IMF-应对气候冲击：空间框架下的粮食安全（英）-2023.8

wpl23l166IMFWorkingPapersdescriberesearchinprogressbytheauthor(s)andarepublishedtoelicitcommentsandtoencouragedebate.TheviewsexpressedinIMFWorkingPapersarethoseoftheauthorsanddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,IMFmanagement,theOECD,theFCDO,orthepartnersintheCCCDI.2023AUGNArNR*WewouldliketoexpressourgOkou,VascoCarvalho,Ti(CCCDI)—Belgium,Canada,China,German©2023InternationalMonetaryFundWP/23/166IMFWorkingPaperResearchDepartment&AsiaandPacificDepartmentCopingwithClimateShocks:FoodSecurityinaSpatialFramework*PreparedbyDiogoBaptista,JohnSpray,andD.FilizUnsalAuthorizedfordistributionbyChrisPapageorgiouandNadaChoueiriAugust2023IMFWorkingPapersdescriberesearchinprogressbytheauthor(s)andarepublishedtoelicitcommentsandtoencouragedebate.TheviewsexpressedinIMFWorkingPapersarethoseoftheauthor(s)anddonotnecessarilyrepresenttheviewsoftheIMF,itsExecutiveBoard,IMFmanagement,theOECD,theFCDO,orthepartnersintheCCCDI.ABSTRACT:Wedevelopaquantitativespatialgeneralequilibriummodelwithheterogeneoushouse-holdsandmultiplelocationstostudyhouseholds’vulnerabilitytofoodinsecurityfromcli-mateshocks.Inthemodel,householdsendogenouslyrespondtonegativeclimateshocksbydrawing-downassets,importingfoodandtemporarilymigratingtoearnadditionalincometoensuresufficientcalories.Becausethesecopingstrategiesaremosteffectivewhentradeandmigrationcostsarelow,remotehouseholdsaremorevulnerabletoclimateshocks.Foodinsecurehouseholdsarealsomorevulnerable,astheirproximitytoasubsistencerequirementcausesthemtoholdasmallercapitalbufferandmoreaggressivelydissaveinresponsetoshocks,attheexpenseoffutureconsumption.Wecalibratethemodelto51districtsinNepalandestimatetheimpactofhistoricalclimateshocksonfoodconsumptionandwelfare.Weestimatethat,onanannualbasis,floods,landslides,droughtsandstormscombinedgeneratedGDPlossesof2.3percent,welfarelossesof3.3percentfortheaveragehouseholdandincreasedtherateofundernourishmentby2.8percent.Undernourishedhouseholdsexperienceroughly50percentlargerwelfarelossesandthoseinremotelocationssufferwelfarelossesthatareroughlytwotimeslargerthaninlessremotelocations(5.9vs2.9percent).Incounterfactualsimulations,weshowtheroleofbetteraccesstomigrationandtradeinbuildingresiliencetoclimateshocks.RECOMMENDEDCITATION:Baptista,Diogo,JohnSpray,andD.FilizUnsal,2023,“CopingwithClimateShocks:FoodSecurityinaSpatialFramework”.IMFWorkingPaper23/166.JELClassificationNumbers:O13,O18,Q53,Q18,R3Keywords:Agriculture;FoodSecurity;Trade,Migration;Climatechange;ClimateshocksAuthor’sE-MailAddress:ds845@cam.ac.uk;jspray@;filiz.unsal@CopingwithClimate INTERNATIONALMONETARYFUND3CopingwithClimateShocks:FoodSecurityinaSpatialFrameworkINTERNATIONALMONETARYFUND41Introduction 2DataandEmpiricalRelationships 92.1.Data 2.2.Context 2.3.EmpiricalRelationships 3Model 3.1.Overview 3.2.ConsumptionandSaving 3.3.FinalGoodsConsumptionandFoodSecurity 3.4.HouseholdHeterogeneity 3.5.FoodMarkets 3.6.FoodProduction 203.7.Non-foodSector 213.8.MarketClearing 224CalibrationandModelSimulation 234.1.ExternalCalibration 234.1.1.Preferences 234.1.2.Population 234.1.3.FarmProduction 244.2.InternalCalibration 254.2.1.ConsumptionPreferences 254.2.2.TradeCosts 254.2.3.Non-foodSectorProductivities 264.2.4.Migration 264.3.Climateshocks 274.4.Determinantsoffoodsecurityincalibratedmodel 284.5.Modelsimulationinresponsetoshocks 285EconomicImpactofHistoricalClimateShocks 295.1.AnnualClimateDamages 295.2.DeterminantsofVulnerability 315.3.Policycounterfactuals 336Conclusion 34References 36Appendix 40INTERNATIONALMONETARYFUND5ListofTables1ExternallyCalibratedParameters 242InternallyCalibratedParameters 26ListofFigures1WeatherVariabilityinNepal 2Distributionofweatherrisk 3Householdimpactsfromclimateshocks 4Householdcopingstrategiestoclimateshocks 5Responseofsavingsratetoclimateshock 6Localprojectionmodelfordistrictlevelfoodpricesfollowingclimateshocks 7ModelEnvironment 8DistributionofDistrict-levelProductivity-PriceElasticities 279ImpulseResponseFunctionsfora-10%ProductivityShock 2910AverageAnnualImpactofHistoricalClimateShocks,2011-2022 3011ImpactofHistoricalClimateShocksacrossDistrictsandTime,2011-2022 3112WelfareLossfromClimateShocksbyRemotenessandUndernourishment,2011-2022 3213EffectofImprovedInfrastructureonWelfareLossesfromClimateChange 3314EffectofImprovedInfrastructureonWelfareLossesfromClimateChangebyDistrict 3461IntroductionIn2021,2.3billionpeoplesufferedfrommoderateorseverefoodinsecuritybeensteadilygrowingforthelastdecade(FAO,2022).Insufficientfoodcanlowerindividualproductivity(Behrmanetal.,1997;ColeandNeumayer,2005),reducetherateofhumancap-italaccumulation(Asfaw,2016;ChakrabortyandJayaraman,2019),andgiverisetopovertytrapsthroughinsufficientphysicalcapitalaccumulation(BarrettandCarter,2013;KraayandMcKenzie,2014).Acoredeterminantofthisworseningtrendistheincreasinginfluenceofcli-matechange.Alongwiththeriseinglobaltemperatures,climatechangeisthoughttoincreasebyloweringagriculturalyields,destroyingcropsanddamaginginfrastructure.1Whiletheseclimateandeconomicvulnerability,aswellaseconomicintegration.Whatistheimpactofcli-mateshocksonfoodsecurity?Whichtypesofhouseholdsaremostvulnerable?Whatfactorsbuildresilience,andwadopt?Toanswerthesequestions,weconducvulnerabledevelopingcountry,thatclimateshocksareassociatedwithsignificantlyloweryields,farmincome,andfoodsecurityandthathouseholdsresponsesincludeincreasedmigrationandreducedinvestment.Motivatedbythisempiricalevidence,wedesignaquantitativespatialgeneralequilibriummodelwhichincorporatesmultiplelocationsandheterogeneoushouseholdstoshowthemacroeconomic,welfare,anddistributionalimpactsofclimateshocks.Themodelassumptionscapturekeyfeaturesoftheeconomiesofdevelopingcountriesincludingrequirements,incomediversificationthroughacombinationoffarmandoff-farmincome,andtemporarymigration.Wecalibratethemodelto51districtsinNepal,whichvarysubstantiallyingeographicconnectedness,productivity,andfoodsecurity.Themodelisusedtoquantifythelocalandmacroeconomicimpactsofclimateshocksusinghistoricaldistrict-leveldataonclimatedamagesovera12yearperiod.Furthermore,westudytheroleofeconomicintegrationinresiliencetoshocksthroughcounterfactualsimulations.Therearethreemaintakeawaysfromthisanalysis.First,historicalclimateshockshavecausedpersistentandsignificantdecreasesinoutput,welfareandfoodsecandfoodinsecurityexacerbatetheimpactsofclimateshocksleadingtomorepersistentanddamagingaggregateimpacts.Third,economicintegrationisanimportantsourceofresiliencetoclimateshocksandimprovedtransportinfrastructurecansubstantiallylowerfutureclimateWebeginbyprovidingempiricalevidenceonhouseholdandmarket-levelremateshocksinadevelopingcountry.2First,weshowthatclimateshocksinNepalaregeograph-icallydispersedanddependongeographicfeaturesofthedistrict.Second,weshowthatatthehouseholdlevel,climateshockscorrelatewithsignificantlyloweryields,lowerfarmincomeandincreasedfoodinsecurity.Theincidenceofclimateshocksisassociatkeycopingstrategies:substitutionawayfromnon-foodexpenditureandtowardshigherfoodexpenditure,increasedmigration,andlowersavingsandcapital.Theimpactsaremagnifiedamongfoodinsecurehouseholds,whodisproportionatelyreducetheirsavings.Finally,weshowthat,atthemarket-level,climateshocksareassociatedwithasignificantincreaseinfoodpriceswhichisapproximatelytwiceaslargeforremoteregions.Collectively,theseresultsindicatethattheappropriateframeworktostudytheaggregateimpactofclimateshocksisamodelMotivatedbytheseempiricalfindings,wedevelopaquantitativespatialgeneralequilibriummodelwithmultiplelocations1See,forinstance,Delletal.(2009,2012,2014);Kahnetal.(2021);Nath(2022).2Wedefineaclimateshockastheincidenceofflood,landslide,drought,orstormwhichisrecordeddamagesinthedistrictbytheGovernmentofNepal.3Bycontrast,aframeworkwhichincludesarepresentativeagentwouldmisssignificantandimportantin-terlinkagesacrossregions.Likewise,anempiricalapproachwhichwasnotabletocapturegeneralequilibriumforcesmayintroducebiasandmisstheinteractionsbetweenregions.7aswellasexternalregionsthroughtradeandmigrationlinkages.Becauseofthis,idiosyncraticclimateshockscanhavelargeandfar-reachingholdsendogenouslylimitreductionsinfoodconsumptionwhenhitbyashockbyutilizingfourresponsechannels:byshiftingalargershareoftheirbudgettowardsfoodconsumption;sellingoffassetstofinancecurrentconsumptionattheexpenseoffutureconsumption,diversifyingincomethroughlabormigration,andimportingadditionalfoodfromotherregions.Theextentwealthandthesizeofspatialfrictions(i.e.thecostsofmigratingandtradinggoods).House-holdslivingingeographicallyconnectedlocationscanimportmorefoodanduseremittancesfrommigranthouseholdmembersinresponsetonegativeshocks,incontrasttothoremotelocationsforwhomtheseoptionsaremorecostly.Asaresult,thelatterwilltendtoendurelargerincreasesinfoodpricetheeffectsofshocks,withconsequencesfortheirfutureproduction.4Conditionalonlocation,latterimpliestheywillholdasmallercapitalstockbufferrelativetotheirincome,makingthemmorevulnerabletoshocks.Moreover,sincetheirutilityismoresensitivetoreductionsinconsumption,theywilldissavedisproportionatelymoretoavoidit.Nepalisanidealsettingforthisstudygiventhepervasivenessoffoodinsecurityamongruralhouseholds,theimportanceofremittancesingrossnationalincomeandasasoandthecomplexanddiversegeographyofthecountryasreflectedinthespatialvariationinagriculturalsuitabilityandnumberofpeoplelivinginremote,difficult-to-access,mountainousregions(Barkeretal.,2020;DeStefanietal.,2022).Wecalibratethemodelfor51districts5(accountingforroughly78percentoftotalNepalpopulation)usingavarietyofdatasourcesincludinghouseholdpanelsurveystocalibrateourbaselineeconomywereobtainedfromtheHouseholdRiskandVulnerabilitySurvey,athree-yearlongitudinalhouseholdsurveyadministeredbytheWorldBankcovering6,000householdsand400communitiesinnon-metropolitanareasofNepal.Thesurveycontainscommunity-leveldataonthemarketpricesofseveralkeyconsumptionitemsdatatoestimatekeyexogenousparametersinthemodelsuchaslocalsectoralproductivitiesandthesizeoftradeandmigrationcosts.WemeasuredamagesfromclimateshocksusingtheBuildingInformationPlatformAgainstDisaster(BIPAD)database,containingaspatially-disagreggatedhistoricalrecordofnaturaldisasterseventsinNepalfrom2011to2022atthemunicipality-levelwhichincludeearthquakes,floods,landslides,droughts,andstorms.Thedatasetrecordsthetimeandlocationofeventsestimatedeconomicdamages.Twofeaturesofthisdatasetmakeitparticularlyvaluableforthispaper.First,thespatialdisaggregationallowstheobservationofthehistoricalsusceptibilityofdifferentdistrictstoclimateshocks.Second,therecordofthedateanddurationoftheshockallowstheestimationofimpulseresponsefunctionsfromtheshocktolocalmarketprices.Whilethistypeofdataissometimesavailableatanational-levelandannualfrequency,6itisraretoanddate.Wedevelopanovelmethodologytoconvertcoarsedataonclimatedamagesintoproductivityshocksbyinferringtheimpactofclimateshocksfrommovementsinthelocalpriceofkeyfooditems.ThelatterisobtainedfromtheWorldFoodProgramme(WFP)GlobalFoodPricesDatabasewhichrecordsmonthlypricesofkeyfooditems(riceandwheat)in2001-2021fofirst,weusethemarketpriceanddisasterdatatoestimateanimpulseresponsefunctionof4Itisadditionalplausiblethatrurallocationsarealsomorepronetoshockswhichisanotherchannelviawhichruralhouseholdscanbemorevulnerable.Thisisincludedinourcalibrationwhichincludesrecordedincidenceofshocksatthedistrictlevel.5Thereisatotalof77districtsinNepal,butdatawasonlyavailablefor51.WemergethedistrictsofKathmandu,LalitpurandBhaktapurintoasinglelocation,KathmanduValley,whichishighlyintegrated.6SeeforinstanceKabundietal.(2022)8localfoodpricestoclimateshocksoccurrences,whichprovidesuswithestimatesoftheaverageThesizeofdamagesreportedinBIPADislikelyanunderestimationofthetruemagnitudeobtainameasureofnormalizeddamages.Thisisthenmultipliedbytheaveragepriceimpactfromstep1toobtaintheestimatedpriceimpactofeachclimateshock.Finally,weconverttheestimatechangesinfoodpricesintoproductivityshocksbyestimatingtheelasticitybetweenthetwothroughmodelsimulationsoflocalidiosyncraticshocksineachdistrict.Thisestimationprocedureaimsatmitigatingissuesofmissingdataandmisestimationfoundinseveraldisasterdatabasesthatmeasureeconomicofreporteddamages-whicharemissingformanyobservationsandarelikelyunder-reportingthefullextentofeconomicdamages-wemeasurelossesthroughtheireffectonfoodproduction,Ourestimatesshowthatclimateshocksthatoccurrerateofundernourishment(definedastheshareofindividualswhoconsumelessthan2,200dailyInaggregate,ruralGDPinNepalisestimatedtobe2.3percentlowerduetoclimateshocks.Wefindthattheaverageannualaggregateimpactofclimateshocksoverthesampleperirelativelystable,butthatthereissubstantialheterogeneityacrossdistricts.Forinstance,in2019theimpactofclimateshocksledthe95thpercentiledistricttoseealmost18percentloweragriculturalyieldscorrespondingtoaroughly13percentlossinwelfareand9percentincreaseinundernourishment.Wehighlighttheimportanceofheterogeneityingeographicandhousehold-levelcharacter-isticsforhouseholdresiliencetoshocks.Weshowthatgeographiclocationandspatialfrictionsarekeydeterminantsoftheimpactofclimateshockswithhouseholdsinthetop30percentmosttherateofundernourishment.Forundernourishedhousehandlonger-lasting,costingthem4.3percentofannualwelfare.Weshowthatthreefactorscanmitigatetheimpactofclimateshocks-higheragriculturalproductivity,accesstomigrationandaccesstomarkets.Thisisshowntobemoreeffectiveforbenefitparticularlyfromalternativestoreducingconsumptionordrawingdownassets.Finally,weshowthatpolicywhichlowerstradeandmigrationcostscansubstantiallyin-creasewelfare,lowerholdswillnecessarilybenefit.Undereither10percentlowertrademigrationcoststhewelfareimpactofsproximately2.7percent(areductionbyafactorof0.18).8Ratesofundernourishmentarereducedbyafactorofalmostathirdfallingfroma2.8percenttoaroughly2percentriseatthehandsofclimateshocks.Whiletheaggregateeffectsarestronglypositive,18percentofdistrictsseeanincreaseinundernourishmentatthehandsoflowertradecostswhile16percentportantdistributionaleffectsevenwithindistricts,withpoorerhouseholdspotentiallyharmedbyimprovementsininfrastructure.Thismaybepartiallyinducedbycross-districtspilloversleadingtobothpositiveandnegativeeffectsonhouseholdvulnerabilitytoexternalshocks.Forinstance,anegativeclimateshockinadistrictcanleadinneighboringdistrictswhenmarketsaremoreintegrated.Ourpaperisrelatedtothreestrandsoftheliterature.First,ourpaperisrelatedtoanextensiveempiricalliteratureindevelopmenteconomicsonthecopingmechanismsemployedby7SeeTamrakarandBajracharya(2020)foranoverviewoftheissuesassociatedwiththeestimationofeconomicdamagesandlossesinBIPAD.8LoweringtradeandmigrationcostsarekeyprioritiesfortheNepalauthorities.SeveralmajorhighwaysareunderconstructionaspartoftheNepalNationalPrideProjectswhileloweringmigrationcostshavebeentargetedthroughmigrantsupportcentersinmigrantdestinationsandprovisionofinformationtomigrants(MinistryofLabour,EmploymentandSocialSecurity,2022;NationalPlanningCommission,2023)9householdsinresponsetoadverseincomeshocksincludinginternalmigrationandremittanc(e.g.McKenzieandYang(2014);McKenzieetal.(2014);Gr¨ogerandZylberberg(2016)),sellingoffassets(e.g.CarterandBarrett(2007);BerloffaandModena(2013)),andoff-farmlabor(e.g.MathengeandTschirley(2015)).9Incontrasttomuchoftheexistingliterature,weembedhouseholddecision-makingwithinageneralequilibriumframework.ThisallowsusMoreover,usingarichmodelallowsustogeneratecounterfactualsandstructurallydecomposetheroleofthevariousshock-copingmechanisms.Wealsocontributetothisliteraturebysheddinglightonthequantitativeroleofspatialfrictionsforhouseholds’foodsecurityoutcomesSecondly,ourpaperisrelatedtoaburgeoningquantitativespatialliteratureemployingquantitativemodelswithrealisticgeographywithafocusontheagriculturalsector.Previousliteraturehasstudiedtheeffectofclimatechangeoncropspecializationpatterns(Costinotetal.,2016),sectoralallocation(Nath,2022)migration(DesmetandRossi-Hansberg,2015;Conte,2022)andinfrastructure(Balboni,2019),theeffectsoftradeonincomediversification(Casellietal.,2020)andfarmers’cropportfoliochoices(AllenandAtkin,2022),theimpactofrailroadnetworkexpansionsontheagriculturalsector(DonaldsonandHornbeck,2016),theroleoftradecostsandpublicinvestmentinstructuraltransformation(Adametal.,2018;Gollinetal.,2014),andthegeneralequilibriumeffectsofagriculturalpolicyinterventions(Bergquistetal.,2019;Asheretal.,2023).Similarlytothisliterature,weplaceeconomicagentsinanenvironmentwithheterogenousgeosubjecttospatialfrictions.Incontrasttomuchofthisliterature,whichstudiesthelong-runeffectsofchangesinkeymodelparameters(e.g.spatialfrictions,policy,technology)westudytheimpactoftemporaryproductivityshocksandhowhouseholdsrespondtothemthroughavarietyofresponsemechanismsthatareparticularlyrelevantforlow-incomecountries.Ourapproachisparticularlywellsuitedforthestudyofthespatialimplicationsofclimateshocks,whichtothebestofourknowledge,hasnotbeenstudiedinasimilarframework.Finally,ourpaperrelatestotheliteratureonfoodsecurity.Previousresearchhasana-lyzedtheimpactoffoodandnutritiononhouseholdincome(e.g.Behrmanetal.(1997)),economicdevelopment(e.g.StraussandThomas(1998),totalfactorproductivity(e.g.ColeandNeumayer(2005),learningoutcomes(e.g.ChakrabortyandJayaraman(2019))andin-tergenerationaleffects(e.g.Asfaw(2016)).Adifferentsetofpapershasdevelopedstatisticalforecastingmodelswiththeaimofpredictingfuturefoodcrisesandsupportingearlyinter-ventions(e.g.SeamanandHolt(1980);Mellor(1986);OkoriandObua(2011);Andreeetal.(2020)).10Ourpaperfocusesonquantifyingtheeffectofclimateshocksonfoodsecurityout-comesinaspatiallydisagTheremainderofourpaperisorganizedasfollows:Section2presentsanumberofeicalrelationshipstoinformthemodel;Section3laysoutthemodel;Section4describesthecalibrationprocedure;Section5showstheresultsfromthequantitativeexercises;Section62DataandEmpiricalRelationshipsInthissectionweoutlinetheprimarydatasourcesusedinthispaper,discussthenatureofclimateshocksandfoodsecurityinNepal,andthenpresentfourempiricalrelationshipswhichsupportourmodellingapproach.9Thereisalsoanextensiveliteraturerelatingagriculturaloutcomesandeducationindevelopingcountries(JacobyandSkoufias,1997;Jensen,2000;Kinda,2010).Althoughthisisn’tdirectlyamechanisminourframework,itiscloselyrelatedandespeciallyifoneiswillingtointerpreteducationasanassetwhichaugmentsproductivity.10Alargeandrelatedliteraturestudiestheroleofagricultureindevelopment.See,forinstance,EvensonandGollin(2003);Gollinetal.(2014,2007)Figure1:WeatherVariabilityinNepal(a)AnnualTemperatureDeviationfrombase-line(b)NumberofClimateRelatedIncidentsNotes:BIPADGovernmentofNepalNotes:Panel(A):FAOandIMFClimateDashboard.Changeincentigradefromabaselineof1951-1980.Panel(B):BIPADGovernmentofNepal2.1DataWecompiletwodatasetsatthehouseholdanddistrictlevelinNepal.VariabledescriptionsandsummarystatisticsareprovidedinTables6and7.HouseholdRiskandVulnerabilitySurvey,athree-yearlongitudinalhouseholdsurveyadminis-teredbytheWorldBankcovering6,000householdsand400communitiesinnon-metropolitanareasofNepal.ThesamplefrCensusdefinition,excludinghouseholdsintheKathmanduvalley(Kathmandu,LalitpurandBhaktapurdistricts).Toincreasetheconcentrationofsampledhouseholds,50ofthe75dis-trictsinNepalwereselectedwithprobabilityproportionaltothenumberofhouseholds.Thesurveycontainsdataonawiderangeofhousehaswellascommunity-leveldataonthemarketpricesofseveralkeyconsumptionitems.Weusethesurveyestimatedmeasureoffoodinsecuritywhichiscompiledfromafoodinsecurityindexscore.11Thedataiscollectedoverthreewavesduring2ToidentifyaggregateimpactsfromclimateshockswecompilemonthlydistrictlevelmarketpricesfromtheWorldFoodProgramme’s(WFP)marketpricedatabasewhichrecordsthemonthlypriceofkeyfooditemsacross41NewithdataonclimateshocksfromtheBuildingInformationPlatformAgainstDisaster(BIPAD)databaseprovidingahistoricalrecordofnaturaldisasterseventsinNepalfrom2011to2022thatincludeearthquakes,floods,landslides,droughts,andstorms.Thedatarecordsthetimethenumberoffatalities,peopleaffected,andestimatedcost.Weincludeallclimateshocks(landslides,storms,cold,heavyrainfall,andfloods)whichhaverecordedanon-missingandpositivevalueforestimatedeconomicdamages.Thisnarrowsdownthesampletoatotalannualaverageof125shocksperyearwithadistrict-levelprobabilityofexperiencingatleastWegenerateadistrictlevelvariableforremotenessusingthepopulation-weightdistancetoalldistricts.12Wedefineadummydenotedremoteifthedistrictisinthetop302.2ContextNepalishighlyvulnerabletoclimateshocksandclimatechange.Theaveragetemperature11IndexgeneratedbytheWorldBankbasedonmethodologyinCoatesetal.(2007).Theindexassignsavalue0,1,2,or3pointstoeachresponseinfourcategories,inordertoarriveatascoreoutof27.ThisisFormoredetailonvariableconstructionseeSection4rerredtoasFormoredetailonvariableconstructionseeSection4Figure2:Distributionofweatherrisk(a)FloodHazard(b)LandslideRiskNotes:Panel(A):BIPADGovernmentofNepalandMETEORproject,mapexportedonMay9,2023.Datashowtheprobabilityofexperiencingagivenwaterdepthinmeterswithinasingleyearovera1000yearreturnperiod.Darkerblueindicateslargerrisk.ThedatawasproducedbyBIPADusingtheFathomglobalfloodhazard-modellingframework(adevelopmentofSampsonetal.(2015)andSmithetal.(2015)).Panel(B):BIPADGovernmentofNepal.MapexportedonMay9,2023.Mapshowslandslideriskbyward.Darkredindicateslargerhazard,blueindicatessmallrisk.(Figure1a).Themonsoonhasbecomeincreasinglyunpredictable,andthenumberofclimateshocksrelatedtofloods,storms,andlandslideshassteadilyrisen(Figure1b).Theseeventscandamageinfrastructure,harmcrops,andimpactconnectivity.ThisisparticularlyimportantinNepalgiventheagriculturalsectormakesup65percentoftotalemploymentand24percentofGDP(ILO,2020;NepalNationalStatisticsOffice,2023).Inasevereclimatechangescenario,theWorldBankestimatethatGDPwouldbe7percentlower(WorldBank,2022).Shocksdonotimpactallareasofthecountryevenly.Figure2showshowexcessrainfallcanimpactdifferentregion