Climate change and global market integration:

Implications for global economic activities,

agricultural commodities, and food security

Background paper for The State of Agricultural Commodity

Markets (SOCO) 2018

ClimatechangeandglobalmarketintegrationImplicationsforglobaleconomic

activities,agriculturalcommodities,andfoodsecurity

Backgroundpaperfor

TheStateofAgriculturalCommodity

Markets(SOCO)2018

HaoDavidCui,MarijkeKuiper,HansvanMeijl,andAndrzejTabeau

WageningenEconomicResearch

FoodandAgricultureOrganizationoftheUnitedNationsRome,2018

Required citation:

Cui, H.D., Kuiper, M., van Meijl, H. and Tabeau, A. 2018. Climate change and global market integration: Implications for global economic activities, agricultural commodities and food security. The State of Agricultural Commodity Markets (SOCO) 2018: Background paper. Rome, FAO.42 pp. Licence: CC BY-NC-SA 3.0 IGO.

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Contents

Acronyms...................................................................................................................................................................v

Executivesummary..............................................................................................................................................vi

1. Introduction.....................................................................................................................................................1

2. Literaturereview..........................................................................................................................................1

3. Descriptionofscenarios............................................................................................................................5

4. Projectionsofthebaseline........................................................................................................................9

5. Theimplicationsofclimatechange...................................................................................................10

Foodandagriculturalproductions,prices,andsectoralincome............................................11

Foodandagriculturaltrade.......................................................................................................................12

Macroeconomicgrowth...............................................................................................................................13

Foodsecurity....................................................................................................................................................16

6. Theimplicationsoftradeliberalisation..........................................................................................20

Foodandagriculturaltrade.......................................................................................................................20

Foodandagriculturalproductions,landuse,andGHGemissions.........................................22

Macroeconomicgrowth...............................................................................................................................23

Foodsecurity....................................................................................................................................................26

7. Theadaptiveeffectoftradeliberalisation.....................................................................................26

Macroeconomicgrowth...............................................................................................................................27

Foodandagriculturalproductions,landuse,andGHGemissions.........................................27

Foodsecurity....................................................................................................................................................28

References..............................................................................................................................................................30

Appendix..................................................................................................................................................................32

iv

Figures

Figure1AssumedgrowthinGDPandpopulation2011-2050........................................................7 Figure2Assumedchangeincropyields2011-2050............................................................................7 Figure3Anillustrationoftherelationshipbetweenscenarios......................................................9Figure4ChangesinGDPintheclimatechangescenariorelativetothebaselineat2050......................................................................................................................................................................................15 Figure5Changesinagriculturalwageunderclimatechangerelativetobaseline.............18 Figure6Changesinskilledandunskilledwagesinagricultureinclimatechangescenariorelativetobaselineat2050............................................................................................................................19 Figure7Changesinfoodandagriculturalnetexportsinnetexporting/importingregions:CCTLvs.CC...........................................................................................................................................21 Figure8Production-shareweightedchangesinfoodproductions:CCTLvs.CC................23 Figure9Theadaptiveeffectmeasuredbythedifferencebetweenclimatechangeimpactswithandwithouttradeliberalisationat2050....................................................................28

Tables

Table1DescriptionofScenarios....................................................................................................................5Table2RegionaleconomiccharacteristicsandGDPchangeinClimateChangeandTradeLiberalisationscenariorelativetoClimateChangescenarioat2050........................................25TableA.1Averageadvaloremimporttariffsonagriculturalandfoodcommoditiesat2011(percent)......................................................................................................................................................32TableA.2Averageadvaloremexporttaxesonagriculturalandfoodcommoditiesat2011(percent)......................................................................................................................................................33TableA.3ChangesinagriculturalandfoodtradeintheClimateChangeandTradeLiberalisationscenariorelativetotheClimateChangescenarioat2050(millionUS$,2011constantprice)..........................................................................................................................................34

v

Acronyms

GDP Grossdomesticproduct

GHG Greenhousegas

IPCC IntergovernmentalPanelonClimateChange

MAGNET ModularAppliedGeNeralEquilibriumTool

RCP RepresentativeConcentrationPathways

SSP SharedSocioeconomicPathways

vi

Executivesummary

Fourscenariosaredevelopedinthispaperincludingabaselinescenario,aclimatechangescenario, a climate change and trade liberalisation scenario, and a trade liberalisationscenario, to explore the impacts of climate change and trade liberalisation, and theadaptationpotentialof trade liberalisation inmitigating theadverse impactof climatechange.

Compared to the baseline as characterised by the assumed shared socio-economicpathway (SSP3) and crop yields growth due to technological progress, the impacts ofclimatechangeareprojectedtobeoverallnegativeforfoodandagriculturalproductionand gross domestic product (GDP) at the global scale. Global food and agriculturalproductions(incl.crops,livestock,processedfood,andfish)areprojectedtodeclineby0.28percentonaverageby2050,duetoclimate-inducedcropyieldlosses,withtheworldGDPprojectedtofallby0.18percentaccordingly.Consistentwiththeproductiondecline,global calorie intake, an indicator of food availability, is expected to decline by 0.28percent. This is connected with a worldwide decline of food purchasing power (2.9percent),anindicatoroffoodaccessibility.

Theimpactoftradeliberalisation,inthecontextofclimatechange,byremovingbordertaxesforfoodandagriculturalcommoditiesinallcountries,areexpectedtobemodestlybeneficialforeconomicgrowth,withtheworldGDPexpectedtoincreaseby0.38percent.Foodandagriculturalproductions,however,areexpectedtoreduceby0.36percentdueto liberalised trade, with global calorie intake expected to decline by 0.39 percentaccordingly. The rise inwages and decline in food prices imply that food purchasingpowerwillbehigherinthetradeliberalisationscenario.Asmeasuredbytheunskilledwageintheagriculturalsectorrelativetofoodprices,foodpurchasingpowerisprojectedtoincreaseby6.3percentwhentradeisliberalised.

Adaptive effects of trade liberalisation are found, as it slightly mitigates the overalladverseimpactofclimatechangeattheglobalscale.ThedeclineoftheworldGDPcausedbyclimatechangewouldbeslightlysmalleriffoodandagriculturaltradeisliberalisedinthecontextofclimatechange.Alsoattributedtotheadaptiveeffectoftradeliberalisation,theclimate-induceddeclineofglobalfoodandagriculturalproductionswouldbesmallerandfoodsecurityissuescausedbyclimatechangewouldbemoderated,attheglobalscale.

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1. Introduction

The2018editionofStateofAgriculturalCommodityMarkets(SOCO)willanalyseandquantify the impacts of climate change on the production and trade of agriculturalcommodities,andassessthepotentialoftradeasanadaptationandmitigationtool.

Anumberofthematicpaperswerecommissionedtoinformthewritingofthepublication,including for instancepaperson the legal aspectsof tradeandclimate change,bordermeasures, country/crop case studies, etc. Toward this end Wageningen EconomicResearch (WEcR) contributesaworkingpaper that simulates (underbaseline, climatechange, and free trade scenarios) the effects of climate change on agriculturalcommodities and the global agricultural trade outlook to 2050, describing possiblechangesinbilateraltradeflows.

TheMAGNETcomputablegeneralequilibriummodellingteamatWEcRhasconductedfoursimulationstoexploretheeffectsofclimatechangeandtradeliberalisationonfoodandagriculturalcommodities,productionsandtradeupto2050.1ThefoursimulationsincludethreesimulationsthatconformtotherequirementoftheTermsofReferenceandoneadditionalsimulationthatallowsforidentificationoftheadaptationpotentialoftradeliberalisation.

2. Literaturereview

TheWorking Group II of the IPCC has provided ample scientific evidence in its fifthassessment report (IPCC, 2014) on awide range of damages that climate change hascaused and may continue to cause to the natural and human systems including, forexample,meltingsnowandicecausingsealeveltorise,moreextremeweathersuchasheatwaves,droughts,floods,cyclones,andwildfires,aswellasoverallnegativeimpactsoncropyieldsandhumanhealth.Theworkinggroupalsosurveyedtheexistingliteratureand found that global annual economic losses for additional temperature increases of~2°Carebetween0.2and2.0percentofincome.

Giventhemulti-dimensionalnatureoftheimpactsthatworkthroughnotonlytheearth’sbiophysical system but also inter-tangled social and economic domains, assessingpreciselytheimpactofclimatechangehasbeenamongthemostchallengingtasksfacingscience communities around the world. While a growing body of literature has beendeveloped to examine the impact of climate change on agriculture and the broader

1Thefoursimulationscoverallmajorfoodandagricultural(aggregated)commoditiesincludingpaddyrice,wheat,othergrains,oilseeds,sugarcane\sugarbeet,vegetables\fruit,othercrops,otheragriculture,bovineanimals(cattle\sheep\goats\horses),otheranimals(pig,poultry),rawmilk,bovinemeatproducts,othermeatproducts,dairyproducts,sugarandmolasses,crudevegetableoil,vegetableoilandfats,processedrice,otherprocessedfood,animalfeed,fishery,forestry,andfertilizers.

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economy,researchfindingsvarysignificantly,dependingontheframeworkand/ordatabeingusedintheassessment.

Broadly,therearetwomajortypesofframeworkbeingusedtoassesssocialandeconomicimpactsofclimatechange–econometricsthatestimatesthedirectrelationshipbetweenobservedclimate/weatherconditions(mainlyrepresentedbytemperatureandrainfall)and the corresponding farming performance (e.g. crop yield, land value, etc.); andeconomicmodellingthatexplicitlymodelshowclimate-inducedcropyieldshocksworkthroughtheinteractionsbetweeneconomicagentsandtypicallyintegrateswithprocess-based biophysical models which provide crop yield estimates for assumed climateconditions. The distinctions between these different approaches are also reviewed byBlancandReilly(2017).

Earlier econometric work relies on what was known as the “production function”approachthatestimatesthedirectrelationshipbetweencropyieldsandclimatevariablessuchastemperature,precipitation,andcarbondioxide(Seeforexample:Adams,1989;Callaway et al., 1982). This approach, by its specification, does not take into accountpossibleadaptationpracticesoffarmersandthushasbeencriticizedinlaterstudiese.g.(Mendelsohn et al., 1994) for tending to overestimate crop yield losses from climatechange. (Mendelsohn et al., 1994) instead propose what they called the “Ricardianapproach” (also known as the “hedonic approach”) that estimates the relationshipbetweenfarmlandvalueandobservedclimatevariablesusingcross-sectionaldatathatarebelievedtohavefactoredinpossibleadaptationsoffarmers.Theirresults,comparedtotheearlierestimatesusingtheproductionfunctionapproach,indicatepotentiallymuchlower negative and possibly small positive impacts of climate change on U.S. farmingincome.Beingintuitivelyappealing,thisapproachbecameastandardforawhileandhasbeenfollowed,andmodified,inmanylaterstudiesincludingSchlenkeretal.(2005),whoclaimthatthepoolingofrainfedandirrigatedfarmlanddatainMendelsohnetal.(1994),wasamisspecificationthatledtoanunderestimationofwaterinputcostinirrigatedareas.Basedonananalysis focusingonrain fed farms,Schlenkeretal. (2005),concludethatclimatechangewillstillhaveanoverallnegative impacton farmincomeintheUnitedStatesofAmerica(USA). InaanotherstudyapplyingthehedonicapproachtoWesternEurope, Van Passel et al. (2017) find European farms are slightly more sensitive towarmingthanAmerican farms,withbothnegativeandpositive impactsbeingpossibledependingonclimatescenarios.

While a great advantage of the hedonic approach is that the full range of farmeradaptationshas inprinciplebeenaccountedfor,someof thetime-variantsettings(e.g.technicalchangeandpolicychange)andtime-invariantcharacteristics(e.g.soilquality)thatmayhavesignificanteffectsonfarm’sperformancearenotcapturedbythisapproach.This has received criticism from, for instance, Deschênes & Greenstone (2007), whoemploy a panel data approach to get around some of the limitations in the hedonicapproachontheimplicitassumptionoftime-invariantcharacteristics.TheirpaneldataresearchindicatesthatagricultureintheUSAwillbenefitfromclimatechange.

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While the advantage of the econometric approach lies in its capability in estimatingdirectlytherelationshipbetweenclimatechangeandagriculturalperformance,oneofthelimitations with this approach is that all the estimations were based on historicalobservationswhichinturnwerebasedongiveneconomicsituations.Thisapproachthuscannot assess how changes in economic situations, e.g. changes in international tradeflows and/or policies,may interferewith the impact of climate change. This iswhereeconomicmodelscancomeintoplay.

Unlike the econometric approach, the economic approach to estimating the impact ofclimatechangeistypicallysubjecttoexplicitmodellingoftheoptimizationbehaviourofeconomicagentsandtheinteractionsbetweenthesewell-definedeconomicagentsacrossdifferent parts of the economy. In particular, economy-wide general equilibriumanalyticalframeworksmodeltheinteractionsbetweentheagriculturalsectorandtherestof the economy and thus are able to capture the flow-on impact of climate changethroughouttheeconomyandtheindirectimpactofclimatechangeviachangesintherestof the economy, e.g. international trade, as demonstrated in several of the followingreviewedpublications.

Inarecentlycompletedstudy(VanMeijletal.,2017),MAGNET,aCGEmodelwithglobalcoverage, was used alongwith four other economic/biophysicalmodels to assess theimpactofclimatechangeonglobalagricultureby2050.Thestudyexaminesanumberofscenarios comprisingdifferent social economicpathways (SSP1-3), alternative climateconditions(RepresentativeConcentrationPathways(RCP)2.6andRCP6),andpossiblemitigationmeasures.TheMAGNETresultsshowthattheRCP6.0forcinglevelhasasmallnegativeeffecton thegrowthofGDP (approximately -0.22percent) and the impactofmitigationonGDPisabitmorenegative(approximately-0.32percent).Overallacrosstheparticipatingmodels,croppricesareprojectedtoincreaseunderclimatechangewhileitseffectonagriculturalproductionswasprojectedtobeasmallnegativeattheglobalscale,withRCP6andRCP2.6showingverysimilarimpacts,asthetwoselectedpathwaysarenotdistinctivelydifferentaround2050.

Hsiang et al. (2017) developed a spatially-explicit integrated assessment frameworkincorporatingaCGEmodelfortheUSAwherepotentialdamageofclimatechangeonarangeof sectors includingagriculture, crime, coastal storms, energy,humanmortality,and labourproductivityaretaken intoaccount.They findthecombinedvalueof thesemarketandnonmarketdamagescostroughly1.2percentofGDPper+1°Conaverage,withthedamagebeingdistributedunequallyacrosslocations.However,bothstudies,VanMeijl et al. (2017) andHsiang et al. (2017),do not discuss how the impact of climatechangemaybeaffectedbyinternationaltrade.

The close relationship and possible interactions between climate change andinternational trade have been examined in a growing body of economic literature. Anumberof issueshavebeencovered in earlierpublications including: a) internationaltradeandagriculturaladaptation(e.g.Randhir&Hertel,2000;Reilly&Hohmann,1993;Rosenzweig&Parry,1994;Tsigasetal.,1997)toclimatechangecarbonleakages–an

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ideathatifonlyasubsetofcountriestaxcarbonemissions,thelevelofemissionsinnon-taxingcountriesislikelytorise(e.g.Babiker,2005;Elliottetal.,2010;Felder&Rutherford,1993)b)thedirectimpactofinternationaltradeonthelevelofcarbonemissionscausedbyinternationaltransportation(e.g.Cristeaetal.,2013).

Cuietal.(2014)usetheGlobalTradeandEnvironmentModel(GTEM)toprojectimpactsof climate change to Australian agriculture and consider whether global tradeliberalisationcanhelpinmitigatingpotentialdamage,andenhanceanybeneficialimpacts,fromclimatechangeonAustralia’sagriculture.Theirresultsshowthat ina liberalisedworld,AustraliaisprojectedtoexperienceaslightimprovementinthepositiveimpactofclimatechangeonitsrealGNPrelativetotheclimatechangeimpactunderthestatusquo.Australia’smajoragriculturalexportsincludingwheat,othergrains,andsugararealsoprojectedtobenefitingeneralinaliberalisedworld.

Costinotetal.(2016)developedageneralequilibriummodelwithwhichtheyestimatethatclimatechangeoverabout110years,untiltheendofthiscentury,maycausea0.26percent loss in global GDP. They also quantify the welfare impact of climate changeinterferedbyadjustmentsinproductionandtradepatterns,concludingthatadjustmentsinproductionpatterns(e.g.cropswitches)contributesignificantlytoalleviationsoftheconsequencesofclimatechangewhileadjustmentsintradepatternsdolittle.

TheENV-LinkagesmodelusedinanOECDreport(OECD,2015)projectsmacroeconomicandsectoraleconomicconsequencesofclimatechange(i.e.climatedamages)inabsenceofnewclimatepolicies,foraselectednumberofimpacts:changesincropyields,lossoflandandcapitalduetosealevelrise,changesinfisheriescatches,capitaldamagesfromhurricanes, labour productivity changes and changes in healthcare expenditures fromdiseasesandheatstress,changes in tourismflows,andchanges inenergydemandforcoolingandheating.Basedontheseshocks,themodellingassessmentsuggeststhatifnofurther climate change actionwill be undertaken, the combined effect of the selectedimpacts(intheclimatedamagesscenario)onglobalannualGDPareprojectedtoriseovertimetolikelylevelsof1.0percentto3.3percentby2060,withacentralprojectionof2percent.Thisrangereflectsuncertaintyonhowsensitivetheearth’sclimatereactstoadoubling of atmospheric CO2 – using a likely range of 1.5 °C to 4.5°C and a centralprojection of 3°C. Assuming a wider range of 1°C to 6°C in the equilibrium climatesensitivity(ECS),GDPlossescouldamountto0.6percentto4.4percentin2060.2Oftheimpactsmodelledintheanalysis,changesincropyieldsandinlabourproductivityareprojectedtohavethelargestnegativeconsequences,causinglosstoannualglobalGDPof0.9percentand0.8percent,respectively,by2060,forthecentralprojection.

Dellink et al. (2017), also using the ENV-Linkage model, provide an analysis of howclimatechangedamagesmayaffectinternationaltradeandhowinternationaltradecanhelp limit the costs of climate change, highlighting the differences in the effects thatclimatechangewillhaveonregionaleconomicactivitiesandtradecompetitiveness.They

2TheOECDreportalsousesanintegratedassessmentmodel(AD-DICE)anditsprojectionssuggestthatGDPmaybenegativelyaffectedbybetween2percentand10percentbytheendofthecenturyrelativetotheno-damagebaselinescenario.

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assessboththedirectimpactofclimatechangeontradeinfrastructure,e.g.morefrequentport closures due to extremeweather conditions, and the indirect impacts thatworkthroughinteractionswithintheeconomicsystem.Theauthorsshowthat,underclimatechange, world exportsmay decrease by 1.8 percent in 2060, relative to the baselinewithoutclimatedamage(expressed in2010USDusingpurchasingpowerparity(PPP)exchange rates). By decomposing the changes in GDP to isolate different channels ofclimate impacts, the authors show that the negative impact of climate change comesmostly from domestic damage, and the effects of international trade in limiting theclimate-induceddamagearesmallinmostregions.

3. Descriptionofscenarios

Ourresearchtaskinthisreportisbasedonthesimulationsoffourpre-definedscenariosincluding:i)abaselinescenariothatconformstotheSSP3storyline;ii)aclimatechangescenario; iii) a climate change and agricultural-food free trade scenario; and iv) anagricultural-food free trade scenario. A comparison of these different scenarios isprovidedinTable1.Allthescenariosimulationsprojectglobaldevelopmentsupto2050usingtheparameterscontainedinRepresentativeConcentrationPathways6(RCP6)andSharedSocioeconomicPathways3(SSP3).

Table1DescriptionofScenarios

ScenarioName SSP3Shocks CropexogenousYieldShocks

BorderTariffs

BaselinePopulationandTechnological(latterswappedwithGDP)a

Technological Asof2011

ClimateChangePopulationandTechnological(latterswappedwithGDP)

TechnologicalandClimatic

Asof2011

ClimateChangeandTradeLiberalisation

PopulationandTechnological(latterswappedwithGDP)

TechnologicalandClimatic

RemovedforAgricultureandFood

TradeLiberalisation

PopulationandTechnological(latterswappedwithGDP)

TechnologicalRemovedforAgricultureandFood

aThetechnologyshocksarederivedfromFAO’sGDPprojections;theycorrespondtothetechnicalchangeneededtoreachtheprojectedGDPinthebaselineandarethenkeptconstantacrossthealternativescenarios.Thisset-upallowsustokeepGDPendogenousandthusresponsivetoclimateandtradescenarios,whileassuringthatthebaselineGDPdevelopmentfollowsFAO’sSSP3storyline.

RCP6.0isoneofthefourgreenhousegas(GHG)concentrationtrajectoriesadoptedbytheIPCCforitsfifthAssessmentReport.UnderRCP6.0,globalGHGemissionsareexpectedto

6

be at the intermediate level and the corresponding increase of global mean surfacetemperature by the end of this century (2081–2100) relative to the beginning of thiscentury(1986–2005)isexpectedtobeintherangeof1.4°Cto3.1°Cwithameanof2.2°C(Pachaurietal.,2014).

SSP3 is also called theRegionalRivalryworld, characterisedwithhighmitigationandadaptationchallengeswithintheSSPworldviews.Itisaworldviewinwhichtheworldisseparatedintoregionscharacterizedbyextremepoverty,pocketsofmoderatewealthanda bulk of countries that struggle tomaintain living standards for a strongly growingpopulation. Regional blocks of countries have re-emerged with little coordinationbetweenthem.Countriesfocusonachievingenergyandfoodsecuritygoalswithintheirownregion.Populationgrowthinthisscenarioishighasaresultoflimitedimprovementsineducationandloweconomicgrowth.

TheBaselineScenario(BS)ismodelledonthisRegionalRivalryworld(SSP3),projectingdevelopments in the world economy up to 2050. In this scenario, the world GDP,population,andcropyieldsduetotechnologicalchange,areassumedtogrowby134.7percent,38.7percent,and38percent,respectively,from2011to2050.3Thelandscapehoweverdiffersacrossregionsandcrops,asshowninFigures1and2,whereitcanbeseen that growth in population andGDP is especially high in developing regions (e.g.North,WestandEastAfrica,CentralAsia,IndonesiaandSoutheastPacific)indicatingaprocessofcatchingupalthoughthiscatchingupismuchlowerthaninotherSSPscenarios(e.g.SSP2).

3ThedataforGDP,population,andcropyieldchangesduetotechnologicalchangeandclimatechange,whichareexogenoustothemodel,areprovidedbytheFAOGlobalPerspectivesTeam,withtheGDPandpopulationdataprovidedatthecountrylevelandcropyieldsdataatthecountryandcroplevel.ThesecountryandcropleveldataarethenaggregatedusingmappingsfortheMAGNETmodel.

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Figure1AssumedgrowthinGDPandpopulation2011-2050

Figure2Assumedchangeincropyields2011-2050

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% Assumed Change in Crops Yield due to Climate Change

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Ontopofthebaselinescenario,threealternativescenariosaredevelopedtoexploretheimplicationsofclimatechangeandtradeliberalisation.

TheClimateChangeScenario(CC)ismodelledinlinewithRepresentativeConcentrationPathway6.0(RCP6.0)whichdefinesthechangesinlandproductivity/cropyieldduetoclimate change. This is essentially the only difference between the climate changescenarioandthebaselineasalltheothershocksintheclimatechangescenario,includingGDP, population, and exogenous crop yield growth due to technological progress, aretaken from thebaseline.Thus, thedeviationsof theClimateChange scenario from theBaselinemeasuretheimpactofclimatechange.

Intheclimatechangescenario,theclimate-inducedcropyieldshocksarecropandregion-specific,withtheaggregatecropsyieldassumedtodeclineby1.13percentattheglobalscalefrom2011to2050.Theseexogenousyieldshocks,asdepictedinFigure2,showthatcropyieldsarerisinginsomeregions(e.g.Canada,restofWesternEurope,Mexico,RestofEastAsia,USAandRestofNorthAmerica,andRussianFederationandtheCaucasus)butdeclininginmanyotherregions,withthehighestyielddeclinesbeingfoundinseveraldevelopingeconomies(Africa,India,restofSouthAsiaandSoutheastAsia).

A Climate Change and Trade Liberalisation scenario (CCTL), modelled on the aboveClimateChangescenario(SSP3andRCP6),includesfurtheraremovalofallbordertaxesincluding both import tariffs and export taxes/subsidies, for agricultural and foodcommodities.Bydesign,thisscenarioisintroducedtocapturethefreetradeeffectunderclimatechange.

Inadditiontotheabovethreescenarios,weintroduceanothercounterfactualscenarioinanattempttoshedlightonthepotentialadaptationeffectsoftradeliberalisation–thatis,towhat extent the impact of climate changemay bemitigated by the impact of tradeliberalisation. While the deviations of the Climate Change and Trade LiberalisationscenariofromtheClimateChangescenario,asdescribedabove,capturetheeffectoftradeliberalisation, this effect per se does not revealwhether the pre-liberalisation climatechange impacts (unaltered by trade liberalisation) may have been alleviated oraggravatedbytheintroductionoftradeliberalisation.Inordertocapturesuchapotentialalterationfromtradeliberalisation,thepost-liberalisationclimatechangeimpactsneedtobeisolatedandcomparedwiththepre-liberalisationclimatechangeimpacts.

Toaccomplishthisisolation,anewscenario-TradeLiberalisation(TL)scenariowithoutclimatechangeimpacts,isintroducedandmodelledonthebaseline.Inthisnewscenariothesametradeliberalisationshocksasin theClimateChangeandTradeLiberalisationscenarioareincludedbuttheclimate-inducedcropyieldchangesareexcluded.Withthisadded scenario, we first compute the deviations of the Climate Change and TradeLiberalisation scenario results from the Trade Liberalisation scenario results – anisolation of the post-liberalisation climate change impacts. These deviations are thencomparedwithpre-liberalisationclimatechangeimpacts,asmeasuredbythedeviationsoftheClimateChangescenarioresultsfromtheBaselineresults.Thegapbetweenthese

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twogroupsofdeviations,whichwerefertoastheinteractioneffect,essentiallygaugestowhatextentclimatechangeimpactsmaybealteredbytheintroducedtradeliberalisation.Ifapositivegapisfound,thatis,thepost-liberalisationclimatechangeimpactsarelessadversethanthepre-liberalisationones,theadaptiveeffectoftradeliberalisationmaybedeclared–theinteractioneffectinthiscasemayalsobereferredtoastheadaptiveeffect.

Figure3providesanillustrationofthescenariosasdescribedabove.Section4willreporttheprojectionsofthebaseline,whichmayberepresentedbythevalueofpointAinthisfigure. Section 5 presents the unaltered climate change impacts, represented by thepercentagedeviationofpointBfrompointA,denotedby(B/A-1)*100.Section6showsthetradeliberalisationeffectsunderclimatechange,asdenotedby(D/B-1)*100.Section7discussestheinteraction/adaptiveeffect,asdenotedby((D-C)-(B-A))/A*100.4

Figure3Anillustrationoftherelationshipbetweenscenarios

4. Projectionsofthebaseline

Asmentionedearlier,thebaselinescenariofollowsaparticularsharedsocialeconomicpathway(SSP3)asdepictedintheinputdatafromFAOwheretheworldGDP,population,andexogenouscropyieldsattributedtotechnologicalprogressareassumedtogrowby134.7percent,38.7percent,and38percent,respectively,from2011to2050.

4ThepercentagedeviationsofvariablesreportedinSection7forgaugingtheinteractioneffectsandtheassociatedclimatechangeimpactsandtradeliberalisationeffectsareallexpressedinbaselinevalues,inordertomakeacommondenominatorforcomparison.

A

B

C

D

2050 Year

Variable

Baseline

ClimateChangeScenario

TradeLiberalisationScenario

ClimateChangeandTradeLiberalisationScenario

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DuetoboththeassumeddemandincreasedrivenbythegrowthinGDPandpopulation,and to the assumed technological progress that boosts crop yields worldwide, globalcropsandlivestockproductionsinthebaselineareprojectedtogrowrespectivelyby58percentand47percent from2011to2050,underpinnedrespectivelybya10percentexpansioninagriculturalland(6percentforcroplandand11percentforpastureland).

Agricultural trade roughlymirrors theexpansionof agriculturalproductionsasglobaltradeincropsincreasesby59percentandtradeinlivestockincreasesby70percent.Theincreaseinlivestocktradeisfasterthantheincreaseinlivestockproductionssincethereisamismatchbetweenregionsthatexperienceafastincreaseinlivestockproductions,e.g.ChinaandEastAsia,andMiddleEast,andregionsthatseeastronggrowthindemand,e.g.IndiaandWestAfrica.Afasterincreasein traderelativetoproductionsessentiallyreflectstheflexibilityofthegloballivestockmarketthatreallocatesincreasedsupplytomeetthegrowingdemand.

The growing demand and expanding agricultural sectors drive both input prices andoutputpriceshigher.By2050,globalcropprices(2011constantprice)areprojectedtoincreaseby24percent, andglobal livestockprices to increaseby18percent.Notably,theseinputandoutputpriceincreasesintheSSP3contextarehigherthanintheSSP2worldwhereMAGNETresultsindicaterelativelymoderatepriceincreases(see,VanMeijletal.,2017).Attheregionallevel,changesinagriculturalpricesvarysignificantlyacrossregionsinthisSSP3astheyincreasemoreindevelopingregions.

Asanimmobileinput,agriculturallandpricesincreasesharply,withpricesforcroplandandpasturelandexpectedtoincreaseoverfourtimesandfivetimes,respectively,overthisprojectionperiod.TheSSP3scenarioalsoleadstoparticularstressonlandmarketsindevelopingcountriesandespeciallyAfrica.Thisinducesasharpincreaseinlandpricesintheseregions.InMAGNET,theriseinlandpricerelativetootherendowmentprices(e.g.labour,capital)alsotriggersanendogenousyieldresponseaslandwillbesubstitutedbylabourandcapital.Inadditiontothe38percentassumedexogenousgrowthincropyieldsglobally, the simulatedendogenousgrowth in cropyields–asmeasuredby thechangeincropoutputsrelativetothechangeinlanduse,isapproximately14percentattheglobalscaleintheBaseline.

5. Theimplicationsofclimatechange

Asmentionedearlier,theonlydifferenceintheClimateChangescenario,comparedtotheBaseline,istheclimate-inducedcropyieldshocks.Thedeviationsoftheclimatechangescenariosimulationresultsfromthebaselinesimulationresultsthuscapturetheimpactsofclimatechangethroughthechangingcropyields.

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Foodandagriculturalproductions,prices,andsectoralincome

Giventheoverallnegativechangesincropyieldsinducedbyclimatechangeattheglobalscale, the climate change scenario is expected to feature relatively lower outputscompoundedwithhigherpricesglobally.Thesimulationresultsconfirmthat,relativetothebaseline,globalfoodandagriculturalproductionsintheclimatechangescenarioareexpected to decline by 0.28 percent at 2050, including crops (0.66 percent), livestock(0.12percent),processedfood(0.16percent),andfish(0.03percent)5.Unsurprisingly,the declining food productions drive food prices higher, with theworld average foodprices (domestic realmarket price for private consumptions) expected to rise by 2.6percent,duetotheimpactofclimatechange.

Theclimate-induceddeclineincropyieldscoupledwiththegrowingdemandasspecifiedbytheSSP3storyline,triggersanincreaseinagriculturalinputsattheglobalscale.Asaresult,croplandexpandsby0.6percentworldwide,atthecostofasmalldeclineinpastureland(0.01percent).Thiscrop-biasiscausedprimarilybyallclimate-relatedexogenousyieldshocksbeingimposedoncropsonly.6

The sectoral incomeof cropsandbroadly thesectoral incomeof agriculture includinglivestockareprojectedtoincreaseby5.9percentand4.8percent,respectively,duetotheimpactofclimatechange.Theseseeminglycounterintuitiveresultsessentiallyreflecthowthe global demand systemworks on food consumption.Given the relatively lowpriceelasticitiesofdemandon foodconsumption, compared tonon-foodconsumptions, thedeclineinagriculturalproductionsduetoclimatechangehavebeenmorethanoffsetbythe rise in prices of agricultural produce, leading to improved sectoral income inagricultureattheglobalscale.

The improved agricultural sectoral income under climate change also occurs at theregional level, with almost all regions, including both climate-resilient and climate-vulnerableregions,showinganimprovedsigninagriculturalincome.Sincebothtypesofregions improve on agricultural income, this implies that the story behind theimprovements would be different for individual regions. While climate-vulnerableregionsare stillsubject to theaforementioned lawofdemandelasticities, i.e., reducedagriculturalproductionsovershadowedbyrising foodprices,regionshaving increasedagriculturalproductionsunderclimatechangetypicallyarenotboundedbythesamerulesince the production increases in these regions are typically absorbed by increasedforeign demand, which helps buffer an otherwise depression of domestic prices inagriculturalproduce.

Theonlyregionthatsuffersalossinagriculturalincomewhileexperiencinganincreasein agricultural productions under climate change is Rest of East Asia, as it is the onlyregion where prices of agricultural products decline under climate change. To help

5Fishproductionsreducemodestlyintheclimatechangescenariosincethefeedinputstoaquaculturebecomelessavailableandthusmoreexpensive.6Oursimulationsuseclimate-relatedyieldshiftersprovidedbytheFAO.SincenolivestockyieldshiftersareprovidedfromFAO,alltheclimate-relatedyieldshiftersinthepresentsimulationsarelimitedtocropyieldsonly.

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understandwhathasoccurredtoagricultureinthisregion,wecomparethisregionwithanother similar region, Rest ofWesternEurope (RWE), as both regions are positivelyaffectedbyclimatechangeandthusbothhaveincreasedagriculturalproductionsintheclimate change scenario.Moreover, both are relatively small regions having a similarexportsize(approximatelyUSD3-4billion)at2050inthebaseline.However,facedwithdifferent trade barriers to food and agricultural commodities, represented by importtariffsleviedbydestinationcountries,agricultureinthetworegionsfaresverydifferentlyunderclimatechange.TheRWEregion,withitsfoodandagriculturalexportsonaveragelevied25percentbydestinationregions,canmanagetogetmajority(82percent)oftheincreasedagriculturalproductionsdisposedofviaexport.TheRestofEastAsiaregion,bycontrast,facesahefty81percentcorrespondingimporttariffsonaverageleviedbyitstradingpartners.Underthehightradebarrier,RestofEastAsiacanonlydisposeofasmallportion of the increased agricultural productions (39 percent) via export, with theremaining majority forced to be absorbed in the domestic market. This creates adampening effect on domestic agricultural prices and effectively hurts agriculturalincomewithintheregion.

Theoverallnegativechangesincropyields,bothexogenousandendogenous,attheglobalscaleindicatethatclimatechangecausesagriculturallandlessproductive.Oursimulationshows that, while global agricultural productions are declining under climate change,agriculturallanduseonaveragegoesup(0.16percent)attheglobalscale,amixtureofanincrease from cropland (0.59 percent) and aminor decrease frompasture land (0.01percent).Again,thisbiastowardscroplandusereflectsthatclimate-inducedyieldshocksareimplementedonlyforcrops.Themagnitudesoftheincreaseincroplanduseandthedecrease in crop productions roughlymirror themagnitude of the built-in crop yieldshocksattheglobalscale(1.1percent).

Climatechangealsocauseslessefficientuseofresourcesinagriculture,particularlyinthecroppingsector,asfarasgreenhousegas(GHG)emissionsareconcerned.7Contrarytothereducedcropproductions,GHGemissionsinthissectorincreasebyabout1percentglobally, in the climate change scenario relative to the baseline. By contrast, GHGemissionsfromthelivestocksectorreduceby0.25percentglobally,largelyinlinewiththe contracted productions in this sector. Taking all sectors into account, global GHGemissions reduce by 0.13 percent globally, consistent with the broad contractions inmacroeconomicgrowthattheglobalscale.

Foodandagriculturaltrade

Whileglobalagriculturalproductionsareexpectedtodeclineduetotheimpactsofclimatechange,globaltradeinfoodandagriculturalproductsisprojectedtoriseby0.9percentunder climate change, including rises in trade for crops (2.2 percent), livestock (0.7

7TheemissionsaccountinginMAGNETincludesallmajorgreenhousegases:CO2,CH4,N2O,andF-Gases.However,GHGemissionsfrominducedlandusechangeareexcluded.

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percent),andprocessedfood(0.5percent),withanexceptionforfishtradewhichisfoundto barely change at the global scale. Rises in global food and agricultural trade arecharacterized not only by the increase in food exports/imports from climate-resilient/vulnerableregions,butalsobytheincreaseinnetexports/importsfromtheseregions.Duetotheimpactofclimatechange,foodexportsfromclimate-resilientregionsareexpectedtoincreaseby1.6percent,metbythecorrespondingincrease(0.6percent)infoodimportsfromclimate-vulnerableregions.Alongwiththeincreaseinfoodexports,netfoodexports(exportsminusimports)alsoincreaseby1.9percentinclimate-resilientregions,matchedbythecorresponding0.5percentincreaseinnetfoodimports(importsminusexports)byclimate-vulnerableregions.

Thesechangesintradeflowsdrivenbyclimatechangeessentiallyreflecttheroleofglobaltradeasadouble-edgedswordunderclimatechange:reallocatingfoodandagriculturalproductsaroundtheworldtominimizeclimate-inducedwelfarelosses,andontheotherhand, strengthening the trade competitiveness of climate-resilient regions, makingclimate-vulnerableregionsasawholeevenlesscompetitiveinglobalfoodtrade.

Thisglobalclimate-tradelinkageisalsomirroredattheregionallevel.CanadaandUnitedStates of America and Rest of North America, for example, have their tradecompetitivenessstrengthenedbyfavourableclimatechange,withfoodexportsinthetwocountries increasingby5.2percentand0.7percent,respectively,andnet foodexportsincreasingby5.3percentand1.1percent,respectively.Bycontrast,foodimportsinIndiaand West Africa increase by 6.1 percent and 5.8 percent, respectively, and net foodimportsinthesetworegionsalsoincreaseby10.3percentand6.7percent,respectively.

Theincreaseinglobalfoodandagriculturaltradeunderclimatechange,asprojectedinoursimulations,meetsacommonexpectationthatinprincipleincreasedtradeflowsarenecessarytocompensateforproductionlossesineconomiesaffectedadverselybyclimatechange.However,ourresultsareincontrastwiththeresultsofDellinketal.(2017)whichinsteadshowacontractioninglobaltradeunderclimatechangeandtheauthorsattributethecontractionintradetothecontractioninglobalfinaldemandasindicatedbythelossinworldGDP.WhilelinkingGDPwithtradesoundsintuitive,oursimulationsrevealthataGDPlossattheglobalscalemayalsobesustainedbyanexpansioninglobaltrade,aswillbeshownbelow.Thus,theGDPloss,evenifitmaybeasource,shouldnotbethemainsource,atleastnotthesinglesource,ofthecontractioninglobaltradeasprojectedintheirstudy.

Macroeconomicgrowth

Whilethesectoralincomeinagriculturemaybenefitfromclimatechange,asexplainedearlier, the other sectors of the economywhich consume agricultural products or useagriculturalproductsasinputsdirectlyorindirectlyareexpectedtobehurtbythefallingsupply and rising price of agricultural inputs. Consequently, the gross product at theworldlevel,theworldGDP,isexpectedtobenegativelyaffectedunderclimatechange.

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Relative to the baseline, theworldGDP in the climate change scenario is projected todecline by 0.18 percent at 2050, which translates to some USD 150 billion (in 2011constantprices).Thisresultisroughlyatthelowerboundoftheeconomiclossestimates(0.2-2 percent) surveyed by the IPCC (IPCC, 2014) for an assumed 2oC increase intemperature.However,itshouldbenotedthat,first,ourprojectionperiodonlyextendsto2050(underRCP6.0globalmeantemperatureincreasewouldbemarkedlylessthan2oC by 2050); and second, different assumptions on climate-induced changes in theeconomywouldpotentiallyleadtoverydifferentestimates.Forinstance,themuchhigherOECDestimate,withameanvalueof2percentlossinglobalGDP(OECD,2015),wasbasedonmanyassumedclimate-relatedchangesintheeconomyincludingcropyieldlosses,lossoflandandcapitalduetosealevelrise,changesinfisheriescatches,capitaldamagesfromhurricanes, labour productivity changes and changes in healthcare expenditures fromdiseasesandheatstress,changes in tourismflows,andchanges inenergydemandforcooling and heating.By contrast, our presentmodelling exercise focusesonly on cropyieldlossesinducedbyclimatechange.

Inthissense,ourGDPestimateismorecomparablewithCostinotetal.(2016)whoalsofocusoncropyieldlossescausedbyclimatechange.UsingFAO’sGlobalAgro-EcologicalZones field leveldatacollected fromabout1.7milliongridcells,Costinotetal. (2016)estimatethatclimatechangeover110yearsuntiltheendofthiscenturywouldcausea0.26percentlossofGDP(2009value).Inordertocompareourestimateof0.18percentloss ofworldGDPwith their estimate,we need to first factor in the difference in theprojectedclimatechangetimespan.Ona“comparingapplewithapple”basis,ifwestretchourprojectiontimespanfrom40yearsto110yearsandassumeclimate-inducedcropyieldchangesfollowingtheexistingtrend,abackoftheenvelopecalculationshowsthatourmodelmayprojectnearlyhalfapercentlossofGDP.However,itisalsonecessarytotaketheverydifferentmodellingframeworksbeingusedintherespectivestudiesintoaccount.For instance, capital supplyhasbeenendogenized inour currentpolicy runswhileitwasnotmodelledinCostinotetal.(2016).Inanearliermodellingexercisewhereweassumeexogenouscapitalsupply,whichmayberegardedasbeingclosertoCostinotetal.’sassumptions,theprojectedworldGDPlossduetoclimatechangewas0.09percentforthe40yearprojectionperiod,whichamountstoabout0.25percentlossofGDPfor110yearswiththesamebackoftheenvelopecalculation,veryclosetoCostinotetal.’sestimate.

WhiletheworldGDPisexpectedtoexperiencealossunderclimatechange,theeffectsonregionalGDPsdonotappeartobehomogenous.AsFigure3shows,thedeviationsofGDPintheclimatechangescenariorelativetothebaselinevarysignificantlyacrossregions.WhiletheGDPgrowthinanumberofregionshasbeenaffectednegatively,theseeffectsarepositiveinsomeotherregions.Moreover,theGDPdeviationsinsomeregionsaretoosmalltoevenbevisible.Amongtheregionsthatarefoundtobehitparticularlyhardbyclimate change are regions located mainly in Africa and South Asia. West Africa, forexample,seestheGDPfallingby2.5percentunderclimatechangerelativetothebaseline.

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These cross-regional variations of theGDP response to climate change can largely beunderstoodbytakingintoaccountthejointeffectoftwomajorcontributingfactors:a)thedirectionsandmagnitudeofclimatechangeimpactsoncropsasdefinedbytheclimate-inducedcropyieldshocks(See,alsoFigure2)andb)howimportantthecroppingsectoristotheeconomy,representedbythevalueshareofthecroppingsectorinallsectors.Thejointeffectofthetwofactorsimpliesthatthepositiveornegativeclimate-inducedcropyield shocksmaybemagnifiedormoderated dependingonhowmuchaneconomy isreliant on the cropping sector. Technically, this joint effect can be quantified bymultiplyingtheexogenouscropyieldchangeswiththesectoralvalueshareofcrops.InFigure 4, the share weighted crop yield shocks are plotted against the deviations ofregionalGDPs.Thetwovariablestraceeachotherverywellinmostregions,suggestingthat deviations of regional GDPs are, to a great extent, predictable by the two knownfactors.

Figure4ChangesinGDPintheclimatechangescenariorelativetothebaselineat2050

Developingeconomies,asFigure4shows,arefoundtobehithardercomparedtotheirdeveloped counterparts due partly to higher climate-induced crops yield losses, andpartlytotheeconomicstructureasthecroppingsectortypicallyaccountsforarelativelylarge portion in the developing economies. At 2050, the output value of crops in thedevelopingeconomiesaccounts foranaverageof3.5percent in theoutputvalueofallsectors, compared to only 0.6 percent on average in the developed economies. West

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Africa,forexample,hasover23percentofitssectoraloutputvaluegeneratedfromcropsat2050.Theheavyrelianceonthecroppingsectorrendersthesedevelopingeconomiesmorevulnerabletoanegativecropyieldshock.

ImpactsinseveralregionsfailtobeexplainedbythefactorsstatedaboveasthedirectionofGDPchangesintheseregionshasasmallyetoppositedeviationfromwhatisindicatedbythedirectionofcropyieldchanges.Theseregionscanbedividedintotwogroups:anegativecropyieldshockbutapositiveGDPchange(RestofCentralEurope,Turkey,andRest of Eastern Europe); and a positive crop yield shock but a negative GDP change(Mexico,CentralAsia,andJapan).These inconsistencesbetweencropyieldshocksandGDPchangestellusthatdespiteplayingamajorroleinexplainingthedirectionofGDPchanges,cropyielditselfisnottheonlydriverguidingwhereGDPisheading,evenifcropyieldshocksaretheonlyshocks indefiningclimatechange.Thedirectionsof theGDPresponse to climate changemay be complicated by other factors that reflect towhatextentaregionwillbeaffectedbyclimatechangerelativetootherregions.Inotherwords,“relativity”alsomatters(Dellinketal.,2017).

Tofigureouthowcropyieldsandtheso-calledrelativitymayinteractwitheachotherinaffectingthedirectionofGDPmovements,weherebydefinethecropyieldeffectasthe“firstorder”effectsincethedirectionofyieldchangesdeterminesthedirectionofGDPchangesinmostofthecaseswherethejointmagnitudesofcropyieldshocksandcropssharesinGDParesignificant.Therelativity,herewerefertoasthe“secondorder”effect,canhoweverbecomeprominentandevendominatethefirstordereffectespeciallywheretheyield-sharejointmagnitudesaresmall,asisthecaseofthesix“defected”regions(See,again,Figure4).

Relativitymayworkthroughmultiplechannels,withanimportantonebeingreflectedbythetermsoftrade(relativepriceofimportsintermsofexports)ofacountryinagri-foodcommodities. Climate change drives crop prices up in global markets, and the 2050baselineresultsshowthatthethreeregionswith“negativeyieldbutpositiveGDP”areallnetcropexporters(totalcropexportsexceedimports)andthereforefaceapositivetermsoftradeeffectduetothehigherpriceoftheirexports.Thispositivetermsoftradeeffectdominatesthesmallnegativeyieldeffect.AnimprovementintermsoftradebenefitsGDPintheseregionsinthesensethattheycanbuymoreimportsforanygivenlevelofexports.Theimpliedincreaseinrealpurchasingpowerofdomesticproductionsisequivalenttoatransferofincomefromtherestoftheworldandthiscanhavelargeimpactsondomesticconsumptions, savings and investment. The three regions with “positive yield butnegativeGDP”areallnetcropimportersfacinganegativetermsoftradeeffectastheircrop(agri-food)importsbecomemoreexpensive.ForthegivenlevelofexportstheycanbuylessimportsandthustheirGDPswillbeeffectednegatively.Thisnegativetermsoftradeeffectinthiscasedominatesthesmallpositiveyieldeffectintheseregions.

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Foodsecurity

Sincelessfoodandagriculturalproductswillbeproducedgloballyunderclimatechange,itfollowsthatclimatechangemaycausefoodsecurityissuesaroundtheworld,aswarnedbyWheelerandVonBraun(2013).Relativeto thebaseline,ourprojectionsshowthatcalorieintakeintheclimatechangescenariowilldrop0.28percentglobally,inlinewiththeproductionreductionsinallfoodcommoditiesincludingcrops,livestock,processedfood,andfish.

Attheregionalscale,developingregionsintotalareprojectedtohaveaslightlygreaterreductionincalorieconsumption(0.283percent)underclimatechangethandevelopedregions (0.265percent) but individual regions differ significantly.Whilemost regionsexperience a fall in calorie consumptions, regions showing an increase in calorieconsumptionsaremostlydevelopingregions including:Mexico,RestofSouthAmerica,NorthAfrica,andRestofSouthAsia.

Alongwithfoodavailabilityindicatedbyreducedcalorieconsumptions,foodaccessibility,asmeasured by the relative change betweenwages and food prices and the resultingchangeinfoodpurchasingpower,willalsobeadverselyaffectedbyclimatechange.Intheclimatechangescenariorelativetothebaseline,theworldaveragewageisexpectedtofallby0.34percent,withtheaveragewageinagriculturalsectors(incl.bothcropsandlivestock)expectedtodeclineby0.24percent.However,thedeclineinagriculturalwagemostlyoccurs indevelopingregions(Figure5).Mostdevelopedregionsexperienceanincreaseinagriculturalwage,duemostlytothefavourabletradepositionsunderclimatechange.

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Figure5Changesinagriculturalwageunderclimatechangerelativetobaseline

Whilewagesearnedbyskilledworkersandunskilledworkersareexpectedtodeclinebyaboutthesamedegreewhenallsectorsaretakenintoaccount,skilledwageinagriculturalsectorsareexpectedtoriseby0.35percentglobally,incontrasttothefallingunskilledwage (0.33 percent) in agricultural sectors. This divergence in the global average issomewhatmisleadingsinceattheregionalscale,asshowninFigure6,itisevidentthatchangesinskilledandunskilledwagesinagriculturalsectorsareverysimilarineachoftheseglobalregions.

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Figure6Changesinskilledandunskilledwagesinagricultureinclimatechangescenariorelative

tobaselineat2050

Rising food prices and falling wages indicate that food purchasing power woulddeteriorateunder climate change.At2050, foodpurchasingpower,used toassess thepovertylevelasmeasuredbytheratioofunskilledwageinagriculturetocerealprices,isexpectedtodeclineby4.7percentgloballyduetotheimpactofclimatechange.8Attheregionalscale,foodpurchasingpowerinalmostallregionsisexpectedtodecline,withtheonlyexception forRestofEastAsia.Again, asexplainedearlier for the fallingsectoralincomeinagricultureinthisregion,thisistheonlyregionwherefoodpricesdeclineunderclimatechangeandaccordinglyfoodpurchasingpowerinthisregionrises.

8Anotherfoodpurchasingpowerindicatorcanbederivedbytakingtheratiooftheaveragewageinallsectorstotheaveragepriceofallfoods,andthisindicatorwoulddeclineby2.9percentgloballyduetoclimatechange.Whilebothindicatorswoulddeclineinresponsetoclimatechange,theyhaveverydifferenttrajectoriesovertime.Overtheprojectionperiodupto2050,theall-sectorwage/foodpriceindicatorisexpectedtoriseinbothBaseline(88percent)andtheClimateChangescenario(82percent),incontrastwiththeagri-unskilledwage/cerealpriceindicatorwhichdeclinesinbothBaseline(-30percent)andtheClimateChangescenario(-34percent).Theriseintheall-sectorwage/foodpriceindicatorisdrivenbythefastereconomy-widewagegrowthrelativetofoodpricegrowth,asopposedtotheagri-unskilledwage/cerealpriceindicatordrivenbytheslowergrowthinagri-unskilledwagerelativetothegrowthincerealprices.Clearly,comparedtothebroadall-sectorwage/foodpriceindicator,theagri-unskilledwage/cerealpriceindicatorusedinthispapertoassessfoodaccessibilityofthepoorastheymayberepresentedbyunskilledworkersinagriculturewhoareconsideredconsumingmainlycereal-basedstaplefood,capturesnotonlythevulnerabilityofthepoortoclimatechangebutalsotheirvulnerabilitytobroadeconomicgrowth–theirfoodpurchasingpowermaybedeterioratingeveniftheeconomyisgrowing.

-10,0-8,0-6,0-4,0-2,00,02,04,06,08,0

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6. Theimplicationsoftradeliberalisation

Asnotedearlier,theClimateChangeandTradeLiberalisationscenarioismodelledontheClimateChangescenariobyremovingallbordertaxes(incl.bothimporttariffsandexporttaxes) for all agricultural and food commodities including all edible crops, livestock,processedfood,andfish,whilekeepingclimate-inducedcropyieldshocksthesameasinthe Climate Change scenario. The free trade effects under climate change can thus becaptured by the deviationsof the simulation results of the Climate Change andTradeLiberalisationscenariofromthesimulationresultsofClimateChangescenario.

Foodandagriculturaltrade

An immediate effect of removing border taxes in all countries is expected to be anexpansionofglobaltrade.Undertheliberalisedregime,globaltradeinagriculturalandfoodcommoditiesisprojectedtoincreaseby12percent,relativetotheClimateChangescenario. While this increase has been shared by all major food commodity groupsincludingcrops(5.3percent), livestock(4.4percent),processedfood(16percent),andfish(2.4percent),theincreaseoftradeinprocessedfoodistheleadingcontributorpartlybecausethemajorityoffoodcommoditytrade(61percent)istradeinprocessedfoodandpartly because processed food tends to have higher initial (i.e. pre-liberalisation)protectionlevelscomparedtoprimaryagriculture.

An expected consequence at the regional scale is thatwhen trade is liberalised, foodexportingregionsare,ingeneral,abletoexportmoreandfoodimportingregionsareingeneralabletoimportmore.Figure7showsthattheexistingnetfoodexportingregions,asidentifiedintheClimateChangescenarioat2050,includingallSouthAmericaregions,SouthAfrica,Turkey,SoutheastAsia,andOceania,willhaveanincreaseinnetexports.Ontheotherhand,theexistingnetfoodimportingregionsincludingUnitedStatesofAmericaandRestofNorthAmerica,mostregionsinAfrica(East,West,andRestofSouthernAfrica),regions around South and Central Asia and Middle East, Russian Federation and theCaucasus, and Japan, will have an increase in net food imports (i.e. decrease in netexports).9Intotal,foodandagriculturalnetfoodexportingregionsareexpectedtohaveanincreaseofUSD63billion(2011constantprice)innetfoodexports,whichisbalancedbythesameamountofincreaseinnetfoodimportsbynetfoodimportingregions.10Thisimplies that overall, the existing comparative advantages in most regions, ascharacterizedbychangesinnetfoodexports/imports,aremaintainedintheliberalisedworld.

9Thenetfoodtradeissuediscussedherealsodemonstratesthatregionsbenefitingfromclimatechangemaynotbenefitfromtradeliberalisationinthesensethattheseregions(e.g.UnitedStatesofAmericaandRestofNorthAmerica,RussianFederationandtheCaucasus,andJapan)experiencingagrowthinnetfoodexportsduetoclimatechangeimpactswouldexperienceadeclineinnetfoodexportsduetotradeliberalisation.10Thebalanceinglobaltradeneedstobeevaluatedusingthesamepricemetric.Hereweuseworldexporterprices(F.O.B)toreachthetradeaccountbalance.

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Figure7Changesinfoodandagriculturalnetexportsinnetexporting/importingregions:CCTLvs.CC

Several regions, however, show a reversal in their trading positions under tradeliberalisation,includingseveraloftheexistingnetfoodexportingregionsexperiencingadeclineinnetfoodexports(e.g.WesternandEasternEurope)andseveraloftheexistingnetfoodimportingregionsseeingariseinnetfoodexports(e.g.NorthAfricaandRestofEastAsia).These reversalsmaybe causedby, amongother things, therelativebordertaxes,includingbordertaxesaregionleviesrelativetothatleviedbytradingpartners,andbordertaxesfacedbyaregionrelativetothatfacedbytheotherregions.WesternEurope,forexample,facesanegativenetbordertax–aggregatebordertaxesforfoodandagriculturalimportsleviedbyWesternEuropeexceedsthecorrespondingbordertaxesleviedbyallregionsonthe foodexportsofWesternEurope,andthis toa largeextent

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WorldOceania

JapanIndonesia and Southeast Pacific

South East AsiaChina and East Asia

Rest of East AsiaRest of South Asia

IndiaMiddle East

Russian Federation & the CaucasusCentral Asia

Rest of Eastern EuropeTurkey

Rest of Central EuropeRest of Western Europe

Eastern EuropeWestern Europe

South AfricaRest of Southern Africa

East AfricaWest Africa

North AfricaRest of South America

BrazilCentral America

MexicoUSA & Rest of North America

Canada

Billion US$, 2011 constant price

Changes in Food and Agricultural Net Exports in Net Importing Regions

Changes in Food and Agricultural Net Exports in Net Exporting Regions

22

explainswhythisregionexperiencesadeclineinnetfoodexportsafterbordertaxesareremoved.

Foodandagriculturalproductions,landuse,andGHGemissions

In principle, trade liberalisation may cause an increase or decrease in food andagriculturalproductionsdependingonwheretradehasbeenliberalisedmoreorless.Inthe Climate Change and Trade Liberalisation Scenario relative to the Climate ChangeScenario,agriculturalproductionsareprojectedtodeclineby0.28percentworldwide,withadecreaseof0.64percentincropsproductionscompensatedbyanincreaseof0.43percent in livestockproduce.The increase inglobal livestockproductions isdrivenbylowerinputcostssincelivestockfeedarecheapernowatthegloballevel.

The overall contraction in agricultural productions also leads to the productions ofprocessed foods declining by 0.42 percent globally. Consistent with the increase inlivestock,productionsoffishalsoincrease(0.13percent)globallyasfishesarecloserfoodsubstituteswith livestock thanwith crops. When all edible commodities (incl. ediblecrops,livestock,processedfood,andfish)arecountedin,globalproductionscontractbyapproximately 0.36 percent under the liberalised trading regime. 11 This contractionessentially reflects a structural adjustment in the economy as a result of tradeliberalisation.Thelowerpricesoffoodandagriculturalproductsboostnon-foodsectors,aswillbeshowninthenextsectiononthemacroeconomiceffect,sincethesesectorsmayuseagri-foodproductsasinputsdirectlyorindirectly.Theexpansioninnon-foodsectorsdemandsmoreprimaryinputsandthusreallocatesmobileprimaryfactors,e.g. labour,fromagri-foodsectors to the restof theeconomy.Amongother things, reallocationofprimaryinputscontributesdirectlytothecontractionofagriculturalproductions.12

Inaliberalisedworld,regionssuchasWesternEurope,India,andJapan,areprojectedtoexperience a significant decline in food and agricultural productions, and given thesubstantialsharesthatfoodandagriculturalproductionsintheseregionsaccountforinglobalproductions,theproductiondeclineintheseregionsweighsheavilyontheglobaldeclineoffoodproductions(

Figure88).Bycontrast,anumberofotherregions,particularlyBrazilandRestofEastAsia, see a substantial increase in food productions, offsetting to some extent thisdecliningmomentum,as illustratedby theproduction-shareweightedchanges in foodproductions. Developed regions on average experience a greater decline in food andagriculturalproductions(0.9percent)thandevelopingregions(0.1percent).

11ContractioninproductionscausedbytradeliberalisationisalsofoundbyLangleyetal.(2003)whouseaglobalpartialequilibriumtoanalysetheinternationaldairysupplyandtrade.12Oursimulationshowsthatemploymentinagri-foodsectorswillshrinkby0.6percentglobally,withacorrespondingemploymentincreaseintheothersectors.Thisemploymentreallocationisdrivenbyahigherwageincreaseinnon-foodsectors(0.62percent)relativetothewageincreaseinagri-foodsectors(0.46percent).

23

Figure8Production-shareweightedchangesinfoodproductions:CCTLvs.CC

Therelativechangebetweencropproductionsandlivestockproductionsnaturallyleadstoarelativechangeintheinputuseintherespectivesector.Globally,0.8percentcroplandmovesoutofthissector,mostlybeingconvertedtopasturelandwhichincreasesby0.4percentattheglobalscale.Thepercentageincreaseofglobalcropland,doublingthatofpastureland,reflects thatglobalpasturelandareadoublesglobalcroplandarea in thebaselinesinceonaverageagriculturallandbarelychanges.

ThechangingsectoralactivitieswithinagriculturearealsomirroredbychangesinGHGemissions. GHG emissions from the cropping sector reduce by 2.2 percent globally,consistentwiththecontractedactivities in thissector.Bycontrast, livestockemissionsincreaseby1.8percentaroundtheworld.TotalGHGemissionsfromallsectorsincreaseby0.3percentgloballyduetotheliberalisation-inducedexpansionineconomicactivities.

Macroeconomicgrowth

The contribution of trade liberalisation to macroeconomic growth has been wellexplainedandtestedinmanyearlierstudies(See,forexample,Bhagwati,1978;Dollar,1992;Krueger,1978;Sachsetal.,1995).Consistentwiththeseearlierfindings,theworldGDPintheClimateChangeandTradeLiberalisationscenarioisprojectedtogrowby0.38percent,relativetotheClimateChangescenario.Twomajorfindingsherethough,areattheregionalscale.

-0,5%-0,4%-0,3%-0,2%-0,1%0,0%0,1%0,2%0,3%0,4%0,5%

Cana

daUS

A &

Res

t of N

orth

Am

erica

Mex

icoCe

ntra

l Am

erica

Braz

ilRe

st o

f Sou

th A

mer

icaNo

rth

Afric

aW

est A

frica

East

Afri

caRe

st o

f Sou

ther

n Af

rica

Sout

h Af

rica

Wes

tern

Eur

ope

East

ern

Euro

peRe

st o

f Wes

tern

Eur

ope

Rest

of C

entra

l Eur

ope

Turk

eyRe

st o

f Eas

tern

Eur

ope

Cent

ral A

siaRu

ssia

n Fe

dera

tion

& th

e Ca

ucas

usM

iddl

e Ea

stIn

dia

Rest

of S

outh

Asia

Rest

of E

ast A

siaCh

ina

and

East

Asia

Sout

h Ea

st A

siaIn

done

sia &

Sou

thea

st P

acifi

cJa

pan

Ocea

nia

Wor

ld

Crops Livestock Processed Food Fish

24

First, compared tonet foodexportingregions,net food importing regions, asawhole,appeartobenefitmoreinmacroeconomicgrowthfromliberalisedtrade,withtheaverageGDPincreaseinnetfoodimportingregions(0.48percent)morethandoubletheaverageGDPincrease innet foodexportingregions(0.21percent)13.This isprimarilybecause,amongotherthings,Netfoodimportingregionsonaveragefaceahigherfoodbordertaxrate(13.9percent),comparedtotheratefacedbynetfoodexportingregions(8.8percent),(Table2).Notably,thismacroeconomicresultisinstarkcontrastwiththeaboveresultsfocused only on agricultural trade, where the net food exporting regions in generalexperienceagreaterexpansioninfoodandagriculturalexportsundertradeliberalisation,whichapparentlysuggeststhattradeliberalisationwouldbenefitfoodexportingregionsmore. The finding here however sheds light on the importance of both sides ofinternational trade through which trade liberalisation may contribute to economicgrowth: the export sidewhere lowered trade costsmake food export easier and thuscontributesdirectly toGDPgrowth through increasedexports; and the import sideascheaper food imports lower domestic food prices and the resulting income andsubstitutioneffectsareexpectedtotriggeranexpansionofthedomesticeconomy.

Second, developing economies on average benefit more in GDP growth than theirdeveloped counterparts. On average, theGDP increase in developing economies (0.61percent)ismuchgreaterthantheGDPincreaseindevelopedeconomies(0.15percent).The increase in GDP in developing economies should be attributed not only to thegenerally higher border tax rates for food and agricultural commodities faced bydeveloped economies (13.6 percent) than that faced by developed economies (8.1percent),butalsototheheavierinvolvementoffoodrelatedeconomicactivitiesinthedevelopingeconomies.Measuredbybothfoodproductionsandconsumptions,thefoodactivity/GDP ratio in developing economies (23 percent) almost doubles that ratio indeveloped economies (12 percent). The heavier presence of food related economicactivities enables developing economies to take better advantage of the benefits fromliberalisedfoodandagriculturaltrade.

13Again,netfoodexportingandimportingregionsareidentifiedintheClimateChangeScenario.

25

Table2RegionaleconomiccharacteristicsandGDPchangeinClimateChangeandTradeLiberalisationscenariorelativetoClimateChangescenarioat2050

Exporting/ImportingRegion

Developed/DevelopingEconomy

GDPChange(%)

FoodBorderTaxRate(%)

FoodActivity/GDP

Ratio(%)

Canada Exporting Developed 0.32 9.5 11USA&RestofNorthAmerica Importing Developed 0.00 7.6 9

Mexico Exporting Developing 0.06 3.6 22CentralAmerica Exporting Developing 0.84 12.1 40Brazil Exporting Developing 0.32 12.8 27RestofSouthAmerica Exporting Developing -0.27 8.0 27

NorthAfrica Importing Developing 1.13 10.7 40WestAfrica Importing Developing 2.14 13.7 41EastAfrica Importing Developing 0.40 12.2 57RestofSouthernAfrica Importing Developing 0.58 13.4 35

SouthernAfrica Exporting Developed 0.36 16.6 21WesternEurope Exporting Developed 0.14 4.1 13EasternEurope Exporting Developed 0.06 3.8 18RestofWesternEurope Exporting Developed 0.49 16.4 12

RestofCentralEurope Exporting Developed 0.44 7.3 20

Turkey Exporting Developing 1.27 19.8 25RestofEasternEurope Exporting Developed

-0.03 7.6 28

CentralAsia Importing Developing 0.66 7.0 20RussianFederationandtheCaucasus Importing Developed 0.11 11.2 16

MiddleEast Importing Developing 0.33 8.9 16India Importing Developing 2.14 50.6 27RestofSouthAsia Importing Developing 2.28 12.9 56RestofEastAsia Importing Developed 1.67 68.5 11ChinaandEastAsia Importing Developing 0.25 10.3 15SoutheastAsia Exporting Developing 0.61 14.1 34IndonesiaandSoutheastPacific Exporting Developing 0.10 31.6 21

Japan Importing Developed 0.04 15.2 14Oceania Exporting Developed 0.03 10.6 14World 0.38 10.8 18

26

Foodsecurity

Since global food and agricultural productions are expected to decline under tradeliberalisation,theavailabilityoffooddeclinesatthegloballevel.Oursimulationsprojectthatglobalcalorieintakewoulddrop0.4percentduetoliberalisedtrade,roughlyinlinewiththedeclineof foodproductions(0.36percent)–withthesmallgapreflectingtheadjustedfoodconsumptionstructureintheliberalisationscenario.

Theglobalreductionincalorieintakehowevermasksthefactthatdevelopedregionsasawholeessentiallygainanincreaseincalorieintake(1.4percent)butthisgainhasbeendwarfedbyadeclineindevelopingregions(0.8percent)sincethetotalcalorieintakeindeveloping regions, driven by a relatively large and fast growing population in theseregions,aremorethanfourtimeshigherthanthatindevelopedregions.

In termsof foodaccessweseeanotherpicture.Theeconomicgrowthdrivenby tradeliberalisationpusheswageshigherattheglobalscale,withanaveragewageincreaseof0.16 percent in the agricultural sector and 0.62 percent in non-agricultural sectors,globally.Within theagricultural sector,wagesearnedby skilledworkersexperience afasterincrease(0.28percent),comparedtoa0.16percentincreaseforunskilledworkers.

Therise inwagesanddecline in foodprices implythat foodpurchasingpowerwillbehigher in the trade liberalisation scenario. Asmeasured by the unskilledwage in theagriculturalsectorrelativetofoodprices,foodpurchasingpowerisprojectedtoincreaseby6.3percentwhentradeisliberalised.

7. Theadaptiveeffectoftradeliberalisation

As stated earlier, climate change impactsmay be altered by the introductionof tradeliberalisation and the alterations, if any, reflect whether and to what extent tradeliberalisation can mitigate the impact of climate change. Identifying the potentialmitigationandadaptationeffectsoftradeliberalisationrequires,asexplainedearlier,anisolationoftheliberalisation-alteredclimatechangeimpactsfromtheunalteredimpacts.Theliberalisation-alteredclimatechangeimpactsaremeasuredbythedeviationsoftheClimate Change and Trade Liberalisation scenario simulation results from the newlyintroducedTradeLiberalisationscenarioresults(withoutclimatechange).ThedifferencebetweenthesedeviationsandthedeviationsreportedinSection5“Theimplicationsofclimatechange”capturestheinteractioneffect-towhatextenttradeliberalisationhasalteredtheimpactsofclimatechange.

Therefore,theinteractioneffectsreportedinthissectionaresubstantiallydifferentfromthe trade liberalisationeffects reported in the last sectionwhere thedeviationsof theClimate Change andTrade Liberalisation scenario simulation results from the ClimateChange scenario simulation results are used to capture the implications of tradeliberalisation under climate change. However, the two effects - the interaction effects

27

reportedbelowand the trade liberalisationeffects reportedabove, are closely relatedsincecomputingtheinteractioneffectsisequivalenttodecomposethetradeliberalisationeffectsreportedinthelastsectionintotwoparts:thepuretradeliberalisationeffect,ascapturedbythedeviationsoftheTradeLiberalisationscenarioresultsfromtheBaseline,and the interaction effect. Thus, the interaction effect may be interpreted from twodifferentbutassociatedperspectives–twosidesofthesamecoin.

Macroeconomicgrowth

Broadly,thesimulationresultsshowthat,withtheintroductionoftradeliberalisation,theoveralladverseimpactsofclimatechangeattheglobalscalewillbeslightlymoderated.ThefirstandmostimportantindicatorusedtomeasurethisadaptiveeffectistheworldGDP,whichwasprojectedtodeclineby0.18percentduetotheimpactofclimatechange(unaltered by trade liberalisation).However, this decline is expected to lower to 0.17percentiffoodandagriculturaltradeisliberalised.TheimprovementonworldGDP,smallyetpositive,signalsanoverallliberalisation-inducedalleviationofthegloballynegativeclimatechangeimpacts.14

Attheregionalscale,theinteractioneffectsonGDPdonotappeartobeuniform.Whileformanyregionstheinteractioneffectsarenotverydifferentfromtheglobalaverage,several regionsexperiencequite largepositiveornegative changes inGDPdue to theinteractioneffect.TheseeffectsinIndia,WestAfrica,andNorthAfrica,forexample,areparticular large and positive, with the climate-induced GDP losses in these regionsreducedby0.11percent,0.04percent,and0.04percent,respectively.Bycontrast,RestofEasternEuropeandRestofSouthAmerica,theonlytworegionsexpectedtobenegativelyaffectedbytradeliberalisationasreportedinSection6.3,arealsosubjecttoanadverseinteractioneffectwhichmakestheoriginalclimatechangeimpactsinthesetworegionsworsenby0.02percentand0.01percent,respectively.

Foodandagriculturalproductions,landuse,andGHGemissions

Asfarasfoodandagriculturalproductionsareconcerned,thegloballynegativeimpactsof climate change on food productions are moderated by nearly 0.03 percent, thepercentagedifferencebetweenthenegativepre-liberalisationclimatechangeimpact(a0.28percentreductioninfoodproductions)andthenegativepost-liberalisationclimatechangeimpact(a0.25percentreductioninfoodproductions),asshownin

Figure9.Themoderationinthebroadfoodcategoryismainlydrivenbythemoderationinthecropsandprocessedfoodsectors(bothsectorsexperienceabouta0.03percentmoderation)andtoalesserextent,amoderationfromthelivestocksector(slightlyover

14Itshouldbenoted,again,thatinoursimulationexercise,theprojectiontimehorizonislimitedto2050,theexogenousclimate-relatedshocksarelimitedtocropyields,andtradeliberalisationisimplementedonlyforfoodandagriculturalcommodities.Theadaptiveeffectdefinedinthispapermaybedifferentiftheselimitationschange.

28

0.01 percent). The fish sector barely changes,meaning that trade liberalisation is notexpectedtoaltertheinitialclimatechangeimpactsonfishproductionsattheglobalscale.

Asmallefficiencygainoninputusesintheagriculturalsectorisalsofoundasaresultofthe interaction/adaptation effects. Cropland uses, which increases by 0.59 percentgloballyduetoclimatechange,areexpectedtoincreasebyonly0.56percentiffoodandagriculturaltradeisliberalised.Thelanduseincreaseintheoverallagriculturalsectorduetoclimatechangewouldreducebysome0.01percentas the interactioneffect forpasturelandislargelynegligible.

While the adaptive effects for GHG emissions at the macro (all-sector) level are notobserved, the emissions from food productions are mitigated by 0.04 percent, withemissionsfromcropproductions,inparticular,mitigatedby0.13percent-downfroma1.05percentincreasetoa0.91percentincrease,indicativeofsomeliberalisation-inducedcorrectionsforclimate-inducedinefficienciesonGHGemissionsattheglobalscale.

Figure9Theadaptiveeffectmeasuredbythedifferencebetweenclimatechangeimpactswithand

withouttradeliberalisationat2050

Foodsecurity

Comparedtotheadaptiveeffectsoftradeliberalisationonfoodproduction,theadaptiveeffectsonfoodpricesaremoresubstantial(see

Figure9).Thepricerisesduetoclimatechangeattheglobalscalewillbemoderatedinalmostallmajorfoodcategoriesincludingcrops(0.69percent),livestock(0.47percent),andprocessed food (0.19percent),withanexceptionof fishes forwhich theadaptive

-4,0

-3,0

-2,0

-1,0

0,0

1,0

2,0

3,0

FoodProductions

AgriculturalLand Use

GHGEmissionsfrom Food

Productions

CalorieIntake

FoodConsumer

Price

SkilledWage in

Agriculture

UnskilledWage in

Agriculture

FoodPurchasing

Power

%Climate Change Impacts without Trade Liberalisation

Climate Change Impacts with Trade Liberalisation

29

effect is not visible. On average, food and agricultural prices are moderated by 0.35percent,downfromtheclimate-induced2.6percentincreaseto2.2percentincrease.

Theincreaseinfoodproductionsanddecreaseinfoodpricesattheglobalscale,thankstotheadaptiveeffect,implythattheclimate-inducedfoodsecurityissuewillbemoderatedaccordingly. The decline of global calorie intake due to climate change is expected toreduceby0.02percent-areductionfromtheinitialdeclineof0.28percentto0.26percent.TheimprovementsonfoodsecurityaremostsignificantinIndiaandTurkey,wherethecalorieintakeisexpectedtoriseby0.42percentand0.15percent,respectively,attributedtotheadaptiveeffect.ThecalorieintakehoweverisexpectedtodeclinemostinRestofEastAsia(0.47percent), Japan(0.38percent),NorthAfrica(0.32percent),andMiddleEast(0.23percent),alsoduetotheinteractioneffect.

Theinteractioneffectalsocausesariseinwagesforunskilledworkersattheglobalscale,althoughthewage forskilledworkersdoesnotseemtobeaffectedglobally.Unskilledwagesinagriculturalsectors(cropsandlivestock)alsohaveagreaterimprovement(0.08percent),relativetotheimprovementofunskilledwagesinnon-agriculturesectors(0.05percent). Accordingly, food purchasing power, measured by the unskilled wage inagriculturerelativetofoodconsumptionprices,improvesby0.15percentglobally,downfromtheclimate-inducedinitialdeclineof2.88percentto2.73percent.

30

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32

Appendix

TableA.1Averageadvaloremimporttariffsonagriculturalandfoodcommoditiesat2011(%)

Note:valuesindiagonalcellsaregenerallyzeroforasinglecountrybutmaybenon-zeroforamulti-countryregion,indicativeofanintra-regionalimporttariff.Source:calculatedfromGTAPdatabase2011.

Canada USA & Rest of North

America

Mexico Central America

Brazil Rest of South

America

North Africa

West Africa

East Africa

Rest of Southern

Africa

South Africa

Western Europe

Eastern Europe

Rest of Western Europe

Rest of Central Europe

Turkey Rest of Eastern Europe

Central Asia

Russian Federation & the Caucasus

Middle East

India Rest of South Asia

Rest of East Asia

China and East

Asia

South East Asia

Indonesia &

Southeast Pacific

Japan Oceania World

Canada 0 1 2 8 7 8 21 9 22 12 5 7 14 40 14 18 1 14 31 4 44 5 21 8 12 6 22 1 7USA & Rest of North America 12 9 1 10 17 14 8 7 14 9 4 8 8 20 8 27 7 32 26 4 29 14 155 4 9 5 22 0 17Mexico 1 0 0 6 7 9 6 8 9 8 7 3 2 32 8 33 3 27 21 10 63 29 38 8 10 11 25 1 2Central America 2 2 9 6 10 11 21 18 22 12 4 3 10 3 4 64 12 21 16 6 39 13 19 27 30 9 8 1 7Brazil 13 3 20 36 0 5 10 12 19 13 16 12 13 42 11 32 10 17 20 8 26 6 139 6 9 8 6 0 12Rest of South America 4 1 6 10 0 3 11 10 6 9 10 8 4 25 8 48 4 17 12 6 20 17 48 4 5 12 9 1 6North Africa 3 2 13 7 9 7 0 16 3 30 12 5 15 21 8 16 2 11 6 1 22 13 7 10 3 6 7 2 5West Africa 0 0 12 6 10 4 21 11 14 14 3 0 0 2 4 3 0 1 5 7 4 11 36 6 3 5 1 0 3East Africa 1 1 10 11 5 4 2 21 2 1 19 0 0 28 5 36 6 1 11 4 45 9 12 5 6 5 5 0 4Rest of Southern Africa 0 2 23 4 4 7 4 23 3 9 0 0 0 90 8 22 1 0 1 27 12 7 2 8 34 5 0 1 7South Africa 3 1 6 13 17 17 18 20 28 13 0 4 2 12 8 7 2 7 6 15 10 33 367 11 5 9 23 3 19Western Europe 19 3 8 16 15 14 20 13 28 15 2 0 0 25 5 24 10 16 20 15 60 21 36 9 17 7 25 3 4Eastern Europe 16 4 17 9 13 8 11 13 14 7 2 0 1 48 3 38 13 20 19 11 33 5 149 10 9 8 36 3 5Rest of Western Europe 48 3 12 6 7 9 86 12 20 20 13 5 4 8 6 14 3 11 11 16 37 23 13 12 11 8 3 3 8Rest of Central Europe 10 5 8 8 11 12 33 9 18 12 10 5 3 7 2 9 9 14 16 21 31 7 13 15 28 9 15 2 7Turkey 2 11 23 13 10 12 14 16 16 13 18 2 4 8 19 0 6 18 7 16 22 17 18 10 20 6 5 3 9Rest of Eastern Europe 47 3 3 21 0 33 7 10 11 11 11 4 4 27 2 45 2 0 0 9 3 7 248 9 14 8 7 1 7Central Asia 0 1 4 0 1 1 6 1 36 0 5 11 5 83 5 46 0 0 0 9 15 5 11 8 15 1 0 0 5Russian Federation & the Caucasus 3 1 24 6 2 3 3 6 8 5 1 6 5 20 2 44 2 1 0 5 41 5 13 12 10 9 4 2 7Middle East 13 3 7 6 7 6 1 15 14 28 9 3 2 17 11 27 6 33 11 4 29 11 17 4 17 9 9 2 6India 2 1 9 62 12 9 12 13 13 21 17 4 6 4 6 21 6 5 8 10 0 12 180 7 10 6 3 0 10Rest of South Asia 1 3 1 10 6 8 9 11 11 10 25 4 2 5 9 44 6 15 11 7 10 9 46 6 16 7 5 0 9Rest of East Asia 8 4 6 8 5 8 12 10 35 8 14 3 1 16 4 11 3 20 17 45 30 11 3 17 17 17 15 3 15China and East Asia 4 3 19 16 11 9 30 15 15 14 8 9 11 19 12 21 7 11 11 15 36 14 51 5 10 4 11 2 13South East Asia 6 2 22 9 11 10 18 14 13 13 5 8 9 8 5 27 3 9 16 9 60 15 29 5 11 7 13 0 11Indonesia &Southeast Pacific 3 1 9 10 66 10 24 27 5 15 14 6 6 5 3 97 0 13 33 10 135 21 45 16 7 14 3 1 28Japan 6 3 12 3 10 6 12 14 10 13 6 8 10 9 5 10 6 4 19 7 33 12 29 13 8 12 0 2 12Oceania 14 4 21 17 13 25 10 11 17 6 8 23 9 36 11 57 7 11 17 4 26 17 43 12 5 8 24 0 14World 12 2 3 12 6 7 13 13 14 13 7 2 2 25 5 32 7 7 15 9 60 13 81 7 10 7 16 1 9

33

TableA.2Averageadvaloremexporttaxesonagriculturalandfoodcommoditiesat2011(%)

Note:negativevaluesareexportsubsidiesinsteadoftaxes.Source:calculatedfromGTAPdatabase2011.

Canada

USA &

Rest of

North

America Mexico

Central

America Brazil

Rest of

South

America

North

Africa

West

Africa

East

Africa

Rest of

Southern

Africa

South

Africa

Western

Europe

Eastern

Europe

Rest of

Western

Europe

Rest of

Central

Europe Turkey

Rest of

Eastern

Europe

Central

Asia

Russian

Federation

& the

Caucasus

Middle

East India

Rest of

South

Asia

Rest of

East Asia

China

and East

Asia

South

East Asia

Indonesia

&

Southeast

Pacific Japan Oceania World

Canada 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

USA & Rest of

North America 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Mexico 0.0 0.0 0.0 -0.3 0.0 -1.3 -6.6 0.0 -0.1 0.0 0.0 -0.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.1

Central America 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Brazil 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Rest of South America 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

North Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

West Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

East Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Rest of Southern Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

South Africa 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Western Europe -0.6 -0.1 -1.1 -1.1 -0.3 -0.5 -4.2 -2.1 -1.0 -1.1 -0.6 0.0 -0.1 -1.9 -1.2 -0.4 -0.6 -2.8 -3.4 -6.2 -0.6 -1.6 -1.6 -1.0 -1.8 -3.1 -1.6 -0.6 -0.6

Eastern Europe -0.4 -0.4 -0.1 -0.1 0.0 -0.1 -0.9 -0.3 -0.1 0.0 -0.3 0.0 0.0 -0.6 -0.7 0.0 -1.6 -8.7 -4.2 -3.4 -0.1 0.0 -1.7 -0.5 -0.2 0.0 -1.7 -0.2 -0.5

Rest of Western Europe -1.0 -0.5 -0.5 -0.3 -0.5 -1.4 -0.6 -0.2 -1.2 -0.1 -0.9 -1.0 -0.3 0.0 -0.4 -0.8 -0.4 -0.7 -0.3 -0.2 -1.4 -1.5 -0.6 -0.4 -0.7 -1.0 -0.1 -1.2 -0.6

Rest of Central Europe 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Turkey 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Rest of Eastern Europe 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Central Asia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Russian Federation &

the Caucasus 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.1 0.0

Middle East 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

India 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Rest of South Asia -0.2 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -0.2 0.0 -0.1 0.0 0.0 -0.2 -0.8 -0.3 -0.1 -3.6 -0.3 0.0 -3.8 -0.4 -0.1 0.0 0.0 -0.5

Rest of East Asia -0.7 -0.5 -0.1 0.0 -0.1 0.0 0.0 -0.2 0.0 -0.4 -0.1 -0.2 0.0 -0.1 0.0 -2.7 0.0 0.0 -0.2 0.0 0.0 0.0 0.0 -0.7 -0.2 -0.2 -1.2 -0.4 -0.6

China and East Asia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

South East Asia 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Indonesia &

Southeast Pacific 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Japan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Oceania 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

World -0.1 0.0 0.0 -0.1 -0.1 -0.1 -1.2 -0.4 -0.2 -0.2 -0.2 0.0 -0.1 -1.3 -0.8 -0.1 -0.4 -0.6 -1.3 -1.0 -0.2 -0.1 -0.2 -0.1 -0.2 -0.2 -0.2 -0.1 -0.2

34

TableA.3ChangesinagriculturalandfoodtradeintheClimateChangeandTradeLiberalisationscenariorelativetotheClimateChangescenarioat2050(millionUS$,2011constantprice)

Source:calculatedfromMAGNETsimulationresults.

CanadaUnited States Mexico

Central America Brazil

Rest of South

AmericaNorth Africa

West Africa

East Africa

Rest of Southern

AfricaSouth Africa

Western Europe

Eastern Europe

Rest of Western Europe

Rest of Central Europe Turkey Ukraine

Central Asia

Russian Federation

& the Caucasus

Middle East India

Rest of South Asia

Rest of East Asia China

South East Asia

Indonesia Japan Oceania World

Canada 0 3228 520 579 16 271 3015 72 183 247 49 507 38 445 0 -136 -2 51 1506 -247 1519 -1078 497 862 904 -107 3989 250 17178USA & Rest of North America 1154 29 -1587 357 285 1178 206 26 223 376 4 1284 96 -27 -1 104 0 85 259 -1402 14 121 3251 509 1933 -512 857 0 8820Mexico 37 -949 0 -340 14 171 -42 1 -1 75 3 37 3 363 1 29 -1 3 134 -4 94 6 128 25 16 8 2461 1 2273Central America -20 847 1670 -1348 18 2579 159 4193 43 19 -7 321 72 -111 0 125 -2 20 72 -49 235 -76 -39 344 396 8 377 29 9874Brazil 8156 -1101 503 3370 0 720 -493 46 -352 -1122 766 21738 660 4893 -69 280 -57 0 693 331 -349 -2020 1252 -3097 316 -319 -3273 -93 31382Rest of South America -837 -2005 -242 -676 -376 -1556 4888 -1837 -186 -167 160 12011 179 333 0 1217 -14 -4 161 -803 28 1679 -424 272 -431 -84 1484 -48 12722North Africa 30 210 20 4 32 26 -919 1136 -1147 862 26 2841 181 -16 5 9 6 7 37 -296 13853 3 -52 48 -12 17 -1 47 16956West Africa -85 114 14 -8 28 7 114 234 43 101 0 171 21 -60 -2 -174 -3 -3 -36 -9 0 32 -14 267 -152 18 -60 5 559East Africa -25 59 51 1 6 7 -142 426 -872 -752 -2 61 2 64 0 52 1 -6 2 171 858 -716 -17 94 -13 14 9 11 -656Rest of Southern Africa 0 16 28 0 2 6 -34 471 -151 70 -57 -36 -2 6 1 6 5 -1 -14 665 51 -46 -7 42 74 36 -1 6 1136South Africa -18 -23 42 -1 9 16 -4 603 2488 1097 0 58 -10 -28 0 -4 -2 -4 -19 287 -293 140 155 565 -75 12 1095 10 6096Western Europe 6695 3173 378 569 780 987 2676 1980 3468 1879 -323 -34054 -728 4976 -56 608 420 147 3300 595 2695 1736 1094 2823 1388 -3 2689 665 10556Eastern Europe 99 420 137 15 11 23 -14 266 -21 -3 3 -4848 -1085 274 -89 1076 460 89 708 313 9 -193 181 89 80 63 399 66 -1473Rest of Western Europe 1932 645 130 -17 50 23 663 383 52 582 28 3333 123 -70 3 233 3 17 142 2337 98 15 -51 256 96 50 -125 116 11048Rest of Central Europe -2 210 5 0 4 10 77 -178 3 0 1 339 20 15 -11 -8 3 0 16 80 -18 -8 -3 90 17 0 7 4 674Turkey 55 1087 33 5 57 53 -183 436 448 354 28 721 207 -55 863 0 50 413 64 6914 -217 227 -27 194 370 12 46 109 12263Rest of Eastern Europe 237 -11 -9 110 1 290 192 -107 18 13 4 227 101 -3 -1 253 -101 -318 -923 848 -2095 -239 26 484 1056 -3 -18 5 36Central Asia -9 -5 -2 -1 1 1 -3 -25 15 -2 0 17 13 -4 0 16 -4 -322 -20 11 -21 -75 0 3 37 0 -11 2 -386Russian Federation & the Caucasus -8 93 7 19 5 32 -1617 -180 -891 139 20 760 114 15 2 948 -49 -286 -334 754 477 57 -215 367 37 4 -71 9 206Middle East 194 201 0 -5 23 24 -468 1913 28 1642 29 431 77 18 4 74 5 1401 70 -1439 159 209 -75 0 369 6 -5 41 4926India 60 869 40 328 25 20 158 736 195 675 59 644 44 9 1 38 15 7 63 1490 0 1722 414 188 1639 334 72 57 9901Rest of South Asia 0 55 3 3 1 5 -32 120 99 79 11 241 8 7 1 154 6 39 54 -24 -295 275 135 6 503 20 7 18 1501Rest of East Asia 323 2606 94 63 70 143 52 1264 215 347 107 1062 73 115 7 60 27 189 421 4098 815 576 19 3578 3019 423 6089 790 26642China and East Asia -81 1061 525 129 244 146 478 779 203 221 95 2668 239 109 4 63 25 -17 179 736 575 472 769 115 529 355 392 159 11171South East Asia 196 2097 450 -308 -8 107 1103 1863 255 1000 52 2951 251 -21 0 14 43 55 -67 424 1721 2395 230 -1398 3370 218 643 290 17928Indonesia &Southeast Pacific 4 -2 90 -8 18 26 287 771 -246 378 51 -160 29 -19 1 13 -22 16 199 79 7726 1231 1705 178 -1164 120 -111 11 11199Japan 9 163 3 -2 5 6 -3 3028 22 18 -1 267 2 -1 0 1 0 0 10 -11 5 -87 73 581 2 19 0 20 4131Oceania 534 -824 184 195 17 601 -68 -77 158 -107 26 4433 82 -33 1 329 0 -12 131 -794 -177 610 1926 572 -1470 429 1047 -510 7201World 18630 12265 3085 3032 1340 5922 10047 18341 4289 8018 1131 18026 811 11192 664 5382 812 1566 6809 15054 27467 6967 10931 8058 12833 1138 17985 2067 233863

35

TableA.4Regionalaggregation

Regionalaggregate CountriesBrazil BrazilCanada CanadaCentralAmerica Anguilla,AntiguaandBarbuda,Aruba,Bahamas,Barbados,Belize,CaymanIslands,CostaRica,Cuba,

Dominica,DominicanRepublic,ElSalvador,Grenada,Guatemala,Haiti,Honduras,Jamaica,Montserrat,NetherlandsAntilles,Nicaragua,Panama,PuertoRico,SaintKittsandNevis,SaintLucia,SaintVincentandtheGrenadines,TrinidadandTobago,TurksandCaicosIslands,BritishVirginIslands,AmericanVirginIslands

CentralAsia Kazakhstan,Kyrgyzstan,Tajikistan,Turkmenistan,UzbekistanChinaandEastAsia China,HongKong,Macao,Mongolia,TaiwanProvinceofChinaEastAfrica Burundi,Comoros,Djibouti,Eritrea,Ethiopia,Kenya,Madagascar,Mauritius,Mayotte,Rwanda,

Seychelles,Somalia,Sudan,SouthSudan,UgandaEasternEurope Bulgaria,Croatia,Cyprus,CzechRepublic,Estonia,Hungary,Latvia,Lithuania,Poland,Romania,

Slovakia,SloveniaWesternEurope ÅlandIslands,Austria,Belgium,Denmark,Finland,France,Germany,Greece,Guadeloupe,Ireland,

Italy,Luxembourg,Malta,Martinique,Netherlands,Portugal,Réunion,Spain,Sweden,UnitedKingdom

India IndiaIndonesiaandSoutheastPacific

AmericanSamoa,CookIslands,Fiji,FrenchPolynesia,Guam,Kiribati,MarshallIslands,FederatedStatesofMicronesia,Indonesia,Nauru,NewCaledonia,Niue,NorthernMarianaIslands,Palau,PapuaNewGuinea,Pitcairn,Samoa,SolomonIslands,Tokelau,Tonga,Tuvalu,UnitedStatesMinorOutlyingIslands,Vanuatu,WallisandFutuna

Japan JapanRestofEastAsia DemocraticPeople'sRepublicofKorea,RepublicofKoreaMexico MexicoMiddleEast Bahrain,IslamicRepublicofIran,Iraq,Israel,Jordan,Kuwait,Lebanon,Oman,Palestine,Qatar,Saudi

Arabia,SyrianArabRepublic,UnitedArabEmirates,YemenNorthAfrica Algeria,Egypt,LibyanArabJamahiriya,Morocco,Tunisia,WesternSaharaOceania Antarctica,Australia,BouvetIsland,BritishIndianOceanTerritory,ChristmasIsland,Cocos

(Keeling)Islands,FrenchSouthernTerritories,HeardIslandandMcDonaldIslands,NewZealand,NorfolkIsland

RestofCentralEurope Albania,Andorra,BosniaandHerzegovina,FaroeIslands,Gibraltar,Guernsey,HolySee(VaticanCityState),IsleofMan,Jersey,theformerYugoslavRepublicofMacedonia,Monaco,Montenegro,SanMarino,Serbia

RestofSouthernAfrica Angola,Botswana,theDemocraticRepublicoftheCongo,Lesotho,Malawi,Mozambique,Namibia,Swaziland,UnitedRepublicofTanzania,Zambia,Zimbabwe

RestofSouthAmerica Argentina,PlurinationalStateofBolivia,Chile,Colombia,Ecuador,FalklandIslands(Malvinas),FrenchGuiana,Guyana,Paraguay,Peru,SouthGeorgiaandtheSouthSandwichIslands,Suriname,Uruguay,BolivarianRepublicofVenezuela

RestofSouthAsia Afghanistan,Bangladesh,Bhutan,Maldives,Nepal,Pakistan,SriLankaRestofWesternEurope Iceland,Liechtenstein,Norway,SvalbardandJanMayen,SwitzerlandRussianFederationandtheCaucasus

Armenia,Azerbaijan,Georgia,RussianFederation

SouthAfrica SouthAfricaSoutheastAsia BruneiDarussalam,Cambodia,LaoPeople'sDemocraticRepublic,Malaysia,Myanmar,Philippines,

Singapore,Thailand,Timor-Leste,VietNamTurkey TurkeyRestofEasternEurope Belarus,RepublicofMoldova,UkraineUnitedStatesofAmericaandRestofNorthAmerica

Bermuda,Greenland,SaintPierreandMiquelon,UnitedStates

WestAfrica Benin,BurkinaFaso,Cameroon,CapeVerde,CentralAfricanRepublic,Chad,Congo,Côted'Ivoire,EquatorialGuinea,Gabon,Gambia,Ghana,Guinea,Guinea-Bissau,Liberia,Mali,Mauritania,Niger,Nigeria,SaintHelena,SãoToméandPrincipe,Senegal,SierraLeone,Togo

CA2332EN/1/11.18

ISBN 978-92-5-131101-1

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