Division of Economics and BusinessWorking Paper Series

Leakage in Regional Climate Policy? Implications ofElectricity Market Design

Brittany TarufelliBen Gilbert

Working Paper 2019-07http://econbus-papers.mines.edu/working-papers/wp201907.pdf

Colorado School of MinesDivision of Economics and Business

1500 Illinois StreetGolden, CO 80401

November 2019

c©2019 by the listed author(s). All rights reserved.

Colorado School of MinesDivision of Economics and BusinessWorking Paper No. 2019-07November 2019

Title:Leakage in Regional Climate Policy? Implications of Electricity Market Design∗

Author(s):Brittany TarufelliLouisiana State University93 South Quad Dr., Baton Rouge, LA 70803, USA

Ben GilbertDivision of Economics and BusinessColorado School of Mines

ABSTRACTWe study how the expansion of a centralized real-time electricity market affects emissions leakagefrom a regional cap-and-trade program. We find the expansion caused modest leakage increases,despite relatively small trading volumes. Natural gas plants just outside the cap-and-trade regionincreasingly balance intermittent renewables. Generation from these plants increases at night whenaverage wind generation is high. These same plants systematically ramp down in response tounexpected solar generation because of friction between how the day-ahead and real-time marketscommit resources. Our results suggest that reduced transactions costs in trade between regulatedand unregulated regions may exacerbate leakage.

JEL classifications: L1, H23, Q48, Q52.Keywords: Electricity market design, Carbon leakage, Emissions, Solar power.

∗Tarufelli is corresponding author.

Leakage in Regional Climate Policy?Implications of Electricity Market Design

Brittany Tarufellia,∗, Ben Gilbertb,

aLouisiana State University, 93 South Quad Dr., Baton Rouge, LA 70803, USA

bColorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA

Abstract

We study how the expansion of a centralized real-time electricity market affects emissions leakage from a

regional cap-and-trade program. We find the expansion caused modest leakage increases, despite relatively

small trading volumes. Natural gas plants just outside the cap-and-trade region increasingly balance in-

termittent renewables. Generation from these plants increases at night when average wind generation is

high. These same plants systematically ramp down in response to unexpected solar generation because of

friction between how the day-ahead and real-time markets commit resources. Our results suggest that re-

duced transactions costs in trade between regulated and unregulated regions may exacerbate leakage.

Keywords: Electricity market design, Carbon leakage, Emissions, Solar power

JEL classification: L1, H23, Q, Q48, Q52

1. Introduction

Goods markets are designed and regulated at a sub-global level. But when we consider the effects of sub-

global environmental regulations, it is typical to assume one set of market clearing rules across regulated

and unregulated regions. In reality, trade occurs across a patchwork of partially overlapping regulations

and market designs. Regulations that do not account for differences in market design could result in unan-

ticipated outcomes for the location and amount of dirty versus clean production, i.e., leakage. For instance,

wholesale electricity market clearing mechanisms differ by region and rarely line up with state-level cli-

mate policies. As electricity markets adapt to the increasing capacity of intermittent renewable genera-

tion, it is important to study their interaction with state-level environmental policies. The leakage effect

of regional differences in market power, emissions markets, and regulations have been extensively stud-

ied (Helm, 2003; Babiker, 2005; Holland, 2009; Carbone et al., 2009; Metcalf and Weisbach, 2011). In this

paper, we focus instead on the effect of differences in output market design in the context of electricity

markets.

∗Corresponding authorEmail addresses: btarufelli@lsu.edu (Brittany Tarufelli), bgilbert@mines.edu (Ben Gilbert)

Preprint submitted to European Economic Review October 25, 2019

In order to better integrate intermittent wind and solar power, many jurisdictions have pursued electricity

market reforms that reduce transactions costs across larger geographic areas. However, reducing trans-

actions costs between carbon-regulated and unregulated regions can increase emissions leakage, reducing

the effectiveness of the environmental regulation. With high transactions costs across regions, an unex-

pected decline in wind generation in the regulated region is likely balanced by a local fossil generator who

pays the local carbon price. With low transactions costs across regions, there may be a fossil generator

outside the regulated region that can more easily meet demand inside the regulated region. Depending on

the policy design, this outside fossil generator may or may not pay a carbon border adjustment that does

not fully internalize the externality. Previous literature has shown that while border adjustments can help,

they rarely eliminate leakage (Fischer and Fox, 2012; Branger and Quirion, 2014). Tarufelli (2019) finds

that all else equal, reduced transactions costs in the form of reduced trading costs between carbon regu-

lated and unregulated regions always result in positive emissions leakage. More generally, reduced trans-

actions costs are likely to rearrange the location of fossil fuel generation unless renewables inside and out-

side of the regulated region exactly offset each other. Estimating the sign and magnitude of this effect is

important for any market reform that reduces transactions costs in output markets between regions with

different environmental regulations.

We investigate this problem in the context of a change in wholesale electricity market design in the West-

ern U.S., and estimate the effect of this change on leakage from California’s carbon cap-and-trade pro-

gram. The Western Energy Imbalance Market (EIM) opened participation in California’s real-time elec-

tricity spot market to generators outside California, extending the spot market over a wider geographic

area and potentially changing production decisions in the new EIM region. Outside California, electric-

ity supply in the West is managed by balancing authorities (BAs) that facilitate bilateral transactions be-

tween local monopolies. The EIM allows generators within these BAs to participate in California’s cen-

tralized uniform price auction for real-time economic dispatch. We ask whether the EIM causes leakage in

fossil fuel generation outside California, by how much, and by what mechanisms.

Like many local jurisdictions pursuing sub-global climate policy, California’s renewables subsidies and

statewide carbon regulation have encouraged growth in solar and wind capacity, and discouraged the use

of fossil fuel generators. But a market design change like the EIM can distort the effects of these policies

by reducing transactions costs and increasing market participation from non-California fossil fuel gener-

ators. Regulators are unable to distinguish low-carbon from high-carbon produced electricity as it flows

into California (Hogan, 2017). Although California applies a carbon price to its electricity imports, ac-

counting for these emissions occurs after the fact. The potential for electricity imports to be higher-emitting

than accounted for in California’s carbon regulation has raised concerns that the EIM is leading to emis-

sions leakage.1 We define emissions leakage as an increase in production and associated emissions from

unregulated producers in the EIM region outside of California. Such leakage could reduce the efficacy of

1California regulators are worried about resource shuffling–a reallocation of who buys from whom. Regulated entitiesbuy the low-carbon version, whereas unregulated entities buy the high-carbon version (Borenstein et al., 2014); CAISO.2018. “Energy Imbalance Market Greenhouse Gas Enhancements Second Revised Draft Final Proposal.” http://www.caiso.

com/Documents/SecondRevisedDraftFinalProposal-EnergyImbalanceMarketGreenhouseGasEnhancements.pdf (accessed6/18/2018). We argue the more pressing concern is emissions leakage.

2

California’s carbon regulation.

We leverage spatial and temporal variation in EIM participation to estimate the average effect on fossil

fuel generation and emissions using a difference-in-differences framework. We exclude California genera-

tors so that our results can be interpreted as the causal effect of market participation on fossil fuel gener-

ation outside California. To address endogenous participation, we preprocess our data by matching BAs

on characteristics known to influence participation as suggested by Ferraro and Miranda (2014). We also

use a triple-differences framework to estimate generators’ marginal responses to California load, wind, and

solar production. We examine how the EIM affected both gas and coal generators’ outcomes, but find our

results are more precise for price-responsive gas generators, and noisier for coal generators.

We find that the EIM allows non-California fossil fuel generators to balance intermittent renewable gener-

ation. EIM gas generators outside California increased their average hourly electricity production by 9.4

megawatt hours (MWh), which caused emissions leakage. This effect is modest – just eight percent of the

average generator’s capacity (114 megawatts) – but statistically significant. The effect is larger and sta-

tistically significant (25.4 MWh) in regressions with both gas and coal generators, but not statistically

significant in coal-only regressions. The effect is largest during hours when wind is a larger share of the

generation mix; average hourly generation from EIM gas plants is about 20 MWh greater at night when

wind plants tend to be running and gas is on the margin.

We also find evidence that EIM gas plants are used to address a problem known as “overgeneration” that

arises from friction between the day-ahead and real-time markets. California is most at risk of overgen-

eration in the daylight hours, due to its solar power capacity.2 If the California Independent System Op-

erator (CAISO) does not procure enough supply in the day-ahead market to meet forecasted load, then

they commit additional fossil generators just after the day-ahead market closes through a process called

Residual Unit Commitment. CAISO does not have a similar process to de-commit those resources in real

time if realized load falls below forecasted load, or if realized solar generation exceeds expectations. In-

stead, the overgeneration is addressed in the real-time imbalance market, the EIM, by turning down dis-

patchable generators, increasing exports or reducing imports, and decommitting fast-start generators.3

Our triple-differences results show that generation from EIM gas plants outside California was lower when

day-ahead load forecasts were higher. The effect is a small but significant decrease of 1.6 kilowatt hours

(kWh) in response to a megawatt (MW) increase in forecasted California load. The effect increases to two

kWh during the daylight hours when solar is a larger share of the generation mix, and is indistinguishable

from zero at night. This effect increases to nine kWh during hours when transmission lines are congested

for exporting power out of California.

Examining the distribution of the EIM treatment effect by BA and by generator capacity, we also find

that large EIM gas generators are selling more in BAs adjacent to California (Nevada Power Company and

Arizona Public Service Company). We conclude the EIM is helping to facilitate integration of renewable

2CAISO. 2018. “Day Ahead Market Enhancements: Revised Straw Proposal.” http://www.caiso.com/Documents/

RevisedStrawProposal-DayAheadMarketEnhancments.pdf (accessed 7/28/2018).3http://www.caiso.com/Documents/2390.pdf (accessed 3/18/2019).

3

resources by shifting fossil generation outside of California, particularly in BAs closest to California load

centers. Although our reduced-form framework does not disentangle emissions leakage from other possible

emissions effects, such as emissions laundering or reshuffling, it measures the change in market outcomes

and resulting emissions due to the change in electricity market design. While the EIM is relatively small

by trading volume, Hogan (2017) argues that it plays an important role in setting prices and expectations

for all other transactions.

Our results are suggestive of changes that can be expected from similar electricity market reforms that are

ongoing, including efforts to expand the footprint of the EIM and to expand CAISO’s larger day-ahead

markets to the Western electric region as well. Other wholesale electricity markets such PJM Intercon-

nection (PJM),4 Southwest Power Pool (SPP),5 and the European Union (EU) are considering adopting

similar market design changes. The EU’s Energy Union, for example, intends to create a fully integrated

energy market across Europe.6 Although there are also EU-wide policies on carbon trading and renewable

energy, each set of policies has been implemented heterogeneously across member states, creating a sim-

ilar patchwork of environmental policies and electricity market rules as in the U.S. (Pollitt, 2009, 2019).

Efforts to integrate EU electricity markets have focused on increasing cross-border trades, as price differ-

entials still persist (Merino and Ebrill, 2018).7 For example, although most member states share one mar-

ket clearing algorithm for day-ahead trades (Gomez et al., 2019), intraday markets are less integrated. A

recent initiative called the European Cross-Border Intraday Market (XBID) allows 14 member states to

match intraday8 electricity orders across zones. Last-minute imbalance markets are still managed by lo-

cal transmission operators, however. These local reserve markets vary widely in their design, but there are

ongoing efforts to enhance coordination among neighboring transmission operators (Gomez et al., 2019).

Each of these reforms will effectively reduce transactions costs between EU member states.

The rest of the paper is organized as follows. Section 2 contrasts our contribution to the existing literature

and Section 3 provides a background discussion of the EIM in the context of our study. Section 4 presents

the data and empirical approach. Sections 5, 6, and 7 present the main results as well as results by hour of

day and region, respectively. Section 8 concludes.

4PJM Interconnection is considering a regional or sub-regional carbon pricing framework for its market footprint, sim-ilar to the carbon pricing framework in CAISO; PJM. 2017. “Advancing Zero Emissions Objectives through PJMs En-ergy Markets: A Review of Carbon-Pricing Frameworks.” https://www.pjm.com/~/media/library/reports-notices/

special-reports/20170502-advancing-zero-emission-objectives-through-pjms-energy-markets.ashx (accessed11/4/2018).

5SPP and Mountain West Transmission Group, which encompasses several BAs in the Western electric region includingPublic Service Company of Colorado (PSCO) and the Western Area Power Administration (WAPA), are currently in discus-sions to explore membership in the SPP Regional Transmission Organization (RTO); SPP. “Mountain West TransmissionGroup Initiative.” https://www.spp.org/mountain-west/ (accessed 10/8/2018). Discussions have recently stalled followingPSCOs decision to withdraw from the potential integration in April 2018; WAPA. “Mountain West Transmission Group Ini-tiative**.” https://www.wapa.gov/About/keytopics/Pages/Mountain-West-Transmission-Group.aspx (accessed 10/8/2018).

6“Building the Energy Union.” https://ec.europa.eu/energy/en/topics/energy-strategy-and-energy-union/

building-energy-union (accessed 10/8/2018).7For example, Spain and France had average price differentials of 13.1 euros/MWh in the second and third quarters of

2017, while Spain and Portugal had prices that converged in 95% of operating hours (Merino and Ebrill, 2018). Further, inthe intraday and balancing market timeframe, cross-border transmission capacity was only efficiently used 50% and 22% ofthe time, respectively (Merino and Ebrill, 2018).

8XBID allows for hourly trades in all participating member states, as well as sub-hourly trades in Germany, Austria, andFrance. https://www.epexspot.com/document/38689/XBID%20launch%20information%20package (accessed 10/21/2019).

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2. Literature

Our study is most closely related to the literature on emissions leakage from carbon regulation in the elec-

tricity sector (Fowlie, 2009; Bushnell and Mansur, 2011; Bushnell and Chen, 2012; Bushnell et al., 2014;

Novan, 2017; Fell and Maniloff, 2018), although we focus on a change in the market rules rather than a

change in emissions regulation. This literature has generally been divided between ex-ante market-based

simulations versus ex-post econometric analyses, with ex-ante simulations being more common. The simu-

lation literature generally finds significant leakage potential from regulated to unregulated regions (Fowlie,

2009; Bushnell and Mansur, 2011; Bushnell and Chen, 2012; Bushnell et al., 2014). Bushnell et al. (2014),

for example, find that California’s carbon cap-and-trade program could cause leakage, highlighting a key

theoretical finding from Bushnell and Mansur (2011)’s more general model that “direct regulation at the

point of emissions results in leakage.” This is a major concern for environmental regulators in general as

well as California regulators’ specific concerns about the EIM, as the carbon price is applied to the first

deliverer of the electricity.

Our research question is most similar to Fowlie (2009) who shows that the institutional details of the in-

dustry meaningfully influence leakage predictions. Specifically, Fowlie (2009)’s simulations predict that

leakage is greater with increased competition, either through reduced oligopoly power or the introduc-

tion of forward markets. Fowlie (2009)’s analysis assumes the market design is the same in the carbon-

regulated and unregulated regions. By contrast, we use ex-post econometric analysis to show that leakage

increases when the unregulated region moves from an opaque bilateral spot market among vertically inte-

grated utilities towards sharing the regulated region’s centrally managed spot market.9

While simulation studies are useful to predict the outcome of counterfactual policies ex ante, ex post econo-

metric analyses are useful because actual market and operating conditions can deviate from assumed con-

ditions (Callaway and Fowlie, 2009). In an ex-post econometric analysis of leakage from a carbon cap-and-

trade policy, Fell and Maniloff (2018) find a positive leakage externality from the Regional Greenhouse

Gas Initiative (RGGI). The finding again depends crucially on industry structure, as non-RGGI producers

happened to be relatively cleaner than RGGI producers. In this application, the electricity market rules

were the same across the RGGI and non-RGGI producers that were studied, in contrast to our study. No-

van (2017) estimates the policy interaction effects of an overlapping cap-and-trade program and a Renew-

able Portfolio Standard when only a subset of fossil fuel generators are covered by the emissions cap. He

finds both a scale effect of overall emissions responses to renewable generation and an emissions composi-

tion effect which occurs when renewable generation reduces emissions permit prices and generators with

different copollutant concentrations begin producing. These differing outcomes underscore the importance

of institutional details for unintended consequences of sub-global climate policy.

Our empirical approach is most closely related to the literature that estimates reduced-form econometric

models on hourly electricity data to uncover marginal emissions offsets from renewable resources (Call-

9We do not identify whether this effect is due to increased competition in the spot market or reduced transactions costs.However, these mechanisms are closely related as reduced transactions costs can itself increase competition.

5

away and Fowlie, 2009; Cullen, 2013; Kaffine et al., 2013; Novan, 2015; Kaffine and Fell, 2017; Callaway

et al., 2018). A key finding from this literature is that the effectiveness of the policy is significantly deter-

mined by the emissions intensity of the fossil fuel production source that is “on the margin” when renew-

able sources come online. Renewable resources such as wind and solar produce electricity at zero marginal

cost, and as a result they typically displace more expensive and emissions-intensive fuels such as coal or

gas. However, the emissions intensity of the marginal fuel source varies across space and time. With the

introduction of the EIM, a new set of generators has the potential to be “on the margin” at any given mo-

ment.

Different from Cullen (2013), Kaffine et al. (2013), and Novan (2015), who exploit variation in wind pro-

duction in Texas to estimate emissions offsets, and Callaway and Fowlie (2009), Graff Zivin et al. (2014),

and Callaway et al. (2018), who exploit variation in electricity demand to estimate emissions offsets, we

identify our results based on variation in electricity market design across the Western electric region. Our

estimates of how the marginal response of non-California fossil generation to California renewable gener-

ation is affected by the EIM have a similar interpretation to this literature, however. Specifically, we esti-

mate how the effect of the EIM on the marginal response of non-California fossil generation to California

load and California renewables generation, both on average and at each hour of the day, as well as at dif-

ferent individual plants throughout the West. Identifying variation in our paper comes from both stochas-

tic renewables as well as heterogeneous EIM participation over space and time.10

3. Background on the Western Energy Imbalance Market

The EIM was launched in response to a set of grid management concerns that began to arise in the mid-

2000s, and as a way to support California’s clean energy goals. Starting in 2002, California passed a series

of regulations to reduce carbon emissions11 that caused changes in CAISO’s generation portfolio and elec-

tricity grid management. For example, from 2006 to 2017, solar power production increased from 0.3% of

California’s in-state generation to 11.7%,12 leading to grid management challenges.

One particular grid management challenge is meeting net load, which is the difference between forecasted

load and expected renewable generation. Net load represents the electricity demand that California has

to meet with dispatchable generators. This is a challenge because the hourly profile of net load has the

10Cullen (2013), Kaffine et al. (2013), and Novan (2015) use exogenous variation in wind generation to identify the ef-fect of renewables on emissions reductions. Kaffine et al. (2013) estimate the direct marginal effect of increases in hourlywind generation on hourly emissions in Texas, finding that the marginal response of emissions varies by hour of day as themarginal fossil fuel changes with the amount of load. Novan (2015) instruments for wind generation with wind speed anddirection to account for potential endogeneity of curtailment and finds somewhat larger marginal effects. Cullen (2013) lever-ages variation in wind generation to estimate whether the costs of renewables subsidies exceed the environmental benefits ofthe pollution avoided in Texas. In a nationwide study, Graff Zivin et al. (2014) estimate the marginal emissions responses tohourly load, finding that marginal emissions vary significantly across space and time. Similarly, Callaway et al. (2018) showhow spatial and temporal variation in marginal emissions and marginal generation technologies affects the value of renewableor energy efficient investment. While we do not study investment, we also estimate marginal emissions responses to load.

11In 2002, California passed regulations to subsidize renewable resources. In 2006, California passed the Global WarmingSolutions Act (Assembly Bill 32) to reduce carbon emissions.

12https://www.energy.ca.gov/almanac/electricity_data/system_power/2006_gross_system_power.html (accessed3/18/2019).

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shape of a duck. California’s net load curve as of 2016 is shown in Figure 1. In the afternoon, when large

amounts of solar production are online, a belly shape forms. This is when California is most at risk of

electricity supply exceeding demand (overgeneration). Overgeneration creates technical and economic chal-

lenges. Some generators cannot turn off or turn down due to minimum voltage and reliability requirements

(Denholm et al., 2015). In the evening, when solar production tapers off, a quick ramp-up of electricity

is needed to meet load, forming the neck of the duck. Few generators are flexible enough to produce the

amount of power needed to manage the quick ramp-up of electricity.13

Figure 1: California Net Load: Actual and Forecasted as of 2016

Notes: California’s net load curve depicts that the risk of overgeneration, when supplyexceeds load, occurs in the daylight hours when solar production is at its peak. Source:CAISO.

The EIM gives CAISO access to a wider pool of generators, improving its ability to address energy imbal-

ances at a lower cost.14 The EIM changes the “effective” installed portfolio of fossil fuel generation avail-

able to California, affecting how CAISO optimizes its least-cost dispatch across this “effective” portfolio.

The EIM achieves this by extending a centrally managed spot market over a region of BAs operated by

vertically integrated utilities.15

CAISO dispatches generators in merit order, with lowest-marginal-cost generators such as wind and so-

lar dispatched first, and higher-cost generators such as natural gas, coal, and natural gas peaking genera-

tors dispatched next. A hypothetical dispatch curve from the Energy Information Administration (EIA) is

shown in Figure 2.16 If the EIM is being used to balance renewable resources, we expect when solar pro-

13CAISO. 2016. “What the duck curve tells us about managing a green grid.” https://www.caiso.com/Documents/

FlexibleResourcesHelpRenewables_FastFacts.pdf (accessed 8/10/2018).14CAISO. 2017. “Western EIM FAQS.” www.caiso.com/documents/EnergyImbalanceMarketFAQ (accessed 6/25/2018).15The Western electric region has 38 different BAs that balance electricity supply and demand across the region, with

most being operated by vertically integrated utilities.16Although declining natural gas prices in the period of this research can alter the merit order of some natural gas and

coal generators in the dispatch curve, it remains informative for setting expectations, this hypothetical dispatch curve doesnot depict CAISO’s specific generation portfolio.

7

duction exceeds its forecast (the dispatch curve shifts to the right), marginal fossil fuel generators are dis-

patched down to accommodate.

Figure 2: Hypothetical Dispatch Curve 2011

Notes: EIA’s hypothetical dispatch curve depicts the merit order dispatch of generators, with wind orsolar dispatched first, and higher cost generators dispatched next. This does not depict CAISO’s specificgeneration portfolio. Source: EIA.

In our results, emissions leakage occurs at night when EIM gas generators are on the margin. But when

California solar production peaks in the late afternoon, marginal EIM gas generators turn down their elec-

tricity production. This effect is in line with our expectations from the hypothetical dispatch curve: when

the dispatch curve is shifted out due to solar production exceeding its forecast, marginal natural gas gen-

erators are dispatched down. This intuition is confirmed by our triple-differences results which show that

EIM generator output declines under overgeneration conditions, such as when forecasted load was high

(i.e., excess resources may have been committed in advance), export transmission lines are constrained

(i.e., exports are not an option to solve overgeneration), and solar plants are at their peak (i.e., in the af-

ternoon).

The EIM started on November 1, 2014 with PacifiCorp. All BAs in the Western electric region were in-

vited to join the EIM. During our study period, five BAs joined, and six more will join by 2020. The EIM

footprint for current and planned participants is shown in Figure 3. Our study period is from April 2010

through December 2016, encompassing BAs shown in orange, (with the exception of Portland General

Electric, which joined in 2017).17 PacifiCorp joined the EIM to improve dispatch and operation of its gen-

17To provide an example of the potential size of the EIM, PacifiCorp has about 11,000 MW of existing generation ca-pacity (http://www.pacificorp.com/content/dam/pacificorp/doc/Energy_Sources/Integrated_Resource_Plan/2015%20IRP%20Update/2015%20IRP%20Update_20160426.pdf accessed 3/20/2019); Nevada Power has about 6000 MW of exist-ing generation capacity (https://www.nvenergy.com/publish/content/dam/nvenergy/brochures_arch/about-nvenergy/our-company/power-supply/GeneratingStations.pdf accessed 3/20/2019); Arizona Public Service Company has about9000 MW of existing capacity (https://www.aps.com/library/resource%20alt/2017IntegratedResourcePlan.pdf ac-cessed 3/20/2019); and Puget Sound Energy has about 3500 MW of existing capacity (https://www.pse.com/pages/

8

eration fleet and transmission facilities, and to create diversity benefits from increasing the pool of avail-

able generators to address its own energy imbalances (FERC, 2013). CAISO anticipated better real-time

access to energy imbalance generators, and more efficient and reliable operation of transmission facilities.18

Since its inception, the EIM’s main purported benefits are from centralized dispatch finding the most eco-

nomic generators in a combined area, diversification benefits from improved integration of renewable re-

sources, and improved grid reliability from increased transparency (Hogan, 2017).

Figure 3: Energy Imbalance Market 2018

Notes: This figure shows the Western EIM. To date, five BAs have joined the EIM. During our study period, PacifiCorp, ArizonaPublic Service Co., NV Energy, and Puget Sound Energy joined the EIM. Source: CAISO.

Regulators are concerned about emissions leakage from the EIM, however.19 Within California, generators

energy-supply/electric-supply accessed 3/20/2019).18CAISO. 2013. “California Independent System Operator Corporation Filing of ISO Rate Sched-

ule Number 73 Docket Number ER13-1372-000.” https://www.caiso.com/Documents/Apr30_

2013EnergyImbalanceMarketImplementationAgreement-PacifiCorpER13-1372-000.pdf (accessed 08/16/2018).19CAISO. 2018. “Energy Imbalance Market Greenhouse Gas Enhancements Second Revised Draft Final Proposal.” http:

//www.caiso.com/Documents/SecondRevisedDraftFinalProposal-EnergyImbalanceMarketGreenhouseGasEnhancements.pdf

(accessed 6/18/2018).

9

are subject to California’s carbon regulation, and the cost of emissions permits are included in their en-

ergy offers. Outside of California, generators have separate energy offers and GHG adders that account for

their emissions factors times an assumed market price of emissions permits (Hogan, 2014). Generators are

responsible for determining the assumed market price of emissions permits, so this GHG adder can distort

market prices (Hogan, 2014).20 Harmonizing the market design across regulated and unregulated regions

could allow for the reallocation of electricity produced by low-carbon resources to California and electricity

produced by high-carbon resources to the EIM region outside California. In our reduced-form analysis we

test the hypothesis that the EIM changed emissions in the EIM region outside of California, against the

null that it did not.

Empirical analyses of leakage outcomes from the EIM have not been undertaken.21 As several wholesale

electricity markets – CAISO, PJM, SPP, and countries within the EU – are considering further expan-

sion of their market designs, understanding EIM generation and emissions outcomes is policy and market-

design relevant. Because the EIM also aids in the integration of renewable resources, assessing how the

EIM balances these resources is also policy relevant.

The EIM is a small market, encompassing less than three percent of CAISO’s wholesale energy costs.22

Relative to the total market size, our leakage finding – that EIM participant gas generators increase their

generation by eight percent on average – is a modest effect. But our findings have broader relevance: the

EIM already sets prices and expectations for all transactions in the Western electric region, and the po-

tential expansion of CAISO’s larger Day-Ahead market processes is expected to produce further opera-

tional efficiencies,23 which could increase emissions leakage, depending on the generation portfolio used to

achieve these efficiency gains.24

20Inside CAISO energy prices include the cost of GHG compliance. Outside of CAISO, energy prices do not includethe cost of GHG compliance. Instead, external resources are paid a price equal to their GHG compliance cost adder whendispatched to serve California load. Inside CAISO, the compliance cost adder is based on a generator’s cost curve. Out-side of CAISO the compliance cost adder can be based on variable cost, which CAISO calculates based on a generator’smaximum heat rate registered with CAISO, the GHG allowance price, and the generator’s emissions rate as defined bythe EPA; or a negotiated rate on file with CAISO. Despite the GHG cost adder procedures in place, regulators remainconcerned that “CAISO’s least-cost dispatch can have the effect of attributing transfers to serve CAISO load to lower-emitting EIM participating resources because these resources face fewer or no costs to comply with ARB’s regulations;”CAISO. 2018. “EIM Greenhouse Gas Enhancements 2nd Revised Draft Final Proposal.” http://www.caiso.com/Documents/

SecondRevisedDraftFinalProposal-EnergyImbalanceMarketGreenhouseGasEnhancements.pdf (accessed 6/18/2018).21CAISO does track EIM benefits which are the cost savings of the EIM dispatch based on a counterfactual of what

the dispatch cost would have been without EIM transfers; CAISO. 2017. “EIM Quarterly Benefit Report Methodology.”https://www.westerneim.com/Documents/EIM_BenefitMethodology.pdf (accessed 5/1/2018). The appropriate counterfac-tual for estimating EIM benefits has also been a matter of ongoing debate (Hogan, 2017). CAISO. 2018. “Energy Imbal-ance Market Greenhouse Gas Enhancements Second Revised Draft Final Proposal.” http://www.caiso.com/Documents/

SecondRevisedDraftFinalProposal-EnergyImbalanceMarketGreenhouseGasEnhancements.pdf (accessed 6/18/2018).22CAISO. 2015 “Regional Coordination in the West: Benefits of PacifiCorp and CAISO Integration.” https://www.caiso.

com/Documents/StudyBenefits-PacifiCorp-ISOIntegration.pdf (accessed 7/6/2018).23https://www.caiso.com/Documents/CaliforniaISOCompletesStudiesonImpactsOf\RegionalEnergyMarket.pdf (accessed

3/18/2019).24CAISO’s Real-Time energy imbalance market, or EIM, operates as a net-pool, as the power producer submits an initial

production (base schedule) which is used to balance load and generation within an entity’s BA. An EIM participant can sub-mit bids to address energy imbalances, or deviations from the base schedule, across the entire EIM footprint. Expansion ofthe Day-Ahead market would mean that base schedules would be used to balance load and generation across the entire mar-ket footprint, potentially resulting in a different portfolio of generators used to supply load (http://www.westerneim.com/CBT/NEWBaseSchedulesFoundationalConcepts/story_html5.html accessed 3/18/2019).

10

4. Data and Empirical Approach

4.1. Data

We utilize hourly generation, emissions, and load data to study the real-time EIM. Our primary data source

for generation and emissions is the Environmental Protection Agency’s (EPA) Continuous Emissions Mon-

itoring System (CEMS) program.25 The EPA requires fossil-fuel generators with over 25 MW of capacity

to provide hourly data on sulfur dioxide (SO2), nitrous oxide (NOX), and carbon dioxide (CO2) emissions

for compliance with emissions regulations.26 Generators were mapped into their respective BAs using their

latitude and longitude coordinates provided by CEMS and a spatial map of BA boundaries from the De-

partment of Homeland Security’s (DHS) Homeland Infrastructure Foundation-Level Database (HIFLD).27

Hourly load is from FERC Form 714 Schedule III.28 FERC requires electric utilities with a planning area

annual peak demand that is greater than 200 MW to report hourly load. Planning areas generally coin-

cide with Balancing Authorities in the WECC.29 Hourly solar and wind production for CAISO are ob-

tained from CAISO’s Daily Renewables Watch reports.30 Hourly California load forecast data from the

Day Ahead Market is obtained from CAISO’s OASIS System Load and Resource Schedules.31 Other vari-

ables, such as generator age, generator efficiency, and generator capacity are constructed from the CEMS

data.

Table 1 reports summary statistics for hourly generation, hourly carbon dioxide emissions, hourly gener-

ator efficiency,32 and generator age for the full sample of non-California Western electric area gas gener-

ators. Hourly California load, wind and solar photovoltaic production, as well as each BA’s own load by

FERC planning area are also reported. The unmatched sample has 355 unique gas generators, 108 be-

long to BAs that participate in the EIM, and 247 belong to BAs that never participate in the EIM. The

unmatched sample has 97 unique coal generators, 37 belong to BAs that participate in the EIM, and 60

25Environmental Protection Agency. “Air Markets Program Data. Continuous Emissions Monitoring System.” https:

//ampd.epa.gov/ampd/ (accessed 4/1/2018).26While generators below 25 MW are not required to report emissions, these generators make up 1 percent or less of total

fossil generation in the EIA 923 dataset, where capacity is from the EIA 860 dataset, in the WECC region for each year ofour sample period.

27WECC was consulted on the accuracy of the DHS HIFLD BA spatial map. Although WECC does not maintain an au-thoritative BA spatial map, they advised using the DHS HIFLD map and refining the boundaries with the EIA 860. As such,we followed this recommended procedure to map generators to their correct BAs; Department of Homeland Security. “Home-land Infrastructure Foundation-Level Data.” https://hifld-geoplatform.opendata.arcgis.com/ (accessed 4/15/2018).

28Federal Energy Regulatory Commission. “ 2006 - 2017 Form 714 Database.” https://www.ferc.gov/docs-filing/

forms/form-714/data.asp (accessed 3/2018).29Exceptions are the Balancing Authority of Northern California (BANC), as two members, Sacramento Municipal Util-

ity District and Modesto Irrigation District report load separately as planning areas, these two planning areas were aggre-gated and matched with BANC. Other exceptions are BAs that are not included in Schedule III; Arlington Valley, Gila RiverPower, Griffith Energy, and New Harquala Generating Company. These excluded BAs make up 2 percent or less of SummerCapacity for each year from 2013 to 2016 according to EIA 860 generator data.

30CAISO. “Daily Renewables Watch.” http://www.CAISO.com/market/Pages/ReportsBulletins/RenewablesReporting.

aspx (accessed 6/2018).31CAISO OASIS System. “Energy: System Load and Resource Schedules.” http://oasis.caiso.com/mrioasis/logon.do

(accessed 8/2018).32Generator efficiency in this study is measured as the generator’s heat input in MMBtu divided by the maximum heat

input in MMBtu for the generator as reported in CEMS in order to capture differences in generator utilization.

11

belong to BAs that never participate in the EIM.33 Summary statistics for coal generators are reported in

Table B1 in Appendix B.

Table 1: Summary Statistics: Non-California Western Electric Area Gas Generators

Ever an EIM participant 0 1 TotalHourly Electrical Generation (MWh) 113.2 133.9 119.6

(94.12) (86.54) (92.35)Hourly Carbon Dioxide Emissions (Short Tons) 52.80 60.75 55.25

(37.18) (34.36) (36.52)Hourly CAISO Load (MW) 27854.0 28165.5 27949.9

(5336.7) (5458.3) (5376.3)California Solar Photovoltaic Generation (MWh) 1059.2 1125.5 1079.6

(1777.2) (1832.3) (1794.6)California Wind Generation (MWh) 1259.7 1280.7 1266.1

(1010.7) (1019.5) (1013.5)Hourly FERC Load by Planning Area 2882.6 4726.9 3450.4

(1888.6) (2316.3) (2201.2)Generator Efficiency(Heat Input/Maximum Heat Input by Fuel Type) 0.618 0.596 0.611

(0.220) (0.195) (0.213)Generator Age 16.90 13.36 15.81

(15.72) (12.91) (15.00)Number of Generators 247 108 355Number of Observations 18729264

Notes: This table reports summary statistics for hourly generation, hourly carbon dioxide emissions,hourly generator efficiency, measured as the generator’s heat input in MMBtu divided by the maxi-mum heat input in MMBtu for the generator as reported in CEMS in order to capture differences ingenerator utilization, as well as generator age for the full sample of non-California, Western electricarea gas generators; and hourly California load, wind and solar photovoltaic production, as well aseach BA’s own load by FERC planning area. Means of each variable are shown with standard devia-tions in parentheses. Variables are shown by group where 1 indicates EIM-participation.

4.2. Empirical Approach

To estimate the causal effect of the EIM, we utilize difference-in-differences and triple-differences with

matching. Under certain assumptions articulated in the potential outcomes framework,34 these methods

will produce unbiased parameter estimates. EIM membership is at the BA level, but EIM participation by

individual generators within the BA is not mandatory. To capture how generators’ generation and emis-

sions outcomes change if their BA joins the EIM, we assign treatment at the BA level, but perform our

analysis at the generator level.

A BA that joins the EIM is considered treated, where Di,t′ = 1, for the ith BA, following the EIM’s estab-

lishment at time t′. EIM membership was offered to all BAs in the Western electric region, but only some

joined, allowing for a comparison group of BAs who never join the EIM and are not considered treated,

33As a reference point, there were 491 gas generators and 72 coal generators in the non-CAISO WECC that were 25 MWor larger, 170 were in EIM BAs from this study period, based on EIA 860 data in 2018.

34See Imbens and Wooldridge (2009) for a survey of the literature.

12

Di,t′ = 0. We exclude any generators already in CAISO from our treatment or control groups. Our difference-

in-differences strategy exploits this variation in market design across time and Western BAs.

Our panel data set allows us to observe pre- and post-treatment outcomes for the jth generator, Yijt(0),

and Yijt′(0) or Yijt′(1). Our treatment effect of interest is the change in generation or emissions outcomes

of generators in BAs that participate in the EIM, which is the average treatment effect on the treated

(ATT),

δ1,att = E[Yijt′(1)− Yijt′(0)|Dit′ = 1];

as well as the change in marginal response to California load, which is the marginal treatment effect on

the treated.

δ2,mtt =∂ E[Yijt′(1)− Yijt′(0)|Dit′ = 1]

∂CaliLoad.

Because we do not observe the potential generation or emissions outcome of the jth generator had their

BA not participated the EIM, we construct a control group of generators from BAs that do not participate

in the EIM to estimate this unobserved potential outcome. We utilize difference-in-differences methods

combined with matching designs as in Ferraro and Miranda (2014) to replicate the experimental outcome

using ex-post econometric methods. We pre-process the data with matching and estimate the difference-

in-differences equation with ordinary least squares. Combining designs is also supported by arguments/evidence

in Ho et al. (2007) and Imbens and Wooldridge (2009).

Our identification strategy hinges on two key assumptions: unconfoundedness and sufficient overlap be-

tween the EIM participants and non-participants. Unconfoundedness requires that there are no unob-

served variables related to both the decision to participate in the EIM, and the potential change in gen-

eration or emissions outcomes from participating in the EIM. This assumption implies we need a suffi-

ciently rich set of predictors in the pre-treatment period t, Xijt, that explain the decision for participat-

ing in the EIM, controlling for these will lead to valid estimates of causal effects (Rosenbaum and Rubin,

1983). Generators in these BAs are part of the same electric grid, situated in the same rate-regulated re-

gion of the Western interconnection, and subject to similar regulatory environments.

Joining the EIM is voluntary, and participation may be endogenous. BAs cited specific reasons for joining

the EIM, including improved utilization of generators, improved utilization of transmission, and increas-

ing the pool of generators available to address energy imbalances. We account for this selection mechanism

by constructing measures of these variables to use in the matching process. To address utilization of gen-

erators, we estimate generator-level annual capacity factors from the observed maximum generation each

year in the CEMS data (Davis and Hausman, 2016; Johnson et al., 2017). We account for transmission

utilization through a measure of the highest grid voltage (kilovolts) at the point of interconnection, where

the plant is connected to the grid from the EIA 860.35 As longer transmission lines have higher voltage,

35Energy Information Administration. “Form EIA-860 detailed data with previous form data (EIA-860A/860B.” https:

//www.eia.gov/electricity/data/eia860/ (accessed 3/2018). Transmission miles are a typical measure of transmission ca-pacity. These are not available at the BA level, due to some transmission owners spanning multiple BAs, preventing aggre-gation to the BA level, and data confidentiality restrictions that prohibit WECC from sharing information about individualBAs.

13

this measure represents access to more transmission capacity. Energy imbalance occurs when supply does

not meet demand, and is a concern during peak hours of demand. To measure the potential for a BA to

meet its own energy imbalance, we construct a measure of excess capacity at the BA level for peak load,

from FERC Form 714 Schedule II Part I. We also construct a measure of propensity to respond to Califor-

nia load,36 which allows us to match on generators that are marginal sellers to California. Last, we match

on a measure of a generator’s annual average heat input (mmBtu), a measure of utilization calculated

by multiplying the quantity of fuel a generator uses each hour by its heat content. We include this mea-

sure to account for differences in thermal efficiency that can affect emissions. As we have panel data, our

matching covariates, Xijt, are measured in the pre-treatment period. We chose the period 2010 - 2012 as

the pre-treatment period as discussions for the EIM began between PacifiCorp and CAISO in April 2013,

although the EIM did not officially begin until November 1, 2014. These are the variables we use in the

matching process.

Unconfoundedness requires that the decision to participate in the EIM be independent of the potential

generation and emissions outcomes from participating in the EIM, conditional on the matching covariates

discussed above, in the pre-treatment period t, which characterize BAs’ decisions for joining the EIM,

Dit′ ⊥ (Yijt′(0), Yijt′(1))|Xijt).

Although the unconfoundedness assumption is not testable, we perform an out-of-sample test in the pre-

treatment period of 2013, and find no treatment effect with the same framework and controls. Results are

provided in Appendix D.

The overlap assumption implies the support of the conditional distribution of Xijt given Dit′ = 0, overlaps

completely with the support of the conditional distribution of Xijt given Dit′ = 1 (Imbens and Wooldridge,

2009).

0 < pr(Dit′ = 1|Xijt = x) < 1, ∀x.

We utilize propensity score matching to estimate the conditional probability of joining the EIM,

E(x) = pr(Dit′ = 1|Xijt = x), ∀x,

and directly test the overlap assumption through our use of propensity scores. In the pre-treatment pe-

riod, covariate balance in the unmatched and matched natural gas sample is shown in Table 2. We report

both a standardized mean difference and a variance ratio for treatment and control covariates. The stan-

dardized mean difference compares the difference in means of the treatment and control covariates in units

of the pooled standard deviation, perfectly balanced covariates will have a standardized mean difference

of zero, and mean differences greater than 20 percent should be considered large (Rosenbaum and Rubin,

1983). The variance ratio is the ratio of the variance of the treatment unit to control unit, where perfectly

36We regress each generator’s hourly generation on hourly California load, hourly own BA load, and control for hour-of-day, day-of-week, and month-by-year time fixed effects.

14

balanced covariates have a variance ratio of one (Austin, 2011). The matched sample with calipers and

trimming for natural gas-fired generators improves covariate balance for most covariates in the sample.

With the exception of the average estimated capacity factor for 2010-2012, and grid voltage as of 2012,

standardized mean differences are reduced. For both of the aforementioned covariates, while the standard-

ized mean difference does not improve, the variance ratio between the treatment and control units does

improve. Note that the original values for both of these covariates were not substantially unbalanced. Co-

variate balance in the unmatched and matched coal sample is shown in Table B2 in Appendix B.

Table 2: Natural Gas Balance of Sample Covariates

Standardized Differences Variance RatioFull Sample Matched Full Sample Matched

Est. Mean Capacity Factor 2010 - 2012 0.003 0.021 1.221 1.106Grid Voltage 2012 0.142 -0.250 1.967 1.593CA Load Response 2011 -0.247 -0.055 0.275 0.312CA Load Response 2012 -0.344 -0.218 0.092 0.097Heat Input 2012 -0.172 -0.107 1.000 0.986BA Avail. Cap. for Peak Demand 2012 -0.176 0.049 1.721 2.500

Notes: This table reports the standardized mean difference which compares the difference in means in units of thepooled standard deviation of the treatment and control. We also report the variance ratio of treatment to controlunits which is the ratio of the variance of the treatment unit to control unit. Perfectly balanced covariates willhave a standardized mean difference of zero, and a variance ratio of one.

Figure 4: Propensity Score Overlap - Natural Gas

Notes: This figure plots the propensity score overlap for natural gas generators for the full sample on the right and the matchedsample on the left.

With respect to the assumption that, conditional on the propensity score, treatment for all units is be-

tween zero and one, we report propensity score overlap in Figure 4 for natural gas-fired generators and

Figure 11 in Appendix B for coal-fired generators. Matching with calipers and trimming the sample im-

proves the balance of propensity scores for treatment and control groups across both gas and coal genera-

tors. Matching with calipers and trimming also reduces the sample size. For gas generators, there are 195

15

unique gas generators in the matched sample, 87 are located in BAs that participate in the EIM and 108

are located in BAs that never participate in the EIM. In the matched sample of coal generators, there are

66 unique generators, 35 are located in BAs that participate in the EIM, and 31 are located in BAs that

never participate in the EIM.

Another potential threat to identification is that the stable unit treatment value assumption (SUTVA)

is violated (Rubin, 1980). SUTVA requires two sub-assumptions: no interference between units, that is

Yijt′(1) and Yijt′(0) are unaffected by actions received by any other units, and no hidden versions of treatments–

that is if unit j received treatment 1, the observed outcome would be Yijt′(1) (Rubin, 2005). SUTVA vio-

lation is a concern as electricity is a network industry, with potential for treatment spillovers. Our match-

ing design helps to address SUTVA as we match on generators under the purview of different BAs (grid

operators). Control generators are located on different sections of the electricity grid than the treatment

generators, lessening the potential for treatment spillovers. We find only small differences in the average

results for our unmatched and matched samples, indicating that treatment spillovers due to the network

structure of the electricity grid are not a significant concern.

We use a reduced-form difference-in-differences model to capture the effect of EIM participation on con-

ventional generation and emissions outside California. Total conventional generation or emissions (Yijt) of

the jth generator in BA i at hour t are regressed against a treatment indicator equal to one if the BA is

ever an EIM participant, EverEIMi, a post-EIM time indicator, Postt, for the period after November 1,

2014 when the EIM began, as well as a vector of controls, Zjt, which is different than the pre-treatment

controls used for matching. Our difference-in-differences model is

Yijt = α+ βPostt + δEverEIMi ∗ Postt + Zjtγ + φi + εijt (1)

where φi are BA fixed effects and absorb the effect of the indicator of a BA ever being an EIM partici-

pant.

Because the EIM is a technological change that enables CAISO to dispatch generators more efficiently

across the EIM, to determine if CAISO changes responses of generators to California load, we use a triple-

differences model.

Yijt = α+ β1Postt + δ1EverEIMi ∗ Postt+ β2CaliLoadt + β3CaliLoadt ∗ EverEIMi + β4CaliLoadt ∗ Postt

+ δ2CaliLoadt ∗ EverEIMi ∗ Postt + Zjtγ + φi + εijt. (2)

In both regressions, all interactions are mean-centered so that base coefficients can be interpreted as the

marginal effect at the mean. Our treatment effects of interest are the level shift in generation or emissions

of a generator that is an EIM participant, δ1; and the change in response to California load if a generator

is an EIM participant, δ2.37

37The triple-differences model measures the response to California load, California wind, and California solar, as we are

16

We control for pre- and post-EIM time period trends. We include BA fixed effects, φi, to account for ob-

served and unobserved factors of BAs that could influence emissions and generation outcomes, such as

load or regional climate and infrastructure that influences electricity production. Zjt includes our various

time fixed effects, two generator-level controls to account for differences in generator efficiency, and con-

trols for hourly California renewables production. For the time fixed effects, we include hour-of-day fixed

effects to account for correlation between emissions or generation and California load, as California load

varies over the course of the day. Day-of-week fixed effects are included to account for differences in emis-

sions and generation that may vary by day due to load variation by weekday or weekends, allowing us to

identify off of within weekday and within hour-of-day variation. We also control for month-by-year fixed

effects to account for long run trends, such as increased capacity of gas or renewable electricity generators

over the long term that may affect generation and emissions outcomes. Last, to account for thermal effi-

ciency differences among generators, which can affect effective capacity (Cullen, 2013), we also control for

the hourly heat input (in mmBtu) as a fraction of the maximum hourly heat input for each generator, and

the generator age.38

We cluster errors at the BA level to allow for within BA, and serial correlation of errors, as suggested by

Bertrand et al. (2004). Although the number of clusters within our unbalanced panel is 19 for the matched

sample of gas generators, and 11 for the matched sample of coal generators, we have many observations

per cluster, which means that our estimates can be reasonably precise and unbiased (Cameron and Miller,

2015). However, with few clusters, the asymptotics have not set in and our variance estimates can be down-

ward biased (Cameron and Miller, 2015). To address this potential problem, as we have G groups of BAs,

N observations, and K regressors, we use the T distribution with G-1 degrees of freedom and scale our

residuals by√c where c = G

G−1N−1N−K to reduce downwards bias in the variance matrix (Cameron and

Miller, 2015).39 Further, we have a large time dimension from using hourly data from April 2010 to De-

cember 2016. Hansen (2007) has shown that even with a fixed group size, G, with a large T the asymp-

totic properties of the estimated variance matrix as T → ∞ will be proportional to the true variance

matrix, provided individual observations are independent and identically distributed (iid) across the in-

dividual observation dimension.40 Based on our model design and various controls, we believe we fulfill

the requirement that observations are iid across BAs. We follow the recommendation of Hansen (2007) to

normalize the Arellano (1987) estimator by GG−1 and use critical values from the T distribution with G-1

degrees of freedom, which will result in an asymptotically unbiased estimator when T →∞ and G is fixed,

provided the iid assumption is met.41

For the matched regressions, we preprocess the data with our matching process and estimate the difference-

in-differences and triple-differences models on the weighted matched sample. In our matched sample we

interested in both the treatment effects in response to California load, and response to California renewables as these are twobalancing challenges.

38Heat input and maximum heat input are available in CEMS and generator age is constructed from the operating datein CEMS data. Although annual average heat input was included as a matching variable, hourly heat input as a percent ofmaximum heat input is included to account for hourly differences in thermal efficiency.

39This is the distribution, critical values, and residuals scaling implemented by using Stata’s vce(cluster) command.40Hansen (2007) notes that this assumption allows for arbitrary correlation and heteroskedasticity within individual obser-

vations, but requires the pattern be the same across individual observations.41This correction is also implemented by Stata’s vce(cluster) command (Hansen, 2007).

17

utilize a weight, wjk, for each non-participant generator, k, that is included in the counterfactual for each

EIM generator, j. We construct our weight in two ways. One is using the propensity score to match the

treated generator with n nearest neighbors. To improve covariate balance of the matched sample, we uti-

lize a trimming rule that discards generators with propensity scores outside of the [0.05, 0.95] range, and

only include nearest neighbors that are within a caliper of one standard deviation of the treated genera-

tor’s propensity score, which provides sufficient overlap in the treated and control groups. Because trim-

ming and calipers reduce the sample to a subset, we identify the conditional average treatment effect on

the treated (CATT). The second way we construct the weight is by nearest neighbor, using the Maha-

lanobis distance to find the matching generator from the control generator pool, which we report in Ap-

pendix C.

We also specify treatment effects for generators’ responses to California wind production and California

solar production. In those regressions, we specify the triple-differences model to capture the marginal re-

sponse to California solar production, or California wind production, while controlling for California load,

solar and wind production. The regression specification is otherwise unchanged from Equation 2.

Last, to examine the heterogeneity in treatment effects across capacity and BA regions, we examine if the

distribution of the counterfactual generator-level outcome, Yjt′(0), differs from the treated generator-level

outcome, Yjt′(1). To determine the distributional effects of the EIM, we estimate Equation 3 for individ-

ual generators and their three nearest neighbors (by propensity score, within the caliper) identified in the

matching process,

Yjt = αj + β1,jPostt + δ1,jEverEIMi ∗ Postt+ β2,jCaliLoadt + β3,jCaliLoadt ∗ EverEIMi + β4,jCaliLoadt ∗ Postt

+ δ2,jCaliLoadt ∗ EverEIMi ∗ Postt + Zjtγj + φi,jEverEIMi + εijt (3)

where an additional control, monthly, state-level CityGate natural gas prices are added to Zjt to account

for regional differences in gas prices that would have previously been captured by BA fixed effects in the

pooled regressions. We use feasible generalized least squares to account for heteroskedasticity and serial

correlation in the errors of the individual generator regressions. For nearest neighbor regressions, we then

investigate the distribution of δ1,j and δ2,j by generator capacity and location.

5. Results and Discussion

Table 3 reports our difference-in-differences and triple-differences results for the EIM’s effect on natu-

ral gas generation, for both the full and matched samples. The difference-in-differences results show how

the EIM changed non-CAISO EIM generators’ average levels of generation and emissions. The triple-

differences results show how the EIM changed non-CAISO EIM generators’ marginal responses to incre-

mental increases in California load, indicating how EIM generators outside of CAISO are used to bal-

ance small changes in California load. The dependent variable is hourly electricity generation in megawatt

hours (MWh). All interactions are mean-centered, so that we interpret base coefficients as marginal ef-

18

fects evaluated at the mean. The first column reports the difference-in-differences results from the full

sample, without hourly California control variables (hourly California load, solar, and wind production).

The second column reports the triple-differences results from the full sample and includes hourly Califor-

nia control variables. The third and fourth columns report the same specifications as the first and second,

estimated using our matching procedure.

Our preferred specification is the matched sample with additional California control variables in the fourth

column. We find that on average, EIM gas generators produce 9.4 MWh more electricity each hour than

non-participants. The EIM increases the average (114 MW) generator’s electricity production by 8 percent

of its capacity. This effect is fairly robust across specifications, though slightly higher in the full sample

than in the matched sample. Controlling for hourly California variables is important for precision, but

dropping them does not appear to create omitted variables bias. This suggests the generation and emis-

sions increase we find is due to the EIM’s reduced transactions costs, rather than reallocating imports

from non-EIM to EIM generators.42 Increased power production also increases carbon emissions by 3.1

tons per hour in the EIM region outside of California. Emissions results are in Table A1 in Appendix A.

In our triple-differences model, we find the same EIM gas generators who produce more power on average,

also turn down (decrement) their power production in response to incremental increases in California load.

While the magnitude of the coefficient is small, a decrement of 1.6 kWh, it also varies throughout the day,

with positive responses in some hours reducing the average magnitude.43

To investigate this counterintuitive result more fully, we decompose hourly California load into hourly Cal-

ifornia forecasted load (from the Day Ahead Market), and deviations of actual load from forecasted load.

We reestimate our triple-differences model, finding that EIM gas generators are turning down in response

to incremental increases in expected California load, as the marginal generator decrements power produc-

tion by 1.6 KWh on average in response to incremental increases in the California load forecast. These

results are reported in Table 4 for the full and matched samples.

The triple-differences model shows how marginal EIM generators behave in response to incremental in-

creases in California load. Because we find the EIM generators decrement their power production signifi-

cantly more than non-participants, this finding indicates that the EIM is using these EIM gas generators

to create more flexibility on the power grid. When the load forecast is too high, EIM gas generators are

turned down more than non-participants. This finding is in line with our expectations from the dispatch

curve, in Figure 2, when the load forecast is a little too high, marginal fossil-fuel generators are turned

down to match actual load.

Because we are also interested in how the EIM balances renewable resources, we examine marginal re-

sponses to California solar and wind production. We reestimate our triple-differences model with Cali-

fornia solar production and California wind production as the triple-interaction and pairwise-interaction

42If EIM generators increase or decrease their power production, holding all other relevant WECC generators fixed,CAISO generators will fill the remaining gap to meet load, and generators balance.

43This effect has a similar magnitude in the full sample, although it is not statistically significant.

19

Table 3: Hourly Electrical Generation from Natural Gas Generators (MWh) in Response toCalifornia Load

Full Sample MatchedEver EIM X Post EIM 13.74 13.18** 9.299*** 9.389***

(8.328) (6.260) (3.144) (3.028)Post EIM -2.831 -6.084 1.076 -0.703

(7.779) (7.275) (6.413) (5.926)Ever EIM X CA Load -0.00146*** -0.00214***

(0.000349) (0.000607)Ever EIM X PostX CA Load -0.00122 -0.00162*

(0.000727) (0.000816)Post EIM X CA Load -0.000323 -0.000168

(0.000284) (0.000151)CA Load -0.00180*** -0.000479**

(0.000301) (0.000198)CA Solar PV -0.000777** -0.00156**

(0.000373) (0.000702)CA Wind -0.000215 -0.000943***

(0.000185) (0.000166)Hourly FERC Loadby Planning Area -0.00531 -0.000634 0.000561 0.00487

(0.00340) (0.00241) (0.00378) (0.00317)Generator Efficiency 179.8*** 180.3*** 203.7** 198.6**

(22.79) (22.97) (88.02) (87.72)Generator Age -1.046*** -1.029*** -1.238** -1.237**

(0.311) (0.307) (0.529) (0.531)Constant 133.0*** 120.2*** 117.5*** 112.0***

(3.518) (3.235) (3.327) (3.450)

Observations 5,682,165 5,666,886 3,809,660 3,799,100R-squared 0.588 0.592 0.526 0.533

Notes: This table reports difference-in-differences and triple-differences results for the effect ofthe EIM on natural gas generation, using both the full and matched samples. The dependentvariable is measured in hourly megawatt hours (MWh). All interactions are mean-centered sothat base coefficients can be interpreted as marginal effects evaluated at the mean. The firstcolumn reports the difference-in-differences results from the full sample, without hourly Cali-fornia control variables. The second column reports the triple-differences results from the fullsample and includes hourly California control variables. The third and fourth columns reportthe same specifications as the first and second, estimated using our matching procedure. Allspecifications have BA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, andmonth-by-year fixed effects. Clustered standard errors by BA are in parentheses. Significancelevels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

20

Table 4: Hourly Electrical Generation from Natural Gas Generators(MWh) in Response to California Load Forecast

Full Sample MatchedEver EIM X Post EIM 13.69** 9.967***

(6.079) (3.103)Post EIM -6.388 -1.227

(7.258) (5.769)Ever EIM X CA Load Forecast -0.00148*** -0.00217***

(0.000363) (0.000616)Ever EIM X PostX CA Load Forecast -0.00124 -0.00164*

(0.000729) (0.000828)Post EIM X CA Load Forecast -0.000314 -0.000180

(0.000286) (0.000147)Ever EIM X CA LoadActual - Forecast 0.000356 -0.00171*

(0.00105) (0.000985)Ever EIM X PostX CA Load Actual - Forecast -0.000919 0.000611

(0.000715) (0.00104)Post EIM X CA LoadActual - Forecast 7.68e-05 -0.000862

(0.000533) (0.000999)CA Forecasted Load (DAM) -0.00183*** -0.000563***

(0.000308) (0.000192)CA Actual - Forecasted Load -0.00172*** 0.000153

(0.000496) (0.000325)CA Solar PV -0.000722* -0.00145**

(0.000349) (0.000637)CA Wind -0.000178 -0.000865***

(0.000177) (0.000167)Hourly FERC Loadby Planning Area -0.000585 0.00502

(0.00241) (0.00315)Generator Efficiency 179.9*** 199.1**

(23.03) (88.22)Generator Age -1.026*** -1.236**

(0.307) (0.533)Constant 120.2*** 111.4***

(3.382) (3.601)Observations 5,568,363 3,730,246R-squared 0.592 0.533

Notes: This table reports difference-in-differences and triple-differencesresults for the effect of the EIM on natural gas generation, using thematched sample. The dependent variable is measured in hourly megawatthours (MWh). All interactions are mean-centered so that base coefficientscan be interpreted as marginal effects evaluated at the mean. The firstcolumn reports the triple-differences results from the full sample and in-cludes hourly California control variables. The second column reports thesame specification as the first, estimated using our matching procedure. Allspecifications have BA fixed effects, hour-of-day fixed effects, day-of-weekfixed effects, and month-by-year fixed effects. Clustered standard errors byBA are in parentheses. Significance levels are denoted by *** p<0.01, **p<0.05, * p<0.1.

21

terms. These results are reported in Tables A2 and A3 in Appendix A. We find that EIM gas genera-

tors produce 14.9 MWh more power than non-participants when California solar is at its mean, and 10.02

MWh more power when California wind is at its mean. On the margin, we find that EIM gas generators

do not respond significantly differently than non-participants to incremental increases in solar or wind pro-

duction. This treatment effect leads us to conclude that the EIM is helping to better balance renewable

resources, but by marginally shifting fossil fuel generation in the EIM region outside of California, creating

more flexibility on the grid when load forecasts are higher than actual load. This treatment effect indi-

cates that the EIM is being used to address overgeneration imbalances.

In the Appendix, we report results for coal generators. While we find heterogeneity in coal generators’ re-

sponses to California load, wind, and solar production, we do not find any significant differences between

EIM participants and non-participants, on average, as shown in Appendix B, Tables B3 through B6. Al-

though our results for gas generators are more precise than those for coal generators, when we pool both

gas and coal generation we find that, on average, generators produce significantly more electricity (25

MWh) than their matched counterfactual generators in the EIM. The marginal generator’s response to

incremental increases in California load is negative and similar in magnitude to the gas generator regres-

sion results, but a marginal EIM generator does not reduce its power more than non-EIM generators. The

pooled generator level regression results are in Appendix F, Table F1.

6. Results and Discussion by Hour of Day

Our average results point to how the EIM is actually dispatching EIM generators outside of California.

Because the duck curve, in Figure 1, shows energy imbalances vary throughout the day, we examine how

the EIM’s average and marginal treatment effects vary over time. We reestimate the triple-differences

model, with California load interactions, for each hour of the day. We find that EIM gas generators are

producing significantly more power than non-participant gas generators in the night hours, from midnight

through 5 AM, as shown in Figure 5. These same marginal EIM gas generators are turning down power

production in response to incremental increases in expected California load in the daylight hours, with

magnitudes increasing in line with when solar production is at it’s peak, in early afternoon, as shown in

Figure 6.

In the non-EIM region, generators can participate in CAISO’s 15 minute real-time energy imbalance mar-

ket, and receive a dispatch instruction every 15 minutes, provided they are competitive and win an elec-

tricity award. Within the EIM, gas generators also participate in the 15 minute real-time energy imbal-

ance market, but with the difference that they receive a dispatch instruction every 5 minutes. In the hourly

results, we identify that the EIM causes EIM gas generators to produce more than their matched counter-

factual generators when gas is likely the marginal fuel in the merit order dispatch, which occurs at night.

Because we matched on characteristics that make both the treatment and control generators marginal to

participating in the EIM, we conclude that this is the effect of efficient dispatch of the EIM design. As

our results are reduced-form, we do not identify if EIM generators are producing more due to displacing

marginal California generators; or if they are producing more due to price effects, increased competition,

22

Figure 5: Hourly EIM-Participant Gas Generation at Califor-nia Load Averages

Notes: This figure compares the difference-in-differences effect ofparticipating in the EIM at hourly CA load averages.

Figure 6: Hourly EIM-participant Gas Generation in Re-sponse to California Load

Notes: This figure compares the triple-difference effect of partici-pating in the EIM for incremental increases in load above hourlyCA load averages.

or increased trading in the EIM region outside of California. We leave the specific mechanism by which

leakage is occurring to the EIM region outside of California to future work.

Reestimating the triple-differences model, with California solar or wind interactions, for each hour of the

day, we find that marginal responses to wind follow the same pattern as marginal responses to load, though

the effects are muted, as shown in Figure 7. Marginal responses to solar are not significantly different than

non-participants, and are not shown. Comparing these results to peak solar and wind production in Fig-

ure 8, marginal generators turn down power production significantly more when solar peaks in the early

afternoon hours, when California is most at risk of overgeneration.

We find heterogeneity in coal generators’ responses to hourly California load, wind, and solar production,

as shown in Appendix B, Figures 12 and 13. But we do not find any significant differences between EIM

participant and non-participant generators’ hourly responses to California variables. Our result does not

rule out emissions reshuffling in the EIM region outside of California, as there was no change in emissions,

but we leave testing for emissions reshuffling to future work.

The EIM was also intended to give CAISO better visibility and utilization of available transmission capac-

ity. Transmission congestion can affect the pattern of dispatch and emissions within the EIM, and also

potentially affect our results. To address this issue we construct a measure of transmission congestion

based on transmission interface and intertie constraints with CAISO, as shown in Appendix E. We find

that EIM participant gas generators turn down significantly to incremental increases in California load

when there is export congestion from CAISO, but do not turn down significantly to incremental increases

in California load when there is import congestion or no congestion at the interties. This finding is in line

with CAISO’s procedures to address overgeneration, which are to turn dispatchable generators, reduce im-

ports, clear exports, and decommit fast-start generators, potentially indicating that when exports cannot

23

Figure 7: Hourly EIM-participant Gas Generation in Re-sponse to California Wind

Notes: This figure compares the triple-difference effect of partici-pating in the EIM for incremental increases in wind power abovehourly CA wind power averages.

Figure 8: California Average Hourly Solar and Wind Produc-tion

Notes: This figure plots average hourly CA solar photovoltaicand wind production for the study period 2010 - 2016.

be cleared due to congestion, EIM-participant gas generators are turned down more to address imbalances.

7. Results and Discussion by Geographic Regions

The Western EIM covers a large portion of the Western U.S., and the distribution of EIM treatment ef-

fects could vary over geographic regions. We examine treatment effects for individual EIM generators, as

specified in Equation 3, by estimating the triple-differences model for each generator and its three matched

nearest neighbor control generators based on the propensity score.44 We plot individual generator responses

according to estimated maximum capacity,45 and the generator’s BA, to analyze the EIM’s treatment ef-

fect heterogeneity over geographic regions.

As shown in Figure 9, we find that smaller EIM gas generators with less than 200 MW capacity have sig-

nificant variation in average treatment effects. In BAs adjacent to California, Nevada Power Company

and Arizona Public Service Company, some generators are producing significantly more power than their

matched counterfactual generators. In Arizona Public Service Company, smaller generators produce more

power, and in Nevada Power Company, larger generators produce more power. Leading us to conclude,

generators closer to California load centers are producing more power. Marginal EIM gas generators in

BAs adjacent to California are also being turned down more to address overgeneration imbalances, as

shown in Figure 10.

44For coal generators, we include only two nearest neighbor control generators.45Estimated maximum capacity is based on the highest observed electricity production of the generator during the sample

period.

24

Figure 9: Individual Generator Power Generation (MWh) at Average Expected CALoad by Capacity by BAA

Notes: This figure compares the difference-in-differences effect of participating in the EIMfor each generator and its three matched nearest neighbor control generators based on thepropensity score.

Figure 10: Marginal Gas Generator Power Production (MWh) from Incremental Ex-pected California Load Increases by Capacity by BAA

Notes: This figure compares the triple-differences effect of participating in the EIM for eachgenerator and its three matched nearest neighbor control generators based on the propensityscore.

8. Conclusion

Changes in electricity market design are cutting across regions with different climate policies, potentially

distorting the efficacy of climate policy goals. We study a key example of a change in electricity market

design that overlaps regional climate policies – the EIM – and empirically estimate how this change in

25

market design affects emissions leakage and fossil fuel dispatch patterns. The EIM created a regional spot

market with centralized bidding and dispatch that connects California, which has a carbon cap-and-trade

program, with multiple BAs in the western U.S. that have no carbon regulation. We estimate the impact

of the EIM on non-California fossil fuel generators using a difference-in-differences approach between par-

ticipants and non-participants. We also estimate the impact on marginal supply decisions using triple dif-

ferences. In order to control for self-selection into the EIM, we preprocess the data using matching proce-

dures that rely on known criteria for the participation decision. Our results show that the EIM is changing

fossil fuel generation decisions and resulting emissions, both on average and at the margin.

Our main finding is that the EIM causes increased gas generation and associated emissions leakage into

the EIM region outside of California. We do not find a statistically significant impact on coal generation

activity. The magnitude of the average effect on gas generation is modest – approximately nine MWh each

hour – but is potentially significant relative to the scale of the program46 and small trading volumes found

within the EIM (Hogan, 2017). The EIM is a real-time balancing market that makes up only three per-

cent of the total wholesale energy costs in CAISO.47 If the full set of CAISO forward and real-time market

mechanisms are expanded to the rest of the Western region, as has been proposed, or if California did not

apply border carbon charges on electricity imports, we expect leakage would occur on a larger scale.

The emissions leakage we find is largely due to gas generators producing more power in the night hours

when gas is likely the marginal fuel. Counterintuitively, EIM participation also caused gas generators to

turn down on the margin in response to incremental increases in expected California load during daylight

hours. Because of the way CAISO commits resources in the forward market, California is most at risk of

overgeneration from excess solar production when daytime load forecasts are high. As a result, California

appears to be using the EIM to decommit fossil resources when solar and load are realized in real time.

These responses follow the general shape of the duck curve. While the EIM appears to be providing ad-

ditional flexibility which facilitates the integration of renewable resources, the mechanism occurs through

shifting fossil generation outside of California. There is also significant heterogeneity in the treatment ef-

fect, with generators close to California load centers selling significantly more power on average, and turn-

ing down more in response to expected increases in California load.

An important caveat to our analysis is that we do not identify the extent to which resource reshuffling, as

well as the California border carbon charges, may have a moderating effect on total leakage. These remain

two sources of concern for California regulators and factor into policy discussions of potential revisions

to EIM participation and trading rules. We do not rule out the potential that resource reshuffling is oc-

curring in the EIM outside of California, given that on average coal generators have negligible emissions

differences from their matched counterfactual generators; but we leave this consideration for future work.

46CAISO. 2015.“Regional Coordination in the West: Benefits of PacifiCorp and CAISO Integration.” https://www.caiso.

com/Documents/ISORegionalEnergyMarketFAQ.pdf (accessed 6/25/2018).47CAISO. 2015.“Regional Coordination in the West: Benefits of PacifiCorp and CAISO Integration.” https://www.caiso.

com/Documents/ISORegionalEnergyMarketFAQ.pdf (accessed 6/25/2018).

26

Our results are informative in the context of continuing changes in Western electricity markets, as well as

potential market expansions elsewhere in the U.S. including PJM’s potential incorporation of regional or

sub-regional carbon pricing in its market design, SPP’s potential expansion to the Mountain West Trans-

mission Group, and internationally with the European Union’s Energy Union, and its goal of integrating

European wholesale electricity markets. Our results also have more general implications for sub-global

climate policy when trade in goods markets occurs across regions with different market clearing rules.

Specifically, reduced transactions costs in trade between regulated and unregulated regions may tend to

exacerbate leakage.

Acknowledgements

Thanks to Dan Kaffine, Charles F. Mason, Thorsten Janus, Robert Godby, Klaas van ‘t Veld, Jonathan

Cook, and seminar participants at Camp Resources, CU Environmental & Resource Economics Workshop,

Front Range Energy Economics Workshop, and the Federal Energy Regulatory Commission Office of En-

ergy Policy and Innovation Intern Seminar for providing valuable comments. We would also like to thank

Peter Collusy (California ISO) for providing feedback on institutional details of the Western Energy Im-

balance Market.

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Appendix A Gas Generator Level Regression Results

Table A1 reports our difference-in-differences and triple-differences results for the effect of the EIM on nat-

ural gas carbon emissions, using both the full and matched samples. The dependent variable is measured

in short tons. All interactions are mean-centered so that base coefficients can be interpreted as marginal

effects evaluated at the mean. The first column reports the difference-in-differences results from the full

sample, without hourly California control variables. The second column reports the triple-differences re-

sults from the full sample and includes hourly California control variables. The third and fourth columns

report the same specifications as the first and second, estimated using our matching procedure. We find

that EIM gas generators produce 3.061 tons more carbon emissions on average than non-EIM participants

in the EIM region outside of California.

Table A2 reports our triple-differences results for the effect of the EIM on natural gas generation and car-

bon emissions, using both the full and matched samples. The dependent variable is measured in either

MWh for generation or short tons for carbon emissions. All interactions are mean-centered so that base

coefficients can be interpreted as marginal effects evaluated at the mean. The first column reports the

triple-differences results for generation from the full sample and includes hourly California control vari-

ables. The second column reports the same specifications as the first, estimated using our matching pro-

cedure. The third and fourth columns report the same specifications as the first and second, for carbon

emissions rather than generation. We find that on average, EIM gas generators produce 14.90 MWh more

30

Table A1: Hourly CO2 Emissions from Natural Gas Generators (Short Tons) in Response toCalifornia Load

Full Sample MatchedEver EIM X Post EIM 4.568 4.147 3.208*** 3.061**

(3.330) (2.462) (1.100) (1.073)Post EIM -1.256 -2.506 0.105 -0.487

(2.931) (2.751) (2.269) (2.073)Ever EIM X CA Load -0.000717*** -0.00103***

(0.000136) (0.000241)Ever EIM X PostX CA Load -0.000421 -0.000593*

(0.000271) (0.000319)Post EIM X CA Load -0.000101 -5.70e-05

(0.000105) (6.73e-05)CA Load -0.000712*** -0.000133

(0.000114) (8.99e-05)CA Solar PV -0.000275* -0.000601**

(0.000133) (0.000281)CA Wind -9.76e-05 -0.000436***

(6.76e-05) (6.17e-05)Hourly FERC Loadby Planning Area -0.00205 -9.47e-05 0.000647 0.00247**

(0.00129) (0.000860) (0.00145) (0.00115)Generator Efficiency 81.76*** 81.94*** 83.40** 81.10*

(8.611) (8.678) (39.42) (39.42)Generator Age -0.256** -0.248** -0.364 -0.363Constant 60.58*** 55.64*** 53.93*** 52.19***

(1.451) (1.166) (1.743) (1.306)

Observations 5,699,772 5,684,428 3,800,748 3,790,210R-squared 0.587 0.592 0.482 0.491

Notes: This table reports difference-in-differences and triple-differences results for the effect ofthe EIM on natural gas carbon emissions, using both the full and matched samples. The depen-dent variable is measured in short tons. All interactions are mean-centered so that base coef-ficients can be interpreted as marginal effects evaluated at the mean. The first column reportsthe difference-in-differences results from the full sample, without hourly California control vari-ables. The second column reports the triple-differences results from the full sample and includeshourly California control variables. The third and fourth columns report the same specifica-tions as the first and second, estimated using our matching procedure. All specifications haveBA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, and month-by-year fixedeffects. Clustered standard errors by BA are in parentheses. Significance levels are denoted by*** p<0.01, ** p<0.05, * p<0.1.

31

power than non-participants, when evaluated at average levels of California solar production. Although we

do not find that the marginal EIM gas generator turns down power production significantly in response to

incremental increases in California solar production, the coefficient of nearly two kWh is in line with the

significant decremental response that we find for incremental increases in California load. EIM gas genera-

tors also produce 5.817 tons more carbon emissions per hour.

Table A2: Hourly Electrical Generation (MWh) and Emissions (Short Tons) from Natural Gas Gener-ators in Response to California Solar Production

Generation EmissionsFull Sample Matched Full Sample Matched

Ever EIM X Post EIM 16.45** 14.90*** 5.843** 5.817***(5.950) (3.018) (2.325) (1.067)

Post EIM -4.540 -0.642 -2.017 -0.761(7.253) (5.461) (2.733) (1.864)

Ever EIM X CA Solar 0.000623 -0.00214** -0.000103 -0.00138***(0.00154) (0.000900) (0.000637) (0.000327)

Ever EIM X PostX CA Solar -0.00353*** -0.00191 -0.00111*** -0.000375

(0.00117) (0.00180) (0.000366) (0.000636)Post EIM X CA Solar 0.000931 -0.000391 0.000326 -0.000368*

(0.000224) (0.000178) (0.000706) (0.000528)CA Load -0.00166*** -0.000442 -0.000643*** -0.000116

(0.000428) (0.000383) (0.000175) (0.000168)CA Solar PV -0.000918*** -0.000810*** -0.000340*** -0.000244*

(0.000273) (0.000199) (9.51e-05) (0.000138)CA Wind -0.000198 -0.000904*** -8.91e-05 -0.000417***

(0.000172) (0.000169) (6.27e-05) (6.81e-05)Hourly FERC Loadby Planning Area -0.00282 0.00169 -0.00107 0.00104

(0.00294) (0.00338) (0.00110) (0.00127)Generator Efficiency 181.0*** 202.8** 82.25*** 83.05**

(23.09) (87.77) (8.738) (39.34)Generator Age -1.039*** -1.238** -0.253** -0.363

(0.310) (0.527) (0.109) (0.242)Constant 120.1*** 112.8*** 55.62*** 52.55***

(3.090) (2.793) (1.165) (1.104)

Observations 5,666,886 3,799,100 5,684,428 3,790,210R-squared 0.590 0.527 0.589 0.484

Notes: This table reports triple-differences results for the effect of the EIM on natural gas generationand carbon emissions, using both the full and matched samples. The dependent variable is measuredin MWh for generation and short tons for carbon emissions. All interactions are mean-centered so thatbase coefficients can be interpreted as marginal effects evaluated at the mean. The first column reportsthe triple-differences results for generation from the full sample and includes hourly California controlvariables. The second column reports the same specifications as the first, estimated using our match-ing procedure. The third and fourth columns report the same specifications as the first and second, forcarbon emissions rather than generation. All specifications have BA fixed effects, hour-of-day fixed ef-fects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors by BA are inparentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

Table A3 reports our triple-differences results for the effect of the EIM on natural gas generation and car-

bon emissions, using both the full and matched samples. The dependent variable is measured in either

MWh for generation or short tons for carbon emissions. All interactions are mean-centered so that base

coefficients can be interpreted as marginal effects evaluated at the mean. The first column reports the

triple-differences results for generation from the full sample and includes hourly California control vari-

32

ables. The second column reports the same specifications as the first, estimated using our matching pro-

cedure. The third and fourth columns report the same specifications as the first and second, for carbon

emissions rather than generation. We find that on average, EIM gas generators produce 10.02 MWh more

power than non-participants, when evaluated at average levels of California wind production, which is in

line with the treatment effect that we find for both California load and California solar production. We

also find that marginal EIM gas generators decrement power production nearly one kWh to incremen-

tal increases in California wind production, which is in line with the decremental response to California

load, though muted. EIM gas generators also produce significantly more carbon emissions of 3.571 tons

per hour more on average than non-participant generators.

Table A3: Hourly Electrical Generation from Natural Gas Generators (MWh) in Response to Cali-fornia Wind Production

Generation EmissionsFull Sample Matched Full Sample Matched

Ever EIM X Post EIM 13.80* 10.02*** 4.658 3.571***(7.796) (3.306) (3.141) (1.188)

Post EIM -3.421 3.664 -1.544 1.192(7.832) (7.549) (2.916) (2.686)

Ever EIM X CA Wind 0.000186 -0.00139 -0.000285 -0.000888(0.00168) (0.00178) (0.000632) (0.000695)

Ever EIM X PostX CA Wind -0.00285** -0.00117 -0.000901** -0.000254

(0.00113) (0.00167) (0.000414) (0.000691)Post EIM X CA Wind 0.000977 0.00115 0.000391 0.000480

(0.000748) (0.00101) (0.000246) (0.000413)CA Load -0.00164*** -0.000391 -0.000638*** -9.33e-05

(0.000424) (0.000375) (0.000172) (0.000165)CA Solar PV -0.00104** -0.00193* -0.000351** -0.000716*

(0.000464) (0.000925) (0.000164) (0.000363)CA Wind -0.000194 -0.000864*** -9.71e-05 -0.000399**

(0.000217) (0.000282) (0.000101) (0.000166)Hourly FERC Loadby Planning Area -0.00298 0.00136 -0.00114 0.000876

(0.00309) (0.00342) (0.00115) (0.00128)Generator Efficiency 181.2*** 203.3** 82.32*** 83.30**

(23.13) (87.78) (8.755) (39.31)Generator Age -1.040*** -1.240** -0.253** -0.364

(0.310) (0.528) (0.110) (0.242)Constant 120.0*** 111.6*** 55.59*** 52.07***

(3.203) (3.323) (1.183) (1.304)

Observations 5,666,886 3,799,100 5,684,428 3,790,210R-squared 0.589 0.526 0.588 0.483

Notes: This table reports triple-differences results for the effect of the EIM on natural gas generationand carbon emissions, using both the full and matched samples. The dependent variable is measuredin MWh for generation and short tons for carbon emissions. All interactions are mean-centered sothat base coefficients can be interpreted as marginal effects evaluated at the mean. The first columnreports the triple-differences results for generation from the full sample and includes hourly Californiacontrol variables. The second column reports the same specifications as the first, estimated using ourmatching procedure. The third and fourth columns report the same specifications as the first andsecond, for carbon emissions rather than generation. All specifications have BA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errorsby BA are in parentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

33

Appendix B Coal Generator Level Regression Results

Table B1 reports summary statistics for hourly generation, hourly carbon dioxide emissions, hourly gener-

ator efficiency,48 as well as generator age for the full sample of non-California, Western electric area coal

generators; and hourly California load, wind and solar photovoltaic production, as well as each BA’s own

load by FERC planning area. The unmatched sample has 97 unique coal generators, 37 belong to BAs

that participate in the EIM, and 60 belong to BAs that never participate in the EIM.49

Table B1: Summary Statistics: Non-California Western Electric Area Coal Generators

Ever an EIM participant 0 1 TotalHourly Electrical Generation (MWh) 322.4 300.0 310.8

(241.7) (179.4) (212.0)Hourly Carbon Dioxide Emissions (Short Tons) 335.1 309.1 321.7

(244.0) (178.9) (213.2)Hourly CAISO Load (MW) 26694.4 26730.2 26713.0

(4866.1) (4874.8) (4870.6)California Solar Photovoltaic Generation (MWh) 851.1 807.4 828.4

(1609.8) (1539.5) (1573.9)California Wind Generation (MWh) 1217.7 1216.2 1216.9

(997.1) (993.2) (995.0)Hourly FERC Load by Planning Area 2901.7 5842.1 4426.0

(1596.7) (2113.7) (2388.0)Generator Efficiency(Heat Input/Maximum Heat Input by Fuel Type) 0.670 0.639 0.654

(0.203) (0.174) (0.189)Generator Age 33.92 39.20 36.66

(12.80) (10.50) (11.96)Number of Generators 60 37 97Number of Observations 3699384

Notes: This table reports summary statistics for hourly generation, hourly carbon dioxide emissions,hourly generator efficiency, measured as the generator’s heat input in MMBtu divided by the maxi-mum heat input in MMBtu for the generator as reported in CEMS in order to capture differences ingenerator utilization, as well as generator age for the full sample of non-California, Western electricarea coal generators; and hourly California load, wind and solar photovoltaic production, as well aseach BA’s own load by FERC planning area. Means of each variable are shown with standard devia-tions in parentheses. Variables are shown by group where 1 indicates EIM-participation.

For coal-fired generators, matching with calipers and trimming the sample improves balance for all covari-

ates, as shown in Figure 11. Though the variance ratio between the treatment and control units does not

improve for the grid voltage as of 2012 and average California load response as of 2012, the variance ratios

decrease by roughly 10%. Covariate balance statistics for the coal-fired generator sample are provided in

Table B2.

Table B3 reports our difference-in-differences and triple-differences results for the effect of the EIM on

coal generation, using both the full and matched samples. The dependent variable is measured in hourly

megawatt hours (MWh). All interactions are mean-centered so that base coefficients can be interpreted

48Generator efficiency in this study is measured as the generator’s heat input in MMBtu divided by the maximum heatinput in MMBtu for the generator as reported in CEMS in order to capture differences in generator utilization.

49As a reference point, there were 72 coal generators in the non-CAISO WECC that were 25 MW or larger based on EIA860 data in 2018.

34

Figure 11: Propensity Score Overlap - Coal

Notes: This figure plots the propensity score overlap for coal generators.

Table B2: Coal Balance of Sample Covariates

Standardized Differences Variance RatioFull Sample Matched Full Sample Matched

Est. Mean Capacity Factor 2010 - 2012 0.014 0.002 1.240 0.982Grid Voltage 2012 0.285 0.094 0.502 0.385CA Load Response 2011 -0.229 -0.063 1.837 1.714CA Load Response 2012 -0.087 0.058 0.923 0.853Heat Input 2012 -0.173 0.023 0.691 0.727

Notes: This table reports the standardized mean difference which compares the difference in means in units of thepooled standard deviation of the treatment and control. We also report the variance ratio of treatment to controlunits which is the ratio of the variance of the treatment unit to control unit. Perfectly balanced covariates willhave a standardized mean difference of zero, perfectly balanced covariates have a variance ratio of one.

as marginal effects evaluated at the mean. The first column reports the difference-in-differences results

from the full sample, without hourly California control variables. The second column reports the triple-

differences results from the full sample and includes hourly California control variables. The third and

fourth columns report the same specifications as the first and second, estimated using our matching pro-

cedure.

Our preferred specification is in the fourth column, we find that coal generators do not respond signifi-

cantly differently than their matched counterfactual generators, either or average, or in response to in-

cremental increases in California load. Although statistically insignificant, coefficients are positive in the

difference-in-difference specification and the marginal coal generators turns down power production in re-

sponse to incremental increases in California load in the triple-differences specification.

Table B4 reports our difference-in-differences and triple-differences results for the effect of the EIM on coal

carbon emissions, using both the full and matched samples. The dependent variable is measured in short

tons. All interactions are mean-centered so that base coefficients can be interpreted as marginal effects

35

evaluated at the mean. The first column reports the difference-in-differences results from the full sample,

without hourly California control variables. The second column reports the triple-differences results from

the full sample and includes hourly California control variables. The third and fourth columns report the

same specifications as the first and second, estimated using our matching procedure. We find that coal

generators produce more carbon emissions than gas generators, with nearly one ton of carbon emissions

per MWh of power production. Although patterns for carbon emissions in response to California load are

similar to natural gas generation; we find that EIM-participant coal generators do not respond statistically

significantly differently than their matched counterfactual generators either on average or in response to

incremental increases in California load.

We also examine EIM treatment effects in response to California renewables production. Tables B5 and

B6 report our triple-differences results for the effect of the EIM on coal generation and carbon emissions,

using both the full and matched samples. The dependent variable is measured in either MWh for gener-

ation or short tons for carbon emissions. All interactions are mean-centered so that base coefficients can

be interpreted as marginal effects evaluated at the mean. The first column reports the triple-differences

results for generation from the full sample and includes hourly California control variables. The second

column reports the same specifications as the first, estimated using our matching procedure. The third

and fourth columns report the same specifications as the first and second, for carbon emissions rather than

generation. We find that although EIM-participant coal generators do not respond differently than their

matched counterfactual generators in response to either California solar or wind production; the sign of

coefficients evaluated at average levels of these variables and marginal responses to these variables follow

similar patterns as noted in response to California load.

We examine responses to California load and California renewables variables on average and in response

to incremental increases in California variables for each hour of the day. We find that while there is some

variation in EIM-participant coal generator response to these variables over time, that response does not

differ significantly from their matched counterfactual coal generators. Figure 12 provides difference-in-

difference results for EIM-participant coal generators compared to their counterfactual generators evalu-

ated at hourly average levels of California load. Figure 13 provides triple-difference results for EIM-participant

coal generators compared to their matched counterfactual generators in response to incremental increases

in California load for each hour of the day. Although EIM participant coal generators do not respond

significantly differently than their matched counterfactual generators, there is variation in their response

throughout the day, with generators tending to increase power production at night, and decrement power

production in response to incremental increases in California load during the day, similar to the pattern

observed for gas generators.

Last, EIM treatment effects were examined at the individual coal generator level to examine heterogeneity

in treatment effects by Balancing Authority and generator capacity. We find that some large coal gener-

ators in Nevada Power Company are producing significantly more power in the EIM compared to their

matched counterfactual generators, while a few large coal generators in PacifiCorp are producing signifi-

cantly less power. On the margin, these marginal coal generators that produce significantly more power

tend to turn down power production in response to incremental increases in expected load. Average EIM-

36

Table B3: Hourly Electrical Generation from Coal Generators (MWh) in Response toCalifornia Load

Full Sample MatchedEver EIM X Post EIM 22.58 21.60 27.85 27.70

(22.73) (22.22) (24.80) (24.58)Post EIM 25.84 29.90* 13.95 19.58

(17.70) (15.79) (26.86) (22.95)Ever EIM X CA Load -0.000785 -0.000252

(0.00109) (0.00114)Ever EIM X PostX CA Load -0.000372 -0.00107

(0.000867) (0.00124)Post EIM X CA Load 0.00102 0.00154

(0.000816) (0.00133)CA Load 0.000657 0.000872

(0.000467) (0.000529)CA Solar PV -0.000705 -0.00131

(0.000777) (0.00113)CA Wind -0.000742 -0.000660

(0.000742) (0.00111)Hourly FERC Loadby Planning Area 0.00424 0.00466 0.00543 0.00490

(0.00576) (0.00701) (0.00809) (0.00875)Generator Efficiency 310.0*** 308.9*** 206.7 205.0

(95.54) (96.93) (181.4) (183.4)Generator Age -3.726 -3.719 -4.091 -4.082

(2.236) (2.239) (3.096) (3.097)Constant 318.3*** 322.1*** 308.8*** 313.0***

(3.355) (4.053) (4.714) (5.176)

Observations 4,489,878 4,442,840 3,140,067 3,107,525R-squared 0.433 0.432 0.534 0.535

Notes: This table reports difference-in-differences and triple-differences results for the ef-fect of the EIM on coal generation, using both the full and matched samples. The depen-dent variable is measured in MWh. All interactions are mean-centered so that base co-efficients can be interpreted as marginal effects evaluated at the mean. The first columnreports the difference-in-differences results from the full sample, without hourly Californiacontrol variables. The second column reports the triple-differences results from the fullsample and includes hourly California control variables. The third and fourth columnsreport the same specifications as the first and second, estimated using our matching pro-cedure. All specifications have BA fixed effects, hour-of-day fixed effects, day-of-weekfixed effects, and month-by-year fixed effects. Clustered standard errors by BA are inparentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

37

Table B4: Hourly Emissions from Coal Generators (Short Tons) in Response to Califor-nia Load

Full Sample MatchedEver EIM X Post EIM 18.74 18.03 26.08 26.05

(24.72) (24.23) (26.08) (25.93)Post EIM 30.70 34.53* 17.62 23.02

(17.19) (15.93) (22.72) (19.61)Ever EIM X CA Load -0.000665 -0.000263

(0.00117) (0.00115)Ever EIM X PostX CA Load -0.000144 -0.000811

(0.000791) (0.00109)Post EIM X CA Load 0.000924 0.00140

(0.000715) (0.00117)CA Load 0.000542 0.000819

(0.000435) (0.000483)CA Solar PV -0.000467 -0.000985

(0.000679) (0.000973)CA Wind -0.00109 -0.00115

(0.000617) (0.00111)Hourly FERC Loadby Planning Area 0.00383 0.00409 0.00466 0.00408

(0.00588) (0.00728) (0.00791) (0.00862)Generator Efficiency 335.9*** 335.1*** 233.4 232.1

(88.70) (90.05) (170.1) (172.0)Generator Age -3.467 -3.462 -3.973 -3.965

(2.291) (2.293) (3.048) (3.049)Constant 328.6*** 331.6*** 318.8*** 322.7***

(3.096) (3.511) (6.334) (5.854)

Observations 4,489,878 4,442,840 3,140,067 3,107,525R-squared 0.433 0.433 0.572 0.572

Notes: This table reports difference-in-differences and triple-differences results for theeffect of the EIM on coal carbon emissions, using both the full and matched samples.The dependent variable is measured in short tons. All interactions are mean-centeredso that base coefficients can be interpreted as marginal effects evaluated at the mean.The first column reports the difference-in-differences results from the full sample, with-out hourly California control variables. The second column reports the triple-differencesresults from the full sample and includes hourly California control variables. The thirdand fourth columns report the same specifications as the first and second, estimated us-ing our matching procedure. All specifications have BA fixed effects, hour-of-day fixedeffects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard er-rors by BA are in parentheses. Significance levels are denoted by *** p<0.01, ** p<0.05,* p<0.1.

38

Table B5: Hourly Electrical Generation from Coal Generators (MWh) in Response to Califor-nia Solar Production

Generation EmissionsFull Sample Matched Full Sample Matched

Ever EIM X Post EIM 25.30 29.76 21.02 27.92(22.29) (24.71) (23.47) (24.82)

Post EIM 26.62 16.14 31.53* 19.76(16.87) (25.62) (16.78) (21.98)

Ever EIM X CA Solar -0.00217 0.000848 -0.00136 0.00128(0.00320) (0.00402) (0.00497) (0.00582)

Ever EIM X PostX CA Solar 0.000705 -0.00243 -5.27e-05 -0.00300

(0.00246) (0.00255) (0.00445) (0.00485)Post EIM X CA Solar -0.000431 0.00121 -2.17e-05 0.00155

(0.000964) (0.00102) (0.00179) (0.00230)CA Load 0.000743 0.000942 0.000606 0.000880

(0.000488) (0.000691) (0.000428) (0.000608)CA Solar PV -0.000166 -0.000832 -2.58e-05 -0.000556

(0.000695) (0.00120) (0.000595) (0.00104)CA Wind -0.000681 -0.000603 -0.00103 -0.00110

(0.000703) (0.00107) (0.000585) (0.00108)Hourly FERC Loadby Planning Area 0.00358 0.00439 0.00327 0.00365

(0.00586) (0.00800) (0.00617) (0.00799)Generator Efficiency 309.3*** 205.3 335.5*** 232.4

(96.51) (182.7) (89.60) (171.3)Generator Age -3.717 -4.081 -3.461 -3.964

(2.238) (3.096) (2.292) (3.047)Constant 322.8*** 313.6*** 332.2*** 323.2***

(4.143) (5.188) (3.551) (5.805)

Observations 4,442,840 3,107,525 4,442,840 3,107,525R-squared 0.432 0.534 0.432 0.572

Notes: This table reports triple-differences results for the effect of the EIM on coal genera-tion and carbon emissions, using both the full and matched samples. The dependent variableis measured in MWh for generation and short tons for carbon emissions. All interactions aremean-centered so that base coefficients can be interpreted as marginal effects evaluated at themean. The first column reports the triple-differences results for generation from the full sampleand includes hourly California control variables. The second column reports the same specifica-tions as the first, estimated using our matching procedure. The third and fourth columns reportthe same specifications as the first and second, for carbon emissions rather than generation.All specifications have BA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, andmonth-by-year fixed effects. Clustered standard errors by BA are in parentheses. Significancelevels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

39

Table B6: Hourly Electrical Generation from Coal Generators (MWh) in Response to Califor-nia Wind Production

Generation EmissionsFull Sample Matched Full Sample Matched

Ever EIM X Post EIM 22.66 28.35 18.74 26.72(22.62) (25.02) (24.30) (25.84)

Post EIM 27.74 16.42 32.31* 19.81(16.68) (25.54) (16.46) (21.78)

Ever EIM X CA Wind 0.000943 0.00141 0.000643 0.000567(0.00243) (0.00198) (0.00288) (0.00245)

Ever EIM X PostX CA Wind 0.00183 -0.000493 0.00214 2.02e-05

(0.00222) (0.00153) (0.00332) (0.00281)Post EIM X CA Wind -0.000871 -0.000557 -0.00114 -0.000979

(0.00117) (0.00109) (0.00166) (0.00179)CA Load 0.000781 0.000951 0.000637 0.000890

(0.000495) (0.000702) (0.000433) (0.000619)CA Solar PV -0.000229 -0.000831 -2.53e-05 -0.000530

(0.000528) (0.000736) (0.000495) (0.000662)CA Wind -0.000632 -0.000465 -0.000964 -0.000966

(0.000593) (0.000790) (0.000575) (0.000945)Hourly FERC Loadby Planning Area 0.00318 0.00431 0.00293 0.00356

(0.00557) (0.00772) (0.00577) (0.00757)Generator Efficiency 309.5*** 205.4 335.7*** 232.5

(96.41) (182.6) (89.47) (171.1)Generator Age -3.717 -4.080 -3.461 -3.963

(2.238) (3.096) (2.292) (3.047)Constant 322.8*** 313.7*** 332.2*** 323.4***

(4.069) (5.022) (3.525) (5.676)

Observations 4,442,840 3,107,525 4,442,840 3,107,525R-squared 0.432 0.534 0.432 0.572

Notes: This table reports triple-differences results for the effect of the EIM on coal genera-tion and carbon emissions, using both the full and matched samples. The dependent variableis measured in MWh for generation and short tons for carbon emissions. All interactions aremean-centered so that base coefficients can be interpreted as marginal effects evaluated at themean. The first column reports the triple-differences results for generation from the full sampleand includes hourly California control variables. The second column reports the same specifica-tions as the first, estimated using our matching procedure. The third and fourth columns reportthe same specifications as the first and second, for carbon emissions rather than generation.All specifications have BA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, andmonth-by-year fixed effects. Clustered standard errors by BA are in parentheses. Significancelevels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

40

Figure 12: Hourly EIM-Participant Coal Generation at Cali-fornia Load Averages

Notes: This figure compares the difference-in-difference effect ofparticipating in the EIM at hourly CA load averages.

Figure 13: Hourly EIM-participant Coal Generation in Re-sponse to California Load

Notes: This figure compares the triple-difference effect of partici-pating in the EIM for incremental increases in load above hourlyCA load averages.

participant coal generator power generation compared to their matched counterfactual generators are in

Figure 14. Marginal EIM-participant coal generator power generation in response to incremental increases

in California load, compared to their matched counterfactual generators are in Figure 15.

41

Figure 14: Individual Generator Power Generation (MWh) at AverageLevels of Expected CA Load by Capacity by BAA

Notes: This figure compares the difference-in-differences effect of participatingin the EIM for each generator and its three matched nearest neighbor controlgenerators based on the propensity score.

Figure 15: Marginal Coal Generation (MWh) due to EIM Participation inResponse to California Load by BA

Notes: This figure compares the triple-differences effect of participating in theEIM for each generator and its three matched nearest neighbor control genera-tors based on the propensity score.

42

Appendix C Robustness of Results Using Nearest Neighbor Matching with Mahalanobis

Distance

To test the robustness of our matching design, we use nearest neighbor matching with Mahalanobis dis-

tance to match each generator with its nearest neighbor. Table C1 reports our difference-in-differences

and triple-differences results for the effect of the EIM on natural gas generation and carbon emissions,

on average, and in response to California load, using the nearest neighbor matched samples. The depen-

dent variable is measured in hourly megawatt hours (MWh) for generation or in short tons for carbon

emissions. All interactions are mean-centered so that base coefficients can be interpreted as marginal ef-

fects evaluated at the mean. The first column reports the difference-in-differences results from the near-

est neighbor matched sample, without hourly California control variables. The second column reports the

triple-differences results from the nearest neighbor matched sample and includes hourly California control

variables. The third and fourth columns report the same specifications.

Tables C2 and C3 report our triple-differences results for the effect of the EIM on natural gas generation

and carbon emissions in response to California renewables, using the nearest neighbor matched sample.

The dependent variable is measured in either MWh for generation or short tons for carbon emissions. All

interactions are mean-centered so that base coefficients can be interpreted as marginal effects evaluated at

the mean. The first column reports the triple-differences results for generation from the full sample and

includes hourly California control variables. The second column reports the same specifications as the first

but for carbon emissions rather than generation.

43

Table C1: Generator Level Natural Gas Generation and Emissions using Nearest Neighbor Matched withMahalanobis Distance

Generation EmissionsMatched Matched Matched Matched

Ever EIM X Post EIM 7.19 7.17∗ 1.96 1.85(6.075) (4.162) (2.593) (1.743)

Post EIM 9.21 0.72 2.52 -0.42(7.700) (5.832) (2.291) (1.890)

Ever EIM X CA Load -0.0017∗∗∗ -0.00078∗∗∗

(0.000370) (0.000147)Ever EIM X Post X CA Load -0.0013∗ -0.00047∗

(0.000673) (0.000249)Post EIM X CA Load -0.00028 -0.000072

(0.000221) (0.0000832)CA Load -0.00098∗∗∗ -0.00038∗∗∗

(0.000325) (0.000127)CA Solar PV -0.00076∗ -0.00026∗

(0.000382) (0.000140)CA Wind -0.00031 -0.00013

(0.000211) (0.0000785)Hourly FERC Load by Planning Area -0.0062∗ -0.0013 -0.0024∗ -0.00034

(0.00329) (0.00231) (0.00126) (0.000803)Generator Efficiency 183.8∗∗∗ 186.2∗∗∗ 84.1∗∗∗ 84.9∗∗∗

(22.84) (23.28) (8.290) (8.438)Generator Age -1.31∗∗∗ -1.25∗∗∗ -0.34∗∗∗ -0.32∗∗∗

(0.338) (0.331) (0.115) (0.111)Constant 34.2∗∗ 26.5∗ 12.8∗∗ 9.57

(13.17) (14.15) (5.444) (5.909)Observations 5487957 5263870 5478789 5254960R-squared 0.61 0.62 0.61 0.61

Notes: This table reports difference-in-differences and triple-differences results for the effect of the EIM onnatural gas generation and carbon emissions, using the nearest neighbor matched samples. The dependentvariable is measured in hourly megawatt hours (MWh) for generation or in short tons for carbon emissions.All interactions are mean-centered so that base coefficients can be interpreted as marginal effects evaluatedat the mean. The first column reports the difference-in-differences results from the nearest neighbor matchedsample, without hourly California control variables. The second column reports the triple-differences resultsfrom the nearest neighbor matched sample and includes hourly California control variables. The third andfourth columns report the same specifications as the first. All specifications have BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors by BA are inparentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

44

Table C2: Generator Level Natural Gas Generation in Re-sponse to CA Solar Production Nearest Neighbor Matchedwith Mahalanobis Distance

Generation EmissionsMatched Matched

Ever EIM X Post EIM 11.5∗∗∗ 3.98∗∗

(4.025) (1.676)Post EIM 1.73 -0.18

(5.615) (1.800)Ever EIM X CA Solar -0.0017 -0.0010∗∗

(0.00123) (0.000499)Ever EIM X PostX CA Solar -0.0016 -0.00034

(0.00106) (0.000286)Post EIM X CA Solar 0.00020 0.000029

(0.000573) (0.000191)CA Load -0.0016∗∗∗ -0.00064∗∗∗

(0.000439) (0.000182)CA Solar PV -0.00076∗∗∗ -0.00026∗∗

(0.000210) (0.000117)CA Wind -0.00028 -0.00012

(0.000201) (0.0000747)Hourly FERC Loadby Planning Area -0.0036 -0.0013

(0.00288) (0.00107)Generator Efficiency 187.1∗∗∗ 85.3∗∗∗

(23.45) (8.517)Generator Age -1.26∗∗∗ -0.33∗∗∗

(0.332) (0.111)Constant 121.7∗∗∗ 56.4∗∗∗

(2.387) (1.049)Observations 5263870 5254960R-squared 0.61 0.61

Notes: This table reports triple-differences results for theeffect of the EIM on natural gas generation and carbon emis-sions in response to California solar production, using thenearest neighbor matched samples. The dependent variableis measured in hourly megawatt hours (MWh) if generationor in short tons if carbon emissions. All interactions aremean-centered so that base coefficients can be interpretedas marginal effects evaluated at the mean. The first columnreports triple-difference results from the nearest neighbormatched sample for generation and includes hourly Califor-nia control variables. The second column reports the triple-differences results from the nearest neighbor matched samplefor emissions and includes hourly California control variables.All specifications have BA fixed effects, hour-of-day fixedeffects, day-of-week fixed effects, and month-by-year fixed ef-fects. Clustered standard errors by BA are in parentheses.Significance levels are denoted by *** p<0.01, ** p<0.05, *p<0.1.

45

Table C3: Generator Level Natural Gas Generation in Re-sponse to California Wind Production Nearest NeighborMatched with Mahalanobis Distance

Generation EmissionsMatched Matched

Ever EIM X Post EIM 7.66 2.27(5.798) (2.474)

Post EIM 3.50 0.58(6.770) (2.233)

Ever EIM X CA Wind -0.00088 -0.00069(0.00194) (0.000717)

Ever EIM X PostX CA Wind -0.0019∗ -0.00046

(0.00106) (0.000375)Post EIM X CA Wind 0.00069 0.00028

(0.000629) (0.000198)CA Load -0.0016∗∗∗ -0.00063∗∗∗

(0.000434) (0.000179)CA Solar PV -0.0010∗∗ -0.00033∗

(0.000485) (0.000178)CA Wind -0.00027 -0.00013

(0.000302) (0.000144)Hourly FERC Loadby Planning Area -0.0039 -0.0015

(0.00300) (0.00111)Generator Efficiency 187.3∗∗∗ 85.4∗∗∗

(23.48) (8.532)Generator Age -1.26∗∗∗ -0.33∗∗∗

(0.333) (0.111)Constant 121.4∗∗∗ 56.4∗∗∗

(2.643) (1.110)Observations 5263870 5254960R-squared 0.61 0.61

Notes: This table reports triple-differences results for theeffect of the EIM on natural gas generation and carbon emis-sions in response to California wind production, using thenearest neighbor matched samples. The dependent variable ismeasured in hourly megawatt hours (MWh) if generationor in short tons if carbon emissions. All interactions aremean-centered so that base coefficients can be interpretedas marginal effects evaluated at the mean. The first columnreports triple-difference results from the nearest neighbormatched sample for generation and includes hourly Califor-nia control variables. The second column reports the triple-differences results from the nearest neighbor matched samplefor emissions and includes hourly California control variables.All specifications have BA fixed effects, hour-of-day fixedeffects, day-of-week fixed effects, and month-by-year fixedeffects. Clustered standard errors by BA are in parentheses.Significance levels are denoted by *** p<0.01, ** p<0.05, *p<0.1.

46

Table C4 reports our difference-in-differences and triple-differences results for the effect of the EIM on coal

generation and carbon emissions, on average, and in response to California load, using the nearest neigh-

bor matched samples. The dependent variable is measured in hourly megawatt hours (MWh) if generation

or in short tons if carbon emissions. All interactions are mean-centered so that base coefficients can be in-

terpreted as marginal effects evaluated at the mean. The first column reports the difference-in-differences

results from the nearest neighbor matched sample, without hourly California control variables. The sec-

ond column reports the triple-differences results from the nearest neighbor matched sample and includes

hourly California control variables. The third and fourth columns report the same specifications.

Tables C5 and C6 report our triple-differences results for the effect of the EIM on coal generation and car-

bon emissions in response to California renewables, using the nearest neighbor matched sample. The de-

pendent variable is measured in either MWh for generation or short tons for carbon emissions. All inter-

actions are mean-centered so that base coefficients can be interpreted as marginal effects evaluated at the

mean. The first column reports the triple-differences results for generation from the full sample and in-

cludes hourly California control variables. The second column reports the same specifications as the first

but for carbon emissions rather than generation.

We find results are similar, in significance and magnitude; to the results found with propensity score match-

ing using multiple nearest neighbors, calipers, and trimming.

47

Table C4: Generator Level Coal Generation in Response to California Load NearestNeighbor Matched with Mahalanobis Distance

Generation EmissionsMatched Matched Matched Matched

Ever EIM X Post EIM 24.0 22.8 20.1 19.2(22.48) (22.14) (24.40) (24.07)

Post EIM 24.8 28.8∗ 29.7 33.4∗

(17.20) (15.19) (16.61) (15.29)Ever EIM X CA Load -0.00088 -0.00076

(0.00106) (0.00114)Ever EIM X PostX CA Load -0.00033 -0.00010

(0.000850) (0.000773)Post EIM X CA Load 0.00098 0.00088

(0.000804) (0.000702)CA Load 0.00062 0.00051

(0.000490) (0.000456)CA Solar PV -0.00075 -0.00051

(0.000772) (0.000673)CA Wind -0.00074 -0.0011

(0.000744) (0.000622)Hourly FERC Loadby Planning Area 0.0045 0.0051 0.0041 0.0045

(0.00578) (0.00697) (0.00590) (0.00725)Generator Efficiency 311.3∗∗∗ 310.0∗∗∗ 336.9∗∗∗ 335.9∗∗∗

(96.58) (97.96) (89.68) (91.01)Generator Age -3.86 -3.85 -3.60 -3.59

(2.332) (2.332) (2.387) (2.386)Constant 320.2∗∗∗ 323.7∗∗∗ 330.6∗∗∗ 333.3∗∗∗

(3.525) (4.196) (3.239) (3.639)Observations 4465337 4419646 4465337 4419646R-squared 0.43 0.43 0.43 0.43

Notes: This table reports difference-in-differences and triple-differences results for theeffect of the EIM on coal generation and carbon emissions, using the nearest neighbormatched samples. The dependent variable is measured in hourly megawatt hours (MWh)if generation or in short tons if carbon emissions. All interactions are mean-centeredso that base coefficients can be interpreted as marginal effects evaluated at the mean.The first column reports the difference-in-differences results from the nearest neighbormatched sample, without hourly California control variables. The second column re-ports the triple-differences results from the nearest neighbor matched sample and includeshourly California control variables. The third and fourth columns report the same specifi-cations as the first. All specifications have BA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors by BAare in parentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

48

Table C5: Generator Level Coal Generation in Responseto California Solar Nearest Neighbor Matched with Maha-lanobis Distance

Generation EmissionsMatched Matched

Ever EIM X Post EIM 26.4 22.1(22.08) (23.20)

Post EIM 25.8 30.7∗

(16.48) (16.35)Ever EIM X CA Solar -0.0014 -0.00060

(0.00316) (0.00491)Ever EIM X PostX CA Solar -0.00010 -0.00084

(0.00234) (0.00433)Post EIM X CA Solar -0.000085 0.00032

(0.000852) (0.00170)CA Load 0.00072 0.00059

(0.000505) (0.000444)CA Solar PV -0.00024 -0.000097

(0.000638) (0.000548)CA Wind -0.00067 -0.0010

(0.000706) (0.000590)Hourly FERC Loadby Planning Area 0.0038 0.0035

(0.00590) (0.00621)Generator Efficiency 310.6∗∗∗ 336.3∗∗∗

(97.55) (90.58)Generator Age -3.85 -3.59

(2.331) (2.385)Constant 324.4∗∗∗ 333.9∗∗∗

(4.275) (3.655)Observations 4419646 4419646R-squared 0.43 0.43

Notes: This table reports triple-differences results for theeffect of the EIM on coal generation and carbon emissionsin response to California solar production, using the near-est neighbor matched samples. The dependent variable ismeasured in hourly megawatt hours (MWh) if generationor in short tons if carbon emissions. All interactions aremean-centered so that base coefficients can be interpretedas marginal effects evaluated at the mean. The first columnreports triple-difference results from the nearest neighbormatched sample for generation and includes hourly Califor-nia control variables. The second column reports the triple-differences results from the nearest neighbor matched samplefor emissions and includes hourly California control vari-ables. All specifications have BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-yearfixed effects. Clustered standard errors by BA are in paren-theses. Significance levels are denoted by *** p<0.01, **p<0.05, * p<0.1.

49

Table C6: Generator Level Coal Generation and Emissions inResponse to California Wind Nearest Neighbor Matched withMahalanobis Distance

Generation EmissionsMatched Matched

Ever EIM X Post EIM 23.9 20.0(22.45) (24.07)

Post EIM 26.8 31.3∗

(16.19) (15.93)Ever EIM X CA Wind 0.0017 0.0014

(0.00219) (0.00258)Ever EIM X PostX CA Wind 0.00099 0.0013

(0.00198) (0.00304)Post EIM X CA Wind -0.00056 -0.00084

(0.00109) (0.00152)CA Load 0.00075 0.00061

(0.000511) (0.00152)CA Solar PV -0.00028 -0.000076

(0.000515) (0.000480)CA Wind -0.00063 -0.00097

(0.000595) (0.000619)Hourly FERC Loadby Planning Area 0.0035 0.0032

(0.00560) (0.00580)Generator Efficiency 310.8∗∗∗ 336.5∗∗∗

(97.44) (90.44)Generator Age -3.85 -3.59

(2.331) (2.384)Constant 324.5∗∗∗ 334.0∗∗∗

(4.206) (3.632)Observations 4419646 4419646R-squared 0.43 0.43

Notes: This table reports triple-differences results for theeffect of the EIM on coal generation and carbon emissionsin response to California wind production, using the near-est neighbor matched samples. The dependent variable ismeasured in hourly megawatt hours (MWh) if generationor in short tons if carbon emissions. All interactions aremean-centered so that base coefficients can be interpretedas marginal effects evaluated at the mean. The first columnreports triple-difference results from the nearest neighbormatched sample for generation and includes hourly Califor-nia control variables. The second column reports the triple-differences results from the nearest neighbor matched samplefor emissions and includes hourly California control vari-ables. All specifications have BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-yearfixed effects. Clustered standard errors by BA are in paren-theses. Significance levels are denoted by *** p<0.01, **p<0.05, * p<0.1.

50

Appendix D Unconfoundedness Assumption

Although the unconfoundedness assumption is not testable, we perform a placebo test to determine if

there are significant differences in our matched sample results in the pre-treatment period. We change the

post-EIM period to be March 2012 through March 2013, before the EIM was announced. We exclude all

data after April 1, 2013. Table D1 reports our difference-in-differences and triple-differences results for the

effect of the EIM on natural gas generation, using the propensity score matched sample. The dependent

variable is measured in MWh. All interactions are mean-centered so that base coefficients can be inter-

preted as marginal effects evaluated at the mean. The first column reports the triple-differences results

from the matched sample and includes hourly California control variables. Tables D2 and D3 use the same

specifications as Table D1, estimated using our matching procedure, but consider the treatment effect in

response to California solar and California wind production, respectively. Tables D4 to D6 report the re-

sults of the tests for coal generators. We find that there are no significant differences between the EIM-

participant and non-participant generators in the out-of-sample tests.

51

Table D1: Natural Gas Generator Response to CALoad, Placebo (Pseudo Post EIM)

PlaceboMatched

Ever EIM X Pseudo Post 3.68(7.606)

Pseudo Post EIM (2013) -2.47(6.206)

Ever EIM X CA Load -0.0021∗∗∗

(0.000604)Ever EIM X Pseudo PostX CA Load 0.00039

(0.000343)Pseudo Post EIM X CA Load 0.000025

(0.000141)California Load -0.00017

(0.000277)CA Solar -0.0013

(0.00158)CA Wind -0.0010∗∗∗

(0.000251)FERC Loadby Planning Area 0.0045

(0.00293)Generator Efficiency 217.6∗∗

(82.53)Generator Age -1.22∗∗

(0.531)Constant 111.3∗∗∗

(2.566)Observations 1663360R-squared 0.53

Notes: This table reports triple-differences resultsfor the effect of a pseudo-EIM treatment in the pre-treatment period on natural gas generation, usingthe matched samples. The dependent variable ismeasured in hourly megawatt hours (MWh). Allinteractions are mean-centered so that base co-efficients can be interpreted as marginal effectsevaluated at the mean. The first column reportstriple-difference results from the matched samplefor generation and includes hourly California con-trol variables. This specification includes BA fixedeffects, hour-of-day fixed effects, day-of-week fixedeffects, and month-by-year fixed effects. Clusteredstandard errors by BA are in parentheses. Signifi-cance levels are denoted by *** p<0.01, ** p<0.05,* p<0.1.

52

Table D2: Natural Gas Generator Response to CASolar, Placebo (Pseudo Post EIM)

PlaceboMatched

Ever EIM X Pseudo Post 3.51(8.280)

Pseudo Post EIM (2013) -2.57(6.115)

Ever EIM X CA Solar -0.017∗∗∗

(0.00496)Ever EIM X Pseudo PostX CA Solar 0.0014

(0.00388)Pseudo Post EIM X CA Solar 0.0015

(0.00162)California Load -0.00012

(0.000360)CA Solar -0.00049

(0.00259)CA Wind -0.00090∗∗∗

(0.000273)FERC Loadby Planning Area 0.0024

(0.00305)Generator Efficiency 219.3∗∗

(82.57)Generator Age -1.22∗∗

(0.530)Constant 111.4∗∗∗

(2.621)Observations 1663360R-squared 0.53

Notes: This table reports triple-differences resultsfor the effect of a pseudo-EIM treatment in the pre-treatment period on natural gas generation, using thematched samples. The dependent variable is mea-sured in hourly megawatt hours (MWh). All inter-actions are mean-centered so that base coefficientscan be interpreted as marginal effects evaluated atthe mean. The first column reports triple-differenceresults from the matched sample for generation andincludes hourly California control variables. Thisspecification includes BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors by BAare in parentheses. Significance levels are denoted by*** p<0.01, ** p<0.05, * p<0.1.

53

Table D3: Natural Gas Generator Response to CAWind, Placebo (Pseudo Post EIM)

PlaceboMatched

Ever EIM X Pseudo Post 1.46(7.737)

Pseudo Post EIM (2013) -2.58(6.263)

Ever EIM X CA Wind -0.0022(0.00144)

Ever EIM X Pseudo PostX CA Wind 0.0020

(0.00163)Pseudo Post EIM X CA Wind -0.00025

(0.000514)California Load -0.000075

(0.000356)CA Solar -0.0010

(0.00150)CA Wind -0.00079

(0.000569)FERC Loadby Planning Area 0.0020

(0.00300)Generator Efficiency 219.6∗∗

(82.54)Generator Age -1.23∗∗

(0.530)Constant 111.5∗∗∗

(2.626)Observations 1663360R-squared 0.53

Notes: This table reports triple-differences resultsfor the effect of a pseudo-EIM treatment in the pre-treatment period on natural gas generation, usingthe matched samples. The dependent variable ismeasured in hourly megawatt hours (MWh). All in-teractions are mean-centered so that base coefficientscan be interpreted as marginal effects evaluated atthe mean. The first column reports triple-differenceresults from the matched sample for generation andincludes hourly California control variables. Thisspecification includes BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors by BAare in parentheses. Significance levels are denoted by*** p<0.01, ** p<0.05, * p<0.1.

54

Table D4: Coal Generator Response to CaliforniaLoad, Placebo (Pseudo Post EIM)

PlaceboMatched

Ever EIM X Pseudo Post 9.17(10.26)

Pseudo Post EIM (2013) -0.42(6.548)

Ever EIM X CA Load -0.00062(0.00113)

Ever EIM X Pseudo PostX CA Load -0.00055

(0.000787)Pseudo Post EIM X CA Load 0.00094

(0.000668)California Load 0.00027

(0.000529)CA Solar -0.00041

(0.000704)CA Wind -0.000055

(0.000946)FERC Loadby Planning Area 0.0071

(0.00441)Generator Efficiency 170.5

(173.0)Generator Age -4.64

(3.017)Constant 305.7∗∗∗

(5.405)Observations 1514224R-squared 0.53

Notes: This table reports triple-differences resultsfor the effect of a pseudo-EIM treatment in thepre-treatment period on coal generation, using thematched samples. The dependent variable is mea-sured in hourly megawatt hours (MWh). All inter-actions are mean-centered so that base coefficientscan be interpreted as marginal effects evaluated atthe mean. The first column reports triple-differenceresults from the matched sample for generation andincludes hourly California control variables. Thisspecification includes BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors byBA are in parentheses. Significance levels are de-noted by *** p<0.01, ** p<0.05, * p<0.1.

55

Table D5: Coal Generator Response to CaliforniaSolar, Placebo (Pseudo Post EIM)

PlaceboMatched

Ever EIM X Pseudo Post 9.09(9.731)

Pseudo Post EIM (2013) -1.34(6.875)

Ever EIM X CA Solar -0.0084(0.00752)

Ever EIM X Pseudo PostX CA Solar -0.0049

(0.0156)Pseudo Post EIM X CA Solar 0.0077

(0.00789)California Load 0.00031

(0.000513)CA Solar 0.0011

(0.00228)CA Wind 0.000052

(0.000865)FERC Loadby Planning Area 0.0068

(0.00391)Generator Efficiency 170.6

(172.8)Generator Age -4.64

(3.016)Constant 306.3∗∗∗

(5.394)Observations 1514224R-squared 0.53

Notes: This table reports triple-differences resultsfor the effect of a pseudo-EIM treatment in thepre-treatment period on coal generation, using thematched samples. The dependent variable is mea-sured in hourly megawatt hours (MWh). All inter-actions are mean-centered so that base coefficientscan be interpreted as marginal effects evaluated atthe mean. The first column reports triple-differenceresults from the matched sample for generation andincludes hourly California control variables. Thisspecification includes BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors byBA are in parentheses. Significance levels are de-noted by *** p<0.01, ** p<0.05, * p<0.1.

56

Table D6: Coal Generator Response to CaliforniaWind, Placebo (Pseudo Post EIM)

PlaceboMatched

Ever EIM X Pseudo Post 7.48(7.381)

Pseudo Post EIM (2013) -1.85(7.322)

Ever EIM X CA Wind 0.00035(0.00380)

Ever EIM X Pseudo PostX CA Wind 0.00084

(0.00343)Pseudo Post EIM X CA Wind -0.00092

(0.00138)California Load 0.00036

(0.000541)CA Solar 0.0014

(0.00123)CA Wind 0.0000050

(0.00119)FERC Loadby Planning Area 0.0065

(0.00387)Generator Efficiency 170.7

(172.8)Generator Age -4.64

(3.016)Constant 307.1∗∗∗

(5.202)Observations 1514224R-squared 0.53

Notes: This table reports triple-differences resultsfor the effect of a pseudo-EIM treatment in thepre-treatment period on coal generation, using thematched samples. The dependent variable is mea-sured in hourly megawatt hours (MWh). All inter-actions are mean-centered so that base coefficientscan be interpreted as marginal effects evaluated atthe mean. The first column reports triple-differenceresults from the matched sample for generation andincludes hourly California control variables. Thisspecification includes BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors by BAare in parentheses. Significance levels are denoted by*** p<0.01, ** p<0.05, * p<0.1.

57

Appendix E Transmission Congestion

We construct a measure of transmission congestion to address the possibility that transmission congestion

could affect our results through changing the pattern of generator dispatch and emissions. In related liter-

ature transmission congestion is typically controlled for through constructing a variable that addresses dif-

ferences in hourly prices across regions as a difference in zonal price typically implies congestion in ISO/RTO

markets (Fell et al., 2019). As CAISO only retains hourly locational marginal price data for 39 months,

sufficient price data is not available to construct a similar measure for the period of our study. CAISO

does however retain transmission interface and intertie constraint shadow prices for the period of our study

in OASIS. This report includes the intertie on which congestion occurred, the direction of the constraint,

and the shadow price for relaxing the constraint by one unit.

We construct a rough measure of transmission congestion by identifying the interties that connect CAISO

to adjacent BAs using both CAISO operating procedures50 and Atlas Reference documentation in OA-

SIS.51 Note that identification of applicable interties was limited to this publicly available documentation,

and may not necessarily encompass all interties.

We perform the triple-differences analysis on the matched sample for hours in which transmission was con-

gested in the export direction from CAISO, the import direction to CAISO, and hours in which no conges-

tion was identified. As shown in Table E2, we find that when interties were constrained for exports out of

CAISO, that EIM-participant gas generators turned down significantly more than non-EIM generators, by

nearly 9 kWh. As shown in Tables E1 and E3, EIM-participant gas generators did not turn down signifi-

cantly in hours where there was import congestion, or no congestion on the interties to CAISO.

50California ISO Market Scheduling Paths, available at https://www.caiso.com/Documents/2510A.pdf (accessed3/29/2019).

51http://oasis.caiso.com/mrioasis/logon.do

58

Table E1: Generator Level Natural Gas Generation when Import Interties are Constrained

Full Sample MatchedEver EIM X Post EIM 49.23*** 36.47*** 20.58*** 16.67***

(1.953) (2.271) (3.079) (3.311)Post EIM -9.248*** -8.496** -0.174 2.014

(1.712) (3.051) (1.675) (2.107)Ever EIM X CA Load -0.00241*** -0.00259***

(0.000299) (0.000305)Ever EIM X Post X CA Load 0.00218 -0.00154

(0.00118) (0.00113)Post EIM X CA Load -0.00370*** -0.000318

(0.000415) (0.000302)CA Load -0.00261*** -0.000254

(7.90e-05) (0.000389)CA Solar PV -0.00208* -0.00362**

(0.00107) (0.00124)CA Wind -0.00104** -0.00150**

(0.000414) (0.000532)Hourly FERC Loadby Planning Area -0.00838** 0.00149 -0.00217 0.00378

(0.00316) (0.000968) (0.00357) (0.00243)Generator Efficiency 211.6*** 212.3*** 259.3*** 252.0***

(9.353) (8.211) (42.33) (39.56)Generator Age -1.403*** -1.378*** -0.958*** -0.966***

(0.200) (0.182) (0.229) (0.203)Shadow Price -0.00481 0.00198 -0.00894 0.00148

(0.00524) (0.00277) (0.0114) (0.00491)Constant 162.9*** 132.0*** 153.4*** 132.5***

(10.65) (0.963) (10.60) (1.379)

Observations 245,113 244,763 200,745 200,440R-squared 0.483 0.497 0.365 0.380

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Notes: This table reports difference in difference and triple-differences results for the effect of CAISOimport transmission congestion on natural gas generation, using both the unmatched and matchedsamples. The dependent variable is measured in hourly megawatt hours (MWh). All interactions aremean-centered so that base coefficients can be interpreted as marginal effects evaluated at the mean.The first column reports difference-in-differences results from the full sample, the second column re-ports the triple-difference results from the full sample for generation and includes hourly Californiacontrol variables. The third and fourth columns report the same specifications as in the first and sec-ond, estimated using our matching procedure. This specification includes BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors byBA are in parentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

59

Table E2: Generator Level Natural Gas Generation when Export Interties are Constrained

Full Sample MatchedEver EIM X Post EIM 71.46*** 53.24** 74.87*** 54.03**

(18.98) (16.33) (16.39) (13.56)Post EIM 22.52 -17.85 -8.729 -35.53

(23.14) (11.55) (31.51) (30.27)Ever EIM X CA Load -0.00406*** -0.00283**

(0.000972) (0.00105)Ever EIM X Post X CA Load 0.00328** -0.00894**

(0.00122) (0.00258)Post EIM X CA Load -0.00287** 0.00805***

(0.00102) (0.00176)CA Load -0.00249* 0.000665

(0.00107) (0.00130)CA Solar PV -0.00396*** -0.00439***

(0.000431) (0.000164)CA Wind -0.000249 -0.00169

(0.00124) (0.000995)Hourly FERC Loadby Planning Area -0.0122 -0.000234 -0.00273 0.00658

(0.00637) (0.00395) (0.00652) (0.00485)Generator Efficiency 201.9*** 204.4*** 279.0** 276.8**

(0.909) (1.761) (87.07) (86.77)Generator Age -1.797*** -1.768*** -0.945 -0.950

(0.235) (0.233) (0.489) (0.475)Shadow Price -0.00450 -0.00292 -0.00296 -0.00314

(0.00306) (0.00402) (0.00640) (0.00766)Constant 121.8*** 115.9*** 129.7*** 130.2***

(12.54) (14.67) (2.721) (9.345)

Observations 105,333 105,333 95,832 95,832R-squared 0.353 0.364 0.236 0.242

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Notes: This table reports difference in difference and triple-differences results for the effect of CAISOexport transmission congestion on natural gas generation, using both the unmatched and matchedsamples. The dependent variable is measured in hourly megawatt hours (MWh). All interactions aremean-centered so that base coefficients can be interpreted as marginal effects evaluated at the mean.The first column reports difference-in-differences results from the full sample, the second column re-ports the triple-difference results from the full sample for generation and includes hourly Californiacontrol variables. The third and fourth columns report the same specifications as in the first andsecond, estimated using our matching procedure. This specification includes BA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errorsby BA are in parentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

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Table E3: Generator Level Natural Gas Generation when Interties are not Constrained

Full Sample MatchedEver EIM X Post EIM 12.39* 11.95** 9.155** 9.104**

(6.831) (5.535) (3.399) (3.761)Post EIM -2.900 -5.982 0.395 -1.505

(7.738) (7.337) (6.205) (5.684)Ever EIM X CA Load -0.00138*** -0.00211***

(0.000349) (0.000631)Ever EIM X Post X CA Load -0.00108 -0.00134

(0.000752) (0.000804)Post EIM X CA Load -0.000302 -0.000220

(0.000236) (0.000150)CA Load -0.00173*** -0.000465**

(0.000315) (0.000199)CA Solar PV -0.000596* -0.00106**

(0.000295) (0.000467)CA Wind -0.000188 -0.000876***

(0.000178) (0.000169)Hourly FERC Loadby Planning Area -0.00514 -0.00120 0.00106 0.00466

(0.00325) (0.00244) (0.00374) (0.00337)Generator Efficiency 177.7*** 178.3*** 195.3* 190.8*

(24.07) (24.28) (96.34) (96.16)Generator Age -0.997*** -0.985*** -1.265** -1.266**

(0.325) (0.321) (0.581) (0.583)Constant 130.6*** 119.0*** 114.1*** 110.5***

(3.596) (3.414) (3.222) (3.819)

Observations 5,331,719 5,316,790 3,513,083 3,502,828R-squared 0.597 0.600 0.542 0.547

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Notes: This table reports difference in difference and triple-differences results for the effect of peri-ods of no transmission congestion on natural gas generation, using both the unmatched and matchedsamples. The dependent variable is measured in hourly megawatt hours (MWh). All interactions aremean-centered so that base coefficients can be interpreted as marginal effects evaluated at the mean.The first column reports difference-in-differences results from the full sample, the second column re-ports the triple-difference results from the full sample for generation and includes hourly Californiacontrol variables. The third and fourth columns report the same specifications as in the first and sec-ond, estimated using our matching procedure. This specification includes BA fixed effects, hour-of-dayfixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors byBA are in parentheses. Significance levels are denoted by *** p<0.01, ** p<0.05, * p<0.1.

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Appendix F Combined Coal and Gas Generator Level Regression Results

Table F1 reports our difference-in-differences and triple-differences results for the effect of the EIM on

pooled coal and gas generation, using both the full and matched samples. The dependent variable is mea-

sured in hourly megawatt hours (MWh). All interactions are mean-centered so that base coefficients can

be interpreted as marginal effects evaluated at the mean. The first column reports the difference-in-differences

results from the full sample, without hourly California control variables. The second column reports the

triple-differences results from the full sample and includes hourly California control variables. The third

and fourth columns report the same specifications as the first and second, estimated using our matching

procedure.

Our preferred specification is in the fourth column, we find that on average, generators produce signifi-

cantly more electricity than their matched counterfactual generators in the EIM. The coefficient on the

marginal generators’ response to incremental increases in California load is negative and similar in mag-

nitude to the gas generator regression results. However, when generators are pooled, we do not find that

generators significantly reduce their power in response to incremental increases in California load, com-

pared to their counterfactual matched generators.

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Table F1: Generator Level Coal and Natural Gas Generation

Full Sample MatchedEver EIM X Post EIM 26.87*** 26.07** 27.01** 25.42**

(9.310) (9.222) (10.83) (11.40)Post EIM -1.683 -4.228 6.540 6.801

(11.49) (11.00) (15.57) (13.71)Ever EIM X CA Load 0.000263 -0.00147**

(0.000599) (0.000666)Ever EIM X PostX CA Load -0.00149 -0.00139

(0.000963) (0.00116)Post EIM X CA Load 0.000262 0.000509

(0.000427) (0.000743)CA Load -0.00208*** 0.000558

(0.000585) (0.000378)CA Solar PV -0.000613*** -0.000835

(0.000208) (0.000595)CA Wind 0.000350 -0.000214

(0.000225) (0.000430)Hourly FERC Loadby Planning Area -0.0110*** -0.00800*** -0.00339 -0.00104

(0.00265) (0.00244) (0.00367) (0.00345)Generator Efficiency 331.8*** 333.3*** 365.8** 365.0**

(56.82) (57.02) (140.9) (141.8)Generator Age 2.087* 2.100* 2.310** 2.329**

(1.016) (1.015) (1.064) (1.064)Constant 223.5*** 209.2*** 196.9*** 194.8***

(7.501) (5.862) (8.558) (7.959)

Observations 10,172,043 10,109,726 6,949,727 6,906,625R-squared 0.301 0.301 0.431 0.432

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Notes: This table reports difference-in-differences and triple-differences results for the effectof the EIM on pooled coal and gas generation, using both the full and matched samples. Thedependent variable is measured in MWh. All interactions are mean-centered so that base coef-ficients can be interpreted as marginal effects evaluated at the mean. The first column reportsthe difference-in-differences results from the full sample, without hourly California controlvariables. The second column reports the triple-differences results from the full sample andincludes hourly California control variables. The third and fourth columns report the samespecifications as the first and second, estimated using our matching procedure. All specifica-tions have BA fixed effects, hour-of-day fixed effects, day-of-week fixed effects, and month-by-year fixed effects. Clustered standard errors by BA are in parentheses. Significance levels aredenoted by *** p<0.01, ** p<0.05, * p<0.1.

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