2015 OKSWP1502 Economics Working Paper … The Disparate Influence of State Renewable Portfolio Standards (RPS) on U.S. Renewable Electricity Generation Capacity Karen Maguire Assistant

The Disparate Influence of State Renewable Portfolio Standards (RPS) on U.S. Renewable Electricity Generation Capacity

Karen Maguire Assistant Professor

Department of Economics and Legal Studies Oklahoma State University

Stillwater, OK

Abdul Munasib Research Scientist

Department of Agricultural & Applied Economics University of Georgia

213 Stuckey Building, 1109 Experiment Street, Griffin, GA 30223

2015 OKSWP1502

Economics Working Paper Series Department of Economics

OKLAHOMA STATE UNIVERSITY http://spears.okstate.edu/ecls/

Department of Economics Oklahoma State University Stillwater, Oklahoma

339 BUS, Stillwater, OK 74078, Ph 405-744-5110, Fax 405-744-5180

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TheDisparateInfluenceofStateRenewablePortfolioStandards(RPS)onU.S.RenewableElectricityGenerationCapacity

KarenMaguireAssistantProfessor

DepartmentofEconomicsandLegalStudiesOklahomaStateUniversity

327BusinessBuilding,Stillwater,OK74075Phone:405‐744‐5112

E‐mail:[email protected]

AbdulMunasibResearchScientist

DepartmentofAgricultural&AppliedEconomicsUniversityofGeorgia

213StuckeyBuilding,1109ExperimentStreet,Griffin,GA30223Phone(770)229‐3419

E‐mail:[email protected]

Abstract

Several papers have used panel data analyses to examine the effectiveness of U.S. state‐levelRenewablePortfolioStandards(RPS)inpromotingrenewablecapacitydevelopment,but the findings are inconclusive. Estimation of average treatment effects, however, canmask the fact that RPS policies across states are disparate and the treatment states areheterogeneous. We use the Synthetic Control Method (SCM) to conduct individual casestudies of the early adopter states. Our findings indicate that the impact of RPS variedacrossstates.WefindTexastobeuniqueamongtheseearlyadoptersinthatRPSinTexashasledtoincreasedrenewablecapacity.Keywords: Renewable portfolio standard (RPS), renewable energy, electricity, synthetic

controlmethod(SCM)JELclassification:Q4,Q42,Q48,H7

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I.Introduction

As of January 2012, 29 U.S. states and theDistrict of Columbia had enacted a Renewable

PortfolioStandards(RPS)orothermandatedrenewableenergypolicies.RPSrequirethatelectricity

producerssupplyaportionoftheirelectricityfromdesignatedrenewableresourcesbyaspecified

futuredate.TheadoptionofRPS ismotivatedbyacomplexsetofpoliticalandeconomic factors,

includingincreasingconcernsoverclimatechangeandenergysecurity(YiandFeiock2012).While

severalpolicieshavebeenproposedtoaddresstheseconcerns,RPSisoneofthemost frequently

advanced policies to promote renewable energy development for electricity generation (Fischer

2010).WeexaminewhetherRPSishavingtheintendedeffectof increasingrenewablegeneration

capacity.

Severalpapershaveimplementedpaneldataanalysestostudytheroleofrenewableenergy

policiesinpromotingrenewablesdevelopment.1They,however,donotprovideanyconsensus.Yin

andPowers(2010)findthatRPShasapositiveinfluenceonthepercentageofnon‐hydrorenewable

generatingcapacity,but the finding ispredicatedon theconstructionofanRPSstringency index.

ShrimaliandKniefel(2011),ontheotherhand,foundanegativeimpactofRPSontheratioofnon‐

hydrorenewablecapacityovertotalnetgeneration.Carley(2009)focusesongenerationandfinds

that in theyearsafterRPSadoptionanadditionalyearofRPShasapositiveeffect,althoughRPS

implementation has no predictive power. Delmas and Montes‐Sancho (2011) analyzed capacity

rather than generation and found that RPS led to declining renewable electricity capacity.

Additionally, a number of studies on wind capacity found no impact of RPS. Hitaj (2013), for

instance,providesacounty‐levelanalysisandfindsthatRPSdidnothaveasignificantinfluenceon

1Carley (2009): 48‐state1998‐2006panel,Delmas andMontes‐Sancho (2011):panel of 650utilities from48‐statesover1998‐2007,Hitaj (2013): county‐level1998‐2007panel,Maguire (2014): state‐level1994‐2012panel, ShrimaliandKniefel(2011):50‐state1991‐2007panel,YinandPowers(2010):50‐state1993‐2006panel.

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wind capacity, andMaguire’s (2014) state‐level analysis also concludes that RPS did not have a

significanteffectonwindcapacity.

Theempiricalliteraturediscussedabovehasgenerallyfailedtofindconclusiveevidenceof

anaveragetreatmenteffectofRPSonrenewablesadoptionacrossRPSstates.Thishighlights the

needforanalysesthataccommodatethepossibilityoftreatmentheterogeneity(Keeleetal.2013),

particularlybecauseRPSareuniquestate‐levelpolicies.Estimationofaverageeffectscanmaskthe

factthatadopterstatesareheterogeneousandstateRPSpoliciesaredisparate.Theassumptionofa

uniform effect of RPS across states can be quite restrictivewhen the states differ in their policy

environment, electricity market characteristics, renewable resource potential, likelihood of

successfulimplementationoftheirRPS,andahostofobservedandunobservedcharacteristics.

TreatingdisparatestatelevelRPSasauniforminterventionisalsoinappropriate.RPSvary

in the amount of electricity generation that must be supplied from renewables, the types of

allowablerenewables,theyearofrequiredimplementationofthefinalmandate,andthemagnitude

and the timing of intermediatemandates.RPS also differ in thenature of theRenewableEnergy

Credit(REC)tradingmarkets,andthedegreeandscopeofrestructuringrequirements(seesection

II.3formoredetails).We,therefore,adoptacasestudyapproachtoexaminetheeffectofastate’s

RPSonitsrenewablecapacity.Weexaminetheperiod1991‐2008andfocusontheearlyadopter

states(seeAppendixAforalistofRPSstatesandfinalmandates).2Oursetoftreatmentstatesare

Nevada(1997),Connecticut(1998),NewJersey(1999),Maine(1999),Texas(1999)andWisconsin

2Theearliestavailablestate‐leveldataforgenerationcapacityis1990.Startingattheendof2008,fiveadditionalstatesadoptedRPS.Extendingouranalysisbeyond2008,therefore,wouldsignificantlyshrinkthesizeofthedonorpool.

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(1999),states thatenactedRPSbetween1997and2000.3Ouroutcomevariableof interest is the

generationcapacityofthemodernrenewables:wind,solar,geothermal,andbiomass.4

Weemploy theSyntheticControlMethod (SCM) for comparativecasestudies (Abadieand

Gardeazabal2003,Abadieetal.2010) toestimate the impactofRPS ineachof thesestates.SCM

constructsauniquecounterfactual(or‘synthetic’)foreachRPS(treatment)stateusingaweighted

averageofthenon‐RPS(control)statesbasedonasetofpre‐intervention(pre‐RPS)characteristics.

Byexaminingeachstateasastand‐alonecasestudyweareabletoallowforheterogeneouseffects

ofRPS.

Wefocusonlyonearlyadopterstates(i.e.,statesthatenactedRPSbetween1997and2000)

in order to allow for sufficient post‐intervention years to capture the effect of RPS.Unlike other

policies such as changes in gun laws or driving restrictions, RPS does not become immediately

binding on its effective date. The renewable mandates are implemented years after the RPS

effectivedatethroughaseriesofintermediategoalsandmandatesleadinguptothefinalmandate.

For instance, Nevada enacted RPS in 1997, and updated the policy in 2001 to establish the

minimum requirement that 2 percent of electricity be supplied from eligible renewable sources,

increasing every two years and culminating in a 15 percent mandate by 2013.5 In Texas, RPS,

passed in 1999, had intermediatemandates in 2002 and 2007with their finalmandate initially

bindingin2010andthensubsequentlyamendedto2015.Asimilarpatternisobservedintheother

RPSstateswherethefinalmandateiseffectiveonafuturedateprecededbyaseriesofintervening

targets. 3IowaistheonlystatethatpassedRPSbefore1997.ButitpasseditsRPSin1983,whichfallsoutsideourdatarange.4 Hydroelectric generation capacity is not considered a modern renewable resource and is excluded. Although itconstitutes52%ofrenewableelectricitygenerationintheU.S.in2013,becausemosthydroelectriccapacitywasaddedprior to the mid‐1970s it is not a newly developed resource.(http://www.eia.gov/energy_in_brief/article/renewable_electricity.cfm)5NevadaRPSwassignificantlyrevisedagainin2009,whichfallsbeyondourstudyperiod.

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OurSCMestimatesshowthattheimpactofRPSindeedvariesacrossstates.Texasisunique

amongtheearlyadopterstates in thatwefindapositive impactofRPSonrenewablecapacity in

Texas.WithinadecadeafterenactingRPS,Texasinstalledmorewindgenerationcapacitythanany

otherstate.6AswediscussindetailinsectionsII.2‐II.4theenergymarketcharacteristicsofTexas

are also quite unique: Texas is the only early adopter state with substantialmodern renewable

potential.Texasisalsotheonlymainlandstatewithitsowngrid,anditsRPS,specifiedintermsof

capacityandnotgeneration,isatypical.

Inwhatfollows,weprovidesomebackgroundinformationontheU.S.electricitymarketand

describetheRPScharacteristicsoftheearlyadopterstatesinsectionII,presentabriefdescription

oftheempiricalmethodologyinsectionIII,describethedatainsectionIV,anddiscusstheresultsin

sectionV.SectionVIconcludes.

II.RenewableGeneration,ElectricityMarkets,andRenewablePortfolioStandards

II.1.Renewablegeneration

Renewableenergysourcesprovided13percentof totalU.S.electricitygeneration in2013,

49percentofwhich is frommodern renewables;wind,biomass, geothermal, and solar, i.e., non‐

hydroelectric sources.Today, theUnitedStatesproducesmoreelectricity fromnon‐hydroelectric

renewablesourcesthananyothercountry,ChinaandGermanyranksecondandthird.7TheEnergy

InformationAssociation(EIA)predictsthatbetween2013and2040,non‐hydroelectricrenewables

willaccountfor24percentoftheoverallgrowthintheUnitedStateselectricitygeneration.Solaris

expectedtoincreasefrom8GWin2012to48GWby2040,whilewindispredictedtoincreasefrom 6In2013,Texasaccountedfor22percentofthe167millionMWhoftotalpowergeneratedfromwindnationwide.IfTexaswereacountryitwouldbesixthintheworldinwindcapacityfollowingChina,theUnitedStates,Germany,Spain,andIndia.SeeHurlbut(2008),EIA‐PTC:http://www.eia.gov/todayinenergy/detail.cfm?id=8870,EIA‐Texas:http://www.eia.gov/todayinenergy/detail.cfm?id=15851,EIA:http://www.eia.gov/state/?sid=TX,ERCOTTime‐line:http://www.ercot.com/about/profile/history,andOfficeoftheGovernor:www.TexasWideOpenForBusiness.com.7http://www.eia.gov/todayinenergy/detail.cfm?id=16051

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60GWto87GWoverthesameperiod.Inaddition,geothermalcapacityispredictedtotripleand

biomass capacity is predicted to double. Finally, modern renewable generation is predicted to

exceedhydroelectricgenerationandcomprisetwo‐thirdsofallrenewablegenerationby2040.8

II.2.ElectricityMarket

The electricity system in the United States consists of three regions: the Eastern

Interconnection, the Western Interconnection, and the Texas Interconnection. Grid connectivity

withinan interconnectionenablesutilities to importandexportgenerationacrossstates.9Within

the Interconnections, there are nine Independent System Operators (ISOs) and Regional

Transmission Organizations (RTOs) that coordinate the trading of electricity generation across

states (SeeFigure1).Theyprovide therates, termsandconditions for thewholesalemarketand

transmissionwithintheregion.

RenewableEnergyCredits(REC)marketsallowforthetradingofrenewableenergybetween

utilitieswithin a particular region.10REC aredesigned toprovide an accurate account of eligible

renewableenergyproduction,andtobetradablebetweenproducersandretailers.Forexample,in

NewEngland,theISONewEnglandRTOcoordinatesthetradingofrenewablegeneratedelectricity

across states using REC.11 Because in these states utilities are allowed to import and export

renewablegenerationfromotherstates,utilitiesmayimportratherthanaddadditionalrenewable 8http://www.eia.gov/forecasts/aeo/MT_electric.cfm#cap_natgas9http://www.un.org/esa/sustdev/publications/energy/chapter2.pdf.10Arizona,Nevada,TexasandWisconsinweretheearlieststatestoallowfororrequiretheuseoftradableRECtomeetRPS.11PowergeneratedfromrenewableresourcesisusedtocreateREC,whicharemeasuredinenergyunits.Forinstance,oneRECmayrepresent1MWhofqualifiedrenewableenergy.TheexistingRECmarketsandtrackingsystemsserveadistinct region: theNEPOOLGeneration InformationSystem(NEPOOLGIS)supportsasix‐statearea inNewEnglandcomprisingtheISONewEnglandcontrolarea,thePJMGenerationAttributeTrackingSystem(GATS)supportsthePJMcontrolarea,whichcovers13statesandtheDistrictofColumbia,whiletheERCOTRECprogramonlyoperatesinTexas.See(Doot,Belval,andFountain2007) formoredetails.TheNewEnglandISOwasestablishedbytheFederalEnergyRegulatory Commission (FERC) in 1997 andwas designated as an RTO in 2005, giving the organization additionalauthority over the regional grid (http://www.iso‐ne.com/about/what‐we‐do/in‐depth/industry‐standards‐structure‐and‐relationships).

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capacityifimportingisalow‐costalternativetomeettheirRPSmandates.Conversely,itmayalso

bemorecosteffectiveforautilitytobecomeanexporterofrenewablegeneration.Accordingtothe

National Renewable Energy Laboratory (NREL), “The primary regionalmarkets for REC exist in

New England and the Mid‐Atlantic states” (Heeter and Bird, 2010, p.6). The NEPOOL_GIS REC

tradingmarketfortheNewEnglandregionbeganin2002,whilethePJM‐GATSRECtradingmarket

servingtheMid‐Atlanticstatesbeganin2005(HeeterandBird,2010,p.9).12

One unique state in terms of interconnectivity is Texas. The Texas Interconnection is

separatedfromtherestofthenation,makingTexastheonlymainlandstatewithitsowngrid.Also,

theTexasRECtradingprogramwasunusualinthatitrequirestheRECgeneratedelectricitytobe

produced in Texas (Hurlbut 2008).13 Nevada is the only other early adopter state that limits

renewablegenerationtowithinstateproducers,buttheydoallowlimitedoutofstateproduction.

II.3.RenewablePotential

The renewable energy potential for each state varies significantly. Texas is the only early

adopterstatewithsubstantialmodernrenewablepotential.14According to theNREL’s renewable

potentialdata,Texasranksfirstinonshorewindandsolarphotovoltaicpotential,fifthinbiopower

(solid) potential, and eighteenth in geothermal‐hydrothermal potential.15 Other states with

significantrenewablespotentialthathaveenactedRPSincludeWashington,California,Oregonand 12TheNEPOOL_GISRECtradingactivityincludedimportsof20,163GWhandexportsofapproximately10,861GWhin2008.Thisrepresentsapproximately6percentand3percentoftotalU.S.renewablegeneration(modernrenewablesandhydroelectricgeneration)in2008.13TheElectricReliabilityCouncilofTexas(ERCOT)whichmanagestheTexasInterconnectionmanageselectricpowerforapproximately85%ofthestate’stotalelectricload.Formoredetails,seeOfficeoftheGovernor(www.TexasWideOpenForBusiness.com),ERCOT(http://www.ercot.com/about,http://www.ercot.com/content/news/mediakit/maps/NERC_Interconnections_color.jpg),andDSIRE(http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=TX03R).14http://www.nrel.gov/gis/re_potential.html15Noneoftheotherearlyadopterstateshasatop10ranking inanycategory,exceptNevadawhichrankssecondingeothermal‐hydrothermalpotential.NRELbiopowerestimates includecrop, forest,primary/secondarymill residues,and urban wood waste from Milbrandt (2005). See NREL 2012 for more information on the calculation of eachrenewableenergypotentialmeasure.

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New York, but they passed their RPS on or after 2003. In addition, in these four states,

hydroelectricityconstitutesthelargestshareofrenewablegenerationandmostofthehydroelectric

capacityexistedinthesestatesbeforetheirrespectiveRPSwereenacted.

II.4.HeterogeneityofRPSacrossStates

RPSarestate‐adoptedpoliciesandthereissignificantvariationinthecharacteristicsofRPS

acrossstates,whichisoneoftherationalesforourcasestudyapproach.OurSCMestimatesallow

ustodeterminetheeffectofastate’suniqueRPSpolicy in thecontextof itsdistinctpoliticaland

marketcharacteristics.

RPSvarynotonlyinthemagnitudeandtimingofthefinalrenewablesmandatebutalsothe

magnitudeandtimingofintermediatemandates(AppendixAdetailsthecurrenttargetsforallthe

RPS states). For instance, Wisconsin’s RPS (passed in 1999) requires 10 percent renewable

generationby2015whileMaine’sRPS(alsopassedin1999)requires40percentby2017,oneof

themoststringentinthenation.TheTexasRPSmandateissetintermsofcapacityandnotinterms

ofthepercentageofgenerationrequiring10,000MWby2025.Kneifel(2007)identifiesthisasan

importantfeaturevis‐à‐vistheeffectivenessofRPS.16

In addition to the finalmandate, states vary in their definitions of ‘renewable resources’.

Thisvariationisafunctionoftheiruniqueresources,politicalconditions,andeconomicstandingin

the regional economy. The mandated renewable sources can include wind, solar, geothermal,

biomass, some typesofhydroelectricity, andother resources suchas landfill gas,municipal solid

waste,andtidalenergy.ForsomestatesmodernrenewablesarelargelycategorizedasClass1and

makeupanincreasingportionoftherenewablerequirementsovertime.Forinstance,Connecticut

16TheonlyotherstatethatsetitsRPSbasedoncapacitywasIowa,buttheirmandatewassmall.Iowa’sRPSmandated105MWofrenewablecapacity.(http://twww.dsireusa.org/incentives/incentive.cfm?Incentive_Code=IA01R&re=1&ee=1)

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and New Jersey mandated three categories of renewables each with their own generation

requirements. The early adopter states included modern renewables in their set of allowable

renewables.

There isalsovariationinthecoverageofthepolicy indifferentstates. Insomestatesonly

specific typesofutilities, investorownedutilities (IOUs),municipal,orruralelectriccooperatives

(Coops)arerequiredtomeetRPS.Forexample,InWisconsintheinitialRPSmandateappliedonly

toIOUsandCoops.TheTexasRPSappliedtobothIOUsandretailsupplierswhilemunicipalutilities

and Coops could opt in. The legislative path of the passing of RPS also varied across states.

WisconsinwasthefirststatetoimplementRPSwithoutrestructuringitselectricitymarket,whilein

therestoftheearlyadopterstates,RPSpassedaspartoflegislationthatincludedrestructuringof

theelectricitymarket.

III.SyntheticControlMethod(SCM)forComparativeCaseStudy

ThereareanumberofadvantagestousingSCMinthisstudy.First, inprogramevaluation,

researchersoftenselectcomparisonsonthebasisofsubjectivemeasuresofsimilaritybetweenthe

affectedandtheunaffectedregionsorstates.But,neitherthesetofallnon‐RPSstatesnorasingle

non‐RPSstatelikelyapproximatesthemostrelevantcharacteristicsofatreatment(orRPS)state.

SCMprovidesacomparisonstate(orsynthetic)thatisacombinationofthecontrolstates,adata‐

drivenprocedurethatcalculates‘optimal’weightstobeassignedtoeachstateinthecontrolgroup

based on pre‐intervention characteristics, thus making explicit the relative contribution of each

controlunit to thecounterfactualof interest (AbadieandGardeazabal2003;Abadieetal.,2010).

Withreduceddiscretioninthechoiceofthecomparisoncontrolunits, theresearcher is forcedto

demonstratetheaffinitiesbetweentheaffectedandunaffectedunits.

Secondly, even when aggregate data are employed, as the case is in this paper, there is

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uncertaintyabouttheabilityofthecontrolgrouptoreproducethecounterfactualoutcomethatthe

affected statewouldhaveexhibited in theabsenceof the intervention.AsBuchmueller,DiNardo,

and Valleta (2011) explain, in a ‘clustering’ framework, inference is based on the asymptotic

assumption,i.e.,thenumberofstatesgrowslarge.Thecomparisonofasinglestateagainstallother

states in the control group collapses the degrees of freedom and results inmuch larger sample

variancecomparedtotheonetypicallyobtainedundertheconventionalasymptoticframeworkand

can seriously overstate the significance of the policy intervention (Donald and Lang 2007;

Buchmueller, DiNardo, and Valletta 2011; Bertrand et al. 2004). We, therefore, apply the

permutationsorrandomizationtestthatSCMreadilyprovides(Bertrand,Duflo,andMullainathan

2004; Buchmueller, DiNardo, and Valletta 2011; Abadie, Diamond, andHainmueller 2010; Bohn,

Lofstrom,andRaphael2014).

Thirdly, because the construction of the optimalweights does not require access to post‐

interventioninformation,SCMallowsustodecideonastudydesignwithoutknowingitsbearingon

thefindings(Abadie,Diamond,andHainmueller2010).Theabilitytomakedecisionsonresearch

designwhileremainingblindtohowaparticulardecisionaffectstheconclusionsofthestudyisa

safeguardagainstactionsmotivatedbya‘desired’finding(Rubin2001).

Finally, Abadie, Diamond, and Hainmueller (2010) argue that unlike the traditional

regression‐based difference‐in‐difference model that restricts the effects of the unobservable

confounders to be time‐invariant so that they can be eliminated by taking time differences, SCM

allows such unobservables to vary with time. In particular, Abadie, Diamond, and Hainmueller

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(2010) show that with a long pre‐intervention matching on outcomes and characteristics a

syntheticcontrolalsomatchesontime‐varyingunobservables.17

III.1.TheSyntheticControl

AtypicalSCManalysisisfeasiblewhenoneormorestatesexposedtoaninterventioncanbe

compared to other states that were not exposed to the same intervention. In this paper, the

interventionisRPS,theoutcomeisrenewablecapacity,andthesetofexposedstatesaretheearly

RPSadopterstates.Thedonorpool(unexposed/controlstates)consistsofstatesthatdidnothave

thepolicyfortheobservedperiod.

To obtain the synthetic control we follow Abadie and Gardeazabal (2003) and Abadie,

Diamond, and Hainmueller (2010). For states 1,...,1 Ji and periods Tt ,...,1 , suppose state

1i isexposedtotheinterventionat ),1(0 TT .Theobservedoutcomeforanystate i attimetis,

(1) ititN

itit SYY ,

where NitY is the outcome for state i at time t in the absence of the intervention, the binary

indicatorvariable itS denotestheinterventiontakingthevalue1if 1i and 0Tt ,and it isthe

effectoftheinterventionforstate i attimet.

Wewant to estimate ),...,( 111 0 TT . Abadie, Diamond, andHainmueller (2010) show that,

under standard conditions, there exist ),...,( 12

JwwW such that pre‐intervention matching is

achievedwithrespecttotheoutcomevariableaswellascharacteristics(orpredictors),andwecan

use,

17 As Abadie et al. (2014) explains the intuition as, “… only units that are alike in both observed and unobserveddeterminantsof theoutcomevariable aswell as in the effect of thosedeterminantson theoutcomevariable shouldproducesimilartrajectoriesoftheoutcomevariableoverextendedperiodsoftime.”

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(2) },...,1{,ˆ 01

211 TTtYwY jt

J

j jtt

,

asanestimator for t1 .Theterm jt

J

j jYw

1

2 ontheright‐hand‐sideof(2) issimplytheweighted

average of the observed outcome of the control states for },...,1{ 0 TTt withweights W . The

proceduretoobtain W isinAppendixB.

III.2.Inference

Once an optimal weighting vector W is obtained, the “synthetic” is constructed by

calculating theweighted averageoutcomeof thedonorpool. Thepost‐intervention valuesof the

synthetic control serve as our counterfactual outcome for the treatment state. The post‐

intervention gapbetween the actual outcome and the synthetic outcome, therefore, captures the

impactoftheintervention.

To begin, we calculate a difference‐in‐difference estimate for the treatment state (Bohn,

Lofstrom,andRaphael2014,MunasibandRickman2015),

(4) presyntheticTR

preactualTR

postsyntheticTR

postactualTRTR YYYY ,,,, ,

where postactualTRY , is the average of the post‐intervention actual outcome of the treatment state,

postsyntheticTRY , istheaverageofthepost‐interventionoutcomeofthecounterfactual.Similarly, pre

actualTRY , is

theaverageofthepre‐interventionactualoutcomeoftreatmentstate,and presyntheticTRY , istheaverage

of thepre‐interventionoutcomeof thecounterfactual. If theoutcomechanged in response to the

interventionintime 0T wewouldexpect 0TR.

To formally test the significance of this estimate, we apply the permutations or

randomizationtest,assuggestedbyBertrandetal.(2004),Buchmuelleretal.(2011),Abadieetal.

(2010)andBohnetal.(2014),onthisdifference‐in‐differenceestimator.Specifically,foreachstate

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inthedonorpool,weestimatethedifference‐in‐differenceasspecifiedinequation(4)asifitwas

exposedtoRPSattime 0T (i.e.,applyafictitiousintervention).Thedistributionofthese“placebo”

difference‐in‐differenceestimatesthenprovidestheequivalentofasamplingdistributionforTR .

Tobe specific, if the cumulativedensity functionof the complete setof estimates is givenby

)(F ,thep‐valuefromaone‐tailedtestofthehypothesisthat 0TRisgivenby )( TRF (Bohnetal.

2014). Note that this answers the question, how often would we obtain an effect of RPS of a

magnitudeaslargeasthatofthetreatmentstateifwehadchosenastateatrandom,whichisthe

fundamental question of inference (Bertrand et al., 2004, Buchmueller et al. 2011, Abadie et al.

2010).

WecarryoutasecondtestwherewecalculatewhatwecalltheDIDrank.Itistherankingof

theabsolutevalueofthemagnitudeofthedifference‐in‐differenceofthetreatmentstateagainstall

the placebo difference‐in‐differencemagnitudes (Bohn et al. 2014,Munasib and Rickman 2015).

Forexample,ifDIDrankis1thentheestimatedimpactoftheinterventioninthetreatmentstateis

greaterthananyoftheestimatedplaceboimpacts.

IV.Data

We collected the data for the outcome variable, renewable capacity, from the EIA. The

information on state RPS is collected from the Database of State Incentives for Renewables &

Efficiency(DSIRE)database(seeAppendixA).Figure2demonstratesthatstatesthathaveadopted

RPS are largely the states that have renewable generation capacity additions. This, of course, is

confoundedbyvariousaggregatefactorssuchas theFederalProductionTaxCredit(PTC).Oneof

the rationales behind our case study approach is thatwe can purge out these aggregate effects,

factorssuchasthePTCapplytobothcontrolandtreatmentstates.

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Much of the remaining energy data, including electricity generation and price, generating

capacity, number of customers, etc., were also collected from the EIA. We used information on

geographicalfeaturessuchassunlightandnaturalamenitiesfromtheEconomicResearchService

(ERS)of theU.S.DepartmentofAgriculture(USDA)andtemperatures fromNationalOceanicand

AtmosphericAdministration(NOAA).Populationaswellaseconomicindicatorssuchaspercapita

personal income andmanufacturing earnings sharewere obtained from theBureauofEconomic

Analysis(BEA).PovertyratesarefromtheCensus.

In addition, we collected data on technical renewable potentials from Pacific Northwest

NationalLaboratory(PNNL)andNREL.Therearetwowindpotentialmeasures.Thefirstmeasure

is derived from wind potential estimates produced by the PNNL in 1991 (Elliott, Wendell, and

Glower1991,p.B‐1).Windpotentialcalculationsindicatetheamountofwindthatastateorregion

istheoreticallycapableofproducingunderaspecificsetoftechnologicalandlanduseassumptions,

excluding transmission limitations.18 The second measure is an updated 2010 wind potential

measurements constructed by NREL.19 Similarly, the photovoltaic potential, biopower (solids)

potential, and geothermal‐hydrothermal potential measures are also 2010 estimates from NREL

(NREL2012).Table1presentsasummarydescriptionforallthevariablesusedintheanalysis.

18Forinstance,theinstalledcapacitycalculationsarebasedonanassumptionof5MW/km2ofinstalledcapacity.19 The twomeasures differ based on technological and land use assumptions. For instance, the 1991measurewasconstructedwithanassumedturbineheightof 50mduetotheavailabilityofwindtechnologyatthetime,whilethe2010measurewasconstructedusingan80mturbineheight(NREL2010).

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V.Results

V.1.SCMEstimatesoftheImpactofRPSonRenewableCapacity

We construct the counterfactual (or synthetic) renewable capacity for each of our early

adopterstates(asdiscussedinSectionIII).Ourdonorpoolconsistsof26statesthatdidnotpassa

lawsimilartomandatoryRPSasof2008.

Figure 3 is a graphical representation of the SCM estimates of the impact of RPS on

renewablecapacityforthesixexposedstates.Ineachpanel,thepictureontheleftshowstheactual

andthesyntheticrenewablecapacitiesfortheperiod1990‐2008.Thepictureontherightpresents

thepermutations/randomizationortheplacebotests:thedarklineisthegapbetweenactualand

syntheticforthetreatmentstate,whereaseachgreylineisthegapbetweenactualandsyntheticof

aplacebo.ThedetailsoftheestimationarereportedinTable2.

TheleftpictureinpanelA(Nevada)showsthatthesyntheticrenewablecapacitycoincides

wellwith theactualrenewablecapacityover1990‐1996.OntherightpictureofpanelA,we find

thatNevada(thedarkline)doesnotstandoutfromtheplacebos(thegreylines).Asexplainedin

section III.2,weexamine thecomparisonof thepost‐predifferenceratios fromtheplacebo tests.

Along the first column of Table 2, we find that the DID rank is 25 and the p‐value of the DID

measuredoesnothaveasignificantp‐value.We,thus,concludethatRPSdidnothaveasignificant

impactonrenewablecapacityinNevada.

WeobservethesamepatternforConnecticut(panelBofFigure3andcolumn2ofTable2),

Maine(panelCofFigure3andcolumn3ofTable2),NewJersey(panelDofFigure3andcolumn4

ofTable2)andWisconsin(panelFofFigure3andcolumn6ofTable2).Ineachofthesecaseswe

findthattheDIDrankishighandnotstatisticallysignificant.

16

For Texas, however, we find that RPS had a significant impact on renewable capacity

addition.OntheleftpictureofpanelEofFigure3,weseethattheactualcapacitystartstodeviate

fromthesyntheticinthepost‐interventionperiod(i.e.,1999,theyearofRPS)andkeepsdiverging.

OntherightpictureofPanelE,weseethatthegapbetweenactualandsyntheticforTexasstands

outinthemidstofalltheplacebogaps.Incolumn5ofTable2,wefindthatTexas’sDIDrankis1,

anditissignificantat1percent.ThemainconstituentsofTexas’ssyntheticasindicatedbythew‐

weights are (in order of importance): Indiana, Illinois and Virginia. The strongest predictors of

renewablecapacityforTexas’ssyntheticare(notshown):coalandnaturalgasgenerationshares,

percapitaincome,growthofcustomersandpercapitaincome,andshareofmanufacturingincome.

V.2.AlternativeSetofPredictors

Totest ifourestimatesarerobusttochangesinthesetofpredictors(forpre‐intervention

matching)we carry out robustness checkswith an alternative set of predictors.We include the

1991 wind potential measure and geographic and weather variables: January sunlight, summer

coolingdegreedays,summerheatingdegreedays.Alaskaisdroppedfromthedonorpoolbecause

the1991windpotentialmeasureandthegeographicvariablesarenotavailableforthisstate.Table

3presentstheseresults.BasedontheDIDranksaswellas thep‐valuesof theDIDmeasures,we

concludethatonlyincaseofTexas,RPShadasignificantimpactonrenewablecapacity.

V.3.AdditionalRobustnessChecksforTexas

IneachSCMreportedinTables2and3,foreachtreatmentstate,thestate’spre‐intervention

outcome(renewablecapacity)isincludedwithacommonsetofpredictors.Then,thematchingis

done to calculate the optimalw‐weight. In the case of Texas, therefore,matching is done on the

commonsetofpredictorsaswellastheoutcomevariable(renewablecapacity)fortheperiod1990‐

17

1998.20 However, Texas’s renewable electricity market did not exist prior to 1998. So, we have

conductedarobustnesscheck,reportedincolumn1ofTable4,wherethematchingisdoneonthe

setofpredictorsthatincludesrenewablecapacityfor1998only.Wefindthatourinferenceremains

unchanged.ThemainconstituentsofTexas’ssyntheticasindicatedbythew‐weightsare(inorder

ofimportance):Oklahoma,Illinois,andSouthDakota.

AnotherissueisthattheTexasRPSincludessomedegreeofrestructuringintheelectricity

market.Todetermineiftheeffectofrestructuringisconfoundingthefindings,incolumn2ofTable

4wepresenttheSCMresultswherewehaveexcludedstatesthathadanykindofderegulation(i.e.,

thedonorpool has onlynon‐RPS andnon‐deregulated states). The set of predictors remains the

sameasthatinTable2.Again,wearriveatthesameconclusionthatRPShadasignificantimpacton

renewablecapacity.

V.4.Discussion:HeterogeneityofRPSImpacts

WefindthatofthesixearlyRPSadopters,TexasistheonlystatewhereRPShadanimpact

on modern renewable capacity. It is important to point out that Texas stands out among these

statesinanumberdifferentways.First,TexasisanexceptioninspecifyingRPSintermsofcapacity.

Allotherstates,withtheexceptionofIowa,specifyRPSasapercentageoftotalgeneration.Kneifel

(2007)arguesthatthetypeofmandateinfluencesitseffectiveness.

Second, the five early adopter states where we do not find an effect are also among the

smallestenergyproducingstates;NewJersey,whichisthelargestproducerofthesefivestates,had

only a 0.5 percent share of the total U.S. generation in 2012. Texas, on the other hand,was the

20ThisisthestandardprocedurefollowedinSCMduetoAbadieetal.(2010)andBohnetal.(2014).

18

largest energy producing state for every year between 1990 and 2012.21 The size of the Texas

electricitymarketmayhavegivenTexasanedgeinaddingrenewablecapacity.

Third,inadditiontosize,gridinterconnectivityhasimportantimplicationsfortheexpansion

of renewable capacity. In New England, the ISO New England RTO coordinates the trading of

renewable generated electricity across states using REC. This may have influenced the pace of

withinstaterenewablecapacityadditionsinConnecticutandMaine,bothintheISONewEngland

region.22NewJersey,whichisinthePJMRTO,promoteswithinstatedevelopment,particularlyfor

solar generation. However, if approved by the New Jersey Board of Public Utilities, renewable

generation can also be generated from regional capacity (Daniel et al, 2014, p. 7). InWisconsin

tradablecreditsarecreatedonlywhenanelectricutilityorcooperativeprovides totalrenewable

energy to its retail electric customers in excess of the RPS requirements (See Berry 2002 for

details).Texas,ontheotherhand,istheonlymainlandstatewithitsowngridandunlikeotherREC

programs,theERCOTRECprogramonlyoperatesinTexas;togenerateaunitofRECtheelectricity

hastobegenerated(fromrenewables)andmeteredinTexas.

InNevada,utilitiesarerequiredtomeetaminimumof5percentoftherequiredrenewables

mandate through solar generated electricity. Nevada did not meet 100 percent of their RPS

obligationuntil2008.23 InNew Jersey, in2005, themandatewasrevisedwhereby theshare that

must come fromClass1 renewableswas set tobe17percentby2021.Until2005,however, the

21http://www.eia.gov/electricity/data/state/22Asarobustnesscheck,weconductedanSCManalysiswheretheNewEnglandregionisconsideredthetreatedunit.The year of intervention was the first year in which a state in New England passed RPS, 1999. The finding wasconsistentwiththestatelevelresults.TherewasnotasignificantinfluenceofRPSonrenewablecapacity.Theseresultsareavailableuponrequest.23In2009,beyondouranalysisperiod,thestringencyoftheinitialpolicywasincreasedandthefinalmandatewasincreasedto25percentby2025.Seehttp://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=NV01R.

19

mandatewasthatthesharethatmustcomefromClass1renewableswas0.74percent.24Thismay

explainwhytherewasnocapacityexpansionthrough2008.

InMaine,atthetimeofthepassageofRPS,thegenerationconstraintwasnotbinding.Maine

hassignificanthydroelectricgenerationcapacity,andgenerationfromtheseresourcesexceededthe

initial mandate. The Maine RPS was subsequently updated to require that a portion of the

renewablecapacitybeinstalledafter2005.Themandatewassmallhowever,requiring1percentof

electricitybeproducedfromnewrenewablecapacityin2008.

InWisconsin, the initialRPSmandateappliedonly to InvestorOwnedUtilities (IOUs) and

RuralElectricCooperatives(Coops),requiringthemtoobtain2.2percentof theirelectricity from

renewablesourcesby2012.Thepolicywasstrengthenedin2006,withautility‐widerequirement

of10percentby2015.25

V.5.Discussion:EfficacyofTexasRPS

WeobservethatTexasproducersreached10,000MWofwindgenerationcapacityby2010

reachingtheRPStargetyearsaheadofthemandatedtimeline.This,however,doesnotindicatethat

RPS was not binding. In the presence of non‐convex adjustment costs, indivisibilities, and

irreversibilities ofwind generation capital, optimal investment is unlikely to be incremental and

more likely toexhibitburstsof large‐scalecapitalaccumulations(AddaandCooper2003,Cooper

and Haltiwanger 2006). As a result, the level and timing of optimal investment may very well

exceedandprecedethemandate,aswasthecaseinTexas.

Additionally, firms may have predated wind generation capacity in order to secure the

federalProductionTaxCredit(PTC)benefits.ThePTCappliestowindfarmsforthefirst10yearsof

24http://www.dsireusa.org/summarytables/rrpre.cfm25http://www.dsireusa.org/incentives/incentive.cfm?Incentive_Code=WI05R&re=0&ee=0.

20

productionandlowersthecostofwindgeneratedelectricityproductionbyaboutonethird(Wiser,

2007).26ThecreditwasoriginallycreatedundertheEnergyPolicyActof1992,butithasexpired

andbeenextendedseveraltimessinceitsinception(Wiser,Bolinger,andBarbose2007,p.1‐2).27

Its lapses over the years are correlatedwith decreases inwind capacity additions and are often

blamedforthosedeclines(AWEA2005,p.4).Barradale(2010)findsthatuncertaintyinthefederal

PTC leads to investmentvolatility, asproducersdelayproduction innon‐PTCyearsand rampup

productionwhenthePTCisactive.

V.6.Discussion:EarlyAdopterStates

BecauseRPSmandatesdonotbecomeimmediatelybindingbutareimplementedthrougha

seriesofintermediategoalsleadinguptothefinalmandate,weonlyfocusedonearlyadopterstates

(i.e.,statesthatenactedRPSbetween1997and2000).Thisallowedussufficientpost‐intervention

years tocapture theeffectofRPS. Indeed,with twoexceptions,MassachusettsandCalifornia, for

statesthatpassedtheirRPSbetween2000and2008,theearliestintermediatemandateis2006.28

Whiletheavailablepost‐interventionperiodsmaynotbesufficientforcarryingoutSCMimpactsof

RPS inMassachusetts andCalifornia,wehave still carried out the estimates.Wedonot find any

impactofRPSonrenewablecapacityinMassachusetts.AsforCalifornia,weareunabletoestablish

apre‐interventionmatching.ThisisbecauseCaliforniahadbyfarthelargestrenewablecapacityfor

thepre‐interventionperiod(1990‐2002);Californiahadatleast8timestherenewablecapacityof

26ThePTCiscurrentlyworth$22perMWh(2011dollars).In2013,Texasaccountedfor22%ofthe167millionMWhoftotalpowergeneratedfromwindnationwide.Seehttp://www.eia.gov/todayinenergy/detail.cfm?id=8870(EIA‐PTC)andhttp://www.eia.gov/todayinenergy/detail.cfm?id=15851(EIA‐Texas).27ThePTCexpiredandwasextendedin2000,2002,2004,and2012.Itwasextendedin2010priortoexpiration.28Massachusetts,whichpasseditsRPSin2002,requiredrenewablegenerationof1percentofsalesin2003,increasingby0.5percentannuallythrough2009.California,whichpassed itsRPS in2003, includedarequirement thatutilitiesincreaserenewablegenerationannuallybyaminimumof1percentoftheirsales.

21

anyotherstateforthisperiod.Asaresult,noweightedaverageofstatescanapproximatethepre‐

interventionrenewablecapacityofCalifornia.29

VI.Conclusion

Variation across states in their policy environment, electricity market structure, and

availabilityofrenewableenergyresourcessuggestthatempiricalidentificationoftheeffectofRPS

reliescruciallyontheaccuratedeterminationofthecontrolstates.WeemploytheSCMcasestudy

approachwhich,weargue,usesamoreappropriatecounterfactualforimpactevaluationcompared

to the approaches estimating average treatment effects. We find that RPS have heterogeneous

impactsonrenewablecapacitydevelopment.

Therenewablepolicyenvironmentacrossstatesisatacrossroads.Thisisparticularlytrue

forRPS in lightof therecent legaland legislativeefforts torepealorweakenRPS inanumberof

states including California, Colorado, Kansas,Massachusetts,Minnesota, andOhio (Plumer 2013;

Gallucci 2013). InMay 2014, Ohio legislators voted to halt the continued implementation of the

state’sRPS,whichwaspassedin2009(Cardwell2014).Similarbillshavealsobeenintroducedin

Wisconsin,WestVirginia,Minnesota andTexas.WhileRPS survived repeal bills early in 2014 in

KansasandNorthCarolina,theyareexpectedtobepickedupagainlaterintheyear.

On the backdrop of the previous findings that RPS are not contributing to renewables

development (Delmas and Montes‐Sancho 2011, Shrimali and Kniefel 2011, Hitaj 2013, and

Maguire2014),theserepealeffortsmaypickupsteam.Butthefindingsinthispapersuggestthat

the impact of RPS may not be generalized; instead, the success of a particular RPS may be

29Incontrast,considerTexas,forinstance.Texas’saveragenon‐renewablecapacityduringthepre‐interventionperiod(1990‐1998)fellbetweenthemedianandthe75thpercentileamongtheU.S.states.Asaresult,thefeasibilityoffindingaweightedaverageofcontrolstatesthatwouldmimicTexas’spre‐interventionnon‐renewablecapacitywasnotanissue.

22

contingent on the features of the policy itself and the characteristics of the pertinent electricity

markets.

23

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26

Figures

Figure1:FERCElectricPowerMarkets:NationalOverview

Source:http://www.ferc.gov/market‐oversight/mkt‐electric/overview.asp

Figure2:U.S.RPSandRenewableGenerationCapacity

27

Figure3:SCMEstimatesoftheImpactofRPSonRenewablesCapacity

PanelA:Nevada

1990 1992 1994 1996 1998 2000 2002 2004 2006 20080.1

63.09

126.08

189.07

252.06

315.05

378.04

441.03

504.02

567.01

630

year

nam

epl

ate

ca

paci

ty

Nevada Renewable Nameplate CapacityNevada vs Control States Synthetic Control

ActualSynthetic

1 3 5 7 9 11 13 15 17 19

-629.9

-503.92

-377.94

-251.96

-125.98

0

125.98

251.96

377.94

503.92

629.9

year

nam

epl

ate

ca

paci

ty

Nevada Nameplate Capacity GapAnd Placebo Gaps in All Control States

PanelB:Connecticut

1990 1992 1994 1996 1998 2000 2002 2004 2006 20080.1

39.45

78.8

118.15

157.5

196.85

236.2

275.55

314.9

354.25

393.6

year

nam

epl

ate

ca

paci

ty

Connecticut Renewable Nameplate CapacityConnecticut vs Control States Synthetic Control

ActualSynthetic

1 3 5 7 9 11 13 15 17 19

-393.5

-314.8

-236.1

-157.4

-78.7

0

78.7

157.4

236.1

314.8

393.5

year

nam

epl

ate

ca

paci

ty

Connecticut Nameplate Capacity GapAnd Placebo Gaps in All Control States

PanelC:Maine

1990 1992 1994 1996 1998 2000 2002 2004 2006 20080.1

22.89

45.68

68.47

91.26

114.05

136.84

159.63

182.42

205.21

228

year

nam

epl

ate

ca

paci

ty

Maine Renewable Nameplate CapacityMaine vs Control States Synthetic Control

ActualSynthetic

1 3 5 7 9 11 13 15 17 19

-227.9

-182.32

-136.74

-91.16

-45.58

0

45.58

91.16

136.74

182.32

227.9

year

nam

epl

ate

ca

paci

ty

Maine Nameplate Capacity GapAnd Placebo Gaps in All Control States

28

PanelD:NewJersey

1990 1992 1994 1996 1998 2000 2002 2004 2006 20080.1

63.59

127.08

190.57

254.06

317.55

381.04

444.53

508.02

571.51

635

year

nam

epl

ate

ca

paci

ty

New Jersey Renewable Nameplate CapacityNew Jersey vs Control States Synthetic Control

ActualSynthetic

1 3 5 7 9 11 13 15 17 19

-634.9

-507.92

-380.94

-253.96

-126.98

0

126.98

253.96

380.94

507.92

634.9

year

nam

epl

ate

ca

paci

ty

New Jersey Nameplate Capacity GapAnd Placebo Gaps in All Control States

PanelE:Texas

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008-500

454.32

1408.64

2362.96

3317.28

4271.6

5225.92

6180.24

7134.56

8088.88

9043.2

year

nam

epl

ate

ca

paci

ty

Texas Renewable Nameplate CapacityTexas vs Control States Synthetic Control

ActualSynthetic

1 3 5 7 9 11 13 15 17 19

-9543.2

-7634.56

-5725.92

-3817.28

-1908.64

0

1908.64

3817.28

5725.92

7634.56

9543.2

year

nam

epl

ate

ca

paci

ty

Texas Nameplate Capacity GapAnd Placebo Gaps in All Control States

PanelF:Wisconsin

1990 1992 1994 1996 1998 2000 2002 2004 2006 20080.1

67.14

134.18

201.22

268.26

335.3

402.34

469.38

536.42

603.46

670.5

year

nam

epl

ate

ca

paci

ty

Wisconsin Renewable Nameplate CapacityWisconsin vs Control States Synthetic Control

ActualSynthetic

1 3 5 7 9 11 13 15 17 19

-670.4

-536.32

-402.24

-268.16

-134.08

0

134.08

268.16

402.24

536.32

670.4

year

nam

epl

ate

ca

paci

ty

Wisconsin Nameplate Capacity GapAnd Placebo Gaps in All Control States

Notes:(a)Outcomevariableisrenewablescapacityofgeothermal,biofuels,solar,andwind.(b)ThesearethepicturesoftheestimatesthatarefurtherdescribedinTable2.

29

TablesTable1:SummaryStatistics(1990‐2008) Donorpool(26states) TreatmentStateMeans

Mean SD Min Max NV CT ME NJ TX WI

Renewablenameplatecapacity(MW) 79.90 166.81 1.00 1130.00 247.32 235.08 78.17 198.31 1162.30 97.23

Totalnameplatecapacitygrowth 37.99 28.49 0.35 117.41 7.15 164.52 13.07 249.06 34.46 25.62

Coalgenerationshare 0.58 0.29 0.00 0.99 0.53 0.13 0.03 0.16 0.39 0.70

Naturalgasgenerationshare 0.10 0.16 0.00 0.65 0.35 0.15 0.21 0.31 0.48 0.04

Realelectricityprice 7.29 1.74 4.48 13.57 7.99 12.50 11.43 11.79 8.11 7.01

Growthoftotalcustomer 1.15 0.13 0.90 1.65 1.56 1.06 1.11 1.08 1.22 1.15

RealPCpersonalincome($) 28709.57 4698.39 18152.14 45222.82 33078.03 43696.99 28482.82 40186.85 29529.13 30577.93GrowthofPCpersonalincome 1.22 0.16 0.98 1.82 1.19 1.21 1.20 1.19 1.24 1.23Percentofpopulationbelowpoverty 13.17 3.57 5.70 26.40 10.32 8.75 11.67 8.71 16.51 9.67

Shareofmfg.earnings 0.11 0.05 0.02 0.22 0.03 0.12 0.11 0.09 0.10 0.18

Windpotential(1991) 239.08 405.16 0.00 1210.00 50.00 5.00 56.00 10.00 1190.00 56.00Windpotential(2010) 227.74 325.94 0.00 952.37 7.25 0.03 11.25 0.13 1901.53 103.76Photovoltaicpotential(2010) 3168.83 2024.67 36.55 9005.30 3742.84 17.13 660.61 276.43 20565.29 3240.76

Biopower‐solidpotential(2010) 1.17 0.77 0.06 3.52 0.04 0.06 0.54 0.15 2.04 1.42

Geo‐&hydro‐thermalpotential(2010) 0.23 0.62 0.00 2.18 5.75 0.00 0.00 0.00 0.00 0.00

Januarymeanhoursofsunlight 147.13 25.34 105.14 197.64 200.00 161.75 156.25 152.24 182.59 133.54

Averagesummercoolingdegreedays 289.38 136.47 23.67 584.33 486.75 168.11 71.42 227.60 531.56 145.00

Averagesummerheatingdegreedays 24.49 36.12 0.00 212.00 13.21 13.14 62.96 5.46 0.00 47.23

Notes:(a)ForthedonorpoolsN=494,exceptfor1991windpotential,sunlightanddegreedaysthatareunavailableforAlaska(i.e.,N=475).(b)Renewablesincludegeothermal,biofuels,solar,andwind.(c)TotalnameplatecapacityismeasuresinMWhper100squaremiles.(d)Allmonetaryvaluesarein2005constantdollars.(e)Standardstatecodesused:Nevada(NV),Connecticut(CT),Maine(ME),NewJersey(NJ),Texas(TX),Wisconsin(WI),Massachusetts(MA).(f)YearoftheenactingRPS in the treatment states: Nevada (1997), Connecticut (1998), Maine (1999), New Jersey (1999), Texas (1999), andWisconsin (1999). (g) Following statesenactedRPSonorbefore2008andthereforeexcludedfromthedonorpool:Iowa(1983),Massachusetts(2002),California(2003),Colorado(2004),Hawaii(2004),Maryland (2004),NewYork (2004),Rhode Island (2004),Delaware (2005),DistrictofColumbia (2005),Montana (2005),Oregon (2005),Pennsylvania (2005),Washington(2006),Arizona(2007),Minnesota(2007),NewHampshire(2007),NewMexico(2007),Michigan(2008),Missouri(2008),NorthCarolina(2008).(h)1991windpotentialisinenergyunits(annual'000GWh),therestofthepotentialmeasuresaremeasuredaspower(GW).

30

Table2:SCMEstimateoftheImpactofRPSonRenewablesCapacity Nevada Connecticut Maine NewJersey Texas WisconsinEstimationsummary

Pre‐interventiondifference(D1) 0.34 0.69 0.03 0.13 ‐0.54 ‐0.54Post‐interventiondifference(D2) ‐4.52 ‐46.59 47.65 1.78 2301.25 42.62DID=|D2|‐|D1| 4.19 45.91 47.62 1.65 2300.71 42.08P‐value:DID 0.89 0.56 0.59 0.96 0.00 0.59DIDrank 25 16 17 27 1 17W‐weightsAlabama 0.00 0.00 0.00 0.00 0.00 0.00Alaska 0.00 0.00 0.23 0.00 0.00 0.00Arkansas 0.00 0.00 0.00 0.00 0.00 0.00Florida 0.25 0.32 0.00 0.17 0.00 0.00Georgia 0.00 0.00 0.00 0.00 0.00 0.55Idaho 0.00 0.00 0.00 0.00 0.00 0.00Illinois 0.00 0.04 0.00 0.00 0.13 0.16Indiana 0.00 0.00 0.00 0.00 0.79 0.15Kansas 0.00 0.00 0.00 0.00 0.00 0.00Kentucky 0.00 0.00 0.00 0.00 0.00 0.00Louisiana 0.00 0.00 0.00 0.00 0.00 0.00Michigan 0.45 0.00 0.00 0.55 0.00 0.00Mississippi 0.00 0.00 0.03 0.00 0.00 0.00Missouri 0.00 0.00 0.00 0.00 0.00 0.00Nebraska 0.00 0.00 0.00 0.00 0.00 0.00NorthDakota 0.00 0.00 0.00 0.00 0.00 0.00Ohio 0.26 0.65 0.54 0.00 0.00 0.00Oklahoma 0.00 0.00 0.00 0.00 0.00 0.00SouthCarolina 0.00 0.00 0.00 0.00 0.00 0.00SouthDakota 0.00 0.00 0.00 0.00 0.00 0.00Tennessee 0.00 0.00 0.00 0.00 0.00 0.00Utah 0.04 0.00 0.13 0.00 0.00 0.00Vermont 0.00 0.00 0.04 0.28 0.00 0.00Virginia 0.00 0.00 0.03 0.00 0.08 0.14WestVirginia 0.00 0.00 0.00 0.00 0.00 0.00Wyoming 0.00 0.00 0.00 0.00 0.00 0.00ListofPredictors (a) Common set of predictors: Total nameplate capacity growth, coal generation share, natural gas generationshare, electricity price, growth of total customer, 2010 wind potential, 2010 photovoltaic potential, 2010biopower‐solid potential, 2010 geo‐ & hydro‐thermal potential, real PC personal income, growth in real PCpersonal income, poverty, share of manufacturing income. (b) 1990 to pre‐intervention renewables capacity(dependingontheyearofinterventionforeachtreatmentstate).Notes:(a)Outcomevariableisrenewablescapacityofgeothermal,biofuels,solar,andwind.(b)YearoftheenactingRPS:Nevada(1997),Connecticut(1998),Maine(1999),NewJersey(1999),Texas(1999),andWisconsin(1999).(c)Donorpool includesAlaska, therefore the set of predictors doesnot include the geographical variables and1991windpotential.However, 2010measure ofwindpotential is included. (d)Weights less than0.01 are reportedaszero.

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Table 3: SCM Estimate of the Impact of RPS on Renewables Capacity (Robustness Check withGeographicalVariables) Nevada Connecticut Maine NewJersey Texas Wisconsin

Estimationsummary Pre‐interventiondifference(D1) 0.35 0.61 ‐0.04 0.69 ‐0.74 ‐0.57Post‐interventiondifference(D2) ‐4.56 ‐45.00 ‐12.32 ‐9.67 2137.23 21.64DID=|D2|‐|D1| 4.20 44.38 12.28 8.97 2136.49 21.07P‐value:DID 0.92 0.62 0.85 0.88 0.00 0.85DIDrank 25 17 23 24 1 23W‐weights Alabama 0.00 0.00 0.00 0.00 0.00 0.00Arkansas 0.00 0.00 0.00 0.00 0.00 0.00Florida 0.25 0.32 0.00 0.24 0.00 0.00Georgia 0.00 0.00 0.00 0.07 0.00 0.00Idaho 0.00 0.00 0.00 0.00 0.00 0.00Illinois 0.00 0.03 0.00 0.06 0.18 0.15Indiana 0.00 0.00 0.00 0.00 0.00 0.60Kansas 0.00 0.00 0.24 0.00 0.63 0.00Kentucky 0.00 0.00 0.00 0.00 0.00 0.00Louisiana 0.00 0.00 0.00 0.01 0.00 0.00Michigan 0.45 0.00 0.00 0.14 0.00 0.00Mississippi 0.00 0.00 0.00 0.04 0.00 0.00Missouri 0.00 0.00 0.00 0.00 0.00 0.00Nebraska 0.00 0.00 0.00 0.00 0.00 0.00NorthDakota 0.00 0.00 0.00 0.02 0.09 0.12Ohio 0.26 0.65 0.54 0.00 0.00 0.00Oklahoma 0.00 0.00 0.00 0.01 0.00 0.00SouthCarolina 0.00 0.00 0.00 0.11 0.00 0.00SouthDakota 0.00 0.00 0.00 0.00 0.00 0.00Tennessee 0.00 0.00 0.00 0.00 0.00 0.00Utah 0.05 0.00 0.14 0.00 0.00 0.00Vermont 0.00 0.00 0.04 0.27 0.00 0.00Virginia 0.00 0.00 0.03 0.02 0.10 0.12WestVirginia 0.00 0.00 0.00 0.00 0.00 0.00Wyoming 0.00 0.00 0.00 0.00 0.00 0.00ListofPredictors (a) Common set of predictors: Total nameplate capacity growth, coal generation share, natural gas generationshare,electricityprice,growthoftotalcustomer,2010windpotential,2010photovoltaicpotential,2010biopower‐solidpotential,2010geo‐&hydro‐thermalpotential,realPCpersonalincome,growthinrealPCpersonalincome,poverty, share of manufacturing income, 1991 wind potential, January sunlight, summer cooling degree days,summer heating degree days. (b) 1990 to pre‐intervention renewables capacity (depending on the year ofinterventionforeachtreatmentstate).Notes:(a)Outcomevariableisrenewablescapacityofgeothermal,biofuels,solar,andwind.(b)YearoftheenactingRPS:Nevada(1997),Connecticut(1998),Maine(1999),NewJersey(1999),Texas(1999),andWisconsin(1999).(c)Geographicvariablesand1991windpotentialmeasurearemissingforAlaska;therefore,Alaskaisexcludedfromthedonorpool.(d)Weightslessthan0.01arereportedaszero.

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Table 4: SCM Estimate of the Impact of RPS on Renewables Capacity in Texas (AdditionalRobustnessChecks) (1) (2)Estimationsummary

Pre‐interventiondifference(D1) 1.34 13.30Post‐interventiondifference(D2) 2060.78 2090.37DID=|D2|‐|D1| 2059.44 2077.07P‐value:DID 0.00 0.00DIDrank 1 1W‐weightsAlabama 0.00 0.00Alaska 0.00 0.00Arkansas 0.00 0.00Florida 0.00Georgia 0.00Idaho 0.00 0.00Illinois 0.24Indiana 0.00 0.00Kansas 0.00Kentucky 0.00 0.00Louisiana 0.00 0.06Michigan 0.00Mississippi 0.00 0.00Missouri 0.00Nebraska 0.00 0.00NorthDakota 0.00 0.00Ohio 0.00Oklahoma 0.76 0.94SouthCarolina 0.00SouthDakota 0.00 0.00Tennessee 0.00 0.00Utah 0.00Vermont 0.00Virginia 0.00WestVirginia 0.00Wyoming 0.00 0.00ListofPredictors(a)Commonsetofpredictors:Totalnameplatecapacitygrowth,coalgenerationshare,naturalgasgenerationshare, electricity price, growth of total customer, 2010 wind potential, 2010 photovoltaic potential, 2010biopower‐solid potential, 2010 geo‐ & hydro‐thermal potential, real PC personal income, growth in real PCpersonalincome,poverty,shareofmanufacturingincome.(b)Column1includes1998renewablescapacityasthe only pre‐intervention outcome. (c) Column 2 includes 1990‐1998 renewables capacity as the pre‐interventionoutcome.Notes:(a)Tocheckifmatchingoncapacitiesisdrivingtheresults,incolumn(1)matchingindoneonlyon1998capacity. In column (2) donor pool includes states that are both non‐RPS and non‐deregulated states. (b)Outcomevariableisrenewablescapacityofgeothermal,biofuels,solar,andwind.(c)Yearofinterventionis1999(theyearRPSenactedinTexas).(d)ThecommonsetofpredictorsisthesameasthatinTable2.(e)Weightslessthan0.01arereportedaszero.

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AppendixA:RPSMandatebyStateandYearofImplementationState Yeareffective FinalMandate State Yeareffective FinalMandateArizona 2007 15%by2025 Montana 2005 15%by2015California 2003 25%by2016 Nevada 1997 25%by2025Colorado 2005 20%by2020 NewHampshire 2007 25%by2025Connecticut 1998 27%by2020 NewJersey 1999 22.5%by2021Delaware 2005 25%by2025 NewMexico 2004 20%by2020Hawaii 2004 40%by2030 New York 2004 29%by2015Illinois 2011 25%by2025 NorthCarolina 2008 12.5%by2021Iowa 1983 105MWby1999 Ohio 2009 12.5%by2024Kansas 2009 20%by2020 Oregon 2007 25%by2025Maine 1999 40%by2017 Pennsylvania 2005 18%by2020Maryland 2004 20%by2022 RhodeIsland 2004 16%by2019Massachusetts 2002 15%by2020 Texas 1999 10,000MWby2025Michigan 2008 10%by2015 Washington 2007 15%by2020Minnesota 2007 25‐30%by2020 Wisconsin 1999 10%by2015Missouri 2009 15%2021 Notes:(a)Statesinboldaretheearlyadopterstates.(b)AlthoughIowaadoptedanRPSin1983,theirimplementationpre‐datesthecapacitydataavailableandtheyarethereforenotanalyzed.(c)Thefinalmandatesofthepolicieshaveevolvedovertime,oftenbecomingmorestringent.Thelatestpolicyineffectduringthe1994‐2012periodislisted.(d)Inthe‘FinalMandate’column,thepercentagesindicatethepercentofelectricitytobegeneratedfromrenewableenergy.

34

AppendixB:Proceduretoobtain W

Let )1( 0 T vector ),...,(01 TkkK define a linear combination of pre‐intervention

outcomesis

T

s si YkY 0

0

~ K . Define )~,...,~,( 11111 MYY KKZX as a )1( k vector of pre‐

intervention characteristics for the exposed statewhere Mrk .30 Similarly, define a

)( Jk matrix 0X that contains the same variables for the unexposed states. The thj

columnof 0X ,thus,is )~,...,~,( 1 Mjjj YY KKZ .

LetVbea )( kk symmetricpositivesemidefinitematrix.Then,

(4) 1and}1,...,2|0{)()(argmin 1

20101

J

j jj wJjwWXXVWXXWW

.

FollowingAbadieandGardeazabal(2003)andAbadie,DiamondandHainmueller(2010),

wechoose V amongpositivedefiniteanddiagonalmatricessuch that themeansquared

prediction error (MSPE) of the outcome variable is minimized for the pre‐intervention

periods.

As Abadie, Diamond and Hainmueller (2010) argue, it is important to note that

unlike the traditional regression‐based difference‐in‐difference model that restricts the

effectsoftheunobservableconfounderstobetime‐invariantsothattheycanbeeliminated

bytakingtimedifferences,SCMallowstheeffectsofsuchunobservablestovarywithtime.

More details of the synthetic control, the procedure to calculate W , and

permutation/randomizationtestsortheinferencecanbefoundinAbadieetal.(2010)or

obtainedfromtheauthorsonrequest.

30 For example, if )0,...,0,1(,2 1 KM and )1,...,0,0(2 K then ),,(

0111 TYYZX , that is the

outcomevaluesofTexasforthefirstyear(year2000)andtheyearbeforethepassingoftheRPS(year2004)

areincludedin 1X .