California s cap-and-trade program and emission leakage: an empirical analysis

Transcription

1 California s cap-and-trade program and emission leakage: an empirical analysis Chiara Lo Prete The Pennsylvania State University Ashish Tyagi Frankfurt School of Finance and Management Cody Hohl The Pennsylvania State University Harvard Kennedy School Energy Policy Seminar November 5 th, 2018

2 California s GHG emissions 600 Peak: : target: target: Million metric tons of CO 2 eq Data source: California Air Resources Board

3 California s GHG cap-and-trade program California s emission trading scheme is the first multi-sector cap-andtrade program in North America It covers about 85% of the state s GHG emissions Status: compliance obligations began in January 2013 extension to 2030 approved with bipartisan support in July 2017 The CARB expects the new cap-and-trade system to contribute at least 25% of total emission reductions by 2030 First deliverer approach: in-state electricity generators and electricity importers are the point of regulation

4 100% 100% 80% 60% 40% 20% 0% Northwest imports 80% 60% 40% 20% 0% CA in-state generation 100% 80% 60% 40% 20% 0% Southwest imports Data source: California Energy Commission, 2016 Unspecified power Natural gas Coal Nuclear Large hydro Renewables

5 Reshuffling as a potential conduit for emission leakage Contract reshuffling: any plan, scheme, or artifice to receive credit based on emission reductions that have not occurred, involving the delivery of electricity to the California grid [Cal. Code Regs. Tit. 17, 95802(a)(251)] Example: changing a high emission source from specified to unspecified to obtain a lower emission factor ( laundering ) Consider three plants producing 438,000 MWh of electricity sold to California load and an allowance price of $30/ton Resource-specific emission factor (MT CO 2 e /MWh) Compliance obligations ($) if resource is: In-state or specified out-of-state Unspecified out-of-state Solar 0 0 5,623,920 Natural gas ,767,100 5,623,920 Coal ,402,800 5,623,920

6 Reshuffling as a potential conduit for emission leakage Reshuffling would result in apparent emission reductions due to changes in the composition of imports to California (although emissions in exporting regions are unchanged or increasing) Simulation-based studies indicated a strong vulnerability to reshuffling under the AB 32 California system (Bushnell et al., 2008; Chen et al., 2011; Bushnell et al., 2014; Borenstein et al., 2014) CARB addressed these concerns by releasing a guidance document that identifies a series of safe harbor provisions for importers

7 Literature Incomplete environmental regulation may enable substantial leakage (Bushnell et al., 2008; Fowlie, 2009; Goulder and Stavins, 2011; Goulder et al., 2012) Emission leakage in regional CO 2 cap-and-trade markets has typically been examined ex ante (Sue Wing and Kolodziej, 2008; Chen et al., 2011; Bushnell and Chen, 2012; Bushnell et al., 2014; Shawhan et al., 2014; Caron et al., 2015) Empirical analyses of leakage are less common (Aichele and Felbermayr, 2015; Fell and Maniloff, 2018) A related literature explores how environmental regulation affects trade flows and the location choice of firms in the long run ( pollution haven effect) (Levinson and Taylor, 2008; Kahn and Mansur, 2013; Aldy and Pizer, 2015)

8 Overview of the paper We conduct the first econometric analysis of leakage from California s cap-and-trade program, with a focus on the U.S. electricity sector in The paper presents three sets of empirical results 1. Differences-in-differences regressions. We estimate the policy impact on baseload power plant operations in WECC applying a DID estimator to a novel panel dataset at the monthly level The policy led to a 11-14% decrease in NGCC capacity factors in California, and a 3-5% increase in coal plant capacity factors in Northwest and Eastern WECC

9 Overview of the paper 2. Matching and DID. We match plants using hour-of-day capacity factors pre ETS, and estimate the policy impact on daytime and nighttime hours of operations using high frequency measures of generation at the plant level The policy induced a reduction of NGCC capacity factors in California by 7% and an increase of coal plant capacity factors by 5% in Northwest WECC during daytime 3. Scheduled power flow regressions. We estimate a model of daily scheduled power flows into CaISO, and test for leakage based on the statistical significance of the AB 32 allowance price as one of the explanatory variables The allowance price is positive and statistically significant as explanatory variable for imports from Northwest WECC

10 Data We construct a detailed plant-level dataset for four NERC regions (WECC, MRO-US, SPP and TRE) from 2009 to 2016 using data from EIA, CEMS, FERC, NOAA and SNL We collect hourly scheduled power flows and available transmission capacity on major CaISO interfaces

11 Empirical strategy ATT = α = E[ Y (1) Y (0) D =1] it ' it ' i y Outcome trend in treatment group Treatment group value A C Control group value Before policy change B α = Treatment Effect E Counterfactual trend in treatment group D Outcome trend in control group After policy change

12 Differences-in-differences regressions Model Y it = α TREAT + α TREAT + X β + γ + γ + γ + ε C L ' C it L it it i y sm it L where i refers to plant-technology and t denotes month (January December 2016) Y it is capacity factor (in %) for plant i at time t C TREAT it is equal to 1 if plant i is in California, and month t is January 2013 or later L TREAT it is equal to 1 if plant i is in leaker region L, and month t is January 2013 or later

13 Differences-in-differences regressions Leaker definition Baseline (a) NGCC Coal steam

14 Differences-in-differences regressions Leaker definition EIM robustness check (b)

15 Differences-in-differences regressions Model Y it = α TREAT + α TREAT + X β + γ + γ + γ + ε C L ' C it L it it i y sm it L X it includes determinants of capacity factors: Electric load in the plant s planning area Input price ratio (levels and square) for the plant Nuclear and renewable generation in the plant s state Heating/cooling degree days and water scarcity indices in the plant s climate division γ i γ y γ sm are plant fixed effects are year fixed effects are state by month-of-year fixed effects

16 Differences-in-differences regressions Estimated treatment effects Baseline (a) Coal Steam NGCC CA *** Northwest 0.04** Eastern 0.04* - Southwest N 14,298 11,938 R Controls Rest of WECC, MRO-US, SPP, TRE Rest of WECC Covariates include plant-level fuel cost ratio (levels and square), log of electric load by planning area, log of nuclear and renewable generation by state, heating/cooling degree days and SPI index by climate division, plant, year and state by month-of-year fixed effects. Robust standard errors are clustered at the plant level. *, **, *** denote statistical significance at the 10, 5, 1% level, respectively. Unit of observation is plant-month.

17 Differences-in-differences regressions Estimated treatment effects Baseline (a) EIM robustness check (b) Coal Steam NGCC Coal Steam NGCC CA *** *** Northwest 0.04** * Eastern 0.04* Southwest N 14,298 11,938 14,298 11,938 R Controls Rest of WECC, MRO-US, SPP, TRE Rest of WECC Rest of WECC, SPP, TRE Rest of WECC Covariates include plant-level fuel cost ratio (levels and square), log of electric load by planning area, log of nuclear and renewable generation by state, heating/cooling degree days and SPI index by climate division, plant, year and state by month-of-year fixed effects. Robust standard errors are clustered at the plant level. *, **, *** denote statistical significance at the 10, 5, 1% level, respectively. Unit of observation is plant-month.

18 Differences-in-differences regressions Implied leakage rate Generation leakage (MWh per month) Coal Steam NGCC CA - -2,083,844 Northwest 405,059 Eastern 158,245 These generation changes imply that production leakage increased leaker emissions by about 8.5 million tons per year, and decreased California emissions by about 12.6 million tons per year * leakage of about 65% Ex ante prediction of leakage under AB 32 from simulation-based studies (Chen et al., 2011): about 85% * Based on an average coal heat rate of 12,454 Btu/kWh and heat content of 208 lb/mmbtu in the Northwest region, coal heat rate of 11,495 Btu/kWh and heat content of lb/mmbtu in the Eastern region, and NGCC heat rate of 8,511 Btu/kWh and heat content of lb/mmbtu in California

19 Matching and differences-in-differences Coarsened exact matching (CEM) Overview of CEM (Iacus et al., 2012): 1. Select pre treatment matching variables 2. Coarsen matching variables into discrete bins 3. Exactly match observations with the same set of attribute bins 4. Assign weights to control units to normalize variance in distribution of attribute bins 5. Run the statistical model using weighted least squares

20 Matching and differences-in-differences Coarsened exact matching (CEM) Overview of CEM (Iacus et al., 2012): 1. Select pre treatment matching variables We match based on two sets of hour-of-day capacity factors pre treatment a) Daytime: average of hourly capacity factors in at 8am,11am, 2pm and 5pm b) Night-time: average of hourly capacity factors in at 8pm, 11pm, 2am and 5am 2. Coarsen matching variables into discrete bins 3. Exactly match observations with the same set of attribute bins 4. Assign weights to control units to normalize variance in distribution of attribute bins 5. Run the statistical model using weighted least squares

21 Matching and differences-in-differences Model Y it = α TREAT + X β + γ + γ + γ + ε C ' C it it i y sm it Y it = α TREAT + X β + γ + γ + γ + ε L ' L it it i y sm it i refers to plant-technology reporting to EPA s Continuous Emissions Monitoring System (CEMS) t denotes daytime (7am-6pm, 01/01/ /31/2016) or night-time (6pm-7am, 01/01/ /31/2016)

22 Matching and differences-in-differences Estimated treatment effects No matching specification Coal Steam NGCC CA *** Northwest 0.04** Eastern 0.04* - Southwest Daytime Night-time Coal Steam NGCC Coal Steam NGCC CA ** Northwest 0.05** * Eastern Southwest

23 Conclusions Simulation-based studies suggested that contract reshuffling may enable substantial leakage under the AB 32 cap-and-trade system We analyze power plant operations in the Western Interconnection applying a DID estimator, in combination with matching methods, to a unique plant-level dataset from 2009 to 2016 Results suggest a policy-induced reduction of NGCC generation in California and an expansion of coal generation in Northwest and Eastern WECC The analysis of daily scheduled flows across major CaISO interfaces further supports this substitution pattern