Unintended consequences of cap-and-trade? Evidence from the Regional Greenhouse Gas Initiative

Energy Economics 80 (2019) 411–422

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Energy Economics journal homepage: www.elsevier.com/locate/eneco

Unintended consequences of cap-and-trade? Evidence from the Regional Greenhouse Gas Initiative夽 Nathan W. Chan a, * , John W. Morrow b a b

University of Massachusetts Amherst, 205E Stockbridge Hall, MA 01003, United States of America TD Bank, United States of America

A R T I C L E

I N F O

Article history: Received 18 June 2018 Received in revised form 19 November 2018 Accepted 11 January 2019 Available online 5 February 2019 JEL classification: Q40 Q51 Q52 Keywords: Cap-and-trade Copollutants Pollution damages

A B S T R A C T Cap-and-trade programs are designed to minimize the overall cost of pollution control by effectively allowing firms with low abatement costs to reduce emissions on behalf of those with higher abatement costs. However, these trades redistribute where emissions are generated, which has important implications for welfare because many pollutants have differential environmental and health impacts depending on where they are emitted. This paper compiles and analyzes a national data set of power plant emissions in order to assess how the Regional Greenhouse Gas Initiative (RGGI), a carbon dioxide (CO2 ) cap-and-trade program involving nine states in the United States, impacts the emissions and damages from copollutants. Our results suggest that, in addition to achieving its goal of reducing CO2 , the program has lowered the quantity of sulfur dioxide (SO2 ) emissions as well as associated damages in the policy region. However, two factors diminish the overall benefits from the program. First, within the RGGI region, trading shifts electricity generation to locations with higher marginal damages for SO2 . Second, there is leakage of electricity generation and emissions to nearby states, although this latter effect is more modest than ex ante analyses predicted. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Given the pressing threat from climate change, there have been growing efforts in the policy sphere to address carbon dioxide (CO2 ) and other greenhouse gases through incentive-based approaches. The European Union’s Emissions Trading System (EU ETS), launched in 2005, was the first large-scale, mandatory cap-and-trade system for carbon emissions, and it operates in all 28 EU member states as well as Iceland, Norway, and Liechtenstein. Parallel efforts in the United States have fallen short of nationwide implementation, so the largest such programs to date are a statewide policy in California and the Regional Greenhouse Gas Initiative (RGGI), which covers nine states in the mid-Atlantic and Northeastern United States. These cap-andtrade programs have generally been lauded by environmentalists and economists, as the strict cap guarantees a target emissions level will be reached while trading allows for flexibility and lower costs of compliance. However, economists have warned that these programs may beget unintended consequences, thus reducing or negating the

夽 Declaration of interests: none. * Corresponding author. E-mail address: [email protected] (N.W. Chan).

https://doi.org/10.1016/j.eneco.2019.01.007 0140-9883/© 2019 Elsevier B.V. All rights reserved.

putative benefits. Of particular concern is the fact that pollutants may be redistributed to more vulnerable areas, which can increase per-unit damages from emissions. Another challenge is emissions leakage, whereby concomitant increases of emissions in unregulated jurisdictions offset reductions in the policy region. In this paper, we provide an ex post policy analysis to quantify the effects of these unintended consequences. We focus on RGGI, America’s first regional CO2 cap-and-trade program, which went into effect on January 1st, 2009 to limit the CO2 emissions from fossil fuel power plants larger than 25 MW. RGGI is attractive to study because it applies only to point sources from the electricity generation sector, making it straightforward to relate emissions to damages, and it has been in effect for nearly a decade, providing sufficient data for analysis. Implementing a difference-in-difference approach, we estimate the causal effects of RGGI on copollutant emissions and accompanying damages. In addition to decreasing CO2 emissions, RGGI also causes large reductions in SO2 emissions, which begets substantial social benefits from avoided damages. However, we find that these benefits are partially eroded, as carbon-trading tends to relocate electricity generation to power plants with higher marginal damages, i.e., those that are located near major urban centers and environmentally sensitive areas. Moreover, while RGGI confers environmental benefits to member states, it also impacts neighboring, non-member

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states. In particular, we observe increases in electricity generation, emissions, and damages in neighboring states, where generators have access to the RGGI region through transmission connections. However, we show that reductions in coal-based generation within RGGI are largely replaced by leakage in the form of cleaner generation from natural gas in Pennsylvania, Ohio, and other nearby states. Because of this change in fuel inputs, the environmental harms from leakage are partially diminished. Our work makes an important contribution to the literature that analyzes the effects of cap-and-trade systems. We apply and extend lessons from Murray and Maniloff (2015), who examine the impact of RGGI on greenhouse gas emissions while controlling for a host of contemporaneous compounding changes in energy markets. A major area of concern in our work is the unintended consequences (or ancillary benefits) from carbon policies, such as leakage and the increased or decreased prevalence of copollutants. As Muller and Mendelsohn (2009) identify, many cap-and-trade policies exchange emissions on a per-ton basis. However, damages from such pollutants are spatially heterogeneous, so trading can unintentionally increase pollution damages if emissions are redistributed to more vulnerable regions. This will tend to occur if marginal abatement costs are positively correlated with marginal damages, which they find to be the case for the trade of sulfur dioxide (SO2 ) under the Acid Rain Program. As such, the authors recommend trading ratios to account for damage disparities, and they estimate that nationwide economic gains from trading ratios could number in the hundreds of millions of dollars per year. In a similar vein, Henry et al. (2011) report that the Acid Rain Program actually increased damages relative to a no-trading baseline, as trades moved SO2 emissions to more damaging areas. Perhaps even more surprisingly, they find that the trading program underperformed relative to a uniform emission standard, as the cost savings from trading were outweighed by the higher marginal damages. For our ex post damage analysis, we build upon the approaches from prior work (Muller and Mendelsohn, 2007, 2009; Henry et al., 2011). However, our focus is unique, as we consider copollutants. That is, we study the effects of a CO2 cap-and-trade program on two primary copollutants, SO2 and nitrogen oxides (NOX ), that are not explicitly targeted by the policy. Whereas prior work has focused on nationwide policies like the Acid Rain Program, we consider a regional program in RGGI. Leakage and spill-overs become paramount concerns here that were not relevant in the analysis of nationwide policies, which presents an additional econometric problem, as incomplete coverage makes it more difficult to identify the causal relationship between the policy and observed patterns of electricity generation and emissions. We address this challenge by using a difference-in-difference approach along with a series of robustness checks. Leakage has been the subject of much attention in research on pollution control (Bushnell et al., 2008). Stavins (2008) proposes an economy-wide cap for CO2 emissions, arguing that a large geographic scope of coverage is useful to prevent leakage domestically. He furthermore suggests applying similar permit requirements to foreign entities that produce carbon-intensive imports, thus reducing leakage across national borders. Fowlie (2009) uses an analytical model to simulate a CO2 regulation in California’s electricity sector, and she projects significant efficiency losses from leakage. Fell and Maniloff (2018) also examine the issue of leakage from RGGI, but they focus on greenhouse gas emissions rather than accompanying pollutants that cause local damages. For RGGI specifically, Chen (2009) simulates the leakage of CO2 to non-RGGI states that arises from energy price differentials, and he also quantifies the predicted “spill-overs” of SO2 and NOX that accompany these changes in electricity generation. He projects that 80% or more of the intended CO2 reductions from RGGI could be offset by leakage, while spill-overs of SO2 and NOX could also be significant. Bushnell and Chen (2012) conduct a similar exercise analyzing the extent of leakage in a hypothetical permit market in the western

U.S. Caron et al. (2015) estimate that leakage in California’s cap-andtrade program would offset emissions reductions by 9%. They argue that the California policy benefits from two important provisions: (1) inclusion of imported electricity in the cap and (2) a ban on shuffling of resources to circumvent regulations; without such measures, the authors estimate that 45% of emissions reductions would be offset by leakage. Burtraw et al. (2003) also use a simulation approach, and they examine a set of hypothetical carbon taxes to analyze the ancillary benefits from accompanying SO2 and NOX abatement. They find that a $25 per metric ton tax on carbon would yield additional ancillary benefits on the order of $12–14 per ton. Our work complements prior simulation exercises, yet it remains distinct in several respects. First, it provides an ex post policy analysis to stand alongside prior predictive exercises (Burtraw et al., 2003; Chen, 2009; Bushnell and Chen, 2012; Caron et al., 2015). With the benefit of hindsight, we show that leakage does occur, as suspected by prior researchers, but substitution to cleaner fuel inputs partially mitigates the harm. Second, this paper examines leakage on multiple dimensions, including electricity generation, CO2 emissions, and copollutants. Third, it provides important insights into the welfare implications of cap-and-trade programs, particularly those stemming from spatial redistribution of pollutants. Our work is closest to that of Chen (2009), but his analysis is restricted only to spill-overs of pollutants from the RGGI region to outside of RGGI, which is understandable given his focus on the quantity of pollution (i.e., tonnage of SO2 and NOX ) rather than damages from pollution. Yet, as we show, the spatial heterogeneity in damages creates an extra layer of concern that warrants attention. Because of this heterogeneity, it is not only important to characterize spill-overs from inside the policy area to outside, but it is also critical to know how emissions are redistributed within the region. This, too, can have important consequences for the net costs and benefits of the program. In what follows, we provide some institutional background and outline our basic research questions and hypotheses in Section 2. We proceed to describe our data and empirical approach in Section 3, and we present and discuss results in Section 4. Section 5 concludes.

2. Background and hypotheses The Regional Greenhouse Gas Initiative (RGGI) is the first mandatory CO2 cap-and-trade program in the United States. It went into effect on January 1st, 2009 and applies to CO2 emissions from fossil fuel power plants in member states larger than 25 MW. RGGI was initially composed of ten states: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont. From 2009 to 2011, the cap was set at 188 million tons of CO2 per year (RGGI CO2 Cap, 2015). At the end of 2011, New Jersey announced that it was withdrawing from the program. Following New Jersey’s departure, the cap was lowered. A 2012 program review set a new 2014 cap of 91 million tons, which declines by 2.5% annually (Regional Greenhouse Gas Initiative, 2015b). RGGI distributes CO2 permits by quarterly auctions. The proceeds from the auctions are invested in supporting renewable energy sources. A report on the returns from investment of 2009–2013 RGGI proceeds estimates that five years of investments will return more than $2.9 billion in lifetime energy bill savings (Regional Greenhouse Gas Initiative, 2015a). The report also estimates that investments in renewable energy sources will further reduce emissions by 10 million tons of CO2 . By their nature, cap-and-trade systems change the spatial distribution of production and pollutants, a fact that is well-noted by Muller and Mendelsohn (2007, 2009), Henry et al. (2011), among others. Thus, our purpose is to empirically investigate how emissions

N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422

and damages for copollutants (SO2 and NOX ) change when CO2 is regulated.1 We proceed with three primary hypotheses: Hypothesis 1. [Emissions] Facilities in RGGI states will decrease CO2 , SO2 , and NOX after the implementation of RGGI in 2009. This reduction will be larger in magnitude than in other regions. Because SO2 , CO2 , and NOX are complements in production (copollutants), we predict that a cap on CO2 will also reduce emissions of SO2 and NOX . Hypothesis 2. [Damages] Damages from SO2 and NOX may increase or decrease in the RGGI region, depending on the overall reduction in emissions of these pollutants and the spatial distribution of electricity generation. RGGI’s effect on damages will be ambiguous a priori, and the outcome will depend on two potentially countervailing effects. First, we predict that total copollutant emissions will drop in the policy region, per Hypothesis 1, which tends to decrease damages. Second, RGGI will likely alter the spatial distribution of emissions by shifting CO2 emissions from plants with low CO2 abatement costs to plants with high CO2 abatement costs. If local SO2 and NOX damages are positively correlated with CO2 abatement costs, then the average unit of emissions will become more damaging, thus eroding or overturning the welfare gains from aggregate reductions in copollutant emissions. The converse is true if SO2 and NOX damages are negatively correlated with CO2 abatement costs. Henry et al. (2011) find that urban areas have higher marginal abatement costs for sulfur as well as higher marginal damages relative to rural areas. Urban areas face higher input costs, which drive up marginal abatement costs. They also have higher population densities, which increases marginal damages. The same mechanism may drive a positive correlation between CO2 abatement costs and copollutant damages in our study, an issue that we will explore in subsequent analysis. Hypothesis 3. [Leakage] Electricity generation, CO2 emissions, and copollutant emissions will increase in regions that neighbor RGGI due to leakage. The cap on CO2 raises production costs for plants in the RGGI region, thus favoring electricity generated in nearby, unregulated areas. Specifically, we will examine this effect for Pennsylvania and Ohio, two large electricity-producing states that provide the primary transmission interface with the RGGI states, and the broader PJM Interconnection, which is a collection of states proximate to (and in some cases, including) states in the RGGI region. Fell and Maniloff (2018) also use Pennsylvania and Ohio to investigate leakage for this reason, although their analysis is focused on CO2 emissions rather than copollutants. 3. Data and empirical approach Our analysis relies upon two primary data sources. First, we obtained daily data for power plants throughout the continental United States from the Environmental Protection Agency’s (EPA) Air Markets Program database for 2002–2016 (U.S. Environmental Protection Agency, 2018). The data are available at the boiler level

1 While it would be interesting to analyze other criteria pollutants in addition to SO2 and NOX , we do not observe their emissions in the EPA power plant data that we use.

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and contain both boiler and facility characteristics, with information on location, power generation, emissions (CO2 , SO2 , and NOX ), and primary fuel type for each boiler. We aggregate across boilers to the facility level and we aggregate the daily data into monthly totals. Our final data set is at the facility-month level, with just under 300,000 observations overall.2 To quantify pollution damages from each plant, we match each plant to county-level marginal damage estimates from the Air Pollution Emission Experiments and Policy (APEEP) analysis model (Muller and Mendelsohn, 2006, 2009). APEEP is an integrated assessment model that combines an air quality modeling module with estimates of exposures and localized damages for six pollutants: sulfur dioxide (SO2 ), nitrogen oxides (NOX ), volatile organic compounds (VOCs), coarse particulte matter (PM10), fine particulate matter (PM2.5), and ammonia (NH3). The air quality module uses a source-receptor matrix framework to model atmospheric transport, identifying how emissions from a given county of origin will be dispersed across receptor counties throughout the United States. At each receptor county, the model uses economic estimates to quantify the damages incurred by one unit of emissions in that county. These damage estimates account for an array of economic losses, including “adverse effects on human health, reduced yields of agricultural crops and timber, reductions in visibility, enhanced depreciation of man-made materials, and damages due to lost recreation services” (Muller and Mendelsohn, 2006).3 By summing the damages at the various receptor counties, the model is able to estimate the overall marginal damage of one additional ton of emission generated in a given source county. We then compute the total damage produced by each plant by multiplying the quantities of SO2 and NOX at the plant by the county-specific marginal damages for that plant. We present summary statistics in Table 1. Notably, there are marked differences in generation, emissions, and damages between RGGI and non-RGGI states. However, it is unclear to what extent these discrepancies are driven by the RGGI program itself or by underlying differences between the states. This will be one of the guiding questions for our econometric analysis. We also split the sample into periods before and after implementation of RGGI, as shown in Table 2. Here, it is clear that there is a general reduction in generation, emissions, and damages over time, but the question is whether there is a more precipitous drop in RGGI states than in non-RGGI states as a virtue of the program. A second important observation is that damages from SO2 are much larger than damages from NOX , by more than an order of magnitude. As such, any welfare effects from copollutants are likely to be driven primarily by SO2 . As such, we will present results for both SO2 and NOX in our primary regressions, but we will drop the latter for most extensions and robustness checks. Fig. 1 compiles these general insights and the essence of our difference-in-difference approach in a single graph for SO2 . Here, we show how total emissions and damages evolve over time for RGGI

2 We do not conduct the empirical analysis at the daily level for several reasons. First, doing so is computationally intensive. Second, there may be anomalies in facility-level generation and emissions on a day-to-day basis that could affect our estimates, especially given our log-linear specification. For example, if a facility is not operating on a given day, that observation will be dropped from our data, but if that same facility is generating (or emitting) a very small but non-zero amount, it will have a strong negative effect on our estimates. Aggregating to the monthly level helps avoid such anomalies from unduly influencing our estimates. Lastly, the timing of the RGGI treatment is based on the year, so we do not expect our primary conclusions to change (anomalies aside) when using a finer temporal resolution, although the precision of our estimates would likely improve. 3 These damage estimates combine estimates of exposures, dose-response functions, and economic valuation of impacts. While the scientific underpinnings of these calculations are beyond the scope of this paper, they are well-documented by Muller and Mendelsohn (2006). A large portion of overall damages are driven by pollutioninduced mortality and morbidity, a subject that has received a great deal of attention in the scientific literature (see, e.g., Liao et al. (2009), Tagaris et al. (2010), Lelieveld et al. (2015), and Di et al. (2017)).

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N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422 Table 1 Summary statistics: Facility-month averages by state classification.

Generation (mwh) SO2 emissions (tons) SO2 damage ($) NOx emissions (tons) NOx damage ($) CO2 (tons) Observations

Full

RGGI

Non-RGGI

Diff

159,483 (289,305) 356 (1178) 13,784,905 (52,389,668) 152 (398) 728,997 (2,259,862) 138,418 (276,612) 275,794

65,933 (127,997) 117 (558) 6,656,444 (34,309,975) 44 (133) 80,161 (311,554) 45,385 (95,686) 45,989

178,204 (308,329) 403 (1260) 15,211,466 (55,192,129) 174 (428) 858,843 (2,451,217) 157,036 (296,505) 229,805

−112,272*** (−127.95) −286*** (−77.41) −8,555,023*** (−43.40) −131*** (−120.17) −778,682*** (−146.49) −111,651*** (−146.40) 275,794

Notes: Standard deviations in parentheses below means. t-statistic in parentheses below differences. Table 2 Summary statistics: Facility-month averages by timing.

Generation (mwh) SO2 emissions (tons) SO2 damage ($) NOx emissions (tons) NOx damage ($) CO2 (tons) Observations

Full

2002–2008

2009–2016

Diff

159,483 (289,305) 356 (1178) 13,784,905 (52,389,668) 152 (398) 728,997 (2,259,862) 138,418 (276,612) 275,794

167,598 (306,186) 597 (1623) 23,905,281 (73,005,151) 234 (538) 1,125,216 (3,136,867) 155,220 (301,357) 112,649

155,114 (280,054) 209 (722) 7,585,268 (31,037,506) 102 (257) 484,014 (1,346,020) 129,395 (262,321) 138,204

−13,718*** (−12.02) −408*** (−79.65) −17,108,332*** (−74.69) −138*** (−80.46) −669,802*** (−67.83) −28,403*** (−25.79) 275,794

Notes: Standard deviations in parentheses below means. t-statistic in parentheses below differences.

states and non-RGGI states, normalized by emissions in base year 2002. As discussed above, both regions have downward trends in emissions and damages over time; however, there is an additional decline in RGGI states starting in 2009, the year that RGGI began. The putative treatment effect is visually striking, providing initial evidence that the RGGI program drives changes in SO2 emissions. On the other hand, no such effect is immediately discernible in a similar graph of NOX emissions and damages (Fig. 2), another reason why NOX will not figure prominently in terms of policy implications. To investigate these relationships more formally, we use a difference-in-difference framework to assess the impact of RGGI on emissions and damages. The basic difference-in-difference strategy is as follows, although some of the variables like rggi and Post will be absorbed by fixed effects in our primary specifications. log(yismt ) =a + b1 rggiis + b2 Postmt + b3 rggi × Postismt + cXismt + mi + lm + ht + eismt ,

(1)

where subscripts i, s, m, and t index by facility, state, month, and year. y is the outcome variable of interest (e.g., generation, emissions, or damages), rggi is a dummy variable that is equal to 1 if the plant resides in a state that belongs to the RGGI program, Post is a dummy variable that is 1 for all years from 2009 onward, and rggi × Post is the interaction between these two.4 The treatment effect of interest

4 New Jersey was initially a member of RGGI but discontinued its membership on January 1, 2012. For our primary regressions, we classify New Jersey as a RGGI state (rggi = 1) and designate rggi × Post = 1 for the years 2009-2011 and rggi × Post = 0 otherwise. We have also rerun these regressions classifying New Jersey as a non-RGGI state, and our results do not change meaningfully. Moreover, this distinction is irrelevant for our primary specification, in which we include facility fixed effects, as the fixed effects absorb the rggi variable.

is b3 , as this captures how the implementation of RGGI influences yismt . X is a vector of control variables. First, it includes dummy variables for a series of 10◦ F temperature bins that equal one if the observed daily average temperature Tismt falls within the range, and zero otherwise.5 Temperature data were compiled from the PRISM Climate Group database (PRISM Climate Group, Oregon State University, 2018). Further, a number of states have Renewable Portfolio Standards (RPS), which are policies that require utilities to use renewable resources for a prescribed proportion of their electricity sales, and the stringency of these standards varies by state and year. We build upon the data used by Johnson (2014), which captures the percentage of renewable energy required by state RPS policies, by extending it to include more recent years using information from the Database of State Incentives for Renewables and Efficiency (NC Clean Energy Technology Center, 2018) and the Berkeley Lab (Berkeley Lab, 2017). We also control for fuel prices using data from the Energy Information Administration, which has fuel price information that is specific to the electricity generation sector. Prior work, such as Murray and Maniloff (2015), uses national-annual prices for their analysis, but we take advantage of more detailed data in terms of spatial and temporal resolution. We use natural gas prices at the state-month level. We use state-year prices for coal, but this data set is only available from 2008 onward, so we use national-annual data for years prior. For our primary specifications, we include these prices as a linear function of the coal-to-gas price ratio, although we show that there

5 This non-parametric form controls for weather-induced energy demand in a more flexible manner than parametric specifications with heating degree days and cooling degree days. The lowest temperature bin captures all temperatures below 40◦ F, while the highest captures temperatures above 80◦ F.

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Fig. 1. Time trends of SO2 emissions and damages for RGGI and non-RGGI states.

Fig. 2. Time trends of NOX emissions and damages for RGGI and non-RGGI states.

is little change in our estimates of interest when using alternative controls for fuel prices.6 Lastly, we include facility fixed effects, mi , month fixed effects, l m , and year fixed effects, ht , in our primary specifications. As mentioned

6 Cullen and Mansur (2017) and Fell and Maniloff (2018) use a nonparametric function of the coal-to-gas price ratio, which is more flexible. For our primary specifications, we maintain a more parsimonious approach. However, we also include robustness checks using a non-parametric function of the coal-to-gas price ratio among other parametric forms for fuel prices, finding similar results in all cases (see section on Robustness and Tables 7 to 9). We suspect that some of the variation in prices is captured by our fixed effects and other controls, thus reducing their impact on our overall findings.

above, these fixed effects will absorb the rggi and Post variables described in Eq. (1) while also removing additional confounding variation from unobservable factors. They help control for a wide variety of time invariant and seasonal factors as well as general trends over time.

4. Results We begin by assessing RGGI’s effectiveness in achieving its stated purpose: decreasing CO2 emissions. We then turn to our primary hypotheses regarding RGGI’s effect on copollutants and associated damages.

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Table 3 Primary results.

RGGI × Post Observations Adjusted R2 PJM filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

(1)

(2)

(3)

(4)

(5)

(6)

CO2 tons

SO2 tons

SO2 damage

NOx tons

NOx damage

mwh

−0.22* (0.11) 96,072 0.11 Y Y Y Y Y Y

−0.48** (0.20) 93,299 0.13 Y Y Y Y Y Y

−0.49** (0.20) 93,060 0.12 Y Y Y Y Y Y

−0.19 (0.14) 103,697 0.13 Y Y Y Y Y Y

−0.19 (0.14) 103,435 0.13 Y Y Y Y Y Y

−0.27*** (0.10) 97,675 0.11 Y Y Y Y Y Y

Notes: Dependent variable is logged. Robust standard errors clustered at the state level are presented in parentheses. Regressions include controls for temperature bins, fuel prices, and renewable portfolio standards along with facility, month, and year fixed effects. The comparison group is non-PJM states; PJM states are excluded from these regressions because they are likely leaker states that may violate the SUTVA conditions.

4.1. Emissions Table 3 displays regression coefficients for Eq. (1), with each column providing results for a different outcome variable. In these primary specifications, we include fixed effects for facility, month, and year, as well as controls for temperature bins, fuel prices, and RPS. However, we will suppress estimates for controls and fixed effects in our tables throughout the paper for compactness; Table A.1 provides the full suite of estimates, including controls, for Eq. (1). Also, our primary specifications focus on RGGI states versus control states that do not belong to the PJM Interconnection, which is a regional transmission organization that coordinates electricity flow for much of the Atlantic seaboard and the eastern portion of the Midwestern United States, including Pennsylvania, Ohio, and several RGGI states. While we retain RGGI states that belong to PJM, we exclude all other PJM states from this regression, as inclusion of these nearby states may violate the stable unit treatment value assumption (SUTVA) via spillovers or leakage between neighboring units. The exclusion of such PJM states is noted in the footers of our regression tables using the “PJM filter” term. We explore variations on our primary specification, including different fixed effects, specifications for temperature, and different control groups in the Appendix. Reassuringly, our primary findings are robust to these various modifications. We focus for now on column 1, where the outcome variable is logged CO2 emissions. The point estimates suggest that RGGI implementation reduced emissions of CO2 from RGGI states, as intended, with significance at the 10% level. The regression coefficient (−0.22) suggests that CO2 reductions at the average RGGI facility were roughly 20% greater than for the average non-RGGI facility.7 This reduction in greenhouse gas emissions is strikingly similar to the finding of Murray and Maniloff (2015), which thoroughly documents the effect of RGGI and a host of other factors on CO2 emissions; they find a RGGI effect on CO2 emissions of 19–24 %. As such, the program achieved its intended goal of reducing CO2 emissions. Did it have additional ancillary benefits or unintended consequences in terms of copollutants? Columns 2–5 of Table 3 provide coefficient estimates from the equation above using (the log of) four related outcome variables: SO2 emissions, SO2 damages, NOX emissions, and NOX damages. Notably, the estimates for regressions based on (1) emissions and (2) damages are virtually identical, which is likely due to the fact that our marginal damage measure,

7 Throughout, we interpret the regression coefficients as percent changes using the transformation %D = eb − 1.

which transforms tons into damages, is facility-specific and timeinvariant, so that it is obviated by the inclusion of fixed effects. Thus, in subsequent analyses, we will exclude explicit analysis of outcomes for damages, as they are so closely related to emissions tonnage. The regression results suggest that RGGI had a causal effect on SO2 , leading to a decline in emissions and damages in affected states after implementation of the program. The magnitude of this effect suggests that average SO2 emissions decreased by roughly 38% compared to non-RGGI facilities (based on the coefficient of −0.48). However, we find no evidence of statistically significant effects on NOX , consistent with Fig. 2. Thus, we partially confirm Hypothesis 1, finding a decrease in SO2 emissions and damages, but not so for NOX emissions and damages. As such, we will focus subsequent analysis on SO2 emissions and damages. We attribute these declines in SO2 to two factors. The first factor is that there are overall reductions in electricity generated via fossil fuels in the RGGI region. In terms of raw, uncontrolled means, fossil fuel generation per plant in the RGGI region fell 15% when comparing the post-RGGI implementation period (2009–2016) to the pre-RGGI period (2002–2008), whereas the corresponding decline in non-RGGI states was a more modest 8%. Secondly, we speculate that RGGI redistributes electricity generation within the RGGI region, whereby less pollution-intensive firms take on a larger share of electricity generation. One channel for such redistribution is through a change in fuel inputs (i.e., transferring generation from coal plants to natural gas plants). To shed light on this channel, we run the same regression as the above, except we do so for two subsets of the data: facilities that use coal and facilities that use combined cycle natural gas (NGCC) generation. Results are presented in Table 4. For coal facilities, we find pronounced reductions in electricity generation and accompanying CO2 and SO2 emissions. These magnitudes are considerably larger than in Table 3, suggesting that the aggregate effects we observed were driven primarily by reductions in coal generation in the RGGI region. Meanwhile, we did not observe any reductions in emissions among NGCC facilities arising from RGGI, although there is weak statistical evidence that NGCC generation decreased due to RGGI. 4.2. Damages We will now explore the implications of these changes in emissions for overall damages, with a focus on SO2 . In principle, there are several factors that contribute to RGGI’s impact on aggregate damages. Most obviously, reductions in SO2 would reduce damages in a direct manner. However, spatial redistribution of pollutants

Observations Adjusted R2 PJM Filter? Facility FE? Month and year FE? Temperature bins? Fuel Prices? RPS?

Notes: Dependent variable is logged. Robust standard errors clustered at the state level are presented in parentheses. Regressions include controls for temperature bins, fuel prices, and renewable portfolio standards along with facility, month, and year fixed effects. The comparison group is non-PJM states; PJM states are excluded from these regressions because they are likely leaker states that may violate the SUTVA conditions.

mwh

−0.20* (0.11) 32,678 0.10 Y Y Y Y Y Y −0.05 (0.10) 33,151 0.09 Y Y Y Y Y Y

NOx tons

NOx damage

−0.14 (0.13) 31,169 0.09 Y Y Y Y Y Y

SO2 damage SO2 tons

−0.15 (0.13) 31,213 0.09 Y Y Y Y Y Y 0.13 (0.11) 31,636 0.10 Y Y Y Y Y Y

CO2 tons NOx damage

−0.24 (0.17) 22,188 0.28 Y Y Y Y Y Y −0.24 (0.17) 22,188 0.28 Y Y Y Y Y Y

NOx tons SO2 damage

−0.89** (0.39) 20,984 0.29 Y Y Y Y Y Y

mwh

−0.79*** (0.17) 21,055 0.20 Y Y Y Y Y Y

−0.46*** (0.10) 19,769 0.20 Y Y Y Y Y Y

SO2 tons

−0.89** (0.39) 20,984 0.29 Y Y Y Y Y Y

CO2 tons

RGGI × Post

(5) (4) (3) Table 4 Primary results by fuel type.

−0.05 (0.10) 33,195 0.09 Y Y Y Y Y Y

(12) (7) (2) (1)

(6)

NGCC Coal

(8)

(9)

(10)

(11)

N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422

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represents a second, indirect channel for changing damages, and this could either increase or decrease damages. To formalize this, note that total damages from SO2 in  a region r MD ×e  i i is can be expressed as TDr = MDr × er , where MDr = i∈r i∈r ei the weighted average marginal damage per ton of SO2 emissions in region r and e is the tonnage of emissions in that region.8 Following the introduction of RGGI, there is an accompanying, observed change in total damages that can be decomposed as follows: dTDr =

MD × de  r r Due to aggregate emissions

+

e × dMD .  r  r

(2)

Due to damage intensity

That is, the change in overall damages in the RGGI (or non-RGGI) region is affected by the change in aggregate emissions as well as the potential change in average damage intensity. The latter, dMDr will increase if RGGI redistributes generation and emissions to facilities with higher values of MDi . We know from Table 3 that there is a drop in the overall level of emissions, so that der < 0. How large a role does the distribution of emissions (dMDr ) play? Table 5 shows estimates for a regression with electricity generation as the outcome variable. In column 1, we run our primary specification with a key modification: we include an additional interaction term, rggiis × Postmt × MDi , where MDi is a facility-level measure of marginal damages. The coefficient on this interaction term will indicate whether electricity generation was redistributed toward plants with higher (positive coefficient) or lower (negative coefficient) marginal damages. Indeed, the term has a positive coefficient that suggests that although RGGI plants tend to decrease their generation overall, those with higher marginal damages take on a larger share of generation after implementation. In column 2, we reproduce these results using a regression that does not use facility fixed effects. We drop facility fixed effects here, as they may soak up meaningful variation in generation loads across facilities with different marginal damages because MDi is a time-invariant, facilitylevel measure. We find a comparable magnitude and the statistical significance is enhanced. The consistency in magnitude across these two specifications gives us confidence that this is meaningful effect— i.e., generation is moving to higher-damage areas—and it is not an artifact of modeling choices. Columns 2 through 6 of Table 5 provide another perspective on this issue. Here, we analyze RGGI (columns 3 and 4) and non-RGGI (columns 5 and 6) subsamples separately to see whether, within region, plants with higher marginal damages generated more electricity beginning in 2009. We see again that there was an increase in generation by more damaging plants in the RGGI region, as we have a positive coefficient on Post × MD among RGGI states for specifications with and without fixed effects. For non-RGGI states, the coefficient on Post × MD is an order of magnitude smaller and equivocal in terms of statistical significance, indicating that the marginal damage of the average plant in non-RGGI states remained virtually unchanged. Thus, RGGI actually increased the average marginal damages from SO2 in the RGGI region by redistributing emissions from lowimpact areas to higher-impact ones. This implies that the plant-level marginal damages are positively correlated with CO2 abatement costs. That is, plants with high MD tend to have higher CO2 abatement cost, and as such, they generate a larger share of electricity in the RGGI region once the cap-and-trade system is implemented.

8 We assume that MDi , the marginal damage from facility i, is constant with respect to emissions, so that total damages from that facility are simply the product TDi = MDi × ei . As shown by Henry et al. (2011), the marginal damage curves for SO2 are almost perfectly horizontal with respect to overall emissions, so our assumption is supported by prior evidence. Even so, at the regional level, the average marginal damage, MDr may still vary when emissions are redistributed across sources. Therefore, any variation in MDr arises entirely from variation in the sources of emissions rather than the level of emissions.

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N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422

Table 5 Shifting generation by marginal damages.

Post × MD RGGI × Post × MD Observations Adjusted R2 PJM filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

All

All

RGGI

RGGI

Non-RGGI

Non-RGGI

(1) mwh

(2) mwh

(3) mwh

(4) mwh

(5) mwh

(6) mwh

−0.01 (0.02) 0.19* (0.10) 97,423 0.11 Y Y Y Y Y Y

0.02 (0.01) 0.23*** (0.05) 97,423 0.10 Y N Y Y Y Y

0.18* (0.10)

0.26*** (0.05)

−0.02 (0.02)

0.02* (0.01)

24,537 0.12 Y Y Y Y Y

24,537 0.04 N Y Y Y Y

72,886 0.11 Y Y Y Y Y

72,886 0.05 N Y Y Y Y

Notes: Dependent variable is logged. The first two columns use the full sample (excluding PJM facilities that are not in the RGGI region, consistent with our primary specification). Sample is restricted to facilities in RGGI states for the third and fourth column and to non-RGGI, non-PJM states for the last two columns. The first, third, and fifth columns use facility fixed effects, as in our primary specification, but the others do not. Robust standard errors are presented in parentheses. Regressions include controls for temperature bins, fuel prices, and renewable portfolio standards along with facility, month, and year fixed effects.

This increase in average marginal damage countervails the averted damages from emission reductions in the region.

can then be interpreted as increases above and beyond broader nationwide trends. log(yismt ) = a + b1 POi + b2 Postmt + b3 PO × Postismt + cXismt

4.3. Leakage

+ mi + lm + ht + eismt , A common concern with regional pollution policies is the issue of leakage. That is, apparent emissions reductions in the policy region may actually be offset by increases in emissions in unregulated regions. To examine this concern in the RGGI context, we investigate whether nearby states experienced an uptick in emissions upon the introduction of RGGI. Specifically, we examine emissions in Pennsylvania and Ohio, two states that border RGGI and where generators have access to the RGGI region. Prior work by Fell and Maniloff (2018) also uses Pennsylvania and Ohio as focal states to examine the prospect of leakage(inCO2 emissions).Inaddition,weconsideraseparatespecification that treats all non-RGGI states in the PJM Interconnection as potential leaker states. To this end, we incorporate an additional dummy variable to denote plants in Pennsylvania and Ohio (PO) as well as an interaction with the RGGI treatment year (POPost). Our leakage regression equation is as follows. We also run an analogous regression using PJM states. In both cases, we exclude RGGI states from the comparison group, so that we do not obtain results that our symmetric to our primary RGGI results by construction, and any positive results

(3)

In Table 6, we present our initial electricity generation (mwh) results from the RGGI region as well as corresponding leakage regression results for (i) Pennsylvania and Ohio and (ii) PJM states. This table offers more nuanced insights on the precise nature of leakage. In particular, RGGI states reduce electricity generation overall, especially from coal-fired power plants. Meanwhile, some of this reduction is taken up by additional generation in Pennsylvania and Ohio and PJM states in the form of natural gas production. The results for the broader PJM regression are similar to those of the Pennsylvania/Ohio regression, although the increase in natural gas production is less pronounced, indicating that other PJM states were less likely to take on leakage than Pennsylvania and Ohio. Still the overall findings on leakage are quite significant, and they indicate that nearby states took on a greater generation load in the wake of RGGI, with accompanying consequences for emissions. However, this leakage substituted cleaner natural gas generation for more pollution-intensive coal-based production, thus reducing the harms from leakage.

Table 6 Leakage results for electricity generation.

RGGI × Post

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

All

Coal

NGCC

All

Coal

NGCC

All

Coal

NGCC

−0.27*** (0.10)

−0.46*** (0.10)

−0.20* (0.11) −0.13 (0.08)

−0.07 (0.09)

1.17*** (0.18) -0.02 (0.07) 103,459 0.10 N Y Y Y Y Y Y

-0.09 (0.06) 31,674 0.14 N Y Y Y Y Y Y

0.93*** (0.20) 26,669 0.14 N Y Y Y Y Y Y

PO × Post PJM × Post Observations Adjusted R2 PJM filter? RGGI filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

97,675 0.11 Y N Y Y Y Y Y

19,769 0.20 Y N Y Y Y Y Y

32,678 0.10 Y N Y Y Y Y Y

103,459 0.10 N Y Y Y Y Y Y

31,674 0.14 N Y Y Y Y Y Y

26,669 0.14 N Y Y Y Y Y Y

Notes: Dependent variable is logged. Robust standard errors clustered at the state level are presented in parentheses. Regressions include controls for temperature bins, fuel prices, and renewable portfolio standards along with facility, month, and year fixed effects.

Table 7 Robustness checks: CO2 .

RGGI × Post

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Base

All States

E of MS

−0.22* (0.11) 96,072 0.11 Y Y Y Y Y Y

−0.19* (0.10) 126,192 0.10 N Y Y Y Y Y

−0.14* (0.08) 71,170 0.10 N Y Y Y Y Y

East Coast

Dereg

Linear Prices

Price Ratio NP

National Price

No Price

No Temp

Robust SE

−0.17* (0.08) 39,941 0.11 N Y Y Y Y Y

−0.15 (0.10) 72,335 0.10 N Y Y Y Y Y

−0.16 (0.09) 92,748 0.11 Y Y Y Y Y Y

−0.19* (0.11) 96,072 0.11 Y Y Y Y Y Y

−0.15* (0.09) 92,748 0.11 Y Y Y Y Y Y

−0.17* (0.09) 135,192 0.10 Y Y Y Y Y Y

−0.19** (0.08) 159,652 0.08 Y Y Y N N Y

−0.22** (0.10) 96,072 0.11 Y Y Y Y Y Y

Notes: Dependent variable is logged. Each column provides a variation on our primary specification, with different control variables and sample subsets. Variations are described in the column heading and in the main text.

Table 8 Robustness checks: SO2 .

RGGI × Post Observations Adjusted R2 PJM filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

Base

All States

E of MS

East Coast

Dereg

Linear Prices

Price Ratio NP

National Price

No Price

No Temp

Robust SE

−0.48** (0.20) 93,299 0.13 Y Y Y Y Y Y

−0.44** (0.17) 123,632 0.12 N Y Y Y Y Y

−0.27 (0.16) 69,987 0.13 N Y Y Y Y Y

−0.08 (0.16) 38,591 0.16 N Y Y Y Y Y

−0.60*** (0.18) 70,131 0.12 N Y Y Y Y Y

−0.54*** (0.18) 89,996 0.12 Y Y Y Y Y Y

−0.45** (0.21) 93,299 0.13 Y Y Y Y Y Y

−0.55*** (0.18) 89,996 0.12 Y Y Y Y Y Y

−0.54** (0.21) 131,813 0.12 Y Y Y Y N Y

−0.53*** (0.19) 155,102 0.12 Y Y Y N Y Y

−0.48*** (0.17) 93,299 0.13 Y Y Y Y Y Y

N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422

Observations Adjusted R2 PJM filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

(1)

Notes: Dependent variable is logged. Each column provides a variation on our primary specification, with different control variables and sample subsets. Variations are described in the column heading and in the main text.

419

−0.27*** (0.09) 97,675 0.11 Y Y Y Y Y Y Notes: Dependent variable is logged. Each column provides a variation on our primary specification, with different control variables and sample subsets. Variations are described in the column heading and in the main text.

No Temp

−0.30*** (0.08) 162,310 0.08 Y Y Y N Y Y −0.26*** (0.08) 136,690 0.10 Y Y Y Y N Y

No Price National Price

−0.21*** (0.08) 94,172 0.11 Y Y Y Y Y Y −0.25** (0.10) 97,675 0.11 Y Y Y Y Y Y

Price Ratio NP Linear Prices

−0.21*** (0.08) 94,172 0.11 Y Y Y Y Y Y −0.21** (0.09) 75,177 0.11 N Y Y Y Y Y

Dereg East Coast

−0.26*** (0.08) 42,161 0.12 N Y Y Y Y Y Observations Adjusted R2 PJM filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

9 We use the classifications from https://www.publicpower.org/system/files/ documents/Retail-Electric-Rates-in-Deregulated-States-2017-Update%20%28003 %29.pdf, which lists CA, CT, DC, DE, IL, MA, MD, ME, MI, MT, NH, NJ, NY, OH, PA, RI, TX as deregulated.

−0.23*** (0.08) 73,776 0.10 N Y Y Y Y Y

E of MS All States Base

Together, our results suggest that RGGI generates substantial benefits. It reduces CO2 emissions while also providing significant ancillary benefits through reductions in SO2 emissions and damages. Although some of the benefits from SO2 abatement are offset by

Table 9 Robustness checks: mwh.

5. Discussion

−0.27*** (0.09) 128,007 0.11 N Y Y Y Y Y

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

In Tables 7 to 9, we show that the primary results reported in Table 3 are robust to a number of variations. First, one may be concerned that the choice of control group influences our results. In our primary specification, we compared RGGI states to other states in the continental United States that were not part of the PJM Interconnection. We also analyze three alternative subsets of states: all 48 contiguous states, states east of the Mississippi River, and states along the Eastern Seaboard. For the most part, our primary treatment effects (for CO2 , SO2 , and mwh) remain statistically significant and comparable in magnitude to our primary estimates, although our SO2 results are attenuated in magnitude and significance when focusing on states east of the Mississippi River or East Coast states. Most RGGI states are deregulated states, so perhaps our results are capturing differences between deregulated and non-deregulated states. To rule out this potential source of confounding variation, we restrict our difference-in-difference regression to deregulated states only.9 Here, we find again that the CO2 and mwh effects are comparable to our primary specification, while, if anything, the effect for SO2 increases in magnitude. Our primary specification uses a simple linear function of the coal-to-gas price ratio. Moreover, as we described before, our statemonth panel of coal prices was incomplete, necessitating the use of a national average price for prior years in our calculation of the fuel price ratio. Here, we show that our results are not sensitive to different specifications of price. Our results are similar when allowing coal and natural gas prices to enter separately as linear terms (column 6); when non-parametrically binning the coal-to-gas price ratio into quintiles (column 7); when calculating our price ratio using national-level coal prices for the entire sample (column 8); or when we exclude fuel prices altogether from the regression (column 9). Similarly, our weather controls are binned, but we continue to have similar results when removing temperature from the regressions entirely (column 10). Altogether, our results are highly robust, as these variations in specifications do little to change the overall story. Lastly, we acknowledge that we may run into problems from having few clusters when we cluster standard errors at the state level. Thus, we rerun the models using robust standard errors, treating each facility as an independent observation (column 11). Our standard errors decrease, thus increasing the statistical significance of our estimates, as one might suspect. However, we continue to report our primary results using state-level clustering to avoid overstating the precision of our estimates.

−0.27*** (0.10) 97,675 0.11 Y Y Y Y Y Y

(10)

4.4. Robustness

RGGI × Post

(11)

We also document leakage consequences for CO2 and SO2 in Tables A.2 and A.3 in the Appendix. Unsurprisingly, the effects on pollutants largely mirror our generation results. That is, we see steep reductions in pollution from coal within the RGGI accompanied by countervailing increases in pollution from natural gas in Pennsylvania, Ohio, and the broader PJM Interconnection.

Robust SE

N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422

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N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422

redistribution of SO2 to higher damage areas, the net effect is still positive for the RGGI region. Likewise, while we find evidence of leakage to nearby states in terms of electricity generation, this shift in generation is unlikely to erode the overall benefits of the RGGI program for several reasons. First, the leaked generation tends to be cleaner and less carbon intensive than the marginal generation in RGGI states, due in large part to different fuel inputs. Second, copollutants tend to be less damaging in the leakage region than in RGGI. Altogether, this ex post analysis provides strong evidence that RGGI was not only effective in reducing CO2 emissions, its primary policy goal, but it also generated substantial cobenefits through reductions in SO2 damages. Our study provides causal estimates for a regional policy. While this analysis is useful for understanding RGGI’s implications for member states and nearby areas, our results should be interpreted with care, particularly if one seeks to draw broader conclusions regarding the national-level impacts of this regional initiative. While

421

it might be desirable to explore RGGI’s effects on a wider geographic scale, our difference-in-difference design captures the relevant scope of analysis from the perspective of a state or regional policy-maker. We provide strong evidence of the local and regional benefits of subnational climate policies, which is especially important given the substantial political barriers to implementing strong national or international policies to curb climate change. Many such efforts have stalled due to fundamental challenges inherent to public good provision, yet our analysis demonstrates that there are sizable private benefits to be reaped by those who act, as ancillary benefits from climate change mitigation efforts can be large.

Acknowledgements We thank Peter Maniloff, Jim Siodla, and John Stranlund for input on earlier drafts of this paper.

Appendix A Table A.1 Primary results: Full table. (1)

RGGI × Post RPS Price ratio (coal/ng) Below 40 40 to 50 50 to 60 70 to 80 Above 80 Observations Adjusted R2 PJM filter? Facility FE?

(2)

(3)

(4)

(5)

(6)

CO2 tons

SO2 tons

SO2 damage

NOx tons

NOx damage

mwh

−0.22* (0.11) −0.85 (0.71) 0.01* (0.00) −0.00 (0.00) −0.01*** (0.00) −0.01** (0.00) 0.02*** (0.00) 0.03*** (0.00) 96,072 0.11 Y Y

−0.48** (0.20) 2.05 (1.98) −0.01 (0.01) 0.01 (0.00) −0.01 (0.01) −0.01 (0.01) 0.01*** (0.00) 0.03*** (0.00) 93,299 0.13 Y Y

−0.49** (0.20) 1.93 (1.93) −0.01 (0.01) 0.01 (0.00) −0.01 (0.01) −0.01 (0.01) 0.01*** (0.00) 0.03*** (0.00) 93,060 0.12 Y Y

−0.19 (0.14) −1.05 (0.76) −0.00 (0.01) −0.00 (0.00) −0.01** (0.00) −0.01** (0.00) 0.02*** (0.00) 0.03*** (0.00) 103,697 0.13 Y Y

−0.19 (0.14) −1.12 (0.74) −0.00 (0.01) −0.00 (0.00) −0.01** (0.00) −0.01** (0.00) 0.02*** (0.00) 0.03*** (0.00) 103,435 0.13 Y Y

−0.27*** (0.10) −1.02 (0.76) 0.01 (0.00) −0.01* (0.00) −0.01** (0.00) −0.01*** (0.00) 0.02*** (0.00) 0.03*** (0.00) 97,675 0.11 Y Y

Notes: Dependent variable is logged. Robust standard errors clustered at the state level are presented in parentheses. Regressions include controls for temperature bins, fuel prices, and renewable portfolio standards along with facility, month, and year fixed effects. The comparison group is non-PJM states; PJM states are excluded from these regressions because they are likely leaker states that may violate the SUTVA conditions.

Table A.2 Leakage results for CO2 .

RGGI × Post

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

All

Coal

NGCC

All

Coal

NGCC

All

Coal

NGCC

−0.22* (0.11)

−0.79*** (0.17)

0.13 (0.11) −0.11** (0.04)

−0.12* (0.06)

1.11*** (0.16) −0.04 (0.06) 104,006 0.10 N Y Y Y Y Y Y

−0.12* (0.07) 33,747 0.13 N Y Y Y Y Y Y

0.90*** (0.16) 26,656 0.13 N Y Y Y Y Y Y

PO × Post PJM × Post Observations Adjusted R2 PJM filter? RGGI filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

96,072 0.11 Y Y Y Y Y Y Y

21,055 0.20 Y Y Y Y Y Y Y

31,636 0.10 Y Y Y Y Y Y Y

104,006 0.10 N Y Y Y Y Y Y

33,747 0.12 N Y Y Y Y Y Y

26,656 0.12 N Y Y Y Y Y Y

Notes: Dependent variable is logged. Robust standard errors clustered at the state level are presented in parentheses. Regressions include controls for temperature bins, fuel prices, and renewable portfolio standards along with facility, month, and year fixed effects.

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N. Chan and J. Morrow / Energy Economics 80 (2019) 411–422

Table A.3 Leakage results for SO2 .

RGGI × Post

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

All

Coal

NGCC

All

Coal

NGCC

All

Coal

NGCC

−0.48** (0.20)

−0.89** (0.39)

−0.15 (0.13) −0.01 (0.11)

−0.06 (0.08)

1.14*** (0.19) −0.03 (0.13) 102,982 0.11 N Y Y Y Y Y Y

−0.13 (0.12) 34,448 0.24 N Y Y Y Y Y Y

0.95*** (0.16) 26,632 0.12 N Y Y Y Y Y Y

PO × Post PJM × Post Observations Adjusted R2 PJM filter? RGGI filter? Facility FE? Month and year FE? Temperature bins? Fuel prices? RPS?

93,299 0.13 Y Y Y Y Y Y Y

20,984 0.29 Y Y Y Y Y Y Y

31,213 0.09 Y Y Y Y Y Y Y

102,982 0.11 N Y Y Y Y Y Y

34,448 0.24 N Y Y Y Y Y Y

26,632 0.12 N Y Y Y Y Y Y

Notes: Dependent variable is logged. Robust standard errors clustered at the state level are presented in parentheses. Regressions include controls for temperature bins, fuel prices, and renewable portfolio standards along with facility, month, and year fixed effects.

Appendix B. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.eneco.2019.01.007.

References Berkeley Lab, 2017. Renewables portfolio standards resources. Technical Report. 10 October 2018. https://emp.lbl.gov/projects/renewables-portfolio. Burtraw, D., Krupnick, A., Palmer, K., Paul, A., Toman, M., Bloyd, C., 2003. Ancillary benefits of reduced air pollution in the {US} from moderate greenhouse gas mitigation policies in the electricity sector. J. Environ. Econ. Manag. 45 (3), 650–673. Bushnell, J., Chen, Y., 2012. Allocation and leakage in regional cap-and-trade markets for CO2. Resour. Energy Econ. 34 (4), 647–668. Bushnell, J., Peterman, C., Wolfram, C., 2008. Local solutions to global problems: climate change policies and regulatory jurisdiction. Rev. Environ. Econ. Policy 2 (2), 175–193. Caron, J., Rausch, S., Winchester, N., 2015. Leakage from sub-national climate policy: the case of california’s cap-and-trade program. Energy J. 36 (2), 167–190. Chen, Y., 2009. Does a regional greenhouse gas policy make sense? A case study of carbon leakage and emissions spillover. Energy Econ. 31 (5), 667–675. Cullen, J.A., Mansur, E.T., 2017. Inferring carbon abatement costs in electricity markets: a revealed preference approach using the shale revolution. Am. Econ. J. Econ. Pol. 9 (3), 106–133. August. Di, Q., Wang, Y., Zanobetti, A., Wang, Y., Koutrakis, P., Choirat, C., Dominici, F., Schwartz, J.D., 2017. Air pollution and mortality in the medicare population. N. Engl. J. Med. 376 (26), 2513–2522. PMID: 28657878. Fell, H., Maniloff, P., 2018. Leakage in regional environmental policy: the case of the regional greenhouse gas initiative. J. Environ. Econ. Manag. 87, 1–23. Fowlie, M.L., 2009. Incomplete environmental regulation, imperfect competition, and emissions leakage. Am. Econ. J. Econ. Pol. 1 (2), 72–112. Henry, D.D., Muller, N.Z., Mendelsohn, R.O., 2011. The social cost of trading: measuring the increased damages from sulfur dioxide trading in the united states. J. Policy Anal. Manag. 30 (3), 598–612. Johnson, E.P., 2014. The cost of carbon dioxide abatement from state renewable portfolio standards. Resour. Energy Econ. 36 (2), 332–350. Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A., 2015. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371. Sep.

Liao, K.-J., Tagaris, E., Manomaiphiboon, K., Wang, C., Woo, J.-H., Amar, P., He, S., Russell, A.G., 2009. Quantification of the impact of climate uncertainty on regional air quality. Atmos. Chem. Phys. 9 (3), 865–878. Muller, N.Z., Mendelsohn, R., 2006. The air pollution emission experiments and policy analysis model (APEEP) technical appendix. Technical Report. 16 May 2016. https://sites.google.com/site/nickmullershomepage/home/ap2-apeep-model-2. Muller, N.Z., Mendelsohn, R., 2007. Measuring the damages of air pollution in the United States. J. Environ. Econ. Manag. 54 (1), 1–14. Muller, N.Z., Mendelsohn, R., 2009. Efficient pollution regulation: getting the prices right. Am. Econ. Rev. 99 (5), 1714–1739. Murray, B.C., Maniloff, P.T., 2015. Why have greenhouse emissions in RGGI states declined? An econometric attribution to economic, energy market, and policy factors. Energy Econ. 51, 581–589. NC Clean Energy Technology Center, 2018. Database of state incentives for renewables and efficiency. http://www.nws.noaa.gov/nwr/coverage/county_coverage.html, Accessed date: 10 October 2018. PRISM Climate Group, Oregon State University, 2018. PRISM climate data. http://www. prism.oregonstate.edu/, Accessed date: 1 September 2018. Regional Greenhouse Gas Initiative, 2015a. Investment of RGGI proceeds through 2013. Technical Report. 16 May 2016. http://www.rggi.org/rggi_benefits/ program_investments. Regional Greenhouse Gas Initiative, 2015b. The RGGI CO2 Cap. Technical Report. 16 May 2016. http://www.rggi.org/design/overview/cap. Stavins, R.N., 2008. Addressing climate change with a comprehensive us cap-and-trade system. Oxf. Rev. Econ. Policy 24 (2), 298–321. Tagaris, E., Liao, K.-J., DeLucia, A.J., Deck, L., Amar, P., Russell, A.G., 2010. Sensitivity of air pollution-induced premature mortality to precursor emissions under the influence of climate change. Int. J. Environ. Res. Public Health 7 (5), 2222–2237. U.S. Environmental Protection Agency, 2018. Air markets program data. http://ampd. epa.gov/ampd/, Accessed date: 1 September 2018.