Technological greenness and long-run performance by OECD

Technologicalgreennessandlong-run performance

S.Battiston(UZurich,UVenice),I.Monasterolo(UUtrecht), M.Montone(UUtrecht)

S.Battiston(UZurich,UVenice),I.Monasterolo(UUtrecht),M.Montone(UUtrecht)

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Greenstocks

Previousresearchanalyzesthe short-runperformance of greenstocks.

Greenstocksareidentifiedasthosewith:

▶ LowerGHGemissions.

▶ HigherESG(orE)scores.

(e.g.,MonasteroloanddeAngelis(2020);BoltonandKacperczyk(2021, 2023);Avramovetal.(2022);P´astoretal.(2021,2022);Zerbib(2022); Aswanietal.(2023))

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Greenstocks(cont’d)

However,therearetwo significantchallenges:

▶ Greennessconfusion.

lackofproperunderstandingofwhatconstitutesgreenness

▶ Greenwashingconcerns.

firms’attempttolookgreenerthantheyactuallyare

Also,littleisknownaboutgreenstocks’ long-runperformance. (see,e.g.,Edmans,2023).

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Long-termperspective

A long-termperspective isimportantforatleasttworeasons:

▶ Greentech becomes moreaccessible and lesscostly overtime. likeanyothertechnology

fullpotentialonlyobservablewithsomedelay overtakingfossilintermsofcapacity/performance(IEA2023)

▶ Regulatoryandmeasurement uncertainty decreaseovertime.

− betterunderstandingofgreenness

− greatercross-countrycooperation

Asaresult, greenfirms graduallyimproveandbecomemoreattractive.

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Long-termperspective(cont’d)

Tothisend,itiscrucialtostudyinvestmentsin greentechnology.

▶ Long-runoriented. importantfor decarbonization goal(ParisAgreement)

▶ Structuralnature. lessproneto misreporting and greenwashing

Thisisthefocusofourpaper:welookat investmentsingreen-tech capacity andtheirimpactonlong-runvaluecreation(5years).

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Long-termperspective(cont’d)

Incontemporaneousresearch,Edmans(2023)arguesthatsustainability shouldfocuson long-termvaluecreation:

▶ Justlikeanyothertypeof firm-levelinvestment.

▶ Sustainabilitytargetsrepresenta meanstoanend.

▶ Moveawayfrom off-the-shelfmetrics.

Throughourfocuson greentech,webuilda morecomprehensive perspective ofwhatconstitutesgreennessandwhyitmattersforthe low-carbontransition (“doingwellbygoinggreen”).

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Shortv.Longrundynamics

Inouranalysis,weidentify twodynamics:

▶ Shortrun.Fundamentalsdonotchange.

greentech/regulationsarefixed stockpricemainlyreflectsgreenpreferences(P > F , E (R) < 0)

▶ Longrun.Fundamentalsdochange.

greentechdevelopments/morestringentregulations

someinvestorsreviseexpectationsslowly(P < F , E (R) > 0).

Keymechanism:evaluatingnewinformationis costly (Hirshleiferand Teoh,2003),especiallyifofhighlytechnicalnature(suchasgreentech).

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Mainfindings

Wefindthat greentechstocks earn higherlong-runreturns:

▶ 1σ ↑ greentech −→ 15.1%increasein5ystockreturns.

▶ Noreversals:suggestinggradualimpoundingofinformation.

Consistentwiththeinformationstory, greenfirms alsoexhibit:

▶ better(andlessvolatile) futureoperatingperformance;

▶ agradualandsteadyincreasein futurevaluations.

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Mainfindings(cont’d)

Wealsostudythe marketreaction to greentechdisclosure:

▶ Disclosureofhightechgreenness −→ higherlong-runreturns. wrtdisclosureoflowgreenness

wrtnon-publishers(nodisclosure)

▶ Effectismorepronouncedafterthe ParisAgreement.

intuition:regulatoryshock

ResultsarestrongerforgreentechcomparedwithGHGandESG disclosure,suggestingthatmarketsprimarily reward sustainability measuresof moretangiblenature suchastechnology.

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Mainfindings(cont’d)

Finally,westudythe marketreaction to greentechdisclosure regardless ofthedegreeofgreenness.

Firmscan decidewhethertodisclose howgreentheyare:

▶ Onlyafewfirmsalreadydisclose,otherfirmsareunknown.

▶ Thenonlygreenerfirmsshouldhaveanincentivetodisclose.

Wefindthatthestockmarket rewards disclosingfirmswith higher long-runreturns.

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Contribution

Ourpapercontributestoaburgeoningliteratureon climatefinance.

(e.g.,Giglioetal.,2021;EdmansandKacperczyk,2022).

Previousresearchfinds mixedevidence on greenassetreturns:

▶ MeasuringGHG:Levelsv.Intensity.

(Aswanietal.,2023;BoltonandKacperczyk,2023)

▶ MeasuringESG:Ratingdisagreement.

(Avramovetal.,2022;Bergetal.,2023)

▶ Measuringreturns:Expectedv.Realized.

(P´astoretal.,2021,2022;Atilganetal.,2023)

▶ Greenpreferences v. Riskconsiderations.

(Alessietal.,2021;P´astoretal.,2021;Zerbib,2022)

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Contribution(cont’d)

Ourfindingsstresstheimportanceofconsideringa longtimehorizon.

Tworeasons:

▶ Marketlearning (e.g.,HirshleiferandTeoh,2003). fullytakesplaceonlyinthelongrun

▶ Mispricingisarbitragedaway (e.g.,Greenwood,2005). reducingdiscrepancyexpectedv.realizedreturns

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Contribution(cont’d)

Recentstudieslookat greentech through patents (e.g.,Kuangand Liang,2022;Cohenetal.,2023;Hegeetal.,2023;RezaandWu,2023).

Thisisalsoa tangiblemetric ofgreenness,but:

▶ Impactona firm’soperations isunclear(howwide?).

(Boltonetal.,2023)

▶ Impacton firmvalue ismixed.

(e.g.,Andriosopoulosetal.,2022;Hegeetal.,2023)

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Contribution(cont’d)

Conversely,wefocusonthe green-techprofileofrevenues. Weobserveutilityfirms’ energytechnologyprofile (%revenues) relatedtoenergycomingfromboth renewable and fossilfuel sources.

▶ Moredirect andcomprehensive measureoftechgreenness.

▶ Morestructuralimpact onfirms’operations.

Moregenerally,ourlong-runfindingssupporttheviewthatgreen innovationis path-dependent (Aghionetal.,2016;Boltonetal.,2023).

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Model

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Setup

Weconsidertheassetpricingmodelfrom HirshleiferandTeoh(2003).

Thesetupincludes:

▶ Onestock.

− wlog(e.g.,Chenetal.,2002;HongandSraer,2013)

intuition:isolatepricingdeterminants(noportfolioanalysis)

▶ Twoinvestortypes.

informed(akaarbitrageurs,or“typeA”)

uninformed(akanaivetraders,or“typeN”)

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Setup(cont’d)

Eachinvestorcan expendresources c onpayingattentiontoinformation, orsparetheresourcesbutalsoendupwithlessinformation.

Withthisinmind:

▶ f (c)= probabilitythataninvestor neglectsrelevantinformation.

− proportionofnaivetradersintheeconomy

− takenasexogenouslygiven

▶ f ′ (c) < 0,i.e.,moreeffortleadstofewerevaluationmistakes.

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Dates

Theeconomyhas threedates:

▶ Time0.Investorsformexpectations.

▶ Time1. New publicinformationarrivesaboutfirmvalue. (investorstradewitheachother)

▶ Time2.Adividendisrealizedandtheeconomyends. (investorsreceivethefinalpayoff)

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Information

Notethatthereis noprivateinformation amonginvestors,everythingis public!But processinginformationis costly.

Naiveinvestors,however:

▶ donotrecognize theirlackofattention;

▶ then donotlearn fromthemarketprice;

▶ specifically,they missthenewinfo attime1. (neglect iswlog,partialrevision wouldyieldsameresults)

Weapplythissetuptostudythepriceofa greentechstock.

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Information(cont’d)

Asin Chenetal.(2002),weassumethatthestock’sinitialfundamental value(attime0)isequalto F + ϵ,where ϵ ∼ N(0, 1).

Attime1,newinfoarrivesongreentechandmodifiesthefundamental valueto F +∆+ ϵ.

▶ Arbitrageurscorrectlyincorporate ∆.

▶ Naivetradersdonot.

Intuition:greentechis difficulttoevaluate andcanonlybepricedby expendingresources c (whicharbsdo).

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Investorpreferences

Weassumethat naiveinvestors:

▶ havea pro-socialpreference forgreenfirms(e.g.,RiedlandSmeets, 2017;Barberetal.,2021;Dittmannetal.,2023);

▶ therefore derivedirectutility frominvestingingreenfirmsoverand abovetheexpectedmonetarypayoff.

Arbitrageurs donot(forsimplicityandwlog).

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Investorpreferences(cont’d)

Pro-socialinvestors arethosewhoexhibitapreferenceforESG characteristics,suchas:

▶ Morallysoundfirms(HongandKacperczyk,2009).

▶ FirmsthatlowCEO-workerpaygaps(Panetal.,2022).

▶ Environmentally-friendlyfirms(RiedlandSmeets,2017).

Greenpreferences characterize retailinvestors and mutualfunds. (e.g.,HartzmarkandSussman,2019;Bri`ereandRamelli,2022)

Suchinvestorsarerelatively lesssopshisticated than hedgefunds. (e.g.,Chenetal.,2002;HongandSraer,2013,2016)

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Constraints

Investorshave:

▶ W0 =initialwealthendowment;

▶ x0 =percapitaendowmentofthestock.

Terminalconsumption isthen:

C = W0 + x0S1 + x(S2 − S1), (1)

where S1 and S2 representthe stockprice attimes1and2.

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Technologicalgreennessandlong-runperformance

Utility

Totalutility ofinvestortype ϕ canbeexpressedas:

U ϕ (x ϕ )= uϕ (x ϕ )+ v ϕ (x ϕ ), (2)

where:

uϕ (x ϕ )= E ϕ 1 x ϕ (S2 S1) γ 2 varϕ 1 x ϕ S2 , (3)

v ϕ (x ϕ )= x ϕ g ϕ with g ϕ ≡ 0if ϕ = A

g > 0if ϕ = N , (4) are additivelyseparablecomponents (e.g.,Lopes,1987;Conlisk,1993; ShefrinandStatman,2000;Barberisetal.,2001).

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Utilitymaximizationproblem

Aninvestoroftype ϕ thensolves:

max {x ϕ } E ϕ 1 x ϕ (S2 − S1) − γ 2 var ϕ 1 x ϕ S2 + x ϕ g ϕ , (5)

where:

▶ γ =coefficientof absoluteriskaversion;

▶ g =coefficientindicating greenpreferences.

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Marketclearing

The marketclearing conditionis:

fx N +(1 f )x A = x0, (6)

where:

▶ x N , x A arethetwodemands(fromthefirst-orderconditions);

▶ x0 isthesecurity’ssupply(given).

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Equilibriumprice

The equilibriumprice isthen:

S1 = fE N 1 (S2)

mispricing +(1 − f ) E A 1 (S2) fundamentals , (7)

whichrepresentsa weightedaverageofvaluations ofinvestortypes.

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Equilibriumprice(cont’d)

Substitutingintotheequation,weobtain:

S1 = fE N 1 (S2)+(1 − f )E A 1 (S2)= = F +∆ fundamentals +(1 f )(g ∆) mispricing . (8)

Inthepresenceofpositivefundamentalnews,the greentechstock:

▶ Trades belowitsfundamentalvalue if∆ > g .

▶ Thatis,ifthenewsshockislargeenough.

▶ Examples:earnings;regulatoryshocks;techimprovement.

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Equilibriumreturns

Definingstockreturnsindollarterms(wlog)asinChenetal.(2002):

E1(r2) ≡ E1(S2) − S1 = = F +∆ − (F +∆+(1 − f )(g − ∆))= =(1 − f )(∆ − g ), (9)

the greentechstock yields positiveexpectedreturns if∆ > g .

Wetestthesehypothesesnext.

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Data

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Sample

Oursampleincludes globalutilityfirms:

▶ financial/accountingdatafor1,000firmsfrom77countries;

▶ dataon green-techcapacity on165firmsfrom31countries;

Thesampleperiodisfrom2011to2021.

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Medianfirm

The medianfirm inoursampleexhibits:

▶ Marketcapof e5.99billion.

▶ PPEof e3.92billion.

▶ Marketbetaof0.7.

▶ Salesgrowthof4%.

The large and stable natureofthesefirmsmakesthem easiertoevaluate, with lessroomformispricing (e.g.,BakerandWurgler,2006,2007; Bakeretal.,2012).

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Medianfirm(cont’d)

Themedianfirm’s greenenergycapacity (expressedasapercentageof totalenergycapacity)includes:

▶ Solar(0.9%).

▶ Waste(0.7%).

▶ Wind(6.3%).

▶ Hydro(15.7%).

▶ Nuclear(18.5%).

Fossilenergycapacity includesgas(32.8%)andcoal(33.2%),sothe medianfirmisthenpredominantly fossil.

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Indexoftechgreenness

Weusethisinformationtoconstructafirm-level indexoftechnological greenness,whichexhibitsthefollowingrange:

▶ Maxscore:+1(indicatingentirely greenfirms).

▶ Minscore:-1(indicatingentirely brownfirms).

Baselinespecification:(solar + waste + wind ) green − (gas + coal ) brown .

(hydro isalreadyestablished; nuclear iscontroversial)

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Indexoftechgreenness(cont’d)

Thisindexgrants severalimportantadvantages withrespecttoESG scores(E),GHGemissions(G),andpatents(P):

▶ Directlyidentifiesenergycapacityinvestments(=P,G,E).

▶ Includesgreenactivitiesinadditiontobrownones(=P,G).

▶ Allowsfordirectcomparisonsacrossfirms(=P,Glevels).

▶ Lesspronetomisreportingorgreenwashing(=P,G,E).

Drawback:smallsamplesize.

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Testequation

Our maintestequation isasfollows:

yi ,t+h = αt + βxi ,t + γ ′ Zi ,t + ϵi ,t+h . (10) where

▶ y =stockreturnoffirm i overperiod t + h;

▶ x = indexoftechnologicalgreenness (standardized);

▶ Z =setoffirm-levelaccountingmeasuresascontrols.

▶ αt =yearFE.

Standarderrorsareclusteredbyfirm(wefindsimilarresultsusing alternativespecifications).

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Testequation(cont’d)

Firm-levelcontrols: book-to-marketratio;

− naturallogarithmofmarketcapandPPE;

− leverage;

− ratiobetweencapitalexpendituresandtotalassets;

− currentstockreturn;

− growthinsalesandearnings-per-share;

− returnonequity; annualizedvolatilityofdailystockreturns.

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Mainfindings

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Futurestockreturns

(1)(2)(3)(4)(5)

Returnt+1Returnt+2Returnt+3Returnt+4Returnt+5

TechGreenness0.0220***0.0231***0.0360***0.0468***0.0269** 2.773.194.314.182.20

ControlsYYYYY

YearFEYYYYY

Observations1095942793652524

R-squared0.10740.10600.12890.12220.0844

Greentechpositivelypredictsfuturestockreturns.

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Futurecumulativestockreturns

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

TechGreenness0.0522***0.0883***0.1335***0.1511***

3.604.514.944.19

ControlsYYYY

YearFEYYYY

Observations942793652524

R-squared0.10590.13960.19400.2068

Greentechpositivelypredictsfuturecumulativestockreturns.

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Robustnesstests

Wefindsimilarresults:

▶ Controllingformarketbeta(fewerobs).

▶ Includinghydroandnuclearenergysources.

▶ Estimatingalternativespecifications.

− FirmFEandclusteringbyyear.

− FirmandyearFE.

PooledOLSregressionswithfirm-yearclustering. Fama-MacBethregressions.

Takeaway:robustresultsandnoreversals,consistentwiththemodel’s predictionof gradualimpoundingofinformation overtime.

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Economicchannel

Caveat:highlong-runstockreturnsmayalsoreflecta riskpremium for adoptinguncertaingreentechnologies.

Therefore,westudythe economicchannel underlyingourresults:

▶ Futurevaluations.

▶ Futureoperatingperformance.

▶ Cross-countryvariationinfinancialdevelopment.

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Futurevaluations

Thepriorsfor futurevaluations areasfollows:

▶ Infostory implies gradualincrease invaluations

initialunderpricing: P < F , E (R) > 0

impoundingofinfo: P −→ F

▶ Riskstory implies decrease invaluations.

impoundingofariskpremium: P ↓, E (R) > 0

− intuition:investorsshunthestock

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Futurevaluations(cont’d)

(1)(2)(3)(4)(5)

MBt+1MBt+2MBt+3MBt+4MBt+5

TechGreenness0.0321*0.0601*0.0943**0.1311**0.1799*** 1.831.832.262.472.79

ControlsYYYYY

YearFEYYYYY

Observations1095942793652524

R-squared0.68580.53310.47070.39830.3848 Greentechpositivelypredictsfuturevaluations.

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Operatingperformance

Thepriorsfor operatingperformance areasfollows:

▶ Infostory implies betterperformance.

− intuition:greentechmakesfirmsmoreefficient

▶ Riskstory implies worseperformance.

− intuition:greentechmakesfirmoperationsmoreuncertain

WelookatboththefirstandthesecondmomentofROE.

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Operatingperformance(cont’d)

(1)(2)(3)(4)(5)

ROEt+1ROEt+2ROEt+3ROEt+4ROEt+5

TechGreenness0.0103***0.0132***0.0180***0.0174***0.0164**

2.812.833.333.042.60

ControlsYYYYY

YearFEYYYYY

Observations1095942793652524

R-squared0.21600.12550.11890.10940.1146

Greentechpositivelypredictsfutureoperatingperformance.

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Operatingperformance(cont’d)

(1)(2)(3)(4)

Cum.ROE1-2Cum.ROE1-3Cum.ROE1-4Cum.ROE1-5

TechGreenness0.0220***0.0372***0.0589***0.0760***

2.702.792.992.95

ControlsYYYY

YearFEYYYY

Observations942793652524

R-squared0.26190.27340.27910.2556

Greentechpositivelypredictsfuturecumulativeoperatingperformance.

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Operatingperformance(cont’d)

(1)(2)(3)(4)(5)

SDROEt+1SDROEt+2SDROEt+3SDROEt+4SDROEt+5

TechGreenness-0.0077***-0.0059**-0.0068**-0.0073**-0.0066

-2.61-1.99-2.10-2.00-1.65

ControlsYYYYY

YearFEYYYYY

Observations1095942793652524

R-squared0.25440.33530.23640.17840.1658

Greentechnegativelypredictsvolatilityoffutureoperatingperformance. S.Battiston(UZurich,UVenice),I.Monasterolo(UUtrecht),M.Montone(UUtrecht) Technologicalgreennessandlong-runperformance

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Operatingperformance(cont’d)

Wefindsimilarresultsalsofor marketbeta asdependentvariable.

Greentechfirms thenseemtoexhibit:

▶ Loweridiosyncraticrisk.

▶ Lowersystematicrisk.

Riskexplanation forhighreturnsseemsunlikely!

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Financialdevelopment

Ahighdegreeofcountry-level financialdevelopment (FD)easesaccessto externalfinance (e.g.,RajanandZingales,1998).

Thisisacrucialrequirementforstructural greentechinvestments,soour resultsshouldbestrongerinhigh-FDcountries.

Wetestthisconjectureexploting cross-countryvariation inFD.

▶ FD=totalbankingcredit /realGDP.

− intuition:ourfirmsdependondebt,sobankingsystemiskey

▶ Wepredeterminethisvariable asa1975-1990average.

− addresspotentialendogeneityissues

− smoothoutboomsandbustsinthefinancialsystem

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Financialdevelopment(cont’d)

Allcountries (1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

TechGreenness0.0264*0.0545**0.0971***0.1095** 1.852.452.902.52

TechGreenness × BankingFD0.0654***0.0859***0.0995**0.1195** 3.593.212.532.36

BankingFD0.0060-0.0063-0.0267-0.0431 0.30-0.22-0.68-0.87 ControlsYYYY

YearFEYYYY

Observations937789649522

R-squared0.11690.15240.20980.2283

Greentechpositivelypredictsfuturecumulativestockreturns,especially infinanciallydevelopedcountries.

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Financialdevelopment(cont’d)

OECDcountriesonly

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

TechGreenness0.0355**0.0481*0.05170.0692 2.061.911.541.44

TechGreenness × BankingFD0.0969***0.1401***0.1924***0.2299*** 4.164.504.904.45

BankingFD-0.0096-0.0081-0.0046-0.0297 -0.40-0.24-0.10-0.48 ControlsYYYY

YearFEYYYY

Observations650554460372

R-squared0.15250.18050.23380.2261

Greentechpositivelypredictsfuturecumulativestockreturnsespecially infinanciallydevelopedcountries,morestronglysointheOECD.

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Financialdevelopment(cont’d)

Allcountries (1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

TechGreenness-0.01250.00750.04630.0422 -0.680.250.980.68

TechGreenness × HighFDDummy0.1119***0.1369***0.1479**0.1868** 4.263.552.552.41

HighFDDummy-0.0389-0.0827*-0.1374**-0.1744** -1.31-1.90-2.22-2.21 ControlsYYYY YearFEYYYY

Observations942793652524

R-squared0.12620.16680.22820.2500

Greentechpositivelypredictsfuturecumulativestockreturnsinthemost financiallydevelopedcountries.

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Takeaways

Altogether, greentechfirms exhibit:

▶ higherlong-runreturns;

▶ highervaluations;

▶ betteroperatingperformance.

Thisinformationisimpoundedprimarilyin efficientfinancialmarkets.

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Disclosure

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Doesitpaytodisclose?

Inthelastpartofthepaper,westudythe marketreaction toafirm’s greennessdisclosure.

Thisanalysisalsoallowsustoexploitthe extendedsample:

▶ the165firmsthatdisclosetechgreenness;

▶ +theremaining835firmsthatdonot.

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Doesitpaytodisclose?(cont’d)

Wedivideourempiricaltestsin threeparts:

▶ Disclosureof technologicalgreenness.

− Top30%,bottom30%,non-publishers

▶ Horserace withalternativemeasuresofgreenness.

− Top30%greentech,top30%ESG,bottom30%GHG(scope1)

▶ Marketreactionafter ParisAgreement andthe Trumpelection. two-yearreturnsafter2016,worldv.US

Publishersinoursample: Tech =13%, ESG =20%, GHG =12%.

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Doesitpaytodisclose?(cont’d)

Weexpectthemarkettoreward greentechdisclosure.

▶ Topgreentech publishersarelikelythe greenestinthesample.

▶ Bottomgreentech publishersarelikely greenerthannon-publishers.

Intuition:itisonlyoptimaltodiscloseforfirmsthatbelievetobe reasonablygreen withrespecttoallothers.

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Greentechdisclosure:Top30%v.Non-publishers

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

Top30%TechGreenness0.1461***0.2615***0.3784***0.4289*** 4.495.145.174.49

YearFEYYYY

Observations4410376731552580

R-squared0.06700.06170.06170.0692

Top30%green-techpublishersearnhigherlong-runstockreturnsthan non-publishers.

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Greentechdisclosure:Bottom30%v.Non-publishers

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

Bottom30%TechGreenness0.0542*0.0840*0.1056*0.1197 1.931.901.771.60

YearFEYYYY

Observations4417378531772605

R-squared0.06750.06050.05490.0603

Bottom30%green-techpublishersearn(weakly)higherlong-runstock returnsthannon-publishers.

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Greentechdisclosure:Publishersv.Non-publishers

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

Publisher0.0699***0.1182***0.1620***0.1810***

3.013.313.292.94

YearFEYYYY

Observations5082434036312965

R-squared0.06170.05650.05420.0611

Green-techpublishers(nomatterhowgreen)earnhigherlong-runstock returnsthannon-publishers.

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Greentechdisclosure:Horserace

Next,wecarryouta horserace between greennessmeasures:

▶ Top30%TechGreenness.

▶ Top30%ESG.

▶ Bottom30%GHG.

Correlations: ρT ,E =0.31, ρT ,G =0.16, ρE ,G =0.32.

(p-value < 0.001forall)

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Greentechdisclosure:Horserace(cont’d)

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

Top30%TechGreenness0.1170***0.2079***0.3131***0.3572***

3.924.384.493.85

Top30%ESG0.03630.0771**0.0963*0.1239*

1.562.141.901.83

Bottom30%GHG0.0749**0.1273***0.1962***0.2180**

2.292.593.032.54

YearFEYYYY

Observations5082434036312965

R-squared0.06440.06290.06540.0731

Top30%green-techpublishersearnhigherlong-runstockreturnsthan non-publishers,controllingfortopESGandbottomGHGpublishing.

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Greentechdisclosure:Horserace(cont’d)

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

Top30%TechGreenness0.1174***0.2107***0.3115***0.3577***

3.954.454.473.83

Top30%E-score0.03350.06340.0980*0.1091

1.351.621.831.56

Bottom30%GHG0.0789**0.1379***0.2071***0.2384***

2.452.883.312.91

YearFEYYYY

Observations5082434036312965

R-squared0.06440.06260.06550.0728

Top30%green-techpublishersearnhigherlong-runstockreturnsthan non-publishers,controllingfortopEandbottomGHGpublishing.

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Technologicalgreennessandlong-runperformance 64/71

Greentechdisclosure:Horserace(cont’d)

(1)(2)(3)(4)

Cum.Returns1-2Cum.Returns1-3Cum.Returns1-4Cum.Returns1-5

TechGreennessPublisher0.0534**0.0829**0.1106**0.1237*

2.262.252.161.89

ESGPublisher0.01710.03920.06500.0620

0.580.861.100.83

GHGPublisher0.03560.0737*0.1030**0.1285**

1.341.852.042.08

YearFEYYYY

Observations5082434036312965

R-squared0.06240.05870.05800.0650

Green-techpublishers(nomatterhowgreen)earnhigherlong-runstock returnsthannon-publishers,controllingforESGandGHGpublishing.

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Greentechdisclosure:2016effect

Dep.Variable:Cum.Returns1-2(1)(2)(3)(4)

Top30%TechGreenness0.1263**0.1233*** 2.463.15

Top30%TechGreenness × 20160.1709***0.0763** 3.502.06

Top30%TechGreenness × 2016 × US-0.2138***-0.2246*** -2.92-3.07

Top30%ESG0.0281-0.0286 0.44-0.46

Top30%ESG × 20160.3114***0.2561*** 4.164.32

Top30%ESG × 2016 × US-0.2054***-0.1158** -2.75-2.13

Bottom30%GHG0.1135**0.1132** 2.022.50

Bottom30%GHG × 20160.2046***0.0695 2.931.56

Bottom30%GHG × 2016 × US-0.2302***-0.0880* -3.61-1.93

ControlsYYYY

Observations5082508250825082

R-squared0.03720.03640.03640.0415

PAeffectforgreentechdisclosureonlyholdsoutsidetheUS,controlling forESGandGHGdisclosure

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Concludingremarks

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Roundup

Inthispaper,westudythe long-runperformance of greentechfirms.

Consistentwithourtheoreticalarguments,greentechfirmsexhibit:

▶ Positivelong-runreturns. especiallyinmoreefficientfinancialmarkets

− disclosinggreentechinfoisrewardedinitsownright

▶ Highervaluations.

− consistentwithgradualimpoundingofinformation

▶ Betteroperatingperformance.

− bothmoreprofitableandlessvolatile inlinewiththefundamentalsstory

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Technologicalgreennessandlong-runperformance

Takeaways

Investingingreenstocksthenseemstobe lucrative inthe longrun. Notjustsociallyresponsible!

Thisisan importantresult:

▶ Financialperformanceofgreenassetsislargelydebated,butstill largelyfocusedonthe shortrun.

▶ Transitiontoa greenereconomy mayentailmoreeconomic advantagesthanpreviouslythought.

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Ourfindingsmakeacasefortheintroductionof systematic requirements for techgreennessdisclosure.

▶ Stockmarketsaroundtheworldseemtorecognizeit.

▶ Setsuptherightincentivesforfirmstogogreen.

Overall,thisisfoodforthoughtfor policymakers.

S.Battiston(UZurich,UVenice),I.Monasterolo(UUtrecht),M.Montone(UUtrecht)

Introduction Model Data Mainfindings Disclosure Conclusion

Takeaways(cont’d)

Technologicalgreennessandlong-runperformance 70/71

Thanksforyourattention!

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