, and to appropriately comp are costs to benefits. Sixth , we have not evaluated whether RPS policies are the least -cost approach to achieving the benefits and impacts assessed . Lastly, our analysis does not seek to attribute the estimated benefits and impacts to RPS policies , given the solely multiple drivers of RE additions . Despite these limitations, the analysis can help inform decision makers of the value of state RPS programs and the scale of various potential benefits and impacts. In addition, the methodology used in the analysi s provides a framework that states and analysts can build upon and refine for their own assessments. x

12 T ABLE OF ONTENTS C ... 1 1. Introduction 1 ... Background ... 2 Scope, Methods, an d Contribution ... 3 Limitations and Caveats ... Roadmap 4 2. Foundational Data and Analysis: RPS Resources and Displaced Fossil Generation 5 ... ... Overview 5 ... 5 Renewable Generation Used for RPS Compliance Displaced Fossil Generation ... 9 RPS Capacity Additions and Displaced Fossil Generation Capacity ... 11 3. Greenhouse Gas Emissions and Climate Change Damage Reduction Benefits ... 13 ... Introduction 13 ... 14 Methods Results ... 17 4. Air Pollution Emissions and Human Health and Environmental Benefits ... 19 Introduction 19 ... Methods ... 19 Results ... 22 5. Water Use Reducti on Benefits ... 26 Introduction 26 ... Methods ... 27 ... 29 Results 33 ... 6. Gross Jobs and Economic Development Impacts ... 33 Introduction ... 34 Methods Results ... 36 city Price Reduction Impacts 39 7. Wholesale Electri ... ... Introduction 39 Methods ... 39 Results ... 42 8. Natural Gas Price Reduction Impacts 44 ... ... Introduction 44 ... 45 Methods Results ... 48 9. Summary and Conclusions ... 50 References 53 ... L IST OF F IGURES Figure ES -1. Benefits and impacts of new RE used to meet 2013 RPS compliance ... ix Figure 1.1. States with RPS policies circa November 2015 ... 1 5 Figure 2.1. Foundational data and analysis to support RPS benefits and impacts estimates ... xi

13 Figure 2.2. AVERT regions ... 7 ... 8 Figure 2.3. New RE for RPS compliance based on plant location 9 ... Figure 2.4. New RE delivered to each AVERT region for 2013 RPS compliance ... 10 Figure 2.5. Displaced fossil generation by state ... 11 Figure 2.6. Displaced fossil generation by AVERT region Figure 2.7. RPS capacity additions by state 12 ... ... Figure 3.1. Summary of life cycle GHG emissions from electricity generation technologies 14 Figure 3.2. GHG benefits methods overview ... 15 Figure 3.3. Combustion -related CO emissions displaced by state from RPS compliance ... 17 2 Figure 3.4. Life cycle GHG emissions impacts of RPS compliance 18 ... Figure 3.5. Estimated benefits of RPS compliance due to reduced global climate change damages 18 ... ... 20 Figure 4.1. Health and environmental benefits methods overview Figure 4.3. National benefits of RPS compliance due to reduced health and environmental damages ... 24 Figure 4.4. Regional benefits of RPS compliance due to reduced health and environmental damages from particulate matter under the ‘COBRA Low’ estimates 25 ... Figure 5.1. Summary of operational water withdrawal and consumption requirements of electricity generation technologies by technology and cooling system ... 27 Figure 5.2. Water use benefits methods overview ... 28 Figure 5.3. National water withdrawal (left) and consumption (right) benefits of RPS compliance ... 29 Figure 5.4. National water withdrawal and consumption benefits of RPS Compliance, by month ... 30 ... 30 Figure 5.5. Water withdrawal and consumption benefits of RPS compliance, by state Figure 5.6. Regional variation of water withdrawal and consumption benefits of RPS ... 31 compliance Figure 5.7. Fresh and saline water withdrawal (left) and consumption (right) benefits of RPS compliance ... 32 ... 34 Figure 6.1. Economic development impacts methods overview Figure 6.2. Estimated total gross domestic jobs from RPS compliance 36 ... bs from RPS compliance, by Figure 6.3. Gross domestic construction and O&M jo technology 37 ... Figure 6.4. Gross onsite jobs from construction and O&M from RPS compliance, by state ... 38 Figure 7.1. Wholesale electricity price impacts methods overview ... 40 Figure 7.2. Example supply curve ... 40 impacts of RPS compliance on consumer savings from wholesale Figure 7.3. Estimated ... 43 price reductions depending on start of decay ... Figure 8.1. Natural gas price impacts methods overview 45 Figure 8.2. Derived inverse price elasticity of supply curve ... 46 Figure 8.3. Application of inverse elasticity curve to 2013 RPS generation under two bounding scenarios 47 ... Figure 8.4. Estimated impacts of RPS compliance on natural gas consumer bill savings by ... sector under two bounding scenarios 49 Figure 8.5. Natural gas consumer bill savings by state under two bounding scenarios ... 49 Figure 9.1. Benefits and impacts of new RE used to meet 2013 RPS compliance ... 51 xii

14 1. I NTRODUCTION ACKGROUND B State renewable portfolio standards (RPS) , which require electricity load -serving entities (LSEs) to meet a wing portion of their load with eligible forms of renewable electricity ( RE gro ), currently exist in 29 U.S. states and Washington , D.C. ( Figure 1.1). The y have been one of the policy drivers for RE growth in the United States, along with federal tax incentives and other forms of state- level support (Leon 2015) . Collectively, 58% of all n capacity built in the United States from 1998 through 2014 on-hydroelectric RE is being used to meet RPS requirements (Barbose 2015). In aggregate, existing state RPS policies require that by 2025, at which point most RPS requirements will have reached their maximum percentage targets, at least 8% of total U.S. generation supply will be met with RPS -eligible forms of renewable electricity, equivalent to roughly 106 gigawatts ( GW ) of renewable generation capacity (Wiser and Bolinger 2015). Figure 1.1. State s with RPS policies circa November 2015 -1990s and 2000s, will reach their during the late Many of these policies, which were enacted primarily terminal targets within the next decade. A s states consid , or otherwise revising er extending, eliminating RPS programs their s, increasing attention is being paid to RPS costs, benefits, , or developing new one and other impacts. Previous work by Lawrence Berkeley National Laboratory and the National Renewable Ene rgy Laboratory summarized RPS compliance costs retrospectively , drawing partly from estimates developed by utilities and state regulatory agencies in the context of annual compliance filings or periodic reports to state legislatures (Heeter et al. 2014; Ba rbose et al. 2015). That work found t hat RPS compliance costs over the 2010–2013 period were generally equivalent to less than 2% of average statewide retail electricity rates , but varied substantially, with the net cost to utilities and other load- 4 -RE). -hour of renewable electricity (kWh -0.4 to 4.8¢ per kilowatt serving entities ranging from In aggregate, total RPS compliance costs represented approximately $1 billion per year, on average, over the 2010–2013 period. 4 RPS compliance costs represent the incremental cost to the utility or other load -serving ent ity with RPS obligations, net of avoided conventional generation costs. Negative RPS compliance costs may occur when renewable electricity procured to meet RPS obligations is less expensive than avoided conventional generation. 1

15 Compared to RPS compliance cost the potential broader societal benefits and estimates, analyses of , and vary considerably impacts of RPS policies are relatively few and far between in scope and methods (Heeter et al. 2014 ). This dearth of comparable RPS benefits analyses ultimately preve nts policymakers comprehensively evaluate the merits of R PS policies , and has prompted the need for from being able to further analysis of the benefits and impacts of RPS programs, based on a consistent approach across states. COPE , M ETHODS S AND C ONTRIBU TION , The present work is part of an ongoing series of analyses that collectively seek to assess the costs, benefits, and other impacts of state RPS programs. In this analysis, we apply a well -vetted set of methods to quantify a number of broad potential societal benefits and impacts of state RPS compliance. We use established methodologies that draw heavily from the existing literature and build on the approaches used in the U.S. Department of Energy (DOE)’s (DOE 2015). The analysis is Wind Vision Report retrospective, focusing specifically on “new” RE resources built after RPS enactment and used to meet the most recent year for which the necessary data were available. RPS compliance obligations in 2013— In evaluating broader societal benefits and impacts, the study does not address either the direct costs of RPS procurement to load -serving entities, or the cost savings from reduced fuel, capital, and operations and maintenance ( ) expenses associated with non- renewable generation. These were evaluated O&M retrospectively in Heeter et al. ( 2014 ) and Barbose et al. ( 2015) and will be evaluated prospectively in future work . The present study encompasses a subset of possible benefits and impacts from RPS programs, including possible environmental benefits associ ated with reductions in greenhouse gas (GHG) emissions, air jobs and . The study also pollution emissions, and water use on evaluates the impacts of state RPS policies economic development, wholesale electricity market price s, and natural gas price further s. As explained se latter three societal benefits. below, the impacts do not necessarily represent e rely on EPA’s AVoided Emissions and geneRation Tool To evaluate these benefits and impacts, w re sources used to (AVERT) to estimate the fuel type and location of generation sources offset by new RE meet RPS compliance obligations in 2013. We then evaluate each of the benefit and impact categories identified above . For each benefit and impact category, we quantify the effects in physical units (e.g., tons of pollutants, gallons of water) and, where possible, in dollar -value terms as well , and estimate key sources of uncertainty. For each benefit and impact ate the aggregate effects of all state RPS , we evalu programs , in combination , reporting results nationally and, where appropriate, by region and state; we do not analyze the effects of individual state RPS programs. M ethodological details specific to each benefit and impact evaluation (and any important limitations therein) are presented in the corresponding section of the report. This analysis represents the first detailed, national -level evaluation of the benefits and impacts from state re not alone in addressing this topic . As already noted , many states have RPS policies. However, we a -level assessments of RPS outcomes, and the previous study in this report series conducted their own state (Heeter et al. 2014) highlighted the widely varying scopes and methods of th ose state- specific analyses, 5 es which complicat comparisons. Chen et al. (2009), meanwhile, summarize state -level forecasts of RPS outcomes. In other cases, researchers have used statistical methods to try to distill the impacts of state RPS programs on GH G emissions (Eastin 2014; Yi 2015), air pollution (Eastin 2014; Werner 2014), jobs (Bowen et al. 2013; Yi 2013), and retail electricity prices ( Caperton 2012; Johnson 2014; Morey and Kirsch 2013). Still others have explored qualitatively or quantitatively a subset of the possible effects of 5 The present study is specifically intended to build on these state-level assessments by applying a more consistent analytical framework. Note that, in addition to state -sponsored studies, a variety of more-academic studies have been conducted for specific states (e.g., for Michigan, see: Johnson and Novacheck 2015a,b; Novacheck and Johnson 2015 ) 2

16 RPS programs, sometimes coming to widely varying conclusions about the merits of these policies (Holt . and Galligan 2013; Michaels 2008a; Michaels 2008b; UCS 2013) L IMITATIONS AND AVEATS C Caveats and limitations associated with each benefit and impact assessment will be presented within the respective section of the report. In addition, a number of broader, cross -cutting considerations, limitations, and caveats deserve explicit note. • Applying Unif orm Methods Nationally: We estimate the potential benefits and impacts of state- level RPS standards in 2013 using common, standardized methods applied to all states collectively. With that in mind, individual states may have used or could use data and meth ods that provide a more -detailed picture of benefits that accrue to those specific states. For example, individual states may have better data on renewable sources used to meet RPS compliance obligations, more wable plants are displaced, or locally vetted approaches sophisticated tools to estimate which non -rene to value benefits and impacts. Summaries of some of these state- level assessments are provided in Heeter et al. (2014). • Uncertainty in Benefits and Impacts Assessment: A variety of uncertainties unde our analysis. rlie Where possible, we quantify the uncertainty in our results. In other cases, however, we qualify the study results and highlight areas of uncertainty not explicitly addressed in the analysis . In presenting the approach and results of our • Distinguishing Potential Benefits from Impacts: analysis, we are careful to distinguish between potential societal benefits of RPS programs (GHG, air of those programs (gross jobs, wholesale pollution, and water use reductions) and other impacts electricity price reductions, and natural gas price reductions). In evaluating potential benefits, we consider both beneficial and detrimental effects from RPS resource s—the latter including, for ing solar power facilities — example, air pollutants emitted by biomass and water use by concentrat the net effects. The impacts we evaluate are sometimes described as and the reported results represent —and indeed, may be benefits when viewed through the lens of an individual state or market benefits participant . However, especially when viewed on a national or global scale, the impacts of RPS programs related to gross jobs and economic development and on wholesale electricity and natural gas prices are best considered resource : benefiting some stakehol ders at the expense of transfers others. For these impact categories, we do not evaluate the effects over the entire country or net global economy and, as such, cannot assess whether or not these impacts reflect net costs or benefits. While such impacts might still be considered when judging the effects of state RPS programs, especially by state policymakers, it is important to acknowledge any offsetting effects involved on sectoral, regional, national, or even international scales. • Coverage of Benefit and Impact Categories: Our analysis considers an important subset of —but not all —potential benefits and impacts . For example, we do not assess the from state RPS programs , or the full set of challenges of integrating renewable energy into bulk power and distribution grids energy supply risks. We also do not quantify the land use impacts on economic development or impacts from the RE deployment serving state RPS policies, nor the offsetting reduction in impacts from fossil energy supplies. Other non- quantified environmental impacts include heavy metal releases, radiological releases, waste products, and water quality impacts associated with power and upstream fuel production, as well as noise, aesthetics, and others. • Considering Costs in the Equation : Ultimately, a full understanding of the ir benefits, impacts, and costs is needed to comprehensively evaluate state RPS programs. The present analysis does not compare the benefits and impacts of RPS programs to their costs. As noted, previous research in this series (Heeter et al. 2014 and Barbose et al. 2015) summarized RPS compliance costs retrospectively, drawing partly from cost estimates developed by utilities and state regulatory agencies, and 3

17 highlighted the divergent methods used to assess RPS costs. ch is needed , and is Additional resear to evaluate planned within this research series, s on a consistent basis across states and in a RPS cost manner that can be more readily compared to benefits . • Evaluating Least -Cost Benefits and Impacts: RPS programs are not the only, or necessarily the least -cost, way of achieving the benefits and impacts discussed in this paper. Rather, as widely recognized in economics literature, “internalizing externalities” is most efficiently achieved by directly p ricing those externalities rather than through technology- or sector -specific policies. This is in part due to possible economy- wide rebound, spillover, or leakage effects and also because such policies more directly target the achievement of public benefi ts (Borenstein 2012; Edenhofer et al. 2013; IPCC 2011; IPCC 2014a 2013; McKibbin et al. 2014; Tuladhar et al. 2014). ; Kalkuhl et al. Research focused on has highlighted these possible effects, finding that the desired RPS policies benefits of RPS programs may not be fully achieved or achieved as cost -effectively as might be desired ( Bushnell et al. 2007; Carley 2011; Fischer and Newell 2008; Fell and Linn 2013; Rausch and ), though other research suggests that the pitfalls of such “second- best” policies may not Karplus 2014 Kalkuhl et al. 2012) be so great ( . • Estimating the Additionality of Benefits: There are multiple drivers of RE additions, and our analysis does not seek to attribute the estimated benefits and impacts solely to RPS policies. Rather, our an alysis evaluates the benefits and impacts of new RE used to meet RPS compliance obligations in 2013 . Because of the leakage and spillover effects noted above, but also because of the multiple e), the estimates presented in this drivers for RE additions (of which state RPS programs are but on 6 only paper may overstate the incremental benefits and impacts attributable to state RPS programs. Previous research, for example, has come to mixed conclusions about the incremental effect of RPS nt ( Carley 2009; Carley and Miller 2012; programs on RE deployme Menz and Vachon 2006; Sarzynski et al. 2012; Shrimali et al. 2015; Shrimali and Jenner 2013; Shrimali and Kniefel 2011; , Staid and Guikema 2013; Yin and Powers 2010). As two potentially offsetting considerations how ever, RPS programs are likely supporting the continu -existing RE ed operation of some pre supply , which we do not consider in our analysis , nor do we consider any benefits or impacts associated with “surplus” RPS resources (i.e., new RE in operation in 2013 that was motivated by , for one of several possible reasons, was not used for RPS requirements but compliance RPS 7 in that year ). obligations R OADMAP The remainder of this paper is structured as follows. Section 2 describes the RPS policies and renewable resources included in our analysis; the approaches used for identifying the capacity, generation, and location of those resources; our approach for including those resources in AVERT; and the outputs of the AVERT analysis. Sections 3 –8 then address each of the potential benefits and impacts in turn: GHG emissions, air pollution emissions, water use, jobs and economic development, wholesale electricity price suppression, and natural gas price suppression. In each of these sections, we introduce the benefit or impact category, describe our methods and their underlying uncertainties, and highlight key results. We conclude with a summary of our findings and their implications in Section 9. 6 For the same reason, estimates of RPS costs also may not be entirely attributable to RPS programs, specifically, as some portion of the renewable development used to serve RPS polici es may have occurred in the absence of those policies. 7 for the purpose of meeting , for example, l ong -term contracts that utilities sign Surplus RPS resources might include future RPS obligations or hedging against contract failures, as well as new RE res ources outbid by other resources competing for RPS -term renewable energy certificate transactions. demand in markets dominated by short 4

18 2. F OUNDATIONAL D ATA AND A : RPS R ESOURCES AND NALYSIS D ISPLACED F OSSIL G ENE RATION was achieved with 98 TWh of new renewable electricity Compliance with RPS obligations in 2013 generation, representing 2.4% of total U.S. electricity generation in that year and resulting in a 3.6% -year period from 2013 through 2014, an average the two reduction in total fossil fuel generation. Over of almost 5,600 MW of new renewable capacity per year was built to service RPS requirements. These effects drive the benefits and impacts analyses described in later sections of this paper. VERVIEW O The RPS benefits and impacts estimated in this study require a common basis of information about - renewable resources used for RPS compliance in 2013 and about the associated displacement of fossil fuel generation, capacity, fuel use, and emissions (see Figure 2.1 ). We compile data on renewable resources used to meet 2013 RPS compliance obligations, drawing from state and utility RPS compliance filings and other relevant sources. Those data are used directly within each of the individual benefit and impact analyses. Those data also compose the key inputs for modeling with AVERT to estimate displaced generation, fuel, and emissions (CO , NO , SO ) from other (primarily fos sil -fueled) power plants. 2 x 2 Separately, data on RPS -related RE capacity additions are compiled and used directly within the analyses of GHG benefits and gross jobs impacts, and also used to estimate displaced fossil generation capacity. section describes each of these data elements and analytical steps in further detail. The remainder of this Avoided Generation = Greenhouse G Gases RPS Generation ) (G,W,E AVERT (G,A,W,J,E,N) = Air Pollution & Health A Avoided Fuel = Water W (N) = Jobs & Econ. Dev. J Avoided CO 2 (G) Displaced Fossil RPS Capacity Electricity Prices = E Capacity (G,J ) , SO Avoided NO 2 x (G) = Natural Gas Prices N (A) Note: Red letters in parentheses indicate in which benefit/impact analyses each data set was used. 2.1. Foundational data and analysis to support RPS benefits and impacts estimates Figure G ENERATION U SED FOR RPS C OMPLIANCE R ENEWABLE Compliance with RPS obligations is typically demonstrated through the use of renewable energy certificates (RECs), which represent th e renewable attribute associated with electricity generated from renewable resources. Some state RPS programs allow RECs to be transacted separately (“unbundled”) from the underlying electricity commodity, while others require RECs to remain bundled with t he associated electricity. Regardless of how RECs are procured, LSEs generally demonstrate compliance by documenting that they have retired the requisite number of RECs to meet RPS compliance obligations in a given year. That documentation provides a recor d of exactly which resources were used for RPS compliance. For the AVERT modeling and the individual benefit and impact analyses within the present study, we specify the quantity and attributes of RPS generation based on the specific set of RECs retired f or each 5

19 8 We employ several key accounting conventions throughout state’s 2013 RPS compliance obligations. these analyses, which collectively tend to constrain the estimated benefits and impacts (in part by limiting the scope of our analysis to those RE pro jects most likely to have been at least partially motivated by state RPS policies): New RE Only : For each state RPS , we count only those RECs associated with RE facilities • 9 constructed after the date of RPS enactment • Exclude : LSEs in some states have over -complied with RPS obligations; Excess RPS Procurement however, we count only those RECs required to meet each LSE’s minimum RPS compliance 10 obligations in 2013. Some states allow resources other than Non -RE Resources Used for RPS Compliance: • Exclude ble electricity —such as energy efficiency or natural gas- fired combined heat and power —to renewa count toward RPS compliance; however, we exclude those resources from our analysis. • Exclude Hawaii: The AVERT model only covers the continental United States, thus Ha waii’s RPS is excluded from our analysis. To assemble these data, we rely principally on annual RPS compliance filings issued by individual LSEs in states with regulated utilities, or by state public utility commissions in restructured states with 11 competit ive retail markets. We supplement those sources with data from regional or state REC tracking -eligible facilities for each state. systems, which provide additional, important details about RPS The various benefit and impact analyses require that we segmen t REC retirement data for each state according to the state in which the plant is located (which may differ from where the associated RECs are retired), the plant vintage, and its fuel type. We use the following fuel type classifications across all states: wind, utility -scale photovoltaics (PV), rooftop PV, concentrating solar power (CSP), landfill gas (LFG), biomass, geothermal, and hydroelectric. We also segment the REC data according to whether the in which the REC is retired. Specifically, AVERT associated electricity is delivered into the same region 12 simulates fossil fuel displacements in each of the ten regio ns shown in Figure 2.2, based on renewable electricity delivered or consumed within the region. Where unbundled RECs were used for RPS to which the compliance, we segment the REC retirement data according to the AVERT region in associated electricity was delivered, building on the approach used by AWEA (2014) to estimate the air quality benefits of wind energy. In making these determinations about physical delivery, we rely on each 8 Depending on the state, some portion of RECs retired for 2013 RPS compliance obligations may have been generated in pr ior years and banked for later use. Similarly, some portion of RECs physically generated in 2013 was banked for use in later year s. For the purpose of estimating RPS benefits and impacts in the year 2013, we ignore distinctions related to REC vintage, and simply treat all RECs retired in 2013 as though they were generated in the same year. 9 In some states, pre-existing resources are ineligible for RPS compliance or are relegated to a secondary tier, while other states es to qualify for RPS compliance. Only in the latter case was any judgement required in may allow certain pre-existing resourc -existing resources. To the extent that RPS policies support the continued operation of existing order to exclude RECs from pre facilities will tend to understate the possible benefits of RPS policies. facilities, limiting the analysis to new 10 This convention is most significant for Texas and Iowa, which have substantially exceeded their RPS requirements. LSEs in a number of other states have also procured more RECs than strictly required for RPS compliance, in some cases banking excess RECs for compliance in future years. 11 Specifically, we relied on annual RPS compliance filings issued by all or most obligated LSEs in Arizona, California, Colorado, Kansas, Michigan, Mis souri, Montana, Nevada, New Mexico, Oregon, and Washington. For the following states, we instead relied primarily on annual summary compliance reports issued by the state public utility commission or other agency: Connecticut, Maine, Massachusetts, Minneso ta, New Hampshire, New York, Rhode Island, Texas, and Wisconsin. Finally, REC retirements were derived entirely from REC tracking system data for LSEs in Delaware, Illinois, Maryland, New Jersey, North Carolina, Ohio, Pennsylvania, and Washington, D.C. 12 AVERT includes a small number of non-fossil generators, and the emissions displaced from these facilities are included in the totals reported for fossil generation. 6

20 13 Importantly, the data sources for many states and state’s geographic eligibility and deliverability rules. regions did not provide exactly the information needed to segment REC retirements in the various ways described above; thus, additional state - or regionally specific intermediate step s and assumptions are often 14 required. Figure 2.2. AVERT regions (Source: EPA 2014) renewable electricity was Based on the data sources and process described above, 98 TWh of new generated to serve RPS co mpliance obligations in 2013. This equates to roughly 2.4% of total U.S. electricity generation and 39% of total U.S. non- hydroelectric renewable electricity generation in that 15 year . Figure 2.3 shows where these RPS resources are located and their relati ve contribution to each state’s total electric generation supply. The percentages shown partly reflect the relative stringency of each state’s target, but are also impacted by the extent to which RPS states rel y on new, in- state 13 Although RPS compliance in restructured states is typically achieved through the purchase and retirement of unbundled RECs, e regional eligibility rules in those states typically require that the associated electricity is either generated in or delivered to th power market, which coincides with the respective AVERT region. Thus, in the vast majority of cases in which unbundled RECs are used for RPS compliance, we assume that the associated electricity is delivered to the corresponding AVERT region. The C retirements from new most significant exception in terms of absolute MWh is California, where roughly 34% of 2013 RE renewables were sourced through “firmed and shaped” products, mostly from the Pacific Northwest; we assume that those contracts result in no incremental electricity delivered into California, and thus that the physical delivery of el ectricity from those facilities remains within their region of origin. The other significant case in which unbundled RECs are used for RPS complia nce without physical delivery of electricity into the region is North Carolina, where we estimate that roughly 55% of REC retirements from new renewables are associated with unbundled RECs procured from wind facilities in Texas. 14 For the Great Lakes/Mid -Atlantic (EMW) region, public reports available through PJM’s GATS -EIS provide data on REC retirements by state and fuel type, but no information about the vintage or location of the underlying resources. To segment those REC retirements by vintage and state-of -origin, we allocated REC retirements for each state RPS across the set of eligible facilities. A similar approach was also used to segment REC retirements for North Carolina’s RPS by vintage and state-of -origin. For most states in the WMW region, no information was readily available about the fuel mix of RECs retired in 2013; thus, tot al REC retirements for e ach state were simply allocated across eligible plants, based on the state(s) for which each facility is eligible, as identified in M -RETS public reports. For other regions, RPS compliance reports directly provided most of the necessary information, though limited assumptions or extrapolations were still often required, for example, where compliance data were unavailable for small LSEs. 15 Total non -hydroelectric renewable generation in the United States equaled 254 TWh in 2013 (EIA 2015a), including both the 98 TWh of new renewable electricity generation applied towards RPS compliance obligations as well as roughly 50 TWh of pre - existing renewables also used to meet RPS requirements. Thus, RPS programs collectively represented 58% of total U.S. non- hydroelec tric renewable generation. Of the 106 TWh of renewable generation not used to meet RPS compliance obligations, roughly 60 TWh was sold into voluntary green power markets (Heeter et al. 2014). 7

21 sting and/or out -of-state resources. Accordingly, the figure also illustrates resources—as opposed to exi the role of cross- state trade in meeting RPS compliance obligations; for example, many states without RPS policies (GA, ID, IN, KY, ND, OK, SD, UT, VA, WV, and WY) produced renew able electricity used to serve RPS compliance obligations in other states in 2013. Figure 2.3. New RE for RPS compliance based on plant location renewable electricity used for 2013 RPS compliance, based of Figure 2.4 shows the distribution new instead on the AVERT region into which the electricity was physically delivered and the composition of those resources. These data compose the primary inputs for the AVERT modeling. The regional dis tribution reflects both the volume of retail sales in each region, as well as the relative stringency of -Atlantic (EMW) region were RPS targets. Thus, for example, RPS deliveries to the Great Lakes/Mid large as a result of the sizable amount of retail load subject to RPS requirements, while RPS deliveries to the SE region were quite small as a result of the absence of RPS policies throughout much of that region. As also shown, wind power was the largest renewable fuel type used in each region, composing 75% of total RPS generation nationally, though clearly some regions have a more diverse mix, reflecting greater -specific carve- outs. availability of particular resource types and/or resource 8

22 25 Hydro Geothermal 20 Biomass 15 LFG TWh 10 Solar CSP Rooftop PV 5 Utility PV 0 Wind SC CA EMW NE NW RM WMW TX SW SE RM CA =Northwest, =Rocky Mountains, , =California =Northeast, EMW =Great Lakes/Mid -Atlantic, NE NW TX =Southwest, SW =Southeast, SE =Lower Midwest, SC Upper Midwest = WMW =Texas, 2.4. New R E delivered to each AVERT region for 2013 RPS compliance Figure D ISPLACED F OSSIL G ENERATION We rely on EPA’s AVERT model to estimate the type and location of fossil generation sources offset by new renewable sources meeting RPS compliance obligations in 2013 , alo ng with the associated reduction in fuel consumption and emissions (CO , SO ), th , NO is model uses ). As described in EPA (2014a 2 2 x historical data to estimate changes in generation and emissions within each AVERT region resulting from an increase in renewabl e electricity (or energy efficiency). In particular, these impacts are estimated on an hourly basis for individual electric generating units, based on statistical relationships derived from 16 historical data reported through the EPA’s Acid Rain Program. y alternative methods exist to Man estimate displacement effects (Synapse 2015). As described in EPA (2014a) and Synapse (2015), AVERT represents an intermediate approach among the available options: it is more complex than using a simple regional marginal dis placement (or emission) rate, but less complex than a full production cost model or a highly region -specific and detailed statistical analysis. AVERT has the benefit of providing a consistent approach to estimating historical displacement on a is and in every region of the contiguous United States. As with any model, however, AVERT national bas has its limitations, as described in the user guide (EPA 2014a). For example, the model is insensitive to nd therefore may not accurately model the the location of renewable generation within a given region a impacts of highly localized RE policies (as is the case in the SE region, where only one state has RPS requirements). Given its calibration to historical data, AVERT also has limited ability to accurately model the impacts of very large RE programs. As a rule of thumb, the user guide recommends limiting RE additions to 15% of the fossil generation load in any hour; this limit was exceeded for a number of regions with relatively aggressive RPS targets (although in no case in our analysis did RE generation exceed 15% of fossil generation on an annual basis). The model also does not capture interactions across regions, which is potentially significant, particularly as balancing authorities seek greater levels of 16 In our analysis, we used AVERT regional data files for the year 2013. Because the statistical relationships in those files already reflect the effects of RPS resources operating in 2013, we modeled the impact of those resources by “removing” them (i.e., treating them as negative generation sources) rather than by add ing them in as additional renewable resources, beyond what was already in operation in 2013. Furthermore, renewable generation quantities were input into AVERT as hourly profiles. This required translating the annual generation quantities shown in Figure 2.4 into aggregate hourly generation profiles for each region, using wind and solar PV profiles built into AVERT and profiles from Brinkman et al. (forthcoming) for other renewable fuel types. 9

23 coordination for renewables integration. These and other model limitations are discussed more fully in EPA (2014a). Based on the renewable generation delivered to each region, the displaced generation resources estimated with AVERT are shown in Figure 2.5 ( by sta te) and Figure 2.6 (by AVERT region). In aggregate, new renewable resources used to serve RPS compliance obligations in 2013 reduced total U.S. fossil generation by roughly 3.6% in that year, though the percentage of fossil generation displaced varies wide ly across regions, from just 0.1% of 2013 fossil generation in the Southeast (SE) region to 13.9% in California, reflecting the varying stringency and prevalence of RPS policies. Most of the displaced fossil generation is estimated by AVERT to have been gas -fired (55% of the total), though substantial amounts -fired generation were also found to be displaced, especially in regions with a coal -heavy fossil of coal -peak hours w hen coal is generation mix, in part reflecting significant levels of wind generation during off on the margin. Other AVERT outputs are summarized in Section 3 (reduced CO emissions), Section 4 2 (reduced SO and NO emissions), and Section 8 (reduced natural gas consumption). x 2 Figure 2.5. Displaced fossil generation by state 10

24 25 20% Other 16% 20 Oil 15 12% Gas TWh 8% 10 Coal 5 4% Total Percent Displaced 0% 0 WMW TX SW SE SC RM NW NE EMW CA CA =Rocky Mountains, , EMW =Great Lakes/Mid -Atlantic, NE =Northeast, NW =Northwest, RM =California =Southeast, WMW =Texas, TX =Southwest, SW Upper Midwest SE =Lower Midwest, SC = 2.6. Displaced fossil generation by AVERT region Figure C APACITY A DDITIONS AND ENERATION ISPLACED F OSSIL G RPS D C APACITY oss jobs impacts of state RPS policies require information Estimates of the life cycle GHG benefits and gr about the amount and type of RE capacity additions associated with these policies, and in the case of the GHG benefits, about the corresponding amount and type of displaced fossil capacity additions. For these capacity -driven benefits and impacts, the relevant set of RPS capacity additions are those that yielded “RPS -driven” benefits and impacts occurring in the year 2013, regardless of whether the renewable capacity was used to meet compliance obli gations in 2013. Specifically, we focus on RPS -related capacity additions completed from 2013 through 2014, and estimate the benefits and impacts based on the average on that particular time per annual capacity additions over that two- year period. We focus iod because some portion of capacity additions completed in 2014 were under construction during 2013, and therefore -year period yielded benefits and impacts in that year. Moreover, averaging capacity additions over a multi smooths out some of the volatilit y in annual renewable capacity build rates. Data on renewable generation capacity additions are derived from multiple sources, including Ventyx (2015), AWEA (2015), GTM Research and SEIA (2015), EPA (2015c), Wiser and Bolinger (2015), and l (2015). Not all renewable capacity additions are associated with state RPS programs; in Bolinger and See general, we consider projects to be “RPS -related” under either of the following conditions: (a) the off - taker of power and RECs is subject to state RPS compliance obl igations, or (b) the facility sells its power on a merchant basis into regional energy markets administered by a regional transmission organization (RTO). The one exception to that decision rule occurs if the individual off -taker (or the RTO market as a wh ole) has already fully met its final -year RPS targets, in which case the renewable capacity addition is -related. This exception is triggered principally for LSEs in Texas and Iowa, which not counted as RPS had already achieved their respective final PS targets. -year R Given the above set of data sources and decision rules, we estimate that RPS -related renewable capacity 5,600 megawatts (MW) per year. As shown in the additions over the 2013–2014 period averaged almost gest portion of these capacity additions is utility -scale PV (48%), pie chart inset into Figure 2.7, the lar 11

25 followed by wind (19%), rooftop PV (17%), CSP (11%), and much smaller contributions from biomass, 17 LFG, and geothermal. most half are These capacity additions are distributed across 33 states, though al located in California, reflecting the rapid and recent build- out of renewable capacity to meet 2020 RPS targets, including the completion of a number of large utility- scale PV projects. 3,000 U.S. Total Biomass 2,500 220 LFG 85 46 Geothermal 2,000 1,036 589 Solar CSP 1,500 Rooftop PV 921 Utility PV 2,681 1,000 Nameplate MW Wind 500 0 IL RI HI IN NJ KS CT AZ SD PA DE MI CA UT NY WI VA DC NC CO OR NV ND NH OH MT ME MA WA MD NM MN MO 2.7. RPS capacity additions by state Figure 2014) (annual average during 2013– Based on the RPS 2014 period, we estimate the -related capacity completed during the 2013– corresponding amount and type of displaced fossil generation capacity. This information is used only for the purpose of estimating avoided GHG emissions associated wit h the construction phase of new fossil generation facilities, which is ultimately a minute component of the overall GHG emission benefits. As such, we employ a simplified approach to estimate avoided fossil capacity additions. For each type of renewable capacity, we stipulate its capacity credit (95% for geothermal, biomass, and LFG; 75% for 18 CSP; 45% for PV; and 15% for wind), drawing from the relevant literature (Sims et al. 2011). We further assume that, for all non- solar renewable capacity additions, 70 % of displaced fossil capacity consists of combined cycle gas turbines (CCGTs), and 30% consists of gas- fired combustion turbines (CTs). For solar capacity additions, whose profiles more closely coincide with peak demand, we assume 30% CCGTs. Based on this simple methodology, we estimate that renewable the inverse: 70% CTs and 00 MW 2014 period displaced, on average, more than 2,5 capacity additions over the 2013- fossil of 19 capacity per year, consisting of 1,600 MW of CCGT MW of CT s. s and 960 . 17 In contrast, the distribution in cumulative RPS capacity additions is more heavily weighted towards wind power, representing 70% of all RPS -related RE capacity additions through 2014, compared to 24% solar, 5% biomass, and 1% geothermal (Barbose 2015). 18 The c apacity credit measures the percent of a renewable power plant ’s nameplate capacity that can be counted on to meet a system’s planning reserve or resource adequacy requirements. In this context, it measures the amount of new fossil or other capacity that would not be needed as a direct result of adding the renew able plant. Capacity credit differs from capacity factor, which is a fraction that reflects the amount of electricity generated by a plant over a certain period of time (typically a y ear) divided by the electricity that could have been produced by that pla nt if it were to operate at its full nameplate rating over the full duration of that period. 19 -world complexities, such as the fact that To be sure, this estimate is highly approximate and does not account for various real avoided fossil capacity addition s might not occur until some future point in time, in cases where a region had surplus generation capacity in 2013 and 2014. That said, these uncertainties and approximations are ultimately insignificant within the context of our analysis, as the GHG emiss ion reductions associated with avoided fossil generation capacity are shown in the next section to constitute less than 1% of the total avoided GHG emissions. 12

26 3. G REENHOUSE G AS E C LIMATE C HANGE D AMAGE MISSIONS AND R EDUCTION B ENEFITS Renewable generation used to meet 2013 RPS compliance obligations reduced national life cycle GHG emissions by an estimated 59 million metric tons of carbon dioxide -equivalent (CO e). These 2 emis sion reductions in 2013 produced an estimated $2.2 billion of global benefits in the form of lower future climate change damages when applying a “central value” for the social cost of carbon (SCC). s the full range of SCC estimates considered Benefits estimates span $0.7 billion to $6.3 billion acros here. These global benefits of new renewable energy meeting RPS policies are equivalent to 2.2¢/kWh RE across the full range. - RE in the central value case, and 0.7 to 6.4¢/kWh - I NTRODUCTION Scientists predict significant changes to the Earth’s climate owing to both past and future GHG emissions. Changes include higher average temperatures, increased frequency and intensity of some types of extreme CC 2013; IPCC 2014b). As highlighted in Melillo et weather, rising sea levels, and ocean acidification (IP al. (2014), these changes are expected to impose a wide range of damages in the United States: threatening human health and well -being through more extreme weather events, wildfire, and decreased air quali ty; putting infrastructure at risk; jeopardizing water quality and supply; disrupting agricultural production; and negatively affecting ecosystems and biodiversity. EPA (2015d) finds that efforts to limit climate change damages through reductions in GHGs can offer many benefits to the United States, and there is growing recognition of the desirability of near -term actions to limit emissions (IPCC 2014a ; Luderer et al. 2013; Nordhaus 2013). Responding to this challenge, EPA has established GHG emission limi ts for —existing and new power —among other things ; EPA 2015 plants (EPA 2015a b). RE technologies generally have low GHG emissions in comparison to fossil energy sources . Most RE stages from upstream sources have no direction emissions and, even when considering all life cycle , RE technologies typically have very low materials requirements to operations and decommissioning . Figure 3.1 summarizes an NREL systematic review of the available life -cycle literature; GHG emissions see also www.nrel.gov/harmonization -level RPS programs are therefore among the many types of . State policies that have been or that might be used to reduce GHG emissions . Previous research has found a (Eastin 2014; Yi 2015). In this statistical link between R PS programs a nd carbon emission reductions life cycle GHG of new RE used for RPS compliance, benefits section, we estimate the potential 20 -related damages. quantifying the value of those GHG reductions in mitigating the severity of climate 20 Current actions to limit GHGs can also be considered as one method of reducing the longer -term cost of future policies to reduce GHGs. Some U.S. states and regions have already enacted carbon -reduction policies; the U.S. Congress has considered such policies in the past; and the EPA has proposed emission limits for power plants (Luckow et al. 2015). As a result, many t RE utilities already regularly consider the possibility of future policies to reduce GHGs in resource planning, and thereby trea sources as options for reducing the possible future costs of climate mitigation (Barbose et al. 2008; Bokenkamp et al. 2005; Luckow et al. 2015). 13

27 erences” Note: “References” refers to the number of literature citations that underlie each “box and whisker”; “estimates” exceed “ref because many literature citations include multiple, independent estimates. 3.1. Summary of life cycle GHG emissions from electricity generation technologies Figure (Source: DOE 2015) M ETHODS Our methods to value the potential GHG benefits from state RPS programs involve first estimating the life cycle GHG reductions from new RE serving RPS compliance in 2013, and then quantifying the economic value of those reductions based on a range of social cost of carbon (SCC) estimates (see Figure 3.2). These methods are similar to those used in DOE’s recent Wind Vision Report (DOE 2015), and are broadly consistent with methods used by U.S. regulatory agencies (GAO 2014) and academic researchers (Buonocore et al. 2015; Callaway et al. 2015; Cullen 2013; Graff Zivin et al. 2014; Johnson et al. 2013; Kaffine et al. 2013; M cCubbin and Sovacool 2013; Novan 2014; Siler -Evans et al. 2013; Shindell 2015). We start with AVERT -estimated power sector CO emissions reductions from new RE used to meet RPS 2 21 ion -related emissions. compliance obligations in 2013. Those estimates, however, only consider combust We adjust these figures to account for life cycle impacts by combining AVERT combustion -related 21 CO from combustion of fuels during the operation of power Combustion -related emissions estimates consider emissions 2 plants. They do not consider two other important sets of emissions attributable to electricity gen eration: GHG emissions from GHG gases. Life cycle assessment procedures are needed to estimate other life cycle stages and GHG emissions from non-CO 2 emissions from upstream materials supply, equipment manufacturing and construction, operations and maintenance, and plant may be particularly important for methane . In addition, consideration of other potent GHGs decommissioning beyond CO 2 released in coal mining, oil production, and natural gas production and transport. 14

28 -combustion emission values for each generation technology , based on emissions with life cycle, non results developed in NREL’s LCA Harmo nization study ( www.nrel.gov/harmonization ). The additional life cycle stages that we consider include: (1) ongoing, non -combustion- related emissions during the fuel cycle, such as those that occur during t he production and transport of fuels and through other operational activities (e.g., operation of maintenance vehicles, auxiliary heating, etc.) ; and (2) construction- related 23 22, emissions (i.e., emissions from materials, equipment, and construction). plying these life cycle By ap adjustments, we capture avoided fuel cycle and construction emissions from displaced fossil generation and capacity while also accounting for increased fuel cycle and construction emissions from RE generation and capacity used to me et RPS standards in 2013. Estimate combustion -related CO 2 emissions reductions (AVERT) Estimate upstream GHG emissions impacts from other life cycle stages Value reductions based on range of social cost of carbon estimates 3.2. GHG benefits methods overview Figure We then estimate the economic benefits of RPS compliance in the form of reduced climate change 24 The SCC reflects, among other elements, monetary damages from the damages using SCC estimates. impacts of climate change on agricultural productivity, human health, property damages, and ecosystem services (IWG 2010; IWG 2015). U.S. government regulatory bodies regularly use the SCC when formulating policy (GAO 2014; Kopp and Mignone 2012), supported by estimates provided by the U.S. Interagency Working Group (IWG) on the Social Cost of Carbon (IWG 2010; IWG 2015). The IWG SCC 22 luded in the analysis, because we are evaluating emissions reductions in 2013, and do not The decommissioning stage is not inc -related presume that state RPS program s impacted plant decommissioning decisions in that year. Furthermore, decommissioning compared to the other life mall GHG emissions are typically very s -cycle stages (<1%) (see studies linked at , and thus any bias introduced by not considering decommissioning will be small. www.nrel.gov/harmonization) 23 s) on electricity generation technologies ts (LCA -cycle assessmen An extensive review and analysis of previously published life ). Based on that literature, was conducted through NREL’s LCA Harmonization project (see http://www.nrel.gov/harmonization -cycle, non the fuel -combustion GHG emission values for each generation technology, and for both we developed median life -combustion GHG emissions from the fuel cycle, we use the electricity production cycle and construction phases. To estimate non renewab le energy (increased under RPS) and fossil energy (displaced estimates (in MWh) provided earlier in Sect ion 2 for both -cycle GHG -combustion fuel -specific, non -derived estimates of technology by RPS) technologies and apply the median, literature , we use the capacity estimates (MW) provided earlier for both the ruction emissions. To estimate GHG emissions from const related displaced the average renewable energy capacity additions over the 2013 to 2014 timeframe to serve RPS programs and -related GHG fossil -fuel capacity, and we then apply the median, litera ture -derived estimates of technology -specific, construction emissions. This approach was used in DOE (2015), where the methods are described in greater detail. 24 -change impacts Research has sought to estimate the magnitude and timing of climate , damages, and associated costs (IPCC 2014a; IPCC 2014b; IWG 2010; IWG 2015; Melillo et al. 2014; Weitzman 2012). Because of the uncertainties involved, estimates of the SCC span a wide range (IPCC 2014b; Tol 2011; Weyant 2014), leading some to suggest pos sible improvements to SCC estimates and procedures (Ackerman and Stanton 2012; Arrow et al. 2013; Johnson and Hope 2012; Kopp et al. 2012; Pizer et al. 2014; Weyant 2014) or even to question the use of these estimates (Pindyck 2013). 15

29 estimates represent global future damages from GHGs emitted in a particular year. Reflecting the uncertainties involved, the IWG (2015) provides four SCC estimates: a “low” case, a “central value” case, a “high” case, and a “higher -than- expected” case intended to account for a much less likely outcome with extreme impact. For our purpose, the monetary value of RPS compliance is estimated based on all a more four IWG estimates, for 2013. The central value SCC for 2013 is $37 metric ton (MT) of CO (in 2 25 2013$). The low case is $12/MT, the high case is $59/MT, and the higher -than- expected case is $106/MT. issues related to the methodology and associated limitations, some discussed earlier, deserve note: Several We do not fully consider the possible erosion of GHG benefits due to increased cycling, ramping, and • part lo ading required of fossil generators in electric systems with higher penetrations of variable renewable generation. This omission, however, will not meaningfully bias our results , as the available literature clearly demonstrates that this impact is quite sm all , even at much higher levels of renewable 26 . energy supply than evaluated here • Our a ssessment of construction -related life cycle emissions is based on average renewable capacity additions over the 2013 through 2014 time frame ( nd—relying on nearly 5,600 MW in total) a capacity credit assumptions —avoided fossil capacity ( more than 2,5 00 MW in total). The application of these emissions to 2013 is somewhat speculative, especially because avoided fossil capacity additions might not be expected to occur until several years after the RE capacity is built. Nevertheless, despite potentially bein g realized in later years, these benefits are arguably attributable to the additional RE supported by the RPS policies. • We assume that biomass combustion emissions (other than landfill gas) are entirely offset by carbon absorption to produce the biomass f eedstocks , and we do not seek to estimate land -use related emissions. Specifically, we do not consider the indirect effects of the provision of biomass to the power plant and whether that provision could have induced a change in land use (whether . This topic lacks a clear s impact domestica lly or internationally) with an associated GHG emission for the purpose of this paper, especially given the consensus by which to quantify GHG emissions -use -related biomass emissions (IPCC 2 ). For wide range in estimates of land 011, Warner et al. 2013 and assume that landfill gas used for in contrast, we take a conservative approach landfill gas, electricity production would otherwise have been flared ( with no net change in GHG emissions at the plant), rather than vented (w hich would avoid more -potent methane GHG emissions). • Though IWG SCC estimates span a large range, an even wider range exists in the literature. Also, as recommended by the IWG and others (IWG 2015; Pizer et al. 2014), we rely upon global SCC is means that the results are reflective of future global benefits (discounted to the estimates: th the United States and the vast majority of present), only a portion of which would directly accrue to in future years. We apply the IWG SCC estimates to the estimated life cycle GHG which would occur 27 emissions savings (in CO e). 2 25 We present all IWG SCC estimates in 2013$. 26 Studies have found that the GHG benefits of variable renewables are diminished by, at most, less than 10% (Göransson and Johnsson 2009; Gross et al. 2006; Pehnt et al. 2008; Perez -Arriaga and Batlle 2012), with far lower levels anticipated at smaller penetrations of RE in large electric systems (Fripp 2011; Valentino et al. 2012). In the most sophisticated of the studies, Lew et al. (2013) find the impact to be negligible (less than 1%). 27 The applic ation of IWG’s SCC estimates to life cycle CO the IWG is clear that its values may e creates some inaccuracy , as 2 other than CO not be appropriate for GHGs e based on global warming potential (IWG 2010). , even when translated into CO 2 2 gases in total life cycle emissions, this inaccuracy is likely The level of inaccurac y is unclear, but given the scale of non -CO 2 considerably smaller than the range in SCC estimates applied in the present analysis. IWG intends, in the future, to develop GHGs . methods to value non-CO 2 16

30 • Finally, our methodology presumes that carbon cap- and- trade programs were effectively non -binding ; otherwise, the benefits of RPS compliance should arguably be valued at carbo in 2013 n allowance prices to reflect savings in the cost of complying with the cap, not at the estimated cost of climate 28 -Evans et al. 2013). damages (Cullen 2013; Siler R ESULTS New RE used to meet RPS obligations in 2013 reduced life cycle GHG emissions by an e stimated 59 million MT of CO e (see Figure 3.4). These emission reductions are driven almost entirely by d irect 2 combustion- related GHG emission s reductions from avoided fossil fuel generation, equal to 61 million MT of CO (3% of 2013 power sector emissions). As shown in Figure 3.3, combustion- related emissions 2 reductions are concentrated in the Great Lakes and Mid- Atlantic regions , Texas, California, Colorado, the regions in the most and Washington, reflecting both the locations with and -aggressive RPS standards emitting coal plants are more likely to be displaced. which high carbon- Because fossil plant displacement occurs not only within RPS states but also outside of those states, GHG emissions reductions extend beyond the 29 states and Washington, D.C. with RPS compliance obligations in 2013. 3.3. Combustion -related CO Figure emissions displaced by state from RPS compliance 2 In addition to direct combustion- related GHG emissions reductions from avoided fossil fuel generation, RPS programs also displace non -combustion fuel -cycle emissions from fossil energy supply and, to a 28 under strictly binding caps, RE does not reduce emissions per se, but instead alleviates the need to reduce This is because, emissions elsewhere in order to achieve the cap. In 2013, GHG cap -and -trade programs existed in the Northeastern United States (via the Regional Greenhouse Gas Initiative, aka RGGI ) and in California. Under RGGI, allowance prices traded at low values in 2013, while the cap was non-binding (Potomac Economics 2014). California policymakers consider the RPS as one of the core mechanisms to reduce GHG emissions, with cap -and -trade used to limit emissions beyond such sectoral policies es is appropriate. reduced damag ). As such, at least for 2013, valuation of GHG benefits based on (http://calcarbondash.org/ 17

31 -related life cycle emissions from fossil plants (see Figure 3.4) . These much lesser extent, construction upstream emissions savings, however, are more than offset by non- combustion life cycle emissions from the construction and—to a lesser degree—operations of RE plants. The non- combustion, life cycle impacts are not assigned to regions (and so are not included in Figure 3.3) because of the challenges of esti mating the location of upstream emissions. 75 Combustion (avoided fossil) e) 2 Fuel cycle (new renewables) 50 -Cycle Fuel cycle (avoided fossil) 25 Construction (new renewables) GHG Emissions 0 Construction (avoided fossil) 2013 Displaced Life (million metric tons CO Net GHG Reduction -25 Figure 3.4. Life cycle GHG emissions impacts of RPS compliance These GHG emissions reductions produce sizable global benefits in the form of reduced climate change damages (see Figure 3.5). Based on the IWG central -value SCC estimate, the global benefits from s in 2013 avoided future damages associated new RE used to meet RPS obligation are equal to $2.2 billion. From the low to the high SCC estimates, benefits range from $0.7 billion to $3.5 billion. The extreme effects case accounts for the possibility of even more , corresponding to higher expected -than- benef its of $6.3 billion. To put these figures in another context, as also shown in Figure 3.5, the global benefits of new RE used to meet 2013 RPS obligations are equal to 2.2¢/kWh- RE , under the central -value range of IWG SCC values, the global ben SCC estimate. Across the full efits of RPS compliance equal 0.7 RE to 6.4¢/kWh- . Figure 3.5. Estimated benefits of RPS compliance due to reduced global climate change damages 18

32 H A IR P OLLUTION E MISSIONS AND 4. UMAN H EALTH AND E NVIRONMENTAL B ENEFITS Renewable generation used to meet 2013 RPS compliance obligations reduced national emissions of SO , NO and PM by an estimated 77,400, 43,900, and 4,800 metric tons, respectively. These 2 x 2.5 to have produced $2.6 billion to emissions reductions are estimated —across a range of approaches— $9.9 billion in health and environmental benefits (average of $5.2 billion), including the prevention of 320 to 1,100 premature mortalities. The health and environmental benefits of new RE used to meet RE across the RPS requirements are equivalent t o 5.3¢/kWh- RE on average, and 2.6 to 10.1¢/kWh- full range of methods. The largest health benefits accrue to the eastern half of the country, and ity -Atlantic, Great Lakes, Northeast, and Texas, due to a combination of proxim especially in the Mid to power sector emissions reductions and population density. I NTRODUCTION Combusting fuels to generate electricity produces air pollutants that harm human health and cause environmental damage (NRC 2010). Epidemiological studies have shown a causal a ssociation between for examples of the association with increased mortality and morbidity and exposure to air pollution; mortality, see Dockery et al. 1993; Krewski et al. 2009; Lepeule et al. 2012. Lim et al. (2012) estimate more than three million premature deaths globally, each year, from outdoor particulate air pollution. In the United States, a number of recent studies have evaluated the potential air quality and public health benefits of reducing combustion- . For example, Driscoll et al. (2015) found based electricity generation -sector CO that policies aimed at reducing power emissions would also reduce PM and ozone, 2 2.5 preventing as many as 3,500 premature mortalities in 2020. Siler -Evans et al. (2013) value the health and environmenta l benefits of displaced conventional generation from new solar and wind power at 1¢/kWh to 10¢/kWh, with the range largely reflecting locational differences. Buonocore et al. (2015) build on this work by further exploring how benefits vary by location and technology. EPA has estimated that its CPP to $34 billion of monetized health benefits in 2030 based mostly on reduc ed would provide $14 billion premature mortality (EPA 2015e ). Though all energy sources —including RE —have environmental impacts, most RE so urces have no direct , and low life cycle, air pollution emissions (IPCC 2011; Turconi et al. 2013). State -level RPS programs are therefore among the many types of policies that can be used to reduce air pollution emissions. Some a link between RPS programs and air pollution concentrations (Eastin 2014; previous research has found Werner 2014). In this section, we calculate the potential air emissions reductions associated with state RPS compliance in 2013, and present the associated public health and enviro nmental benefits. M ETHODS Our methods to value the potential air quality benefits from RPS programs involve first estimating the net related emissions of SO reductions in direct combustion- serving associated with , NO new RE , and PM 2.5 x 2 (see Figure 4.1) . We then quantify the public health and environmental benefits RPS compliance in 2013 of those changes in emissions in the form of reduced mortality and morbidity, and translate those effects into monetary terms. Given uncertainty in pollutant transport, tr ansformation, and exposure as well as uncertainty in the human response to ambient PM and ozone exposure, we use multiple established 2.5 methods to quantify the health and environmental outcomes and monetary benefits of the emissions (DOE 2015), and is changes. Our overall approach is similar to that used in DOE’s Wind Vision Report broadly consistent with methods used in NRC (2010), Fann et al. (2012), Cullen (2013), Johnson et al. 19

33 (2013), McCubbin and Sovacool (2013), Siler -Evans et al. (2013), EPA (2015 e), Novan (2014), Buonocore et al. (2015), and Driscoll et al. (2015). Estimate combustion -related SO , NO , and x 2 PM emissions reductions (AVERT) 2.5 Estimate emissions from 2013 RPS biomass electricity production Calculate impacts and monetized benefits of net emission reductions with multiple methods (COBRA, AP2, EPA) 4.1. Health and environmental benefits methods overview Figure , and PM , NO power sector SO As the first step in this analysis, EPA’s AVERT tool is used to estimate 2.5 2 x 29 emissions reductions from state RPS programs in 2013. Those estimates consider avoided emissions due emissions associated with to increased renewable generation but do not include any offsetting increases in - e therefore separately estimate emissions from RPS ation used to meet RPS programs. W biomass gener estimates of reduced from the AVERT , and subtract those amounts related biomass combustion -derived 30 emissions , in order to calculate the net c hange in emissions . -renewable generation from non We then calculate a range of health and environmental benefits (including reduced morbidity and mortality outcomes and total monetary value) from these net emissions changes based on three different s for pollutant transport and chemical transformation as peer -reviewed approaches. Each approach account the Air Pollution Emission Experiments and Policy well as population exposure and response: (1) AP2, formerly APEEP, described in Muller et al. 2011), (2) EPA’s benefi t-per -ton analysis model ( methodology developed for the CPP (EPA 2015e) , and (3) EPA’s COBRA model ( EPA 2014b ). While approaches ( 2) and ( 3) both come from EPA, they differ from each other and from AP2 in multiple 29 and NO Although SO reductions are not reported by AVERT. To reductions are estimated directly in the AVERT tool, PM 2.5 x 2 , we estimate emissions by plant as a function of avoided MWh generation (from AVERT) and average reductions calculate PM 2.5 emissions rates (reported by Argonne National Laboratory, Cai et al. 2012; Cai et al. 2013) by generation type (coal, gas, or oil) and U.S. state. 30 compliance obligations We estimat e emissions for each new biomass plant used to meet RPS in 2013. For plants combusting -gas, we assume emission rates equal to state-level emission rates from natural gas plants (reported by Argonne National bio Laboratory, Cai et al 2012; Cai et al 2013). To estimate emissions from combustion of solid and liquid biomass, we multiply total are directly available and NO . Year 2013 emission rates for SO and PM , NO MWh by a per -MWh emission rate for SO x 2 2.5 x 2 m Data (AMPD, EPA 2015b) for a subset of the biomass plants. We use these plant -level data from EPA’s Air Markets Progra -average emissions rates of 2.3 where available. For all biomass plants where AMPD data are not available, we assume weighted and SO ively. These average emission rates were developed from all biomass plants (defined , respect and 0.5 lbs/MWh for NO x 2 as listing biomass as their primary fuel in the 2013 AMPD data) that began operation in 1990 or later. AMPD does not provide emis sion rates we combine plant emission rates, so to estimate PM -level emissions from EPA’s most recent, year 2011, PM 2.5 2.5 National Emission Inventory (EPA 2015c) with generation data from AMPD for that same year; these were used to estimate developed a weighted -average emission rate for PM emissions rates, where feasible. We also in a similar plant -level PM 2.5 2.5 manner as above, but without the 1990 cutoff due to sample size limitations. We apply this average PM emission rate of 0.3 2.5 lbs/MWh to all biomass plants that did not have available plant-level data. A final detail: for a number of plants that reported , based only heat input in mmBTU but not energy generated, we use a weighted-average conversion rate of 0.07 MWh/mmBTU on all biomass plants that began operation after 1990 and had available data in the 2013 AMPD dataset . 20

34 31 (2) and ( 3) also each include two es timates of the health impacts that reflect respects. Approaches 32 uncertainty in the underlying epidemiological studies. Henceforth , we refer to the multiple outputs from the EPA approaches as ‘EPA Low’ and ‘EPA High’ for the EPA benefit -per -ton methodology developed for the CP P and as ‘COBRA Low’ and ‘COBRA High’ for the COBRA model approach. The ‘high’ and ‘low’ classification corresponds to differences only between the underlying health impact functions l five benefit estimates as the employed by the particular EPA approach. We take the simple average of al “central” value. One important assumption across all methods used is the monetary value of preventing a premature mortality (or the Value of Statistical Life, VSL). Consistent with the broader literature, all use 33 a VSL of ap proximately $6 million in 2000, but updated for year 2013. issues related to the methodology and associated limitations, some discussed earlier, deserve note: Several evaluate other , NO . We do not , and PM Our focus is on a subset of air emissions impacts: SO • 2.5 x 2 nvironmental impacts, includ potential e heavy metal releases, radiological releases, waste ing products, land use, and water quality impacts associated with power and upstream fuel production, as well as noise, aesthetics, and others. We also only consider air emissions from power plant operations, and so do not assess upstream and downstream life cycle impacts. • Uncertainties in PM and biomass emissions rates are more substantial than for fossil -based SO 2.5 2 and NO e mission reductions dominate the benefit estimates, however, -based SO . Because fossil X 2 uncertainties in PM and biomass emissions are not the largest drivers of uncertainty in the 2.5 overall analysis. We do not fully consider the possible erosion of air quality benefits due to the increased cycling, • ramping, and part loading required of fossil generators in electric systems with higher penetrations of variable renewable generation. Literature suggests tha t this omission will not 31 Benefits calculated by AP2, EPA CPP, and EPA COBRA differ in a number of respects. For example, the AP2 model accounts for not only mortality and morbidity, but also air pollution induced decreases to timber and agriculture yields, visibility reductions, accelerated materials degradation, and reductions in recreation services; the benefits calculated with the EPA CP P benefit -per -ton approach and with the EPA COBRA model only include mortality and morbidity. Additionally, while the EPA CPP benefit -per -ton approach and the AP2 model both include the benefits of ozone reduction (as well as particulates), EPA related benefits and ignores the smaller ozone-related impacts. EPA CPP and EPA COBRA also COBRA includes only PM 2.5 rely on different air quality models to characterize the effects of emissions changes on ambient pollution. Finally, each approach -ton is based on three large regions, whereas EPA applies a different geographic resolution in its analysis: EPA CPP benefit-per CO BRA and AP2 allow for more geographic detail in the location of the resultant benefits. Overall, each approach has advantages and limitations, and so each is treated equally when establishing a “central” (average) value. 32 EPA Low and COBRA Low are based on research summarized in Krewski et al. (2009) and Bell et al. (2004), whereas EPA High and COBRA High are based on research presented in Lepeule et al. (2012) and Levy et al. (2005). Both sets of epidemiology research have different strengths and weaknes s, and EPA does not favor one result over the other. 33 The AP2 model contains monetized benefit -per -ton estimates based on emissions in the year 2008, but based on year 2000 dollars and a year 2000 VSL value. To find 2013 benefits we updated the 2000 numbe r for increased population exposure at the national level by multiplying the AP2 2008 values by the ratio of population in 2013 to population in 2008 (U.S. Census Burea u 2014) and then also by the ratio of the 2013 CPI to the 2000 CPI (U.S. BLS 2014). We a lso multiplied the AP2 value by 1.131 to adjust VSL for per -capita real income growth over 13 years. This income growth adjustment was made to mirror the EPA methodology by matching the linearized annual income change in VSL described in Table 3 of the rel evant technical support document (EPA 2013a). We adjusted EPA CPP benefit -per -ton values and EPA COBRA values in a similar fashion, except, EPA benefit -per -ton values for the CPP were developed for a base year of 2020 (in 2011$) and EPA COBRA values were developed for a base year of 2017 (in 2010$). Health outcomes were also scaled by population to match year 2013, with population beyond 2013 characterized with Census projections (U.S. Census Bureau 2012). 21

35 the basic results reported here, especially at the relatively low levels of dramatically alter 34 renewable energy penetration analyzed. Following the earlier assumptions for GHG impacts (Section 3 ), we assume that landfill gas was • flared prior to conversion to controlled combustion for RPS electricity generation. We do not , because we assume the conversion account for any emissions changes due to this conversion between flaring and electricity production causes only marginal differences in emissions, 35 especially for SO . 2 Finally, our methodology presumes that SO cap and NO -trade programs, such as the Clean • -and x 2 Air Interstate Rule (CAIR), were not binding in 2013 ; otherwise, the benefits of RPS compliance should arguably be valued at allowance prices, to reflect savings in the cost of complying with 36 s (Siler -Evans et al. 2013). those emissions cap ESULTS R Compliance with RPS obligations in 2013 reduced national power sector emissions of SO , NO and 2 x PM , with reductions in by an estimated 77,400, 43,900, and 4,800 metric tons in 2013, respectively 2.5 each pollutant equivalent to 2% of the corresponding total U.S. power sector emissions in 2013. These emissions reductions are concentrated in the Midwest, Mid -Atlantic, Great Lakes, and Texas, driven by the locations of the most aggressive RPS standards but also by the regions in which higher - emitting fossil plants are displaced (see Figure 4.2). Fossil plant displacement occurs not only within RPS states but also outside of those stat es. A few states with biomass plants serving RPS compliance are estimated to have had small (relative to emission reductions in other states) emission increases for some of the pollutants, which is a function of the assumed emissions of those biomass plant s. These emission increases do not necessarily lead to increases in state- level total pollutant exposure or related health damages, however, because reductions in other pollutant emissions or reduced transport of pollutants from neighboring states can counteract the effect of the local emission increases. In fact, the COBRA model results presented later indicate that, after taking pollutant dispersion into account, no region saw an increase in average annual PM levels in 2013 as a result of state RPS com pliance. 2.5 34 Though our analysis does not fully capture these effects (AVERT may capture some), results from recent research suggest that air pollutant emissions are reduced by variable renewables, even after accounting for any emissions penalties associated with plant cycling and part -loading (Oates and Jaramillo 20 13; Valentino et al. 2012). Lew et al. (2013) find that accounting for emissions of wind and solar impacts related to increased coal plant cycling slightly improves (by 1% –2%) the avoided NO x relative to the avoided emissions based on an assumption of a fully loaded plant, but that accounting for cycling impacts reduces emissions of wind and solar by 3% –6%. A similarly detailed analysis in the Mid -Atlantic region reports more the avoided SO 2 substantial emissions penalties (GE Energy Consulting 2014). In bo th cases, however, the impacts are not large enough to dramatically alter the basic results reported here. 35 Kaplan et al. (2009) indicate that the range of SO emission rates for landfill gas -to -energy and landfill flaring overlap and are 2 magnitude lower than the SO emission rate for conventional coal power plants. an order of 2 36 Under strictly binding caps, RE does not reduce emissions per se, but instead alleviates the need to reduce emissions elsewhere in order to achieve the cap. In support of our binding caps, in 2013, the largest regional SO assumption of non- - and NO -and cap x 2 trade program (CAIR) was non -binding (EPA 2013b; Schmalensee et al. 2013). It is possible that some more localized cap -and - trade programs were binding in 2013; however, the geographic extent of these programs was limited, so will not substantially bias our results. 22

36 Figure 4.2. Change in emissions of (a) SO2, (b) NOx, and (c) PM2.5 from RPS compliance 23

37 These emissions reductions in 2013 led to improved air quality and health outcomes across the continental United States. Specifically, total U.S. health and environmental benefits fall in the range of $2.6 billion to $9.9 billion, with a “central” estimate (defined as the average of the five primary estimates) of $5.2 billion (Figure 4.3 ). To put these figures in another context, as also shown in Figure 4.3, the “central” estimate is equivalent to a value of 5.3¢/kWh -RE used to meet 2013 RPS requirements. A cross the total range of valuation estimates, the benefits of RPS compliance equal 2.6 to 10.1¢/kWh- RE. Note: AP2, EPA Low and EPA High allow one to separate monetary benefits by the three specific pollutants. EPA COBRA does not allow for such segmentati on. The Central estimate is a simple average of the five primary estimates. Figure 4.3. National benefits of RPS compliance due to reduced health and environmental damages (primarily from coal) and the subsequent reduction of particulate sulfate concentrations Reduction of SO 2 accounted for a majority of the monetized benefits (Figure 4.3 ). For example, SO emissions reductions 2 37 accounted for 77%, 86%, and 83% of the AP2, EPA Low, and EPA Hi gh benefit estimates, respectively. The benefits of reduced tropospheric ozone (from reduced NO emissions) were relatively small, x 38 accounting for 4% and 7% of the EPA Low and EPA High benefit estimates, respectively. This shows that exposure to particula tes (directly or indirectly from emissions of SO ) is the primary , NO and PM x 2.5 2 driver of health outcomes. Most of the health benefits come from avoided premature mortality, primarily associated with reduced chronic exposure to ambient PM (which largel y derive from the transformation of SO to sulfate 2.5 2 particles). For example, 98% of COBRA -estimated benefits come from reduced mortality with the rest RE attributed to morbidity benefits. Based on the two EPA methods (and four EPA -based estimates), new 39 meet ing RPS programs in 2013 prevented 320–1,100 deaths that year. We also estimate that RPS compliance in 2013 generated a range of benefits in the form of reduced morbidity, including avoiding between 160–290 emergency department visits for asthma, 195 –310 hospital emissions for respiratory 41 40 –560 non- and cardiovascular symptoms, 40 fatal heart attacks, 64,000 lost work days. and 38,000– 37 Benefits were not separated by pollutant with the COBRA model. 38 An estimate of ozone benefits, separate from the total benefits, corresponding to the AP2 valua tion was not available within the model. 39 An estimate of mortality reduction corresponding to the AP2 valuation was not available within the model; similarly, AP2 does not report morbidity results. 40 This morbidity outcome has a wide range relative to the other outcomes as it is estimated based on two different studies, reflecting the uncertainty related to this outcome. 24

38 The benefits reported here are net of emissions from biomass electricity used for RPS compliance. We estimated emissions of 1,800, 6,200, and 900 metric tons of SO from new , NO , respectively, , and PM 2.5 2 x biomass meeting RPS requirements in 2013. Biomass electricity therefore reduced total RPS emissions benefits by 2.3%, 12.3%, and 15.8% for SO , respectively. Because health benefits are , NO , and PM x 2.5 2 dominated by SO reductions, the biomass emissions only marginally reduce total monetized benefits; for 2 example, the EPA Low value would have been 4% larger without the biomass emissions. Finally, the largest benefits accrue to the eastern half of the country, and especially in the Mid- Atlantic, Great Lakes, Northeast, and Texas. As one example, Figure 4.4 illustrates the regional allocation of 42 ach. monetized benefits from fine particulate exposure calculated using the COBRA Low appro Figure 4.4. Regional benefits of RPS compliance due to reduced health and environmental damages from particulate matter under the ‘COBRA Low’ estimates 41 Other morbidity benefits: RE 730 incidents of acute estimated to avoid between 440 – serving RPS programs in 2013 was also bronchitis (ages 8 -12), 5,700 – 9,400 incidents of lower respiratory symptoms (ages 7-14), 8,200 – 14,000 incidents of upper -11), 225,000 – 380,000 minor restricted-activity days, and 14,000 – 20,000 incidents respiratory symptoms (in asthmatics age 9 emission reductions associated with RPS compliance of asthma exacerbation (age 6 -18). Additionally, we estimated that NO x reduced ozone levels and thus avoided 80 respiratory related hospital admissions , 30 emergency room visits due to respiratory symptoms, 20,000 school loss days, and 58,000 incidents of acute respiratory symptoms. Note that the COBRA model does not produce estimates of ozone air quality and thus only a single value for the ozone impacts is presented, based on EPA CPP -per -ton estimates. benefit 42 These m onetized benefits (based on the COBRA Low results) focus exclusively on fine particulates. Ozone is also found to be reduced in many locations, but is not accounted for in the COBRA model used to generate this figure. The monetized benefits reductions, however, so the exclusion of ozone in from ozone reductions are of second order compared to the benefits of PM 2.5 the figure does not create substantial bias. The EPA CPP benefit -per -ton approach and APP model do not output the locations of fits, so are now shown here. the resultant bene 25

39 5. W ATER U SE EDUCTION B ENEFITS R Renewable generation used to meet 2013 RPS compliance obligations reduced national water withdrawals and consumption by an estimated 830 billion gallons and 27 billion gallons, respectively. -sector water use reductions are equivalent to savings of 8,420 gallons of withdrawal and These power 270 gallons of consumption with each MWh of generation of renewable electricity. Water use reductions vary seasonally and come predominantly from freshwater sources, with reductions varying regionally due to geographic differences in power plant fuel types and cooling system configurations. The largest withdrawal and consumption reductions were in California and Texas, respectively, stressed regions. - demonstrating the important benefits RPS compliance can have in water I NTRODUCTION The electric sector is heavily dependent on water —and can affect —primarily for thermal plant cooling water resources through water withdrawals, water consumption, changes in water quality, and changes in water temperature. Withdrawals are defined as the amount of water removed or diverted from a water source for use, while consumption refers to the amount of water that is evaporated, transpired, nt (Kenny incorporated into products or crops, or otherwise removed from the immediate water environme et al. 2009). The U.S. electric sector is the largest withdrawer of water (38% of total) in the nation (Maupin et al. 2014), whereas its share of consumption is much lower, around 3% (Solley et al. 1998). As such, water availability impacts the electric system, including impacts on new capacity decisions (Macknick et al. 2015) and on power plant operations and reliability (DOE 2014; Rogers et al. 2013). In turn, water availability and quality for other competing uses are impacted by the electricit y sector. Moreover, future uncertainties with regard to water availability and temperature, including those associated with climate change, may further exacerbate vulnerabilities in the electric sector (Cohen et al. 2014; DOE 2014). Many RE technologies h ave low water withdrawal and consumption intensities compared to fossil and nuclear generating technologies, whether only considering operational demands (see Figure 5.1) or the entire life cycle (Macknick et al. 2012a; Meldrum et al. 2013). Prior studies have evaluated the impact of a wide range of U.S. electricity sector futures on water demands (Arent et al. 2014; Chandel et al. 2011; Clemmer et al. 2013; DOE 2015; Macknick et al. 2012b; Roy et al. 2012; Tidwell et al. 2013a; van Vliet et al. 2012). This body of work generally finds that scenarios designed to meet carbon reduction goals would also result in water savings, particularly when renewable -based pathways are followed (Clemmer 43 et al. 2013; Macknick et al. 2012b). While the literature contains a substantial amount of scenario analysis of water use by the U.S. power sector, no studies have quantified—on a national scale —the potential water benefits that have resulted from RPS mandates. RPS programs are generally not designed around water reduction goals, though water savings could be a significant co -benefit in some regions. In this section, we calculate the water withdrawal and consumption impacts of RPS compliance, providing insights on regional and temporal dynamics. 43 Recently, the DOE (DOE 2015) demonstrated how achieving 35% wind penetration by 2050 could result Wind Vision Report 15). in water consumption savings of 23% over a scenario with wind capacity held constant at 2013 levels (DOE 20 26

40 Note: G eothermal technol ogies are not displayed in the figure due to the multiple configurations (e.g., binary, flash) and cooling system types (e.g., wet, dry, hybrid) commonly utilized, but water withdrawal and consumption impacts are calculated in this analysis for geothermal technology configurat ions based on data reported in Macknick et al. (2012b). Figure 5.1. Summary of operational water withdrawal and consumption requirements of electricity generation technologies by technol ogy and cooling system (Source: Averyt et al. 2011) M ETHODS from state RPS programs Our methods to assess potential water withdrawal and consumption benefits renewable generation used to serv e RPS compliance of involve first estimating the quantity and type -based generation obligations in 2013 along with the associated reductions in fossil (using AVERT) , and then estimating both water use reductions from displaced fossil -based generation as well as increased water use from RPS resources (see F igure 5.2). This approach build s on established methods for assign ing generation from individual electric generating units or aggregate regional generation to corresponding cooling system type have been applied in multi ple studies evaluating s (Averyt et al. 2013; UCS 2012) that national and regional water impacts of the U.S. electricity sector (Clemmer et al. 2013; DOE 2015; 44 Macknick et al. 2012b; Macknick et al. 2015; Rogers et al. 2013). Water withdrawal and consumption impacts from changes in fossil gen eration are calculated by applying fuel/prime mover/cooling system -specific water intensity rates (in gallons per MWh of electricity generated) to generating unit -level changes in monthly generation, as estimated with AVERT. A similar process is used to calculate increases in water use resulting from renewable electricity technologies, inclusive of all operational water demands (e.g., cooling in the case of biomass, module cleaning in the case of PV). Water intensity rates are based on a literature review o f nationally applicable estimates of 44 -specific outputs from AVERT model for fossil generation displaced by new RE for RPS compliance in We start with the unit 2013 (Section 2). To match AVERT generators with cooling system characteristics, we make an initial assignment based on the Office of Regulatory Information Systems Plant Location (ORISPL) Plant ID number, which is included in the AVERT outputs , as well as the UCS EW3 Energy -Water Database (UCS 2012), with updates from Ventyx (2013). From this preliminary matching of power plants, we assign prime mover technology, cooling system technology, and water type (freshwater or saline water) to each AVERT generat ing uni t based on matches of nameplate capacity. Additional details can be found in Averyt et al. (2013). For cases in which there is no obvious match, prime mover types and cooling systems are assigned based on similar generators within the same power plant or v ia internet research. For cases in which this procedure did not lead to new information (for a total of 20 generating units with 437 MW of capacity), no cooling systems are assigned. 27

41 power plant water use (Macknick et al. 2012b). Water use increases and decreases are aggregated, with results summarized on a net basis nationally, by month, and by state. Estimate renewable and fossil changes in generation from RPS Compliance (AVERT) Match power plants in AVERT with -plant water use database of power intensity estimates Quantify national, regional ,and temporal net water use reductions 5.2. Water use benefits methods overview Figure Several issues related to the methodology and associated limitations, some discussed earlier, deserve note: We consider only the operational water requirements of the power sector and do not estimate full life • upstream water cycle water uses, including upstream processes and fuel sources. Including effects on requirements would likely increase the overall water savings from RPS resources, but associating ld be challenging and introduce greater uncertainty to our regional upstream water uses to regions wou results. Moreover, prior work has demonstrated that operational water requirements are more than an order of magnitude greater than the combined water requirements of other life es, so this cycle stag not substantially alter our results (Meldrum et al. 2013). exclusion does The assessment of operational water use impacts relies on assumptions about which prime mover • s. In practice, individual technology type and cooling system are associated with individual generator generators at a specific power plant might be associated with multiple cooling system types, with . We use methods described in Averyt et al. (2013) to ies substantially varying water use intensit associate individual generating units with cooling systems. Biomass generating units have many different configurations that affect water use. We categorize • biomass plants into three broad categories: biomass (non -gas), biomass (gas), and landfill gas. - are assign Biomass (non- gas) sources ed water use characteristics of simple- cycle steam turbine solid assigned water use characteristics of biogas- based biomass power plants, biomass (gas) sources are lues in assumed to require no water for operations, per va power plants, and landfill gas plants are Macknick et al. (2012). We do not consider any hydropower -related evaporation in this analysis due to uncertainties in • allocation of evaporative consumption among multiple uses of reservoirs, such as flood control, in addition to the inherent variability in evaporation rates with drinking supply, and recreation, Given the small amount of incremental hydropower reservoir and local climate characteristics. -of-river, this assumption is serving RPS standards, and the fact that most of those facilities are run . or bias unlikely to be a major driver of uncertainty 28

42 • Finally, we do not quantify the benefits of water use reductions in monetary terms due to challenges associated with quantifying the value of water resource services (DOE 2015). ESULTS R New RE used for RPS obligations in 2013 reduced net national water withdrawals by an estimated 830 billion gallons and net national water consumption by 27 billion gallons (see Figure 5.3). These reductions both amount to 2% of the corresponding power sector totals for 2013. E ach MWh of electricity generated for RPS compliance obligations in 2013 represent s an a verage sav ings of 8,420 gallons of water withdrawal and 270 gallons of water consumption. Additional withdrawal needs from new RPS generation are less than 1% of the withdrawal reductions from decreased fossil -based generation. This result is largely because fossil -based generators that use once -through cooling systems have high water withdrawal intensity, and renewable generators typically do not use once -through cooling (with exceptions for some biomass -based systems). Additional equal to 15% of water -complia consumption needs from RPS nt new renewable generation are consumption reductions -based generation. This higher percentage of fossil associated with displaced water consumption from RPS -compliant generation, as compared with that for withdrawals, is largely a result of the water use characteristics of displaced fossil -based generation, which have relatively low er water withdrawal rates. In addition, some biomass, CSP, water consumption rates and relatively high y configurations that use recirculating cooling systems similar to those used by and geothermal technolog fossil -based generators can have water consumption rates similar to the displaced fossil generators. Notwithstanding the fact that water use by renewable generators somewhat offsets water use reductions from displaced fossil generation, the net effect is positive, as shown in Figure 5.3. Figure 5.3. National water withdrawal (left) and consumption (right) benefits of RPS compliance Figure 5.4 highlights monthly trend s in net water withdrawal and consumption savings. The highest levels of water savings occur in October through May, while July and August represent the lowest levels of water savings. Summer savings are lower because ren ewable generation in these months often displaces -intensive technologies (e.g., natural gas combustion turbine) and because some renewable less water technologies with higher water intensities (e.g., CSP) produce more electricity in the summer. 29

43 Figure 5.4. National water withdrawal and consumption benefits of RPS Compliance, by month Water withdrawal and consumption savings are not uniform throughout the continental United States (Figures 5.5 and 5.6). M oreover, b ecause fossil plant displacement and RPS generation occur not only in benefits RPS states but also outside of those states, water , extend beyond the 29 states and Washington obligations in 2013. Most states are estimated to have seen decreases in both D.C. with RPS compliance withdrawal and consumption in 2013. Importantly, there are reductions in water use in many drought - prone regions (e.g., California, Texas, and parts of the southwest) , with the largest withdrawal savings in California, and the largest consumption savings in Texas. Certain states experience large withdrawal savings but lower consumption savings (e.g., New York), . whereas other s experience lower withdrawal savings but larger consumption savings (e.g., Pennsylvania) This regional variation reflects the type of generators and cooling systems being displaced and the water requirements of local renewable technologies deployed. Six s increases in tates experienced slight net water consumption: Maine, New Hampshire, North Carolina, Nevada, Virginia, and Vermont. For Nevada, water consumption requirements of new geothermal facilities outweighed the water consumption displaced coal - and gas -fired generators . For the five other states with an increase in net water savings at consumption, the assumed water consumption requirements of biomass -fired technologies were greater than the water consumption savings of the displaced fossil es. Only one state (Vermont , which technologi did not have an RPS in 2013) experienced a slight increase in water withdrawals, the result of in -state biomass projects serv ing RPS demands in other states. ater withdrawal and consumption benefits of RPS compliance, by state Figure 5.5. W 30

44 (a) Water withdrawal benefits (b) Water consumption benefits Figure 5.6. Regional variation of water withdrawal and consumption benefits of RPS compliance 31

45 We estimate reductions of b freshwater and saline water demands resulting associated with RPS oth policies awal reductions and 97% of the consumptio n reductions were from freshwater : 82% of the withdr sources. Saline water savings were concentrated in California, Connecticut, Florida, New York, and Texas. Figure 5.7 highlights estimated water savings in just these five states, showing the split between fresh and saline water savings. Most water consumption savings in even these states come from primarily of saline water in California, freshwater sources. However, withdrawal savings consist Connecticut, and Massachusetts due to the types and the coastal location of generators in those states. Reducing saline water withdrawals can have important impacts for the health of marine ecosystems (EPA 2011). Figure 5.7. Fresh and saline water withdrawal (left) and consumption (right) benefits of RPS compliance Though standard methods do not exist to value —in monetary terms —these water use impacts, water use reductions from RPS policies can be considered a benefit, especially where water is scarce. Reductions in water demands also r educe the vulnerability of electricity supply to the availability or temperature of water, potentially avoiding electric -sector reliability events and/or the effects of reduced thermal plant efficiencies —concerns that might otherwise grow as the climate ch anges (DOE 2014). Reduced power - sector water use also frees water for other uses, whether for other productive purposes (e.g., agricultural, industrial, or municipal use) or to strengthen local ecosystems (e.g., benefiting wildlife owing to greater water availability, lack of temperature change, etc.). Finally, by avoiding upstream water demands from fossil fuel supply, RE may help alleviate other energy -sector impacts on water resource quality and quantity, e.g., water otherwise used in mining, coal washing, and hydraulic fracturing (Averyt et al. 2011). 32

46 G ROSS J OBS AND E CONOMIC D EVELOPMENT I MPACTS 6. Renewable generation used to meet 2013 RPS compliance obligations, along with average annual RPS -related capacity additions in 2013 and 2014, supported an estimated 200,000 U.S. -based gross jobs earning average annual salaries of $60,000, and driving over $20 billion in GDP. Of these gross jobs, over 30,000 are related to ongoing operations and maintenance (O&M) while the remaining 170,000 are supported by construction activity. Labor -intensive PV installations account for the majority of construction jobs, while established wind plants account for the majority of O&M jobs. California had the most significant renewable capacity expansion and generation associated with RPS compliance obligations, and thus also had more “onsite” renewable energy jobs (i.e., in stallers, ) than any other state in 2013. construction workers, plant operators, and other maintenance personnel I NTRODUCTION Renewable electricity generation infrastructure requires workers and expenditures. These include onsite truction and O&M work, as well as workers across the country (and personnel performing cons worldwide) in manufacturing, business services, and other aspects of the energy supply chain. Workers, in turn, spend money that further supports jobs and other economic activity. The physic al location of supply —and related induced impacts chain impacts —are, of course, greatly affected by where materials, manufacturing, and business services occur, whether domestic or international in origin. d net impacts of RE deployment on jobs and economic Much research has sought to quantify the gross an development (e.g., Böhringer et al. 2012; Böhringer et al. 2013; Breitschopf et al. 2011; Brown et al. 2012; Chien and Hu 2008; Frondel et al. 2010; Haerer and Pratson 2015; Hillebrand et al. 2006; Lehr e t al. 2008; Lehr et al. 2012; Marques and Fuinhas 2012; Menegaki 2011; Meyer and Sommer 2014; Wei et al. 2010). This literature typically finds that RE deployment does increase gross jobs in and related to the RE sector. At the same time, the evidence and economic underpinnings for any “net” impacts on jobs or economic development are limited , because RE directly displaces demand for other sources of electric generation, impacting employment and economic development associated with those sectors. Additional ly, to the extent that RE deployment impacts the cost of energy, or has other macroeconomic effects, this too may affect employment in the broader economy. In general, there is little reason to e positive or negative direction, at least on a believe that net impacts are likely to be sizable in either th global or even national level (e.g., Rivers 2013). Moreover, even if net positive effects occur, which is omic possible at the local or state level, questions remain as to whether such effects serve as strong econ justification for government policy (e.g., Borenstein 2012; Edenhofer et al. 2013; Gillingham and Sweney 2010; Morris et al. 2012). These considerations notwithstanding, s tate RPS policies have been motivated in part by potential local economic devel opment and employment impacts (Rabe 2008). Previous research has sought to establish a statistical link between state RPS programs and jobs, but has found mixed results (Bowen et al. 2013; Yi c jobs and other economic impacts 2013). In this section, we estimate the potential gross, domesti supported by RPS policies. Such estimates may provide governments, stakeholders, and other interested parties valuable information about how RE expenditures translate to gross jobs, gross domestic product (GDP), earnings, and economic activity in the United States. At the same time, it must be emphasized that 45 our analysis does not include an assessment of economy -wide net impacts, and we therefore make no is reason, we refer to the jobs and economic effects as claim of net economy- For th wide benefits or costs. impacts . benefits rather than 45 The JEDI and IMPLAN models used in this analysis do not represent a full spectrum of economic impacts that could arise as a , and therefore do not allow for a comprehensive assessment of net effects. result of renewable electricity generation 33

47 ETHODS M As shown in Figure 6.1, the first step in the analysis is to identify new RE generation used to meet RPS compliance obligations in 2013 and RE capacity built over t he 2013 and 2014 period to serve RPS requirements (see Section 2). We then rely primarily on NREL’s Jobs and Economic Development Impacts (JEDI) suite of models to estimate the gross jobs and economic development impacts of RPS - related RE projects in 2013. Specifically, we use JEDI models for wind, solar, geothermal, hydropower, —a proprietary input and biomass. A JEDI model does not exist for landfill gas, so IMPLAN -O) -output (I —is used instead, with landfill gas expenditure cost distributions from Je nsen et al. (2010). Both model JEDI and IMPLAN have been commonly used for the type of analysis conducted here (e.g., Adelaja and Hailu 2008; Bamufleh et al. 2013; Croucher 2012; DOE 2015; Navigant 2013; Slattery et al. 2011; You et al. 2012). The methodologies employed by the two models are broadly consistent and use the same set of economic multipliers. Costs and assumptions for the “domestic content” for all renewable technologies 46 other than landfill gas are based on JEDI default data. New RE serving RPS obligations in 2013 and average annual RPS - 2014 capacity additions for 2013 JEDI models and IMPLAN Gross jobs, earnings, output, and GDP impact estimates Figure 6.1. Economic development impacts methods overview JEDI produces impact estimates for four metrics: gross jobs, earnings, output, and GDP. Jobs are expressed as full time equivalent (FTE). One FTE is the equivalent of one person • 47 working 40 hours per week for one year. Earnings include wages, salaries, and employer -provided supplements such as health insurance • and retirement contributions. 46 ssible to maintain consistency. JEDI default Default data are used to ensure that each technology is treated as similarly as po costs come from interviews with project developers, operators, consultants, engineers, and others familiar with construction, installation, and operation of different types of energy projects. Model estimates us ing default data have historically come close t to job counts at actual projects and numbers published by other researchers (Billman and Keyser 2013). That said, JEDI defaul costs are based primarily on actual projects, with some lag. As such, rapid changes in—for example —prices for solar PV installations could merit adjusting these defaults to reflect actual 2013 projects. Solar PV costs have decreased below JEDI defaults and, as of this publication, continue to decline. A primary driver for PV price declines through 2013, however, was module prices (BNEF 2015). JEDI does not, by default, include the impacts from module manufacturing in its estimates, however, as JEDI assumes that this manufacturing occurs outside of the United States. As such, while our use of default values for PV in 2013 may impose some error, the size of that error would not likely have a dramatic effect on the results. 47 Two workers who work 20 hours per week, for example, would be reported as a single FTE. 34

48 Output is a measure of overall economic activity. At an individual business, it could be thought of • as revenue. This revenue would include payments for inputs as well as payroll, taxes, and property type payments (including profits). GDP is solely a measure of the value of production. It includes payments to workers, property • 48 come, and taxes. type in JEDI reports these four metrics in three categories: onsite, supply chain, and induced. Onsite impacts are most directly related to the project; these include installers, construction workers, plant operators, and other maintenance personnel. Supply chain impacts arise as a result of expenditures by the developer, installer, or operator; these include construction material providers, equipment manufacturers, and consultants. Induced impacts arise as a result of onsite and supply chain wor kers making expenditures within the United States; these impacts are often in retail sales, leisure and hospitality, education, and health services. In all cases, JEDI reports domestic jobs and impacts. We use JEDI and IMPLAN to estimate domestic jobs and economic development impacts associated with of RE both the operation and construction (including manufacturing of equipment subsequently installed) facilities used to support state RPS programs. Operational impacts are based on RE facilities used to meet RPS compliance obligations in 2013, while construction impacts are based on average annual RPS capacity additions in 2013 and 2014; both sets of assumptions are described in Section 2. Average annual additions from 2013 through 2014 are used for the const ruction -related impacts in order to mitigate variability in RE capacity build rates from year to year , and also to reflect the fact that construction - related jobs and economic impacts occurring in 2013 may be associated partly with projects completed in the following year, given lags between manufacturing, construction, and commercial operations. All results produced by JEDI and IMPLAN are for the equivalent of a single year ; although O&M jobs duration. Results are can be assumed to be ongoing, construction- related jobs are inhe rently of limited 49 -state basis. reported on a national and, for onsite jobs only, state -by Two both noted earlier, deserve reiteration : issues related to the methodology and associated limitations, • s. JEDI and IMPLAN results represent the workforce needs Our estimates represent gross impact and economic impacts that could feasibly have been supported by the construction and operation of RPS -related renewable generation facilities; they do not reflect other potential economic impacts of an RPS , such as those resulting from displaced fossil generation or capacity, changes in utility electricity rates, or changes in property values or other prices. As such, the results should not be considered net economy- wide impacts or societal benefits. • Several aspects of the methodology may produce uncertainty or inconsistency in the results. For example, landfill gas is treated somewhat differently than the remaining technologies, and we assume that average construction over the 2013 -related jobs -2014 period influe nces construction in 2013. Additionally, we use default JEDI data and assumptions to estimate impacts, which does not provide perfect cost information for all projects. Any uncertainty created by these or other factors may be more significant for state -level , as opposed to national , results. 48 GDP is also known as value added, which is how it is labeled in the JEDI model. Property type income is also known as gross operating surplus or simply “capital.” 49 We do not estimate supply chain or induced economic impacts by state because of uncertainty relating to where in the Un ited ia States those impacts occur. A solar installer in New Jersey, for example, may purchase IT services from a company in Californ but could just as easily purchase these services from a company in another state. 35

49 ESULTS R We estimate that new RE serving state RPS policies supported nearly 200,000 gross domestic jobs in 2013 (Figure 6.2 ), each earning an average annual salary of $60,000, with RE expenditures driving over $20 billion in gross GDP in the United States (Table 6.1 ). Of the total gross domestic jobs in 2013, the vast majority —nearly 170,000—a re associated with construction of new facilities, with the remaining 30,000 associated with operation of renewable projects serving RPS compliance obligations in 2013. Most gross jobs a re either onsite or within the supply chain, with a smaller number of induced jobs, reflecting the predominance of jobs tied to construction of new facilities. Simi lar trends are also apparent when comparing gross GDP impacts between construction -related and O&M activities, and among onsite, (Table 6.1) . supply chain, and induced jobs Hydro 200,000 Geothermal Landfill Gas O&M 180,000 Induced Biomass Solar CSP 160,000 Wind 140,000 Onsite 120,000 100,000 Const - ruction 80,000 Solar PV Total Gross Jobs (FTE) 60,000 40,000 Supply Chain 20,000 - domestic jobs from RPS compliance 6.2. Estimated total gross Figure 6.1. Estimated jobs and economic impacts from RPS c ompliance domestic gross Table Output GDP Jobs Annual Earnings Total Earnings Per FTE ($2013) (Million $2013) $2013) (Million (FTE) (Million $2013) Construction $67,000 $6,000 Onsite $7,100 $4,500 67,000 Supply Chain 62,700 $3,800 $60,000 $10,500 $6,200 $1,700 Induced 37,700 $46,000 $5,100 $3,000 $10,000 Total 167,400 Sub- $60,000 $22,700 $15,200 O&M $450 5,700 $400 $70,000 $570 Onsite Supply Chain 14,000 $840 $60,000 $5,260 $3,340 $1,180 Induced 12,500 $670 $53,000 $2,020 $7,840 $4,970 Total Sub- 32,200 $1,910 $59,000 Construction + O&M Total Onsite 72,700 $4,900 $67,000 $7,670 $6,450 76,700 Supply Chain $60 ,000 $15,760 $9,540 $4,640 Induced $2,370 50,200 $7,120 $4,180 $47,000 $11,910 $30,540 $60,000 $20,170 199,600 Total 36

50 Note: may not divide precisely due to rounding Totals may not sum due to rounding. Similarly, average annual earnings The distribution of jobs among renewable technologies reflects both the contribution of each technology to RPS generation and capacity additions, as well as the labor -intensi ty of their construction and operation phases. As such, solar PV —including both rooftop and utility -scale—accounted for the lion’s share of construction jobs in 201 3 (left -hand panel in Figure 6.3 ), driven by its large fractional share of recent RPS 50 capacity addition s (as shown earlier in Section 2) and the relatively high labor intens ity of the . O installation and construction process (especially for rooftop solar) -induced construction f the total PV jobs, 62% are associated with compared to 38% from utility -scale PV systems), rooftop applications ( despite the fact that rooftop installa tions accounted for just 26% of the underlying RPS -related PV capacity additions. In contrast, when focusing on the operations phase (right -hand panel in Figure 6.3), wind energy supported the largest portion of O&M jobs in 2013, followed by landfill gas, biomass, and geothermal. During O&M, PV installations supported a disproportionately small fraction of jobs (4%, compared to PV’s 6% share of RPS generation in 2013) due to the low labor intensity of O&M. Construction Jobs O&M Jobs Landfill Gas Biomass Landfill Gas 6,600 8,300 8,300 Wind 2,200 Geothermal 500 Solar CSP Hydropower Wind 800 12,400 17,900 Biomass 3,300 Geothermal 3,300 Solar PV 135,600 Solar CSP Solar PV 200 1,200 Figure 6.3. Gross domestic construction and O&M jobs from RPS compliance, by technology Of the three categories of jobs—onsite, supply chain, and induced—only onsite jobs can be readily linked The distribution of onsite jobs (including those associated with both construction and to specific states. additions O&M ) across states largely corresponds to the distribution in RPS capacity , given the struction- see Figure 6 .4). In particular, n early 50% of RPS capacity related jobs ( dominance of con additions in 2013 through 2014—primarily PV —was built in California , as shown earlier in Section 2. Correspondingly , most onsite job impacts from construction activities occurred in California, followed by Arizona, North Carolina, a -related onsite O&M jobs in nd Massachusetts. California also led states in RPS 2013, followed by New Jersey and New York. 50 historical dominance among In earlier years, wind would have been the largest source of construction jobs RPS new its , given . capacity additions (Barbose 2015) 37

51 Figure 6.4. Gross onsite jobs from construction and O&M from RPS compliance, by state 38

52 7. W HOLESALE E LECTRICITY P R EDUCTION I MPACTS RICE Renewable generation used to meet 2013 RPS compliance obligations potentially shifted the supply curve for electric power, reducing wholesale electricity prices and yielding an estimated $0 to $1.2 ted States. These consumer savings are billion in savings to electricity consumers across the Uni RE. The wide range of estimates reflects bounding assumptions about equivalent to 0.0 to 1.2¢/kWh- how the effects of renewable generation on wholesale spot market prices decline over time and the extent to which consumers are exposed to those prices. Importantly, these impacts are best considered wealth transfers from owners or shareholders of electricity generating companies to electricity consumers rather than net societal benefits. NTRODUCTION I s pushes out the wholesale power supply curve, an impact sometimes RE generation with low marginal cost ” ( Sensfuß et al. —that is, within the time it takes -order effect merit referred to as the “ 2008) . In the short run —the shifting of the supply curve reduces market clearing prices, new generation to be built or to retire expensive units are no longer needed to meet demand in hours with RE generation. Retail because more- sumers as the utility serving electricity con sofar electricity rates are impacted by wholesale power prices in wholesale purchases power on wholesale spot markets or via contracts with generators that are indexed to power market prices. Reduced from increased deployment of RE can ing wholesale market prices result s. use consumer prices and bills paid by end- there by potentially reduce retail electricity The wholesale price effect has been estimated for RE through both model simulations (e.g., Sensfuß et al. 2008; Fagan et al. 2013; GE Energy Consulting 2014) and empirical analysis (e.g., Woo et al. 2011; Woo et al. 201 3; Gil and Lin 2013) . The effect has also been considered in a wide array of regulatory and policymaking forums, including those that have considered RPS policies (e.g., IPA 2013), transmission (e.g., Perez et al. 2012), along with cost effectiveness expansion (e.g., Fagan et al. 2012), and solar value -side efficiency programs (Hornby et al. 2013) estimates of demand -level analyses of RPS policies . State sometimes considered this wholesale price effect (Barbose et al. 2015; Heeter et al. 2014). have also In this section, we quantify the potential effects of state RPS programs on wholesale electricity prices and savings to electricity consumers. estimate the associated bill It is important to recognize, however, that come at the expense of owners or shareholders these cost savings to electrici ty consumers of electricity imply reduced revenue s for generators. In other generating companies, because lower wholesale prices also producers to -induced reduction in wholesale prices represents a transfer of wealth from RPS words, any Keeping with the terminology used throughout , rather than a net societal benefit (Felder 2011). consumers fect as an impact this report, w benefit rather than a . e therefore refer to the wholesale price ef ETHODS M potential wholesale price reduction impacts from state RPS programs in 2013 Our methods to value the , based on the AVERT outputs, involve first developing regional supply curves that relate wholesale prices to electricity demand (see Figure 7.1). We then us e those supply curves to generate “unadjusted” wholesale prices with and without RPS generation, and finally we quantify the resulting cost reductions seen by end- use customers, based on a set of adjustmen ts that will be described later in this section. These methods are - broadly similar to those used in previous analyses of the wholesale price effect of renewables and demand use of side measures (see citations above) . The unique contribution of our AVERT to analysis is the develop supply curves , and the application of a common methodology across all regions of the United States. 39

53 Estimate supply curves relating elecricity demand to wholesale prices (AVERT) Generate "unadjusted" wholesale prices for each region with and without RPS generation Adjust the wholesale price effect by a decay factor and the portion of demand purchased at spot market prices. 7.1. Wholesale electricity price impacts methods overview Figure supply curves for each AVERT region, we calculate the wholesale price in each hour as the To develop change in production cost for that hour’s change in total generation, using outputs from the AVERT ction cost modeling described in Section 2. Specifically, t he change in produ in each hour is estimated based on the change in fuel consumption, derived from AVERT outputs, and U.S. Energy Information 51 Administration (EIA) data on historical fuel prices for each AVERT region. We then plot the calculated wholesale price an see the data points labeled d the corresponding demand level for all hours of the year ( ). The final “Raw AVERT” in the left -hand panel of Figure 7.2, which illustrates a single AVERT region -order polyno regional supply curve is estimated by fitting a third mial with a positive slope to the -hand panel of Figure 7.2). We then use that the line labeled “Smoothed” in the left price/demand pairs ( smoothed supply curve to estimate hourly wholesale prices with and without RPS generation in each right -hand panel of Figure 7.2). As expected, lower wholesale prices occur more frequently AVERT region ( (as indicated by a narrower with RPS generation than without it probability density curve with a shorter upper tail ). Figure 7.2. Example supply curve RT Upper Midwest AVERT region (left) and “unadjusted” wholesale prices with and without RPS generation for the Upper Midwest AVE region (right) 51 The change in production cost is based on AVERT’s estimates of the change in fuel consumption at each plant for each hour and rly estimates of fuel costs from EIA. Fuel prices from each quarter of 2013 by region were collected from quarte http://www.eia.gov/electricity/data/browser/ 40

54 ; however, these are Through this process, RPS generation is found to drive down wholesale prices that overstate actual wholesale price reductions and consumer savings. To properly “unadjusted” effects estimate both the wholesale price reductions and potential consumer savings, we adjust for two factors: (1) the degree to which the impact of RPS generation on wholesale prices persists into 2013 (i.e., the decay factor), and (2) the extent to which retail electricity consumer bills are exposed to wh olesale prices . The first adjustment is required because the impact of RPS generation on wholesale prices may erode over . In the short -run, l ower wholesale prices lead to reduced time s for generators, dampening the revenue incentive for new generators to enter remain in operation (Traber the market or for existing generators to Over time, the market will re -equilibrate as lower wholesale prices and Kemfert 2011; Woo et al. 2012). ifting the supply curve back delay the entry of new generators and accelerate the retirement of others, sh toward its original position . Additionally, in the long run, a number of studies show that , with high levels of variable generation, the overall generation mix will shift away from technologies with high up- front costs and low variable costs (i.e., coal and nuclear plants) and toward technologies with low up- front costs and higher variable costs (i.e., natural gas plants) (Sáenz de Miera et al. 2008; Lamont 2008; Bushnell 2010). The increased investment in plants with higher var iable costs further suggests that wholesale market prices . may not decline over the long term to the same degree observed in the short term We account for this impact through a “decay factor” that reduces the short -run price effect based on the length of t ime that the market has had to re -adjust to RPS generation . Unfortunately, there is no clear empirical foundation for . In previous studies, application of a decay the duration of this adjustment period factor rests on analyst judgment. We therefore bound o ur results based on the range of decay factors used in the literature (e.g., Hornby et al. 2011; Hornby et al. 2013) and assume that the wholesale price effect decays linearly over 5, 10, or 20 years, with no effect after the final year . In addition to the duration of the decay period, a related methodological question is at what point the market adjustment commences. In the absence of any significant literature on this topic, we bound our results by considering two options: either the decay begins at the date of RPS enactment (RPS Vintage) or it begins as new renewable generator s are built (RE Project Vintage). The RPS Vintage assumption implies that market the participants have the foresight to anticipate when new RPS generation will enter the market as soon as , and alter their investment and retirement decisions accordingly . The RE Project Vintage RPS becomes law assumption implies that market participants only begin to change investment and retirement decisions once generation enters the market and can be observed to impact dispatch and power prices . This same new RPS methodological issue and source of uncertainty also arises in the analysis of natural gas price impacts, as described in Section 8. A second adjustment is needed not all end- use customer demand is supplied by to reflect the fact that generation purchased at wholesale spot market prices. Rather , some portion of customer demand is hedged, either through long -term power contracts or utility ownership of generation ; only the portion supplied by purchases tied to wholesale prices wholesale price is impacted by reductions in s. To roughly approximate the portion of retail electricity generation costs exposed to wholesale market prices, we use historical EIA ges in average wholesale prices to estimate the relationship between data on changes in retail rates and chan 52 the two. We find that historical changes in wholesale prices flow through to the energy portion of retail –80% of the wholesale price changes, depending on the re gion and analysis electricity rates at 30% 53 approach. We assume that regions with organized wholesale power markets tend to feature greater levels of purchases tied to those prices than elsewhere. Based on this analysis and assumptions, we define two cases: a “Low Share” case, where 50% of energy purchases are based on wholesale prices in regions with 52 Retail price data were collected from http://www.eia.gov/electricity/data/state/avgprice_annual.xls , and wholesale price data were collected from http://www.eia.gov/electricity/wholesale/ 53 A, EMW, NE, SC, TX, and WMW. The AVERT regions with organized wholesale market exchanges are C 41

55 organized markets and 30% elsewhere; and a “High Share” case, where 80% of energy purchases are based . Becau on the wholesale price in regions with organized markets and 50% elsewhere se these are broadly aggregate national results and applied estimates, we focus on do not present results on a regional basis. Several issues related to the methodology and associated limitations, some discussed earlier, deserve note: • It is important to reiterate that the electricity consumer benefit calculated here represents a wealth transfer from electricity producers to consumers. No net societal benefit is claimed. • Though broadly consistent with other studies, assumptions related to the decay factor ar e uncertain. To reflect and bound this uncertainty, w e apply a wide range of assumptions, both for when the 54 decay begins and its duration, yielding a correspondingly wide range of impact estimates. Finally, most electricity consumers are not fully exposed to wholesale price. We address • uncertainty around the degree of exposure by estimating the impacts under a wide range of wholesale electricity market price assumptions about the share of purchases tied to s. R ESULTS Based on these methods, we first estimate -induced wholesale price reduction s prior the magnitude of RPS to any adjustments for decay or wholesale price hedging in the retail market . Across AVERT regions, the unadjusted wholesale price effect ranges from 0.0 to 0.5¢/kWh -load, or from 0.8 to 4.7¢/kWh- RE . The 55 magnitude of this unadjusted effect is within the range suggested by the broader literature. T he variation across regions also aligns with expectations based on market fundamentals. For example, the effe cts of additional renewable generation on wholesale prices are greater in regions with steeper supply curves, which occur when the wholesale electricity market is supplied by many different types of generators with a 56 variety of fuel types and efficiencies. More importantly, after adjustments, the magnitude of aggregate, national consumer savings resulting from wholesale price reductions range from an estimated $0 to $1.2 billion (Figure 7.3). To put these figures in e 7.3, these consumer savings are equivalent to a levelized impact another context, as also shown in Figur 54 The generation -weighted average age of new -weighted average age of existing RPS policies is nearly 12 years and the generation renewable energy delivered for 2013 RPS compliance is just over 4 years. As such, the choice of de cay with RPS Vintage or with RE Project Vintage along with the assumption of whether the wholesale price effect decays over 5, 10, or 20 years greatly imp act the estimate of the wholesale price effect from 2013 RPS generation. 55 For example, IPA (2013) est imated a value of 2.1 ¢/kWh -RE for wind in the Midwest, while our AVERT method estimated 2.5 ¢/kWh -RE for the WMW and EMW region. More generally, a broad literature review conducted by Würzburg et al. (2013) created a common metric of $/MWh -RE per % of renew ables across many studies, primarily from Europe. The median value across -1.0 studies was $0.73/MWh-RE per % RE. Using this value would lead to estimates of 0 -RE ¢/kWh , depending on the region, for the RE penetration levels we are considering. On the other hand, other studies find much larger possible effects. A report on transmission in MISO (Fagan et al. 2012), for example, estimated a price suppression benefit of $7.9 billion for 20 GW of wind or impact of 9.9- -RE. Moreover, Perez et al. (2012) estimate $12.2 billion for 40 GW of wind, implying a wholesale price 12.9¢/kWh -Atlantic region to be around 5.5¢/kWh-RE, comparable to the high end of our range. the wholesale price effect of solar in the mid 56 To understand regional variation in the wholesale price effect, we created a number of supply curves based on data from the Ventyx Velocity Suite for regions that loosely match AVERT regions. These give some indication of the slope of the supply cur ve in each region , between maximum and minimum load. Based on this assessment, the effect is found to be large in the Northeast due in part to high natural gas prices, particularly in Q1 2013, and the high cost for oil units. The supply curve is much steepe r in the Northeast than in other parts of the country, leading to the high wholesale price reduction value. The supply curves in the Southwest, Texas, and Lower Midwest regions, meanwhile, are all relatively flat. In many cases, this is due to a high share o f coal or a large number of efficient CCGTs that have produ ction costs similar to coal plants (at 2013 natural gas prices). The Upper Midwest region, on the other hand, has very low coal prices and a large supply of coal generation, but few efficient CCGTs. T hat means that the supply curve jumps from coal to less efficient CTs or natural gas steam generators relatively quickly, thus leading to a higher effect here than other nearby regions. The California supply curve appears flat due to a large share of natural gas. The Northwest has a substantial portion of coal (with most having a relatively high heat rate of 10 MMBTU/MWh) along with a large number of efficient CCGTs: this generation stack leads to a relatively flat supply curve and a low wholesale price effect. The large California may also play a role in flattening the supply curve. amount of hydropower in the Northwest and 42

56 from RPS programs of 0.0 to 1.2¢/kWh- RE. The high level of uncertainty reflected in these ranges is consistent with the broad range of assumptions used for the decay of price effects and th e portion of retail exposed to wholesale spot market prices. electricity sales Low Share: Billion 2013$ (left axis) Low Share: Billion 2013$ (left axis) High Share: Billion 2013$ (left axis) High Share: Billion 2013$ (left axis) Low Share: cents/kWh-re (right axis) Low Share: cents/kWh-re (right axis) High Share: cents/kWh-re (right axis) High Share: cents/kWh-re (right axis) $1.4 $1.4 1.4 1.4 $1.2 $1.2 1.2 1.2 $1.0 $1.0 1.0 1.0 $0.8 $0.8 0.8 0.8 -renewables) $0.6 $0.6 0.6 0.6 -renewables) $0.4 0.4 $0.4 (Billion 2013$) 0.4 (Billion 2013$) (¢/kWh (¢/kWh $0.2 0.2 $0.2 0.2 Wholesale Price Savings Wholesale Price Savings Wholesale Price Savings Wholesale Price Savings 0.0 $0.0 0.0 $0.0 5 20 10 10 5 20 Duration of Effect (Years) Duration of Effect (Years) RE Project Vintage RPS Vintage 7.3. Estimated impacts of RPS compliance on consumer savings from wholesale price Figure reducti ons depending on start of decay RPS vintage (left) vs. RE project vintage (right), duration of decay, and hedging assumptions If wholesale price impacts begin to decay at the date of RPS enactment (the left -hand panel of Figure 7.3), the aggregate consumer savings are relatively small, given the age of most RPS policies. At the low end, if the decay occurs over only 5 or 10 years, the wholesale price impact is found to be negligible. If the price effect s instead decay over 20 years, the consumer savi ngs are more sizeable, ranging from $0.4 billion to $0.6 billion in 2013, depending on assumptions about the share of customer energy purchased at wholesale . power prices Assuming s begin to decay only once RE projects are built ( the right - , instead, that wholesale price effect hand panel of Figure 7.3 ), the estimated consumer savings are larger, though still uncertain. If the effect lasts for only five years after projects are built, then the wholesale price effect is $0.1 billion to $0.2 billion in 2013. B ut if the effect lasts for 20 years, the consumer benefits from the wholesale price effect can be as large as $0.7 billion to $1.2 billion in 2013, again depending on assumptions about the share of customer energy purchased at wholesale power rates. 43

57 N ATU RAL G AS P RICE R EDUCTION I MPACTS 8. Renewable generation used to meet 2013 RPS compliance obligations reduced demand for natural gas by an estimated 0.42 quads (422 million MMBtu). This reduction is estimated to have lowered average natural gas prices by $0 .05- $0.14/MMBtu in 2013, resulting in consumer savings ranging from $1.3 RE. The range in billion to $3.7 billion. These consumer savings are equivalent to 1.3 to 3.7¢/kWh- estimates reflects two bounding assumptions about when projected natural gas demand reductions begin to affect prices. Importantly, these impacts are best considered wealth transfers from natural gas producers to consumers, rather than net societal benefits. NTRODUCTION I RE’s generation, due to -fueled lower fuel price risk than fossil is frequently noted to impose RE generation reliance primarily on inexhaustible fuels that face little or no price uncertainty. Quantifying the benefits of has a single, standard approach to benefit proven to be controversial, however, and these fuel characteristics quantification has not emerged. Methods that have been used to assess these benefits, as well as the benefits -adjusted discount rates (Awerbuch 1993), Monte of electricity supply diversity more generally, include risk -based portfolio theory (Awerbuch ysis (Wiser and Bolinger 2006), mean variance Carlo and decision anal - -based assessments of the cost of conventional fuel and Berger 2003; Bazilian and Roques 2008), market price hedges (Bolinger et al. 2006), various diversity indices (Stirling 1994; Stirling 2010), comparisons of empirical wind contract prices to gas price forecasts (Bolinger 2013), and estimates of a generation Graves and portfolio’s sensitivity to high and low fuel prices under high renewable penetration scenarios ( Jenkin et al. 2013). Litvinova 2009; While standardized tools for quantifying the benefits of energy supply diversity and fuel risk reduction do be quantified way that can fossil fuel price risks in one not yet exist, increased use of RE does mitigate . Specifically, i n many parts of the methods —accepted d and—with appropriate caveats using recognize -fired generation is commonly the marginal supply resource within the electricity United States, natural gas ts comes online, it therefore tends to sector. As RPS -driven renewable generation with low marginal cos displace gas- fired generation from the bid stack. This displacement results in a reduction in demand for —all else being equal -fired generators, which —should place downward pressure on natural gas among gas 57 natural gas prices (e.g., Wiser and Bolinger 2007). As a result of these natural gas price effects, previous , while RPS programs drive up retail electricity prices in certain cases (Barbose et al. research has found that 2015), these increases could be partially or entirely offset by suppression of natural gas prices (e.g., Fischer 2009). In this section, we estimate the potential effect of state RPS programs on natural gas prices and in all natural gas consumers in natural gas prices benefit ing that reductions related energy bills, recogniz - consuming sectors of the economy . These consumer savings, —that is, not just within the electricity sector however, come at the expense of natural gas produc l states may experience net benefits . While individua ers. —others will experience —e.g., states that consume more natural gas than they produce from these transfers . the opposite. For this reason, we refer to this effect as an impact rather than a benefit 57 Within a simple economics supply/demand framework, this gas displacement can be represented by the demand curve for natural gas shifting inward along the supply curve. Presuming the supply curve has an upward slope and does not change in response to the demand shift, the demand shift will result in a lower price. Although demand for coal within the electricity sector also declines as a results , w e do not analyze potential impacts on coal prices, because the result of increased RE generation, per the earlier AVERT 07). long -term inverse price elasticity of supply is generally thought to be lower for coal than for natural gas (Wiser and Bolinger 20 44

58 ETHODS M Our methods to value the potential natural gas price reductions from state RPS programs in 2013 are generally consistent with the approaches summarized in Wiser et al. (2005) and Wiser and Bolinger (2007) and recently applied in the DOE’s Wind Vision Report (DOE 2015). T hese methods depend on data and assumptions about the amount of natural gas demand reduction, the shape of the natural gas supply curve, -induced and total natural gas demand in the contiguous United States ( Figure 8 .1). The amount of RPS natural gas demand reduction in 2013 is estimated through the AVERT modeling process described in Section 2, while total natural gas demand comes from the EIA. The shape of the supply c urve —measured 58 by its elasticity —is more uncertain. Derive "inverse price elasticity of natural gas supply" curve from the EIA's NEMS -induced Apply elasticity curve to RPS natural gas displacement in 2013 (AVERT) Apply resulting natural gas price change to nationwide gas demand in 2013 (EIA) 8.1. Natural gas price impacts methods overview Figure If the supply curve is steep (or “inelastic”), a demand reduction will cause a larger price reduction than if the supply curve is flatter (or more “elastic”). Because supply is relatively fixed in the near term but has more time to adjust to market conditions over longer periods, supply curves are typically thought to be steeper in the near term than over longer terms. This distinction has important implications for this analysis, ble to renewable generators in 2013 is attributa given that renewable generation serving RPS requirements that have been built (or, alternatively, to RPS policies that have been enacted) over an extended period, ranging from as far back as the 1990s to as recently as 2013. Presuming that gas supply will eventually s-displacing renewable generators (or RPS policies), the older the renewable generator (or adjust to these ga RPS policy), the less of an impact it should have on gas prices in 2013. For this analysis, we use modeling output from the EIA’s National Energy Modeling System (NE MS) to derive an implied inverse elasticity curve for natural gas supply. Figure 8.2 shows two curves derived by 59 gas demand, -modeled scenarios of natural comparing different EIA along with the corresponding smoothed curve used for the rest of this analysis. These inverse elasticity curves conform to the 58 For this analysis, however, anges in response to a change in price (%∆Q/%∆P). Elasticity is a measure of how much demand ch inverse we are more interested in how prices change in response to a change in demand (%∆P/%∆Q), which we refer to as . elasticity 59 b), to characterize (EIA 2015 The comparisons involve two sets of scenarios from the EIA’s Annual Energy Outlook 2015 how changes in gas demand impacted modeled prices. Specifically, we compared the High Economic Growth scenario (i.e., a high gas In Low Economic Growth demand scenario) to the Reference scenario, and also to the scenario (i.e., a low gas demand scenario). nge in both comparisons, the inverse price elasticity of supply starts out relatively high at around 4.0 (implying an initial 4% cha or every 1% change in demand), and declines somewhat smoothly over the next ten years before settling somewhere between price f 0.5 and 1.0. 45

59 expectations laid out above: prices are more responsive in the short term than over the long term. Rather than event ually trending to zero (no price response) over time, however, the inverse elasticity curve -term steady eventually settles on a long -state price impact that is larger than zero, reflecting the fact that 60 natural gas is an exhaustible resource. 5.0 AEO15 High Economic Growth Q) ∆ vs. Reference 4.0 AEO15 High vs. Low Economic P/% 3.0 ∆ Growth Smoothed Estimate of Inverse 2.0 Elasticity 1.0 Inverse Price Elasticity of Supply (% 0.0 2034 2036 2038 2040 2022 2020 2018 2016 2026 2028 2030 2024 2032 8.2. Derived inverse price elasticity of supply curve Figure Next, we apply this derived inverse elasticity curve to the natural gas demand reductions estimated by the AVERT model (and attributed to specific renewab le generators and RPS policies, each of which is associated with a particular year) in two different ways, in order to establish a range of potential gas price reductions in 2013. Under one approach, we assume that enactment of an RPS policy was a sufficiently momentous and transparent occasion to cause gas suppliers to anticipate future demand reductions and begin to adjust supply in response. Alternatively, w e assume that gas suppliers do not begin to adjust supply until each new renewable generator is bu ilt and begins to displace gas- fired generation. The first approach RPS vintage rather than by renewable project vintage— —measuring the price impact by results in a muted natural gas price response in 2013. This is expected because many RPS policies were enacted in the late 1990s, which places them within the lower, steady -state portion of the inverse elasticity 61 curve (see dark blue bars in Figure 8.3 ). The second approach—measuring the price impact by renewable project vintage rather than by RPS vintage—results in a larger price response because even those RPS policies enacted long ago have still, in many cases, driven the addition of new renewable generators through 2013, impacting prices through the higher, steeper portion of the inverse elasticity curve (see the light blue ). bars in Figure 8.3 60 This point is perhaps best understood by considering an increase, rather than decrease, in gas consumption: a unit of exh austible natural gas that is consumed today will not be available for future consumption, and hence drives up the price of all remaini ng natural gas. 61 The inverse elasticity curve shown in Figure 8.3 is the mirror image of that shown earlier in Figure 8.2 , to reflect that 2013 is the end point rather than the starting point of the analysis period. 46

60 5.0 50% Q) ∆ 4.0 40% P/% ∆ 3.0 30% 2.0 20% 1.0 10% Gas Displacement Proportion of 2013 of Supply (% Inverse Price Elasticity 0.0 0% 2002 1996 1995 1994 2001 1999 2013 2012 2011 1998 2010 2009 2008 2007 2006 1997 2000 2003 2004 2005 Proportion of 2013 RPS-induced gas displacement by RPS vintage Proportion of 2013 RPS-induced gas displacement by RE project vintage Application of inverse elasticity curve to 2013 gas displacement (left scale) on under two bounding 8.3. Application of inverse elasticity curve to 2013 RPS generati Figure scenarios (by RPS vintage and by project vintage) he range of natural gas price reductions to nationwide and The final step in the methodology is to apply t 62 -level natural gas demand across all sectors of the economy in 2013. state The result provides an s stemming from natural gas price redu indication, in dollars, of the total size of consumer benefit ction at the sector, state, and national level. limitations, some discussed earlier, deserve A number of issues related to the methodology and associated note: • reiterate that —on a national basis —the consumer benefit calculated here It is important to sents a wealth transfer from gas producers to consumers. While individual states, such as those repre that consume more natural gas than they produce, may experience net benefits from these transfers, others will experience the opposite. nt with estimates gleaned from past literature as well as previous • Though roughly consiste 63 approaches to estimating elasticity, the accuracy of the inverse elasticity curve derived for this analysis is uncertain, both in terms of magnitude as well as the timing and rate of decay . • As described earlier, there is uncertainty over whether the elasticity decay should begin on the date of RPS enactment or once specific renewable generators have been built and have started to displace natural gas. We have bounded this uncertainty by presenting a range of results that are book- ended by these two extreme possibilities. • The estimated gas price reductions are for average nationwide wellhead prices, and it is unclear to what extent changes in national average wellhead price flow through to delivered gas prices in various states and sectors of the economy. Our analysis assumes 100% pass- through to all states and sectors equally, with no differentiation in elasticities or pass -through by region or sector. • Related to the p revious point, some gas consumers may not be fully exposed to “spot” wellhead price contracts for gas supply, price changes, either because they have entered into long -term, fixed- or because they have implemented some type of price hedge (e.g., through the futures market). Our 62 Data on natural gas consumption is provided by the Energy Information Administration. 63 Wiser and Bolinger (2007) reviewed the extant empirical literature for estimates of the elasticity of natural gas supply, but did not find much material and ultimately largely resorted to a similar modeling -based approach as used in this analysis. The model -based elasticity estimates reviewed by Wiser and Bolinger (2007) are consistent with those used here. 47

61 analysis assumes that all consumers (in all states and sectors of the economy) are 100% exposed to 64 wellhead price changes. • Our analysis does not directly account for the possibility of a “rebound effect,” whereby RPS - induced gas pri ce reductions spur additional demand, which in turn boosts gas prices somewhat. Although any such rebound effect would result in less of a price reduction than estimated here, the , because the smaller price reduction wou ld be applicable to a overall dollar impact is less clear larger amount of demand. • Finally, although we present results by state, many commercial and industrial facilities that benefit from lower natural gas prices may be owned by publicly traded corporations whose shareholders reside in dif ferent states than those in which the facilities themselves are located. R ESULTS ompliance with RPS obligations in 2013 reduced Based on the AVERT modeling presented earlier, c -fired generators by an estimated 0.42 quads ( 422 million MMBtu ), demand for natural gas among gas representing 1.6% of total natural gas consumption in the contiguous United States (and 5% of natural gas consumption within the electricity sector). This reduction in gas demand is, in turn, estimated to have reduced natural gas prices by $0.05 to $0.14/MMBtu, depending on whether the decay of natural gas price effects is tied to RPS vintage or RE project vintage, respectively. 65 When applied to all gas -consuming sectors of the economy within the contiguous states, the aggregate consume r savings in 2013 range from $1.3 billion to $3.7 billion (Figure 8.4). Primary beneficiaries include gas consumers within the electricity, industrial, and residential sectors, followed by the commercial sector. se potential price reductions and Importantly, while individual states may exp erience net gains or losses, the consumer savings are likely to be primarily , or even exclusively transfer payments from gas producers (and , those that benefit from gas production, such as owners of mineral rights [through rents] and governments and taxpayers [through taxes]) to gas consumers on a national basis . 64 Given that both gas producers and consumers clearly hedge away gas price risk to some extent, this assumption of “no hedging” is a simplification. That said, long -term, fixed -price contracts are uncommon in the gas indust ry (Bolinger 2013), and most gas price hedging is instead done on a short -term (e.g., 3 months to several years) basis. As these short -term hedges roll off, they can only be re -implemented at prices that reflect interim movements in the spot market price o f gas. In other words, using short -term hedges, one cannot completely insulate oneself over the long -term from spot gas price movements; spot price movements can only be avoided as long as the hedge is in place, and will “catch up” once the hedge rolls off . Given that this analysis focuses on the year 2013, and that the gas price reduction is estimated based either on when RPS policies were enacted or when renewable generato rs achieved commercial operation – either of which is well in advance of 20 13 in mos t cases (see Figure 8.3 ) – it is reasonable to assume that any spot gas price reductions resulting from RPS policies have, by 2013, also been largely reflected in the price of short -term hedges. If so, our simplifying assumption of “no hedging” may not dis tort our results to any great extent. 65 The application of the price reduction to the contiguous states reflects an implicit assumption that these states comprise a single market for natural gas (as opposed to multiple different regional markets) (see Wiser and Bolinger 2007). 48

62 Electricity Sector 4.0 4.0 3.5 3.5 Transport Sector 3.0 3.0 Industrial Sector 2.5 2.5 Commercial Sector 2.0 2.0 -renewables) Residential Sector 1.5 1.5 (Billion 2013$) 1.0 1.0 Pipeline & Distribution Use Natural Gas Savings Natural Gas Savings (¢/kWh 0.5 0.5 Lease & Plant Fuel 0.0 0.0 Total, in cents/kWh- RE Project Vintage RPS Enactment Vintage renewables (right axis) Basis for Elasticity Decay 8.4. Estimated impacts of RPS compliance on natural gas consumer bill savings by sector Figure under two bounding scenarios To put these figures in another context, as also shown in Figure 8.4, consumer savings from reduced natural RE from new RE used to meet RPS requirements in gas prices are equivalent to a value of 1.3 to 3.7¢/kWh- roadly consistent with those reported in Wiser et al. (2005) and Wiser and Bolinger 2013. These values are b (2007). 66 Figure 8.5 breaks out the consumer gas savings by state. Many of the largest state beneficiaries—e.g., Texas, Louisiana, and Pennsylvania—also happen to be large g producing states as well, so those states as- 67 are also impacted by the offsetting negative effect on natural gas producers. 0.6 Elasticity Decay by RE Project Vintage 0.5 Elasticity Decay by RPS Enactment Vintage 0.4 0.3 0.2 (Billion 2013$) Natural Gas Savings 0.1 0.0 IL RI IA FL ID IN NJ SC KS CT AL LA TX KY AZ VT SD DE CA PA MI NE UT TN DC AR WI NY NC VA CO OK OR GA NV ND NH OH MS ME MT WY MA WV MD WA NM MN MO 8.5. Natural gas consumer bill savings by state under two bounding scenario Figure s 66 Note that these results by state could be misleading, as some of the commercial and industrial facilities that reap gas savin gs may be publicly traded and widely held by shareholders across the United States. 67 Though, just as with gas consumers, many gas producers may be publicly traded companies with shareholders located across the United States, in which case attributing lost production revenue to a specific state may also be misleading. 49

63 9. S UMMARY AND C ONCLUSIONS 98 TWh of new renewable electricity generation, In 2013, RPS compliance obligations were met with ricity generation in that year. This renewable generation reduced life representing 2.4% of nationwide elect cycle GHG emissions by 59 million metric tons of CO e; reduced emissions of SO , NO , and PM by 2 2 x 2.5 77,400, 43,900, and 4,800 metric tons, respectively ; and reduced water withdrawals and consumption by 830 billion gallons and 27 billion gallons, respectively. Though there is uncertainty in economic value of these effects, using average values, the monetary value of the GHG and air pollution emissions reduction totals approximately $7.4 billion in 2013, or 7.5¢/kWh -RE, under our central estimates. The monetary values of GHG and air pollution emission reductions span $3.3 billion to $16.2 billion, or 3.3¢/kWh- RE to RE. Monetary quantification of the reductions in water withdraws and consumption was not 16.5¢/kWh- a lack of available data and consistent methodology for such valuation. estimated due to Prior studies summarizing state assessments of RPS costs (Heeter et al. 2014; Barbose et al. 2015) found cost s—that is, the incremental cost net of avoided conventional generation that annual RPS compliance costs—were generally equival ent to less than 2% of average statewide retail electricity rates over the 2010 - 2013 period, but varied substantially, with the net cost to utilities and other load -serving entities ranging 68 -0.4 to 4.8¢ per kilowatt -hour of renewable electricity (kWh -RE). from In aggregate, total RPS compliance costs represented approximately $1 billion per year over the 2010 to 2013 period. These costs are borne by utilities and other LSEs subject to state RPS compliance obligations and are generally passed , at a national level, the study, we find that on to retail e lectricity consumers. Based on analysis in this monetary benefits in 2013 solely from reductions in GHGs, SO outweigh the approximate , NO and PM x 2.5 2 annual costs borne to meet state RPS requirements in that single year . Benefits from reduced water use were societal benefits estimated only in physical, but not monetary, terms for this study, but add further to the This loose comparison notwithstanding, for the reasons discussed in associated with RPS compliance. Section 1, additional research would be necessary to formally and accurately compare the costs of RPS policies with the associated benefits and impacts, and considerable uncertainty and nuance underlies any such comparison. In addition to these health and environmental benefits, RE used to meet 2013 RPS compliance obligations to have supported nearly 200,000 U.S.- based gross jobs and more than $20 billion in GDP. is estimated Average natural gas prices were lowered by $0.05 -$0.14/MMBtu, resulting in consumer savings from $1.3 billion to $3.7 billion, or 1.3 to 3.7¢/kWh- RE. The supply curve for wholesale power was potentially shifted, reducing wholesale electricity prices and yielding consumer savings of $0 to $1.2 billion, or 0.0 to 1.2¢/kWh- RE. The analysis of e ach of these three impacts only quantifies the magnitude of the resource transfer from others without attempting to determine if there are net societal benefits some stakeholders to on a state, national, or global scale from these impacts , such impacts might still be considered . However relevant when evaluating state RPS programs . Figure 9.1 summarizes the benefits and impacts of new RE used to meet 2013 RPS compliance. 68 RPS compliance costs represent the increm ental cost to the utility or other load-serving entity with RPS obligations, net of avoided conventional generation costs. Negative RPS compliance costs may occur when renewable electricity procured to meet nventional generation. RPS obligations is less expensive than avoided co 50

64 Figure 9.1. Benefits and impacts of new RE used to meet 2013 RPS compliance These findings may help to inform evaluations of new or existing RPS programs. That said, it is important to acknowledge that multiple drivers exist for RE additions ; the benefit and impact e stimates presented in this study are therefore not fully attributable to state RPS programs. Moreover, state RPS policies are not necessarily the least -cost approach to achieving the benefits assessed in this report. Although this study does not evaluate individual state RPS programs, the standardized methods applied in . this study may serve as a model for states seeking to evaluate the benefits and impacts of their policies age more specialized tools Individual states, however, may have access to better data or the ability to lever s of the benefits and impacts of their particular RPS policies. that provide more -detailed assessment Depending on the particular benefit or impact, access to more state- specific data or more specialized tools may help to reduce s ome of the uncertainty surrounding the results presented in this report; for example, advanced modeling tools could be used to more more precisely estimate wholesale market price suppression impacts. Finally, state -level tools and methods may enable an assessment of a broader set of benefits, costs, and impacts than covered in the present paper. In order to properly compare benefits with costs, a dditional work is needed to understand the cost that RPS , have shown implications of RPS compliance . Past state- level assessments, using diverse methods 51

65 compliance costs constituted less than 2% of average retail rates in most U.S. states over the 2010 –2013 period, albeit with a sizable range over time and across states , and with data unavailable for a number of key states (Heeter et al. 2014; Barbose et al. 2015). Future work is also needed to understand future Such benefits, impacts, and costs of RPS polices— as currently enacted and as plausibly expanded. prospective analysis could leverage many of the same analy tical tools and methods employed in the present study. 52

66 R EFERENCES rank , and E Stanton. 2012. “Climate Risks and Carbon Prices: Revising the Ackerman, F lizabeth A. -Access, Open Economics: The Open -Journal 6(2012- 10): Social Cost of Carbon.” -Assessment E 1–25. Adelaja, S oji ohannes G. Hailu. 2015. Renewable Energy Development and Implications to , and Y Agricultural Viability. Accessed September 2015. http://ageconsearch.umn.edu/bitstream /6132/2/470566.pdf . AWEA) . 2015. “ AWEA U.S. Wind Industry Annual Market American Wind Energy Association ( Report: Year Ending 2014.” Washington, D.C.: American Wind Energy Association. ———. 2014. “ The Clean Air Benefits of Wind Energy.” W ashington, D.C.: A merican Wind Energy Association. Arent, D , Jacquelyn Pless, T rieu Mai, R yan oug aureen Hand, S am Baldwin, G arvin Wiser, M Heath, J ordan Macknick, M organ Bazilian, A dam Schlosser, and Paul Denholm. 2014. “Implications of High Renewable Electricity Penetration in the US for Water Use, Greenhouse Gas Emissions, Land- Use, and Materials Supply.” Applied Energy 123: 368- 77. Arrow, K., M. Cropper, C. Gollier, B. Groom, G. Heal, R. Newell, W. Nor dhaus, R. Pindyck, W. Pizer, P. Portney, T. Sterner, R.S.J. Tol, and M. Weitzman. 2013. “Determining Benefits and Costs Science –50. for Future Generations.” 341 (6144): 349 and S. Averyt, K. -Lee, A. Lewis, J. Macknick, N. Madden, J. Rogers, Huber , J. Fisher, A. Tellinghuisen. 2011. “ Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource. ” Cambridge, MA: UCSUSA. Averyt, K., J. Macknick, J. Rogers, N. Madden, J. Fisher, J. Meldrum, and R. Newmark. 2013. “Water Use for Electricity i n the United States: An Analysis of Reported and Calculated Water Use Information for 2008.” Environmental Research Letters 8 (015001). Awerbuch, S. 1993. “The Surprising Role of Risk in Utility Integrated Resource Planning.” 6(3): The Electricity Journal P. 20–33. doi: 10.1016/1040- 6190(93)90048- Awerbuch, S., and M. Berger. 2003. Applying Portfolio Theory to EU Electricity Planning and -Making . IEA/EET Working Paper EET/2003/03. Paris: International Energy Agency. Policy Bamufleh, Hisham, Jose Ponce , and Mahmoud El -Halwagi. 2013. "Multi -Objective -Ortega Optimization of Process Cogeneration Systems with Economic, Environmental, and Social Tradeoffs." Clean Technologies and Environmental Policy 15 (1): 185- 97. Barbose, G . 2015. “Renewables Portfolio Standards in the United States: A Status Update.” alen Presented at the 2015 National RPS Summit, Washington, D .C. Barbose, G ., L . Bird, J . Heeter, F. Flores, and R. Wiser. 2015. "Costs and Benefits of Renewables . Portfolio Standards in the United States." Renewable and Sustainable Energy Reviews 52: 523- 33 53

67 Barbose, G., R. Wiser, A. Phadke, and C. Goldman. 2008. “Managing Carbon Regulatory Risk in Energy Policy Utility Resource Planning: Current Practices in the Western United States.” 36(9): 3300–11. doi:10.1016/j.enpol.2008.04.023. Bazilian, M., and F. Roques, eds. 2008. Analytical Methods for Energy Diversity & Security. Elsevier Global Energy Policy and Economics Series. Oxford: Elsevier. Bell M. L., A. McDermott, S. L. Zeger, J. M. Samet, and F. Dominici. 2004. “Ozone and Short - Term Mortality in 95 US Urban Communities, 1987–2000.” Journal of the American Medical Association 292(19): 2372–8. Billman, L., and D. Keyser. 2013. Assessment of the Value, Impact, and Validity of the Jobs and Economic Development Impacts (JEDI) Suite of Models . NREL/TP -6A20 -56390. National Renewabl e Energy Laboratory, Golden, CO (US). Data for H1 2015 North American PV Outlook.” Bloomberg New Energy Finance (BNEF). 2015. “ rf. 2013. “Are Green Hopes Too Böhringer, Christoph, Andreas Keller, and Edwin van der We Rosy? Employment and Welfare Impacts of Renewable Energy Promotion.” Energy Economics 36(March): 277 –85. Böhringer, Christoph, Nicholas J. Rivers, Thomas F. Rutherford, and Randall Wigle. 2012. “Green Jobs and Renewable Elect ricity Policies: Employment Impacts of Ontario’s Feed -in Tariff.” The B.E. Journal of Economic Analysis & Policy 12(1). Bokenkamp, K ., H. LaFlash, V. Singh, and D. Bachrach Wang. 2005. “Hedging Carbon Risk: Protecting Customers and Shareholders from the F inancial Risk Associated with Carbon Dioxide Emissions.” 18(6): 11–24. doi:10.1016/j.tej.2005.05.007. The Electricity Journal Bolinger, M. 2013. Revisiting the Long- Term Hedge Value of Wind Power in an Era of Low Natural Gas Prices . LBNL- 6103E. Lawrence Be rkeley National Laboratory , Berkeley, CA . Bolinger, M., and J. Seel . 2015. Utility -Scale Solar 2014: An Empirical Analysis of Project Cost, . Berkeley, CA: Lawrence Berkeley National Performance, and Pricing Trends in the United States Laboratory. Bolinger, M., R. Wiser, and W. Golove. 2006. “Accounting for Fuel Price Risk When Comparing Renewable to Gas -Fired Generation: The Role of Forward Natural Gas Prices.” Energy Policy 34(6): 706–20. doi: 10.1016/j.enpol.2004.07.008. Borenstein, Severin. 2012. “The Private and Public Economics of Renewable Electricity Generation.” Journal of Economic Perspectives 26 (1): 67 –92. Bowen, William M., Sunjoo Park, and Joel A. Elvery. 2013. “Empirical Estimates of the Influence of Renewable Energy Portfolio Standards on the Green Economies of States.” Economic 51. Development Quarterly 27(4 ): 338- 54

68 Breitschopf, Barbara, Carsten Nathani, and Gustav Resch. 2011. “ Review of Approaches for ” The Netherlands: IEA Employment Impact Assessment of Renewable Energy Deployment. TD. RE Brinkman, G., J. Jorgenson, J. Caldwell, A. Ehlen. 2015. The California 2030 Low Carbon Grid . NREL -TP -6A20- Study l Renewable Energy Laboratory, Golden, CO (US). 64884. Nationa Brown, J ., J. Pender , R. Wiser, E. Lantz, B. Hoen. 2012. “ Ex Post Analysis o f Economic Impacts from Wind Power Development in US Counties.” Energy Economics 34(6): 1743- 54. Buonocore, Jonathan J., P Luckow, G regory Norris, J ohn D. Spengler, B ruce Biewald, atrick Jeremy Fisher, and J onathan I. Levy. 2015. “Health and Climate Benefits of Different Energy - Efficiency and Renewable Energy Choices.” Nature Climate Change (August). doi:10.1038/nclimate2771. Bushnell, J. 2010. “Building Blocks: Investment in Renewable and Non- Renewable Technologies.” In Harnessing Renewable Energy in Electric Power Systems: Theory, Practice, . Washington, D.C.: RFF Press. Policy Bushnell, J., C. Peterman, and C. Woldfram. 2007. California’s Greenhouse Gas Policies: Local Berkeley, CA : UC Energy Institute. Solutions to a Global Problem? Cai, H., M. Wang, A. Elgowainy, and J. Han. 2013. Updated Greenhouse Gas and Criteria Air . Argonne National Pollutant Emission Factors of the U.S. Electric Generating Units in 2010 Laboratory, Lemont, IL (US). ———. 2012. Updated Greenhouse Gas and Criteria Air Pollutant Emission Factors and Their Probability Distribution Functions for Electric Generating Units . ANL/ESD/12 -2. Argonne National Laboratory , Lemont, IL (US) . http://www .osti.gov/scitech/biblio/1045758 . Location, Location, Location: The Variable Callaway, D., M. Fowlie, and G. McCormick. 2015. Value of Renewable Energy and Demand Berkeley, CA: Energy -Side Efficiency Resources. Institute at Haas. Caperton, R. 2012. Renewable Energy Standards Deliver Affordable, Clean Power. Washington, D.C .: Center for American Progress. Carley, S. 2011. “Decarbonization of the U.S. Electricity Sector: Are State Energy Policy Portfolios the Solutions?” Energy Economics 33: 1004- 23. ———. 2009. “State Renewable Energy Electricity Policies: An Empirical Evaluation of Effectiveness.” Energy Policy 37(8) (August): 3071–81. Carley, S. , and C. Miller. 2012. “Regulatory Stringency and Policy Drivers: A Reassessment of 40(4): 730- 56. Renewable Portfolio Standards.” Policy Studies Journal 55

69 Center for the New Energy Economy (CNEE). 2015. “ Summary of State Renewable Portfolio Advanced Energy Legislation Tracker. Ft. Collins, CO: Standard Legislation in 2015.” Colorado State University. Chandel, M.K., L. F. Pratson, and R. B. Jackson. 2011. "The Potential Impacts of Climate -Change Energy Policy 39: 6234–42. Policy on Freshwater Use in Thermoelectric Power Generation." Weighing the Costs and Benefits of State Re Chen, C., R. Wiser, and M. Bolinger. 2007. newables -Level Policy Impact Projections . LBNL- Portfolio Standards: A Comparative Analysis of State , Berkeley, CA . 61590. Lawrence Berkeley National Laboratory Chien, Taichen, and Jin- Li Hu. 2008. “Renewable Energy: An Efficient Mechanism to Improve GDP.” Energy Policy 36(8): 3045–52. Clemmer, S. Rogers, S. Sattler, J. Macknick, and T. Mai. 2013. "Modeling Low -Carbon US , J. Electricity Futures to Explore Impacts on National and Regional Water Use." Environmental Research Letters 8: 015004. Croucher, M. 2012. "Which State is Yoda?" Energy Policy 42: 613- 5. Cullen, J. 2013. “Measuring the Environmental Benefits of Wind- Generated Electricity.” American Economic Journal: Economic Policy 5 (4): 107–33. doi:10.1257/pol.5.4.107. Dockery , D. W., C. A. Pope, X. Xu, J . D. Spengler, J. H. Ware, M. E. Fay , B. G. Ferris, and F. E. The New Speizer. 1993. “An Association Between Air Pollution and Mortality in Six U.S. Cities.” England Journal of Medicine : 1753− 9. 329 DOE (Department of Energy). 2015. Wind Vision: A New Era for Wind Power in the United States. DOE/GO- 102015 -4557. U.S. Department of Energy , Washington, D.C . ———. 2013. . U.S. Energy Sector Vulnerabilities to Climate Change and Extreme Weather DOE/PI -0013. U.S. Department of Energy , Washington, D.C . Driscoll, C. T., J. J. Buonocore, J. I. Levy, K. F. Lambert, D. Burtraw, S. B. Reid, H. Fakhraei , and Benefits.” J. Schwartz . 2015. “US Power Plant Carbon Standards and Clean Air and Health Co- Nature Climate Change . DOI: 10.1038/NCLIMATE2598. Eastin, L. 2014. “An Assessment of the Effectiveness of Renewable Portfolio Standards in the United States.” The Electricity Journal 27(7):126- 37. Edenhofer, Ottmar, Lion Hirth, Brigitte Knopf, Michael Pahle, Steffen Schlömer, Eva Schmid, and Falko Ueckerdt. “On the Economics of Renewable Energy Sources.” Energy Economics 40, Supplement 1 (December): S12 –S23. Energy Information Administration (EIA). 2015a . Electric Power Annual . Washington, D .C. : EIA. EIA. ———. 2015b. Annual Energy Outlook 2015 . DOE/EIA 0383(2015) . Washington, D.C.: 56

70 EPA (Environmental Protection Agency). 2015a. Carbon Pollution Emission Guidelines for . Washington, D.C.: Environmental Existing Stationary Sources: Electric Utility Generating Units Protection Agency. ———. 2015b. Standards of Performance for Greenhouse Gas Emissions from New, Modified, and Reconstructed Stationary Sources: Electric Utility Generating Units. Washington, D.C.: Environmental Protection Agency. ———. 2015c. Database of Operational LFG Energy Projects . Landfill Methane Outreach Program. shington D .C.: EPA . Wa http://www.epa.gov/lmop/projects -candidates/operational.html . ———. 2015d. Climate Change in the United States: Benefits of Global Action . Washington, D.C.: Environmental Protection Agency. Docket ID ———. 2015e. Regulatory Impact Analysis for the Clean Power Plan Final Rule. EPA 003. Research Triangle Park, NC: U.S. Environmental Protection Agency. -15- -452/R Air Markets Program Data (AMPD). ” Assessed July 2015. ———. 2015f. “ . http://ampd.epa.gov/ampd/ ———. 2014a. AVoided Emissions and geneRation Tool (AVERT) User Manual, Version 1.2 . Washington, D.C.: Environmental Protection Agency. ———. 2014b. -Benefits Risk Assessment (COBRA) Screening Model User’s Manual for the Co Version: 2.61. Washington, D .C .: U.S. Environmental Protection Agency. ———. 2013a. Technical Support Document, Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors . Research Triangl e Park, NC: U.S. Environmental Protection Agency. . Emissions, Compliance, and Market Analyses and NO Progress Report: SO ———. 2013b. 2 X Washington, D .C .: U.S. Environmental Protection Agency. http://www.epa.gov/airmarkets/progress/ progress -reports.html. ———. 2011. Environmental and Economic Benefits Analysis for Proposed Section 316(b) Existing Facilities Rule . EPA 821- R-11- 002. Washington, D.C.: EPA. atrick Fagan, B ob, M ax Chang, P Hausman, and Knight, M elissa Schultz, T yler Comings, E zra Wilson. 2012. “The Potential Rate Effects of Wind Energy and Transmission in the Rachel Midwest ISO Region.” Cambridge, MA: Synapse Energy Economics, Inc. Accessed November 17, -Rate - http://cleanenergytransmission.org/wp -content/uploads/2012/05/Full -Report -The -Potential 2015. . -of-Wind- Energy- and -Transmission -in-the -Midwest -ISO -Region.pdf Effects Fagan, B., P. Luckow, D. White, and R. Wilson. 2013. “ The Net Benefits of Increased Wind Power in PJM.” Cambridge, MA: Synapse Energy Economics, Inc. Accessed November 17, 2015. www.synapse -energy.com/Downloads/SynapseReport.2013- 05.EFC.Increased -Wind -Power -in-PJM.12- 062.pdf . 57

71 Fann Baker, and C. M. Fulcher. 2012. “Characterizing the PM2.5- Related Health , N., K. R. Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across the U.S.” 49: 141–51. Environment International /dx.doi.org/10.1016/j.envint.2012.08.017 . http:/ Felder, F.A. 2011. “Examining Electricity Price Suppression Due to Renewable Resources and The Electricity Journal 24(4): 34 –46. doi:10.1016/j.tej.2011.04.001. Other Grid Investments.” Fell, H. , and J. Linn. 2013. “Renewable Electricity Policies, Heterogeneity, and Cost Effectiveness.” Journal of Environmental Economics and Management 66(3): 688–707. Fischer, C. 2009. “Renewable Portfolio Standards: When Do They Lower Energy Prices?” 30(4): 81- 99. The Energy Journal Fischer, C. , and R.G. Newell. 2008. “Environmental and Technology Policies for Climate Mitigation.” Journal of Environmental Economics and Management 55(2): 142–62. Fripp, M. 2011. “Greenhouse Gas Emissions from Operating Reserves Used to Backup Large - Scale Wind Power.” Environmental Science & Technology 45(21): 9405–12. Frondel, Manuel, Nolan Ritter, Christoph M. Schmidt, and Colin Vance. 2010. “Economic Impacts from the Promotion of Renewable Energy Technologies: The German Experience.” Energy Policy 38(8): 4048–56. GAO (Government Accountability Office). 2014. Regulatory Impact Analysis: Development of GAO- 663. Washington, D.C.: Government Accountability Social Cost of Carbon Estimates. 14- Office. GE Energy Consulting. 2014. “ PJM Renewable Integratio n Study.” Schenectady, NY: General Electric Energy Consulting Group. Report for PJM Interconnection. http://www.pjm.com/committees -and -groups/task -forces/irtf/pris.aspx . Gil, H.A., and J. Lin. 2013. “Wind Power and Electricity Prices at the PJM Market.” IEEE Transactions on Power Systems 28(4): 3945–53. doi:10.1109/TPWRS.2013.2260773. Gillingham, Kenneth, and Jim Sweney. 2010. “Market Failure and the Structure of Externaliti es.” In Harnessing Renewable Energy . Washington D.C.: RFF Press. Göransson, L., and F. Johnsson. 2009. “Dispatch Modeling of a Regional Power Generation Integrating Wind Power.” Renewable Energy 34(4): 1040–49. System - doi:10.1016/j.renene.2008.08.002. Gr aff Zivin, J.S., M.J.Kotchen, and E. Mansur. 2014. “Spatial and Temporal Heterogeneity of Marginal Emissions: Implications for Electric Cars and Other Electricity -Shifting Policies .” Journal of Economic Behavior & Organization 107: 248- 68. Graves, F., and J. Litvinova. 2009. “Hedging Effects of Wind on Retail Electricity Supply Costs .” 55. The Electricity Journal 22 (10): 44- 58

72 Gross, R., P. Heptonstall, D. Anderson, T. Green, M. Leach, The Costs and and J. Skea. 2006. An Assessment of the Evidence on the Costs and Impacts of Intermittent Impacts of Intermittency: . U K Energy Research Centre. London: Imperial Generation on the British Electricity Network College London. , and SEIA. 2015. U.S. Solar Market Insight 2014 Year GTM Research . Boston, MA: -in-Review GTM Research and Solar Energy Industries Association. Haerer, D., and L. Pratson. 2015. “Employment Trends in the U.S. Electricity Sector, 2008- 2012.” Energy Policy 82: 85- 98. Heeter, J., G. Barbose, L. Bird, S. Weaver, F. Flores, K. Kuskova -Bu rns, and R. Wiser. 2014. A Survey of State . National -Level Cost and Benefit Estimates of Renewable Portfolio Standards Renewable Energy Laboratory , Golden, CO . Heeter, J., K. Belyeu, and K. Kuskova -Burns. 2014. Status and Trends in the U.S. Voluntary Green Power Market (2013 Data) . National Renewable Energy Laboratory , Golden, CO . Hillebrand, Bernhard, Hans Georg Buttermann, Jean Marc Behringer, and Michaela Bleuel. 2006. “The Expansion of Renewable Energies and Employment Effects in Germany.” Energy Policy 34(18): 3484–94. Holt, L., and M. Galligan. 2013. “States’ RPS Policies: Serving the Public Interest?” The Electricity Journal 26(10): 16 –23. ick , John Rosenkranz, Ron Denhardt, Elizabeth A. Hornby, R White , P aul Chernick, D avid Stanton, Jason Gifford, B ob Grace, Max Chang, Patrick Luckow, Thomas Vitolo, Patrick Knight, Ben Griffiths, and Bruce Biewald. 2013. “Avoided Energy Supply Costs in New England: 2013 Report.” Cambridge, MA: Synapse Energy Economics, Inc. http://www.synapse - energy.com/sites/default/files/SynapseReport.2013 -07.AESC_.AESC -2013.13- 029- Report.pdf . Hornby, Rick, P aul Chernick, C arl Swanson, David White, Jason Gifford, Max Chang, Nicole Hughes, Matthew Wittenstein, Rachel Wilson, and Bruce Biewald. 2011. “Avoided Energy Supply Costs in New England: 2011 Report.” Cambridge, MA: Synapse Energy Economics, Inc. -energy.com/sites/default/files/SynapseReport.2011- http://www.synapse -Study- 07.AESC_.AESC 2011.11 - 014.pdf . Illinois Power Authority (IPA). 2013. “Annual Report: The Costs and Benefits of Renewable Re source Procurement in Illinois Under the Illinois Power Agency and Illinois Public Utilities Acts.” Chicago, IL: Illinois Power Authority. http://www.illinois.gov/ipa/Do cuments/201304- IPA -Renewables- Report.pdf . Intergovernmental Panel on Climate Change (IPCC). 2014a. Climate Change 2014: Mitigation of Climate Change: Working Group III Contribution to the Fifth Assessment Report of the IPCC . rsity Press. Cambridge, UK: Cambridge Unive 59

73 ———. 2014b. Climate Change 2014: Impacts, Adaptation, and Vulnerability: Working Group II . Cambridge, UK: Cambridge University Contribution to the Fifth Assessment Report of the IPCC Press. ———. 2013. Climate Change 2013: The Physical Science Basis: Working Group I Contribution . Cambridge, UK: Cambridge University Press. to the Fifth Assessment Report of the IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation ———. 2011. (SRREN). Cambridge, UK: Cambridge University Press. IWG. 2015. Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866. July 2015 Revision. Washington D.C.: Interagency Working Group on S ocial Cost of Carbon. ———. 2010. Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 . Washington D.C.: Interagency Working Group on Social Cost of Carbon. Jenkin, T., V. Diakov, E. Drury, B. Bush, P. Denholm, J. Milford, D. Arent, R. Margolis, and R. Byearne . 2013. The Use of Solar and Wind as a Physical Hedge against Price Variability Within a Generation Portfolio . NREL/TP -6A20- 59065. National Renewable Energy Laboratory , Golden, CO . Dayton M. enard, Burton C. English, Wan Xu. 2010. Jensen, Kimberly L., Lambert, R. Jamey M “Projected Economic Impacts of Green Jobs Development in the Appalachian Region. ” Department of Agricultural & Resource Economics Institute of Agriculture. Knoxville, TN: The Univers ity of Tennessee. Accessed June 2015 . http://beag.ag.utk.edu/pub/GreenJobsImpactARC2.pdf . Johnson, E.P. 2014. “The Cost of Carbon Dioxide Abatement from State Renewable Portfolio .” Resource and Energy Economics 36(2): 332–50. Standards Johnson, J., and J. Novacheck. 2015a. “Emissions Reductions from Expanding State -Level Renewable Portfolio Standards.” Environmental Science & Technology 49: 5318- 5325. Johnson, J., and J. Novacheck. 2015b. “Environmental Benefits of Renewable Portfolio Standards in an Age of Coal Plant Retirements.” 28(8): 59- 68. The Electricity Journal Johnson, Laurie T., and Chris Hope. 2012. “The Social Cost of Carbon in U.S. Regulatory Impact Analyses: An Introduction and Critique.” Journal of Environmental Studies and Sciences 2(3): 205–21. Johnson, L., S. Yeh, and C. Hope. 2013. “The Social Cost of Carbon: Implications for Modernizing Our Electricity System.” Journal of Environmental Studies and Sciences 3 (4): 369– 75. Kaffine, D., B. McBee , and J. Lieskovsky. 2013. “Emissions Savings from Wind Power 75. doi: 10.5547/01956574.34.1.7. 34(1): 155- Generation in Texas.” The Energy Journal 60

74 Kalkuhl, M., O. Edenhofer, and K. Lessman. 2013. “Renewable Energy Subsidies: Second- Best Resource and Energy Economics Policy or Fatal Aberration for Mitigation?” 35(3): 217–34. ———. 2012. “Learning or Lock- In: Optimal Technology Policies to Support Mitigation.” Resource and Energy Economics 34(1): 1 –23. , P. O., J. Decarolis, an d S. Thorneloe. 2009. “Is It Better To Burn or Bury Waste for Clean Kaplan Environmental Science & Technology Electricity Generation?” 7. 43: 1711– , N. L. Barber, S. S. Hutson, K. S. Linsey, J. K. Kenny, J. F. Lovelace, M. A. Maupin. 2009. Estimated Use of Water in the United States in 2005 . No. CIR - 1344. Reston, VA: United States Geological Survey. Kopp, R. E., A. Golub, N. O. Keohane , and C. Onda. 2012. “The Influence of the Specification of Climate Change Damages on the Social Cost of Carbon.” Economics: The -Access, Open - Open -Journal Assessment E -ejournal.ja.2012- 13. . doi:10.5018/economics Kopp, R.E., and B.K. Mignone. 2012. “The U.S. Government’s Social Cost of Carbon Estimates after Their First Two Years: Pathways for Improvement.” Economics: The Open- Access , Open - Assessment E -Journal . doi:10.5018/economics -ejournal.ja.2012- 15. Krewski , Daniel , Michael Jerrett, Richard T. Burnett, Renjun Ma, Edward Hughes, Yuanli Shi, Michelle C. Turner, C. Arden Pope III, George Thurston, Eugenia E. Calle, Michael J. Thun, Bernie Beckerman, Pat DeLuca, Norm Finkelstein, Kaz Ito, D. K. Moore, K. Bruce Newbold, Tim Zev Ross, Hwashin Shin, and B arbara Tempalski. 2009. “Extended Follow Ramsay, -Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Po llution and Mortality.” Resear ch report 140. Boston, MA: Health Effects Institute . http://pubs.healtheffects.org/view.php?id=315 . Lamont, A. D. 2008. “Assessing the Long- ermittent Electric Generation Term System Value of Int Technologies.” Energy Economics 30(3): 1208–31. doi:10.1016/j.eneco.2007.02.007. Lehr, Ulrike, Christian Lutz, and Dietmar Edler. 2012. “Green Jobs? Economic Impacts of Renewable Energy in Germany.” Energy Policy 47 (August): 358–64. Lehr, Ulrike, Joachim Nitsch, Marlene Kratzat, Christian Lutz, and Dietmar Edler. 2008. Energy Policy “Renewable Energy and Employment in Germany.” 36(1): 108 –17. Leon, W. 2015. Clean Energy Champions: The Importance of State Programs and Policies . Montpelier, VT: Clean Energy States Alliance. Lepeule, J., F. D. Dockery, and J. Schwartz. 2012. “Chronic Exposure to Fine Particles and Laden, Mortality: An Extended Follow -Up of the Harvard Six Cities Study from 1974 to 2009.” Environmental Health Perspectives 120: 965−70. Levy, J. I., S. M. Chemerynski, and J. A. Sarnat. 2005. “Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analysis.” Epidemiology 16( 4): 458–68. . ://www.ncbi.nlm.nih.gov/pubmed/15951663 http 61

75 Lew, D., G. Brinkman, E. Ibanez, A. Florita, M. Heaney, B.- M. Hodge, M. Hummon, G. Stark, J. , and S. A. Lefton. 2013. . NREL/TP - King The Western Wind and Solar Integration Study Phase 2 5500- 55588. National Renewabl e Energy Laboratory , Golden, CO . Lim, Stephen S., Theo Vos, Abraham D. Flaxman, Goodarz Danaei, Kenji Shibuya, Heather -Rohani, Mohammad A. AlMazroa, Markus Amann, H. Ross Anderson, Kathryn G. Adair Andrews, Martin Aryee, Charles Atkinson, Loraine J. Bacchus, Adil N. Bahalim, Kalpana Balakrishnan, John Balmes, Suzanne Barker -Collo, Amanda Baxter, Michelle L. Bell, Jed D. Blore, Fiona Blyth, Carissa Bonner, Guilherme Borges, Rupert Bourne, Michel Boussinesq, Michael Brauer, Peter Brooks, et al . 2010. “A Comparative Risk Assessment of Burden of Disease and Injury Attributable to 67 Risk Factors and Risk Factor Clusters in 21 Regions, 1990−2010: A Systematic Analysis for the Global Burden of Dise ase Study.” The Lancet 380: 2224− 60. Luckow, P., E. Stanton, S. F , and R. Wilson. 2015. 2015 ields, B. Biewald, S. Jackson, J. Fisher Carbon Dioxide Price Forecast . Cambridge, MA: Synapse Energy Economics, Inc. Luderer, G unnar , R obert C. Pietzcker, Christoph Bertram, E lmar Kriegler, M alte Meinshausen , and O ttmar Edenhofe r. 2013. “Economic Mitigation Challenges: How Further Delay Closes the Door for Achieving Climate Targets.” Environmental Research Letters 8(3): 034033. . "Operational Water Consumption Macknick, J., R. Newmark, G. Heath, and K. C. Hallett. 2012a and Withdrawal Factors for Electricity Generating Technologies: A Review of Existing Literature." 7: 045802. doi:10.1088/1748- 9326/7/4/045802. Environmental Research Letters K. Averyt, S. Clemmer , and J. Rogers. 2012b. "The Water Implications of Macknick, J., S. Sattler, Generating Electricity: Water Use Across the United States Based on Different Electricity Pathways Through 2050." Environmental Research Letters 7: 045803. doi:10.1088/1748- 9326/7/4/045803. Marques, António Cardoso, and José Alberto Fuinhas. 2012. “Is Renewable Energy Effective in Promoting Growth?” Energy Policy 46(0): 434–42. Maupin, M. A., J. F. Kenny, S. S. Hutson, J. K. Lovelace, N. L. Barber, and K. S. Linsey. 2014. Estimated Use of Water in the United States in 2010. Circular 1405. Washington, D.C.: U.S. Geological Survey McCubbin, D., and B.K. Sovacool. 2013. “Quantifying the Health and Environmental Benefits of Wind Power to Natural Gas.” Energy Policy 53(February): 429–41. doi:10.1016/j.enpol.2012.11.004. McKibbin, W.J., A.C. Morris, and P.J. Wilcoxen. 2014. “Pricing Carbon in the U.S.: A Model - Based Analysis of Power -Sector -Only Approaches.” Resource and Energy Economics 36(1): 130– 50. Meldrum, J., S. Nettles -Anderson, G. Heath, and J. Macknick . 2013. “ Life Cycle Water Use for Electricity Generation: A Review and Harmonization of Literature Estimates. ” Environmental 8: 015031. Research Letters 62

76 Melillo, J.M., T.C. Richmond, and G.W. Yohe. 2014. Climate Change Impacts in the United ational Climate Assessment States: The Third N . Washington D.C.: U.S. Global Change Research Program. Menegaki, Angeliki N. 2011. “Growth and Renewable Energy in Europe: A Random Effect Model with Evidence for Neutrality Hypothesis.” Energy Economics 33(2): 257–63. dric C., and Stephan Vachon. 2006. “The Effectiveness of Different Policy Regimes for Menz, Fre Promoting Wind Power: Experiences from the States . Energy Policy 34( 14) (September): 1786– ” 96. Meyer, I na, and Mark Wolfgang Sommer. 2014. Employment Effects of Renewable Energy Supply – A Meta Analysis . Document SSH.2011.1.2- 1. Vienna, Austria: WWWforEurope. Michaels, R.J. 2008a. “A National Renewable Portfolio Standard: Politically Correct, Economically Suspect.” 21 (3): 9 -28. The Electricity Journal The Electricity Journal ———. 2008b. “Renewable Portfolio Standards: Still No Good Reasons.” 21(8): 18- 31. Mills, S arah B. , B arry G. Rabe, and C hristopher Borick. 2015. “ Widespread Public Support for Renewable Energy Mandates Despite Proposed Roll backs. ” Issues in Energy and Environmental 22 (June). Policy Morey, M.J., and L.D. Kirsch. 2013. “Retail Rate Impacts of State and Federal Electric Utility Policies.” 26(3): 35 –49. The Electricity Journal Morris, Adele C., Pietro S. Nivola, and Charles L. Schultze. 2012. “Clean Energy: Revisiting the Challenges of Industrial Policy.” 34, Supplement 1 (0): S34–S42. Energy Economics Muller N. Z., R. Mendelsohn, and W. Nordhaus. 2011. “Environmental Accounting for Pollution Amer ican Economic Review 101( in the United States Economy.” 5): 1649–75. DOI: 10.1257/aer.101.5.1649. National Renewable Energy Laboratory. 2012. Renewable Electricity Futures Study . NREL/TP - 6A20- 52409. Golden, CO: National Renewable Energy Laboratory. National Research Council (NRC). 2010. “ Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use.” Washington, D .C .: National Academies Press. http://www.nap.edu/catalog/12794/ hidden- -of-energy -unpriced- consequences -of- energy - costs production- and. Navigant. 2013. Offshore W ind Market and Economic Analysis: Annual Market Assessment. DE - EE0005360. Prepared for the U.S. Department of Energy. Burlington, MA: Navigant. Nordhaus, W.D. 2013. The Climate Casino: Risk, Uncertainty, and Economics for a Warming Yale University Press. World . New Haven, CT: 63

77 Novacheck, J., and J. Johnson. 2015. “The environmental and cost implications of solar energy Energy Policy 261. preferences in Renewable Portfolio Standards.” 86: 250- Novan, K. 2014. Valuing the Wind: Renewable Energy Policies and Air Pollution Avoided. Working paper. Davis, CA : University of California, Davis. Oates, D. L., and P. Jaramillo. 2013. “Production Cost and Air Emissions Impacts of Coal Cycling -Scale Wind Penetration.” in Power Systems with Large 8(2): Environmental Research Letters 024022. doi: 10.1088/1748- 9326/8/2/024022. Pehnt, M., M. Oeser, and D.J. Swider. 2008. “Consequential Environmental System Analysis of Expected Offshore Wind Electricity Production in Germany.” Energy 33(5): 747–59. Perez, R., B.L. Norris, and T.E. Hoff. 2012. “The Value of Solar to New Jersey and Pennsylvania.” Napa, CA: Clean Power Research. http://asrc.albany.edu/peopl e/faculty/perez/2012b/MSEIA.pdf . Perez -Arriaga, I. J. , and C. Batlle. 2012. “Impacts of Intermittent Renewables on Electricity Economics of Energy & Environmental Policy 1(2). Generation System Operation.” doi:10.5547/2160- 5890.1.2.1. Pindyck, R. S. 2013. “Climate Change Policy: What Do the Models Tell Us?” Journal of Economic Literature 51(3): 860–72. Pizer, W illiam, M atthew Adler, J oseph Aldy, D avid Anthoff, Maureen Cropper, K enneth Gillingham, M ichael Greenstone, Brian Murray, Richard Newell, Richard Ri chels, Arden Rowell, Stephanie Waldhoff, and Jonathan Wiener . 2014. “Using and Improving the Social Cost of Science 346(6214): 1189–90. Carbon.” Allowances: 2013. Potomac Economics. 2014 . Annual Report on the Market for RGGI CO 2 Prepared for RGGI Inc. Fai rfax, VA: Potomac Economics. Rabe, B. 2008. “States on Steroids: The Intergovernmental Odyssey of American Climate Policy.” Review Policy Research 25(2): 105- 28. Rausch, S., and V. Karplus. 2014. “Markets V l ersus Regulation: The Efficiency and Distributiona Impacts of U.S. Climate Policy Proposals.” The Energy Journal 35(SI1):199- 227. Rivers, Nicholas. 2013. “Renewable Energy and Unemployment: A General Equilibrium Analysis.” Resource and Energy Economics 35(4): 467–85. Clemmer, M. Rogers, J., K. Averyt, S. Flores Davis, F. -Lopez , D. Kenney, J. Macknick, N. Madden, J. Sattler, E. Spanger -Siegfried, and D. Yates . 2013. “ Water -Smart Power: Meldrum, S. Strengthening the U.S. Electricity System in a Warming World. ” Cambridge, MA: UCSUSA. Roy, S.B., L. Chen, E. H. Girvetz, E. P. Maurer, W. B. Mills, and T. M. Grieb . 2012. "Projecting Water Withdrawal and Supply for Future Decades in the U.S. under Climate Change Scenarios." 46: 2545– 56. doi:10.1021/es2030774. Environmental Science & Technology 64

78 Sáenz de Miera , P.R. Gonzalez, and I. Vizcaino. 2008. “Analysing the Impact of Renewable , G. Energy Electricity Support Schemes on Power Prices: The Case of Wind Electricity in Spain.” 36(9): 3345–59. doi:10.1016/j.enpol.2008.04.022. Policy Sarzynski, J., and G. Shrimali. 2012. “The Impact of State Financial Incentives on Market Energy Policy 46: 550- 7. Deployment of Solar Technology (in the US).” Schmalensee R. , and R. N. Stavins. 2013. “The SO2 Allowance Trading System: The Ironic History of a Grand Policy Experiment.” Jou rnal of Economic Perspectives 27( 1): 103–22. https://www.aeaweb.org/articles.php?doi=10.1257/ jep.27.1.103. Sensfuß, F., M. Ragwitz, and M. Genoese. 2008. “The Merit -Order Effect: A Detailed Analysis of the Price Effect of Renewable Electricity Generation on Spot Market Prices in Germany.” Energy Policy 36(8): 3086–94. doi:10.1016/j.enpol.2008.03.035. Shindell, D. 2015. “The Social Cost of Atmospheric Release.” Climate Change . doi: 10.1007/s 10584- 015- 1343- 0. Shrimali, G., G. Chan, S. Jenner, F. Groba, and J. Indvik. 2015. “Evaluating Renewable Portfolio Standards for In -State Renewable Deployment: Accounting for Policy Heterogeneity.” Economics of Energy & Environmental Policy 4(2). Shrimali, G. , and S. Jenner. 2013. “The Impact of State Policies on Deplo yment and Cost of Solar PV in the US: A Sector Specific Empirical Analysis.” Renewable Energy 60: 679- 90. Shrimali, G. , and J. Kneifel. 2011. “Are Government Policies Effective in Promoting Deployment of Renewable Electricity Resources (in the US)?” Policy 39(9): 4726- 41. Energy Siler -Evans, Kyle, Inez Lima Azevedo, M. G ranger Morgan , and J ay Apt. 2013. “Regional Variations in the Health, Environmental, and Climate Benefits of Wind and Solar Generation.” 110(29): 11768–73. Proceedings of the National Academy of Sciences http://www.pnas.org/content/110/29/11768 . Sims, R., P. Mercado, W. Krewitt, G. Bhuyan, D. Flynn, H. Holttinen, G. Jannuzzi, S. Khennas , Y. Liu, M. O’Malley, L. J. Nilsson, J. Ogden, K. Ogimoto, H. Outhred, Ø. Ulleberg, and F. van Hulle . 2011. “Integration of Renewable Energy into Present and Future Energy Systems.” In IPCC Special Report on Renewable Energy Sources and Climate Change Mit igation , edited by O. Edenhofer, R. Pichs tschoss, S. Kadner, T. Zwickel, G. -Madruga, Y. Sokona, K. Seyboth, P. Ma Hansen, S. Schlömer, and C. von Stechow . Cambridge, UK and New York, NY, USA: Cambridge University Press. Slattery, Michael C., Eric Lantz, an d Becky L. Johnson. 2011. “State and Local Economic Impacts from Wind Energy Projects: Texas Case Study.” Energy Policy 39 (12): 7930–40. Solley, W. B., R. R. Pierce, and H. A. Perlman. 1998. “ Estimated Use of Wat er in the United States in 1995.” Circular 1200. Reston, VA: U .S . Geological Survey, U .S . Department of the Interior. 65

79 Staid, A., and S.D. Guikema. 2013. “Statistical Analysis of Installed Wind Capacity in the United Energy Policy States.” 60(September): 378–85. Stirling, A. 2010. “Multicriteria Div ersity Analysis: A Novel Heuristic Framework for Appraising Energy Portfolios.” Energy Policy 38(4): 1622–34. doi: 10.1016/j.enpol.2009.02.023. ———. 1994. “Diversity and Ignorance in Electricity Supply Investment: Addressing the Solution Rather than the Pr Energy Policy 22(3): 195–216. doi: 10.1016/0301- 4215(94)90159- 7. oblem.” Synapse Energy Economics. 2015. Air Emissions Displacement by Energy Efficiency and Renewable Energy: A Survey of Data, Methods, and Results . Cambridge, MA: Synapse Energy Economics. Tidwell, V. C. , L. A. Malczynski, P. H. Kobos, G. T. Klise, and E. Shuster. 2013a. "Potential Impacts of Electric Power Production Utilizing Natural Gas, Renewables and Carbon Capture and Sequestration on U.S. Freshwater Resources." & Technology 47: 8940–7. Environmental Science doi:10.1021/es3052284. C. , K. Zemlick, and G. T. Klise . 2013b. Nationwide Water Availability Data for Tidwell, V. Energy -Water Modeling . SAND2013 -9968. Sandia National Laboratories , Albuquerque, NM . Tol, R. 2011. “The Social Cost of Carbon.” Annual Review of Resource Economics 3(1): 419–43. Traber, T., and C. Kemfert. 2011. “Gone with the Wind? -- Electricity Market Prices and Energy Incentives to Invest in Thermal Power Plants under Increasing Wind Energy Supply.” Economics 249–56. doi:10.1016/j.eneco.2010.07.002. 33(2): Tuladhar, S., S. Mankowski, and P. Bernstein. 2014. “Interaction Effects of Market -Based and Command- and -Control Policies.” The Energy Journal 35(SI1): 61- 88. Turconi le Assessment (LCA) of Electricity , R., A. Boldrin, and T. Astrup. 2013. “Life Cyc Generation Technologies: Overview, Comparability and Limitations.” Renewable and Sustainable Energy Reviews 28: 555–65. doi: 10.1016/j.rser.2013.08.013. Union of Concerned Scientists (UCS). 2012. UCS EW3 Energy -Water Data base. V.1.3. Cambridge, MA: Union of Concerned Scientists. . www.ucsusa.org/ew3database ———. 2013. Cambridge, MA: How Renewable Electricity Standards Deliver Economic Benefits. Union of Concerned Scientists . U.S. BLS (U.S. Bureau of Labor Statistics). 2014. “Table 24. Historical Consumer Price Index for All Urban Consumers (CPI -U): U. S. city average, all items. ” In CPI Detailed Report: Data for January 2014 . http://www.bls.gov/cpi/cpid1401.pdf . U.S. Census Bureau. 2014. “ Table P -1. CPS Population and Per Capita Money I ncome, All Races: 1967 to 2013.” In Current Population Survey, 2014 Annual Social and Economic (ASEC) .C .: Bureau of the Census for the Bureau of Labor Statistics. Supplement . Washington, D 66

80 ———. 2012. “ Table 1. Projections of the Population and Components of Change for the United In . Washington, States: 2015 to 2060.” 2012 National Population Projections Summary Tables D.C .: U.S. Census Bureau Population Division. . https://www.census.gov/population/projections/data/national/2012/summarytables.html -Wide Valentino, L., V. Valenzuela, A. Botterud, Z. Zhou, and G. Conzelmann. 2012. “System Emissions Implications of Increased Wind Power Penetration.” Environmental Science & Technology 46(7): 4200– 6. doi: 10.1021/es2038432. Van Vliet, M. T. H. , J. R. Yearsley, F. Ludwig, S. Vogele, D. P. Lettenm aier, and P. Kabat. 2012. "Vulnerability of US and European Electricity Supply to Climate Change." Nature Climate Change . doi:10.1038/nclimate1546. Ventyx. 2015. Velocity Suite Data Product. Accessed May 2015. Ventyx. 2013. Ventyx Energy Velocity Suite. Warner, E., Y. Zhang, D. Inman, and G. Heath. 2013. “Can We Measure It? Challenges in the Estimation of Greenhouse Gas Emissions from Biofuel -Induced Global Land Use Change.” Biofuels, Bioproducts, Biorefining 8(1): 114- 25. en. 2010. “ Putting Renewables and Energy Efficiency to Work: Wei, M. , S. Patadia, and D. Kamm How Many Jobs Can the Clean Energy Industry Generate in the US? ” 38: 919- 31. Energy Policy Weitzman, M.L. 2012. “GHG Targets as Insurance Against Catastrophic Climate Damages.” Journal of Public Economic Theory 14(2): 221–44 . Werner, D. 2014. “The Local Environmental Impacts of Renewable Portfolio Standards.” Working Paper. College Park, MD : University of Maryland, College Park. Weyant, J. 2014. “Integrated Assessment of Climate Change: State of the Literature.” Journal of Benefit -Cost Analysis 5(3): 377- 409. Wiser, R. 2014 Wind Technologies Market Report , and M. Bolinger. 2015. . Washington, DC: U.S. Department of Energy. ———. 2007. “Can Deployment of Renewable Energy Put Downward Pressure on Natural Gas Prices?” Energy Policy 35( 1) ( January ): 295- 306. ———. 2006. “Balancing Cost and Risk: The Treatment of Renewable Energy in Western Utility Resource Plans.” The Electricity Journal 19(1): 48 –59. doi: 10.1016/j.tej.2005.10.012. Wiser, R., M. Bolinger, and M. St Clair. 2005. Easing the Natural Gas Crisis: Reducing Natural Gas Prices through Increased Deployment of Renewable Energy and Energy Efficiency . LBNL- . , Berkeley, CA 56756. Lawrence Ber keley National Laboratory 67

81 Woo, C.K., I. Horowitz, B. Horii, R. Orans, and J. Zarnikau. 2012. “Blowing in the Wind: -Gas -Fired Generation of Electricity in Vanishing Payoffs of a Tolling Agreement for Natural Texas.” Energy Journal 6574- EJ. 33(1): 207–29. doi:10.5547/ISSN0195- Woo, C.K., I. Horowitz, J. Moore, and A. Pacheco. 2011. “The Impact of Wind Generation on the Electricity Spot- Market Price Level and Variance: The Texas Experience.” Energy Policy 39 (7): 3939–44. doi:10.1016/j.enpol.2011.03.084. Woo, C.K., J. Zarnikau, J. Kadish, I. Horowitz, J. Wang, and A. Olson. 2013. “The Impact of Wind Generation on Wholesale Electricity Prices in the Hydro -Rich Pacific Northwest.” IEEE Transactions on Power Systems 28(4): 4245–53. doi:10.1109/TPWRS.2013.2265238. Würzburg, K., X. Labandeira, and P. Linares. 2013. “Renewable Generation and Electricity Prices: Taking Stock and New Evidence for Germany and Austria.” Energy Economics 40, Supplement 1 –71. doi:10.1016/j.eneco.2013.09.011. (December): S159 Yi, H. 2015. “Clean- Energy Policies and Electricity Sector Carbon Emissions in the U.S. States.” Utilities Policy 34: 19- 29. Yi, Hongtao. 2013. “Clean Energy Policies and Green Jobs: An Evaluation of Green Jobs in U.S. Energy Policy 56: 644- 52. Metropolitan Areas.” , and N. Powers. 2010. “Do State Renewable Portfolio Standards Promote In- Yin, H. State Renewable Generation?” Energy Policy 38: 1140- 9. You, F., L. Tao, and D. J. Graziano, and S. W. Snyder. 2012. "Optimal Design of Sustainable Cellulosic Biofuel Supply Chains: Multiobjective Optimization Coupled with Life Cycle 58(4): 1157–80. AIChE Journal Assessment and Input –Output Analysis." 68

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