W hat is the most economically efficient way to reduce greenhouse gas emissions? essay

Journal of Economic Perspectives—Volume 32, Number 4—Fall 2018—Pages 53–72 W hat is the most economically efficient way to reduce greenhouse gas emissions? The principles of economics deliver a crisp answer: reduce emissions to the point that the marginal benefits of the reduction equal its marginal costs. This answer can be implemented by a Pigouvian tax, for example a carbon tax where the tax rate is the marginal benefit of the emissions reduction or, equivalently, the monetized damages from emitting an additional ton of carbon dioxide (CO2). The carbon externality will then be internalized and the market will find cost-effective ways to reduce emissions up to the amount of the carbon tax. However, most countries, including the United States, do not place an econ- omy-wide tax on carbon, and instead have an array of greenhouse gas mitigation policies that provide subsidies or restrictions typically aimed at specific technologies or sectors. Such climate policies range from automobile fuel economy standards, to gasoline taxes, to mandating that a certain amount of electricity in a state comes from renewables, to subsidizing solar and wind electrical generation, to mandates requiring the blending of biofuels into the surface transportation fuel supply, to supply-side restrictions on fossil fuel extraction. In the world of a Pigouvian tax, markets sort out the most cost-effective ways to reduce emissions, but in the world The Cost of Reducing Greenhouse Gas Emissions ■ Kenneth Gillingham is Associate Professor of Economics, Yale University, New Haven, Connecticut. James H. Stock is the Harold Hitchings Burbank Professor of Political Economy, Harvard University, Cambridge Massachusetts. Gillingham is a Faculty Research Fellow and Stock is a Research Associate, both at the National Bureau of Economic Research, Cambridge Massachusetts. Their email addresses are kenneth.gillingham@yale.edu and james_stock@ harvard.edu. † For supplementary materials such as appendices, datasets, and author disclosure statements, see the article page at https://doi.org/10.1257/jep.32.4.53 doi=10.1257/jep.32.4.53 Kenneth Gillingham and James H. Stock mailto:kenneth.gillingham@yale.edu mailto:james_stock@harvard.edu mailto:james_stock@harvard.edu 54 Journal of Economic Perspectives we live in, economists need to weigh in on the costs of specific technologies or narrow interventions. This paper reviews the costs of various technologies and actions aimed at reducing greenhouse gas emissions. Our aim is twofold. First, we seek to provide an up-to-date summary of costs of actions that can be taken now using currently available technology. These costs focus on expenditures and emissions reductions over the life of a project compared to some business-as-usual benchmark—for example, replacing coal-fired electricity generation with wind or weatherizing a home. We refer to these costs as static because they are costs over the life of a specific project undertaken now, and they ignore spillovers. In the environmental economics literature, these static costs are an element in creating what is called a marginal abatement cost (MAC) curve, which plots out the marginal costs of achieving a cumulative level of emissions abatement in order from the lowest- to highest-cost technology or measure. To economists not in the energy-environment field, these marginal abatement costs might contain some surprises. Although we are skeptical of most “free lunch” static estimates, for some technologies the cost of emissions reductions is remark- ably low. For example, blending corn ethanol into gasoline up to a 10 percent ratio provides essentially costless emissions reductions (our point estimate is in the “free lunch” range) in the United States because ethanol is a less-expensive octane booster than alternatives derived from petroleum. Another low or negative static cost source of emissions reductions is replacing coal-fired electricity generation with natural gas, a switch that has been widely adopted by power generators located where gas prices are low because of the fracking revolution. On the other hand, some actions that might seem green are, from a static perspective, anything but. For example, driving a Ford Focus electric vehicle in a region in which electricity is generated by coal has approximately the same CO2 footprint as a Ford Explorer sport utility vehicle that averages 25 miles per gallon, and costs nearly as much. We find a wide range of costs for interventions currently being employed, both across and within different types of interventions. This heterogeneity in costs implies that we could achieve the same amount of greenhouse gas emissions reductions that we are achieving now at a much lower static cost, or greater emissions reductions for the same cost. Possible reasons for the use of more expensive policies include the chosen policies having less transparent costs, individual policies having justifica- tions beyond just climate policy, differences in the marginal costs across locations, and lobbying by businesses that could potentially be affected by lower-cost policies. In some cases, especially policies aimed at developing nascent technologies, the policies are developed with a longer-term vision in mind. These estimates of static costs help to inform discussions about climate policy, but they miss the critical consideration that climate change is a long-term problem. As a result, the proper answer to our opening question is not necessarily what is the least expensive mitigation strategy among options available today, but what are the actions if, taken today, will minimize the cost of mitigation both today and into the future, recognizing that actions taken today can influence future costs. We refer to such costs as dynamic, because they outlive the life of a specific project. Kenneth Gillingham and James H. Stock 55 Our second aim is to distinguish between dynamic and static costs and to argue that some actions taken today with seemingly high static costs can have low dynamic costs, and vice versa. We make this argument at a general level and through two case studies, of solar panels and of electric vehicles. The cost of both technologies has fallen sharply, arguably driven in part by demand-side incentives that in turn stimu- lated learning-by-doing and technological improvements, the benefits of which are only partially captured by the manufacturing firm. In addition, purchasing an elec- tric vehicle today drives the demand for charging stations, which in effect reduces the cost (here, the cost of time and worry) to potential future purchasers. Under the right circumstances, such dynamic effects can offer a justification for policies that a myopic calculation suggests have high costs. Estimates of Static Abatement Costs Before we begin, we briefly digress on units. The standard units of emissions costs and benefits are dollars per metric ton (1,000 kilograms) of CO2 emissions avoided. As a point of comparison, the social cost of carbon is an estimate of the net present value of monetized social damages from emission of an additional metric ton of CO2; under the Obama administration, the US government estimated the social cost of carbon to be approximately $46 in 2017 dollars for a ton of emissions in 2017 (IWG 2016).1 Burning one gallon of petroleum gasoline produces roughly nine kilograms of CO2, so a social cost of carbon value of $46/metric ton CO2 corre- sponds to $0.41 per gallon. Also, carbon dioxide is only one of many greenhouse gases; others include methane, nitrous oxide, and hydrofluorocarbons. To facilitate comparisons, it is conventional to convert costs for reducing non-CO2 greenhouse gases into CO2-equivalent units, and we adopt that convention here. 2 Brief Background on Marginal Abatement Cost Curves The marginal abatement cost (MAC) curve plots measures to abate emissions in order from the least to most expensive. For each, there is a cost per ton of emis- sions reduced and a quantity of emissions reductions available at that cost. The 1 The Trump administration withdrew this estimate by executive order and forbid agencies from using the underlying research for regulatory purposes; as of this writing, the Environmental Protection Agency is using two estimates, $1 and $6 per ton, depending on the discount rate (3 or 7 percent) (Newell 2017). The estimate of $46/ton is in the range of the academic literature, although some estimates are much higher (as one example, see Gillingham et al. 2018). There is currently a cross-institutional interdisciplinary effort to provide a comprehensive update to the social cost of carbon based on recom- mendations made by the National Academy of Sciences (2017), which is discussed on the Resources for the Future website at http://www.rff.org/research/collection/rffs-social-cost-carbon-initiative. 2 A complication in developing CO2-equivalent estimates is that the atmospheric residence time of greenhouse gases varies. The most common approach, the global warming potential approach, is only an approximation when used to calculate the social cost of non-CO2 greenhouse gases. See Marten and Newbold (2012) for a more comprehensive approach to calculating the social cost of non-CO2 greenhouse gases. http://www.rff.org/research/collection/rffs-social-cost-carbon-initiative 56 Journal of Economic Perspectives use of MAC curves to support climate policy analysis dates back at least a quarter century (for an early review, see Grubb, Edmonds, ten Brink, and Morrison 1993). All models that estimate the mitigation costs of climate policy either implicitly or explicitly use a MAC curve. The most prominent attempt at developing a comprehensive marginal abate- ment cost curve is the well-known McKinsey curve, which is constructed using engineering estimates of the cost of implementing new technologies or other measures. Figure 1 displays the global version of the McKinsey curve (McKinsey & Company 2009). A striking feature of the McKinsey curve, which is shared by MAC curves more generally (for example, see figure 2 in Grubb et al. 1993), is that some interventions have negative abatement costs: that is, emissions can be reduced, and money saved, at the same time. Economists, including ourselves, are often skeptical of these “free lunch” estimates, unless they are supported by convincing evidence and explanations. Negative costs require institutional entities, such as firms, not to be optimizing, or require the existence of behavioral failures in consumer deci- sion-making (like consumers acting myopically). In some cases, entities such as Figure 1 The McKinsey (2009) Marginal Abatement Cost Curve: “Global GHG Abatement Cost Curve Beyond Business-As-Usual-2030” Source: Global GHG Abatement Cost Curve v2.0. Figure and notes reproduced with permission from McKinsey (2009). Note: The curve presents an estimate of the maximum potential of all technical GHG abatement measures below €60 per tCO2e if each lever was pursued aggressively. It is not a forecast of what role different abatement measures and technologies will play. The Cost of Reducing Greenhouse Gas Emissions 57 governments are institutionally complex and/or not minimizing costs, so these free- lunch savings are potentially valid but institutionally difficult to realize. When these negative costs are for energy efficiency programs, this is often called the “energy efficiency gap” and there is a continued debate in the literature on whether there is a real gap or whether the gap can be explained by unaccounted-for costs (Gerarden, Newell, and Stavins 2017; Gillingham and Palmer 2014; in this journal, Allcott and Greenstone 2012). The concern over negative costs highlights a limitation of marginal abatement curves like the McKinsey curve in Figure 1: specifically, that they are based on engi- neering estimates, which have their own assumptions and typically do not include behavioral considerations. An example of such a behavioral effect is turning the heat up because the cost of doing so has declined because of weatherization. Econo- mists are typically interested in the combined effect of behavioral responses and the engineering costs. Static Cost Comparisons In addition to these and other methodological concerns, the cost estimates in the McKinsey curve in Figure 1 are out of date. We therefore turn to more current estimates of marginal costs. These estimates are drawn from the economics and trade literatures, supplemented by our own calculations. To fix orders of magnitude, we begin with some “bottom-up” or engineering cost estimates for the power sector, presented in Table 1. These estimates compare the cost per ton of CO2 abated by replacing electricity generated by an existing coal-fired power plant with electricity generated by a cleaner alternative. The esti- mates are based on the US Energy Information Administration’s (2018) so-called Table 1 New Source Generation Costs when Compared to Existing Coal Generation (ordered from lowest to highest) Technology Cost estimate ($2017/ ton CO2) Onshore wind 24 Natural gas combined cycle 24 Utility-scale solar photovoltaic 28 Natural gas with carbon capture and storage 42 Advanced nuclear 58 Coal retrofit with carbon capture and storage 84 New coal with carbon capture and storage 95 Offshore wind 105 Solar thermal 132 Source: Author’s calculations updating methodology from Clean Air Task Force (2013) based on Energy Information Administration estimates from the 2018 Annual Energy Outlook. Costs are projected for facilities that come online in 2022. Costs do not incorporate federal renewable tax credits. 58 Journal of Economic Perspectives “levelized” cost of electricity for the different sources, which combines discounted capital, operating, and maintenance expenses to produce a cost of energy per megawatt-hour, given the typical utilization rate or capacity factor for each genera- tion type. These estimates are similar to private sector estimates, such as those by Lazard (2017). According to these estimates, the least expensive technologies to reduce emissions relative to existing coal are onshore wind, natural gas combined cycle, utility-scale solar photovoltaics, and natural gas with carbon capture and storage technology. Advanced nuclear technologies are more expensive, followed by other carbon capture and storage technologies, offshore wind, and solar thermal. The technologies in this set of estimates that are less expensive (when replacing existing coal) than the Obama administration’s social cost of carbon estimate of $46 per ton of CO2 are onshore wind, natural gas combined cycle, utility scale photovoltaic, and natural gas with 90 percent carbon capture and storage. In comparison, offshore wind and solar thermal are currently quite expensive ways to reduce emissions (although offshore wind costs are falling). These estimates only consider climate benefits of switching from coal, not any other health co-benefits arising from reduc- tions in local air pollutants. From a policy perspective, engineering cost estimates such as those in Table 1 have important limitations. Some of these technologies are in wide current use, so cost estimates are reasonably reliable (onshore wind, natural gas combined cycle), whereas other technologies have demonstrated technical feasibility but current projects are subject to large cost overruns, so the engineering costs could be under- estimates (for example, advanced nuclear, carbon capture and storage). Another limitation is that these are national averages, and costs vary regionally depending on local conditions (for example, local fuel prices, wind conditions, and insolation). In addition, these are costs of switching technologies, which differ from the costs of a policy designed to encourage technology switching. These engineering estimates do not incorporate behavioral responses or any indirect emissions such as fugitive methane emissions from the production and transport of natural gas. We therefore turn to a systematic review of costs of interventions—typically policies—aimed at reducing greenhouse gas emissions. This review draws on more than 50 recent articles in the economics literature. We selected papers based on a few criteria. First, the paper must be an economic analysis, so we draw most heavily from papers published in economics journals and economics working paper series. Second, the paper must either have enough information so that we can calculate a cost per ton of emissions reduction or include an explicit estimate of this cost. Most papers we review have an explicit estimate in dollars per ton CO2. Third, we focus on papers published in the past decade, and nearly all of the papers included in our review are published after 2006. In some cases, we have supplemented the estimates from the economics literature with studies from the trade literature and/or our own calculations. The results are summarized in Table 2. The table presents ranges of estimates whenever there are multiple estimates from either the same study or multiple studies; the online Appendix available with this paper at http://e-jep.org provides Kenneth Gillingham and James H. Stock 59 an expanded version of Table 2 with sources and methods. As in a marginal abate- ment cost curve, we have ordered the estimates in Table 2 from lowest to highest cost. We highlight seven features of Table 2. First, the range of costs of these interventions is extremely wide, from less than $10 per ton to over $1,000 per ton. What is striking about this range is that all the interventions in Table 2 are either policy steps that have been implemented, at least in some jurisdiction, or have been actively proposed and considered. Most of the costs are relatively expensive, in the sense that they exceed $46/ton. Evidently, static cost is only one consideration when a policy is proposed or considered. This heterogeneity likely stems from multiple sources, including the carbon intensity of the displaced fuel (for example, is the electricity on the grid coming from coal or hydropower?) and the other policies in place. Second, there is a wide range of costs within a type of intervention. For example, subsidies to wind generation, such as the wind production tax credit in the United States, have estimated carbon abatement costs ranging from $2 to more than $260 Table 2 Static Costs of Policies based on a Compilation of Economic Studies (ordered from lowest to highest cost) Policy Estimate ($2017/ ton CO2e) Behavioral energy efficiency −190 Corn starch ethanol (US) −18 to +310 Renewable Portfolio Standards 0–190 Reforestation 1–10 Wind energy subsidies 2–260 Clean Power Plan 11 Gasoline tax 18–47 Methane flaring regulation 20 Reducing federal coal leasing 33–68 CAFE Standards 48–310 Agricultural emissions policies 50–65 National Clean Energy Standard 51–110 Soil management 57 Livestock management policies 71 Concentrating solar power expansion (China & India) 100 Renewable fuel subsidies 100 Low carbon fuel standard 100–2,900 Solar photovoltaics subsidies 140–2,100 Biodiesel 150–250 Energy efficiency programs (China) 250–300 Cash for Clunkers 270–420 Weatherization assistance program 350 Dedicated battery electric vehicle subsidy 350–640 Note: Figures are rounded to two significant digits. We have converted all estimates to 2017 dollars for comparability. See Appendix Table A-1 for sources and methods. CO2e denotes conversion of tons of non-CO2 greenhouse gases to their CO2 equivalent based on their global warming potential. 60 Journal of Economic Perspectives per ton of reduced CO2. For wind power, one reason for the large range is that there is large variation across sites in wind potential. The range is even wider for subsidies for solar photovoltaics, in part because there is wide variation in solar potential across locations (the solar power potential in southwestern Arizona is roughly twice that in upstate New York3), in part because of the timing of the programs (for example, earlier programs faced higher solar panel costs than later programs), and in part because of differences in scale (utility-scale arrays cost much less to install per kilowatt than rooftop arrays) (Baker, Fowlie, Lemoine, and Reynolds 2013). The wide ranges of estimates in Table 2 underscore that policies may have very different costs per ton of CO2 depending on the empirical setting and/or the meth- odology of the study. The ranges of the estimates should not necessarily be taken as a proxy for uncertainty, for they simply show how estimates vary across studies. Due to within-study uncertainty, values above and below the ranges are likely to occur with some probability. Third, some of the interventions that have negative economic costs in the McKinsey curve (and in other marginal abatement cost curves) have positive costs here. For example, engineering estimates of weatherization programs often suggest that they have negative costs. So why have such changes not already been under- taken? This is the energy efficiency paradox. In a randomized controlled trial, however, Fowlie, Greenstone, and Wolfram (2018) found that the actual costs of the weatherization exceeded the savings, leading to the $350/ton estimate of the mitigation cost reported in Table 2. They attribute the difference between the nega- tive engineering costs and the actual positive costs for the homes in their study primarily to flaws in the engineering models. Fourth, some of the costs in Table 2 are negative. A striking estimate arises from behavioral economics studies of how small nudges can get consumers to reduce their energy consumption, thereby saving money while reducing emissions; the esti- mate in Table 2 is taken from Allcott and Mullainathan’s (2010) meta-analysis of behavioral interventions. An example of such a nudge is the OPOWER program, in which an insert in the residential electricity bill compares the homeowner’s usage to that of neighbors, costing the utility very little and leading to consumer savings. One concern, which we share, is that while the cost of such reductions is negative, the total emissions reductions from such nudges are likely to be relatively small and partially transitory. The other negative estimate in Table 2 is for corn ethanol, which some might find surprising. In the United States, petroleum gasoline blend stock must be blended with an octane booster to bring it up to the 87 octane standard of regular unleaded gaso- line. Ethanol is a lower-cost octane booster than its petroleum alternatives (Irwin and Good 2017). In 2012—a year in which there were no direct federal subsidies and the federal ethanol mandate under the Renewable Fuel Standard was not binding—ethanol comprised just under 10 percent of the US retail gasoline supply. 3 See the National Renewable Energy Laboratory (NREL) National Solar Radiation Database (NSRDB) Data Vewer at https://maps.nrel.gov/nsrdb-viewer/. https://maps.nrel.gov/nsrdb-viewer/ The Cost of Reducing Greenhouse Gas Emissions 61 The California Air Resources Board (2018) estimates that ethanol from new corn ethanol plants has roughly 70 percent of the life-cycle CO2 emissions of petroleum, including the carbon effects of induced land use change. Thus, for blends up to 10 percent, ethanol has negative greenhouse gas emissions reductions costs, and indeed is the market choice. Blending ethanol up to approximately 30 percent continues to enhance octane. The US fueling infrastructure, however, generally cannot handle blends above 10 percent, nor are engines designed to harness those octane advantages to improve energy efficiency, a situation known as the “E10 blend wall.” As a result, subsidies are needed to incentivize ethanol consumption in blends higher than E10, and those costs increase quickly when measured in dollars per ton of CO2 avoided. Fifth, a few of the interventions have very low costs. Some, like the Clean Power Plan—the Obama administration’s rulemaking for CO2 emissions standards in the power sector—and regulations to reduce methane flaring from fracked oil wells that coproduce natural gas, are examples in which the regulation intensity was chosen with cost in mind. The Clean Power Plan is notable for its low cost per ton of emissions reductions (this estimate is taken from the Regulatory Impact Analysis for the Clean Power Plan, US Environmental Protection Agency 2015). This cost per ton is less than any of the engineering costs in Table 1, for two reasons. First, some of the emissions reduction comes from switching generation from existing coal- fired plants to existing gas-fired plants, and so does not require building a new plant as in Table 1. Second, because the Clean Power Plan allowed interstate trading of emissions permits, new low-greenhouse gas generating facilities would be built where it is most economically efficient to do so, yielding lower costs than the generic plant replacement costs in Table 1. The Clean Power Plan is also notable because its projected CO2 emissions reductions are the largest, or nearly so, among the inter- ventions in Table 2. Sixth, some of the interventions have very high static costs. The United States and Europe have programs that require blending biodiesel into the diesel fuel supply. Biodiesel can be made from many oil feedstocks, including waste grease, but on the margin it is made from food-competing feedstocks such as soybean oil. These food oils are expensive and production of soy biodiesel requires a large subsidy, which is provided in the United States primarily through a tax credit and through the Renewable Fuel Standard. In other cases, the high costs are a result of inefficien- cies in program design. For example, the temporary Cash for Clunkers program was installed at the depth of the recession in 2009 to provide an infusion of demand for new cars to support the auto industry and to provide countercyclical fiscal stim- ulus. Because the program exchanged old vehicles for more efficient new ones, it boosted fleet fuel economy. However, it had substantial temporary inframarginal transfers that were not a problem for its primary purpose—to pull forward auto demand—but made it a costly way to reduce emissions. Seventh, the literature suggests that the cost of reducing carbon is low for some land use policies (see “Reforestation” on Table 2). In a randomized controlled exper- iment that lasted two years, Jayachandran et al. (2017) found that cash payments 62 Journal of Economic Perspectives for forest conservation in Uganda substantially reduced deforestation and cost $1 per ton of carbon sequestered. They do not, however, provide evidence on what happened after the payments stopped, and a natural concern is that there would be a reversion to the deforestation baseline. If so, the emissions reduction would be temporary, that is, the emissions would simply be postponed, not eliminated.4 This distinction between permanent and temporary sequestration, along with the difficulty of ascertaining whether the payments actually induce incremental carbon retention in practice (something that was in fact found in Jayachandran et al.’s experiment), are at the heart of the controversy over the use of carbon offsets (for example, van Benthem and Kerr 2013; Bento, Kanbur, and Leard 2016). One sobering insight from the estimates in Table 2 is that many of the least- expensive interventions cover a small amount of CO2 reductions, whereas the scalable technologies that that are at the center of discussions about a transfor- mation to a low-carbon economy—electric vehicles, solar photovoltaic panels, and offshore wind turbines—are among the most expensive on the list. Behavioral nudges are a very small step towards deep decarbonization. In contrast, the more expensive scalable technologies have a much greater potential for substantial emis- sions reductions. For these technologies, what matters most are not the static costs today, but the costs and consequences of these interventions over time, that is, the dynamic costs of the intervention. It is informative to know what are the cheapest interventions to do today, but we would argue that it is even more important to know what interventions might most effectively drive down the price of large-scale reductions in emissions in the future. Dynamic Costs The long residence time of CO2 in the atmosphere makes climate change a long-term problem, in which (to a first approximation) what matters is the total number of tons emitted over some long horizon. As a result, the key to reducing emissions in the future is to have low-cost alternatives to fossil fuels that are zero- or low-carbon. The true total cost of investments or interventions today therefore must include both their static or face-value cost, and any spillovers those investments have for future costs of emissions reduction. The importance of a dynamic perspec- tive is hardly new—see Popp, Newell, and Jaffe (2010) for a review—but it is often neglected both in the public debate and in the literature on costs of abatement. Yet, the welfare benefits of even small growth rates in the efficiency of clean technolo- gies may be large, as suggested by simulations in Hassler, Krussel, Olovsson, and Reiter (2018). 4 The distinction between temporary and permanent forest sequestration is important. Temporary rain- forest sequestration is equivalent to storing emissions then releasing them later. In a manner analogous to how generating electricity from wind displaces retired coal-fired electricity, permanent sequestration permanently keeps the CO2 in question out of the atmosphere. Kenneth Gillingham and James H. Stock 63 Conceptual Framework The static cost estimates of the previous section focus on direct reductions in emissions in the relatively short-run. However, expenditures on certain kinds of short-run reductions in emissions today can also affect emissions in the future, above and beyond direct emissions from the project. There are at least four reasons why this second component of emissions reduction could be nonzero and possibly large for some green technologies. Three of these stem from externalities, while the fourth is the difference between myopic and dynamic cost minimization. First, many of these low-carbon technologies are nascent, and there could be substantial gains in production efficiency as more units are produced. Such gains can arise from engineering and managerial improvements made as production increases, a channel referred to as learning by doing, and from scale economies. To the extent that such gains are only partially appropriable by the firm, an expendi- ture today provides a positive externality that reduces costs in the future. The first case study that we discuss in the next subsection—solar panels—focuses on this learning-by-doing effect. Second, a related externality arises from research and development spillovers because research results are only partially appropriable. These spillovers also repre- sent a market failure, and economists have argued that the spillovers are likely to be particularly large for emerging clean technologies (Nordhaus 2011). To the extent that purchases today spur additional research, which then reduces costs, expen- ditures today reduce emissions tomorrow. It can often be difficult to separate the effects of research and development spillovers from learning-by-doing spillovers, for as a firm ramps up production, it also may ramp up research. For this reason, econo- mists have often encouraged caution in relying too heavily on learning-by-doing to model technological change (Nordhaus 2014). Third, a separate externality that is present for some technologies is a network or “chicken and egg” externality, in which an expenditure today influences the options that are available to others in the future. For example, purchases of electric vehicles today will, on the margin, stimulate demand for charging stations, which once installed will lower the effective cost for future potential purchasers of elec- tric vehicles. Our second case study, of electric vehicles, in principle includes both learning-by-doing and network externalities. Fourth, energy investments typically have substantial irreversible components, which in general implies state dependence so that the dynamically optimal path may differ from a sequence of myopic optimizations each chosen at a point in time. This potential for lock-in is at the heart of the debate about the merits of natural gas as a bridge fuel towards decarbonizing the power sector, in which renewable proponents argue that natural gas is cheaper only if one optimizes myopically and fails to recog- nize that the power sector will eventually need to be decarbonized. This intuition underlies Vogt-Schilb, Meunier, and Hallegatte (2018), who show that if abatement is achieved through investment in long-lived capital, it can be optimal to begin emissions abatement with expensive abatement investments that have large emissions reduction potential because they crowd-out dirtier long-lived investments. Irreversibility (state 64 Journal of Economic Perspectives dependence) also underlies the results of Fischer and Newall (2008), Acemoglu, Aghion, Bursztyn, and Hemous (2012), and Acemoglu, Akcigit, Hanley, and Kerr (2016), who show that a carbon price combined with research subsidies for low- greenhouse-gas technologies may be desirable to attain dynamically efficient outcomes. Of course, long-term considerations may not always lower the cost of emissions reductions. For example, nuclear power has long had major federal research subsi- dies but its cost has gone up, not down (Davis and Hausman 2016). Additionally, as the marginal ton of displaced electricity becomes cleaner (for example, displacing natural gas instead of coal), the cost per ton abated by low-carbon renewables will tend to increase. One major reason why dynamic considerations are often ignored is that they tend to be highly uncertain. But that uncertainty should be viewed as a research challenge rather than an excuse to ignore dynamic considerations. And there is some evidence from the recent literature. Dynamic Cost Case Study 1: Solar Panels From 2010 to 2015, the price of solar photovoltaic panels fell by two-thirds, while annual global panel installations grew by 250 percent, as shown in Figure 2. The fact that panel sales increased when their price fell is hardly surprising, but more intriguing is that the steepest decline in panel prices after about 2007 post-dated the initial growth in panel sales, which began around 2002. The growth in sales in the mid-2000s was associated with policies that provided aggressive financial support for installing rooftop photovoltaic arrays through the German Energiewende, which provided a substantial feed-in tariff that allowed solar installations to be compensated at a very high rate for electricity fed into the grid, and the California Solar Initiative, which provided generous upfront subsidies for solar installations.5 These early panel purchases were very expensive and account for some of the high photovoltaic cost estimates in Table 2. As stressed by a number of researchers (for example, Borenstein 2017), the static cost per ton of CO2 reduced from policies to encourage solar installations tends to be high. Our literature review finds costs ranging from more than $100 per ton of CO2 to in the thousands per ton of CO2. On the lower end, Hughes and Podolefsky (2015) estimate costs of the California Solar Initiative at between $130 and $196 per ton. On the high end, Abrell, Kosch, and Rausch (2017) find a static cost per ton of €500–1300 (roughly $574–$1,492 in 2017 dollars) for solar feed-in tariffs in Germany and Spain (a solar feed-in tariff is a long-term fixed price contract for purchasing electricity from a solar array). However, both the timing shown in Figure 2 and recent research suggests that the early push in demand, stimulated by deep government subsidies, did in fact help to drive down the price of solar panels. One channel is that current subsidies may 5 Many US states have generous net metering policies that act as implicit subsidies by compensating solar fed into the grid at the retail rate. Some states have Renewable Portfolio Standards that require utilities to procure certain amounts of renewable power (sometimes with a solar carve-out) by certain dates. At the federal level, since 2008, there has been a 30 percent investment tax credit for the installa- tion of a residential solar system. The Cost of Reducing Greenhouse Gas Emissions 65 encourage firms to innovate to reduce their future costs. Gerarden (2018) estimates that this induced innovation effect, which does not include learning-by-doing, contributed to the decline in solar array prices and increased the long-run external social benefits from global government subsidies to solar adoption by at least 22 percent. His results further suggest an important spillover from any single country that subsidizes solar to the rest of the world due to the investment in innovation by international firms. In this sense, the German Energiewende subsidized lower-cost solar for the rest of the world. Other channels for cost reduction in the production of solar panels include learning-by-doing and economies of scale. Nemet (2006) decomposes the reduc- tion in cost into the manufacturing plant size, module efficiency, and cost of silicon, finding that between 1980 and 2001, economies of scale from larger manufacturing plant sizes accounted for 43 percent of the cost reduction. Most of the remaining cost reduction could be attributed to improvements in module efficiency due to research and development investment. The substantial cost declines in solar module prices over the past decade are often attributed to economies of scale (Carvalho, Dechezleprêtre, and Glachant 2017). Economies of scale and learning-by-doing can in many cases be appropriable by the firms making decisions to scale up (this appears to be the case for learning-by-doing among rooftop solar installers, as Bollinger and Gillingham 2018 explain), so that learning-by-doing and scale economies do not by Figure 2 Solar Panel Price Indexes Excluding Subsidies and Cumulative Worldwide Installed Capacity, 1983–2015 1983 0 1 2 3 4 5 6 7 8 9 0.1 1 10 100 1985 1987 1989 $/ W Price (left axis) G W (lo g scale) Annual Installation (right axis) 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Source: International Energy Agency (2017), Navigant Consulting (2009), and Gerarden (2018). 66 Journal of Economic Perspectives themselves necessarily constitute reasons for policy intervention. Absent a carbon price, however, the demand for solar panels will be less than it would be were there a carbon price. As a result, second-best policies that are initially expensive (like the German Energiewende) can in principle stimulate production that would not normally happen because fossil fuels are cheaper than they would be, were their externality priced. For solar panels, at least, all this seems to have been the case. Going forward, we might continue to see policy-induced cost reductions for solar technology. As the penetration of solar rises, and as the rest of the electricity system decarbonizes, such cost reductions will have to continue to be substantial to offset the higher potential costs of additional storage needed because of solar intermittency. Dynamic Cost Case Study 2: Electric Vehicles Like solar panels, the static costs of CO2 reductions obtained by using elec- tric vehicles is high in Table 2 (the last row). Today, many electric vehicles in the United States are charged using electricity that on the margin is produced by fossil fuels. Holland, Mansur, Muller, and Yates (2016) use the method of Graff Zivin, Kotchen, and Mansur (2014) for computing marginal emissions to examine the static optimal second-best purchase subsidy on electric vehicles accounting for both greenhouse gases and local air pollution. Holland, Mansur, Muller, and Yates (2016) find that the subsidy ranges from a subsidy of $2,785 in California (with rela- tively clean electricity on the margin) to a penalty of $4,964 in North Dakota, where electricity is generated from coal. Archsmith, Kendall, and Rapson (2015) perform similar calculations that additionally include life-cycle considerations, and find that on average electric vehicles currently only slightly reduce greenhouse gases relative to gasoline-powered vehicles. From a dynamic perspective, however, the case against programs to support electric vehicle purchases is far less clear. The static calculations ignore the fact that the grid is evolving and becoming cleaner. Moreover, the general issues raised for solar panels—induced innovation, learning by doing, and economies of scale that would not otherwise be achieved because carbon is not priced—apply to electric vehicles as well. Unlike the case of solar panels, however, we are not aware of any research that investigates drivers of price dynamics for electric vehicles, perhaps because the cost declines and sales growth are so recent. The available data are, however, suggestive that the analogy to demand-pull effects for solar panels also applies to electric vehicles. Figure 3 plots electric vehicles that entered the market from model years 2011 to 2018 based on their suggested retail price (y-axis) and battery range (x-axis). The price-range frontier has strikingly shifted out: more recent market entrants have greater battery range at lower cost, underscoring this rapid improvement in technology. The large declines in price for vehicles with the same range is mainly due to the ongoing decline in battery prices. From 2009 to 2015, the price of batteries for electric vehicles fell by 75 percent (US Department of Energy 2016). Like solar Kenneth Gillingham and James H. Stock 67 photovoltaic arrays, electric vehicles have been a target of demand-pull subsidy programs. Since the Energy Policy Act of 2005, there has been a US federal income tax credit of $7,500 (which phases out with production by any given manufacturer). Many states have additional incentives, such as a $3,000 rebate in Connecticut, eligi- bility for driving in a high-occupancy lane with only a single occupant in California, and a zero-emissions vehicle mandate in 10 states that requires automakers to sell a certain number of zero tailpipe emission vehicles (including electric vehicles) for every non-zero-emissions vehicle sold. Numerous papers in the transporta- tion literature have provided evidence suggesting that electric vehicle subsidies increase demand for electric vehicles, as one would expect (reviewed in Zhou, Levin, and Plotkin 2016). The general pattern of demand-pull policies combined with subsequent sharp declines in costs is similar to that found for solar panels. We note that it is consistent with learning-by-doing and scale economy effects, and that confirming or refuting this hypothesis is an important area for future research by economists. Figure 3 Electric Vehicle Manufacturers Suggested Retail Price (MSRP) Plotted against the Battery Range Shows Impressive Technology Improvements within a Short Time Source: J. Li (2017) and authors’ calculations. Note: Dates indicate year the model is introduced. Regression lines are fit with a common slope and different intercept for each group of model years. $100,000 $80,000 $60,000 $40,000 $20,000 50 100 150 200 250 Battery range (miles) B as e M SR P 2011–2012 2013–2015 2017–2018 Tesla Roadster Tesla Model X Ford Focus Nissan Leaf Tesla Model 3 Chevrolet Bolt 68 Journal of Economic Perspectives Electric vehicles also exhibit network effects, whereby the purchase of an additional electric vehicles makes the installation of a charging station more profitable. Thus, a positive feedback can exist, leading to multiple equilibria. For example, there may be one equilibrium with few charging stations and few or no electric vehicles, and another with many charging stations and electric vehicles. There is a growing literature on electric vehicles and network effects. Zhou and S. Li (2017) point out the possibility of multiple equilibria in electric vehicles and argue that a subsidy targeted at the marginal electric vehicle purchaser can be much more efficient than a policy that provides large inframarginal gains to those who would purchase an electric vehicle anyway. Yu, S. Li, and Tong (2016) discuss how network effects can lead the market solution to underinvest in electric vehi- cles compared to what is socially optimal. J. Li (2017) develops a structural model of two-sided market estimated with vehicle registration data from the United States and finds that mandating compatibility in charging stations would benefit consumers, enhance network effects, and increase the size of the electric vehicle market. Springel (2018) uses vehicle registration data from Norway—the country with the highest penetration of electric vehicles—to estimate a structural model showing that subsidies for charging stations are more effective for increasing electric vehicles uptake than are purchase subsidies for electric vehicles, but their effectiveness tapers off with increased subsidy. The findings of these papers on network effects point to how a static perspective on policies to encourage technologies such as electric vehicles miss important aspects germane to the long-term cost-effectiveness of different policy approaches. Static versus Dynamic Costs: Other Examples Our two case studies present the sanguine view that seemingly expensive investments today result in lower costs in the future, a finding broadly akin to the theoretical work of Vogt-Schilb, Meunier, and Hallegatte (2018), Newbery (2018), Acemoglu et al. (2012), and Acemoglu et al. (2016). This happy result, however, is not preordained. For example, taking the dynamic approach may lead one to invest less in a carbon abatement technology if costs are expected to increase, rather than decrease, over time. Nuclear technology may fall into this category as construction costs of nuclear energy have risen, not fallen (Davis and Hausman 2016). Increasing costs of integrating renewable electricity into the electric grid can also work in this direction. In other cases, the static approach is perfectly appropriate. Consider policies to reduce methane leaks from the natural gas distribution system: the costs of sealing these leaks is likely to be similar in the near future as it is today because the process of sealing leaks is well understood but costly (digging up pavement and replacing pipes). Still other cases are less clear. Policies that would promote fuel switching to natural gas may reduce emissions in the short-run, but have potential to lead to investments in long-lived capital assets, and possibly even technological lock-in (Gillingham and Huang 2018). The Cost of Reducing Greenhouse Gas Emissions 69 Challenges The costs of reducing carbon emissions discussed in this paper pose several challenges. One of these challenges is that some politically appealing programs, such as support for biodiesel or subsidies for energy efficiency programs, can be quite costly either for technological reasons or because of behavioral responses. Because the costs for these programs are often masked and only apparent upon scrutiny by economists, they appear low-cost—but are not. A second challenge is the reverse, where highly visible programs are perceived as high-cost, but are not. A prominent example is the Clean Power Plan, which would have resulted in large emissions reductions for a cost far below that of many other programs already in place. A third challenge is that the static costs provide at best an incomplete picture of the true costs of a particular action, which must include the dynamic consequences. The sign of those dynamic consequences in general depends on the intervention. If the intervention is replacing coal electricity generation by natural gas, low short- term costs might lead to higher longer-term costs if the result is long-lived natural gas infrastructure that is locked in and costly to abandon as the price of renewables drops. In contrast, if the intervention is providing subsidies for purchasing electric vehicles, the demand-pull effects of induced learning by doing and economies of scale can make dynamic costs much lower than a myopic static calculation would suggest. Because climate change is a long-term problem and the changes ultimately needed to reduce emissions are vast, the dynamic costs are far more important than the static ones. A fourth challenge is to the economic research community, and it stems from the previous observation. As is clear from our review, most of the empirical studies of costs by economists focus on static costs, typically static costs of programs that have already been in place. This is natural because there is data on these programs, and understanding the costs of previous programs is a helpful guide to designing future programs. But particularly in the field of climate change research, more attention is needed on the determinants of dynamic costs. This exciting field of research merges environmental and energy economics with the extant literature on productivity, diffusion, and learning-by-doing. We have highlighted two areas— solar photovoltaics and electric vehicles—in which demand-pull policies appear to have induced cost reductions; however, that need not always happen and magni- tudes surely vary from one case to the next. Climate change is a long-term problem, and the focus of policy must be on long-term solutions. To make major progress on climate goals, like 80 percent decarbonization by 2050 in the United States, will require new technology deployed on a vast scale. Even if each technological step is evolutionary—cheaper electric vehicle batteries, connecting the grid to harness the wind potential in the Midwest, reducing the cost of offshore wind, developing and commercializing low-carbon fuels for air transport—the overall change will be revolutionary. If a price on carbon is not politically feasible—and arguably even if it is—these long-term considerations 70 Journal of Economic Perspectives need to be incorporated into our short-term policy tradeoffs. From the perspective of the cost calculations in this paper, one clear implication is that choosing low-cost interventions without a future, including ones that lock in fossil fuel infrastructure, can result in too much emphasis being placed on what is cheapest to do today. We are always surprised by the specifics of technological progress, but as economists, we are not surprised that it is more likely to occur when the right incentives are in place. ■ The authors thank Todd Gerarden and Jing Li for providing data and comments, and Tim Bialecki for research assistance. The authors also thank Joe Aldy, Rick Duke, Matt Kotchen, Derek Lemoine, Will Rafey, and Gernot Wagner for helpful discussions and/or comments on an early draft of this paper. References Abrell, Jan, Mirjam Kosch, and Sebastian Rausch. 2017. “The Economic Cost of Carbon Abatement with Renewable Energy Policies.” CER-ETH (Center of Economic Research at ETH Zurich) Working Paper 17/273. Acemoglu, Daron, Philippe Aghion, Leonardo Bursztyn, and David Hemous. 2012. “The Environ- ment and Directed Technical Change.” American Economic Review 102(1): 131–66. Acemoglu, Daron, Ufuk Akcigit, Douglas Hanley, and William Kerr. 2016. “Transition to Clean Technology.” Journal of Political Economy 124(1): 52–104. Allcott, Hunt, and Michael Greenstone. 2012. “Is There an Energy Efficiency Gap?” Journal of Economic Perspectives 26(1): 3–28. Allcott, Hunt, and Sendhil Mullainathan. 2010. “Behavior and Energy Policy.” Science 327(5970): 1204–1205. Archsmith, James, Alissa Kendall, and David Rapson. 2015. “From Cradle to Junkyard: Assessing the Life Cycle Greenhouse Gas Benefits of Electric Vehicles.” Research in Transportation Economics 52: 72–90. Baker, Erin, Meredith Fowlie, Derek Lemoine, and Stanley S. Reynolds. 2013. “The Economics of Solar Electricity.” Annual Review of Resource Economics 5: 387–426. Bento, Antonio, Ravi Kanbur, and Benjamin Leard. 2016. “On the Importance of Baseline Setting in Carbon Offsets Markets.” Climatic Change 137(3–4): 625–37. Bollinger, Bryan, and Kenneth Gillingham. 2018. “Learning-by-Doing in Solar Photovoltaic Installations.” Unpublished paper, Yale University. Borenstein, Severin. 2017. “The Private Net Benefits of Residential Solar PV: The Role of Elec- tricity Tariffs, Tax Incentives and Rebates.” Journal of the Association of Environmental and Resource Economists 4(S1): S85–S122. California Air Resources Board. 2018. “LCFS Pathway Certified Carbon Intensities.” at https:// www.arb.ca.gov/fuels/lcfs/fuelpathways/pathway- table.htm and https://www.arb.ca.gov/fuels/lcfs/ fuelpathways/current-pathways_all.xlsx, accessed August 2, 2018 Carvalho, Maria, Antoine Dechezleprêtre, and Matthieu Glachant. 2017. “Understanding the Dynamics of Global Value Chains for Solar Photo- voltaic Technologies.” WIPO Economic Research Working Paper 40, World Intellectual Property Organization. http://www.wipo.int/edocs/ pubdocs/en/wipo_pub_econstat_wp_40.pdf. Clean Air Task Force. 2013. “Comparison of CO2 Abatement Costs in the United States for Various Low and No Carbon Resources.” Fact- sheet, September. Available at: http://www.catf. us/resources/factsheets/ Davis, Lucas W., and Catherine Hausman. 2016. “Market Impacts of a Nuclear Power Plant https://www.arb.ca.gov/fuels/lcfs/fuelpathways/pathwaytable.htm https://www.arb.ca.gov/fuels/lcfs/fuelpathways/pathwaytable.htm https://www.arb.ca.gov/fuels/lcfs/fuelpathways/pathwaytable.htm https://www.arb.ca.gov/fuels/lcfs/fuelpathways/current-pathways_all.xlsx https://www.arb.ca.gov/fuels/lcfs/fuelpathways/current-pathways_all.xlsx http://www.wipo.int/edocs/pubdocs/en/wipo_pub_econstat_wp_40.pdf http://www.wipo.int/edocs/pubdocs/en/wipo_pub_econstat_wp_40.pdf http://www.catf.us/resources/factsheets/ http://www.catf.us/resources/factsheets/ http://pubs.aeaweb.org/action/showLinks?system=10.1257%2Fjep.26.1.3&citationId=p_4 http://pubs.aeaweb.org/action/showLinks?pmid=20203035&crossref=10.1126%2Fscience.1180775&citationId=p_5 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.retrec.2015.10.007&citationId=p_6 http://pubs.aeaweb.org/action/showLinks?crossref=10.1146%2Fannurev-resource-091912-151843&citationId=p_7 http://pubs.aeaweb.org/action/showLinks?system=10.1257%2Fapp.20140473&citationId=p_16 http://pubs.aeaweb.org/action/showLinks?crossref=10.1007%2Fs10584-016-1685-2&citationId=p_8 http://pubs.aeaweb.org/action/showLinks?crossref=10.1086%2F691978&citationId=p_11 http://pubs.aeaweb.org/action/showLinks?system=10.1257%2Faer.102.1.131&citationId=p_2 http://pubs.aeaweb.org/action/showLinks?crossref=10.1086%2F684511&citationId=p_3 Kenneth Gillingham and James H. Stock 71 Closure.” American Economic Journal: Applied Economics 8(2): 92–122. Fischer, Carolyn, and Richard G. Newell. 2008. “Environmental and Technology Policies for Climate Mitigation.” Journal of Environmental Economics and Management 55(2): 142–62. Fowlie, Meredith, Michael Greenstone, and Catherine Wolfram. 2018. “Do Energy Efficiency Investments Deliver? Evidence from the Weather- ization Assistance Program.” Quarterly Journal of Economics 133(3): 1597–1644. Gerarden, Todd. 2018. “Demanding Innova- tion: The Impact of Consumer Subsidies on Solar Panel Production Costs.” Unpublished paper. Available at: https://scholar.harvard.edu/ gerarden/publications/innovation-solar-panel- manufacturing. Gerarden, Todd D., Richard G. Newell, and Robert N. Stavins. 2017. “Assessing the Energy- Efficiency Gap.” Journal of Economic Literature 55(4): 1486–1525. Gillingham, Kenneth, and P. Huang. 2018. “The Environmental and Welfare Implications of Abundant Natural Gas.” Unpublished Paper, Yale University. Gillingham, Kenneth, William Nordhaus, David Anthoff, Geoffrey Blanford, Valentina Bosetti, Peter Christensen, Haewon McJeon, and John Reilly. 2018. “Modeling Uncertainty in Integrated Assessment of Climate Change: A Multimodel Comparison.” Journal of the Association of Environ- mental and Resource Economists 5(4): 791–826. Gillingham, Kenneth, and Karen Palmer. 2014. “Bridging the Energy Efficiency Gap: Policy Insights from Economic Theory and Empirical Evidence.” Review of Environmental Economics and Policy 8(1): 18–38. Graff Zivin, Joshua S., Matthew J. Kotchen, and Erin T. Mansur. 2014. “Spatial and Temporal Heterogeneity of Marginal Emissions: Implications for Electric Cars and Other Electricity-Shifting Poli- cies.” Journal of Economic Behavior & Organization 107(Part A): 248–68. Grubb, Michael, Jae Edmonds, Patrick ten Brink, and Michael Morrison. 1993. “The Costs of Limiting Fossil-Fuel CO2 Emissions: A Survey and Analysis.” Annual Review of Energy and the Environ- ment 18: 397–478. Hassler, John, Per Krusell, Conny Olovsson, and Michael Reiter. 2018. “Integrated Assessment in a Multi-region World with Multiple Energy Sources and Endogenous Technical Change.” Unpublished paper. Holland, Stephen P., Erin T. Mansur, Nicholas Z. Muller, and Andrew J. Yates. 2016. “Are there Environmental Benefits from Driving Electric Vehicles? The Importance of Local Factors.” American Economic Review 106(12): 3700–29. Hughes, Jonathan E., and Molly Podolefsky. 2015. “Getting Green with Solar Subsidies: Evidence from the California Solar Initiative.” Journal of the Association of Environmental and Resource Economists 2(2): 235–75. International Energy Agency. 2017. “Renew- ables 2017.” https://www.iea.org/publications/ renewables2017/. Intragency Working Group on Social Cost of Greenhouse Gases (IWG), United States Govern- ment. 2016. “Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866.” Interagency Working Group on the Social Cost of Greenhouse Gases, United States Government. At https://www.epa.gov/ sites/production/files/2016-12/documents/ sc_co2_tsd_august_2016.pdf. Irwin, Scott, and Darrel Good. 2017. “On the Value of Ethanol in the Gasoline Blend.” farmdoc daily, March 15. Jayachandran, Seema, Joost de Laat, Eric F. Lambin, Charlotte Y. Stanton, Robin Audy, and Nancy E. Thomas. 2017. “Cash for Carbon: A Randomized Trial of Payments for Ecosystem Services to Reduce Deforestation.” Science, July, 21, 367(6348): 267–73. Lazard. 2017. “Lazard’s Levelized Cost of Energy Analysis Version 11.0.” Report. https:// www.lazard.com/perspective/levelized-cost-of- energy-2017/. Li, Jing. 2017. “Compatibility and Investment in the U.S. Electric Vehicle Market.” Unpublished Paper. Available at: http://www.mit.edu/~lijing/. Marten, Alex L., and Stephen C. Newbold. 2012.“Estimating the Social Cost of Non-CO2 GHG Emissions: Methane and Nitrous Oxide.” Energy Policy 51: 957–72. McKinsey & Company. 2009. “Pathways to a Low-Carbon Economy: Version 2 of the Global Greenhouse Gas Abatement Cost Curve.” Report. National Academy of Sciences, Committee on Assessing Approaches to Updating the Social Cost of Carbon. 2017. Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide. Wash- ington, DC: National Academies Press. Navigant Consulting. 2009. Navigant Consulting PV Module Index (a proprietary data source). Nemet, Gregory F. 2006. “Beyond the Learning Curve: Factors Influencing Cost Reductions in Photovoltaics.” Energy Policy 34(17): 3218–32. Newbery, David. 2018. “Evaluating the Case for Supporting Renewable Electricity.” Energy Policy 120: 684–96. Newell, Richard G. 2017. “Unpacking the Administration’s Revised Social Cost of Carbon.” https://scholar.harvard.edu/gerarden/publications/innovation-solar-panel-manufacturing https://scholar.harvard.edu/gerarden/publications/innovation-solar-panel-manufacturing https://scholar.harvard.edu/gerarden/publications/innovation-solar-panel-manufacturing https://www.iea.org/publications/renewables2017/ https://www.iea.org/publications/renewables2017/ https://www.epa.gov/sites/production/files/2016-12/documents/sc_co2_tsd_august_2016.pdf https://www.epa.gov/sites/production/files/2016-12/documents/sc_co2_tsd_august_2016.pdf https://www.epa.gov/sites/production/files/2016-12/documents/sc_co2_tsd_august_2016.pdf https://www.lazard.com/perspective/levelized-cost-of-energy-2017/ https://www.lazard.com/perspective/levelized-cost-of-energy-2017/ https://www.lazard.com/perspective/levelized-cost-of-energy-2017/ http://www.mit.edu/~lijing/ http://pubs.aeaweb.org/action/showLinks?system=10.1257%2Fjel.20161360&citationId=p_21 http://pubs.aeaweb.org/action/showLinks?system=10.1257%2Faer.20150897&citationId=p_29 http://pubs.aeaweb.org/action/showLinks?crossref=10.1086%2F681131&citationId=p_31 http://pubs.aeaweb.org/action/showLinks?crossref=10.1086%2F698910&citationId=p_23 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.enpol.2012.09.073&citationId=p_39 http://pubs.aeaweb.org/action/showLinks?crossref=10.1093%2Freep%2Fret021&citationId=p_25 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.jebo.2014.03.010&citationId=p_26 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.jeem.2007.11.001&citationId=p_18 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.enpol.2005.06.020&citationId=p_43 http://pubs.aeaweb.org/action/showLinks?crossref=10.1146%2Fannurev.eg.18.110193.002145&citationId=p_27 http://pubs.aeaweb.org/action/showLinks?crossref=10.1093%2Fqje%2Fqjy005&citationId=p_19 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.enpol.2018.05.029&citationId=p_44 72 Journal of Economic Perspectives October, 10. http://www.rff.org/blog/2017/ unpacking-administration-s-revised-social-cost- carbon. Nordhaus, William. 2011. “Designing a Friendly Space for Technological Change to Slow Global Warming.” Energy Economics 33(4): 665–73. Nordhaus, William D. 2014. “The Perils of the Learning Model for Modeling Endogenous Technological Change.” Energy Journal 35(1): 1–13. Popp, David, Richard G. Newell, and Adam B. Jaffe. 2010. “Energy, the Environment, and Tech- nological Change.” Chap. 21 in Handbook of the Economics of Innovation, vol. 2, edited by Bronwyn H. Hall and Nathan Rosenberg. Elsevier. Springel, Katalin. 2018. “Network Externality and Subsidy Structure in Two-Sided Markets: Evidence from Electric Vehicle Incentives.” Unpublished paper. US Department of Energy. 2016. “Revolution … Now: The Future Arrives for Five Clean Energy Technologies—2016 Update.” Primary author: Paul Donohoo-Vallett. Contributing authors: Patrick Gilman, David Feldman, James Brodrick, David Gohlke, Roland Gravel, Amy Jiron, Carol Schutte, Sunita Satyapal, Tien Nguyen, Paul Schei- hing, Blake Marshall, Sarah Harman. Available at: https://www.energy.gov/eere/downloads/ revolution-now-future-arrives-five-clean-energy- technologies-2016-update. US Energy Information Administration. 2018. “Levelized Cost and Levelized Avoided Cost of New Generation Resources in the Annual Energy Outlook 2018.” March. US Environmental Protection Agency. 2015. Regulatory Impact Analysis for the Clean Power Plan Final Rule. October 13. https://www3.epa.gov/ ttnecas1/docs/ria/utilities_ria_final-clean-power- plan-existing-units_2015-08.pdf. van Benthem, Arthur, and Suzi Kerr. 2013. “Scale and Transfers in International Emissions Offset Programs.” Journal of Public Economics 107: 31–46. Vogt-Schilb, Adrien, Guy Meunier, and Stéphane Hallegatte. 2018. “When Starting with the Most Expensive Option Makes Sense: Optimal Timing, Cost and Sectoral Allocation of Abatement Investment.” Journal of Environmental Economics and Management 88: 210–33. Yu, Zhe, Shanjun Li, and Lang Tong. 2016. “Market Dynamics and Indirect Network Effects in Electric Vehicle Diffusion.” Transportation Research Part D: Transport and Environment 47: 336–56. Zhou, Yan, Todd Levin, and Steven E. Plotkin. 2016. Plug-in Electric Vehicle Policy Effectiveness: Literature Review, Argonne National Laboratory ANL/ESD-16/8, May. https://www.energy.gov/ sites/prod/files/2017/01/f34/Plug-In%20Elec- tric%20Vehicle%20Policy%20Effectiveness%20 Literature%20Review.pdf. Zhou, Yiyi, and Shanjun Li. 2017. “Technology Adoption and Critical Mass: The Case of the U.S. Electric Vehicle Market.” NET Institute Working Paper 15-10. http://www.rff.org/blog/2017/unpacking-administration-s-revised-social-cost-carbon http://www.rff.org/blog/2017/unpacking-administration-s-revised-social-cost-carbon http://www.rff.org/blog/2017/unpacking-administration-s-revised-social-cost-carbon https://www.energy.gov/eere/downloads/revolution-now-future-arrives-five-clean-energy-technologies-2016-update https://www.energy.gov/eere/downloads/revolution-now-future-arrives-five-clean-energy-technologies-2016-update https://www.energy.gov/eere/downloads/revolution-now-future-arrives-five-clean-energy-technologies-2016-update https://www3.epa.gov/ttnecas1/docs/ria/utilities_ria_final-clean-power-plan-existing-units_2015-08.pdf https://www3.epa.gov/ttnecas1/docs/ria/utilities_ria_final-clean-power-plan-existing-units_2015-08.pdf https://www3.epa.gov/ttnecas1/docs/ria/utilities_ria_final-clean-power-plan-existing-units_2015-08.pdf https://www.energy.gov/sites/prod/files/2017/01/f34/Plug-In%20Electric%20Vehicle%20Policy%20Effectiveness%20Literature%20Review.pdf https://www.energy.gov/sites/prod/files/2017/01/f34/Plug-In%20Electric%20Vehicle%20Policy%20Effectiveness%20Literature%20Review.pdf https://www.energy.gov/sites/prod/files/2017/01/f34/Plug-In%20Electric%20Vehicle%20Policy%20Effectiveness%20Literature%20Review.pdf https://www.energy.gov/sites/prod/files/2017/01/f34/Plug-In%20Electric%20Vehicle%20Policy%20Effectiveness%20Literature%20Review.pdf http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.jpubeco.2013.08.004&citationId=p_53 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.jeem.2017.12.001&citationId=p_54 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.eneco.2010.08.005&citationId=p_46 http://pubs.aeaweb.org/action/showLinks?crossref=10.1016%2Fj.trd.2016.06.010&citationId=p_55 http://pubs.aeaweb.org/action/showLinks?crossref=10.5547%2F01956574.35.1.1&citationId=p_47 This article has been cited by: 1. Jonas Meckling, Eric Biber. 2021. A policy roadmap for negative emissions using direct air capture. Nature Communications 12:1. . [Crossref] 2. Omar Isaac Asensio, M. Cade Lawson, Camila Z. Apablaza. 2021. Electric vehicle charging stations in the workplace with high-resolution data from casual and habitual users. Scientific Data 8:1. . [Crossref] 3. R. Daniel Bressler. 2021. The mortality cost of carbon. Nature Communications 12:1. . [Crossref] 4. David Schlund, Max Schönfisch. 2021. Analysing the impact of a renewable hydrogen quota on the European electricity and natural gas markets. Applied Energy 304, 117666. [Crossref] 5. Sonia Yeh, Dallas Burtraw, Thomas Sterner, David Greene. 2021. Tradable performance standards in the transportation sector. Energy Economics 102, 105490. [Crossref] 6. Derek Machalek, Kasra Mohammadi, Kody M. Powell. 2021. State-by-State comparison of combined heat and power to photovoltaic installations at manufacturing facilities with heat and power loads. Sustainable Energy Technologies and Assessments 47, 101502. [Crossref] 7. John Bistline, Geoffrey Blanford, Trieu Mai, James Merrick. 2021. Modeling variable renewable energy and storage in the power sector. Energy Policy 156, 112424. [Crossref] 8. Joanna I. Lewis, Gregory F. Nemet. 2021. Assessing learning in low carbon technologies: Toward a more comprehensive approach. WIREs Climate Change 12:5. . [Crossref] 9. Sebastian Schäfer, Lisa Altvater. 2021. A Capacity Market for the Transition towards Renewable- Based Electricity Generation with Enhanced Political Feasibility. Energies 14:18, 5889. [Crossref] 10. Haoye Liu, Rafiu Olalere, Chongming Wang, Xiao Ma, Hongming Xu. 2021. Combustion characteristics and engine performance of 2-methylfuran compared to gasoline and ethanol in a direct injection spark ignition engine. Fuel 299, 120825. [Crossref] 11. Shengqing Xu, Shuiping Dai. 2021. CCUS As a second-best choice for China's carbon neutrality: an institutional analysis. Climate Policy 21:7, 927-938. [Crossref] 12. Zhangqi Zhong, Zhifang Guo, Jianwu Zhang. 2021. Does the participation in global value chains promote interregional carbon emissions transferring via trade? Evidence from 39 major economies. Technological Forecasting and Social Change 169, 120806. [Crossref] 13. Ariel Chiche, Claudia Andruetto, Carina Lagergren, Göran Lindbergh, Ivan Stenius, Luca Peretti. 2021. Feasibility and impact of a Swedish fuel cell-powered rescue boat. Ocean Engineering 234, 109259. [Crossref] 14. Sumit Sharma, Yog Raj Sood. 2021. Optimal planning and sensitivity analysis of green microgrid using various types of storage systems. Wind Engineering 45:4, 939-952. [Crossref] 15. Sam Uden, Paul Dargusch, Chris Greig. 2021. Cutting through the noise on negative emissions. Joule 5:8, 1956-1970. [Crossref] 16. Chikati Roick, Kabir Opeyemi Otun, Nkazi Diankanua, Gorimbo Joshua. Non‐Edible Feedstock for Biodiesel Production 285-309. [Crossref] 17. Angie Ruiz, Jose Guevara. 2021. Energy Efficiency Strategies in the Social Housing Sector: Dynamic Considerations and Policies. Journal of Management in Engineering 37:4, 04021040. [Crossref] 18. Astrida Miceikienė, Kristina Gesevičienė, Daiva Rimkuvienė. 2021. Assessment of the Dependence of GHG Emissions on the Support and Taxes in the EU Countries. Sustainability 13:14, 7650. [Crossref] 19. Torfinn Harding, Julika Herzberg, Karlygash Kuralbayeva. 2021. Commodity prices and robust environmental regulation: Evidence from deforestation in Brazil. Journal of Environmental Economics and Management 108, 102452. [Crossref] https://doi.org/10.1038/s41467-021-22347-1 https://doi.org/10.1038/s41597-021-00956-1 https://doi.org/10.1038/s41467-021-24487-w https://doi.org/10.1016/j.apenergy.2021.117666 https://doi.org/10.1016/j.eneco.2021.105490 https://doi.org/10.1016/j.seta.2021.101502 https://doi.org/10.1016/j.enpol.2021.112424 https://doi.org/10.1002/wcc.730 https://doi.org/10.3390/en14185889 https://doi.org/10.1016/j.fuel.2021.120825 https://doi.org/10.1080/14693062.2021.1947766 https://doi.org/10.1016/j.techfore.2021.120806 https://doi.org/10.1016/j.oceaneng.2021.109259 https://doi.org/10.1177/0309524X20941475 https://doi.org/10.1016/j.joule.2021.06.013 https://doi.org/10.1002/9781119724957.ch10 https://doi.org/10.1061/(ASCE)ME.1943-5479.0000937 https://doi.org/10.3390/su13147650 https://doi.org/10.1016/j.jeem.2021.102452 20. Stewart A. Isaacs, Mark D. Staples, Florian Allroggen, Dharik S. Mallapragada, Christoph P. Falter, Steven R. H. Barrett. 2021. Environmental and Economic Performance of Hybrid Power-to-Liquid and Biomass-to-Liquid Fuel Production in the United States. Environmental Science & Technology 55:12, 8247-8257. [Crossref] 21. Wolfgang Buchholz, Todd Sandler. 2021. Global Public Goods: A Survey. Journal of Economic Literature 59:2, 488-545. [Abstract] [View PDF article] [PDF with links] 22. Juan A. Correa, Marcos Gómez, Andrés Luengo, Francisco Parro. 2021. Environmental misallocation in the copper industry. Resources Policy 71, 102003. [Crossref] 23. Felipe Gonçalves, Rafael Perna, Emília Lopes, Rubens Maciel, Laura Tovar, Melina Lopes. 2021. Strategies to improve the environmental efficiency and the profitability of sugarcane mills. Biomass and Bioenergy 148, 106052. [Crossref] 24. Rafael J P Schmitt, Noah Kittner, G Mathias Kondolf, Daniel M Kammen. 2021. Joint strategic energy and river basin planning to reduce dam impacts on rivers in Myanmar. Environmental Research Letters 16:5, 054054. [Crossref] 25. Pejman Bahramian, Glenn P. Jenkins, Frank Milne. 2021. The displacement impacts of wind power electricity generation: Costly lessons from Ontario. Energy Policy 152, 112211. [Crossref] 26. G. Sánchez-Barroso, J. González-Domínguez, J. García-Sanz-Calcedo. 2021. Impact of urban mobility on carbon footprint in healthcare centers in Extremadura (Spain). International Journal of Sustainable Transportation 63, 1-18. [Crossref] 27. Mohamed Elhagar, Nour El-Emam, Mostafa Awad, Abu Zied Ahmed, Tarek M. Aboul-Fotouh. 2021. Increase flared gas recovery and emission reduction by separator optimization. International Journal of Energy and Environmental Engineering 12:1, 115-130. [Crossref] 28. Michael Grubb, Paul Drummond, Alexandra Poncia, Will McDowall, David Popp, Sascha Samadi, Cristina Peñasco, Kenneth Gillingham, Sjak Smulders, Matthieu Glachant, Gavin Hassall, Emi Mizuno, Edward S Rubin, Antoine Dechezlepretre, Giulia Pavan. 2021. Induced innovation in energy technologies and systems: a review of evidence and potential implications for CO 2 mitigation. Environmental Research Letters . [Crossref] 29. Ziwei Li, Zhiming Qi, Qianjing Jiang, Nathan Sima. 2021. An economic analysis software for evaluating best management practices to mitigate greenhouse gas emissions from cropland. Agricultural Systems 186, 102950. [Crossref] 30. Pavel Tcvetkov. 2021. Climate Policy Imbalance in the Energy Sector: Time to Focus on the Value of CO2 Utilization. Energies 14:2, 411. [Crossref] 31. Ayça Tokuç. Carbon Management 1-7. [Crossref] 32. Seongwon Im, Alsayed Mostafa, Dong-Hoon Kim. 2021. Use of citric acid for reducing CH4 and H2S emissions during storage of pig slurry and increasing biogas production: Lab- and pilot-scale test, and assessment. Science of The Total Environment 753, 142080. [Crossref] 33. Philip J. Ball. 2021. A Review of Geothermal Technologies and Their Role in Reducing Greenhouse Gas Emissions in the USA. Journal of Energy Resources Technology 143:1. . [Crossref] 34. Shyam Nath, Yeti Nisha Madhoo. Climate Change, Sea Level Dynamics, and Mitigation 185-203. [Crossref] 35. Joachim Betz. Klima und Entwicklung 237-253. [Crossref] 36. Massimo Minesso Ferrari, Maria Sole Pagliari. 2021. No Country is an Island. International Cooperation and Climate Change. SSRN Electronic Journal 124. . [Crossref] 37. Edward B. Barbier, Joanne C. Burgess. Policy Implications 143-174. [Crossref] 38. Svetlana Ushak, Yanio E. Milian, Paula E. Marín, Mario Grageda. Applications of wastes based on inorganic salts as low-cost thermal energy storage materials 429-465. [Crossref] https://doi.org/10.1021/acs.est.0c07674 https://doi.org/10.1257/jel.20191546 http://pubs.aeaweb.org/doi/pdf/10.1257/jel.20191546 http://pubs.aeaweb.org/doi/pdfplus/10.1257/jel.20191546 https://doi.org/10.1016/j.resourpol.2021.102003 https://doi.org/10.1016/j.biombioe.2021.106052 https://doi.org/10.1088/1748-9326/abe329 https://doi.org/10.1016/j.enpol.2021.112211 https://doi.org/10.1080/15568318.2021.1914794 https://doi.org/10.1007/s40095-020-00363-z https://doi.org/10.1088/1748-9326/abde07 https://doi.org/10.1016/j.agsy.2020.102950 https://doi.org/10.3390/en14020411 https://doi.org/10.1007/978-3-030-02006-4_81-1 https://doi.org/10.1016/j.scitotenv.2020.142080 https://doi.org/10.1115/1.4048187 https://doi.org/10.1007/978-981-15-4883-3_10 https://doi.org/10.1007/978-3-658-32467-4_19 https://doi.org/10.2139/ssrn.3868947 https://doi.org/10.1007/978-3-030-78698-4_8 https://doi.org/10.1016/B978-0-12-821592-0.00009-1 39. Karolina Drela. 2021. Harnessing solar energy and green hydrogen – the energy transition. Procedia Computer Science 192, 4942-4951. [Crossref] 40. Jun Yang, Xiaoming Li, Shoujun Huang. 2020. Impacts on environmental quality and required environmental regulation adjustments: A perspective of directed technical change driven by big data. Journal of Cleaner Production 275, 124126. [Crossref] 41. Adrien Fabre, Gernot Wagner. 2020. Availability of risky geoengineering can make an ambitious climate mitigation agreement more likely. Humanities and Social Sciences Communications 7:1. . [Crossref] 42. Taekyung Lim, Yunju La, Ok Sung Jeon, Sang Yoon Park, Young Joon Yoo, Keun-Hyeok Yang. 2020. Pore Structure Analysis to Adsorb NOx Gas based on Porous Materials. Journal of the Korean Physical Society 77:9, 790-796. [Crossref] 43. Katja Heinisch, Oliver Holtemöller, Christoph Schult. 2020. Power generation and structural change: Quantifying economic effects of the coal phase-out in Germany. Energy Economics 39, 105008. [Crossref] 44. Na Wu, Zoriana Demchuk, Andriy Voronov, Ghasideh Pourhashem. 2020. Sustainable manufacturing of polymeric materials: A techno-economic analysis of soybean oil-based acrylic monomers production. Journal of Cleaner Production 124939. [Crossref] 45. Ravihari Kotagoda Hetti, Hirushie Karunathilake, Gyan Chhipi-Shrestha, Rehan Sadiq, Kasun Hewage. 2020. Prospects of integrating carbon capturing into community scale energy systems. Renewable and Sustainable Energy Reviews 133, 110193. [Crossref] 46. Reiner Eichenberger, David Stadelmann. 2020. Die politische Ökonomik der Klimapolitik: So wird ein Land mit Kostenwahrheit zum Vorbild beim Klimaschutz. GAIA - Ecological Perspectives for Science and Society 29:3, 148-153. [Crossref] 47. Yashar Manteghi, Jamal Arkat, Anwar Mahmoodi, Hamid Farvaresh. 2020. Competition and cooperation in the sustainable food supply chain with a focus on social issues. Journal of Cleaner Production 124872. [Crossref] 48. Mathias Reynaert. 2020. Abatement Strategies and the Cost of Environmental Regulation: Emission Standards on the European Car Market. The Review of Economic Studies 124. . [Crossref] 49. John E.T. Bistline, Maxwell Brown, Sauleh A. Siddiqui, Kathleen Vaillancourt. 2020. Electric sector impacts of renewable policy coordination: A multi-model study of the North American energy system. Energy Policy 145, 111707. [Crossref] 50. Thomas A. Deetjen, Inês L. Azevedo. 2020. Climate and Health Benefits of Rapid Coal-to-Gas Fuel Switching in the U.S. Power Sector Offset Methane Leakage and Production Cost Increases. Environmental Science & Technology 54:18, 11494-11505. [Crossref] 51. Sheikh Aminur Rahaman, Tania Urmee, David A. Parlevliet. 2020. PV system defects identification using Remotely Piloted Aircraft (RPA) based infrared (IR) imaging: A review. Solar Energy 206, 579-595. [Crossref] 52. Tianyang Wang, Zhe Jiang, Bin Zhao, Yu Gu, Kuo-Nan Liou, Nesamani Kalandiyur, Da Zhang, Yifang Zhu. 2020. Health co-benefits of achieving sustainable net-zero greenhouse gas emissions in California. Nature Sustainability 3:8, 597-605. [Crossref] 53. Edward B. Barbier. 2020. Greening the Post-pandemic Recovery in the G20. Environmental and Resource Economics 76:4, 685-703. [Crossref] 54. Martin C. Hänsel, Moritz A. Drupp, Daniel J. A. Johansson, Frikk Nesje, Christian Azar, Mark C. Freeman, Ben Groom, Thomas Sterner. 2020. Climate economics support for the UN climate targets. Nature Climate Change 10:8, 781-789. [Crossref] https://doi.org/10.1016/j.procs.2021.09.272 https://doi.org/10.1016/j.jclepro.2020.124126 https://doi.org/10.1057/s41599-020-0492-6 https://doi.org/10.3938/jkps.77.790 https://doi.org/10.1016/j.eneco.2020.105008 https://doi.org/10.1016/j.jclepro.2020.124939 https://doi.org/10.1016/j.rser.2020.110193 https://doi.org/10.14512/gaia.29.3.4 https://doi.org/10.1016/j.jclepro.2020.124872 https://doi.org/10.1093/restud/rdaa058 https://doi.org/10.1016/j.enpol.2020.111707 https://doi.org/10.1021/acs.est.9b06499 https://doi.org/10.1016/j.solener.2020.06.014 https://doi.org/10.1038/s41893-020-0520-y https://doi.org/10.1007/s10640-020-00437-w https://doi.org/10.1038/s41558-020-0833-x 55. Keaton S. Miller, Wesley W. Wilson, Nicholas G. Wood. 2020. ENVIRONMENTALISM, STIMULUS, AND INEQUALITY REDUCTION THROUGH INDUSTRIAL POLICY: DID CASH FOR CLUNKERS ACHIEVE THE TRIFECTA?. Economic Inquiry 58:3, 1109-1128. [Crossref] 56. Khalid Anwar, Sandip Deshmukh, Saad Mustafa Rizvi. 2020. Feasibility and Sensitivity Analysis of a Hybrid Photovoltaic/Wind/Biogas/Fuel-Cell/Diesel/Battery System for Off-Grid Rural Electrification Using homer. Journal of Energy Resources Technology 142:6. . [Crossref] 57. Mohammad Shaterabadi, Mehdi Ahmadi Jirdehi, Nima Amiri, Sina Omidi. 2020. Enhancement the economical and environmental aspects of plus-zero energy buildings integrated with INVELOX turbines. Renewable Energy 153, 1355-1367. [Crossref] 58. M. Jean Blair, Warren E. Mabee. 2020. Evaluation of technology, economics and emissions impacts of community-scale bioenergy systems for a forest-based community in Ontario. Renewable Energy 151, 715-730. [Crossref] 59. John Freebairn. 2020. A Portfolio Policy Package to Reduce Greenhouse Gas Emissions. Atmosphere 11:4, 337. [Crossref] 60. Bismark Ameyaw, Yao Li, Augustine Annan, Joy Korang Agyeman. 2020. West Africa’s CO2 emissions: investigating the economic indicators, forecasting, and proposing pathways to reduce carbon emission levels. Environmental Science and Pollution Research 27:12, 13276-13300. [Crossref] 61. Sashwat Roy, Parikhit Sinha, Syed Ismat Shah. 2020. Assessing the Techno-Economics and Environmental Attributes of Utility-Scale PV with Battery Energy Storage Systems (PVS) Compared to Conventional Gas Peakers for Providing Firm Capacity in California. Energies 13:2, 488. [Crossref] 62. James H. Stock. 2020. Climate Change, Climate Policy, and Economic Growth. NBER Macroeconomics Annual 34, 399-419. [Crossref] 63. Mark Burris. Cost-effectiveness of projects and policies 455-470. [Crossref] 64. Joseph Chi, Mathieu Pellerin, Jacobo Rodriguez. 2020. The Economics of Climate Change. SSRN Electronic Journal 100. . [Crossref] 65. Pierre-Olivier Pineau, Nima Rafizadeh. 2020. The Welfare Loss of Subsidies in Global Electricity Markets. SSRN Electronic Journal 33. . [Crossref] 66. Kent D. Daniel, Robert B. Litterman, Gernot Wagner. 2019. Declining CO 2 price paths. Proceedings of the National Academy of Sciences 116:42, 20886-20891. [Crossref] 67. Jared Langevin, Chioke B. Harris, Janet L. Reyna. 2019. Assessing the Potential to Reduce U.S. Building CO2 Emissions 80% by 2050. Joule 3:10, 2403-2424. [Crossref] 68. Bin Zhao, Tianyang Wang, Zhe Jiang, Yu Gu, Kuo-Nan Liou, Nesamani Kalandiyur, Yang Gao, Yifang Zhu. 2019. Air Quality and Health Cobenefits of Different Deep Decarbonization Pathways in California. Environmental Science & Technology 53:12, 7163-7171. [Crossref] 69. Ulka Kelkar, Apurba Mitra. Rethinking Technology for a 1.5 °C World 143-155. [Crossref] 70. Adrien Fabre, Gernot Wagner. 2019. Risky Geoengineering Option Can Make An Ambitious Climate Mitigation Agreement More Likely. SSRN Electronic Journal . [Crossref] 71. Michael Greenstone, Richard McDowell, Ishan Nath. 2019. Do Renewable Portfolio Standards Deliver?. SSRN Electronic Journal . [Crossref] 72. Bryan K. Bollinger, Kenneth Gillingham. 2014. Learning-by-Doing in Solar Photovoltaic Installations. SSRN Electronic Journal . [Crossref] https://doi.org/10.1111/ecin.12889 https://doi.org/10.1115/1.4045880 https://doi.org/10.1016/j.renene.2020.02.089 https://doi.org/10.1016/j.renene.2019.11.073 https://doi.org/10.3390/atmos11040337 https://doi.org/10.1007/s11356-020-07849-7 https://doi.org/10.3390/en13020488 https://doi.org/10.1086/707193 https://doi.org/10.1016/B978-0-12-818122-5.00018-1 https://doi.org/10.2139/ssrn.3715848 https://doi.org/10.2139/ssrn.3679848 https://doi.org/10.1073/pnas.1817444116 https://doi.org/10.1016/j.joule.2019.07.013 https://doi.org/10.1021/acs.est.9b02385 https://doi.org/10.1007/978-981-13-8106-5_8 https://doi.org/10.2139/ssrn.3499684 https://doi.org/10.2139/ssrn.3374942 https://doi.org/10.2139/ssrn.2342406 The Cost of Reducing Greenhouse Gas Emissions Estimates of Static Abatement Costs Brief Background on Marginal Abatement Cost Curves Static Cost Comparisons Dynamic Costs Conceptual Framework Dynamic Cost Case Study 1: Solar Panels Dynamic Cost Case Study 2: Electric Vehicles Static versus Dynamic Costs: Other Examples Challenges References -research paper writing service
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