Post on 15-Mar-2020
California’s cap-and-trade program and emission leakage: an empirical analysis
Chiara Lo PreteThe Pennsylvania State University
Ashish TyagiFrankfurt School of Finance and Management
Cody HohlThe Pennsylvania State University
Harvard Kennedy School Energy Policy SeminarNovember 5th, 2018
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California’s GHG emissions
Peak: 487.63
2020 target: 431
2030 target: 256
2014: 441.53
Data source: California Air Resources Board
California’s emission trading scheme is the first multi-sector cap-and-trade program in North America
It covers about 85% of the state’s GHG emissions
Status: compliance obligations began in January 2013extension to 2030 approved with bipartisan support in July 2017
The CARB expects the new cap-and-trade system to contribute at least25% of total emission reductions by 2030
First deliverer approach: in-state electricity generators and electricity importers are the point of regulation
California’s GHG cap-and-trade program
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CA in-stategeneration
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80%
100%
Northwestimports
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100%
Southwestimports
Natural gas
Data source: California Energy Commission, 2016
Large hydro
Coal
Nuclear
Renewables
Unspecified power
Reshuffling as a potential conduit for emission leakage
Contract reshuffling: “any plan, scheme, or artifice to receive creditbased on emission reductions that have not occurred, involving thedelivery of electricity to the California grid” [Cal. Code Regs. Tit. 17, §95802(a)(251)]
Example: changing a high emission source from specified to unspecifiedto obtain a lower emission factor (“laundering”)
Consider three plants producing 438,000 MWh of electricity sold toCalifornia load and an allowance price of $30/ton
Compliance obligations ($) if resource is:
Resource-specific emission factor (MT CO2e /MWh)
In-state or specified out-of-state
Unspecified out-of-state
Solar 0 0 5,623,920
Natural gas 0.515 6,767,100 5,623,920
Coal 1.02 13,402,800 5,623,920
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
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 CO2 cap-and-trade markets has typicallybeen 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 andFelbermayr, 2015; Fell and Maniloff, 2018)
A related literature explores how environmental regulation affects tradeflows and the location choice of firms in the long run (“pollutionhaven” effect) (Levinson and Taylor, 2008; Kahn and Mansur, 2013;Aldy and Pizer, 2015)
Literature
We conduct the first econometric analysis of leakage from California’scap-and-trade program, with a focus on the U.S. electricity sector in2009-2016
The paper presents three sets of empirical results
1. Differences-in-differences regressions. We estimate the policyimpact on baseload power plant operations in WECC applying a DIDestimator 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 inNorthwest and Eastern WECC
Overview of the paper
2. Matching and DID. We match plants using hour-of-day capacityfactors pre ETS, and estimate the policy impact on daytime and night-time hours of operations using high frequency measures of generationat the plant level• The policy induced a reduction of NGCC capacity factors in
California by 7% and an increase of coal plant capacity factors by5% in Northwest WECC during daytime
3. Scheduled power flow regressions. We estimate a model of dailyscheduled power flows into CaISO, and test for leakage based on thestatistical significance of the AB 32 allowance price as one of theexplanatory variables• The allowance price is positive and statistically significant as
explanatory variable for imports from Northwest WECC
Overview of the paper
We construct a detailed plant-level dataset for four NERC regions(WECC, MRO-US, SPP and TRE) from 2009 to 2016 using data fromEIA, CEMS, FERC, NOAA and SNL
We collect hourly scheduled power flows and available transmissioncapacity on major CaISO interfaces
Data
Empirical strategy
y
Before policy change
A
C
α = Treatment Effect
B
E
D
Outcome trend in treatment group
Counterfactual trend in treatment group
Outcome trend in control group
After policy change
Treatment group value
Control group value
' '[ (1) (0) | =1] it it iATT E Y Y Dα= = −
where i refers to plant-technology and t denotes month (January 2009 -December 2016)
is capacity factor (in %) for plant i at time t
is equal to 1 if plant i is in California, and month t isJanuary 2013 or later
is equal to 1 if plant i is in leaker region L, and month t isJanuary 2013 or later
Differences-in-differences regressionsModel
CitTREAT
LitTREAT
itY
'itY C L
C it L it it i y sm itL
TREAT TREATα α β γ γ γ ε= + + + + + +∑ X
Baseline (a)
NGCC Coal steam
Differences-in-differences regressionsLeaker definition
EIM robustness check (b)
Differences-in-differences regressionsLeaker definition
Xit 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
are plant fixed effectsare year fixed effectsare state by month-of-year fixed effects
'itY C L
C it L it it i y sm itL
TREAT TREATα α β γ γ γ ε= + + + + + +∑ X
iγyγsmγ
Differences-in-differences regressionsModel
Baseline (a)
Coal Steam NGCC
CA - -0.14***
Northwest 0.04** -0.05
Eastern 0.04* -
Southwest 0.01 -0.03
N 14,298 11,938
R2 0.60 0.67
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 ofnuclear 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.
Differences-in-differences regressionsEstimated treatment effects
Baseline (a) EIM robustness check (b)
Coal Steam NGCC Coal Steam NGCC
CA - -0.14*** - -0.12***
Northwest 0.04** -0.05 0.03* -0.03
Eastern 0.04* - - -
Southwest 0.01 -0.03 -0.03 0.01
N 14,298 11,938 14,298 11,938
R2 0.60 0.67 0.60 0.67
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 ofnuclear 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.
Differences-in-differences regressionsEstimated treatment effects
Coal Steam NGCC
CA - -2,083,844
Northwest 405,059
Eastern 158,245
These generation changes imply that production leakage increasedleaker emissions by about 8.5 million tons per year, and decreasedCalifornia emissions by about 12.6 million tons per year*→ leakage ofabout 65%
Ex ante prediction of leakage under AB 32 from simulation-basedstudies (Chen et al., 2011): about 85%
Differences-in-differences regressionsImplied leakage rate
Generation leakage (MWh per month)
* Based on an average coal heat rate of 12,454 Btu/kWh and heat content of 208 lb/MMBtu in theNorthwest region, coal heat rate of 11,495 Btu/kWh and heat content of 207.93 lb/MMBtu in the Easternregion, and NGCC heat rate of 8,511 Btu/kWh and heat content of 118.66 lb/MMBtu in California
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 indistribution of attribute bins
5. Run the statistical model using weighted least squares
Matching and differences-in-differencesCoarsened 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 treatmenta) Daytime: average of hourly capacity factors in 2009-2010 at
8am ,11am, 2pm and 5pmb) Night-time: average of hourly capacity factors in 2009-2010
at 8pm, 11pm, 2am and 5am
2. Coarsen matching variables into discrete bins3. Exactly match observations with the same set of attribute bins4. Assign weights to control units to normalize variance in
distribution of attribute bins5. Run the statistical model using weighted least squares
Matching and differences-in-differencesCoarsened exact matching (CEM)
i refers to plant-technology reporting to EPA’s Continuous EmissionsMonitoring System (CEMS)
t denotes daytime (7am-6pm, 01/01/2009-12/31/2016) or night-time(6pm-7am, 01/01/2009-12/31/2016)
Matching and differences-in-differencesModel
'itY C
C it it i y sm itTREATα β γ γ γ ε= + + + + +X
'itY L
L it it i y sm itTREATα β γ γ γ ε= + + + + +X
Matching and differences-in-differencesEstimated treatment effects
No matching specification
Coal Steam NGCC
CA - -0.14***
Northwest 0.04** -0.05
Eastern 0.04* -
Southwest 0.01 -0.03
Daytime Night-time
Coal Steam NGCC Coal Steam NGCC
CA - -0.07** - -0.02
Northwest 0.05** -0.01 0.05* -0.02
Eastern 0.03 - 0.04 0.01
Southwest 0.05 0.02 0.03 0.01
Simulation-based studies suggested that contract reshuffling mayenable substantial leakage under the AB 32 cap-and-trade system
We analyze power plant operations in the Western Interconnectionapplying a DID estimator, in combination with matching methods, to aunique plant-level dataset from 2009 to 2016
Results suggest a policy-induced reduction of NGCC generation inCalifornia and an expansion of coal generation in Northwest andEastern WECC
The analysis of daily scheduled flows across major CaISO interfacesfurther supports this substitution pattern
Conclusions