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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100%

CA in-stategeneration

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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