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Will African Consumers Buy Cleaner Fuels and Stoves? A Household Energy Economic Analysis Model for the Market Introduction of Bio-Ethanol Cooking Stoves in Ethiopia, Tanzania, and Mozambique Takeshi Takama, Fiona Lambe, Francis X. Johnson, Anders Arvidson, Boris Atanassov, Milkyas Debebe, Linda Nilsson, Patricia Tella and Stanzin Tsephel Research Report, Stockholm Environment Institute, 2011

Will African Consumers Buy Cleaner Fuels and Stoves€¦ · Will African Consumers Buy Cleaner Fuels and Stoves? A Household Energy Economic Analysis Model for the Market Introduction

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Page 1: Will African Consumers Buy Cleaner Fuels and Stoves€¦ · Will African Consumers Buy Cleaner Fuels and Stoves? A Household Energy Economic Analysis Model for the Market Introduction

Will African Consumers Buy Cleaner Fuels and Stoves?

A Household Energy Economic Analysis Model for the Market Introduction of Bio-Ethanol Cooking Stoves

in Ethiopia, Tanzania, and Mozambique

Takeshi Takama, Fiona Lambe, Francis X. Johnson, Anders Arvidson, Boris Atanassov, Milkyas Debebe,

Linda Nilsson, Patricia Tella and Stanzin Tsephel

Research Report, Stockholm Environment Institute, 2011

Page 2: Will African Consumers Buy Cleaner Fuels and Stoves€¦ · Will African Consumers Buy Cleaner Fuels and Stoves? A Household Energy Economic Analysis Model for the Market Introduction
Page 3: Will African Consumers Buy Cleaner Fuels and Stoves€¦ · Will African Consumers Buy Cleaner Fuels and Stoves? A Household Energy Economic Analysis Model for the Market Introduction

Will African Consumers Buy Cleaner Fuels and Stoves?

A Household Energy Economic Analysis Model for the Market Introduction of Bio-Ethanol Cooking Stoves in Ethiopia, Tanzania, and Mozambique

Takeshi Takama,1 Fiona Lambe,2 Francis X. Johnson,3 Anders Arvidson, Boris Atanassov, Milkyas Debebe, Linda Nilsson, Patricia Tella and Stanzin Tsephel

1 [email protected] [email protected] [email protected]

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Stockholm Environment InstituteKräftriket 2B106 91 Stockholm Sweden

Tel: +46 8 674 7070Fax: +46 8 674 7020Web: www.sei-international.org

Head of Communications: Robert Watt Publications Manager: Erik WillisLayout: Richard Clay

Cover photo: © Gaia Association

This publication may be reproduced in whole or in part and in any form for educational or non-profit purposes, without special permission from the copyright holder(s) provided acknowledgement of the source is made. No use of this publication may be made for resale or other commercial purpose, without the written permission of the copyright holder(s).

Copyright © April 2011 by Stockholm Environment Institute

ISBN 978-91-86125-25-7

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CONTENTS

List of acronyms vList of figures viList of tables viiAcknowledgements and disclaimer viii

Executive summary ix

1 Introduction 1

Strengthening energy, environment and development processes 1

Choice of case studies 1

Structure of the report 2

2 Energy access and household cooking: background and policy context 3

Household energy access and development 3

Clean cooking alternatives 4

Policy relevance 5

3 Approach and methodology 7

Attributes influencing cooking stove choice: gaps in the literature 7

Types of determinant attributes: socio-economic and product-specific 8

Choice experiment and analysis 9

Stated preference survey 9

4 Case study descriptions 12

Ethiopia 12

Tanzania 15

Mozambique 16

5 Stated preference survey design 20

Ethiopia 23

Tanzania 24

Mozambique 25

6 Results 27

Utility parameters 27

The general model 28

Specific models 28

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7 Fuel switch simulation 37

8 Overall discussion 40

Suggestions on the modelling for further research 40

Wish-list approach vs. trade-off approach 41

Product-specific attributes and socio-economic attributes 42

Policy relevant findings 44

9 Conclusions and recommendations 47

Appendix 1: Choice experiment and analysis 48

Appendix 2: Policy outreach and follow up activities 53

Ethiopia 53

Tanzania 54

Mozambique 54

International 54

References 56

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LIST OF ACRONYMS

ASC AlternativeSpecificConstantCOPD ChronicObstructivePulmonaryDiseaseCSA CentralStatisticsAuthorityDALY Disability-AdjustedLifeYearDCA DiscreteChoiceAnalysisDFID UKDepartmentforInternationalDevelopmentEAC EastAfricanCommunityETB EthiopianBirrFAO FoodandAgricultureOrganisationoftheUnitedNationsGIZ DeutscheGesellschaftfürInternationaleZusammenarbeit

(GermanAgencyforInternationalDevelopment)HHEA HouseholdEnergyEconomicAnalysisIAP IndoorAirPollutionLDC LeastDevelopedCountryLPG LiquidPetroleumGasLRI LowerRespiratoryInfectionMDG MillenniumDevelopmentGoalMME MinistryforMinesandEnergyMNL MultinomialLogitMt MegaTonneMWTP MarginalWillingnessToPayMZN MozambicanMeticalNORAD NorwegianAgencyforDevelopmentCooperationPISCES PolicyInnovationSystemsforCleanEnergySecurityPCIA PartnershipforCleanIndoorAirRP RevealedPreferenceRPTES RegionalProgramforTraditionalEnergySectorSEED StrengtheningEnergyEnvironmentandDevelopmentProcessesSIDA SwedishInternationalDevelopmentCooperationAgencySP StatedPreferenceSSA SubSaharanAfricaTZS TanzanianSchillingUDSM UniversityofDaresSalaamUNDP UnitedNationsDevelopmentProgramUSAID UnitedStatesAgencyforInternationalDevelopmentUSD UnitedStatesDollarWHO WorldHealthOrganizationWWF WorldWildlifeFederation

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LIST OF FIGURES

Figure1: ExampleofStatedPreference(SP)survey 10Figure2: TherangesofattributesincludedinSPandRPtechniques 11Figure3: Designprocessofchoiceexperiment 11Figure4: TrendsinsourceofprimaryfuelsforcookinginAddisAbaba

between1996and2004(CSA2004) 13Figure5: KerosenepriceincreaseBirr/LitreinAddisAbaba,1990-2009 14Figure6: HistogramofenergyexpenditureinAddisAbabain2000 14Figure7: Illustrationofpricedevelopmentbyprimarycookingfuelin

DaresSalaam. 17Figure8: HouseholdenergyuseforcookinginMozambique 17Figure9: HouseholdenergyuseforlightinginMozambique 18Figure10: PhotographofchoicecardandsurveyinEthiopiacasestudy 23Figure11: Comparisonbetweenusagecostandstovepriceofmenin

EthiopiaandTanzania(inpartialutility) 33Figure12: Comparisonbetweenusagecostandstovepriceofwomenin

EthiopiaandTanzania(inpartialutility) 33Figure13: Comparisonofsmokelevelcoefficientsbetweenmenand

womeninEthiopiaandTanzania 33Figure14: Comparisonofstovepriceandusagecostcoefficients in the

Ethiopia Case 35Figure15: MWTPofsoftattributesbetweenincomegroupsinEthiopia 36Figure16: MWTPofsoftattributesbetweenincomegroupsinTanzania 36Figure17: Demandsforthreestovesunderchangingethanolfuelprice

scenario 38Figure18: Demandsforethanolstovesinthreeincomegroupsunder

changingethanolfuelpricescenario 39Figure19: Fourcategoriesofcleancookingstovedeterminants 44Figure20: Relationshipbetweenattributesandalternatives 48Figure21: Indifferencecurvesofcookingstovechoice. 49Figure22: Deterministicchoicemodel 51Figure23: Stochasticchoicemodel 51

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LIST OF TABLES

Table1: Summaryofproduct-specificandsocio-economicattributes 9Table2: Distributionofhouseholdsbyenergysourceforcooking

(%ofhouseholds)inDaresSalaam 16Table3: Allocationoflevelsandlabelsforthefourattributesinthe

Ethiopiancasestudy 25Table4: Allocationoflevelsandlabelsforthefourattributesinthe

Tanzaniacasestudy 26Table5: Allocationoflevelsandlabelsforthefourattributesinthe

Mozambiquecasestudy 26Table6: ThegeneralparametersderivedfromtheBIOGEMEmodel 29Table7: SpecificmodeloutputoftheEthiopiacasestudy 30Table8: SpecificmodeloutputoftheTanzaniacasestudy 30Table9: SpecificmodeloutputoftheMozambiquecasestudy 31Table10: Trade-offbetweenattributes(MWTP)basedonstoveprice 35Table11: Simulationinputs:thestovepriceandusagecostofanethanol

stovevaryandtheremainingattributesarefixed 37

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ACKNOWLEDGEMENTS AND DISCLAIMER

We are thankful to all of the households inAddisAbaba, Dar esSalaam, and Catembewhoparticipated in thesurvey.Wearealsograteful toProf.David

BannisteroftheTransportStudiesUnitattheUniversityofOxfordforhiswelcomedsuggestions regarding socio-economic variables and for reviewing the experimentdesign.Wewould also like to thankDr.NigelTapley andNikolaosThomopoulos(University of Leeds, UK) for reviewing and helping to improve the experimentdesign.ThanksalsotoPhilipMann(ECI,UniversityofOxford)foranexpertreviewof the report.The studywas supported by the Swedish InternationalDevelopmentCooperationAgency(Sida)throughtheStockholmEnvironmentInstitute’sresearchand capacity-building programme, Strengthening Energy, Environment andDevelopmentProcesses(SEED).However,Sidawasnotinvolvedinthedesignofthestudyanddoesnotnecessarilysupporttheviewsexpressedinthereport.Thestudywas jointlymanagedandled throughtheSEI-HQinStockholmandtheSEIAfricaCentreinDaresSalaam.

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EXECUTIVE SUMMARY

Switching from traditional biomass to cleaner, safer and more efficient fuels forcookingcanenhancethewelfareofthe2.5billionpeopleworldwidewhocurrently

donothaveaccesstomodernenergysources,whilehelpingtoreducethenegativehealthandenvironmentalimpactsassociatedwithtraditionalbiomassuse.Despitethenumerousbenefitsassociatedwithcleaneralternatives,thetransitiontoimprovedcookingstovesandfuelshaslargelystalledinSub-SaharanAfrica(SSA).Inordertodesigneffectivepoliciesandprogramstopromotetheuseofcleanercookingalternatives,thebarrierstoimprovedcookingtechnologiesmustbeunderstoodatthehouseholdlevel.Todate,researchregardingthedeterminantsofstovechoiceatthehouseholdlevelhasfocusedmainlyonsocio-economicattributes,suchasincome,age,genderandeducation,whiletheroleofproduct-specificattributes,suchassafety,indoorsmoke,usagecostandstoveprice,hasbeendisregardedorgivenlessattention.

ThisreportpresentsastudyconductedbytheStockholmEnvironmentInstitutetoaddressthisknowledgegapbyempiricallyassessingtheroleofsocio-economicattributesandproduct-specific attributes as determinants of cooking stove choice at the householdlevel. The study involved a stated preference survey to investigate household-levelpreferencesofcookingfuelsandstoves;thesurveyincluded200householdsinAddisAbaba,Ethiopia,564householdsinDaresSalaam,Tanzania,and402householdsinMaputo,Mozambique.Theresearchteamappliedanalternativemethodology,discretechoiceanalysis,whichiscommonlyusedintransportationstudies,toassessthetrade-offsamongattributesaffectinghouseholdcookingchoice.Theresearchmethodologyincludedfocusgroupdiscussionswithkeystakeholdersaswellasindividualinterviews,tovalidatethemodelandtoidentifyanysignificantsocial,culturalandlocalattributesthatmayhavebeenoverlookedintheapplicationofthemodel.

Thefindingsofthisstudyillustratetherespectiverolesofsocio-economicandproduct-specific attributes as determinants of stove/fuel choice and permit estimation of therelativestrengthsofproduct-specificattributesindeterminingstove/fuelchoiceatthehousehold level. Such information is crucial for stove producers, policy-makers andprogrammedevelopersinterestedintargetingparticularmarketssinceittellsthemnotonlywhichstovecharacteristicsareimportanttotheconsumer,butalsobyhowmuchtheconsumervaluesonestoveattributeoveranother,orhowtrade-offsaremadeattheindividuallevel.Thistypeofmarketinformationcanbeusedtoaccuratelytargetdifferentmarketsegmentsanddesignstovesthatconsumerswillactuallybuyanduse.Thestudyarguesthatproduct-specificattributesareasimportantassocio-economicattributestocreateamarketforcleancookingstoves,andthatfutureresearchshouldstrikeabalancebetweenbothtypesofattributes.Inanear-termperspective,product-specificattributesaremore important, as socio-economicattributes tend tochange slowly, in linewithlonger-termpatternsofeconomicgrowthandhumandevelopment.Thestudy resultsdemonstratetheinsightsthatcanbegainedfromdetailedconsumerchoiceanalysis,andhowtheseinsightscansupportpolicymakersandcookingstoveprogrammedesignersinterestedinevaluatingmarketsfornewstovesandcookingfuels.

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Stockholm Environment Institute

1 INTRODUCTION

Thisresearchreportpresentsahouseholdenergyeconomicanalysisstudyconductedby researchers at theStockholmEnvironment Institute between2008 and2010

in three locations in Sub SaharanAfrica:AddisAbaba, Ethiopia, Dar es Salaam,TanzaniaandMaputo,Mozambique.Anovelmethodologybasedondiscretechoicetheorywasdesignedtoinvestigateconsumerchoiceforfuelsandcookingstovesandthismethodologywasthenappliedineachofthethreelocations.Tothebestoftheauthors’knowledge,thisisthefirsttimesuchamethodologyhasbeenappliedinanexperimentalsettingtoanalyseconsumerdecision-makingaroundcookstovesandfuelchoicesinadevelopingcountrycontext.Theaimoftheanalysiswastoaddressgapsin the literatureregardingtheconsumer-leveldeterminantsofstoveandfuelchoiceandtodemonstrateamethodologythatcansupportpractitionersandpolicymakersindesigningnewprogrammes,productsandpoliciesinthehouseholdcookingsector.

Strengthening energy, environment and development processes

TheHouseholdEnergyEconomicAnalysismodel(HHEA)wasdevelopedaspartoftheSEIprogrammeonStrengtheningEnergyEnvironmentandDevelopmentProcesses(SEED), an initiative aimed at new approaches for improving energy access andenhancinglivelihoods.Theprogrammealsoincludedsupportinpolicyprocessesforimprovingenergyaccess;evaluationofmodernbioenergyoptionsforsocio-economicdevelopment;andruralandrenewableenergyenterprisedevelopment.

Aspecialfocushasbeenondevelopingevaluationmethodsforassessingthetransitionawayfromtraditionalfuelssuchaswoodandcharcoaltocleancookingfuelssuchasbio-ethanol.Traditionalbiomassprovidesonlylowqualityenergyservicesandraisessignificant environment, health and socio-economic welfare concerns. The HHEAaimsforabetterunderstandingof themarketopportunitiesforhouseholds toadoptclean cooking fuels by evaluating consumer response through a fuel/stove choiceexperimentinseveralcasestudyregionsinsub-SaharanAfrica.

Choice of case studies

ThefirstcasestudywascarriedoutinJuly2008inAddisAbaba,Ethiopia.Ethiopiawaschosenasaninitialstudysite,sincetheintroductionofcleancookingstoveshasbeenidentifiedasapriority.Moreover,deforestationisasevereenvironmentalprobleminEthiopiaanditisacknowledgedthatreducingfirewoodandcharcoalconsumptioncanreduceresourcepressuresthatcontributetodeforestation.Also,alocalNGO—GaiaAssociation—hasbeenplanningfortheproductionanduseofanethanolstoveinAddisAbaba;therefore,intermsofpolicyrelevance,AddisAbabaofferedahighlyappropriateinitiallocationtocarryoutthechoiceexperiment.

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Will African Consumers Buy Cleaner Fuels and Stoves?

ItwasdecidedthatadditionalHHEAstudieswouldbeconductedinDaresSalaam,Tanzania, and inMaputo,Mozambique to build upon and strengthen the approachthatwasusedintheAddisAbabacasestudy.Byapplyingthesamemethodologyinadditionalsitesandbymaintainingthesameresearchparameters,itwaspossibletomakecomparisonsacrossallthreestudies.Toensureconsistency,similarsitestothoseinEthiopiawereselected,thatis,urban/periurbanneighbourhoods.Thestudyisalsoofstrategicrelevanceforthecountriesinquestion:bothTanzaniaandMozambiquehave been investigating biofuels options as a contribution to a more sustainableenergysupply,toimproveenergysecurityandtostimulateruraldevelopmentandtheagriculturalsector.Themainpurposeofthethreecasestudiesistoanalyseaswitchtothebio-ethanolstove/fuelbasedonthespecificchoiceexperimentsthatwereused,ratherthanamoregeneralexplorationofthedifferentalternativesthatareavailable,orcouldbemadeavailable,inthesethreelocations.

Structure of the report

Given the study objectives presented above, Chapter two sets the policy contextfor theHHEAbyexploring theenergyaccess-development-environmentnexusanddescribingthehouseholdenergycrisiscurrentlyaffectingmanydevelopingcountries,particularlyinSubSaharanAfrica.Thepotentialroleofcleanfuelssuchasbio-ethanolforaddressing thisproblem isdiscussedand thepolicy relevanceof theHHEAforeachofthecasecountriesispresented.ChapterthreeintroducestheDiscreteChoiceAnalysismethodologyappliedinthecasestudiesandtheideaofchoicedeterminantattributes,uponwhichitisbased.Thestatedpreferencesurveytoolisthendescribedand an overview of the experimental design process is given. There follows, inChapterfour,adetaileddescriptionofeachofthecasestudysitesintermsofpolicybackground andhousehold energyprofile.This is followedby a description of thestatedpreferencesurveydesignprocessineachofthecasecountriesinChapterfive,highlightingparticularlocalfactorsinfluencingthestudydesignandimplementation.

ThemainfindingsofthestudyareexpoundedinChaptersix,withparticularattentiongiventotheresultsacrossvarioussocio-economicstrataineachcasecountryandthetrendsobservedintermsoftrade-offsmadebyhouseholdsbetweendifferentproduct-specificfactorswhenselectingacookingstove.InChapterseven,thepotentialofthemethodologyforsimulatingfuelswitching,undergivenmarketscenariosisexploredtaking the case exampleofEthiopia.Anumber ofmethodological issues emergingfromtheHHEAarediscussedindetailinChaptereightwithparticularattentiongivento possible improvements that could bemade in future applications of themodel.Different approaches to understanding the relative strengths of attributes affectingstovechoiceareevaluatedandtheimportanceofanalyzingbothproduct-specificandsocio-economic factors affecting stove choice is explained. Several policy relevantfindingsarethendiscussed.Finally,Chapterninesummarizesthemainfindingsandofferssomerecommendationsintermsofthefuturedevelopmentandapplicationofthemethodologyformakingsimilarassessments.

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Stockholm Environment Institute

2 ENERGY ACCESS AND HOUSEHOLD COOKING: BACKGROUND AND POLICY CONTEXT

Thissectionlooksatthecentralroleofhouseholdenergyforhumandevelopment,highlighting thehealth,environmentalandsocio-economicconsequencesofnot

havingaccesstocleanandsafeenergyservicesatthehouseholdlevel.Thepotentialofcleancookingfuelsforaddressingthehouseholdenergyprobleminsub-SaharanAfricaisoutlinedandthepolicyrelevanceoftheHHEAstudyisdescribedforeachcase study country. The HHEA is placed in the context of regional and nationalpolicygoalsrelatedtohouseholdcooking,andsomebackgroundisprovidedonthedevelopmentofthemethodologyfromtheinitialcasestudyinEthiopia.

Household energy access and development

Although there is noMillenniumDevelopmentGoal (MDG) specifically related toenergy,accesstoenergyisintrinsicallylinkedtopovertyreductionandplaysacrucialroleunderpinningeffortstomeettheMDGs(UNDP,2005).ForpoorhouseholdsinLeastDevelopedCountries(LDCs),cookingoftenaccountsfor90percentormoreoftotalenergydemand.Withoutamassivescale-upofaccesstomodernenergyservicesinthecomingyears,itisunlikelythattheMDGswillbemet(DFID,2002).Intheyear2000,indoorairpollutionfromsolidfuelusewasresponsibleformorethan1.6millionannualdeathsand2.7percentoftheglobalburdenofdisease(inDisability-AdjustedLifeYearsorDALYs),makingindoorairpollutionthesecondbiggestenvironmentalcontributor to ill health afterunsafewater and sanitation.Therefore, cooking stoveandenergyareoftenquiterelevanttoMDGpolicies,suchasMDG4-ChildMortality;improvementstocookingstovesandhouseholdenergyusecansupportachievementoftheMDGs.

InSSAalone, an estimated500,000people die eachyear fromdiseases causedbyexposure to indoor air pollution (IAP) from burning biomass.Without systematicchanges, household biomass use will result in an estimated 8.1 million LowerRespiratoryInfection(LRI)deathsamongyoungchildrenand1.7millionCOPDdeathsamongadultwomen inSSAbetween2000and2030(Bailis,Ezzatti,andKammen2007p.6).Sincealargenumberofdeathsfromillnessescausedbyexposuretoindoorairpollutionoccuramongwomenandchildren,biomassuseforcookingisrelatedtomultipleMDGsandhasbeenidentifiedasakeyfocusareainpovertyreductionefforts.

In addition to the act of cooking itself, the task of gathering fuelwood also fallsmainlyonwomenandchildrenandcomeswithahighsocio-economiccost,typicallynotaccountedforbygovernmentpolicies.Forexample, therearesignificantsocio-economic impacts due to the opportunity costs of spending several hours per daygatheringfuelwood.Thepossibilitytousethattimetoengageinincome-generatingandeducationalactivitiescontributestothestabilityandadvancementofhouseholds

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Will African Consumers Buy Cleaner Fuels and Stoves?

andcommunities(UNDP,2005).Therearealsosafetyrisksforwomenandgirlsthatmusttravellongdistancesbyfootaloneorinsmallgroups.Wherefuelispurchased,spending money on large quantities of fuel due to an inefficient stove constrainshouseholdbudgets(WHO,2006).

Finally, there is agrowingbodyofknowledge linkinghouseholduseof traditionalbiomass to emissionsof black carbon,which is a significant contributor to climatechange.Residential biomass burning is responsible for an estimated 18percent ofglobalblackcarbonemissions(BondandSun,2005).Thecombinationofclimateandhealthbenefitscreatesevengreaterimpetustoaddressthehouseholdenergyproblem.

Clean cooking alternatives

Modernor cleancooking fuels are considered tobe thoseused in stoves thathavehighenergydensity,highcombustionefficiencyandhighheat-transferefficiencywithsufficientheatcontrolcharacteristics.BiogasandLPGarecommongaseouscookingfuels,whileethanolandkerosenearethemainalternativeliquidcookingfuels(SchlagandZuzarte,2008).Efficientstovesarealsoavailableforwoodorcharcoal,sometimesrelyingonpreparedfuelssuchaswoodchipsorpellets.Aprogressiveshifttocleanerfuels and technologies for basic energyneeds is a cost-effectivemeans of creatingnewdevelopmentalternatives,reducinghealthimpactsandmitigatingenvironmentaldegradation.Despite themanyadvantagesofcleancookingoptionsover traditionalbiomass, their use remains limited in SSA. The failure of clean fuels to achievewidespreaddisseminationinhouseholdsistheresultofacombinationofeconomic,politicalandsocialattributes(Goldemberg et al., 2004).

Atthesametime,newmarketopportunitiesareemergingfordevelopingcountriestoimproveaccesstomodernenergysourcesthroughthedomesticuseoflocallyproducedbiofuelswhile reducing reliance on imported fossil fuels.This has led to renewedoptimismregardingthefeasibilityofprogrammestoaddresshouseholdenergyaccessindevelopingcountries.AnumberofAfricancountriesarecurrentlyproducingethanolatsignificantscales,includingEthiopia,Kenya,MalawiandZimbabwe.Theethanolismainlyusedasanadditiveintransportationfuelsbutastheindustrycontinuestoexpand,ethanolcouldoffertheprospectofmeetinghouseholdcookingneeds(SchlagandZuzarte,2008).

Ethanolisamongthecleanestofhouseholdfuelswhenburnedinproperappliances,andiswidelycost-competitivecomparedwithelectricity,LPG,andkerosene.Itcanbedistributedthroughexistinginfrastructureandmarkets;andithasahighefficiency,lowsmoke/sootlevel,andhigh‘cleanliness’comparedtothosefuelsmostprevalentfor cooking.The use of alcohol fuels in the household results in greatly improvedair quality in the kitchen compared to firewood or charcoal, safer handling withreduced danger of fires, burns, and explosions compared to kerosene, and reducedenvironmentalimpactscomparedtotraditionalstovesandfuels(Kassa,2007).

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Stockholm Environment Institute

Policy relevance

Althoughtheapproachusedinthisstudyisbroadlyapplicable,thisresearchusedasa case study the option of bio-ethanol stoves as a clean cooking alternative.Othersolutionssuchasusingmoreefficienttraditionalwoodorcharcoalcookingstovesarealsoimportant,especiallyinperi-urbanareas;however,theseoptionsarebeyondthescopeoftheapplicationchoseninthisresearchandwillnotbediscussedindetailinthis report.Nevertheless, themethodology itself isgeneralenough tobeapplied tobasicallyanytypeofhouseholdcookingoption.

Inadditiontoalternativecookingfuelsbasedonrenewableenergysuchasbio-ethanol,there are also non-renewable options such as LPG (Liquefied PetroleumGas) thatwouldhaveimmediatebenefitswithrespecttoindoorairpollutionaswellasreducingotherenvironmentalandsocialimpacts.However,arenewableoptionoffersadditionalbenefitsintheformofclimatemitigation,developmentopportunitiesfornewdomesticagro-industriesandimprovementsinforeignexchange.ManySSAcountriescurrentlyproduce somebiofuels and several countries are in theprocessofdraftingnationalbiofuels development strategies to outline the targets and sustainability criteria forthegrowthofthisindustry.Thereappearstobepotentialforlocallyandsustainablyproducedbiofuelsasanalternativesourceofcleanenergyforthehouseholdsector;it is nevertheless important when designing policies and programmes to considerpotentialconflictsrelatedtofoodsecurity,wateruseandsocio-economicimpactsonlandandlivelihoods.Thisstudyfocusesonthedemandsideoftheequationforcleancookingfuel,althoughinthebroaderpolicycontextitwillofcoursebeimportanttoaddressthesupplysideaswell.

InEthiopiawheretheproductionofethanolisbeingrapidlyscaledup,thegovernmenthasdevelopedabiofuelspolicythatincludestheuseofethanolforhouseholdcooking,inadditiontoitsuseinthetransportsector.BothTanzaniaandMozambiquehavebeeninvestigatingbiofuelsoptionsasacontributiontoamoresustainableenergysupply,to improve energy security and to stimulate rural development and the agriculturalsector.Abiofuelstaskforcehasbeenestablishedineachcountrytoassessthevariousoptionsforthedevelopmentofpoliciesandstrategiestoincreasetheuseofbiofuelsinthetransportsector.

However, there has been little analysis on the role of ethanol and other renewablecookingfuelsinthehouseholdsector.Oneconcepttobeexploredistousethetransportfuelmarket,whichismuchlargerinvolume,tosupportthecreationofahouseholdfuelmarket.Inthismanner,thehousehold-levelorenergyaccessbenefitsofabiofuelprogram can be extended tomany households and not just to those few that haveaccess to vehicles. Policymakers, programmedevelopers and stoveproducerswillbenefitfromamoreaccuratepictureofthepotentialmarketforethanolasacookingfuel.TheHHEA(HouseholdEnergyEconomicAnalysis)methodologycanbeusedtogeneratedetailedinformationonfuelandstovemarketsegmentation,whichisvitalforexpandingthemarketforimprovedstovesandfordesigningmechanismstoincrease

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Will African Consumers Buy Cleaner Fuels and Stoves?

accesstoimprovedfuelsandtechnologiesforlowerincomehouseholds,e.g.throughsubsidiesormicrofinanceprogrammes.

In order to reach energy targets for improved cooking options, such as the targetsestablished in the East African Community (EAC), policy makers and stoveprogrammedesignersneedtohaveaclearunderstandingoftheattributesthatinfluenceahouseholds’choiceofcookingfuelandstoves.However,accurateknowledgeinthisareahashistoricallybeenlackingand,asaresult,alargenumberofimprovedcookingstoveprogrammeshavefailed(Barnes et al., 1994).TheHHEAmethodologyappliedinthisprojectwasdevelopedspecificallytoaddressthisknowledgegapandthusbeofpracticalusetopolicymakersandstoveprogrammedesigners.

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Stockholm Environment Institute

3 APPROACH AND METHODOLOGY

This sectionbeginsby reviewing researchoncookingstovechoiceover thepastthree decades and highlighting some major omissions in terms of the lack of

academicfocusonproduct-specificfactorsasdeterminantsofchoice.Theimportanceof studying both product-specific and socio-economic factors to gain an accuratepictureofhouseholddecisionmakingisdiscussedandthemethodologyappliedinthethreecasestudies,discretechoiceanalysis,togetherwiththestatedpreferencesurveytechnique, is introduced.Amore detailed description of discrete choice analysis isprovidedinAppendixone.

Attributes influencing cooking stove choice: gaps in the literature

Sincetheearly1970s,academicresearchandtheoreticalmodelshavegenerallyviewedincomeasthesinglemostimportantattributeresponsibleforvariationincookingfuelandtheassociatedstovechoiceat thehouseholdlevel(Briscoe,1979;Davis,1998;HosierandDowd,1987;PachauriandJiang,2008).Atthesametime,muchresearchhas in fact considered non-income attributes affecting cooking stove choice at thehouseholdlevel(GuptaandKöhlin,2006;Heltberg,2004;HosierandDowd,1987;NarasimhaRaoandReddy,2007).Areviewof20academicjournalsindicatedthat47non-incomeattributeshavebeenidentifiedasaccountingforsomevariationinstovechoice,withthemostcommonlyreportedattributesbeingage,occupation,education,household size, and gender (for example, readGupta andKöhlin, 2006;Heltberg,2004;Ouedraogo,2006).

Theacknowledgementintheliteratureofmanynon-incomeattributesaffectingcookingstove choice combined with a common assertion about the lack of understandingof those attributes at household level raises questions about the methodologicalapproachesuseinpreviousresearchonstovechoiceanalysis.Acloseexaminationoftheidentifiedattributesandthemethodologiesadoptedindicatedanumberofmajorshortcomings.Oneofthefundamentalshortcomingsinthepublishedliteratureisthattheresearchfocusishighlyskewedtowardssocio-economicattributesandconsistentlyfailstodistinguishbetweenthedifferentrolesofproduct-specific-attributesandsocio-economic attributes.This is amajor omission because failing to consider product-specificattributes is tantamount to ignoring theagency1ofhouseholdconsumers infuelandstoveselection,akeyattributeinpredictingthemarketforimprovedstoves.Fromafuel-switchingperspective, it isespecially important todistinguishproduct-specificandsocio-economicattributes,asdiscussedfurtherbelow.

1 Agency is a term from economic theory, and in this case refers to the role of the (household) con-sumer as an economic decision-maker (i.e. an economic agent).

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Types of determinant attributes: socio-economic and product-specific

In theory, all determinant attributes of household cooking choice fall into eitherindividual socio-economic characteristics or product-specific characteristics. Thesetwotypesofattributescanbedistinguishedbasedonvariations“between”and“within”individuals.Theformertypearecharacteristicsbetweenindividualsastheseattributesaredifferentbetweendifferentindividualsanddonotchangeforanindividualoverashortperiodoftime.Thelatteristhevariationthatanindividualfaceswhilemakingachoice.Foragivenindividualatagivenmoment,socio-economiccharacteristicsarealwaysfixed;hencetheattributesresponsibleforvariationinchoice‘within’individual(foragivenindividual)areproduct-specific.Itisimportanttoclusteralldeterminantsofstovechoiceintothetwocategories,sincetheusagesandrolesoftheseattributesaredifferentineachcategory.Itisinstructivetonoteherethatthiscategorisationisanalogoustoshort-runandlong-runcostsinproduction/consumptiontheory,i.e.thatsomeattributesofindividualsarefixedintheshort-termbutvariableoverthelong-term,justlikedifferentattributesofproductionarefixedorvariable.

Theoretically,asocio-economicattributethatdeterminescookingstovechoicecouldbeanythingthatdefinesanddescribespeopleandhasacorrelationwithvariationinstovechoice.Asocio-economicfocusedanalysisseekstoidentifyindividualorhouseholdcharacteristicsandassessifstovechoicesdifferacrossthespecifiedsocio-economicvariables.Commonlyreportedsocio-economicattributessuchasage,income,gender,andeducationdonotvarywithin individuals in the short term, i.e. thevariation is“between”individuals.Theseattributesareusefulinidentifyingthetargetmarketandunderstandingtheprofileofitsconsumers.

In contrast, product-specific attributes refer to attributes or characteristics of thecooking stoves (and the associated fuel or fuels) themselves.As an individual cantestorevaluatedifferentstoveswithinashortperiodoftime,theseattributeschange“within”theindividual.Amongtherelevantproduct-specificattributesarestoveprice,usagecost,convenienceand levelofsmoke.Previous researchon thedeterminantsof cooking stove choice lacked a focus on product-specific attributes. This biasedapproach and limited understanding of product-specific attributes is a significantconstraint for the successfuldesignand/orpromotionof fuel/stove switching.Onlyproduct-specificcharacteristicscanbeeasilymodifiedinarelativelyshortperiodoftimetodevelopanappropriatestovedesignwithahighprobabilityofacceptanceinatargetmarket.

As summarised inTable1, theproduct-specificandsocio-economicattributeshavedifferentcharacteristicsandrolesinthepromotionofmodernenergyefficientcookingstoves; therefore, an innovative approach andmethod is required to categorise andstudyeach.Furthermore,thequantificationoftheseattributesisnecessarytoprovideguidanceforpracticalprojectimplementation.

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Choice experiment and analysis

Accurate demand predictions are vital for the uptake of innovative clean cookingalternativessuchasethanolorsolarcookingstoves;without this information,stoveproducers cannot risk producing new stoves and policymakers are unable to givesuitablesupporttotheprojects.InordertomakesuchaccuratedemandpredictionsforhouseholdfuelsandstovesinEthiopia,TanzaniaandMozambique,theprojectteamdevelopedafairlynovelapproachbasedonastatedpreferenceSurveyandtheuseofDiscreteChoicetheory(seeAppendix1foradetaileddiscussion).Choiceanalysishasbeensuccessfullyappliedasanapproachformakingeconomicassessmentsinvariousmarketresearchandtransportationstudies.

The research team applied a discrete choice analysis model, in this case using amultinomial logit model (MNL), in order to evaluate the trade-offs inherent inhouseholdchoiceofcookingstovesandfuels.Thismodelwasselectedasitallowsforthequantitativeassessmentofbothsocio-economicandproduct-specificfactors,andbecausetheresearchteamwasinterestedinknowingnotonlywhetheraparticularproduct-specificfactorisimportant,butalso,howimportantitisinrelationtootherfactors.

Consumers derive utility not from a cooking stove as such, but from its specificcharacteristics/attributessuchasheatenergydelivered,smokelevel,safety,conveniencetouseandsoon.Hence,thestrengthofthefactorsaffectingastovechoiceisderivedfromtheweightoftheutilitythatanindividualderivesfromeachattributeofastoveandhowmuchtheyarewillingtopayforthoseattributes.Therelativeweightofeachattributecanbeestimatedbydesigningachoiceexperiment.

Stated preference survey

CentraltotheDCAmethodologyistheStated Preference(SP)surveytechnique.InaSPsurvey,ananalystaskspeopletochoosealternativeswithstatedattributesinahypotheticalsituationusingquestionnaires,cards,telephoneinterviews,web,etc.Forexample,preferencebetweenethanolandfirewoodstovescanbeaskedusingcardsas showninFigure1.

Table 1: Summary of product-specific and socio-economic attributes

Product-specific attributes Socio-economic attributes

Specific to: Product Person

Characteristics: Universal in nature Specific to context

Variation in choice: Within individuals Between individuals or groups

Change in short-term: Relatively easy Difficult

Useful for: Product design, demand forecast, policy formulation

Market segmentation/profiling and policy formulation

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Inthissituation,anindividualisaskedtotrade-offbetweenthepricesandsmokelevelsoftwostoves.Ifs/heiswillingtopayextra$50toreducethesmokelevelfrom“High”to “Low”, he/she should choose an ethanol stove; otherwise, it is better to choosea firewood stove.An analystmay askmultiple questions in different combinationsofattributes; therefore,SPtechnique isconsideredmoreefficient thanconventionalquestionnaires, in thatanumberofquestionscanbeaskedtooneperson,andthus,morethanonesampleisgeneratedfromaSPsurvey.

Moreover,realobservedchoices,i.e.Revealed Preference (RP),giveonlyalimitedvariation of data due to technological constraints and competition between similarproducts.Forexample,aproducercannotmakestovesthataremuchmoreexpensivethansimilarproductsdue tomarketcompetition.For thesamereason,onewillnotfindgoodquality(lowsmokelevel)productsatcheapprice.Inotherwords,inRP,ananalystcannotcontroltheselectionofattributesandattributelevels.Incontrast,usingaSP survey, the analyst is free to choose any combinationof attributespotentiallyaffectingchoicebehaviour.Therefore,achoiceagainstanon-existingsituationcanbetestedwiththistechnique.AnanalystcanobtainamuchwiderrangeofdatawithSPthanRP.Inthefirstindifferencecurveexample,potentiallyobservedsmokeleveland

Figure 1: Example of Stated Preference (SP) survey

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pricecombinationofastovewillbeafractionofI2.Incontrast,theanalystcantestanycombinationonI1,I2,andI3asinFigure2.

Experimental design processTheSPsurveyisacoreofthechoiceanalysisasanempiricalexperiment.AccordingtoHensher,et al.,(2005),anappropriatedesignprocessissummarisedasFigure3.Thechoiceanalysisshouldstartwitharefinementofthestudyproblem.Then,ananalystis required to identify and refinealternatives andattributes through feedback loopsasinverifyingtheattributes,ranges,etc.beforecompletingachoiceexperimentandconstructinganactualsurvey.

Figure 2: The ranges of attributes included in SP and RP techniques

Figure 3: Design process of choice experiment

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4 CASE STUDY DESCRIPTIONS

The following section provides background information for each case countryincludingthepolicyrelevanceoftheHHEAstudy,householdenergyconsumption

patternsandtrendsineachcity(AddisAbaba,DaresSalaamandCatembe),andothercasespecificrelevantdatasuchas,forexamplethestatusofnationalbiofuelsstrategiesandongoingcleanstoveprogrammes.

Ethiopia

InJuly,2008,thefirstofthreeHouseholdEnergyEconomicAnalyseswasconductedinEthiopiatoestimatetherelativeimportanceofproduct-specificandsocio-economicdeterminantsofcookingstovechoiceatthehouseholdlevel.ThestudywasconductedinclosecooperationwithGaiaAssociation,alocalNGOengagedinthepromotionofethanolandethanolfuelledstovesasacleanandsafealternativetotraditionalbiomassandkeroseneforhouseholdcookinginEthiopia.

Background and policy relevance Ethiopiacurrentlyproduceseightmillionlitresofethanolannuallyandplansareinplace toscaleupproductionto130millionlitersbytheyear2012/13(HeckettandAklilu,2009).Becauseofthisgreatpotential,in2004DometicAB(amajorSwedishproducerofalcoholappliances)introducedtheethanol-burningCleanCookStoveandinpartnershipwithGaiaAssociation,conductedapilotstudyamong500AddisAbabahouseholds. The study confirmed the stove’s popularity among users, who cited anumberofbenefitsincludingimprovedhealthandsavingsintimeandincome(Kassa,2007;Lambe,2006;StokesandEbbeson,2005).TheGovernmentofEthiopiahasbeenreceptivetotheideaofdevelopingthehouseholdcookingmarketforethanolandhasincludedlegislationtothiseffectinitsdraftbiofuelspolicy.TheCleanCookstoveisanadaptationoftheleadingalcoholstoveinuseinnicheapplicationsintheDevelopedWorldforthreedecades.Itisestablishedtechnologyandthoroughlyconsumer-tested.Since it is non-pressurized, there is no risk of explosion.The stove is constructedentirelyofstainlesssteelandisdurableandlonglastingwithanestimated10-yearlife.Itiscurrentlyavailableeitherwithoneortwoburners;eachburnerprovides1.5kWofheatoutputandhasitsownfuelcanisterthatholds1.2litresoffuelsufficientfor4.5hoursofcooking.IthasheatingpowerequivalenttoanLPGstoveandaturn-downcapability that allowsusers to conserve fuel and simmer food.Moreover, it has anefficiencyratingof55percentormore,whichiscomparablewithtypicalefficienciesofLPGstoves.Thestovehasbeenredesignedfor theEthiopianmarket tobemorecheaplymanufactured,tobestronger,holdlargerpots,andholdround-bottomedpots(StokesandEbbeson,2005).Thelowemissionsresultinlowerindoorairpollutionandthusfewerhealthimpacts.

Household cooking in Addis AbabaThe most widely used cooking fuel inAddisAbaba is kerosene (42.2percent ofhouseholds) followed by fuelwood (29.4percent). Electricity, LPG, charcoal and

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residuesareusedbyamuchsmallershareofurbanhouseholds.Theprimarycookingstoveused inAddisAbaba is thesingleburnerkerosenewickstove,which isusedin98percentofAddisAbabahouseholds (Kassa,2007;FAOandPISCES,2009).2Fuelwoodcookingistypicallydoneoveranopen,three-stonefire.CharcoalcookingisdonewiththeLakech,whichisatypeofimprovedstove,andothertypesofmetalstoves.LPGisusedbythehighest incomegroupin thecityandthisgroupusuallyownsmultipleburnerLPGstoves.However,LPGisoftendifficulttopurchaseduetounstablesupplyinAddisAbaba.Themostwidelyusedelectricstoveisasingle-burnerhotplate.

Cookingfueluseintheurbanareasfluctuatesfrequentlyduetochangesinpricesandavailabilityoffuels.Asignificantchangetookplacebetween1996and2004wherethenumberofhouseholdsreportingkeroseneastheirprimarycookingfueldeclinedinbothrelativeandabsoluteterms(Figure4).Besidesthepotentialadditionalusagein fuelwooddue tomigration intoAddisAbaba, the reduction inkeroseneusewasaccompanied by an increase in the number of households cooking with fuelwoodwhichsuggeststhatalargeproportionofkeroseneusersmayhaveshiftedtofuelwoodormovedfromusingmostlykerosenetousingmostlyfuelwood.Suchashifttowardsalessconvenientandlessefficientfuelsuggeststhatpricemayhavebeenthemaindriver.Indeed,thepriceofkeroseneincreasedsteadilybetween1996and2004(seeFigure5).

Increasedcosttotheconsumerofenergyacquisitionerodedhouseholdwelfare(Figure6).Morethan75percentofhouseholdsinurbanareashaveanannualincomeoflessthanETB6,000.Theaveragehouseholdspends10percentofitsincomeonenergy.Forurbanhouseholdswithmoreincome,theaverageexpenditureisabout6percentoftotalincome.Householdsinthelowestsegmentspendasmuchas16percentoftheirincomeonenergyandthoseatthehighestsegmentabout4percent(Figure6)(Kassa,

2 Households may have multiple fuels, since wood or charcoal may be needed for cooking injera (bread) and the traditional coffee ceremony.

Figure 4: Trends in source of primary fuels for cooking in Addis Ababa between

1996 and 2004 (CSA 2004)

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Figure 5: Kerosene price increase Birr / Litre in Addis Ababa, 1990-2009

Figure 6: Histogram of energy expenditure in Addis Ababa in 2000

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2009)The proportion of income spent on energymay have increased even further(thanthatshowninthefigure)becausepriceshaveincreasedsignificantlysince2000whereasincomeshavenot.Thusenergyexpenditureisclearlyerodingtheincomesofpoorandmiddleincomehouseholdsinurbanareas.

Tanzania

TheTanzanianHouseholdEnergyEconomicAnalysiswasconductedinDaresSalaambetweenSeptemberandOctoberof2009.Thepurposeofthestudywas—aswiththeEthiopiacasestudy—toestimatetherelativeimportanceofproduct-specificandsocio-economicdeterminantsofcookingstovechoiceatthehouseholdlevel.

Background and policy relevance By 2010, the Tanzanian Ministry of Energy and Minerals aims to increase thenumber of grid-connected households by 20percent and to decrease charcoal andfirewoodusageby80percent.Atpresent,14percentofurbanand2percentofruralhouseholds are connected to the grid.3 Of total energy consumption in Tanzania,90percentisstillderivedfromfirewoodandcharcoal,andlessthanonepercentofthenationalenergybalanceisgeneratedfromsustainablerenewableenergysources.TheJointEnergySectorReviewstateintheir2009(finaldraft)reportthat:“Themainenergychallengeistoreversetherelianceonfirewoodandcharcoalasthesourceofenergyasthisisunsustainable.”4

Household cooking in Dar es SalaamAscanbeseeninTable2below,CharcoalisthedominantsourceofenergyinDaresSalaam.Itistheprimarycookingfuelforalmost75percentofhouseholds.Fueluseismorediversethanthisnumberwouldimply,however,withthemajorityofhomesusingmorethanonekindofcookingfuel(Sanga,2003).

CharcoalisusedbyallincomelevelsinDaresSalaam,andinadditiontobeingthemostaffordabletypeoffuel,itisalsoapartofTanzaniancultureandtradition.Householdsare by far the largest consumer of the fuel (Varmola, Valkonen, and Tapaninen,2008).Useofcharcoalisalsofavorableduetoverylowstoveprices.Thenumberofhouseholdsusingefficientcharcoalstoveshasincreasedfrom49percentin2002to72percentin2007.Charcoaliscommonlyusedincombinationwithkerosene,orlessfrequentlywithelectricity.High-incomegroupsgenerallyonlyuseelectricity(Yahya,2000).Theincreaseofcharcoaluserscomparedtotheearly1990sisatleastpartlyanoutcomeofthedecreaseinitsprice,comparedtoalternativefuels(Ghanadan,2004).

ThetrendincookingfuelusagepatternsoverthelastfifteenyearsinDaresSalaamhasbeentowardsincreasinguseofsolidfuels,despitegovernmenttargetstoreducetheiruse.Firewoodusagehasincreasedfroma1991/1992levelof1.2percenttobeing

3 Joint Energy Sector Review final draft report 2009, p. 15

4 Joint Energy Sector Review final draft report 2009, p. 15

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thethirdmostcommonlyusedprimarycookingfueltodayat8percent,andcharcoalhasstrengtheneditsdominancefrombeingusedbyoneintwohouseholdsintheearly1990stobeingtheprimarychoiceof75percentofhouseholdsby2009.Conversely,electricityhasdeclinedoverthelast15yearsfrom7.5percenttotoday’slevelofamodest2.2percent.

Mozambique5

The Mozambican Household Energy Economic Analysis was conducted duringSeptemberandOctoberof2009.

Background and policy relevance The Human Development Report highlights that 18.6 million people are withoutaccesstoelectricitycountrywide(UNDP2008p.305)inMozambique.Thisaccountsforabout90percentofMozambique´s20.5millionpopulation.Tosatisfyhouseholdenergyneeds,Mozambicanslargelydependontraditionalbiomass,suchasfirewoodand charcoal, for cooking andheating, as seen inFigure8;firewood andkeroseneare used for lighting purposes, as demonstrated byFigure 9.With 69.4percent ofthepopulation livingbelow thenational poverty line and largelydependent on thecountry’s forestry resources for their energy needs, there is a need to introducealternativesthatmitigatethistrend,whileremainingeconomicallyviableforthepoor.

Asalargemajorityofthepopulationdirectlydependontraditionalbiomassfortheirenergy needs, the National Energy Policy dedicates Section 3.6 to the promotionof sustainable use of the country’s biomass resources. The policy calls for a

5 Household Budget Survey 2007, Dar es Salaam January 2009, Ministry of Finance and Economic affairs, National Bureau of Statistics, Tanzania

Table 2: Distribution of households by energy source for cooking

(% of households) in Dar es Salaam

1991/1992 2000/2001 2007

Electricity 9.7 4.8 2.2

Gas-industrial 1.2 0.4 2.2

Gas- biogas N/A 0.2 0.1

Paraffin/Kerosene* 33.7 43.0 12.4

Coal 1.1 0.6 0.4

Charcoal 52.1 46.2 74.9

Firewood 1.2 4.6 8.0

Wood/Farm residuals N/A N/A 0.0

Other 1.0 0.3 1.1

Source: National Bureau of Statistics Tanzania5 * Only paraffin in 1991/92 and 2000/01

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Figure 7: Illustration of price development by primary cooking fuel in

Dar es Salaam. Comments: The 2009 costs are based on an investment time of ten years. Source: (Ghanadan, 2004) and SEI survey of fuel prices in Dar es Salaam, September 2009.

Figure 8: Household energy use for cooking in Mozambique Source: Ministerio de Energia, 2005, Estatistica de Energia, pp. 34

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gradual reduction in traditional biomass consumption and advocates environmentalsustainabilitythroughreforestationprogrammesandthepromotionofmoreefficientandenvironmentallyconsideratemeanstoproduceandconsumebiomassproducts.6Furthermore, the national strategy on climate change and adaptation focuses ondeforestationasaresultoffuelwoodextraction.Accordingtothedocument,18millioncubicmeters of forest are exploited annually for biomass collection,with no clearsustainabilitymeasuresinplace.Obstacleshighlightedincludethelackofcommunitylevel involvement, weak institutional capacity in protecting and controlling forestareas,lackofdemarcationsonprotectedregions,aswellasthelackofaclearstrategyforforestmanagement.7

The government of Mozambique has recently introduced its Biofuels Policy andStrategywith the core aimsof ridding the countryof itsdependenceon fossil fuelimports, promoting socio-economic development, and contributing to the globalinitiative of greenhouse gas reduction.8 Mozambique is considered as one of theAfricancountrieswith the largestbiofuelproductionpotential,with thecapacity toproduce around seven Exajoules of biofuels sustainably–a figure that assures fullenergy independence, with surplus to supply international markets (WWF, 2008).Furthermore,thepolicypromotestheuseofethanolgelfuelasameanstoreducethe

6 Governo de Mocambique, Estrategia de Energia, 3 de Outubro de 2000, electronic copy: http://www.portaldogoverno.gov.mz/docs_gov/estrategia/energia/estrat_energia.pdf

7 Ministerio para a coordincao da accao ambiental, 2005, Avaliacao da vulnerabilidade as mudancas climaticas e estrategias de adaptacao. Electronic copy: http://www.portaldogoverno.gov.mz/docs_gov/estrategia/agricultura/avaliacao_vulnerab_mud_climat_estrateg_adapt.pdf

8 Governo de Mocambique, 2009, Politica e Estrategia de Biocombustiveis, 21 Maio 2009

Figure 9: Household energy use for lighting in Mozambique Source: Ministerio de Energia, 2005, Estatistica de Energia, pp. 34

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massuseoffirewoodandcharcoal.Thefocusongelfuel,incontrasttoliquidfuels,canbeunderstoodtobeinfluencedbytheMillenniumGelfuelInitiative,pioneeredbytheWorldBank´sRegionalProgramforTraditionalEnergySector(RPTES).

Household cooking in MaputoInMaputo,around75percentofthepopulationrelyoncharcoalandfirewoodfortheircookingenergyneeds.ElectricityandLPGaccountsfortherestandaregenerallyusedbymiddleandhigherincomegroups.Charcoalisbyfarthemostprominentfuel,anditisusedby63percentofthecity’spopulation.9Asameanstoestimatehouseholdexpendituresoncharcoal,abaselinesurveywascarriedoutinSeptember2009.Theresultsshowthatanaveragefamilyoffiverequiresaround70kgofcharcoalmonthly.A70kg sackof charcoal is soldat the localmarket for anaverageof480Mt (17USD).Thisfigureisequivalenttoathirdofthenationalminimumwage(1480Mt,equivalentto53USD).10Itwasalsonotedthatmanypeoplepurchasesmallquantitiesofcharcoalata time tosatisfy theirdailycookingneeds.Thisoccursbecause theyareeitherunabletoaffordasinglepaymentforalargesackortotransportthe70kgsack from themarket. In this instance, ahousehold spendsonaverage25Meticais(0.84USD)forabouttwokgofcharcoaldaily.Theresultingmonthlyexpenditureis64percentmorethanitwouldcosttobuythe70kgsackofcharcoal.

9 Ministerio da Energia, 2006, Estatistica de energia 2005, pp 34

10 http://www.meusalario.org/Mocambique/main/salario-minimo/salario-minimos-em-mocambique

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5 STATED PREFERENCE SURVEY DESIGN

This sectiondescribeshow the statedpreference survey for each case studywasdesigned and pilot tested to ensure that the alternative choices and attributes

selectedwere relevant foreachsite.Particularattention isgiven toexplaininghowsomewhatsubjectiveattributessuchas“smokiness”and“safety”levelofstoveswereincorporatedinto thedesign.Thesurveyprocessateachstudysite, includingfocusgroupdiscussionsisthendescribed.

Acombinedstatedpreferenceanddiscretechoiceanalysis,namely themultinomiallogitmodel, enables a researcher to quantitatively study the two categories (socio-economicandproduct-specificattributes).Astatedpreference-basedchoiceexperimentsurveywasconductedinAddisAbaba,EthiopiainJune2008,andinDaresSalaam,TanzaniaandasuburbofMaputo,MozambiqueinSeptember2009.Forastatisticallysound representation of respondents with mutually non-exclusive socio-economicstrata(e.g.,age,income,educationandgender),astratifiedclusterrandomsamplingtechniquewasadopted.Thesamplesizeforthehouseholdsurveywas200,564,and402households inEthiopia,Tanzania, andMozambique, respectively, and for eachstratum,aminimumof30sampleswasensured.

The choice experiment design involved eight repeated games or treatments perrespondent, in order to collect data about purchasing of eight different stoves. Inaddition, one experiment was randomly repeated for each respondent in order toverify consistency; thus, the total sample size for the choice experiment was, forexample,1800inEthiopia,andeachfeaturedthreedifferentalternativeschoices.Theexperimentrequiredrespondentstochoosebetweenvariouscookingstoveattributesorcharacteristicswhichwererepresentedpictorially,ratherthantextually(seeFigure1).Thepictorialapproachallowedrespondents tovisualise thevariouschoicesandavoidedpotentialconfusionandfatiguewhichcanoccurwhenrespondentsareaskedaseriesofsimilarquestions.Usingpicturesalsoeliminatedtheriskofmisunderstandingduetoilliteracywhichisprevalentineachprojectsite.Howevertheuseofpicturesinsuchsurveyscanalsoleadtobiases.InthecaseofEthiopia,toeliminatethebiases,theresearchersusedseveralstovepicturesandensuredthattheywererandomized;inDaresSalaamandCatembe,illustrationsoffuels,ratherthatpicturesofstoveswereused.

A structured household questionnaire survey was also distributed to collect socio-economic informationand the respondent’s revealedpreference for cookingenergychoice,inanattempttogatherindividualperceptionsofthehealthandenvironmentalimpacts of firewood and charcoal use. In Maputo, 105 households were asked toindicatetheiropinionsontheseissues.

IntheEthiopiancase,basedontheliteraturereviewanddiscussionswithenergyexperts,severalattributeswereconsideredforthestatedpreference(SP)andlaterdiscounted,suchasstart-uptime,durability,fuelavailabilityandeaseofuse.Onlyfourattributeswereultimatelyselected:stoveprice,monthlyusagecosts, indoorsmokelevel,andrisk.Sincemonetaryattributesarethemostimportantfactorsforthedecisionmaking

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onstovepurchase,weincludebothstovepriceandmonthlyusagecost.ThesmokelevelwaschosenbecausethereductionofindoorairpollutionisoneofthemainstudypurposesandthelocalNGO,GaiaAssociation,requestedthattheattribute“risklevel”be added to this study.Hensher et al., (2005) state that it is appropriate to chooseattributesbasedonastudypurposeandarequestfromastakeholder.Theselectionofattributesdoesnotaffectthevaluationoftrade-offsbetweentheattributes,whichistheprimaryobjectiveofthisresearch(McFadden,1987).

Originally, fuel types were considered as an attribute; however, since each stoveburns justone typeof fuel, the fuel typebecame the labelof thestove,andoneoftheobjectiveswas toassessfuelswitching.Fuelprice,efficiency,andfuelquantityattributesaffecttheusagecostatthehouseholdlevel;therefore,thesethreeattributeswerecombinedintoasingleattribute,termed‘usagecost’.Thelevelofindoorsmokedepends not only on the smoke emitted by the stove, but also on the kitchen size,shape/layout,thekitchen’swindowsize,otherventilationattributesandthedurationofcooking(Heltberg,2004).Thisresultedinanumberofmeasurementcomplications.For example, a given quantity of smoke as such has no consistent meaning forscientists because the same level of smoke could have a varying composition ofmanygases andparticulates (Naumoff ,2005).On the other hand, smoke level hasno consistentmeaning amongdifferent respondents.For example, the level termed‘highlysmoky’isverysubjective.Hence,itwasdecidedtotreatthesmokeattributeasapseudo-categoricalvariable,whichassumedlineardistancebetweeneachcategoricalresponse,i.e.,Nosmoke=1,Verylittlesmoke=2,Moderatelysmoky=3,andHighlysmoky=4. It isacknowledged that the linearassumption isa simplificationand isunlikelytorepresenttherealityofpeoples’perceptionsortherealimpacts(e.g.dose/responserelationshipbetweenhealthimpactsandlevelofsmoke).Dependentontherealperceptions,thesmokeattributewillthereforetendtobeunderorover-estimatedandthisisthesamefortheriskattribute.Theselinearassumptionscouldbeimprovedinthefuturestudy.

Safety/riskisanotherhighlysubjectivetermandoftenelicitsvaryinginterpretations(of same risk level or label) among respondents; therefore, we treat risk as apseudo-categorical variable, like smoke: Highly unsafe = 1, Highly safe = 4, etc.Thecategoricalnumbersinsmokeandriskwereusedonlyformodellingpurposes;therefore,respondentswereinformedofonlythedescriptionandthetitlesofattributelevelsintheEthiopiancase.Riskandsmokethushavesimilarmeasurementproblems.In order tominimise themeasurement problem associatedwith pseudo-categoricalvariables, a case description approachwas applied for the defined labels of smokeandriskattributes(e.g.,Table3fortheEthiopiacasestudy).Beforeconductingtheexperiment,eachofthepseudo-categoricalvariableswasdefinedfortherespondents–forexample,thelabel“moderatelysmoky”wasdefinedasasituationwhenthesmokelevel inside the kitchen is so high that it causes some discomfort to the eyes andirritationtothethroat.Likewise,thelabel‘highlysmoky’wasdefinedasasituationwhensmokeinsidethekitchenissuchthattherespondentcannotstayinsidekitchenwithoutopeningthekitchenwindowsordoors.Similarly,thefollowingcasesituationwasused for the riskattribute: i)HighlySafe:zero riskofburnandexplosion; ii);

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ModeratelySafe:noriskofexplosion,butriskofburn;iii)LittleUnsafe:riskofbothburnandexplosion;andiv)HighlyUnsafe:highriskofburnandexplosion(specifiedasoneburnandminorexplosioneverytwomonths).Thesespecificationswereusedtodescribethecasebecausepeoplecouldnotdifferentiatebetweenminorriskandhighrisk.

In theTanzania andMozambique case studies,we improve the pseudo-categoricalsituationbydisplayingactualnumbers to the respondents, i.e., “0:No”,“3:A lot”,etc.Therefore,theequaldistancesbetweenlevelsarenolongerimplicitlyassumed,but explicitly indicated. In addition to improving the modelling process, this alsorespondstotherememberedlevelsofsmokeandrisksbetweenalternatives,asmanyrespondentscouldreadnumbersbutnotthealphabet.Moreover,inthesetwocases,theattributethathadbeenpresentedas“safety”intheEthiopiacasestudywaspresentedas“risk”tomakethenatureoftheattributeconsistentwiththesmokeattribute,i.e.,thegreaterthelevel,theworsetheattribute.

Attheendoftheexperiment,therespondent’sunderstandingofthelabelsandlevelsofsmokeandsafety/riskattributeswastestedbyaskingeachtomatchtheabove-defineddifferent labelsandlevelsofriskandsmokeattributeswithsomeof thecommonlyusedstoves,whichhavesimilarfeatures.Forexample,ifarespondentunderstoodtheterm“moderatelyrisky”correctly,inthequestionnaire,therespondentshouldmatchmoderatelyriskywiththewoodstoveoption,sincethewoodstovehasaburnrisk,butnotanexplosionrisk.Thetestwasdoneforalllabelsofriskandsmokeattributes.Althoughthissub-projectdoesnotaddresstheperceptionaboutissuessuchasindoorairpollutionanddeforestation,theseconceptsareevaluatedatthesametimeastheconductionoftheSPsurvey.

Usinganorthogonaldesigntechnique,thenumberoftreatmentswasreducedto32,or fourblockswitheight treatmentsper respondent in theEthiopiacase study,and16ortwoblocksofeighttreatmentsintheTanzaniaandMozambiquecasestudies.Theblockingdesigndivides theoverall treatments into twoorfourgroupsofeighttreatmentsandadifferentsetoftheeighttreatmentsareaskedtoadifferenthousehold.The number of treatments asked to a respondent should not bemore than eight toavoid the fatigueof the respondent.Theexperimentswerecarriedout in twostepsaspair-wisecomparisons, i.e.,first,a respondentcomparedexistingstoves,suchasawoodstoveandakerosenestove,andthenaselectedstovewascomparedwithanethanolstove(Figure10)inEthiopia.Whenthetwo-step-pair-wisedatawereenteredintoBIOGEMEsoftware,thisexperimentwastreatedasaone-step-three-alternativechoice.Theexperimentswereconductedinone-step-three-alternativesinTanzaniaandMozambique,asthepair-wiseapproachinEthiopiatookexcessiveamountsoftime.Cookingstoveandfuelchoiceliteraturestressedincomeasthesinglemostimportantattributeresponsibleforvariance(Heltberg,2005;PachauriandJiang,2008).Hence,forthepurposeofthestudy,income-basedstratawereused.

Finally,softattributeslikecomfort,userconvenience,associatedstatusorbrandalsoplayanimportantroleinchoice,butaredifficulttoobjectivelyassessandquantify.

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Hence,a‘labelledexperiment’wasusedforeachalternative,sothatthe‘alternativespecificconstant’(ASC)capturesthecumulativestrengthofthisattributeanditseffectonchoice(Hensher,Rose,andGreene2005p.113;Louviere,Hensher,andSwait2000p.220).

Ethiopia

PilottestingofthequestionnaireandchoiceexperimentwasconductedintwostagesinAddisAbaba,Ethiopia.Thefirstandsecondpre-testswereconductedbythestaffoftheGaiaAssociationbetweenthe25thand28thofJune,andbetweenthe9thand10thJuly,2008.Themainsurveywasexecutedbetweenthe10thand20thofJuly2008.Todesign thechoiceexperiment, initialdataanalysiswascarriedoutonabaselinedatasamplecollectedin2005.Furthermore,afocusgroupdiscussionwasconductedinStockholmon31stMay2008withfourenergyexperts, twoofwhomwerefromEthiopia. Upon the completion of the household survey, one more focused groupdiscussionwas held inAddisAbabawith 18 cooking fuel consumers, representingdifferent socio-economicgroups andparts ofAddisAbaba.Thismeetingwasusedto gather qualitative information about cooking stove and fuel choice. SincemanyrespondentsconsiderethanoltobeunsafeduetotheK-50problem11inEthiopia,therisklabelwasusedwiththisfuel.

Alternatives for thecookingstovechoiceexperimentwereevaluatedbasedonfourcriteria:(1)numberofusers;(2)distributionacrossdifferentincomegroups;(3)usage

11 In 2005, a local company produced kerosene and ethanol blend as cooking fuel and termed it as K-50. It developed a reputation for being explosively volatile due to poor blend and bad quality stoves; now, many respondents associate ethanol with explosions and consider it unsafe.

Figure 10: Photograph of choice card and survey in Ethiopia case study

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level;and(4)relevancetothestudyobjectives.Forexample,thelocalenergyexpertssuggested that almost every household uses charcoal primarily for the traditional‘coffeeceremony’inEthiopia.Wedonotaddresstheproblemthatsomehouseholdsmay prefer to use only charcoal over other alternatives (e.g. because of the coffeeceremony).Since forecasting isnotamajorobjectiveof thisstudy, it is reasonableto exclude thedominant alternative in this choice experiment, namely the charcoalalternative.Whicheverthelevelofattributesinanexperiment,somerespondentsmaystickwiththedominantalternative.Forexample,nomatterwhatthepricesandthehardwarespecsofPCs,manypeoplemaybuyaWindowsPC,butnotaMacorLinuxPC. If the purpose is to evaluate themonetaryvalue of hardware specs, it is goodto excludeWindows alternative to make respondents consider price and hardwareattributes.Thus,onthebasisoftheabovecriteriaanddiscussion,keroseneandwoodwereselectedasrelevantcookingfuelalternatives.Inordertounderstandtheswitchingpatternfromtraditionaltoanewermodernfueltheethanolalternativewasadded.Theallocationoflevelsandlabelsofthefourattributesamongstthethreealternativesisa critical part of the choice experiment (Table 3).The levels of price andmonthlycostweredeterminedbasedontheliteraturereviewsandinterviewswithexperts.Forexample,therangeofkerosenestovewasfoundbetween30and150EthiopianBirr(ETB),sothatwetestedthreelevelssuchas30,70,and150ETBintheexperiments.

Tanzania

Prior to themainsurvey,apre-pilot survey in focusgroup formatandpilot surveywerecarriedoutattheUniversityofDaresSalaam(UDSM).Focusgroupdiscussionswereheldinordertoevaluatetheapplicabilityofthecookingstovechoiceexperimentdesign,aswellasthesurveyquestionnaireusedintheEthiopianstudy,andtofine-tune the pilot survey.The pre-pilot study took place in focus group format duringthreesessionsoverthe27thand28thofAugust,2009.Thegroupswerecomposedof11participantsselectedtorepresentlow,mediumandhigh-incomelevels.Inadditiontodiscussions about cooking stove/fuel choiceand theevaluationof fuel attributesand adoption of themodel toTanzania, amore open conversation about fuels andstoveswasheld.Focusgroupdiscussionswerefollowedbyapilotsurveyonthe4thofSeptember.Themainsurveywascarriedoutbetweenthe14thand18thofSeptember2009 by twelve research assistants fromUDSM.The survey period was precededwithonedayoftrainingforthesurveyors.Fourareas–Mikocheni,Bunju,TemekeandKigamboni–ofDaresSalaamwereselectedasthemainsurveyareas,astheyincludedlow,mediumandhigh-incomegroups.AdditionalsurveyareasincludedChangombe,TabataSegerea,Kijitonyama,Mwenge,Karakata,Makumbusho,YombovitukaandTabata.

InDaresSalaam,charcoalis,aspresentedearlier,byfarthemostcommonlyusedfuelforcooking,withapproximately75percentofthepopulationusingitastheprimarycookingfuel.Charcoalwasnot includedasanalternativeduetoitsdominanceandriskofbiasasexplainedaboveonthediscretechoicemodelapplicationsandalsotomake thecase studyconsistentwith theEthiopiancase.Havingexcludedcharcoal,

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firewoodwasselectedinstead.FirewoodisbecomingalesscommoncookingfuelinDaresSalaamandotherurbanareas,butisstillwidelyusedinrurallocations;thus,itwaschosentorepresentatraditionalfuelwithmanysimilarpropertiestocharcoal.Inadditiontofirewood,kerosenewasselectedasanimportantfuel(secondaftercharcoalinDaresSalaam),andethanol,asarepresentationofamodernfuel.

RangesofpricesforstovesandmonthlycostswereobtainedfromInternetresearchandconfirmedbyafieldsurveyinDaresSalaaminAugust2009.ThefindingsarepresentedinTable4below.IncomparisontotheEthiopianstudy,stovepricesincludedfourlevelsinsteadofthree,andindoorsmokeandsafetyrisksalsofeaturedfourlevels,bothuniformlyona0-3scale.

Mozambique

The stated preference-based survey was administered in the municipal district ofCatembe, an extension to the city ofMaputo,with a distinct blend of urban, peri-urban and rural characteristics. The region can be seen as a representation of thecountryatlarge.Catembehasanestimatedpopulationof21,000inhabitants,ofwhich85percentIsengagedinagriculturalactivities,and10percentinfishing.Thesurveyteam covered four neighbourhoods–Guachene, Chali, Incassane and Inguide. Eachneighbourhood offers a different setting: Guachene and Incassane are commercialcentresinhabitedbymostlylowerandmiddleincomepeople,Chaliisamoreaffluentarea,andInguidehasadistinctruralaspectwithstrawandthatchhousing.Themodeldesign used for the choice experiment corresponds to those used for theEthiopianandTanzanian studies.One difference, however,was the choice of alternatives, ascharcoalreplacedkerosene(Table5).Duetoalackofpublishedandupdateddataonfuelandstovepriceranges,itwasnecessarytocarryoutastudypriortothesurveytodetermine theprice ranges for themodel.The studycovered threemarketplacesinMaputoandoneinthedistrictofCatembe.Inaddition,afocusgroupdiscussion

Table 3: Allocation of levels and labels for the four attributes in the Ethiopian case

study

Ethanol Kerosene Wood

Stove price (ETB) {500, 250, 100} {150, 70, 30} {120, 50, 20}

Usage cost (ETB) {60, 120, 160, 320} {50, 100, 150, 300} {40, 80, 140, 250}

Indoor smoke{No smoke, Very little smoke}

{Very little smoke, Moderately smoky}

{Very little smoke, Highly smoky}

Safety risk{Little unsafe, Highly safe}

{Highly unsafe, Little unsafe}

{Moderately unsafe, Highly safe}

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amongsthouseholdcooksinMaputoservedtoconfirmthechoiceofalternativesandlevelsofattributeschosen.

Trainingof thesurveyteamtookplaceon the17thand18thSeptember2009.Fivesurveyorswererecruited.Apilotsurveywascarriedoutby thesurvey teamon the19thSeptember2009andallowedforthefine-tuningofthechoicemodelandsocio-economic questionnaire. The implementation of the choice experiment and socio-economicquestionnairetookplacebetweenthe21stSeptemberandthe1stOctober2009.

Table 4 : Allocation of levels and labels for the four attributes in the Tanzania case

study

Ethanol Kerosene Firewood

Stove price (TZS) {20 000, 30 000, 40 000, 50 000}

{5 000, 10 000, 15 000 20 000, }

{100, 10 000, 20 000, 30 000}

Usage cost (TZS) {30 000, 50 000, 70 000, 90 000}

{1 000, 10 000, 20 000, 30 000}

{10 000, 20 000, 30 000, 40 000}

Indoor smoke {0: No smoke, 1: Little smoke, 2: Moderately smoky, 3: Very smoky}

{0: No smoke, 1: Little smoke, 2: Moderately smoky, 3: Very smoky}

{0: No smoke, 1: Little smoke, 2: Moderately smoky, 3: Very smoky}

Safety risk {0: No risk, 1: Little unsafe, 2: Moderately unsafe, 3:Highly safe}

{0: No risk, 1: Little unsafe, 2: Moderately unsafe, 3:Highly safe}

{0: No risk, 1: Little unsafe, 2: Moderately unsafe, 3:Highly safe}

Table 5: Allocation of levels and labels for the four attributes in the Mozambique

case study

Ethanol Charcoal Firewood

Stove price (MZN) {100, 500, 1000, 1500}

{ 50, 200, 400, 600}

{1,50, 100, 150}

Usage cost (MZN) {400, 700, 1000, 1300}

{ 300, 600, 900, 1200}

{1, 200, 400, 600}

Indoor smoke {0: No smoke, 1: Little smoke, 2: Moderately smoky, 3: Very smoky}

{0: No smoke, 1: Little smoke, 2: Moderately smoky, 3: Very smoky}

{0: No smoke, 1: Little smoke, 2: Moderately smoky, 3: Very smoky}

Safety risk {0: No risk, 1: Little unsafe, 2: Moderately unsafe, 3:Highly safe}

{0: No risk, 1: Little unsafe, 2: Moderately unsafe, 3:Highly safe}

{0: No risk, 1: Little unsafe, 2: Moderately unsafe, 3:Highly safe}

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6 RESULTS

Thissectionbeginsbypresenting theutility functions for the threestovechoicesineachcasestudy.Thegeneralmodelis thenexaminedandsometrendsacross

thethreecasesareidentified.Therefollowsaexaminationoftheresultsintermsoftheimportanceofproduct-specificattributesacrossthevarioussocio-economicstrataforinfluencingchoiceofstoveineachcasestudy.Therelativestrengthsofproduct-specific attributes for different market segments across socio-economic strata arethen discussed and some significant findings are presented, both in terms of stovepreferencesbutalsothetrade-offsmadebetweenattributesatdifferentincomelevels.Trends in decision making across the socio-economic strata in all three cases arehighlighted.

Utility parameters

Thefollowingutilityfunctionshavebeenspecifiedforthethreealternativesacrossthedifferentsocio-economicstratawithoutanerrorterm,i.e.,U=V+ε:AUrepresents,unobserved utility and an ε represents errors in observation, so that aV representobservedutility.Aβrepresentsaweightingparameterofarelevantattributeandanαrepresentspartialutilityassociatedwithatypeofastovewhichisnotcapturedbyotherβs.i.e.AlternativeSpecificConstant(ASC).

Ethanol:Vi

E = αE + βcost(cost_e) + βprice(price_e) + βrisk(risk_e) + βsmoke(smoke_e)

Kerosene (Charcoal):

ViK = αK + βcost(cost_k) + βprice(price_k) + βrisk (risk_b) (risk_k) + βsmoke(smoke_k)

Wood: ViW = αW+ βcost(cost_w) + βprice(price_w) + βrisk_b(risk_w) + βsmoke(smoke_w)

Thismodelisthebasisofallthreecases;however,oneofthealternativesischarcoalinsteadofkeroseneintheMozambiquecase.Inaddition,αKintheTanzaniacaseandβsmokeintheMozambiquecasewereremoved.EachvariablenamerepresentsarelevantattributeshowninTable3,anditssuffixrepresentsalternativefuelusedforthestoves.Theαoffirewoodisfixedaszero.Allalternativesusethesameparameters,excepttheriskoffirewoodandcharcoal,βrisk_b,becauseriskissuesaredifferentbetweenliquidandsolidfuels,i.e.,firewoodandcharcoaldonotpresentexplosionrisks.Hence,thelogisticformofthefittedmodelforchoosingtheethanolstoveis:

( ))(V+)(V+)(V

)(V=P Wi

Ki

Ei

Ei

expexpexpexpEthanol

Thelogisticformsfortheothertwooptionsaresimilartotheoneabove.P(Ethanol)is the probability of choosing an ethanol stove in a given situation. Themodel isbasedontheworkofMcFadden(1973),andithasbeenusedtostudycookingstovechoice(Heltberg,2004;NarasimhaRaoandReddy,2007;Ouedraogo,2006;Pundo

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andFraser,2006).BIOGEMEsoftware12wasusedfortheparameterestimationofthediscretechoiceanalysis.

The general model

P-valuesinTable6showthatalltwoconstantsandfiveparametersaresignificantat5percentlevelsinthethreecases.TheASCofcharcoalfortheTanzaniacaseandthesmokeparameterforMozambiquewereremoved,as theseASCandparametersareconsistentlyinsignificantinalmostallsocialsegregatedmodels,tobeexplainedinthenextsection.Thisindicatedthatthesecoefficientsaremostlikelytobeinsignificantattributesinthecontextofthetwocases.Weretaintherestofthemodelcomponentsforthecomparisonbetweenthethreecases.ApositiveASCsignifiesthattheassociatedalternativegeneratesutilityfortherespondent,whileanegativeparametershowsthatanyincreaseintherespectiveattributelevelwillreduceutility.Allmonetaryvalueswere translated into U.S. dollars; the exchange rates used are 12.5 Ethiopian Birr(ETB),1,300TanzanianSchilling(TZS),and26MozambicanMetical(MZN).

The three casesmay not be directly comparable, asmonetary parametersmay notcorrectly reflect purchasing power parity between the three countries, and risk andsmokeparameterscanbebiasedduetoperceptiondifferences.Giventheabove,someinterestingtrendswereobserved.ASCswellreflecttheactualsituationsofthestudiedregions.Forexample,inEthiopia,peoplearenegativeaboutkerosenestovesduetoseriousexplosionissuesthataroseseveralyearsago.ThenegativeαKfortheEthiopiancase reflects the unpopularity of kerosene stoves. In addition, charcoal stoves arethedominantstoveinMozambique,sotheASCforcharcoalstovesisverystronglypositive.Anethanolstoveismorepositivelyacceptedthanafirewoodstoveinallthreecases.AnASCrepresentsonlyrelativeutility,sotheASCofonealternativeisfixedaszero.Inthiscase,theASCoffirewoodisfixedatzero.

Inβparameters,theEthiopiaandTanzaniacaseshavemoresimilarresultsthanthoseoftheMozambiquecase,sotheusagecostandstovepricehavesimilarcoefficientvalueseveninabsoluteterms.Inrelativeterms,theeffectsoftheusagecostareslightlyhigherthanthoseofstovepriceinallthreecountries.Wheretheyexist,allriskparametershavestrongercoefficientsthanthecorrespondingsmokecoefficients.TheinsignificantsmokeparameterintheMozambiquecaseisconfirmedbyfieldobservationandexpertopinion.Unlikeothercases,peopleintheMozambiquecasestudyoftencookoutside,sotheyarenotasconcernedaboutsmokelevels.

Specific models

Thissectionanalysestherelativestrengthsofproduct-specificattributesfordifferentmarketsegments,differentiatedonthebasisofsocio-economiccharacteristics.Dueto

12 http:// biogeme.epfl.ch/

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Ethiopia Tanzania Mozambique

Parameter Description Coeff. P-value Coeff. P-value Coeffi. P-value

αE ASC for Ethanol

.415 .05 . 666 <.01 . 427 <.01

αK/C ASC for Kerosene or Charcoal

-.392 .04 -- -- . 558 <.01

αW ASC for Firewood

Fixed --- Fixed --- Fixed ---

βcost Usage Cost -.029 <.01 -.024 <.01 -.009 <.01

βprice Stove Price -.024 <.01 -.022 <.01 -.008 <.01

βrisk Explosion risk -.272 <.01 -.296 <.01 -.357 <.01

βrisk_b Burn risk -.256 <.01 -.298 <.01 -.118 <.01

βsmoke Indoor Smoke -.101 <.01 -.214 <.01 -- --

Table 6: The general parameters derived from the BIOGEME model

thelackofapanelstructure(mentionedabove),thecomparisonofmodelcoefficientsfor different socio-economic groupsmay underestimate the effects of other socio-economic characteristics in a particularmodel because such characteristics are notcontrolledinthemodels.However,thisapproachyieldedclearoutcomes,evenwhenconsideringtheinevitable“noise”orerrorintheSPsurveyduetolowadultliteracyrates (e.g., less than 50percent inEthiopia) (Watkins, 2008), the un-computerisedsurveyprocess,andrelatedattributes.Theresultsofthechoiceexperimentinestimatingproduct-specific attribute strengths across different socio-economic attributes arepresentedinTable7,Table8,andTable9,respectively.Thefiguresinsquarebracketsrepresentinsignificantvariablesatthe10percentlevel.

TheTanzaniacasehasthemostsignificantoutcomes,asallparametersacrossallthesocio-economicclassesaresignificant,excepttheomittedASCsofkerosene,whichindicatethatpeopleinthiscasedonothavesignificantpreferencesbetweenkeroseneandfirewoodstoves.Overall,theEthiopiaandTanzaniacasestudiesshowrelativelysimilar results to explain the differences in parameters andASCs amongst socio-economicclasses.TheresultsfromtheMozambiquecasestudyalsohavesignificantvalueswhencomparedwiththeothertwocases.AlthoughsomeestimatedparametersintheEthiopianandMozambicancasesdidnotshowsignificantoutcomes,allestimatedparametersacrossthetensocio-economicclassesinthethreecasesdisplaynegativecoefficients,exceptthesmokecoefficientforthelow-incomeclassinEthiopia,whichis statistically insignificant.Thenegativecoefficients are expected, ashigherusagecost,higherprice,morerisk,andhighersmokelevelsareexpectedtoreducetheutilityofarespondent,regardlessofgeographicallocationandsocio-economicstatus.Thissimplebutimportantresultconfirmedthatthesestoveattributesas“product-specificcharacteristics”tosomeextentuniversallyinfluencestovepurchasedecision-making.

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Models Coefficients

αE αK βcost βprice βrisk βrisk_b βsmoke

General: .415 -.392 -.029 -.024 -.272 -.256 -.101

Income: Poor .508 [.119] -.043 -.045 -.236 -.298 [.068]

Middle [.077] -.71 -.035 -.030 -.340 -.253 -.155

High [.633] [-.602] -.015 [-.008] -.336 -.310 -.322

Gender: Female .369 -.488 -.024 -.024 -.250 -.208 -.113

Male .479 -.280 -.031 -.023 -.281 -.300 -.089

Education: Low edu.

[.405] -.259 -.029 -.029 -.293 -.274 [-.020]

High edu.

[.419] -.531 -.028 -.019 -.257 -.246 -.198

Age: Below 30

[.045] -.618 -.043 -.019 -.262 -.184 [-.062]

31-40 Yrs

.469 -.515 -.026 -.033 -.249 -.195 -.134

Above 41

.672 [-.057] -.019 -.016 -.300 -.381 -.102

Models Coefficients

αE αK βcost βprice βrisk βrisk_b βsmoke

General: .666 -- -.024 -.022 -.296 -.298 -.214

Income: Poor .659 -- -.029 -.025 -.244 -.191 -.16

Middle .734 -- -

023 -.025 -.345 -.371 -.274

High .555 -- -.018 -.013 -.303 -.369 -.19

Gender: Female .742 -- -.024 -.027 -.392 -.382 -.275

Male .617 -- -.025 -.019 -.236 -.244 -.183

Education: Low edu.

.953 -- -.033 -.037 -.304 -.219 -.241

High edu.

.555 -- -.021 -.016 -.297 -.346 -.203

Age: Below 30

.7 -- -.023 -.024 -.286 -.28 -.184

31-40 Yrs

.919 -- -.026 -.034 -.235 -.204 -.295

Above 41

.421 -- -.025 -.012 -.359 -.410 -.195

Table 7: Specific model output of the Ethiopia case study

Table 8: Specific model output of the Tanzania case study

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Models Coefficients

αE αC βcost βprice βrisk βrisk_b βsmoke

General: .427 .558 -.009 -.008 -.357 -.118 --

Income: Poor .416 .557 -.008 -.007 -.336 -.0868 --

Middle .511 .476 -.009 -.009 -.245 -.132 --

High .37 .647 -.01 -.008 -.515 -.157 --

Gender: Female .412 .532 -.009 -.006 -.393 -.123 --

Male .531 .758 -.01 -.019 [-.144] [-.0753] --

Education: Low edu.

.337 .43 -.008 [-.003] -.347 -.0908 --

High edu.

.535 .706 -.01 -.014 -.371 -.152 --

Age: Below 30

.474 .514 -.008 -.012 -.338 -.142 --

31-40 Yrs

.264 .501 -.007 [-.003] -.362 [-.058] --

Above 41

.47 .705 -.011 [-.004] -.401 -.122 --

Table 9: Specific model output of the Mozambique case study

Thenegativesignoftheusagecost(βcost)coefficientimpliesthatincreasingtheusagecost generates negative utility for respondents. In addition, the stove price (βprice)coefficient,asexpected,issimilartotheusagecostcoefficient,inthatitissignificantlynegativeforallsocio-economicstratums,exceptthehigh-incomeclassintheEthiopiacasestudyandsomeclassesintheMozambiquecasestudy.Agivenchangeinstovepricewillaffectthelow-incomegrouputilitymorethanfivetimesthatofthehigh-incomegroupintheEthiopiancase,andtherespectiverateisapproximatelydoubledinTanzania.Thenegativeindoorsmokecoefficient(βsmoke)intheTanzanianandEthiopiancasesindicatesthatincreasingsmokelevelsreducesutility.However,theinsignificantcoefficientsof someparticulargroupsmayalso indicate that thedecisions in thesegroupscannotbeaffectedbysmokeattributes,andhence,therespectivecoefficientvaluehasnomeaning.Somereportedthatindoorsmokeplayedarolekillingbugs;therefore,theymightactuallyprefertohaveindoorsmoke.Theexplosionrisk(βrisk)coefficientis,asexpected,negativeforallstrataintheEthiopianandtheTanzaniancases,andthereisonlyoneinsignificantestimateintheMozambiquecasestudy.Thisindicatesthatpeoplepreferasafestovetoariskyone.

TheASC(α)coefficientsrevealdifferenttrendsbetweenthethreecases.InEthiopia,allstrataexceptthelow-incomegrouphavethefollowingpreferenceorder:ethanol,followedbywood,thenthekeroseneoption.Thisindicatesthat,allthingsbeingequal,peoplepreferethanoltowoodandkerosene;furthermore,itdemonstratesthatexceptfor the low-incomegroup,peoplepreferwoodtokerosene.Acrossmanystrata, theethanolASCcoefficientisinsignificant;suchresultsareexpected,giventhatthesefueloptionsarepresentlynon-existentornon-utilisedformanypeopleinEthiopia.

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InTanzania, the ethanol stove is also themost preferred alternative, if all attributelevelsaresimilar,i.e.,ethanolASCsalwayshavehighsignificantvalues,butkeroseneASCsdonot.Asaresult,ASCsforkerosenewereremovedfromtheTanzaniacasestudy.Thisindicatesthatpeoplepreferanethanolstovebyasignificantmarginoverafirewoodorkerosenestove.InMozambique,charcoalisbyfarthemoredominantstovechoice,andtheresultssupportthistrend.Unliketheothercases,thepreferenceorderisalwayscharcoal,ethanol,andthenfirewoodformostsocialclassesinMozambique.

Socio-economic strataThesocio-economicspecificmodelshowsthatinallthreecasestudies,barringfewexceptions, all product-specific attributes (usage cost, stove price, risk and smokelevels) affect choice decisions across the entire specified socio-economic strata,includingage,education,incomeandgender.However,asindicatedbythedifferentcoefficient values, the relative importance of attributes and trade-offs are differentwitheachstratum.Abriefbutdetaileddiscussiononthegeneralpatternobservediswarrantedforeachstratum.

Thecoefficientvalueofusagecost ishighest for the low-incomegroupand lowestfor thehigh-incomegroupin theEthiopiaandTanzaniacasestudies,butnot in theMozambicancase.Thetrendsinthefirsttwocountriesindicatethatagivenincreaseintheusagecostwillhavethemostnegativeimpactonpoorhouseholdsandtheleastnegativeimpactontherich.TheimpactismoresignificantintheEthiopiancasethantheTanzaniancase.InEthiopia,thelow-incomegroupisaffectednearlythreetimesasmuch as the high-income group,while theTanzanian low-income group is lessthantwiceasmuchaffectedthanthehigh-incomegroup.Thecoefficientsforriskaregenerallyweakerinthelow-incomegroupacrossallcases.Thisindicatesthatwithanincreaseinincomelevel,peoplearetosomeextentmoresensitivetosafetyoraversetotheriskofexplosionandburning.

ThemalegroupismoresensitivetousagecostthanthefemalegroupintheEthiopianandTanzanian cases (Figure11 andFigure12), possiblybecauseof the traditionalhouseholddivisionoflabour;womeninthesecountriesareresponsibleforcooking,whilemen are responsible for the household budget and for shopping.Theunit ofthesefiguresisapartialutilityinarelativeterm,sothatthesenumbersdonothaveanymeaningintheabsoluteterm.Therelativevaluesshowthatmenaremoresensitivetobudgetaffairs.WomenareindifferenttotheusagepriceandfuelpriceintheEthiopiancaseandmoresignificantlyaffectedbystovepriceintheTanzaniancase,whilemenaremoresensitivetousagecostthanstovepriceintheEthiopianandTanzaniancases,which further substantiates the aboveobservation.Focusingon smoke coefficients,thefemalegroupcoefficientishigherthanthatofthemalegroupintheEthiopianandTanzaniancases,suggestingthatwomenaremoreconcernedaboutsmokelevelswhilecooking,so this result isconsistent (Figure13).Thedivergencesbetween the threecountriescanbeduetotheculturalreasonsorinefficiencyinthestatisticalanalysis.Ineithercase,weneedfurtherstudiessuchasin-depthsocialassessmentsandfurtherstatisticalanalysestounderstandtherealcauses.

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Figure 13: Comparison of smoke level coefficients between men and women in

Ethiopia and Tanzania

Figure 11: Comparison between usage cost and stove price of men in Ethiopia and

Tanzania (in partial utility)

Figure 12: Comparison between usage cost and stove price of women in Ethiopia

and Tanzania (in partial utility)

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Education is classified into two strata, namely “low” and “high.”A respondent isclassifiedashavingalowlevelofeducationifheorshehasonlyreceivedprimaryeducationornoformaleducation.Educationandincome-basedstrataresulttrendsaresimilar,indicatingastrongcorrelationbetweenthetwocharacteristics.Forexample,smokeisasinsignificanttothelow-incomegroupasitisforthelesseducatedgroupintheEthiopiacasestudy,i.e.,thelow-incomegroupismoresensitivetoburnsthanexplosions,asisthelesseducatedgroup.Moreover,likeincomeclasses,bothmonetaryestimatesaremorenegativelysignificantinlow-educatedhouseholdsratherthanhigh-educatedhouseholdsintheEthiopianandTanzaniancases.Thehighlyeducatedgroupismorelikelyawareoffuelattributesandthedangersofindoorairpollution.Hence,somenon-incomeattributessuchaseducationcouldplayarole indecisionmakingprocessesregardingcookingstoveandfuel.Asagingrespondentsarelesssensitivetopriceandcost,thisindicatesthattheolderpopulationisgenerallylesslikelytoswitchstovesduetotheseattributesintheEthiopiancase,butnotintheothertwocases.

Trade-off between product-specific attributesThe trade-off between attributes is presented as a ratio of an individual attributecoefficientandthestovepricecoefficient(Table10).The trade-off canbeinterpretedasthevalue(describedinstovepriceunit, inUSD) thatarespondentwouldpaytoreceiveonemoreoronelessunitofanotherattributespecifiedinthemodel;therefore,thiscanbeviewedastheMarginalWillingnesstoPay(MWTP)basedonthe stoveprice.Itmayalsobeunderstoodasan exchangeratebetweenattributesbased on the stoveprice.

Thevaluesinsquarebracketsareinsignificantvalues,aseitherasubjectcoefficientorastovepricecoefficientwasdeterminedtobenotstatisticallysignificant.

Thetrade-offtrendsarenotuniversal,butsomeinterestingtrendsareobserved.Forexample, thefirst columnof thefigure showshowmuchhouseholdsarewilling topayforoneunit(1USD)worthreductioninthestoveprice.InEthiopia,low-incomehouseholdswant topayonly0.956USD,comparedto1.167USDand1.875USD,whichthemiddleandhigh-incomegroupsarewillingtopay,respectively.Ingeneral,withincreasesinthelevelofincome,peopleinEthiopiaarewillingtopaymoreforaunitreductionintheusagecost.Thisresultisquiteintriguing:itreflectstheveryhighdiscount rate associatedwith the low-incomegroup, and the capital cost constraintdifferencebetweenthepoorandotherincomegroups,i.e.,paynoworpaylater.

Itisalsoimportanttoemphasisethetrade-offbetweenproduct-specificattributes,asthis indicates relative importance and significance amongst attributes.As shown inFigure14, theusagecostgenerallyaffectsdecisionmakingsignificantlymore thanthestovepricedoesbecausetheusagecostofthegeneralmodelismoresignificantlynegative than thatof the stoveprice.However, thisdoesnotuniversally impactallindividuals.Inthesocio-economicspecificmodels,thestovepriceismoresignificantlynegative in low-incomehouseholds than theusagecost, indicating that low-incomepeoplecaremoreaboutthestovepricethantheusagecost.Incontrast,asevidencedbythebarplots,usagecostismoresignificantthanstovepriceformiddleandhigh-

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incomegroups.Alowerusagecostreducestheoverallcostofacookingstoveinthelongterm.Therefore,thelow-incomegroupconsidersaninitialinvestment,suchasstoveprice,tobemoresignificantintheshorttermbutlesssointhelongterm.

This trade-off phenomenon between attributes amongst different socio-economicclassesisevenmoreimportantwhennon-monetaryattributessuchassmokeandriskarecompared (Figure15andFigure16) in theEthiopianandTanzaniancases.For

βcost βrisk βrisk_b βsmoke

Ethiopia General 1.208 11.333 10.667 4.208

Low 0.956 5.244 6.622 [-1.511]

Middle 1.167 11.333 8.433 5.167

High [1.875] [42.000] [38.750] [40.250]

Tanzania General 1.091 13.455 13.545 9.727

Low 1.160 9.760 7.640 6.400

Middle 0.920 13.800 14.840 10.960

High 1.385 23.308 28.385 14.615

Mozambique General 1.125 44.625 14.750

Low 1.143 48.000 12.400

Middle 1.000 27.222 14.667

High 1.250 64.375 19.625

Table 10: Trade-off between attributes (MWTP) based on stove price

Figure 14: Comparison of Stove Price and Usage Cost Coefficients in the Ethiopia

Case

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example,itisinterestingtonotethatforaunitreductioninthesmokelevel,thelow-incomegroupiswillingtopayonly6.4USD,whilethewealthiergroupiswillingtopaymorethantwicethatamount(14.615USD)intheTanzaniacasestudy.Asawhole,similartousagecost,withincreasesinincomelevel,thewillingnesstopayforaunitwithreducedsmokelevelsandriskattributesincreasessignificantlyinthetwocases.

Figure 15: MWTP of soft attributes between income groups in Ethiopia

Figure 16: MWTP of soft attributes between income groups in tanzania

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7 FUEL SWITCH SIMULATION

Thefollowingsectionexaminesthepotentialofthemethodologyforsimulatingfuelswitchingpatternsunderdifferentscenarios.Giventhetrade-offsmadebetween

variousstoveattributesatdifferentsocio-economiclevels,andtakingexistingmarketconditionsinagivenlocation(inthiscase,theexampletakenisEthiopia),stovepriceandusagecost(fuelcost)oftheethanolstovearespecifiedtoestimatethemarketshareunderdifferingscenarios.Fromthis,thedemandforthreestoves(ethanol,woodfuelandkerosene)undervariousethanolpricingscenarios issimulated. Inaddition, thedemandfortheethanolstoveamongthreeincomegroups(high,middleandlow)undervariousethanolfuelpricingscenariosissimulated.

Fuelswitchingpatternscanbesimulatedbasedontheestimatedcoefficientsandtheapplicationofextrameasuresdescribedinthemethodsection.Thisisderivedfromestimating thepotential demand for the three alternatives in thedifferent scenariosofattributelevel–suchasusagecost,stoveprice,smokelevel,andrisklevel–withinthe observed range of the experiment.This scenariomakes an attempt to simulatethedemandforethanolunderexistingmarketscenarios.Theattributes/inputsofthekeroseneandwoodalternativehavebeenspecifiedonthebasisofthecurrentmarketscenario(Table11).Asthemarketforethanoldoesnotexist,theinputswerespecifiedbasedonthefollowingconditions:(i)thestovepriceof500ETB(i.e.,40USD)isthelikelyimportedpriceofthestove;(ii)theethanolriskhasbeenspecifiedasmoderatelynot risky, as some respondents have the perception of it being unsafe because ofpreviousnegativeexperienceswithkeroseneandethanolblendedfuels;and(iii)usagecosthasbeensimulatedintheobservedrange,namelybetween60to320ETB(i.e.,4.8to25.6USD).Inaddition,aloweredethanolstovepriceistestedtosimulatethescenarioofasubsidyappliedontheethanolstove.

PPlot (A) inFigure17 showsdemandcurves for the threealternative stovesunderdifferentethanolusagecostscenarios,withthemostlikelystovepriceof40USD.Theslopeindicatesswitchingpatternsforthesimulatedscenariosabove.Thegraphshowsthatifethanolfuelisprovidedattheminimumobservedcost,itcansecureonlyathird

Table 11: Simulation inputs: the stove price and usage cost of an ethanol stove vary

and the remaining attributes are fixed

Ethanol Kerosene Wood

Stove price ($) {20, 40} 9.6 8

Usage cost ($) (4.8, 25.6) 12 12

Indoor smoke No smoke Moderately not smoky

Moderately smoky

Safety risk Moderately not risky Highly risky Moderately risky

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oftheexistingmarket.Ifethanolfuelispricedequallytokerosene(usagecostof12USDpermonth),ceteris paribus,themarketsharewillbereducedtoapproximately27percent,while(otherthingsbeingequal)keroseneandfirewoodstoveswillhaveamarketshareof20percentand52percent,respectively.Despiteitsadvantageouscharacteristics,suchascleanlinessandsafety,theethanolstovecannotdominatethemarket.

Thepopularityoftheethanolstovecanbeachievedbyacutinitsprice.Plot(B)ofFigure17showsthatwhenthestovepriceisreducedto20USD(e.g.,bygovernmentalsubsidy, technological innovation, carbon finance), the ethanol stove is the mostpopular stove,when itsmonthly usage cost is also set at the lowest scenario.Thissimulationshowedthatalthoughitisstilldifficulttomaketheethanolstovethemostpopularstoveinthemarketitisnotanimpossiblescenarioifstovepriceandmonthlycostarereducedatthesametime.

When we look at segmented markets, the trend in market shares is more clearlyexplained.Plot(A)ofFigure18showsthedemandsfortheethanolstovewhenthestovepriceis40USDastheplot(A)ofFigure17.However,Figure18showsthesegmenteddemandsbydifferentincomegroupssuchaslow,middleandhighincome.AsFigure17,alldemandcurvesinFigure18havedownwardtrendandthedemandcurveofthelowincomegroupisthesteepestastheusagecostofthelowincomegrouphasthelargestnegativecoefficient(Table7).Alsoatanypointsintheplot,demandfortheethanolstovebythehighincomegroupisthemostandfollowedbythelowandmiddleincomegroups.

Plot(B)ofFigure18showsthatthedemandofethanolstoveincreaseswhenthestovepriceisreducedto20USDasexplainedabove.Thepointoffocusisthechangeinthedemandofthelowincomegroup.If,forexample,theusagecostbecomesthelowestbythedropintheethanolfuelprice,thedemandofthelowincomegroupbecomeshigherthanthatofthehighincomegroup.Thismeansthatthechangeinthedemand

Figure 17: Demands for three stoves under changing ethanol fuel price scenario

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ofthelowincomegroupbecomesakeydrivingforcebehindamovementinpopularityoftheethanolstoveintheplot(B)ofFigure17.

Thissimulationdemonstratedtheeffectsofproduct-specificattributessuchasstovepriceandusagescosttothemarketshareofstovesandhowthesocio-economicattributesalsocontributetotheanalysisinthesimulation.Basedontheestimatedcoefficientsandtrade-offamongtheproduct-specificattributesindifferentsocioeconomicgroups,simulation for stove switching patterns under different scenarios can be conductedforpolicyconsiderationtopromotecleancookingstoves.Thissimulationtaughtthatit iseffectivetoreducebothusageandpurchasecoststopromotetheethanolstoveespeciallyifthelowincomegroupisitstargetmarket.

Figure 18: Demands for ethanol stoves in three income groups under changing

ethanol fuel price scenario

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8 OVERALL DISCUSSION

This sectionopenswitha reflectionon themethodologyused, focusingon someareasforimprovementinfutureapplications.Amongthepossibleimprovements

discussedare thepossibilityof includingadditional attributes in futuremodels andways to improvethecategorisationof themoresubjective,“softattributes”suchassmokinessandsafety.Therefollowsadiscussionontheadvantagesofusinga“tradeoff” as opposed to a “wish list” approach in identifying attributes affecting stovechoice.Thisleadsontoadetaileddiscussionontheneedtoconsiderproduct-specificaswellassocio-economicfactorsasdeterminantsofcookingstovechoice.

Suggestions on the modelling for further research

Althoughthemodelusedinthisstudyshowssomeinterestingandsignificantoutcomes,therecouldbesomeareastoimprove–namely1)additionalattributes,2)paneldatastructure,and3)dummyvariablesforsoftattributes.Thisshortsectiondiscussesthepossibilityofthesethreeimprovements,whileexplainingtherationaleforadoptingthecurrentmodelinthisresearch.

Firstofall,likeotherregression-basedmodels,thefitcanbeimprovedbyincludingadditional variables, such as income level, age, education, occupationor anyothersocio-economic attributes affecting variability. However, the inclusion of suchattributeswoulddefeatthestudy’spurposeofseparatelyassessingattributesthatrelatetoproduct-specificandsocio-economicvariables.Furthermore,themainpurposeofthemodel is thevaluationof trade-offbetweenattributes,butnot to forecast; thus,noadditionaleffortwasmadetoimprovetheρ2valuewhichindicatesmodelfitness.Havingsaidthat,infutureresearch,additionalattributescanbeincludediftheyhelptoformulatebettercleancookingprojectdesignsorpolicies.Forexample,fromthefocusgroupdiscussion,usability–suchasstart-uptimeandfinishingtime–canbeimportanttomakingapopularcleancookingstove.Althoughconvenienceisusuallynotthemainpurposeofacleancookingstoveproject,ifthisattractsextrademand,stovedesignersshouldtaketheattributeintoaccount.Anotherspecificfactorinfluencingstovechoicethathasnotbeenassessedisthelife-timeofthestove,andhencethe(medium-term)runningcosts,whichtheresultsshowtobeveryimportanttousers.Someimprovedstoveslastonlyamatterofthreetofiveyears.Inthiscircumstance,themedium-termrunningcostsshouldtakeintoaccountthestovereplacementcost.Bothtimeattributewillbequantifiedeasily,sothatfurtherresearchregardingthenewattributewillhelptoestablishmorerealisticrepresentationofhypotheticalalternatives.

Second,theoretically,thepanelnatureofthedatamustbeconsideredbecausetheSPsurveyusesmultipleobservationsfromagivenrespondent(ReveltandTrain,1998).Forexample, amodelcouldhavea randomcoefficient,whichwillnotvaryacrossobservations from the same respondent.That is, panel data could be addressed byusingaMixedLogitmodel,inwhichanindividualspecificerrortermisaddedtofixitsmeanvalueatzero,whileitsstandarddeviationisestimated.However,thepanel

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structuredidnotworkinthemodel,andasaconsequence,thestandarderrorsreportedhereareunderstated, i.e., the results in themodelmustbediscounted(Wooldridge,2002).Asthismodeldoesnotconsidertheeffectsofpreviouschoicesonsubsequentquestions,oneofthepotentialproblemsisincorrectstartingvaluesfortheparameterestimates.Forfutureresearch,a jackknifemethodshouldbeapplied to theoriginalresults to estimate less biased and more conservative parameters, as discussed byCirlloet al.,(2000).

Third, for the risk and smoke attributes, dummy variables could have been moreappropriate, as thesearecategorical attributes;however, asmentionedabove, thesearepseudo-categoricalattributes in thismodel,while theyarecontinuousinreality.These attributes differ from “age” categories, which, although also continuous innature,mayevenchangesign(+/-)acrosstheagecategories;theparametersforsmokeandrisklevels,incontrast,areunlikelytoalterinsignacrosslevels.Moreover,theriskandsmokevariableswerepresentedwithlevelnumbers,suchas“0:No”and“3:Very high” in theSPof theTanzania andMozambique case studies; therefore, thelinearassumptionismoreexplicitlysuggestedinthisstudy.Sincethesesoftattributesaredifficult tomeasureandestimate, futurestudiescouldadoptamoreappropriateapproach,suchasordinalvariablesusingalatentvariableframework,inwhichthesevaluesareindicatorsofalatentvariable.

Wish-list approach vs. trade-off approach

Although researchon the relative strengthsof attributesaffecting stovechoicewaslacking,afewstudieshaveattemptedtoestimatetherelativestrengthsofattributes(GuptaandKöhlin,2006;Pohekar,Kumar,andRamachandran,2005).However,theiradoptedmethodologyisquestionable;forexample,thestrengthsoftheattributeswereestimatedby asking respondents to rank/ratedifferentproduct-specific attributes asthebasisof theirpreference levels.Suchanapproachhas the riskofpeople listingeverypositiveattribute–suchassafety,convenienceofuseandcleanliness–ashighlypreferred,whilenegativeattributes–suchassmoke,priceandoperatingcost–arelistedasleastpreferred.Ifthereisafailuretochecksuchbiases,thisresultsinthegenerationofapotentiallyunrealistic‘wishlist’attheindividuallevel,withtheextrapolationofsuchdata foragivenpopulation throughaggregationresulting ina ‘democraticallyexpressed wish list’. Choices are therefore assessed in terms of a hypotheticalcollectionofattributes,ratherthananalysingthetrade-offsamongchoicesavailabletoconsumers.PohekarandRamachandran’s(2005)resultsshowedsuchatendency.TheirstudyrevealedthatLiquidPetroleumGas(LPG)forcookingisthemostpreferredalternative,whilethestudyregioninIndiaisknownforitsuseofbiomassfuelsandverylittleuseofLPG.

In the ranking- and rating-based studies, respondents are not required to expresspreference in terms of trade-offs between the attributes. In choice experiments,theadoptedmethodology for this research, thestrength lies in itsability tocapturepreferenceintermsofatrade-offbetweentheattributes,whichinturncanbeusedfor

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theestimationofdemand,priceelasticity,incomeelasticity,thevalueofeachattributeandtrade-offsbetweenthem.Theoreticallymostproductattributeswillaffectchoicesifthelevelsoftheproductattributesareraisedorloweredbeyondthetrade-offrangeofanindividualorgroup.Forexample,theresultofthisstudyindicatesthatthestoveprice is not significant to the high-incomegroup, perhaps because the study testedamaximumstovepriceof40USD(i.e.,500ETB).However, for thesameincomegroup,ifthestovepriceisraisedcontinuously,atsomepointthepricewillbecomehigherthanthehighestwillingness-to-paypriceofthegroup(e.g.,ifapriceishigherthanincomeandsavings,onecannotpaytheprice);hence,itwouldaffectthechoicesmade.Thus,itisimportanttodesignaproducttomeetconsumerneedsbasedonthetrade-offsamongstattributes.

Product-specific attributes and socio-economic attributes

Thepreviousstudiesondeterminantsoffuel/stovechoicearehighlyskewedtowardssocio-economicattributesandlackafocusonproduct-specificattributes.Asmentionedpreviously,socio-economicattributesareresponsibleforvariationsinchoicebetweendifferent individualsorgroups.Forexample, thestudyresultspresentedinTable7,Table8,andTable9showthatthestrengthsofproduct-specificattributes–suchasthecoefficientofsmoke,stoveprice,usagecost,andsafety–differacrossvarioussocio-economic attributes like age, income, education, and gender. The product-specificattributes analysis assumes that people’s choices are different because they havedifferenttastes,asindicatedbythecoefficientsoftheproduct-specificattributes.Forexample,forthelow-incomegroup,thesmokeattributeisinsignificant,indicatingthatsmokedoesnotaffectthestovechoiceofalow-incomehousehold,whileforthehigh-incomegroup,thesmokecoefficientishighest,indicatingthehighlevelofaversiontosmokewithinthehigh-incomegroup.

The World Bank’s research on ‘attributes determining fuel choice in Guatemala’(Heltberg,2005)foundincome,electricityconnection,lifestyle,householdsize,andeducation to be the determinants of cooking stove and the associated fuel choice.Asdescribedabove, the identifieddeterminantsofstovechoicearesocio-economicattributes, which are responsible for variations in cooking stove and fuel choicebetweendifferentpeople,andfail toexplainwhatdeterminesstovechoicein termsoftheidentifiedsocio-economicattributes,suchasincome,lifestyle,householdsizeandeducation.Inotherwords,thestudysuccessfullyidentifiesthecharacteristicsofatargetmarket,butitissilentregardingthedescriptionofstovesthatbestfittheneedsandcharacteristicsofthatmarket.Forexample,manyresearchershavecitedgenderasanimportantattributeaffectingstovechoice.Attributeslikegenderareessentiallytwomutuallyexclusivecategories–maleandfemale.Theidentificationofgenderasaattributeindeterminingstovechoicemayhelptoexplainhowstovechoicediffersbetween individuals or groups belonging to either of the two categories; however,whatdeterminescookingstovechoiceforeitherofthecategoriesremainsunanswered.Therefore, it is important that research includesbothproduct-specificattributesandsocio-economicattributes.Theproduct-specificdeterminantsofstovechoicearelikely

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tobelimitedinnumberanduniversalinnature.Hence,theimportantquestionmaynotbewhatdeterminesstovechoiceforagiventargetmarket,butmoreimportantly,whatarethemagnitudesandtrade-offsbetweentheproduct-specificattributesforagiventargetmarket, specified in termsof socio-economicattributes.This study (Table7)illustratesthatwhileproduct-specificattributesdeterminingchoicesremainreasonablyconsistentacrossallothertestedsocio-economicattributes,thedifferenceliesinthemagnitudeandtrade-offsrepresentedbythecoefficientsoftheattributes.

Understandingthestrengthofeachattributeandthetrade-offsamongattributesisamorepractical,efficient,andeffectiveapproach.Assuch,reducingthecostofthestove(βcost)ismore“efficient”thanreducingthepriceoffuels(βprice),asthemagnitudeoftheformerparameterislargerinthegeneralmodel(Table6).However,ifthedecisionmakersinthetargetmarketarewomen,relativelymoreattentionmustbegiventofuelprice,as thisstudyindicatesthatwomengivemoreconsiderationtofuelpricethanmen (gender-segregatedmodels). Similarly, the trade-offs andmagnitudes of otherproduct-specificattributescanbeanalysedfordifferentsocio-economicattributestoidentify efficient interventions. Furthermore, an “effective” implementation needstoconsider informationonmarket-specificprofilesandpreferences throughvariousindicators, such as the variability of trade-offs andmagnitudes of product-specificattributesacrossvarioussocio-economicvariables.Inthisway,bothproduct-specificattributesandsocio-economicattributesareimportant,eachprovidingratherdifferentinformation.Thusacleardistinctionbetweenthetwomustbeconsidered.

A focus on product-specific attributes is as important as socio-economic attributesbecauseinordertobringchangestoenergyusagepatternsintheshort-run,itismucheasier to change products or product attributes than to change the socio-economiccharacteristics of an individual or a household.Transformation through changes insocio-economicattributesistantamounttochangingenergyusagepatternsbyalteringpeople or their circumstances, which is almost impossible in the short term anddifficult even in the long run.For example, commonly reportedattributes like age,income,gender,andlevelofeducationcannoteasilybechanged.Atthesametime,socio-economicattributesareimportanttoidentifytargetgroupsforlongtermpolicychanges.

Theabovediscussionabouttherolesofsocio-economicandproduct-specificattributesaresummarisedinFigure19.PreviousresearchhasonlyfocusedonQuadrants1and3(Q-1andQ-3),helpingtoidentifytargetgroupsormarketsegmentation.ThisstudyhasidentifiedtheimportanceofQ-2andQ-4andestimatedthetrade-offsamongsuchattributes,whichcouldhelptodesignacookingstoveprojectwithahigherprobabilityof acceptance.The ideaof balancingbetween socio-economic andproduct-specificattributes is strongly related to “pragmatism,” as expressed by the philosopherCharlesSandersPeirce(Peirce,1877;Peirce,1878).Peirceclaimedthattheproblemof social science, comparedwith physical engineering, is perfectionism and a lackof pragmatism.An engineer finds a solution and then designs approaches tomovestepbystep towards the targetor theperfectsolution.Incontrast,asocialscientistsetsupaperfecttargetandthenskipsintermediateprocessesanddesigningstagesto

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attempttoreachtheperfectsolutiondirectly(Sobrinho,2001pp.235-7).Thisdoesnotnecessarilymeanthattheperfectsolutionhasnoanalyticalvalue;itsimplyhaslessanalyticalvaluewhenitcomestotheshorter-termgoalofimprovingthefuel/stoveanditsimpacts,bydesigningbetterprogrammesormarketinstruments.

Apragmaticapproachisnecessaryinthecleancookingsector.Theperfectsolutiontopromoteacleancookingstovemaybetoachieveamoreequitableandstablesociety,sothateveryonecanaffordsuchstoves;however,thisisnotpossiblewithoutdesigningaspecificprocessanddefiningstepstotaketowardsthegoal.Asapartoftheprocess,itisimportanttoincreasethefocusonproduct-specificattributessoastomakeastovemoredesirable to the targetpopulation,consequentlycreatingandexpandingarealmarket.

Moreover,althoughtherearemanysimilaritiesamongstthecases,especiallybetweenEthiopia and Tanzania, the case study approaches also show distinct differences,indicatingcase-specificissues.Forexample,ethanolASCsarenotalwayssignificant,unlikeothercasesandthiscanbeexplainedbyanEthiopian-specificissue,asexplainedabove, i.e., someEthiopiansdonot like theethanolstovebecauseofpreviousfatalaccidentscausedbymisusedethanol-kerosenestoves.

Policy relevant findings

Byexaminingthetrade-offsthattheconsumermakesbetweenproduct-specificfactors,onecanselectastovedesigntofitspecifiedmarketsegments.Thisisnotpossiblebyconsideringsocio-economicfactorsalone.

Moreover,asillustratedabove,whenthevarioustrade-offsareunderstood,itbecomespossible to make predictions about the market for specific products given certain

Figure 19: Four categories of clean cooking stove determinants

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conditions,forexample,underdifferentfuelpricescenarios.ThistypeofinformationisofparticularinteresttocountriessuchasEthiopiawherepolicymakersareintheprocessofdevelopingstrategiesforallocatingbiofuelsresources(ethanol,inthecaseofEthiopia) todifferent sectors (e.g. export, transport,household)and requirefirmguaranteesabouttheexistenceandsizeofahouseholdmarketforthefuelatagivenpriceinordertoallocatesufficientquantitiestosupportthedevelopmentofthissector.

Although themethodologyapplied in this studycouldbeused toassesshouseholdpreferencesforanytypeofcookingstove,ineachofthecasestudiespresentedhere,theethanolcookingstovewasincludedasanalternative.InthecasesofDaresSalaamandCatembe,whereethanolstoveshavenotyetbeenfieldtested,theresultsgeneratedareofinteresttostoveprogrammedesignersandpolicymakerslookingatthepotentialmarketforthistechnologyandfuel.InthecaseofDaresSalaam,householdspreferredtheethanolstoveoverfuelwoodandkerosenebyasignificantmargin,whichsuggestsastrongpotentialmarketforthisstove.However,inordertopenetratethelowerincomemarket inTanzania, the price of the stovemust be carefully considered; the studyshowed thatachange instoveprice indaresSalaamwouldaffect the low incomegroup’sutilitymorethan10timesthatofthehighincomegroup.

ResultsinMozambiqueshowthatalthoughtherespondentsvaluedtheethanolstoves’safeandclean(smokefree)attributes,thecharcoalstovewasstillthepreferredcookingoption(followedbytheethanolstoveandthenthefuelwoodstove.).However,sincethe research was conducted, the price of charcoal in Mozambique has increasedsignificantlyandisnowlessaffordableformanylowandmiddleincomehouseholds.Ethanol is now a more viable option for low and middle income households inMozambiquethanpreviouslyandaprivatecompanyiscurrentlypilottestingethanolstoves in lowandmiddle incomehouseholds inMaputo.Thegoal is toproduceanethanolstove locally thatmeets theneedsof theusers. In thecaseofCatembe, theHHEAdemonstratedthatmostimportantfactorsdeterminingchoiceofcookingstovefor themajorityof consumers are stoveprice, runningcost and safety.TheprivatecompanyistargetingthelowtomiddleincomemarketsinMaputoandisseekingtostrikeabalancebetweentheseattributesinthedesignandfuturemarketingofthenewstoves.(Atanassov,personalcomment,2011.03.02).

In the Ethiopian case, the results of theHHEAwere particularly pertinent from apolicyperspectivegiventheongoingethanolstoveprogrammeandthegovernment’sefforts todevelopastrategyforfuturebiofuelsdevelopmentandmarketallocation.The Addis Ababa study clearly confirms that ethanol is the preferred option forcooking;thisresultvalidatesthefindingsoftheGaiaAssociationpilotethanolcookingstoveproject.Themostinteresting,policyrelevantresultsarerelatedtotheimpactofincreasingstovecost andstove runningcostonvarious socio-economic strata.Thestudyshowedthatintermsoftradingoffbetweenproduct-specificattributes,thelowerincomehouseholdsweremoresensitive to theupfrontcostof thestove than to theusage cost as comparedwithmiddle and upper income households.Moreover, thefact that an increase in stove pricewill affect the low incomegroup’s utilitymorethanfive times thatof thehigh incomegroupwouldseemtosuggest that,unlessa

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cheaper stove can be produced, particular financingmechanisms, e.g. subsidies ormicrofinancewillbeneededinorderforthecurrentstovemodeltobeaccessibleforpoorerhouseholds.FormiddleincomehouseholdsinAddisAbaba,thestoverunningcost(fuelcost)isamoresignificantfactoraffectingdecisionthanthecapitalcostofthestove.ThisisasignificantresultgiventhattheEthiopianGovernmentisinterestedininitiallypromotingethanolcookingstovesforuseinmiddleincomecondominiumdevelopmentsaroundAddisAbaba.Itwouldsuggestthatalthoughthesehouseholdswill likelybeable toafford topayfor thestove, inorderfor thisprogrammetobesuccessful,carefulconsiderationmustbegiventoensurethatthepriceofethanoldoesnotexceedthatofkeroseneandcharcoal.ForfurtherpolicyoutcomesinTanzaniaandMozambique,aswellasoutreachandfollowonactivitiesineachcasecountry,pleaserefertoAppendix2.

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9 CONCLUSIONS AND RECOMMENDATIONS

A switchfromtraditionalbiomassfuelstovestomodern,clean,safeandefficientstovesisexpectedtoenhancethewelfareofthe2.5billionpeopleworldwidewholackenergyaccessandtohelptoreducenegativeenvironmentalimpactsassociatedwith traditional biomass use.This research empirically investigated the applicationofacutting-edgedeconomicmodelthatwasbasedonastatedpreferencesurveyanddiscretechoiceanalysis,forevaluatingcleancookingandenergydemandassessmentsatahouseholdlevelinEthiopia,Tanzania,andMozambique.Thispaperfirstexplainedthemethodologicalcontextsandthetheoreticaldimensionsofattributesdeterminingstove choice at thehousehold level.The study found that as compared to the low-incomegroup,thehigh-incomegroupwaswillingtopaytentimesasmuchforaunitreductioninindoorsmoke,twotimesmoreforincreasedefficiency,andtentimesmoreforincreasedsafety,insomecases.Thestudyalsorevealedthestructureofhouseholdpreferencesforlesssmoky,lessrisky,andlesscostlycookingoptions,indicatingtherelativeimportanceamongsuchattributesandthetrade-offsamongsttheseattributes.Oneofthemostsignificantfindingsinthisresearchwasdistinguishingbetweensocio-economic and product-specific attributes as the determinants of cooking stove andfuelchoice,withtheformerresponsibleforvariationinchoicebetweenindividualsorgroups,andthelatterforvariationwithinindividualsorgroups.

Thisresearchintroducesamethodology(discretechoiceanalysis)usedextensivelyintransportationandmarketingstudies.Theapplicationincludessimulationofhouseholdcookingstove/fuelchoicesandpreferenceassessment.Thesimulationmodelusedinthisstudycanhelptoimprovehouseholdcookingprogrammedesignsandtoidentifypolicy alternatives with potentially greater impacts at local and national levels.Additionalresearchisneededtoadaptthechoiceexperimentmethodologyforstovesandfuelchoiceanalyses.Theissuesregardingthesurveydesign–suchastherefinementof the smoke labels and levels, the inclusion of more product-specific attributes,additionalalternatives,anddifferentsurveytechniques–couldgiveinterestinginsightsintothedeterminantsofchoiceandimprovethechoiceexperimentdesign.

If theswitch from traditionalbiomassstoves tomodern,cleancookingstoves is tobeaccelerated,futureresearchshouldstrikeabalancebetweensocio-economicandproduct-specificattributesandplaceagreateremphasisonproduct-specificattributestocompensateforwhathasbeenlackinginpastresearch.Whenacleancookingstoveproject is implemented in thefield, thesefindingsmust be considered seriously, assomelevelofpragmatismisnecessaryinthissectorinordertomakeprogresstowardsthe goal of improving energy services and reducing the social and environmentalimpactsoftraditionalbiomassuse.

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APPENDIX 1: CHOICE EXPERIMENT AND ANALYSIS

Thissectionbrieflyexplainsthemechanicsofthechoiceanalysisanddemonstrateshowthiscanbeusedforthedemandassessmentofcleancookingstoves.

Choice is lifeEveryoneisconstantlymakingchoices.Almosteverythingonedoesis theresultofa choicemade.—Every action taken, from reading this report to reading the localnewspaperisachoice(even“nottoreadanything”isachoice).Thesetofthesechoiceoptionsiscalledalternatives.Thereareanumberofreasonswhyonemaycontinuereadingthisreport,e.g.,thelengthofthisarticle,timingofyourbusinesshour,colourof the paper, etc. Regardless of what the reasons are, the factors influencing yourdecisionarecalledattributes.

“Attribute”and“alternative”canbeviewedas“input”and“output”ofachoiceprocessandthe“behaviouralrules”explainthelogicfrominputtooutput(Figure20).Choicemodellingisaquantitative/statisticalapproachtounderstandthebehaviouralrulesbasedonmicroeconomictheorieswithempiricaldataaboutattributesandalternatives.

In termsofcookingstoves, thereareanumberofalternatives inanygivenmarketsuchasa“cheap”and“dirty”firewoodstove,“clean”and“expensive”ethanolstove.Cheapnessandcleanlinessareattributes,whichinfluenceuswhenpurchasingacertaintypeofstove.So,ifweunderstandbehaviouralrules,whichuseattributestomakeachoicewithinasetofalternatives,wecanunderstandwhyonestoveisselectedoveranother.Also,ifweknowattributesdescribingthecharacteristicsofstovebuyers,wecanalsounderstand“Whyacertainstoveischosenmorebyaparticulartypeofbuyer”.

Sincechoiceisafundamentalhumanbehaviourandelementsofmanyhumanactivities,ananalystmayfindmanyinterestingsocialphenomenabyscalinguptheresultsoffindings. For example, an analysis can demonstrate themarket share of an ethanol

Figure 20: Relationship between attributes and alternatives

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stoveandthesensitivityofdemandchangesinitsstoveprice(i.e.priceelasticityofdemand).Estimatingthesemacrolevelphenomenaisusuallytheinterestofananalystandher/hisclientssuchaspolicymakersandstovedevelopers.Itisimportanttonotethat thisapproachrequiresahighdegreeofknowledgeofattributes; therefore, it isimportanttoworkwithlocalstakeholderstoidentifythem.

Choice with multi-attributes Thissectionexplainshowachoiceanalysisusesattributesasthesourcesofpreference,bylookingatwhathappenswhenwegotoamarkettobuyacookingstove.Tomakethedemonstrationsimple,onlytwoalternatives,namelyafirewoodstoveandethanolstove,andtwoattributes,suchasthepriceofstovesandsmokelevelsareused.13

If your choice between the firewood stove and the ethanol stove is determined bytheirpricesonly,yourshoppingisveryeasy,i.e.youwillbuythecheapestalternative.However, ifyoualsoconsidersmoke levelsandyourobjective is tobuyacheaperandcleanerstoveyouwillsometimesneedtocompromiseoneattributeoveranother,i.e.ifanethanolstoveisveryclean,youmaygiveupthecheapnessofthestoveandviceversa.ThepreferencebetweenthetwoattributesforanethanolstovewillbelikeFigure21.

At any points on the same concave-shaped line (Let’s say I2), an individual hasthesamelevelofsatisfaction. Inotherwords,aconsumer is indifferentamonganycombinationof stove characteristics on the I2 curve, so that the curve is called anindifferencecurve.Forexample,astoveisrelativelysmoky,butcheapatthepointA,andastoveisrelativelyexpensivebutnotsmokyatapointBonI2curve.Althoughthecharacteristicsofthetwostovesaredifferent,theoverallsatisfactionsofthetwostovesarethesameasthedisadvantageofoneattributeiscompromisedbytheadvantageoftheotherattributeintheoryaswellaspractice.Anethanolstoveontheupperright

13 It is not necessary to have all alternatives in an experiment, but if an analyst has all alternatives, they simulate the market share of each alternative. If not, the focus of research is usually the valuation of trade-offs between attributes.

Figure 21: Indifference curves of cooking stove choice. An indifference curve is a set of stove characteristics (in this example, with two attributes), such that the consumer does not have any special preference between them. Therefore, Stove A and Stove B on the I2 curve give the equal satisfaction. Moreover, any stove on the I3 is better than any stove on I2 for the consumer because those stoves have less smoke AND lower price.

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corneristhecheapestandcleaneststove,whichmeansthataconsumerwillgainthemaximum satisfaction or utility.Thismeans that if an individual canmoveher/hisindifferencecurvefromI1toI3,s/hecanincreasetheirsatisfactionandtheyhaveanintentiontodothat,i.e.utilitymaximisingbehaviour.However,anindividualhastostickwithanindifferencecurveatsomepointintimeduetotechnologicalorincomeconstraints, e.g. technologically it is impossible tomake the cleanest and cheapestethanolstoveintheworldatthesametime.Meanwhile,itisnotrationaltochooseacombinationontheI1curveasonecanchoosebettercombinationsonI2becauseofutilitymaximisingbehaviour.

Inshort,thepreferencesanindividualmakesarenotonlybetweenalternatives,butalsoattributes.Moreover,inthisexample,whataconsumercaresaboutisthecleannessandthecheapnessofanystove,notwhetherthestoveusesethanolorfirewood.Thismeansthat,thechoiceaconsumermakesatthetimeofshoppingisatradeoffbetweenthetwoattributes,butnotthealternatives.Ineconomics,thisconceptiscalledmulti-attributeutilitytheory.

This tradeoff concept between attributes is important to operate a clean cookingstoveproject successfully. If an analyst asks a local consumer “whatkindof stovewouldyouliketobuy?”themostlikelyresponsewillbe“thecheapestandcleaneststove!” However, as explained above, it is not possible to create such a cookingstove.Therefore,ifacleancookingstoveprojectmanagertriestofollowtheresponse(assumingitisactuallypossibletomakethecheapestandcleaneststove),thecleaneststovehastobesubsidisedoritsproductioncostwillnotbecoveredbyitsprice.Ineithercase,thecleancookingstoveprojectwillnotbesustainableinthelongterm.A choice analysis must carefully assess the trade-off between attributes, which isobservedasachoicebetweenasetofalternatives.Asanoutcome,theanalysiswillbeabletopresentaproper“shoppinglist,”butnota“wishlist,”sincethelatterignoresthetrade-offsbetweenattributes.

There is an implicit assumption in this approach that consumerswill alwayswantmoreofagoodthing(e.g.lowercost)andlessofabadthing(e.g.smoke).Theremaybesomecaseswheresucharelationmaynotbeappropriate;forexample,theremaybeapointatwhichfurtherreductioninsmokewillnotproduceanyextra(perceived)utility.Aneconomicmodeldesignbasedonmulti-attributeassumptionscaninsomecasesadjustthemodelstructuresoastorecognisesuchlimitationswheretheymayimpact the results.Furthermore,by introducinga randomelement in themodellingapproach,methodssuchasdiscretechoiceanalysisaremoreflexibleinrepresentingconsumerchoices(seebelow).

Discrete choice analysisPractically, the choice analysis uses statistical tools and experimental surveytechniquestoestimatetherelationshipbetweensourcesofpreferencesuchasattributesandalternatives.DiscreteChoiceAnalysis(DCA)isoriginatedbyProf.McFadden’sworkin1960’sand70’s,forwhichhewasawardedtheNobelPrizeforEconomicsin2000.Hecombinedthemulti-attributeutilitybasedonconsumerchoicetheorywith

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statistical randomutilitymaximizationmodel and developed the originalmodel ofDCAnamelymultinomiallogit(MNL)model.Conventionalconsumerchoicetheorystatesthatiftheutilityofagoodisgreaterthanthatofanothergood,aconsumerwillchoose theformergood.Thus, ifbuyinganethanolstovebringshigherutility thanbuying a firewood stove, i.e.U(Ethanol) –U(Firewood) > 0, a consumerwill buyan ethanol stove.The relationshipof utility and the stove selection is explained asFigure22.

Crossmarksinthefigureareassumedaspotentialobservedchoicesandthe“zigzag”shapeperfectlymatchwithalltheobservations,i.e.allcrossmarksareonthezigzagline.For example, as theutilities of buying an ethanol stove is higher than that offirewoodstoveatobservationAandB,anethanolstoveischosen,viceversa,i.e.itshowsthatatacertainpoint,“0”,itsuddenlymakessensefortheconsumertochoosetheethanolstove.

However,inrealityitisnotpossibletoestimatetheutilityofstovepurchaseduetounobserved attributes,measurement errors, etc.Therefore, the utilitymodel has tohaveanerrorcomponent,i.e.randomutilitymodel.Becauseoftheerrorcomponent,ananalystmayobserve irrationalchoicesuchaschoosinganethanolstovewhenafirewoodstoveappearstobringgreaterutility.Tofitamodelwiththeseobservations,achoicemodelbecomesstochasticandits“S”curvedisplaysaprobabilisticoutcomeasinFigure23.

Figure 22: Deterministic choice model

Figure 23: Stochastic choice model

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Forexample,whentheutilityofchoosinganethanolstoveoverfirewoodstoveisthesame,i.e.U(Ethanol)–U(Firewood)=0,theprobabilityofbuyingtheethanolstoveis50percentasthetwoalternativesareindifferentintermsofsatisfaction.Also,theprobabilityishigheratobservationGthanFastheutilityishigheratF.DCAusesamathematicalfunctiontodescribethe“S”shape.

Policymakersandstovedeveloperswillbemoreinterestedinthemacroleveloutcomessuchasthemarketshareandpriceelasticityofastovethanprobabilityofbuyingaparticularstove.TheDCAwillanswer thesequestionsas theprobabilityofastovechoiceisthesameasthemarketshareofthestoveinprobability theoryandtheslopeofthe“S”curveisitspriceelasticity.

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APPENDIX 2: POLICY OUTREACH AND FOLLOW UP ACTIVITIES

Thissectionprovidesanoverviewofthevariouspolicyoutreachactivitiesundertakentodisseminatethestudyresultstoarangeofstakeholders,fromtheprivatesector

to local and national government officials, stove programme implementers and theinternational donor community. The impact of the study at the local level is alsohighlighted, with particular reference to ethanol stove programmes (ongoing andrecentlylaunched)intwoofthecasecountries.

Ethiopia

TheresultsoftheEthiopianstudywerepresentedbySEIandGaiaAssociationataspecialworkshophostedinAddisAbabainMarch,2009attendedby,amongothers,theMinistry forMines and Energy (MME), The Ethiopian SugarAgency and theFederalEnvironmentalProtectionAuthority.Theimportanceofdisplacingkeroseneasacookingfuelintheurbanareaswashighlightedand,fromanationalpolicypointofview,governmentofficialswereparticularly interested in thepotential forusingthestudyresultstopredictthehouseholdmarketforethanolamongvariousincomegroupsinAddisAbaba.AnumberoftechnicalexpertsfromwithintheMMEexpressedan interest in havingmore information on demand forecasting as a tool for policyformulation.Inseparatemeetings,thestudyresultswerepresentedtothePresidentofEthiopia,andtheDirectorGeneraloftheFederalEnvironmentalProtectionAuthority.Both officials were particularly interested in the potential of the ethanol stove forreachinglowerincomehouseholdsandwerethereforekeentohavespecificestimatesregardingthemarginalwillingnesstopayofthevarioussocio-economicstrata.

Crucially,theresultsofthestudywerealsopresentedtoMakobuEnterprisesPLC.,thelocalethanolstovemanufacturerandkeyprojectstakeholder.Makobuisintheprocessofestablishinglocalproductionoftheethanolstove,requiringsignificantinvestmentontheirpartandisthereforeparticularlyinterestedinthetradeoffbetweenvariousstove attributes amongconsumers at different income levels.The current projectedpriceforthelocallyproducedstoveis$20,apricewhich,accordingtotheanalysisinsection5above,wouldbeacceptable to lower incomehouseholds,providedthatthepriceofethanol(usagecost)wouldbebelowthatofkeroseneandotherwidelyusedcookingfuels.Sincethestudywascompleted,thepriceofethanolinEthiopiahasincreasedto$0.50perliter-stilllowerthanthepriceofkerosenewhichtopped$0.80 in December 2010. Ethiopian ethanol is currently used for fuel blending inthe automotive sector but the Government is supportive about expanding into thehouseholdmarket,particularly if ethanolcandisplacekerosenewhichconstitutesasignificantdrainonforeignexchange.

The GaiaAssociation is now planning to establish small scale, community basedmicrodistilleriestoproduceethanolfromvariousagri-wastesforhouseholduseinandaroundAddisAbaba.TheNGOisseekingfundingtopilottestonemicrodistilleryin

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alowincomecommunityinAddisAbabaandhasrequestedthatSEIconductthesameeconomicanalysisinthiscommunityifandwhenfundinghasbeensecured.

Tanzania

ThehouseholdenergyeconomicanalysiswasalsowellreceivedbypolicymakersinTanzania.Preliminary resultswerepresentedat theMinistry forEnergy inOctober2009andtherewereindicationsthatsuchanalysescouldbeusefulinfuturenationallevel policy formulation on biofuels and household energy. In April 2010, thehouseholdenergystudywashighlightedduringa roundtablediscussiononbiofuelsledbyDirectoroftheDepartmentforEnvironment,ClimateChangeandSustainableServicesoftheSwedishInternationalDevelopmentCooperationAgency(Sida)attheSwedishEmbassyinDaresSalaam.InJanuary2011SEIParticipatedinaconsultationmeetingattheSwedishEmbassyinDaresSalaamandpresentedthefullSEEDreportincluding the HHEA results as input into their work drafting a new developmentcooperationstrategyforTanzania-Sweden.Sidaviewsthehouseholdenergyanalysispositively and gave the impression that energy, environment, and private sectordevelopment could become focal areas for Swedish development cooperation inTanzaniaasSwedenrefinesandfocusesitscooperationwithTanzania.

In January2011, the researchcarriedoutunder theSEEDprogramme (particularlythe issue of applying a Strategic EnvironmentalAssessment approach to assessingalternativestohouseholdenergyandtheworkonhouseholdenergyeconomics)waspresented to theMinisterofNaturalResourcesandTourismandhis technicalstaff,followingapleafromtheministertofindworkablesolutionstomakeatransitionfromcharcoaltocleanercookingfuels.

Mozambique

InNovember2009,thestudywaspresentedduringaseminarhostedbytheSwedishEmbassyinMaputo.Theaudienceconsistedofpolicy-makers,high-levelgovernmentofficials,representativesofdonorgroups(SIDA,USAID,GTZ,Italiancooperation,Norad, and the French DevelopmentAgency), International organizations (WorldBank) and some private sector representatives. A private company, CleanStarVentures,isnowundertakingthetaskofintroducingEthanolcookstovestothemarketinMozambique.Thedecisiontolaunchthisinitiativeaswellasmuchoftheinitialmarketanalysisfortheventureisbasedonthehouseholdenergyeconomicanalysisstudy.CleanStarVentures has nowengaged a consultant tomanage the stove pilottestingandinitialmarketroll-outphase.

International

ThestudywaspresentedduringthePartnershipforCleanIndoorAir(PCIA)biennialforum in Kampala, Uganda inMarch 2009 for more than two hundred improved

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cookingstovepractitioners(NGOs),researchersandpolicymakersfromaroundtheworld.Thepresentationgeneratedmuchinterest, inparticularfrommembersof thehouseholdenergyNGOcommunityinterestedinconductingsimilarstudies.

InMarch2010,thestudywaspresentedforSidaattheSwedishembassyinLusaka,Zambia.Sidaindicatedaninterestinworkingattheinterfaceoftechnologyend-usersandtheprivatesectorasapossiblealternativeroutetoworkingthroughGovernmentagenciessuchastheDepartmentofEnergy.

Thereisnowarenewedinternationalcommitmenttotacklingtheissueofhouseholdenergyaccess.InSeptember,2010USSecretaryofState,HilaryClintonannouncedanewinitiative,theGlobalAllianceforCleanCookstoves,apartnershipbetweentheUSgovernmentandothernationsandcharitablefoundationswhichisthefirstmajorattempt toaddress the issueworldwide.Theprojectaimsto introducemodern low-pollutionstovestothehomesof100millionpoorpeopleby2020.

TheAlliance is seeking to support a thrivingglobalmarketmadeupof a rangeoforganizations—from cottage industries to large-scale companies—that are bothsustainablysupplyingclean,efficient,affordable,anduser-desiredcookingsolutions(stovesandfuels)atgreaterscaleandthatareconstantlyinnovatingtoimprovedesignandperformance,whileloweringcost.14Buildingthecapacityofsuchorganizationswillrequireathoroughunderstandingofthefactorsaffectinghouseholdleveldecisionmakingineachspecificmarket.Themethodologyappliedinthisstudycouldofferaverypracticalmeansofrapidlyassessingthefeasibilityofparticularapproachesandpoliciesforscalingupaccesstocleanercookingoptionsandensuringthat,fromtheperspectiveoftheconsumer,themostappropriatetechnologiesarepromoted.

14 http://cleancookstoves.org/

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sei-international.org

Stockholm Environment InstituteSEI is an independent, international research institute. It has been engaged in environment and development issues at local, national, regional and global policy levels for more than a quarter of a century. SEI’s goal is to support decision making for sustainable development by bridging science and policy.

Fiona Lambe is a Research Associate at SEI in Stockholm. Her main areas of focus include socio-economic aspects in the design and implementation of household energy projects in developing countries as well as institutional and policy frameworks for sustainable energy transitions.

Anders Arvidson is Director of the Stockholm Environment Institute’s Africa Centre. He has more than 15 years of experience in consultancy, research and capacity building related work in developing countries in the area of energy, environment and development. Boris Atanassov is the founding director of GreenLight, a Mozambique based research and development organization, focused on the promotion of environmental technology for community advancement. He worked as an intern for SEI (2009-2010) during which time he coordinated the HHEA in Mozambique. Milkyas Debebe is Managing Director of Gaia Association, an Ethiopian NGO engaged in the promotion of ethanol and ethanol fuelled cooking stoves as an alternative to the traditional use of biomass for cooking in Ethiopia. Linda Nilsson is a MSc student of Political Science at Uppsala University in. During 2009 -2010 she conducted an internship as a Research Associate at the Stockholm Environment Institute’s Africa Centre, contributing to SEI’s energy sector related research and capacity building activities. Patricia Tella is a Research Associate at SEI, Stockholm where her main research focus is on the analysis of sustainability criteria for agricultural production, in particular fuel crops. She has also worked on the socioeconomic dimensions of energy access in developing countries. Stanzin Tsephel conducted research and fieldwork for the Ethiopian case study in this report in completing his Masters thesis at Oxford University, UK in the Environmental Change Institute (ECI). He now works with Ladekh Ecological Development Associates in Leh Ladakh, India.

Switching from traditional biomass to cleaner, safer and more efficient fuels for cooking can enhance the welfare of the 2.5 billion people worldwide who currently do not have access to modern energy sources, while helping to reduce the negative health and environmental impacts associated with traditional biomass use. Despite the numerous benefits associated with cleaner alternatives, the transition to improved cooking stoves and fuels has largely stalled in Sub-Saharan Africa (SSA). Why is it that well-designed, efficient and clean stoves often fail to penetrate the market in developing countries, as expected? This report presents a study conducted by the Stockholm Environment Institute to address this knowledge gap by empirically assessing the role of socio-economic attributes and product-specific attributes as determinants of cooking stove choice at the household level.

ISBN: 978-91-86125-25-7

Takeshi Takama is a Japan International Cooperation Agency (JICA) expert working on a climate change project as well as a visiting Research Fellow at the SEI Asia Centre. He previously worked at the SEI Oxford office and collaborated with the Stockholm Centre on projects related to energy, climate change and human behaviour in Africa and Asia.

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Francis X. Johnson is Senior Research Fellow in Energy and Climate at SEI in Stockholm, and conducts analysis, research and strategic assessments related to biofuels and bioenergy in the context of global and regional climate and development policies.

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Will A

frican

Con

sum

ers Buy C

lean

er Fuels a

nd

Stoves?

Taka

ma, La

mb

e, Joh

nso

n, et al.

SEI