A Ricardian analysis of the impact of climate change on agriculture in Germany
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A Ricardian analysis of the impact of climate change on agriculture in Germany
ClimaticChange(2009)97:593–610
DOI10.1007/s10584-009-9652-9
ARicardiananalysisoftheimpactofclimatechange
onagricultureinGermany
C.Lippert·T.Krimly·J.Aurbacher
Received:14March2008/Accepted:22May2009/Publishedonline:18August2009
©SpringerScience+BusinessMediaB.V.2009
AbstractBasedonaRicardiananalysisaccountingforspatialautocorrelationandrelyingonrecentclimatechangeforecastsatalowspatialscale,thisstudyassessestheimpactofclimatechangeonGermanagriculture.Giventhelimitedavailabilityofdata(e.g.,theunknownaveragesoilqualityatthedistrictlevel),aspatialerrormodelisusedinordertoobtainunbiasedmarginaleffects.TheRicardiananalysisisperformedusingdatafromthe1999agriculturalcensusalongwithdatafromthenetworkofGermanweatherobservationstations.Thecross-sectionalanalysisyieldsanincreaseoflandrentalongwithbotharisingmeantemperatureandadecliningspringprecipitation,exceptforintheEasternpartofthecountry.ThesubsequentsimulationoflocallandrentchangesunderthreedifferentIPCCscenariosisdonebyenteringintotheestimatedregressionequationsspatiallyprocesseddataaveragesfortheperiodbetween2011and2040fromtheregionalclimatemodelREMO.Theresultingexpectedbene?tsarisingfromclimatechangearerepresentedinmapscontainingthe439Germandistricts;thecalculatedoverallrentincreasecorrespondstoapproximately5–6%ofnetGermanagriculturalincome.However,inthelongrun,whentemperatureandprecipitationchangeswillbemoreseverethanthosesimulatedfor2011–2040,incomelossesforGermanagriculturecannotbeexcluded.1Introduction:climatechangeinGermany
Asaconsequenceofthegreenhouseeffect,anon-goingchangeoftheglobalclimateisprojectedforthenextdecades.TheIPCCreport(2007a)expectsanincreaseofthemeanglobaltemperatureby1.8?Cto4.0?C.Furthermore,precipitationandtheoccurrenceofextremeweathereventswillincrease.Overthepast100yearsthe
C.Lippert(B)·T.Krimly·J.Aurbacher
InsititueforFarmManagement(410a),SectionofProductionTheoryandResource
Economics,UniversitätHohenheim,SchlossOsthof-Süd,70593Stuttgart,Germany
e-mail:clippert@uni-hohenheim.de
594ClimaticChange(2009)97:593–610averagetemperatureincreaseinEuropewas1?C,comparedtoaglobalaveragetemperatureincreaseofabout0.7?C(IPCC2007a).ThemeantemperatureinEuropeisexpectedtoincreaseby2.1?Cto5.3?Cbytheendofthiscentury;again,Europeshowsastrongerwarmingtrendthantheglobalaverage.Sinceagricultureisaneconomicactivitywhichstronglydependsontheclimatesettingandisparticularlyresponsivetoclimatechanges,itisimportanttounderstandhowsuchchangesmayaffectagriculturalproductivityandpro?tability.
Inprinciple,therearetwomainapproachestoassessingtheimpactofclimatechange(Mendelsohn2007):onewayistorunsimulationmodels,theparametersofwhichhavetobeobtainedfromcontrolledexperiments;theotherwayistoconductacross-sectionalanalysisobservingthe(economic)systemacrossdifferentlocationsinordertodeterminehowthesystemmayadapttodifferentclimates.Thismethod,usuallyreferredtoasaRicardianapproach,http://wendang.chazidian.comingobservedlandprices,itsbasicpurposeis“[...]toinferthewillingnesstopayinagriculturetoavoida3?Ctemperaturerise(forexample)byexaminingtwoagriculturalareasthatarethesameinallrespectsexceptthatonehasaclimateonaverage3?Cwarmerthantheother”(Kolstad2000:317;forabroaderdescriptionoftheunderlyingtheorycf.Mendelsohnetal.1994;MendelsohnandReinsborough2007:10f.;Lang2007:425f.).Inthecaseofcompetitivemarkets,assumingthatlandpricesatdifferentlocationshavereachedtheirlong-runequilibrium,thisapproachaccountsforboththedirecteffectsofclimateoncropyieldsandtheindirecteffectsresultingfromthesubstitutionoradaptationoffarmingactivities.
WhereastheRicardianapproachhasbeenfrequentlyusedforNorthernAmerica(e.g.,Mendelsohnetal.1994;PolskyandEasterling2001;Schlenkeretal.2005,2006;DeschênesandGreenstone2007;MendelsohnandReinsborough2007),http://wendang.chazidian.comng(2007)analysedweatherdataalongwith1990through1994paneldatafromfarmersinformerWestGermany,andfound,amongotherresults,aninverselyu-shapedrelationshipbetweenthelocaltemperaturesumduringthegrowingseasonandlandrentalprices.Hepredictedthat“[...]Germanfarmerswillbewinnersofclimaticchangeintheshortrun,withmaximumgainsoccurringatatemperatureincreaseof+0.6?Cagainstcurrentlevels”(Lang2007:423).
Whencomparedwiththementionedexperimental-simulationapproach,onead-vantageofaRicardiananalysisisthatitisbasedonreal-worldadaptationmeasureswhichhavebeenbroughtaboutbyatrial-and-errorprocessinvolvingmanyfarmerswellacquaintedwiththeirspeci?clocalproductionconditions.AmajorweaknessoftheRicardianapproachconsistsintheinadequacyofextrapolatingitforclimaticsettings(e.g.,temperature,CO2-fertilisation)whichhavenotbeenobservedsofar(i.e.,settingswhicharenotcoveredbythedatasetusedtoestimatetheHedonicPricingfunction).Furthermore,theapproach“mustworkhardnottobebiasedbyomittedvariablesthatarecorrelatedwithclimate”(Mendelsohn2007:2).
OnepromisingwaytocopewiththeproblemofspatialautocorrelationistoexplicitlyconsiderspatialautocorrelationoftheresidualswhenestimatingtheparametersoftheHedonicPricingmodel.Uptonow,thishasrarelybeendoneinthecontextofclimatechangeimpactassessment.ExceptionsareSchlenkeretal.(2006:116),DeschênesandGreenstone(2007:366),whoadjustedthestandarderrorsoftheirestimatedmodelsforspatialdependence,andtoacertainextentPolskyand
ClimaticChange(2009)97:593–610595Easterling(2001),whoincludedadditionalexplanatoryvariablesreferringtoalargerspatialscale(districts)intheircounty-basedanalysis.
Ourapproachtakesonlylong-termclimaticvariablesintoconsiderationalthoughSchlenkerandRoberts(2006)indicatethatalreadysingledayeventscanhavesigni?cantin?uenceonyields.However,dailyweatherdataforGermanywasnotavailabletous.DeschênesandGreenstone(2007)criticizedtheRicardianapproachwhichintheiranalysisturnedouttobestronglyin?uencedamongotherthingsbythechoiceofvariablesincludedintotheestimatedequation.Alternatively,theysuggestedandappliedanapproachwheretheyusedtheobservedyear-to-yearvariationofprecipitationandtemperaturetoexplainagriculturalpro?tsintheUnitedStates.However,astheyadmit,indoingsofarmers’damagesduetoclimaticchangearesystematicallyoverstatedbecausethestatisticalmodelthendoesnotaccountforcompleteadaptationwhichisimpossiblewhenonlyreactingtotheweathereventsofsingleyears.
TheobjectiveofthispaperistoassesstheimpactofclimatechangeonGermanagricultureusingrecentclimatechangeforecastsatalowspatialscale,relyingonaRicardiananalysiswhichaccountsforspatialautocorrelation.Inthenextsectionwewillpresentanappropriatestatisticalmodelrelyingonaspatialweightmatrix(Section2.1)aswellasthedata(Section2.2)thatwill?nallybeusedtoestimatetwoHedonicPricingfunctions(Section2.3).Then,bymeansofthesefunctions,theeconomicimpactofthreedifferentclimatechangescenarios(Section3.1)onthepro?tabilityofGermanAgriculturewillbepresented(Section3.2)anddiscussed(Section4).
2Empiricalanalysis
2.1Statisticalmodel
Inthefollowing,afunctionalrelationshipbetweentherentalpriceriforfarmlandatlocationianddifferentexogenousfactorsxcandxncisassumed:
ri=f(xc,xnc),(1)
wherexcisavectorofclimatecharacteristicssuchasmeanannualtemperatureoraverageprecipitationindifferentmonths,andxncstandsforavectorofnon-climatevariablessuchasgrasslandshareofoverallagriculturallandorsoilquality.Sinceitisimpossibletoobtainsuf?cientdataforallrelevantvariablesxncwhenestimatingtheHedonicPricingfunction1,weexplicitlyconsideredspatialautocorrelation.Equations2and3outlinethegeneralversionofacorrespondingspatiallyautoregres-sivemodel(Anselin1988:34ff.;LeSage1999:52f.)whichaccountsforbothspatiallagdependenceandspatialerrordependence(cf.PattonandMcErlean2003:37):
r=ρW1r+Xβ+u
u=λW2u+ε
with
????ε?N0,σ2I,(2)(3)
596ClimaticChange(2009)97:593–610where
r
X
Ws
I
u
εn×1vectorcontainingthereportedaveragefarmlandrentalprices,eachassociatedwithaspeci?cadministrativedistricti(i=1,...,n);n×(1+k)designmatrixcontainingasetofobservationsforkexplanatoryclimateandnon-climatevariables;givenn×nspatialweightmatrices(s=1,2;W1andW2maybeidentical);n×nidentitymatrix;n×1vectorofthespatiallycorrelatedresiduals;
n×1vectorofnormallydistributederrors(mean=0,variance=σ2).
Theparameterstobeestimatedare
ρβ
λspatiallagcoef?cient;(1+k)×1vectorcontainingtheregressioncoef?cientsfortheexplanatoryvariables;
coef?cientre?ectingthespatialautocorrelationoftheresidualsui.
Forthefollowingestimations,wewillalwaysuseastandardised?rst-ordercontigu-itymatrix(W=W1=W2).Noticethatsuchamatrixre?ectssimpleneighbourhoodalone(inourcasebetweenthen=439districtsofGermany):ineveryrowia0isassignedtoeverydistrictj=ithatdoesnotadjoinwithdistricti;thesameisdoneforalldiagonalelementsofthen×nmatrix.Whentwodistrictsiandjarecontiguous,1/giwillbeassignedtotheintersectionoftheithrowandthejthcolumn(wheregiisthenumberofdistrictswhichhaveacommonborderwithdistricti).WithWsetuplikethis,then×1vectorWrgivesforeverydistrictithemeanrentalpriceobservedinitscontiguousdistricts.Asigni?cantpositiveparameterρwouldhintataself-enforcingeffectofhigherfarmlandrentalprices(acaseofspatialdependencyofrentalprices).SolvingEq.3forthevectorofthespatiallycorrelatedresiduals(u)andenteringtheresultingtermintoEq.2gives:
r=ρWr+Xβ+(I?λW)?1ε
<=>(I?λW)r=(I?λW)ρWr+(I?λW)Xβ+ε
<=>r=ρWr+λW(r?ρWr)+Xβ?λWXβ+ε.(4)(4a)
Aproblemoccurswhenoneormoreoftheoftenspatiallycorrelatedfactorswhichin?uencerentalpricesarenotaccountedforinthestatisticalmodel,ascanbeeasilydemonstratedbylookingatEq.4a:neglectingpossiblespatialdependency(i.e.,assumingρ≈0),Eq.4aisreducedto
ε
<=>ε==r?Xβ?λWr+λWXβu?λWr+λWXβ.(4b*)
IfallrelevantexplanatoryvariableswerecontainedinX,thevaluesriwouldincreaseanddecreaseinlinewithXiβ(apartfromthe“whitenoise”ε;i.e.,ε=u).Theparameterλthenwouldbeclosetozero.Ontheotherhand,ifimportantspatiallycorrelatedexplanatoryvariableswerenotcontainedinXβ,theresidualsuiwouldhavetobecorrectedbyλWrinordertoobtainavectorofnormallydistributed
ClimaticChange(2009)97:593–610597residualsε.(Notethatinthelattercase,λWXβwillcontainonlynormallydistributedelements,whereasλWrwillpositivelydependonu.)Asigni?cantvalueforλmeansthatthereisatleastonespatiallycorrelatedfeaturewhichisnotre?ectedbytheexogenousvariablesusedinthemodel,butwhichaffectstheobservedrentalprice.Subsequentregressionanalysesalwaysyieldedahighlysigni?cantMoran’sIaswellasahighlysigni?cantvalueforλ(whichindicatesthatsomeoftherelevantexplanatoryvariablesxcandxncwerenotincludedinX),whereasasigni?cantparameterρcouldnotbefound.Consequently,ourparameterestimatespresentedinthefollowingarebasedonthesimplespatialerrormodel:
r=Xβ+(I?λW)?1ε.(4b)
TheEq.4bwereestimatedusingMATLABalongwiththe“EconometricsToolbox”byLeSage(2003)(forthespatialerrormodelandtheiterativemaximumlikelihoodestimationemployed,cf.LeSage1999:48f.).
2.2Data
SourcesforlanddataDataregardingthedistricts’utilisableagriculturalarea(UAA)andthegrasslandshareofthatareaweretakenfromthe1999agriculturalcensus(StatistischeÄmterdesBundesundderLänder2001).Alltogether,theUAAofthe439Germandistrictsamountedtoatotalof17,157,906hectares.The1999yearlyrentalpricerforfarmland(inEuroperhectareUAA)bydistrict(Landkreis)waskindlycommunicatedbyStatistischesLandesamtBaden-Württemberg(2007).For14ofthe439Germandistricts,partoftheagriculturalcensusdatawaslackingandhadtobereplacedbycarefulassumptionsbasedonobservationsfromsimilardistricts(usingaspatialweightmatrixW,itwasimpossibletoomitthecorrespondingdistrictsfromtheanalysis).Relyingonrentalpricesinsteadoffarmlandpriceshastheadvantagethatweneednotconsidersomefactorswhichstronglydistortfarmlandprices,especiallyinthedenselypopulatedregionsofCentralEurope.Forexample,thehighpricesforfarmlandwhichmaybecomebuildinglandinthemediumtermhavenothingtodowithrealagriculturalproductivity.RentingfarmlandisquitecommoninGermany:in1999,68.4%ofGermanfarmsrentedatleastpartoftheirland;theshareofrentedlandwasabout50%ofoverallcultivatedfarmlandinWestGermanyandabout90%inEastGermany(BMVEL2001:12).Sincelandrentcontractsalwaysexpireaftersomeyearsthereportedlandrentalpricestobepaidonayearlybasisbythetenant(whoisnotentitledtosellthelandandwhowillnotbene?tfromalandsalebytheowner)donotcontaintheoptionvalueofthecorrespondingplotsoflandforapossibleurbandevelopment.Theyarejustre?ectingtheagriculturalproductivityoftheland.Incontrast,purchasepricesforfarmlandalsoincludethementionedoptionvalue.
SourcesforclimatedataThisanalysisuseddatafromweatherobservationstationsfromtheGermanWeatherService(DWD2007).Thisdatasetcontainsthelatitudeandlongitudeofthestation(ingeographiccoordinates),altitude,andaveragesoftemperature,precipitationandsunshinedurationover30years(1961–1990)forthewholeyearandforeachmonth.Theprecipitationdatasetconsistsof4748stations,whilethedatasetfortemperaturesincludes675stations.Forthefuturescenarios,climatedatafromtheREMOmodel(MPIonbehalfoftheUmweltbundesamt2006)
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- 多媒体
- 软件测试
- 计算机硬件与维护
- 网站策划/UE
- 网页设计/UI
- 网吧管理