Exploring
the
economic
case
for
climate
action
in
cities
Andy
Gouldson
a,*
,
Sarah
Colenbrander
a,
Andrew
Sudmant
a,
Faye
McAnulla
a,
Niall
Kerr
a,
Paola
Sakai
b,
Stephen
Hall
a,
Effie
Papargyropoulou
c,
Johan
Kuylenstierna
c,daESRCCentreforClimateChangeEconomicsandPolicy,UniversityofLeeds,UK
b
SustainabilityResearchInstitute,UniversityofLeeds,UK
c
Malaysia-JapanInternationalInstituteofTechnology,UniversitiTeknologiMalaysia,Malaysia
d
StockholmEnvironmentInstitute,UniversityofYork,UK
ARTICLE INFO
Articlehistory:
Received25November2014 Receivedinrevisedform24June2015 Accepted19July2015
Availableonline
Keywords: Climate Mitigation Carbon Economics Cities
ABSTRACT
Thereis increasinginterestinthepotentialofcitiestocontributetoclimatemitigation.Multiple assessmentshaveevaluatedthescaleandcompositionofurbanGHGemissions,whileothershave evaluatedsomeaspectsofurbanmitigationpotential.However,assessmentsofmitigationpotential tendtobebroadlyfocused,fewifanyhaveevaluatedurbanmitigationpotentialona measure-by-measurebasis,andfewerstillhaveconsideredtheeconomiccaseforinvestinginthesemeasures.This isasignificantknowledgegapasaneconomiccaseforactioncouldbecriticalinbuildingpolitical commitment, strengthening institutional capacities, securing large-scale finance and targeting investmentandimplementationincities.Inthispaper,weconductacomparativeanalysisofthe resultsoffiverecentlycompletedstudiesthatexaminedtheeconomiccaseforinvestingin low-carbonmeasuresinfivecities:LeedsintheUK,KolkatainIndia,LimainPeru,JohorBahruinMalaysia andPalembanginIndonesia.Theresultsdemonstratethatthereisacompellingeconomiccasefor citiesinbothdevelopedanddevelopingcountrycontextstoinvest,atscale,incost-effective low-carbonmeasures.Theresultssuggestthattheseinvestmentscouldgeneratesignificantreductions(in therangeof15–24%relativetobusiness-as-usualtrends)inurbancarbonemissionsoverthenext10 years.Securingthesesavingswouldrequireanaverageinvestmentof$3.2billionpercity,whichif spreadover10yearsequatesto0.4–0.9%ofcityGDPperyear.However,thesavingsgeneratedinthe formofreducedenergybillswouldbeequivalenttobetween1.7%and9.5%ofannualcity-scaleGDP, andtheaverage paybackperiodof investmentswould beapproximately 2yearsat commercial interest rates. We provisionally estimate that if these findings were replicated and similar investmentsweremadeincitiesglobally,thentheycouldgeneratereductionsequivalentto10– 18% ofglobal energy-related GHGemissionsin 2025. Whilethe studiesoffersomegrounds for optimism,they alsoraise important questions about the barriers tochange that prevent these economicallyattractiveoptionsfrombeingexploitedandaboutthescopeformitigationbasedonthe exploitation of only the economically attractive options. We therefore discuss the institutional capacities,policyenvironmentsandfinancingarrangementsthatneedtobedevelopedbeforeeven theseeconomicallyattractiveopportunitiescanbeexploited.Wealsodemonstratethat,inrapidly growingcities,thecarbonsavingsfromsuchinvestmentscouldbequicklyoverwhelmed–inaslittle as 7 years – by the impacts of sustained population and economic growth. We conclude by highlightingtheneedtobuildcapacitiesthatenabletheexploitationnotonlyoftheeconomically attractiveoptionsintheshorttermbutalsoofthosedeeperandmorestructuralchangesthatare likelytobeneededinthelongerterm.
ß2015TheAuthors.PublishedbyElsevierLtd.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/).
* Correspondingauthorat:SchoolofEarthandEnvironment,UniversityofLeeds,LeedsLS29JT,UK. E-mailaddress:[email protected](A.Gouldson).
ContentslistsavailableatScienceDirect
Global
Environmental
Change
j ou rna l hom e pa ge : w w w. e l s e v i e r. c om/ l o ca t e / gl oe n v cha
http://dx.doi.org/10.1016/j.gloenvcha.2015.07.009
1. Introduction
Ofthe7.1billionpeoplealivetoday,morethan3.6billionlivein cities.By2050theurbanpopulationispredictedtopass6.7billion (UNDESA,2014).Forecastssuggestthatthevastmajorityofthis urbanpopulation–some5.2billionpeople–willliveinlow-and middle-incomecountries, wherethenumber ofcity-dwellers is increasingby1.2millionpeopleperweek(WHO,2014).Although theurbanpopulationinhigh-incomecountriesisgrowingmore slowly,it is still forecastthat around1.2 billionpeople willbe livingincitiesinhigh-incomecountriesby2050(WHO,2014).
The rapidly growing significance of cities in the developing world,andtheirsustainedimportanceinthedevelopedworld,has profound implications for the mitigation of climate change. Arguably,whencomparedtothebodyofresearchoninternational ornationalclimatestrategies,thebodyofresearchonthelinks betweencitiesandgreenhousegas(GHG)emissionsisrelatively small. However, a growing number of studies have evaluated theemissionsthatcanbedirectlyattributedtodifferentcities(cf. Brownetal.,2009;Dhakal,2009;GlaeserandKahn,2010;Bietal., 2011;Kennedyet al.,2012; Creutzigetal.,2015; Colenbrander et al.,2015a, 2015b). Otherstudies have considered the wider carbonfootprintoftheconsumptionthattakesplaceindifferent cities(cf.Khan,2012;Hoornwegetal.,2011;Fengetal.,2014).
Therehavealsobeenvarioushigh-levelassessmentsofurban energyuseandofthecarbonemissionsthatcanbeattributedto cities(cf.GEA,2012;IEA,2013a;IPCC,2014).Theseassessments note that the estimates of urban energy use and emissions generatedtodatehaveoftenbeenbasedondifferentapproaches thathavebeenappliedatdifferentscaleswithvarying assump-tions and units of analysis. Variousauthors also note that are frequentlyissueswithaccessingrobustandcomparabledataatthe cityscale(Brownetal.,2009;Dhakal,2010;WeiszandSteinberger,
2010; Sovacool and Brown, 2010; Minx et al., 2013). Recent
initiativeshavesoughttoadvance andstandardizethewaysin whichurbanGHGinventoriesareprepared.Informedbyprevious research such as Kennedy et al. (2011), the Global Protocol for CommunityScale GHG EmissionInventories (the GPC) was launchedin2014(GHGProtocol,2014).TheGPCisbeingadopted widelyandisexpectedtounderpinemerginginitiativessuchasthe CompactofMayors.
TheIPCChasestimatedthatthefuelthatisconsumedandthe otheractivitiesthattakeplacewithincitiesdirectlyaccountfor 44% of global GHG emissions (IPCC, 2014). However, when consideringtheir finalconsumptionofelectricity andexcluding non-CO2GHGemissions,theIPCCestimatesthat71–76%of the globalCO2emissionsfromfinalenergyusecanbeattributedto cities(IPCC,2014). Various analyseshave suggestedthat when wider consumption-based impacts are taken into account the shareofglobalenergy-relatedCO2emissionsattributabletocities would be higher (Satterthwaite, 2008; Khan, 2012; Hoornweg etal.,2011;GEA,2012;Fengetal.,2014).
Thishasledsometosuggestthatitisnotcitiespersethatare responsibleforahighproportionofglobalemissionsbutthelevels ofeconomicactivityandthenumbersofaffluentconsumersthat areoftenconcentrated withinthem (Satterthwaite,2008; Dod-man,2009;Hoornwegetal.,2011;Minxetal.,2013). However, others emphasize that urbanization offers opportunities to improvelivingstandardswhilereducingthecarbonintensityof development, particularly through more compact and energy efficient forms of development (cf. Khan, 2012; Weisz and
Steinberger, 2010; Hoornweg et al., 2011; Feng et al., 2014;
Floateretal.,2014;Creutzigetal.,2015).Thereisthereforeaneed for a nuanced attribution of energy consumption and GHG emissions to cities and the activities that take place and the people that live within them, and of the opportunities that
different forms of urbandevelopment offerfor climatechange mitigation (Sovacool and Brown, 2010; Weisz and Steinberger, 2010;Hoornwegetal.,2011).
Despitetheon-goingdebatesandtheremaininguncertainties, itiswidelyacceptedthattheurbandevelopmentdecisionstaken inthenextfewyearswillbecrucialindeterminingthesuccessof globalclimatemitigationefforts.Thishasfuelledresearchonthe driversofurbanenergyuseandcarbonemissions,andtheassociated scopeforclimate mitigation.Asummaryofthe keyfactors that emergefromthewiderliteratureispresentedinTable1.
AlthoughthestudiescitedinTable1haveconsideredthebroad mitigationpotentialinkeysectorsincities,asyettherehavebeenno analyses of the economicsof low carbon urban development the economiccaseforinvestmentinlowcarbonurbandevelopmentona measure-by-measure basis. Of course, there have been various estimatesofglobalinvestmentneedsrelatingtolow-carbon develop-ment(Stern,2007;GEA,2012;WEF,2013;McCollumetal.,2013;IEA, 2013a),anditcanbeassumedthataconsiderableproportionofthese investmentneedswilloccurincities.Therehavealsobeenestimatesof thelow-carboninvestmentneedsinspecificareassuchas infrastruc-ture(KennedyandCorfee-Morlot,2013)orbuildings(IEA,2013b).But therehavebeenveryfewassessmentsrelatingdirectlytotheeconomic caseforurbanactiononclimatechange.
Thispaperisbasedontheviewthatthelackofresearchonthe economicsoflow-carboncitiesissignificantforseveralreasons.
1)Citiesaretheplaceswheresubstantialsectionsofinternational andnationalclimatepolicy‘hittheground’.Localauthorities have critical powers with respect to climate mitigation, including land use planning, urban transport provision and theenforcementof energyregulation(Dodman,2009). Inter-national bodies, national governments and local authorities thereforeneedtounderstandtheopportunitiesinandpriorities ofcitiesiftheyaretodesigneffectivepoliciesanddevelopthe multi-levelgovernanceframeworksneededtodeliverthese. 2)Cities are distinguished by the concentration of social and
economicactivitythattakesplacewithintheirboundaries.This means that local authorities have unique opportunities to deliver certain low-carbonmeasures in a cost-effective way (suchasmasstransitordistrictheating),andtoencouragemass adoptionofbothtechnicalandbehaviourallow-carbonoptions (Dodman, 2009). Identifying economically attractive options canfacilitatetheseactions.
3)Many cities are showing leadership on climate change, for example by establishingmore ambitious emission reduction targetsthannationalgovernments.However,toovercomethe barrierstoortosecuretheresourcesforaction,decision-makers
Table1
Keyfactorsshapingurbancarbonemissions.
Historyandlevelsoflock-intoexistinginfrastructure Geography,climateandweatherandresourceendowments Levels,ratesandformsofsocialandeconomicactivityandgrowth Urbangovernancecapacitiesandabilitiestoinfluencedevelopment
patterns
Levelsofpressureand/orsupportfrominternationalandnationalpolicy Levelsofaccesstofinanceandanabilitytoinvestininfrastructure Thecarbonintensityofelectricitysupplyandenergyprices Urbanform,landusemixandtheextentofcompact,connected
development
Levelsandformsoftransportdemandandeaseofaccesstopublic transport
Buildingsefficiencyandlevelsofdemandforheatingandcooling Levelsofincome,formsoflifestyleandpatternsofconsumption
often require an economically compelling case for action. Presentinganeconomiccasecanchangethepoliticaldynamics ofclimateaction.
4)Local authorities and other decision makers need locally relevantevidenceonthemostcarbonandcost-effectiveways ofdeliveringtheirclimatetargets.Studiesoftheeconomiccase forlow-carboninvestmentatthecityscalecanhelptoinform decision-making,andsubsequentlytosecurethemajor invest-mentsnecessarytomeettheirclimatetargets.
Thispaperrespondstothegapintheliteraturebyconductinga comparative analysis of five recently completed studies that assessedthepotentialforcost-effectiveinvestmentsinlow-carbon measuresinfivecities:LeedsCityRegionintheUnitedKingdom, JohorBahru(includingPasirGudang)inMalaysia,Lima-Callaoin Peru,PalembanginIndonesiaandKolkatainIndia(seeGouldson et al., 2012, 2014a, 2014b, 2014c, 2014d; Colenbrander et al., 2015a,2015b).Whilethesecitiescannotbesaidtofullyrepresent the variety of cities that exist today, they are geographically diverse,arefoundinhigh-income(theUK),uppermiddle-income (Malaysia and Peru) and lower middle-income (India and Indonesia) countries and arepursuing a range of development modes.Thecomparativeanalysisthereforeillustratesarangeof thedifferentlevelsandtrendsinpopulationandeconomicgrowth, energyconsumption andcarbon emissionsthat arelikely tobe foundinmanycitiesaroundtheworld.
The paper is structured as follows. Section 2 outlines the methodologiesemployedforthecitystudiesandthiscomparative analysis.Section3presentsandcomparestheheadlinefindings fromthefivecitystudies,andseekstocontextualizetheimpactof investmentincost-effectivelow-carbonmeasures.Section4looks inmoredetailateachofthefivecities,discussingthespecific low-carbonopportunitiesavailableineachcity.Section5considersthe globalimplicationsofthisresearchandidentifiesthecapacities thatneedtobedevelopediftheeconomicallyattractiveoptionsare tobewidelyexploitedinawaythatmovesthecitytowardsdeeper decarbonization. Section 6 presentsconclusions and offers two differentscenariosforlarge-scalelow-carboninvestmentincities.
2. Methodology
Themethodologyusedineachofthecitystudiesincludesthree stages:
1)Anassessmentofrecenttrendsinthecity’senergyuse,energy expenditureandGHGemissions,andprojectionofthesetrends overthenextdecade(thebusinessasusual(BAU)baselines); 2)Anevaluationofthecosts,benefitsandcarbonsavingpotential
of a wide range of the low-carbon measures that could be adoptedindifferentsectorsinthecityinthenextdecade;and 3)An aggregation of the findings and the presentation of the economic case for investment in these options at scale in differentsectorsinthecityoverthenextdecade.
Thiscomparativeanalysisadditionallyincludesanew calcula-tion–theTimetoReachBAULevelsofEmissions(TREBLE)point– fordifferentlevelsoflow-carboninvestmentineachcity.Thisis explainedindetailbelow.
Asis discussed below, Stage 1 of themethodology adopted manybutnotalloftheelementsofthenewGPC(GHGProtocol, 2014).However,theissueswithgainingaccesstosuitablyrobust city-scaledatathatwereraisedabovewereapparentineachofthe casestudycities.Inresponse,thestudiesadoptedaformofiterated participatoryappraisal(seeFraseretal.,2006).Thisapproachwas basedonthecollationofpreliminarydatafromextensivereviews of academic literature, government publications and industry
reports, and its presentation to project steering groups and stakeholder panels.Thesegroups includedrepresentativesfrom national governments, city authorities, development agencies, industrygroups,civilsocietyorganizationsandlocaluniversities. Thepanelsreviewedandrefinedthepreliminarydatatoensureits relevancetoandaccuracyinthelocalcontext,aswellasassessing themethods,assumptionsandoutputsoftheresearch.Fulldetails ofthesteeringgroupsandmembershipofthestakeholderpanels arepresentedintheoriginalcity-reportsandinrelatedacademic publications (see Gouldson et al., 2012, 2014a, 2014b, 2014c, 2014d;Colenbranderetal.,2015a,2015b).Althoughthisapproach enabledtheresearchtotakeplaceinawaythatbuiltconfidencein andlocalownershipoftheresults,thescopeandcomplexityofthe researchthesteeringgroupsandstakeholderpanelswerenotable todiscussuncertaintyrangesorconfidenceintervalsforthedata theyhelpedtogenerate.
2.1. Settingthescopeandboundariesofthecitystudies
Geographically,eachstudyfocusedonametropolitanareaor cityregionwithboundariesdeterminedinconjunctionwithlocal authorities. Thisallowed us toconsider energy use withinthe broadertravel-to-workareathatwasundertheinfluenceofthe metropolitangovernment.
Technically, in terms of carbon accounting, the baseline assessments in each of the studies considered GHG emissions fromthemetropolitanarea,includingthosefromdirect consump-tion of fuels and waste management facilities within city boundaries(so-calledScope1emissions)andthoseproducedby generating the electricity consumed within the city (Scope 2emissions).Industrialprocessemissions,whichfallwithinScope 1,had tobeexcludedduetolackof data.Emissions toorfrom transport areasoutside ofthemetropolitan travel-to-workarea werealsoexcluded,aswereemissionsfromagriculture,forestry andlanduse.Noneofthestudiesconsideredembeddedenergyor carboninthegoodsorservicesconsumedwithinthecity(Scope 3emissions).Thestudiesthereforefocusedprimarilyonterritorial emissions withineach city, withconsumption-based emissions considered for electricity use only. Drawing on the review of accounting procedures for urban GHG emissions presented in
Kennedy et al. (2011) and the recently published GPC (GHG
Protocol,2014),asummaryofthetechnicalscopeoftheemissions inventories,thebaselineforecastsandtheoptionsappraisalstages ispresentedinTable2.
Temporally,thestudiesfocusedonthemediumterm,basing BAUcalculationsonthelast10–15yearsandassessingtheimpacts of adopting low-carbon options in the next 10 years. This timeframehelpstoensurethatthefindingsarerelevanttocurrent decision-makers.LikeMoharebandKennedy(2012),forecastsof future baselineswerebased onextrapolationsof recenttrends, althoughthetimelinesforthisresearchranto2025ratherthanto 2050.Giventheirshorttomediumtermfocus,thestudiesdonot factorinanyoftheprospectiveimpactsoftechnologicallearning (seebelow).
Thisis not meanttodownplay their significance:thepresence of co-benefits such as improved public health or employment creationwouldstrengthenthecaseandthepresenceofco-costs suchasdeterioratedpublichealthorinducedskillsshortagescould weakenthecase forinvestment inparticularmeasures.Careful designanddeliverywillbeneededtomaximizeco-benefitsand minimizeco-costs.However,thenarroweranalysispresentedin the studies reflects the reality that often the direct private economiccasehastobedemonstratedbeforepolicy-makerscan starttoconsiderpotentialinvestmentsandtheirwiderimpacts.
2.2. Calculatingbusiness-as-usualtrends
Thestudiesfirstsoughttomapthelevelsandcompositionof energy supply and demand in each city between 2000 and 2014. This history was developed using activity data for the residential,commercialandpublic,industrial,transportandwaste sectors. These assessments used a wide range of data suchas energysalesbysector,ratesofapplianceownership,floorspaceby type, fleet size by vehicle type and efficiency, mode share by transport type, average passenger kilometres, per capitawaste productionandwastecompositionanddestination.Fulldetailsof thekeyvariables bysectorarepresentedintheSupplementary Material,andfurtherinformationoncity-specificdatasourcesare presentedinthereportsforeachcitystudyandinrelatedacademic papers (Gouldson et al., 2012, 2014a, 2014b, 2014c, 2014d; Colenbranderetal.,2015a,2015b).
TheBAUbaselinesfor2015–2025werebasedonan extrapola-tion of historical trends in activity data between 2000 and 2014.Theseprojectionstookintoconsiderationprojected popula-tiongrowthandlarge-scaleplannedinvestmentscurrentlyunder construction in each city. Changing consumer behaviour and backgroundimprovementsinenergyefficiencywerealso implic-itlyincludedinthetrendsinactivitydata.Thekeyassumptionat theheartofthestudieswasthattrendsinthedifferentcitieswould continueinthenearfutureastheyhadintherecentpast.Asis widelyrecognized(cf.GEA,2012),theurbanfuturemaynotlook liketheurbanpast,butsuchassumptionsarecommoninsuchas assessmentsofBAUtrends,aremorelikelytoholdinthenearterm thaninthelongertermandwerenecessarytomaketheanalysis feasible.
Baseline estimatesofGHGemissionsandenergyexpenditure werebased on activity data. When estimating emissionsfrom
electricity consumption, this analysis took into account the changingcarbonintensityofelectricitybetween2000and2025 (includingtransmissionanddistributionlosses)basedoncurrent infrastructureandplannedinvestmentsinthegridsupplyingeach city. Nominal energy prices between 2000 and 2014 were converted into realprices at 2014levels using context-specific consumerpriceindices.Basedonanassessmentofrecenttrends, weassumedthatenergypricesareexpectedtorise2%peryearin realtermsinLeeds,LimaandKolkata,and3%peryearinIndonesia and Malaysia.Allfuture activitieswerecompared againstthese baselines.
2.3. Identifyingandevaluatinglow-carbonoptions
Preliminarylonglistsofthelow-carbonmeasuresthatcould beadoptedintheresidential,commercial,industrial,transport weredevelopedforeachcity.Thewastesectorwasalsoincluded in all of the cities other thanLeeds where itwas deemed to be outside of the project scope. Reflecting the approach to iteratedparticipatoryappraisaloutlinedabove,projectsteering groupsandstakeholderpanelsin eachcityhelpedtoturn the longlistsintoshortlistsofthosemeasuresthatwereconsidered appropriate for local climates, cultures and socio-economic structures.
Theperformanceoftheshortlistedmeasuresandthescopefor deployment of all measures were then assessed, drawing on literaturereviewsandconsultationswiththestakeholdergroups. Basedontherevisedfiguresthatemergedfromtheconsultations, capital costs,operatingcostsand estimates ofthepotentialfor deploymentofeachmeasureintheperiodto2025werecombined to determine the net present value (NPV) of each measure. Similarly,themitigationpotentialofeachmeasurewasbasedon calculations of the renewable energy generated, energy use avoidedor thewasteemissionsprevented,comparedwithBAU levels.Thepreliminaryfigureswereagainreviewedandrefinedby stakeholdergroupsbeforebeingfinalized.
The NPV calculation for each measure focused narrowly on directprivate costsand benefits.Thecostsconsideredwerethe incrementalcostsofbuying,installingandrunningthelow-carbon measureswhencomparedtothestandard,higher-carbon equiva-lentsthatarecommonlyinplaceineachcity.Ineachcaseweused a standard real interest rateof 5%, which wasdeemed by the projectsteeringgroupstobearealisticborrowingrateineachcity
Table2
Scopeofbaselineinventories,BAUforecastsandmeasuresappraisal.
Electricitya
Transmission losses
Buildings energy use
Transport fueluseb
Aviation Marine Railwaysb
Biofuels Industrial processes
Industrial energyuse
AFOLUc Wasted
Upstream fuels
Embodied carbon
JohorBahru, Malaysia
B B B&M B&M – – B&M B&M – B&M – B&M – –
Kolkata, India
B B B&M B&M – – B&M B&M – B&M – B&M – –
Leeds,UK B B B&M B&M – – B&M B&M – B&M – – – –
Lima, Peru
B B B&M B&M – – B&M B&M – B&M – B&M – –
Palembang, Indonesia
B B B&M B&M – – B&M B&M – B&M – B&M – –
Source:AdaptedfromKennedyetal.(2011)andGHGProtocol(2014). Key:
B–includedinthebaselineinventoryandthebusinessasusualforecast. M–includedinthemeasuresappraisal.
a
Thepotentialapplicationoflowcarbonmeasuresinthelarge-scalegeneratorssupplyingelectricitytotherelevantnationalorregionalgridswasnotconsidered. However,thepotentialfortheadoptionofsmall-scalerenewableswithinthecitywasconsidered.
b
Fortravelwithinthecityonly.
cAgriculture,forestryandlanduse.
afteradjustingforinflation.Ineachcasewealsoheldthecostsof measuresconstantat2014prices.Incontrasttostudiessuchas thoseconductedbyMoharebandKennedy(2012,2014),wedid notconsidertheimpactoftechnologicallearningonthecostand performanceofmeasures.Thismakesourestimatesofeconomic andenvironmentalperformancemoreconservative.Furtherdetail on the specific costs, benefits and deployment rates of each measureineachcityarefullydetailedinGouldsonetal.(2012,
2014a, 2014b, 2014c, 2014d) and Colenbrander et al. (2015a,
2015b).
TheestimatesofNPVandcarbonsavingswerethenusedto rankthemeasuresinorder of(i)thetotalcarbonsavings that they could generateto 2025 and (ii) the cost-effectivenessof these carbon savings, calculated by dividing the NPV of the measureovertheirlifetimesby thecarbonsavings throughto 2025.Manyofthesemeasureswillexistbeyond2025,therefore thelifetime carbonsavings are likely tobe higherthan those reportedhere.Theseleaguetablesindicatethepotentialimpacts ofdeployinganyindividualmeasureindependently,i.e.without relying onthe adoption of any other measure. Short versions of the league tables for the five cities are included in the SupplementaryMaterial.
Theleaguetablescontainequivalentinformationtothatusedas thebasisformarginalabatementcost(MAC)curves.Wepresent results in the form of league tables as steering groups and stakeholderpanelsgenerallydeemedthesetobemoreaccessible and useful than MAC curves. However, we also present city-specificMACcurvesintheSupplementaryMaterial.MACcurves havebeencriticizedbecausetheyfailtoconsidercost uncertain-ties, interactions between measures, unintended consequences and transaction and policy implementation costs (IPCC, 2007;
Kesicki and Ekins, 2012; Vogt-Schilb and Hallegate, 2014).
However,inouranalysis,costuncertaintieswereaddressedboth throughtheselectionofconservativefiguresandtheparticipatory reviewprocessesoutlinedabove.Whiletheleaguetablesdonot account for interactions between measures, we explore these impactsonthenetfinancialandcarbonsavingsintheinvestment scenarios(seeSection2.4).Byconsideringnotonlypurchasebut alsoinstallationandmaintenancecostsforeachmeasurewealso consideredsomeofthekeytransactionscoststhatareoftenleftout ofthedataunderpinningMACcurves.However,ourfocusonlyon thedirectcostsandbenefitsofdifferentmeasuresmeantthatthe analysisdoesnotconsiderpotentialunintendedconsequencesor policyimplementationcosts.Althoughwearguethattheoutputs of our analysis have value as they help analysts and decision makerstounderstandthescopeforclimateactionatthecityscale and to quantify and rank the different low-carbon measures available,wedonotclaimthatthedatapresentedarecomplete, robust or detailed enough to underpin, for example, specific investmentdecisions.
2.4. Calculatingpotentialsavingsatthecityscale
Twoaggregatedscenarioswerethendevelopedbasedon the assessmentofthemeasuresandtheleaguetablesoftheircostand carbon effectiveness. In the first scenario, the cost-effective measures are deployed. Where two measures were mutually exclusive or where interactions led to individual measures no longerpresentingapositiveeconomiccase,themoreeconomically attractivemeasure wasincludedin thescenario.In the second scenario,itwasassumedthatthereturnsfromthecost-effective scenario would be reinvested in those measures that do not independentlyhaveapositivenetpresentvalue.Thisproduceda ‘‘cost-neutral’’bundleofmeasuresthatcouldbeadoptedatnonet costoncommercialtermsoverallofthemeasures’lifetimes.These bundlesthereforeincludedtheeconomicandcarbonsavingsthat
couldberealizedifthereturnsfromthecost-effectivemeasures wererecycled and re-investedin further low-carbonmeasures. Whilethisanalysisconsiderscostsandbenefitsacrossthecityasa whole,withthecityitselfbeingseenasthefunctioningeconomic unit, it shouldbe noted that cost-recovery mechanisms would needtobeinplaceforfinancialsavingsfromcost-effective low-carbon measuresto becaptured and re-invested. Thisscenario shouldthereforebeviewedashighlytheoretical.
Whendevelopingthesescenarios,itisimportanttohighlight thattheperformanceofameasuredependsonwhether,andto whatextent,otheroptionsaredeployed(Bajzˇeljetal.,2013).For example, when adopting mandatory energy performance stan-dards for air conditioners, we assume that the introduction of green buildingstandards has already reduced theneed for air conditioning and the savings from more energy efficient air conditioners.Whiletheleaguetablesassesstheperformanceof measuresdeployedindividually,asoutlinedabove,weaccounted forthesekindsofinteractionsbetweenmeasureswhendeveloping scenarios to better illustrate potential city-scale financial and carbonsavings.
2.5. TheTREBLEpoint
The significance of the rebound effect – where additional energy consumption is enabled by improved energy efficiency (MadlenerandAlcott,2009)–iswidelyacknowledged.Berkhout etal.(2000)estimatethat,althoughtheyvarybymeasure,rebound effects reduce the savings from energy efficiency measures by between 0%and 15%, while Hertwich (2005) and Sorrell et al. (2009)suggestthatreboundeffects,althoughvariablearetypically lessthan30%.Itthereforeseemslikelythattheenergyandhence thecostandcarbonsavingspredictedabovecouldbereducedby reboundeffects.Astherearemultiplecomponentsandsystemic dimensionstoreboundeffects,andassucheffectsareoftenspecific tobothanindividualmeasureandtothecontextofitsapplication, wejudgedthatacalculationofthereboundeffectsthatarelikelyto beexperiencedineach ofthecase studycitieswasoutsidethe scopeofthispaper.Instead,wehighlightthetimeitwouldtakefor continuedurban growth–even in amore energyefficient and lowercarbonform–tocanceloutthesavingsmadethroughthe investmentsinthelow-carbonmeasuresthatarethemainfocusof thepaper.
Todothis,weproposetheconceptoftheTREBLEpoint.The TREBLE point compares the time taken for emissions with investmentinlow-carbonmeasurestoreachthelevelthatwould have been realized without such investment under the BAU scenarioinareferenceyear,inthiscase2025.Apositivenumber suggeststhatwithinvestmenttheBAUlevelofemissionsforecast for 2025would still berealizedbuta number ofyears later;a negativenumbersuggeststhattheBAUlevelofemissionsforecast for2025wouldberealizedanumberofyearsearlier.Ifemissions afterinvestmentsareunlikelytoreachtheBAUreferencepointin theforeseeablefuture,thereisnoTREBLEpoint.
3. Results
3.1. Business-as-usualtrends
BAUbaselinesforeconomicdevelopment,energyconsumption, emissionsintensityofeconomicactivityandtotalcarbonemissions foreachofthefivecitiesarepresentedinFig.1.Theaveragesfor membercountriesoftheOrganizationforEconomicCo-operation andDevelopment(OECD)arealsoshownforreference.Accordingto ourBAUprojections,intheperiodfrom2014to2025:
-Averagepercapitaenergyconsumptionwillrisesignificantlyin JohorBahruandPalembang,drivenbyindustrialexpansion,and riseslightlyin Kolkata,Lima andLeeds. Allcitieswillremain significantlybelowOECDaverages.
-Averagepercapitaemissionswillcontinuetoriseinallcities except Leeds, where it will fall markedly in line with OECD trends. Emissions per capita in Johor Bahru will exceed the averageinOECDcountries.
-Theemissionsintensityofeconomicactivitywillfallinallcities except Palembang, where fuel switching to more carbon-intensiveenergysourcesisanticipated.
Putting these baselines together reveals the trajectories for absolute emissions levels in each city between 2014 and 2025 under BAU conditions. Absolute carbon emissions will increasein thefourdevelopingworldcities:by54%in Kolkata, 52%inLima,84%inJohorBahruand165%inPalembang.InLeeds, however,theywillfallby13%.
Moredetailaboutrelevantdevelopmentsineachcityisgivenin thecasestudiesinSection4.
3.2. Comparingtheeconomiccasesforlow-carboninvestmentinthe fivecities
IncontrasttotheBAUscenarios,theeconomiccasesfor low-carboninvestmentinthefivecitiesshowsomestriking similari-ties.Thesummaryresultsoftheeconomicanalysisforinvestments
in cost-effective and cost-neutral bundles of measures are presentedinTable3.
Theseresultssuggestthatthereareverysignificant opportu-nitiesforcitiestoexploiteconomicallyattractiveinitiativesthat wouldcuttheir carbonemissions.Thepackageofcost-effective investmentswould payforthemselvesthroughtheenergythat theysavequickly,inonecaseinamatterofafewmonths,and relativetoBAUtrendstheywouldgeneratecarbonsavingsinthe rangeof15–24%by 2025.Asmostofthelow-carbonmeasures considered have lifespans beyond their payback periods, the investmentsmadewouldcarryongeneratingbothfinancialand carbonsavingsoveramuchlongerperiod.
If the savings from these cost-effective investments were capturedandreinvestedinfurtherlow-carbonmeasuresuptothe pointwhereallinvestmentswouldbecost-neutralthenlevelsof investmentwouldatleastdoubleinmostofthecities.Whilethe payback periods of these cost-neutral bundles of investments would belonger – upto 8years – by 2025theycould reduce emissionsby22–45%relativetoBAUtrends.
3.3. TREBLEpoints:theimpactsinalonger-termperspective
AnalysisoftheTREBLEpointsrevealsthatwithcost-effective investments,thefourdevelopingworldcitiescouldkeepemissions belowtheBAUlevelsprojectedfor2025forafurther7–15years (seeFig.2andTable1).However,theanalysisalsoshowsthatthe impactsofsustainedpopulationandeconomicgrowthwouldthen morethanoffsettheseimprovements.FortheLeedsCityRegion, whereBAUlevelsofemissionsarefalling,investinginthe cost-effectiveoptionscouldbringthereducedBAUlevelofemissions projectedfor2025forwardbyasmuchas6years.
Theprospectsforcost-neutrallevelsofinvestmenttoreduce overallemissionsinthelonger-termareevenmorecompelling.In thecost-neutralscenarios,Palembangwouldkeepemissionslevels belowtheBAUlevelprojectedfor2025for10yearsandLimafor 15 years.In Johor Bahruand Kolkata,there is noTREBLE point inthecost-neutralscenario:inotherwords,iftheimpactofthe cost-neutralbundleofmeasuresis sustained,thesecities could effectively shift to low-carbon development trajectories at no net cost. This is an even more substantial contribution, as it suggeststhat,insomecitiesatleast,economicallyneutrallevelsof
low-carboninvestmentscouldhavelong-termimpactsoncarbon emissions.
Itisworthemphasizingthattheemissionreductionsfromthese low-carboninvestments representa substantial contributionto climatemitigation.Withtheexploitationofallcost-effective low-carbonmeasures,thefivecitiescouldavoidemissionsofbetween 2.6and9.4MtCO2;withthefurtherdeploymentofallcost-neutral options,thefivecitiescouldavoidemissionsofbetween3.6and 17.5MtCO2-e (see Table 3 and Fig. 2). While these are very significantreductionsincarbonemissions,theanalysisofTREBLE pointsmakesitclearthatcitiescannotdeliversustainedemission reductionsbyonlyexploitingeconomicallyattractiveoptions.It willbenecessarytoinvestinlesseconomicallyattractiveoptions, andpossiblywideranddeeperchangesinurbanformandfunction, if growing cities want to achieve deeper cuts in their carbon emissionsinthelonger-term.
3.4. Globalimplications
This comparative analysis indicates that there is scope for economicallyattractiveinvestmentstoreduceenergyuse,energy billsandcarbonemissions,relativetoBAUtrends,indiversecitiesat differentstagesofdevelopment.Ofcourse,thesmallsampleofcities examinedhereisunlikelytoberepresentativeofthewiderrangeof cities and the multiple factors shaping their carbon emissions. However,iftheopportunitiesavailabletothefivecasestudycities were broadly representative, and allcities were toidentify and exploit similar opportunities, then this would lead to very substantialinvestmentsinthelow-carboneconomyandreductions incarbonemissionsthatwouldbesignificantattheglobalscale.
Asa verybroadandvery preliminaryestimate, if71–76%of global energy-related GHG emissions come from cities (IPCC, 2014),andcitiescouldbereducetheirGHGemissionsby15–24% throughcost-effectiveinvestments(asinoursmallsample),then very cautiously we could estimate that cities could achieve reductionsequivalentto10–18%of globalenergy-related emis-sionsin2025.Further,ifGHGreductionsof22–45%areavailable through cost-neutral levels of investment in all cities, then – equallycautiously– wecouldestimatethatcities coulddeliver carbon savings equivalent to 15–34% of global energy-related emissionsatnonetcost.
Table3
Summaryoftheestimatedcostsandbenefitsoftwolevelsoflow-carboninvestmentinthefivecities.
Leeds JohorBahru Lima Palembang Kolkata
Cost-effectivescenario
Investmentneeds(US$billion) 7.7 1.0 5.0 0.4 2.0
Investmentneeds(%ofcityGDP) 8.9 3.7 7.5 8.8 6.3
Annualsavings(US$billion) 1.9 0.8 2.1 0.4 0.5
Annualsavings(%ofcityGDP) 2.2 2.9 3.2 9.5 1.7
Paybackperiod(years) 4.1 1.3 2.4 <1 3.9
Carbonsavingsin2025(MtCO2-e) 2.6 9.4 3.5 3.2 7.8
Carbonsavingsin2025(%ofBAU) 15.6 24.2 14.7 24.1 20.7
TREBLEpoint(years)a 6 11 7 8 15
Cost-neutralscenario
Investmentneeds(US$billion) 18.1 5.6 10.8 1.5 3.6
Investmentneeds(%ofcityGDP) 21 20.8 16.3 33.6 11.4
Annualsavings(US$billion) 2.5 0.8 2.4 0.5 0.6
Annualsavings(%ofcityGDP) 2.9 3.1 3.6 10.2 1.8
Paybackperiod(years) 7.3 6.8 4.5 3.3 6.2
Carbonsavingsin2025(MtCO2-e) 3.6 17.5 5.2 3.7 13.6
Carbonsavingsin2025(%ofBAU) 21.8 45.4 22.4 28.3 35.9
TREBLEpoint(years)a
7 NA 15 10 NA
a
4. Casestudies
Thissectionprovidesmoredetailabouteachofthefivecities studied.Wepresentthebroadercontextofthecitytoillustrateits relativecarbonintensityanddevelopmentlevel,andexplorethe economicandcarbonsavingsofdeployingthecost-effectiveand cost-neutralbundles of low-carbonmeasures. Each case study alsozeroesinona particularsectorwherethemostinteresting opportunitiescanbefoundinthatcity.Summariesofkeydataand findingsforeachcitycanbefoundintheSupplementaryMaterial.
4.1. TheLeedsCityRegion,UK
The Leeds City Region – that includes the local authority districts of Barnsley, Bradford, Calderdale, Craven, Harrogate, Kirklees,Leeds,Selby,WakefieldandYork–hasapopulationof
over three million and an economy worth over
£
52 billion (US$86.2billion),approximately5%oftheUKeconomy.Percapita GDPin thearea is approximately£
17,000(US$26,500)and per capitaenergyconsumptionis75%oftheOECDaverage.Thecarbon intensityofelectricityis0.27tCO2-e/MWhbutthisfigureisfalling aslesscarbon-intensiveelectricitysourcescomeonline.Thecity region’saggregateenergyuseisrelativelystable,butitsannual energybillof£
5.4billion(US$8.4billion)–approximately10%of cityGDP–issteadilyincreasing.TheLeeds CityRegionfacesmany oftheenergy andcarbon challengesofotherestablishedcitiesinthedevelopedworld.Ithas alargelyde-industrialized,service-basedeconomywithrelatively highlevelsofwealth,energyconsumptionandcarbonemissions whencomparedtoworldaverages.Itsinfrastructureisextensive butrelativelyold,andmayneedtobesubstantiallyretrofittedto reduceemissionsintensity.Transitioningtoalow-carboncitywill
therefore demand substantial investment, although ongoing decarbonizationofelectricitysupplyatthenationalscalemeans thatthecity’sannualemissionsarefallinginabsoluteterms.
We find potential for
£
4.9 billion (US$7.7 billion) of cost-effective investments in different energy efficiency, renewable energyandotherlow-carbonmeasureswithinLeeds.Thesewould generateannualsavingsof£
1.2billion(US$1.9billion),meaning that theycouldpay for themselvesin around 4 years. If these investmentsweremade,weestimatethatLeedscouldreduceits annualcarbonemissionsby2025by16%,relativetoBAUlevels. Theemissionsavingswouldbedistributedamongthecommercial (30%),domestic(29%),industrial(33%)andtransport(7%)sectors. Acost-neutralpackageofmeasureswouldmobilize£
11.6billion (US$18.1 billion) oflow-carbon investments and would deliver annualemissionreductionsof22%in2025relativetoBAUlevelsat nonetcosttothecity.Muchofthecarbonsavingpotentialisfoundinthedomestic sector.MuchofthehousingstockinLeedswasbuiltbefore1920, andis poorly insulatedand energyinefficient andthere is also somepotentialforthedeploymentofsmall-scalerenewables.
Mini-windturbineswithafeed-intariff,forexample,havean economic value of
£
467(US$729)/tCO2 measure, although the scope for their deployment and hence their aggregate carbon savingpotentialis comparativelysmall. Biomass boilerswitha renewableheatincentivearethenextmostcost-effectivemeasure for thesectorat£
325(US$507)/tCO2 and also becauseof their widespreadscopefordeploymentthatofferlargepotentialcarbon savings.Reducing householdheatinglevels by18Ccouldavoid 201ktCO2cost-effectively,whilesolidwallinsulationwouldsavea further198ktCO2andcouldbeincludedinacost-neutralbundleof measures if it was cross-subsidized by the cost-effective low-carbonmeasures.4.2. JohorBahru,Malaysia
Johor Bahru, which for the purposes of the research here includesPasirGudang–isthethirdlargestcityinMalaysia,and servesasanimportantindustrial,logisticsandcommercialcentre. Thepopulationiscurrently1.5millionbutisexpectedtogrowto 2.8 million by 2025. Massive additional investment in urban infrastructureandindustrialdevelopmentisplannedoverthenext decadeinordertomeettheneedsofthegrowingpopulationand diversifyingeconomy.
Thecityenjoyshighgrowthratesafterbecomingthefocusof IskandarMalaysiaregionaleconomiccorridor.Percapitaincomes intheareaare48,880Malaysianringgit(MYR;US$14,790)andper capita energy consumption is 70.2% of the OECD average in 2014.Theserelativelyhighlevelsofenergyuseareunsurprising considering the city’s large industrial base which includes oil refiningandrubberprocessing.Furthereconomicandpopulation growth will see substantial increases in absolute levels of emissions (84%), energy use (79%) and energy bills (140%) in JohorBahruovertheperiod2014–2025.
WeestimatethatJohorBahrucouldreduceitscarbonemissions by 24% in 2025, relative to BAU trends, through cost-effective investmentsworthMYR3.3billion(US$1.0billion).Thesewould generateannualsavingsofMYR2.6billion(US$0.77billion),with theemissionreductionsdistributedamongthecommercial(1%), domestic(20%),industrial(18%),transport(52%)andwaste(9%) sectors.Reinvestingthereturnsontheseinvestmentsinother low-carbon measures could enable investment in a cost-neutral package of measures worth MYR18.5 billion (US$5.6 billion), whichwoulddeliveremissionsreductionsof45%relativetoBAUat nonetcosttothecity.
Manyoftheselow-carbonmeasuresarefoundintheindustrial sector. The petroleum refinery and petrochemical industry in
particularcouldbenefitfrommoreefficientpumps,compressors, furnaces,boilers,heatexchangersandutilities.Theseareall cost-effectiveoptions,withanaveragevalueofMYR252(US$76)/tCO2 -e andcollectivelyabletosave4.4MtCO2-eby2025.Therubber industryalsooffersseverallow-carbonmeasuresthatentailonly smalladditionaloperationalcostsinreturnforlargeenergyand carbonsavings.Leakpreventionandloweringfunctionalpressure in boilers,forinstance,couldreduceemissionsbyanestimated 9.7MtCO2-eby2025.Thetransportsectoralsocontainsanumber ofhighlycosteffectivemeasures,theadoptionofEuroIVvehicle standards for example could save 9.1MtCO2-e by 2025 and substantial savings could also be made through vehicle fuel switchingandthediffusionofhybridvehicles.
4.3. Lima-Callao,Peru
With a populationof 9.2 million, Lima Metropolitan Area which includes both Lima andCallao isthe fifth largestcity inSouthAmericaand byfar thelargestmetropolitan areain Peru, accounting for51%of nationalGDP and84% ofthe tax
base (INEI, n.d.). While Lima’s GDP per capita reached
approximately18,590PeruvianNuevo Sol(PEN;US$6990)in 2014,provisionofhousing,transportandsanitation infrastruc-turehasnotkeptpacewiththeincreasingpopulation.Absolute povertyinthecityfellfrom44.8%in2004to15.7%in2011,but approximately onein ten peoplecontinues to lackaccess to water and electricity (Sedepal,2010). There has also beena substantialexpansionofinformalsettlementsontheperiphery ofthecity.
Limapossessesadistinctadvantageintheshifttowardsa low-carbon economy because of the availability of low-cost, low-carbon (0.24tCO2-e/MWh) electricity, largely generated from hydropowerandnatural gas,andbecauseofaclimatein which neitherheatingnorairconditioningarewidelyrequired.Projected BAU trendssuggest that, while energy consumption percapita grew32%between2000and2014,currentlevelsareonly10%of theOECDaverage.However,economicdevelopmentandagrowing population will see substantial increases in absolute levels of emissions(52%),energyuse(48%)andenergybills(92%)inLima between2014and2025.
Weestimatethat,comparedtoBAUtrends,Limacouldreduce its carbon emissions by 2025 by 15% through cost-effective investmentsofPEN13.2billion(US$5.1billion).Theseinvestments wouldgeneratesavingsofPEN5.5billion(US$2.1billion),withthe emission reductions distributed among the commercial (10%), domestic(15%),industrial(24%),transport(42%)andwaste(8%) sectors. We calculate that reinvesting the returns from these investmentsinotherlow-carbonmeasureswouldenableanextra PEN19.7billion(US$7.1billion)ofinvestmentsthatwoulddeliver emissionreductionsof22%relativetoBAUlevelsatnonetcostto thecity.
4.4. Palembang,Indonesia
PalembangistheseventhlargestcityinIndonesia,thecapital andmajor industrial centreof thestate of SouthSumatra, and animportantport for theisland ofSumatra. Thepopulation of 1.5 million has an average income of 34.6 million Indonesian rupiah(IDR;US$2940)andconsumes30%oftheOECDaverageper capita energy consumption. The carbon intensity of electricity supply is projected to rise from 0.84tCO2-e/MWh in 2014 to 0.94tCO2-e/MWhin2025asnewcoal-firedpower plantscome online.Thelargeindustrialbasecombinedwiththerisingcarbon intensityofelectricitymeanthatPalembanghasanincreasingly energy-andcarbon-intensiveeconomy.
Energyuseinthecityhasdoubledsince2000,andisprojected tomorethandoubleagainbetween2014and2025.Asubstantial expansion of electricity generation is required to meet new demand.Thecurrent energy billfor thecity is IDR10.1 trillion (US$857million)or18.7%ofGDP.Totalexpenditureonenergyis projected to rise 155% by 2025, while carbon emissions are projectedtoincreaseby165%overthesameperiodwiththelarge jumpinemissionspartlyattributabletotheconstructionofalarge fertilizerfactorywithinthecityboundaries.
WeestimatethatPalembangcouldreduceitscarbonemissions in2025by24%,comparedtoBAUlevels,throughcost-effective measures. This would require investment of IDR4.8 trillion (US$405.6 million), which would pay for itself within a year throughannualsavingsofIDR5.1trillion(US$436.8million).The carbonsavingswouldbedistributedamongthecommercial(1%), domestic(24%), industry (51%),transport (9%) andwaste (15%) sectors. We calculate that reinvesting the returns from these investmentsinotherlow-carbonmeasureswouldenableatotal investmentofIDR18.2trillion(US$1.5billion)andwoulddeliver emissionreductionsof28%relativetoBAUlevelsatnonetcostto thecity.
The provision of reliable,low-carbon electricity would sub-stantially expandtheemission reduction measuresavailable to PalembangandotherIndonesiancities.Ouranalysisidentifiestwo cost-and carbon-effectivesmeasuresavailable totheSumatran grid.514MWofexistingnaturalgasgenerationcapacitycouldbe retrofitted with best available technologies at a return of IDR745,193(US$62)/tCO2, and some 1200MW of geothermal generation capacity could be built instead of coal-fired power plantswitha return of INR95,712(US$8)/tCO2.These measures wouldsave38.6MtCO2by2025.
4.5. Kolkata,India
Kolkataisthethirdlargestandthemostdenselypopulatedcity inIndiaandthe19thlargesturbanareaintheworld(Government ofWest Bengal, 2009). Itsofficial population of 14.1million is currentlygrowingatarateof6.9%peryear(GovernmentofIndia, 2011), but its unofficial population could be much larger. ElectricitysupplytothecitycomeslargelyfromtheWestBengal grid.Inefficienciesandlossesinthisgridmeanthattheelectricity consumedinKolkatahasacarbonintensityof1.52tCO2-e/MWh, morethandoubleglobalbestpracticeforlow-grade,non-coking coal(IEA,2010).
AveragepercapitaincomeinKolkatais125,109Indianrupees (INR;US$2139)andaverageannualpercapitaenergyconsumption is3.6MWh(7%oftheOECDaverage).Therearestarkinequalities withinthecity.MorethanathirdofKolkata’spopulationlivesin slums,wheremostpeopleworkininformalsectorsandathirdare unemployed (UN Habitat, 2003). Even so, electricity demand is growingrapidly.Weestimatethat,underBAUconditions,Kolkata’s totalenergyconsumptionwouldriseby44%,expenditureonenergy by112%andcarbonemissionsby54%between2014and2025.
We estimate that Kolkata could reduce its annual carbon emissionsby21% by2025,relativetoBAUlevels,through cost-effectiveinvestmentsof INR119.3billion(US$2.0billion). These would pay for themselves through annual savings of INR 30.4billion(US$520.7million)within3.9years,andthencontinue togeneratesavingsforthelifetimeofthemeasures.Thecarbon savings would be distributed among the commercial (25%), domestic(28%), industry(15%),transport (9%),andwaste(23%) sectors. By reinvesting the returns from these cost-effective options,KolkatacouldinvestatotalofINR205.6billion(US$3.6 billion)inlow-carbonmeasuresandreduceitscarbonemissions by36%relativetoBAUtrendsatnonetcosttothecity.
Kolkata’swastesectorhighlightstheimportanceofevaluating the social and environmental implications of low-carbon mea-sures,as wellastheeconomiccase forinvestment. Poorwaste managementimpactspublichealthinthecity,butalsoprovides importantsourcesofinformalemploymenttosomeofthemost vulnerablepopulationsinthecity.Anylow-carbonmeasuresneed to capture potential health benefits while protecting these livelihoods.Acommunity-ledrecycling schememight therefore provideoneoption,reducingemissionsby226ktCO2-e. Gasifica-tionisalsoeconomicallyattractive,yieldingINR135(US$2)/tCO2-e that could cross-subsidize investment in energy-from-waste infrastructure. These measures have the potential to yield a doubledividendbydisplacinggridelectricitygeneratedfromcoal, therebyavoiding2.8MtCO2-eby2025.
5. Discussion
These findings reinforce the widely made claims about the significance of cities inclimate mitigation,and particularlythe importanceofrapidlygrowingcitiesinmiddle-incomecountries (GEA,2012;IPCC,2014).Theanalysishighlightstheconsequences of ‘business as usual’development in thesecities, withcarbon emissionsforecasttoincreaseby52–164%inthenextdecadeifthe citiesgrowatthesameratesandinthesamewaysastheyhave since2000.Thelargestabsoluteandrelativegrowthinemissionsis seeninmoreindustrialcitiessuchasJohorBahruinMalaysiaand Palembang in Indonesia, compared to service-sector intensive citiessuchasLeedsintheUKorLimainPeru.Thishighlightsthe importance of a consumption-based approach to urban GHG accounting,suchasthatadoptedbyKhan(2012),Hoornwegetal. (2011)orFeng etal.(2014).Theapplicationof a consumption-based approach would decrease the emissions attributed to ‘producer’ cities and increase them for ‘consumer’ cities ( Sat-terthwaite,2008;Hoornwegetal.,2011;GEA,2012).Asfaraswe areaware,theimplicationsofsuchanapproachfortheselectionof urbanmitigationoptionshaveyettobeevaluated.
similar opportunities for economically attractive low-carbon investment,theannualestimatedemissionsreduction– equiva-lentto10–18%ofglobalenergy-relatedcarbonemissions–would beaverysignificantcontributiontoclimatechangemitigation.
WhenexpressedasashareofGDP,thecity-level investment needsidentifiedaboveareslightlylowerthanthoseidentifiedat thegloballevelbyStern(2007)andothers.Thisisatleastpartly duetothenarrowerfocusofouranalysisoncurrentlyavailable mitigationoptionsandtheexclusionofothercosts,relatingfor exampletothecostsoflowcarbonR&Dortheneedforinvestment inresilienceandadaptation.Morebroadly,althoughStern(2007) illuminatedthebroaderlonger-termeconomiclogicforaddressing climatechangeattheglobalscale,itisnotalwaysclearthatthis logicholdsforspecificinvestmentdecisionsatthelocallevel.The findingspresentedhereprovideanimportantcomplementtosuch analysesbydemonstratingthatinvestmentintheearlystagesof thelow-carbontransitioncouldappealtolocaldecisionmakers andinvestorsondirect,short-termeconomicgrounds.Thisinturn indicatesthatclimatemitigationoughttofeatureprominentlyin economicdevelopmentstrategiesaswellasintheenvironment andsustainabilitystrategiesthatareoftenmoreperipheralto,and lessinfluentialin,city-scaledecisionmaking.
There aremany reasonsthatcities havenot exploitedthese substantialeconomicopportunities.ESMAP(2012,p.3)statesthat ‘Cities face major barriers to implementing sustainable energy measures.Evenwherethereisadesiretoimprovetheirefficiency levels, cities often lack the requisite information, supportive national-levelpolicies,accesstofinancingandothersupport.City managers and mayors are often not equipped with adequate informationorresourcestoidentifyandprioritizeenergyactions’. TheIPCC(2014,p.928)similarlystatesthatcitiesneedtodevelop institutional arrangements that facilitate the integration of mitigationwithotherhigh-priorityurbanagendas,amulti-level governance context that empowers cities to promote urban transformationsand sufficient financial flowsand incentives to adequatelysupportmitigationstrategies.
Thepresenceofaneconomiccaseforlow-carboninvestmentin citiescouldencouragecity-leaderstobuildtherelevantcapacities and‘mainstream’climatechangeintocore policyareas suchas planning, economic development, infrastructure, transport and housing.Theimportanceof suchclimatepolicyintegrationhas beenwidelyrecognizedatthenationallevel(JordanandLenschow, 2010;AdelleandRussell,2013)butresearchonthisthemerarely considerstheurbanlevel.
Whiletheeconomiccasemightcapturetheinterestof decision-makers,inabsolutetermsthefinancialneedsidentifiedforeachof thecasestudycitiesareverysubstantial,particularlyrelativeto citybudgets.However,itisimportanttorecognizethatmanyof theopportunitiescouldbesufficientlyeconomicallyattractiveto takeplacewithoutgovernmentsupport,and manyoftheother optionscouldbepromotedthroughpolicyratherthanfundedby government(seeColenbranderetal.,2015a,2015b).Ashasbeen widelydiscussed,governmentpolicycanincentivizelow-carbon investmentthroughfeed-in-tariffsortheremovalofsubsidiesfor fossil fuels. It can also enable actors to respond to market opportunitiesandpolicysignalsthroughinformationprovision,for instancebyenvironmentallabelling,orthroughsupportforR&D. Andultimately,itcanmandateinvestmentthroughregulation– forexamplethrough theadoptionof toughervehicleemissions standardsorbuildingenergyperformancestandards.
Giventherangeoflow-carbonmeasuresidentifiedinthefive cities,it isclear that suchpolicyinterventions arelikely to be needed both across levels (national, regional and local) and between policy areas (energy, finance, housing, transport and economic development, as well as environment).The need for effective multi-level governance arrangements to help cities
respond to climate change is widely recognized (Betsil and Bulkely,2006;Corfee-Morlotetal.,2009;Franze´n, 2013;Acuto, 2013;Matsumotoetal.,2014).However,asNCE(2015)recognize, thedetailsofsucharrangementsareoftenunder-developedand cities arefrequentlyconstrained in their abilityto pursue low-carbon development paths. This suggests that climate change needstobemainstreamedbyallactorsandsupportedbyvertical learning networks across scales horizontal learning networks betweencities.
The potentialfor horizontallearning between citiesis clear. Althoughtherearesomesignificantdifferencesbetweenthecities, relatingforexampletothecarbonintensityofelectricitysupplyor thelevelsofdemandforheatingandcooling,therearealsosome notablesimilarities.Eachofthecitiesforexamplecouldexploit low-carbon options relating to green building standards and buildings retrofitand theadoption of more efficient modes of transport.Andmanyofthegovernancechallengesexperiencedby thedifferentcitiesare alsoverysimilar.The scopefor learning betweencities–evenwhentheyareverydifferent–istherefore readilyapparent.Wearguethattheprospectsforsuchlearningare likelytobestrongerwhereacompellingeconomiccasehasbeen putforward.
In the longer-term, the positive TREBLE point for each developingcountrycityhasshownthattheemissionreductions fromthecost-effectiveoptionsarelikelytobeoverwhelmedbythe impactsofongoingeconomicandpopulationgrowth.Theanalysis thereforeclearly shows thatcities cannottransition onto low-carbon development paths just by exploiting economically attractivelow-carbonmeasures.Inordertodeliversustainedcuts incarbonemissions,citieswouldhavetoexploredeeperandmore structuralformsofdecarbonizationthatmaynotbeas economi-callyattractive(atleastnotondirecteconomictermsintheshort to medium term) and that may also be more challenging technically,politicallyandsocially.However,ambitious interven-tionstoalterurbanformandfunctionwillbenecessaryifcitiesare to combine high quality of life with low GHG emissions (Satterthwaite,2008;Floateretal.,2014).Exploitingcost-effective low-carbon measurescouldcreate the enabling conditions and momentum for thesekinds of long-term,transformative urban actionsonclimatechange.
6. Conclusions
Althoughclearlywemustbecarefulaboutextrapolatingfroma sampleofonlyfivecities,ourfindingsofferusefulinsightsintothe opportunitiesfor,and thelimits of,investmentineconomically attractive options for low-carbon cities. Given the growing importanceofcities,suchopportunitieshavemajorimplications for climate change mitigation; economically attractive invest-ments in cities couldlead toglobally significant reductions in carbonemissions.Reinvestingthereturnsfromsuchinvestments up to the point where all investments are cost-neutral would increasecarbonsavingssubstantially.
Socouldtheexploitationoftheseeconomicallyattractive low-carbonmeasuresincitiesintheshorttomediumtermprovidea platformformoretransformativechangeinthelongerterm?The answerperhapsdependsonwhetherlowcarbonurban develop-ment represents what Hajer (1996) refers to as institutional learningoratechnocraticproject.
development.Institutionalcapacitieswouldbebuilt,newfinancing arrangements would evolve and important lessons would be learntover time.Othercitieswouldthen beencouragedtoadopt similar models,and the pioneering orfront-runningcities would decarbonizefurther.Inotherwords,earlysuccesseswouldinspire othercities, whilestrengthening capacitiesthatenablethe front-runningcitiestoexploremoreambitiousclimatemitigationstrategies suchasstructuralchangesinurbanform.
Under conditionsoftechnocracy,citiesmightimplementthe measureswithoutappropriateforms ofengagementand gover-nance.Thetransitionwouldbecomeatechnicalexercisethatruns theriskofgeneratingsocial,economicandenvironmentalco-costs andunderminingsocialandpoliticalsupportandmomentumfor further change. Institutional capacities would be built and financing arrangements would emerge, but cities would only usetheseto‘‘cherrypick’’theeasiestandmosteconomicoptions. Thefront-runningcitieswouldthenloseinterestinfurtherchange andothercitieswoulddecideonlytoinvestinlow-carbonoptions where they are economically attractive. Cities would maintain their resistance totransformative changesthat are likely tobe more challenging, and would lock into what is at best only a marginallydecarbonizedfuture.Inotherwords,thereisariskthat citieswouldspendvaluabletimeexploringoptionsthatareatbest apartialresponsetoapressingglobalproblem,andintheprocess they would crowd out the potential for deeper and more transformativechange.
The policychallengeis tofindwaysof enabling low-carbon transitionsunderconditionsofinstitutionallearning.Forthis to happen,policy-makersandotherswouldhavetoexploittheearly stagesofthelow-carbontransitionwherethereareeconomically attractiveoptions,whileensuringthattheycreatetheconditions forthelaterstagesoftransitionthatcouldbemorechallenging. Low-carbon transitions would need to be seen by city-level decision-makers as an opportunity rather than a threat, and climateactions would needto betaken from theperiphery of urbandecision-making andmainstreamedintothekeyareasof urban policy such as planning, energy, housing, transport and economicdevelopment.Appropriatestakeholderengagementand governancecapacitieswouldneedtobeestablishedtoensurethat thetransitionisnotatechnocraticexercisebutis‘sociallysteered’ sothatchoicesreflectdifferentsocialconcernsandbuildpublic support over time. New financing arrangements and delivery models need to be built, and enabling policies need to be introduced at different scales. Lessons from the front-runners then need to be identified – for example through robust evaluationsofearlyexperiences– sothat goodpracticecanbe rapidlydevelopedwithinandtransferredbetweencities.Andallof thisneedstobedoneinawaythatstimulatesalong-termvisionof, andacommitmentto,moredeeplydecarbonizedcities.Ifthiscan beachieved,thenexploitingeconomicallyattractivelow-carbon optionsincitiesintheshorttermcouldbeamajorcontributionto successfulclimate changemitigation at the globalscale in the longerterm.
Acknowledgements
This paperwas preparedas a workingpaper for theGlobal CommissionontheEconomyandClimate’sNewClimateEconomy project.WewouldliketothankNickGodfreyandCasparTrimmer fortheirhelpwiththepreparationofthepaper,andthereviewers fortheir usefulfeedback.Thevarious projectsthatfedinto the comparativeanalysispresentedinthispaperwereconductedwith thehelpoffriendsandcollaboratorsincludingCorradoTopi,Ellie Dawkins,RichardPearce,JoyashreeRoy,SayantanSarkar,Debalina Chakravarty,DiyaGanguly,SofiaCastroandCayoRamos.Funding
forthecity-scaleprojectswasprovidedbyorganizationsincluding theUKEconomicand SocialResearchCouncil(ESRC)Centrefor ClimateChangeEconomicsandPolicy(ES/K006576/1),theCentre forLowCarbonFutures,theUKDepartmentforEnergyandClimate Change, the UK Foreign and Commonwealth Office, the Inter-American Development Bank, the Peruvian Ministry for the Environment,theIndonesianMinistryofTransport,theIskander RegionalDevelopmentAgency,and theUniversity ofLeeds.We gratefully acknowledge the support provided by all of these partnersandfundingbodies.Westressthattheviewsexpressedin thispaperarethoseoftheauthorsalone.
AppendixA.Supplementarydata
Supplementarydataassociatedwiththisarticlecanbefound,in theonlineversion,atdoi:10.1016/j.gloenvcha.2015.07.009.
References
Acuto,M.,2013.Cityleadershipinglobalgovernance.Glob.Gov.19(3),481–498. Adelle,C.,Russell,D.,2013.Climatepolicyintegration:acaseofDe´ja` Vu?
Environ.PolicyGov.23(1),1–12.
Berkhout,P.,Muskens,J.,Velthuijsen,J.,2000.Definingthereboundeffect. EnergyPolicy28,425–432.
Betsil,M.,Bulkely,H.,2006.Citiesandthemultilevelgovernanceofglobal climatechange.Glob.Gov.12(2),141–159.
Bajzˇelj,B.,Allwood,J.M.,Cullen,J.M.,2013.Designingclimatechangemitigation plansthataddup.Environ.Sci.Technol.47(14),8062–8069.
Bi,J.,Zhang,R.,Wang,H.,Liu,M.,Wu,Y.,2011.Thebenchmarksofcarbon emissionsandpolicyimplicationsforChinesecities:caseofNanjing.Energy Policy39,4785–4794.
Brown,M.,Southworth,F.,Sarzynski,A.,2009.Thegeographyofmetropolitan carbonfootprints.PolicySoc.27,286–304.
Colenbrander,S.,Gouldson,A.,Sudmant,A.,Papargyropoulou,E.,Chau,L.,Ho,C., 2015a.Exploringtheeconomiccaseforearlyinvestmentinclimatechange mitigationinmiddle-incomecountries:acasestudyofJohorBahru, Malaysia.Clim.Dev..
Colenbrander,S.,Gouldson,A.,Sudmant,A.,Papargyropoulou,E.,2015b.The economiccaseforlowcarbondevelopmentinrapidlygrowingdeveloping worldcities:acasestudyofPalembang,Indonesia.EnergyPolicy80,24–35. Corfee-Morlot,J.,Kamal-Chaoui,L.,Donovan,M.,Cochran,I.,Robert,A.,Teasdale,
P.-J.,2009.Cities,ClimateChangeandMulti-levelGovernance,OECD EnvironmentWorkingPapers.OrganisationforEconomicCooperationand Development,Paris.
Creutzig,F.,Baiocchi,G.,Bierkandt,R.,Pichler,P.,Seto,K.,2015.Aglobal typologyofurbanenergyuseandpotentialsforanurbanizationmitigation wedge.Proc.Natl.Acad.Sci.U.S.A.112,6283–6288.
Dhakal,S.,2010.GHGemissionsfromurbanizationandopportunitiesforcarbon mitigation.Curr.Opin.Environ.Sustain.2,277–283.
Dhakal,S.,2009.UrbanenergyuseandcarbonemissionsfromcitiesinChina andpolicyimplications.EnergyPolicy37,4208–4219.
Dodman,D.,2009.Blamingcitiesforclimatechange?Ananalysisofurban greenhousegasemissionsinventories. Environ.Urban.21,185–201. ESMAP,2012.EnergyEfficientCitiesInitiative,EnergySectorManagement
AssistanceProgramme.WorldBank,Washington.
Feng,K.,Hubacek,K.,Sun,L.,Liu,Z.,2014.Consumption-basedCO2accounting ofChina’smegacities:thecaseofBeijing,Tianjin,ShanghaiandChongqing. Ecol.Indic.47,26–31.
Floater,G.,Rode,P.,Friedel,B.,Robert,A.,2014.SteeringUrbanGrowth: Governance,PolicyandFinance,NCEWorkingPaper2.GlobalCommission ontheEconomyandClimate.
Franze´n,M.,2013.Localgovernmentsasnodesforgreenhousegasabatement: climatechangegovernanceinmulti-levelframeworks.Eur.J.Sustain.Dev.2, 361–372.
Fraser,E.,Dougill,A.,Mabee,A.,Reed,M.,McAlpine,P.,2006.Bottomupandtop down:analysisofparticipatoryprocessesforsustainabilityindicator identificationasapathwaytocommunityempowermentandsustainable environmentalmanagement.J.Environ.Manag.78(2),114–127. Glaeser,E.,Kahn,M.,2010.Thegreennessofcities:carbondioxideemissions
andurbandevelopment.J.UrbanEcon.67,404–418.
Gouldson,A.,Kerr,N.,Topi,C.,Dawkins,E.,Kuylenstierna,J.C.I.,Pearce,R.,2012. TheEconomicsofLowCarbonCities:AMiniSternReviewfortheLeedsCity Region.CentreforLowCarbonFutures,Leeds.
Gouldson,A.,Colenbrander,S.,Papargyropoulou,E.,Sudmant,A.,2014a.The EconomicsofLowCarbonCities:JohorBahruandPasirGudang,Malaysia. CentreforLowCarbonFutures,Leeds.
Gouldson,A.,Kerr,N.,McAnulla,F.,Hall,S.,Colenbrander,S.,Sudmant,A.,Roy, J.,Sarkar,S.,Chakravarty,D.,Ganguly,D.,2014c.TheEconomicsofLow CarbonCities:Kolkata,India.CentreforLowCarbonFutures,Leeds. Gouldson,A.,McAnulla,F.,Sakai,P.,Sudmant,A.,Castro,S.,Ramos,C.,2014d.
TheEconomicsofLowCarbonCities:Lima,Peru.CentreforLowCarbon Futures,Leeds.
GovernmentofIndia,2011.CensusofIndia.GovernmentofIndia,NewDelhi. GovernmentofWestBengal,2009.DistrictStatisticalHandbook:South
24-Parganas.BureauofAppliedEconomicsandStatistics,Kolkata,India. GEA,2012.GlobalEnergyAssessment:TowardaSustainableFuture.Cambridge
UniversityPress/InternationalInstituteforAppliedSystemsAnalysis, Cambridge,UK/Laxenburg,Austria,pp.1307–1400.
GHGProtocol,2014.GlobalProtocolforCommunity-ScaleGreenhouseGas Inventories:AnAccountingandReportingStandardforCities.Greenhouse GasProtocol,WRI,C40andICLEI.
Hajer,M.,1996.Ecologicalmodernizationasculturalpolitics.In:Lash,S., Szerszynski,B.,Wynne,B.(Eds.),Risk,EnvironmentandModernity:Towards aNewEcology.Sage,London, pp.246–268.
Hertwich,E.,2005.Consumptionandthereboundeffect:anindustrialecology perspective.J.Ind.Ecol.9,85–98.
Hoornweg,D.,Sugar,L.,TrejosGomez,C.,2011.Citiesandgreenhousegas emissions:movingforward.Environ.Urban.20,1–21.
IEA,2010.PowerGenerationfromCoal:MeasuringandReportingEfficiency PerformanceandCO2Emissions.InternationalEnergyAgency,Paris,France. IEA,2013a.WorldEnergyOutlook2012.InternationalEnergyAgency,Paris. IEA,2013b.TransitiontoSustainableBuildings:StrategiesandOpportunitiesto
2050.InternationalEnergyAgency,Paris.
INEI,n.d.Principalesindicadoresmacroeconomicos(Principalmacro-economic indicators).InstitutoNacionaldeEstadı´sticaeInforma´tica,Lima,Peru. IPCC,2007.ClimateChange2007–IPCCFourthAssessmentReport.
IntergovernmentalPanelonClimateChange/CambridgeUniversityPress, Cambridge,UK.
IPCC,2014.ClimateChange2014–IPCCFifthAssessmentReport.
IntergovernmentalPanelonClimateChange/CambridgeUniversityPress, Cambridge,UK.
Jordan,A.,Lenschow,A.,2010.Environmentalpolicyintegration:astateofthe artreview.Environ.PolicyGov.20(3),147–158.
Kahn,M.,2009.Urbangrowthandclimatechange.Annu.Rev.Resour.Econ.1, 333–349.
Kennedy,C.,Corfee-Morlot,J.,2013.Pastperformanceandfutureneedsforlow carbonclimateresilientinfrastructure:aninvestmentperspective.Energy Policy59,773–783.
Kennedy,C.,Ramaswami,A.,Carney,S.,Dhakal,S.,2011.Greenhousegas emissionsbaselinesforglobalcitiesandmetropolitanregions.In:Hoornweg, D.,Freire,M.,Lee,M.,Bhada-Tata,P.,Yuen,B.(Eds.),CitiesandClimate Change:RespondingtoanUrgentAgenda.TheWorldBank,Washington. Kennedy,C.,Steinberger,J.,Gasson,B.,Hansen,Y.,Hillman,T.,Havranek,M.,
Pataki,D.,Phdungsilp,A.,Ramaswami,A.,Mendez,G.,2009.Greenhousegas emissionsfromglobalcities.Environ.Sci.Technol.43,7297–7302. Kennedy,C.,Demoullin,S.,Mohareb,E.,2012.Citiesreducingtheirgreenhouse
gasemissions.EnergyPolicy49,774–777.
Kesicki,F.,Ekins,P.,2012.Marginalabatementcostcurves:acallforcaution. Clim.Policy12(2),219–236.
Khan,M.,2012.Urbanpolicyeffectsoncarbonmitigation.In:Fullerton,D., Wolfram,C.(Eds.),TheDesignandImplementationofUSClimatePolicy. UniversityofChicagoPress,Chicago.
Madlener,R.,Alcott,B.,2009.Energyreboundandeconomicgrowth:areviewof themainissuesandresearchneeds.EnergyPolicy34,370–376.
Matsumoto,T.,Nutall,C.,Bathan,G.,Gouldson,A.,Laloe,F.,Pathak,M.,Robert, A.,Welch,D.,2014.Nationalandsub-nationalintegration.In:GreenGrowth inPractice:LessonsfromCountryExperiencesGlobalGreenGrowthInstitute, EuropeanClimateFoundationandClimateandDevelopmentKnowledge Network.
McCollum,D.,Nagai,Y.,Riahi,K.,Marangoni,G.,Calvin,K.,Pietzcker,R.,van Vliet,J.,vanderZwaan,B.,2013.Energyinvestmentsunderclimatepolicy:a comparisonofglobalmodels.Clim.ChangeEcon.4(4),3–32.
Minx,J.,Baiocchi,G.,Wiedmann,T.,Barrett,J.,Creutzig,F.,Feng,K.,Forster,M., Pichler,P.,Weisz,H.,Hubacek,K.,2013.Carbonfootprintsofcitiesandother humansettlementsintheUK.Environ.Res.Lett.8,1–10.
Mohareb,E.,Kennedy,C.,2012.Greenhousegasemissionscenariomodellingfor citiesusingthePURGEmodel:acasestudyofthegreaterTorontoarea.J. Ind.Ecol.16(6),875–888.
Mohareb,E.,Kennedy,C.,2014.Scenariosoftechnologyadoptiontowardslow carboncities.EnergyPolicy66,685–693.
NCE,2014.BetterGrowth,BetterClimate,FirstReportoftheGlobalCommission onEconomyandClimate.
NCE,2015.CatalysingInternationalCooperationforGrowthandClimateAction, 2ndReportoftheGlobalCommissionontheEconomyandClimate. Satterthwaite,D.,2008.Cities’contributiontoglobalwarming:notesonthe
allocationofgreenhousegasemissions.Environ.Urban.20,539–549. Sedepal,2010.PlanEstrate´gicodeTecnologı´asdeInformacio´nyComunicacio´n
2009–2013(StrategicPlanforInformationandCommunicationsTechnology, 2009–2013).SEDEPAL,Lima.
Sorrell,S.,Dimitroploulos,J.,Sommerville,M.,2009.Empiricalestimatesofthe directreboundeffect:areview.EnergyPolicy37,1356–1371.
Sovacool,B.,Brown,M.,2010.Twelvemetropolitancarbonfootprints:a preliminarycomparativeglobalassessment.EnergyPolicy38,4856–4869. Stern,N.,2007.TheEconomicsofClimateChange:TheSternReview.Cambridge
UniversityPress,Cambridge,UK.
UNDESA,2014.WorldUrbanizationProspects.UnitedNationsDepartmentof EconomicandSocialAffairs,PopulationDivision,http://esa.un.org/unpd/ wup/CD-ROM/Default.aspx.
UNHabitat,2003.GlobalReportonHumanSettlements–TheChallengeof Slums.UNHabitat,Nairobi.
Vogt-Schilb,A.,Hallegate,S.,2014.Marginalabatementcostcurvesandthe optimaltimingofmitigationmeasures.EnergyPolicy66,645–653. Weisz,H.,Steinberger,J.,2010.Reducingenergyandmaterialflowsincities.
Curr.Opin.Environ.Sustain.2,185–192.
WEF,2013.TheGreenInvestmentReport:TheWaysandMeanstoUnlock PrivateFinanceforGreenGrowth.WorldEconomicForum,Geneva. WHO,2014.UrbanPopulationGrowth.WorldHealthOrganisationGlobal
