climate change impacts and household resilience: prospects for 2050 in brazil, mexico, and peru

8/18/2019 Climate change Impacts and Household Resilience: Prospects for 2050 in Brazil, Mexico, and Peru

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FOOD POLICY

REPORT

LYKKE E. ANDERSEN, CLEMENS BREISINGER, LUIS CARLOS JEMIO, DANIEL MASON-D’CROZ, CLAUDIA RINGLER, RICHARD

ROBERTSON, DORTE VERNER, AND MANFRED WIEBELT

Prospects for 2050 in Brazil, Mexico, and Peru

CLIMATE CHANGE IMPACTS

AND HOUSEHOLD RESILIENCE


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APRIL 2016

Climate Change Impacts andHousehold Resilience

Prospects for 2050 in Brazil, Mexico, and PeruLykke E. Andersen, Clemens Breisinger, Luis Carlos Jemio, Daniel Mason-D’Croz,

Claudia Ringler, Richard Robertson, Dorte Verner, and Manfred Wiebelt

A Peer-reviewed PublicaionInernaional Food Policy Research Insiue

Washingon, DC


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ABOUT IFPRI

Te Inernaional Food Policy Research Insiue (IFPRI), esablished in 1975,provides research-based policy soluions o susainably reduce povery and end hungerand malnuriion. Te Insiue conducs research, communicaes resuls, opimizesparnerships, and builds capaciy o ensure susainable food producion, promoe healhyfood sysems, improve markes and rade, ransform agriculure, build resilience, andsrenghen insiuions and governance. Gender is considered in all of he Insiue’s work.IFPRI collaboraes wih parners around he world, including developmen implemeners,public insiuions, he privae secor, and farmers’ organizaions.

Copyrigh ©2016 Inernaional Food Policy Research Insiue. All righs reserved.For permission o reprin, conac [email protected]: Sandra YinDesign: David Popham Layout: Deirdre Laun and Julia Vivalo Cover photo: Panos/D. elemansISBN: 978-0-89629-581-0DOI: htp://dx.doi.org/10.2499/9780896295810

mailto:[email protected]://dx.doi.org/10.2499/9780896295810http://dx.doi.org/10.2499/9780896295810mailto:[email protected]


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CONTENTS

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii

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

Modeling Suite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Socioeconomic Impacts of Climate Change in Brazil. . . . . . . . . . . . . . . . . . . . . . .

20

Socioeconomic Impacts of Climate Change in Mexico . . . . . . . . . . . . . . . . . . . . . . . 32

Socioeconomic Impacts of Climate Change in Peru . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Summary, Conclusions, and Proposed Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Appendix: Supplementary ables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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TABLESable 1 Brazil: Projeced annual crop yield changes (percenage per year), 2000–2050 ....................9

able 2 Mexico: Projeced annual crop yield changes (percenage per year), 2000–2050 ...............10

able 3 Peru: Projeced annual crop yield changes (percenage per year), 2000–2050 .....................11

able 4 Summar y of GDP, populaion, and GDP per capia assumpions forShared Socioeconomic Pahway Number 2, by region ..............................................................................13

able 5 Counry model characerisics .................. .................... .................... ..................... .................... .......... 17

able 6 Brazil: Agriculural value-added by region and agriculural rade orienaion, 2008 ..........21

able 7 Brazil: Disribuion of households by gender and locaion(percenage of all households), 2008 .................... .................... .................... ..................... .................... ......... 30

able 8 Brazil: Per capia household income, by household ype

(reais per monh per person), 2008.............. .................... .................... .................... ..................... ................... 30

able 9 Brazil: Household Income Diversificaion Index, by household ype, 2008 .......................... 31

able 10 Brazil: Probabiliy of being highly vulnerable, by household ype (percenage), 2008 ....31

able 11 Brazil: Probabiliy of being highly resilien, by household ype (percenage), 2008 ........ 31

able 12 Mexico: Agriculural value-added by region and agriculural rade orienaion, 2008 ....33

able 13 Mexico: Disribuion of households by gender and locaion, 2008 .........................................41

able 14 Mexico: Per capia household income, by household ype(pesos per monh per person), 2008.................................................................................................................41

able 15 Mexico: Household Income Diversificaion Index, by household ype, 2008 .....................41able 16 Mexico: Probabiliy of being highly vulnerable, by household ype (percenage), 2008 .41

able 17 Mexico: Probabiliy of being highly resilien, by household ype (percenage), 2008 ...... 41

able 18 Peru: Agriculural value-added by region and agriculural commodiy, 2008 ....................45

able 19 Peru: Disribuion of households by gender and locaion(percenage of all households), 2008 .................... .................... .................... ..................... .................... ......... 50

able 20 Peru: Per capia household income, by household ype,(nuevos soles per monh per person), 2008 ................... .................... ..................... .................... ................... 50

able 21 Peru: Household Income Diversificaion Index, by household ype (percen), 2008 ......50

able 22 Peru: Probabiliy of being highly vulnerable, by household ype (percen), 2008 .............51

able 23 Peru: Probabiliy of being highly resilien, by household ype (percen), 2008 .................. 51

able 24 Summar y of resuls: Climae-change induced changes in agriculural price ..................... 54

able A1 Dyna mic Compuable General Equil ibrium model variables and parameers...................56

able A2 Full Dynam ic Compuable General Equilibrium model equaions .....................................58

able A3 Brazil: Economic srucure in base year (percen), 2008. ....................................................... 64

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able A4 Mexico: Economic srucure in base year (percen), 2008 .......................................................66

able A5 Peru: Economic sruc ure in base year (percen), 2002 .......................................................... 68

able A6 Brazil: Summary of resuls .................... .................... .................... .................... ..................... .............69

able A7 Mexico: Summary of resuls .................... .................... ..................... .................... .................... ..........69

able A8 Peru: Summary of resuls ................................................................................................................... 70

able A9 Sensiiviy of macroeconomic resuls o income elasiciy variaion(percenage poin deviaion of annual average growh raes) ...................................................................71

FIGURESFigure 1 Schemaic overview of he modeling suie ........................................................................................4

Figure 2 Comparing CO2 concenraion and radiaive forcing assumpions .........................................5

Figure 3 Projeced changes in average annual maximum emperaures (in degrees Celsius)in Brazi l, Mexico, and Peru for four downscaled climae scenarios .........................................................6

Figure 4 Projeced changes in oal annual precipiaion in Brazil, Mexico,and Peru for four climae scenarios ....................................................................................................................7

Figure 5 Te Impac Sysem of Models .................. .................... ..................... .................... .................... ..........12

Figure 6 Global food price scenarios by climae model, 2010–2050........................................................14

Figure 7 Agroecological zones of Brazil, Mexico, and Peru ........................................................................15

Figure 9 Brazil: Impacs of global agriculural price changes on ne presen valueof agriculural GDP, by produc group ............................................................................................................22

Figure 10 Brazil: Impacs of global agriculural price changes on ne presen valueof agriculural GDP, by region ...........................................................................................................................23

Figure 11 Brazil: Impacs of global agriculural price changes on ne presen valueof GDP, by secor.................... .................... .................... ..................... .................... .................... .................... ...... 24

Figure 12 Brazil: Impacs of global agriculural price changes on household welfare, by income decile .................................................................................................................................................... 24

Figure 13 Brazil: Impacs of local yield changes on ne presen value of agriculural GDP, by region ...................................................................................................................................................................25

Figure 14 Brazil: Impacs of local yield changes on household welfare .................................................. 26

Figure 15 Brazil: Combined impacs of climae change on ne presen value ofsecoral and oal GDP .........................................................................................................................................27

Figure 16 Brazil: Combined impacs of climae change on ne presen value ofagriculural GDP, by region ................................................................................................................................27

Figure 17 Brazil: Combined impacs of climae change on ne presen value ofagriculural GDP, by produc group ...............................................................................................................28

Figure 18 Brazil: Combined impacs on household welfare, by region ...................................................29

Figure 19 Brazil: Combined impacs on household welfare, by income decile .....................................29

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Figure 20 Mexico: Impacs of global agriculural price changes on ne presen valueof agriculural GDP, by region .......................................................................................................................... 34

Figure 21 Mexico: Impacs of global agriculural price changes on ne presen valueof agriculural GDP, by produc group ...........................................................................................................35

Figure 22 Mexico: Impacs of global agriculural price changes on ne presen valueof household welfare, by region .........................................................................................................................36

Figure 23 Mexico: Impacs of global agriculural price changes on ne presen valueof household welfare, by household head’s gender ......................................................................................36

Figure 24 Mexico: Impacs of local yield changes on ne presen valueof agriculural GDP, by region ...........................................................................................................................37

Figure 25 Mexico: Impacs of local yield changes on ne presen valueof oal GDP, by produc group ..........................................................................................................................38

Figure 26 Mexico: Combined impacs of global price changes and local yield

changes on ne presen value of agriculural GDP, by region ...................................................................39

Figure 27 Mexico: Combined impacs of global price changes and local yieldchanges on ne presen value of oal GDP, by produc group ..................................................................39

Figure 28 Mexico: Combined impacs of global price changes and local yieldchanges on ne presen value of household welfare, by income deci le ..................................................40

Figure 29 Mexico: Combined impacs of global price changes and local yieldchanges on ne presen value of household welfare, by household head’s gender..............................40

Figure 30 Peru: Impacs of global agriculural price changes on he ne presen value of agriculural GDP, by gender of household head .......................................................................... 44

Figure 31 Peru: Impacs of global agriculural price changes on secoral GDP, by produc group ................................................................................................................................................... 44

Figure 32 Peru: Impacs of global agriculural price changes on household welfare, by locaion ............................................................................................................................................................... 46

Figure 33 Peru: Impacs of local yield changes on agriculura l GDP, by region ..................................46

Figure 34 Peru: Impacs of local yield changes on agriculural GDP, by produc group ...................47

Figure 35 Peru: Impacs of local yield changes on household welfare, by locaion............................. 48

Figure 36 Peru: Combined impacs of climae change on oal GDP ..................................................... 48

Figure 37 Peru: Combined impacs of climae change on agriculural GDP, by produc .................49

Figure 38 Peru: Combined impacs of climae change on household welfare, by locaion ...............49

BOXESBox A1 Descripion of seleced equaions relevan for model simulaions ................................................60

Box A2 Counry-specific deails of Dynamic Compuable General Equilibrium models ....................61

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ACKNOWLEDGMENTSTis repor has been produced under a projec commissioned by he Office of Evaluaion

and Oversigh (OVE), Iner-American Developmen Bank (IDB) and was carried ou wihhe Inernaional Food Policy Research Insiue (IFPRI). Te auhors hank JoaquimBeno de Souza Ferreira Filho a he Universiy of São Paulo for conribuing he Brazilsocial accouning marix and are graeful for he research assisance provided by MarceloCardona a he Insiue for Advanced Developmen Sudies. Te auhors also hank eunis van Rheenen of IFPRI for his valuable conribuions o his projec. Te eam also hanksChanning Arnd, Alvaro Calzadilla Rivera, John Nash, and David Suarez for heir valuablecommens and graefully acknowledges financial suppor from OVE, which made hisrepor possible. We also hank IFPRI’s Publicaion Review Commitee and wo anonymousreviewers for heir consrucive commens, which helped o furher improve he repor.

Te findings, inerpreaions, and conclusions expressed in his paper are enirely hose ofhe auhors. Tey do no necessarily represen he view of IDB, is execuive direcors, or isclien counries.

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This food policy repor is a response o growing concerns abou he impacs of climaechange on Lain American economies, agriculure, and people. I assesses boh localand global effecs of changing agriculural yields on he economy, subnaional regions,and differen household ypes, including male- and female-headed households in Brazil,Mexico, and Peru. Te hree counries reflec economic and geographic diversiy in Lain America and more han half of he region’s populaion.

MAIN FINDINGS

Climae change impacs end o be relaively small a an economywide level in all hreecounries. However, secoral and household-level economic impacs end o be diverseacross counries and subnaional levels. Tey mainly depend on projeced changes in

agriculural yields, he share of agriculure in regional gross domesic produc (GDP),crop-specific inernaional rade balances, ne food buyer/seller posiion, and incomediversificaion of households. As for gender, resuls from his sudy sugges ha female-headed households may be less vulnerable han male-headed households o he effecs ofclimae change, highlighing he imporance of considering women as a source for soluionsfor building resilience o climae change. Given he relaively small impacs of climaechange and he degree of uncerainy associaed wih hem, i is oo early o define specificpolicy recommendaions.

POLICY IMPLICATIONS

All hree counries should ry o maximize he benefis ha may come wih higher

agriculural world marke prices and o minimize he losses from reducions in agriculural yields.

EXECUTIVE SUMMARY

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Introduction

C .Te mos climae-sensiive secor is agriculure, as rising emperaures and changes inrainfall paterns affec agriculural yields of irrigaed and dryland crops (Kang e al. 2009;Mendelsohn and Dinar 2009). Counries ha are more dependen on rainfed agriculure,

including many in Africa souh of he Sahara (SSA), are more vulnerable o a changing climae wih projeced large losses in heir naional oupu (Arnd e al. 2012). Bu even counries wiha larger share of irrigaed land, including many Arab counries, are projeced o be hi hard by

adverse, local impacs of climae change (Wiebel e al. 2013; Wiebel e al. 2015). Furhermore,he sum of worldwide adverse climae change effecs on agriculure is expeced o have srongnegaive implicaions for global food supply, rade flows, and commodiy prices (Parry e al.

2004; Nelson e al. 2010). Accouning for changing global food prices is herefore an imporanpar of he expeced impac a he counry level.

Depending on he ne rade posiion of a counry and hene food producing and consuming saus of households,socioeconomic impacs will likely differ. For example, coun-

ries ha are heavily dependen on food impors, especiallyhose in he Arab world, are paricularly hard hi by risingglobal food prices (Breisinger e al. 2011). Climae changeimpacs may also differ by gender. Te lieraure suggessha rural women in developing counries are among hemos vulnerable groups (Lambrou and Piana 2006; IPCC2013) because hey are responsible for he mos climae-sensiive aciviies, such as waer collecion and fuelwoodcollecion and, in many insances, agriculural aciviies

(Byrne and Baden 1995; Denon 2009). Wihin heseclimae-sensiive aciviies gendered differences in accesso waer, land, and resources oen exis (Sachs 1996; UN

Women Wach 2009; Ringler e al. 2014). A series of previous sudies on he poenial economic

impacs of climae change in Lain America and he Carib- bean (LAC) up o 2100, coordinaed by he EconomicCommission for Lain America and he Caribbean (CE-PAL), found quie modes climae change impacs on hedifferen economies in Lain America. Resuls for he sudyfor Mexico indicaed ha GDP in he scenario wih srongclimae change was on average only abou 0.1 percen

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lower han he no climae change scenario (Galindo 2009).Similar modes impacs for Mexico were indicaed by he

economeric esimaions presened in Andersen and Verner(2010). Te nex sudy in he CEPAL series, from Cenral America, found adverse impacs ha averaged abou 0.4percen of GDP for he res of he cenury (CEPAL 2010).Te mos adverse impacs found in his series were forBolivia, which averaged up o 4.8 percen of GDP duringhe res of he cenury (BID-CEPAL 2014). None of hesesudies, however, ook ino accoun ha climae changein he res of he world may have a significan effec on well-being in Lain American counries hrough changes ininernaional commodiy prices and rade paterns. Tus,

a major conribuion of his sudy is o incorporae heseglobal price effecs, compare hem wih local yield effecs,and incorporae boh in a general equilibrium framework o visualize how counries are likely o reac o hese changes.

Te rend oward urbanizaion in LAC, as elsewhere,means ha fewer people are living off and producing foodon he land. Overall in L AC, 15.9 percen of he labor forceis direcly employed in agriculure, bu here is large varia-ion hroughou he region: 15.3 percen, 13.4 percen, and25 percen of he oal workforce is employed in agriculurein Brazil, Mexico, and Peru, respecively.1 Hence, climae

change is expeced o affec fewer households direclyhrough agriculural income change and poenially morehouseholds hrough he indirec effecs of climae change-induced flucuaions in food prices. o quaniaivelyassess hese direc and indirec economic effecs of climaechange, his food policy repor uses an innovaive model-ing suie o address four specific ypes of impacs, namely(1) impacs from increased world prices of agriculuralproducs due o global climae change, (2) impacs of local yield changes due o local changes in emperaures andprecipiaion,2,3 (3) combined impacs following from hese

wo impacs as he economies adjus. Te repor appliesfour climae models o illusrae he variabiliy of resuls forhree case-sudy counries: Brazil, Mexico, and Peru. Tesehree counries ogeher cover more han half of LAC’soal populaion. Te ime period covered is he nex fourdecades, o 2050.

Te repor disinguishes iself from previous climaechange sudies addressing impacs in LAC in hree ways.Firs, i addresses no only he local impacs of chang-

ing crop yields due o climae change bu also he globalimpacs from changing crop yields in he res of he world.

Second, i akes ino accoun he indirec effecs ha heselocal and global effecs on agriculure will have on he resof he economy hrough facor reallocaion, changes in hecos of inermediae inpus, and final consumpion levels.Tird, his sudy includes a gender-differeniaed analysis, which has previously been largely absen in he op-downclimae change impac lieraure. Te gender dimension was fully included in he analysis of Mexico, as householdsin he Social Accouning Marix and compuable generalequilibrium (CGE) model were no only disaggregaed byregion, level of educaion, and level of income, bu also by

he gender of he heads-of-household. Since gender impacsfound in he CGE modeling framework, however, wereinsignifican for he case of Mexico, we used he frameworkof Andersen and Cardona (2013) o conduc a householdsurvey-based analysis of vulnerabiliy in all hree counries.Tis complemenary analysis did no find any evidence hafemale-headed households are more vulnerable han male-headed households. Indeed, in all hree counries analyzed,female-headed households have boh higher and morediversified incomes, which would end o make hem moreresilien han male-headed households.

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Modeling Suite

because he new condiions or policies havenever been observed.Te models and resuls presened in his repor rely onsrucural relaionships and explici causal chains and followhe srucural models described and used in a special issue

of Review o Development Economics (Arnd e al. 2012)and in Verner and Breisinger (2013). More specifically,he major componens of he modeling framework em-ployed in his sudy are he downscaling and debiasing ofglobal climae scenarios, a crop model, a global agriculuraleconomy model, and counrywide economic models. Asshown in Figure 1, he downscaled global climae modelscenarios feed precipiaion and minimum and maximumdaily emperaures ino he Decision Suppor Sysem for

Agroechnology ransfer (DSSA), which, in urn, gener-aes changes in yields for boh rainfed and irrigaed crops inhe hree economies. Yield changes are communicaed fromDSSA o boh he Inernaional Food Policy ResearchInsiue’s (IFPRI’s) Inernaional Model for Policy Analysisof Agriculural Commodiies and rade (IMPAC) and

o counrywide dynamic compuable general equilibrium(DCGE) models. Changes in world food prices derivedfrom IMPAC are communicaed o he DCGE model oassess he impacs of climae change on he economic sruc-ure and consumpion of represenaive household groups.Only in he case of Mexico are household groups differeni-aed by he gender of he head-of-household in he DCGEmodel, so we complemen he gender analysis by calcula-ing gender-differeniaed vulnerabiliy and resilience indica-

P incorporaed ino biophysical modeling sysems o ulimaely assess economic impacshrough so-called inegraed assessmen models (IAM). ol and Fankhauser (1998) providean overview of hese reduced-form models. While hese models have he advanages of

being easy o use and of providing a firs order esimae of empirical impac, hey also haveserious disadvanages. In paricular, hey lump a long causal chain of evens ino a simplified

algebraic relaionship. If his causal chain of evens naurally evolves hrough ime or is changeddeliberaely by policy, he only opions for capuring hese effecs is hrough change in heparameers. Unforunaely, he empirical basis for hese changes is oen lacking precisely

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ors using household survey daa as suggesed by Andersenand Cardona (2013).

OVERVIEW OF RECENT CLIMATIC

CHANGES IN BRAZIL, MEXICO, AND

PERU

Knowledge abou climae change in LAC has increased

significanly over he pas 5–10 years as climae observa-ions have become more accessible (IDB 2014). A leashree sudies have analyzed pas emperaure and precipia-ion rends in hese hree counries. Andersen, Román, and Verner (2010) analyzed climae daa from May 1948 oMarch 2008 for 34 high-qualiy meeorological saions inBrazil and found ha of hese, 31 saions show significan warming, 3 show no significan change, and none show sig-nifican cooling.4 Te auhors found ha he Norh region is

warming abou wice as fas as heSouh region and he Norheas and

Cenerwes regions are warminga inermediae raes. In conraso he resuls for emperaure, heauhors found no clear endencies wih respec o precipiaion.

Using he same daa source, Andersen and Verner (2010) ana-lyzed he daa from 21 high-qualiysaions in Mexico during he sameperiod and found more mixedresuls. Ou of he 21 saions, 12

showed a significan posiive rendfor emperaure, 3 showed a signifi-can negaive rend, and he remain-ing 6 showed no significan rend(using a 95 percen confidencelevel). Since individual saions aresubjec o idiosyncraic variaions,i is necessary o average he resulsfrom several saions o ge reliablerends for a region. Te auhorsfound indicaions ha he cenral

zone of Mexico is warming abouhree imes faser han he coasalzones. A rend analysis reveals nosysemaic changes in rainfall dur-

ing he 1948–2008 period as all saions excep one showedno significan rend in monhly precipiaion anomalies.

Finally, for he case of Peru, Andersen, Suxo, and Verner(2009) analyzed similar daa for 24 high-qualiy saionsand found ha 15 of hese show a significan warmingrend, ypically by 0.2 o 0.3 ºC per decade, 4 show a signifi-can negaive rend of beween –0.1 and –0.2 ºC per decade,

and 5 show no significan rend. Te auhors found hai was no possible o esablish any sysemaic differences beween regions. Tey also concluded ha here have beenno sysemaic rends in precipiaion in Peru during he passix decades.

As we will see in he following secion, he ambiguousresuls abou pas rends in precipiaion carry over o heprojecions abou fuure changes in precipiaion.

FIGURE 1 Schematic overview of the modeling suite

Source: Authors’ elaboration.

Global climate models downscaledand historical meteorological data

Decision Support System for Agrotechnology

Transfer (DSSAT) Crop Model

International Model for Policy Analysis of Agricultural

Commodities and Trade (IMPACT) Model

Dynamic Computable General Equilibrium (DCGE) Model• Macroeconomic variables

• Economic structure (agriculture and other sectors)

• Incomes and expenditures of representative household groups

Vulnerability and Gender Analysis• Changes in vulnerability

• Gender difference in vulnerability and impacts



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OVERVIEW OF CLIMATE CHANGESCENARIOS

For his sudy, five climae change scenarios were usedo simulae poenial effecs of climae change by 2050.5

Te perfec miigaion scenario follows hisorical paterns wih regard o emperaure and precipiaion and heirsubsequen effecs on crop yields, and would be a closeproxy of represenaive concenraion pahways (RCP) 2.6, which assumes CO

2 concenraion levels of jus 60 pars

per million greaer han in 2000. Tis scenario serves as acounerfacual o allow us o isolae he effecs of climaechange on agriculural and food sysems. Te four addi-ional fuure climaes were drawn from resuls running RCP8.5 in four earh sysem models (ESMs), which was used inhe Inergovernmenal Panel on Climae Change’s (IPCC’s)Fih Assessmen Repor, he Iner-Secoral Impac ModelInercomparison Projec (ISIMIP), and he AgriculureModel Inercomparison and Improvemen Projec.6 RCP8.5 was seleced as i was he concenraion pahway ha

represened he mos exreme case of radiaive forcing (8.5 wats per square meer by 2100), and because by 2050 bohRCP 4.5 and 6.0 are sill relaively close o RCP 2.6 wihonly 8.5 providing a significan divergence (Figure 2).7

Te use of a perfec miigaion scenario (similar o RCP2.6) as well as a more exreme scenario (RCP 8.5) providesa variey of poenial climae change effecs due o poenialgreenhouse gas buildup in he amosphere similar o Nelsone al. (2010) and several recen AgMIP sudies (Nelson eal. 2014a; and Nelson e al. 2014b). Tese ESM resuls alsoprovide he climaic daa needed o run he crop models

(see secion below). Williamson (1994) observed haESMs increasing complexiy leads o decreasing precisionacross all variables being simulaed. o beter handle hisuncerainy a mulimodel ensemble of four ESMs was used. All formed par of ISIMIP, ensuring a sandard reporing ofmodel resuls. Te four ESMs used are he following:

• GFDL-ESM2M: designed and mainained by heNaional Oceanic and Amospheric Adminisraion’s

FIGURE 2 Comparing CO2 concentration and radiative forcing assumptions

Source: Downloaded from the RCP Database version 2.0.5 (IIASA 2015). RCP 2.6: van Vuuren et al. (2006); van Vuuren et al. (2007); RCP 4.5: Clarke et al.(2007); Smith and Wigley (2006); Wise et al. (2009); RCP 6.0: Fujino et al. (2006); Hijioka et al. (2008); RCP 8.5: Riahi and Nakicenovic (2007).

Note: RCP = representative concentration pathway, radiative forcing = the amount of extra power that is driving the climate (the amount of sunshine com-

ing in minus the energy radiated back out). Radiative forcing is measured in terms of power per area: watts (joules of energy per second) passing through the

space above a square meter on the Earth's surface. Greenhouse gases act like a blanket, reducing the amount of energy that can be radiated back out intospace. The different RCPs represent different ideas on how this blanketing effect might change over time. Their names reflect total radiative forcing assump-

tions for the year 2100. They follow different trajectories to get to 2100. The CO2 equivalent concentrations include all forcing agents.

0

200

400

600

800

1000

1200

1400 RCP RCP

2000 2010 2030 2050 2070 2090

0

1

2

3

4

5

6

7

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9

2000 2010 2030 2050 2070 2090

8.5

6.0

4.5

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MODELING SUITE 5


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Geophysical Fluid Dynamic Laboraory (GFDL) ( www.gfdl.noaa.gov/earh-sysem-model);

• HADGEM2-ES: he Hadley Cenre’s Global Environ-men Model, version 2 ( www.meoffice.gov.uk/research/modelling-sysems/unified-model/climae-models/hadgem2);

• IPSL-CM5A-LR: he Insiu Pierre Simon Laplace’s

ESM (htp://icmc.ipsl.fr/index.php/icmc-models/icmc-ipsl-cm5); and

• MIROC-ESM: Model for Inerdisciplinary Research onClimae, developed by he Universiy of okyo, NIES(Naional Insiue for Environmenal Sudies), and JAMSEC (Japan Agency for Marine-Earh Scienceand echnology) ( www.geosci-model-dev-discuss.ne/4/1063/2011/gmdd-4-1063-2011.pdf ).

Figures 3 and 4 show how average annual maximumemperaures and oal annual precipiaion are projeced ochange beween ime slices 1991–2010 and 2041–2060 forhe four downscaled ESMs used in his projec.

From Figure 3 we can see ha he climae scenarios ingeneral projec higher emperaure increases in he ineriorof each counry and lower increases along he coas. MI-ROC is generally he mos exreme climae scenario, wih

emperaure increases of up o 6°C in he Amazon basinduring he nex 50 years. In conras, he GFDL scenarioprojecs more modes emperaure increases, ypically of1–2 °C over 50 years.

While all scenarios projec emperaure increases, al-hough of quie differen magniudes for he same exremeRCP, here is much less agreemen abou precipiaionchanges. Figure 4 shows, for example, ha MIROC projecssevere drying in cenral Brazil (he soybean-producing re-

FIGURE 3 Projected changes in average annual maximum temperatures (in degrees Celsius)in Brazil, Mexico, and Peru for four downscaled climate scenarios


Note: GFDL = Earth System Model (ESM) designed and maintained by the National Oceanic and Atmospheric Administration’s Geophysical Fluid DynamicLaboratory, HADGEM2 = Hadley Centre’s Global Environment Model (version 2), IPSL = Institut Pierre Simon Laplace’s ESM, and MIROC = Model for Interdis-

ciplinary Research on Climate. The four climate scenarios depict average annual maximum temperature changes from 2041–2060 minus 1991–2010.

HADGEM26

5

4

3

2

1

Brazil

Mexico

Peru

MIROC GFDL IPSL


http://www.gfdl.noaa.gov/earth-system-modelhttp://www.gfdl.noaa.gov/earth-system-modelhttp://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2http://icmc.ipsl.fr/index.php/icmc-models/icmc-ipsl-cm5http://icmc.ipsl.fr/index.php/icmc-models/icmc-ipsl-cm5http://www.geosci-model-dev-discuss.net/4/1063/2011/gmdd-4-1063-2011.pdfhttp://www.geosci-model-dev-discuss.net/4/1063/2011/gmdd-4-1063-2011.pdfhttp://www.geosci-model-dev-discuss.net/4/1063/2011/gmdd-4-1063-2011.pdfhttp://www.geosci-model-dev-discuss.net/4/1063/2011/gmdd-4-1063-2011.pdfhttp://icmc.ipsl.fr/index.php/icmc-models/icmc-ipsl-cm5http://icmc.ipsl.fr/index.php/icmc-models/icmc-ipsl-cm5http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2http://www.gfdl.noaa.gov/earth-system-modelhttp://www.gfdl.noaa.gov/earth-system-model


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gion) while IPSL projecs a subsanial increase in precipia-ion in he same region. In conras, IPSL projecs severedrying of souhern Mexico while GFDL projecs a much weter climae for he same region.

IPSL seems o be an oulier in erms of precipiaion,alhough i looks inermediae in erms of emperaureincreases. By simulaing he impacs of all four climaescenarios, a range of possible resuls, even wihin he sameRCP scenario, can be assessed.

For he modeling of impacs of climae change on crop yields, we use no only hese average rends bu also heinra-annual changes projeced in each scenario.

DSSA Crop Model

Locaion-specific effecs on crop produciviy can beprojeced using process-based crop simulaion models. In

urn, he crop models are driven by weaher, consisen wihcondiions indicaed by he ESMs, compleing he connec-ions beween climae condiions a one end and economicoucomes a he oher.

Crop models have long been used o assess he possibleeffecs of climae change on agriculure. One of he seminalconribuions was Rosenzweig and Parry (1994) which brough ogeher crop models run on weaher daa for 112sies modified by global circulaion model (GCM) oupusand passed hose changes hrough a basic global economicmodel. Ta invesigaion found negaive effecs for global yields from climae change in he range of 10–30 percenlosses from abou 1990 o 2060. Convering o a yearlychange, ha amouns abou 0.2–0.4 percen losses per year, which is comparable o he range found in his sudy. Manyindividual sudies of paricular crops and/or regions have

FIGURE 4 Projected changes in total annual precipitation in Brazil, Mexico, and Peru for four climatescenarios


Note: GFDL = Earth System Model (ESM) designed and maintained by the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamic

Laboratory, HADGEM2 = Hadley Centre’s Global Environment Model (version 2), IPSL = Institut Pierre Simon Laplace’s ESM, and MIROC = Model for Interdis-ciplinary Research on Climate. The four climate scenarios depict total annual precipitation changes from 2041–2060 minus 1991–2010.

HADGEM2400

200

0

–200

–400

Brazil

Mexico

Peru

MIROC GFDL IPSL

MODELING SUITE 7


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been performed (see Whie e al. 2011 for a comprehensivesurvey up o ha ime). Te approach used in his research

follows recen global approaches like hose described inRosenzweig e al. (2014) and Nelson e al. (2010).

Crop models require a basic se of weaher informaiono adequaely represen how he plans grow and respondo heir changing environmen. Te crop models employedhere are from he DSSA family and work on daily highemperaure, low emperaure, rainfall, and shorwave solarradiaion reaching ground level.8 Since all hose quaniiesneed o be idenified for he crop models o work, we arelimied o using climae projecions from ESMs ha makeall he differen values available o ouside researchers.

Comparaively few of he ESM research groups provide helevel of deail needed for use in crop modeling. empera-ure daa ypically are he easies o find and are hough o be he mos reliable. However, for agriculure, precipiaion(and o a lesser exen, sunshine) is really where he game isplayed for climae change purposes. As a resul, he choiceof fuure climae circumsances is consrained by pragmaicconcerns of daa availabiliy and usabiliy.

Te daa employed were drawn from four ESMs work-ing under RCP 8.5 condiions as described in he secionabove: HADGEM2, MIROC, GFDL, and IPSL.

Te raw ESM oupus canno be direcly inpu inocrop models. Te spaial represenaions are oen differen beween models and in any case are much oo coarse foragriculural purposes. emporally, hey operae a scales ofminues or hours while he crop models usually employ adaily ime sep (and in his case use muliple realizaionsof generaed daily weaher based on monhly averages).Hence, several seps are needed o obain and process hedaa from differen sources ino a common spaial grid as well as emporally aggregae he fine ime slices ino monh-ly averages. Tese asks were accomplished by colleagues a

he Posdam Climae Insiue resuling in maps wih globalcoverage a ½-arc-degree spaial resoluion and furheraggregaed emporally o monhly averages by colleaguesa Columbia Universiy. A dela-mehod approach adjusedhe values by puting he changes beween he baselineperiod and fuure periods on a common baseline. Daily weaher realizaions were generaed by he Simulaion ofMeeorological variables (SIMMEEO) random weahergeneraor based on he adjused monhly averages.9 Finally,

he crop models wihin DSSA used he daily weaher asinpus o deermine yields under he various condiions.

Te crop models generae gridded maps of yields. Eachcrop was modeled for boh rainfed and irrigaed condiionsunder perfec miigaion as well as he four fuure climaesiuaions, meaning 2 × (1 + 4) = 10 maps of yields percrop. One imporan issue is when each crop is planed. Ahe purely daa level, we do no have full confidence in heaccuracy of he arge planing monh for every locaion.Bu, a he subsanive level, adjusing he planing monh isa significan way for producers o adap o changing climaiccondiions. o deal wih hese possibiliies, he arge plan-ing monh is used as he cener of a hree-monh window.

Each of he hree cases are simulaed and he highes of hehree yields is chosen for each pixel. Addiionally, each cropis represened by several varieies since differen varieiesare appropriae for differen locaions. So, he final yieldmaps for each crop and waer source reflec boh simplisicopimizing behavior regarding planing monh and hegeographic diversiy of varieies.

Te economic models operae a a more aggregae scale, wih scale depending on he paricular applicaion. o makehe differen scales work ogeher, he pixel-level yields wereaggregaed wihin he appropriae regional boundaries.

An area-weighed average was employed by using maps ofphysical area allocaions by crop from he Spaial Produc-ion Allocaion Model as he weighing facors (You e al.2014; You, Wood, and Wood-Sichra 2006).10 More deaileddiscussions of he aggregaion process and is implicaionscan be found in Mueller and Roberson (2014) and Rober-son e al. (2013). Te economic models use hese regional yields o adjus he evoluion of produciviy hrough ime wihin heir frameworks. Please noe, however, ha hesepurely biophysical modeling inpus only parially reflecadapive behavior on he par of producers. Oher auono-

mous adapaions are refleced hrough changes in demandand supply as a resul of changing food prices under climaechange and are parly capured in he economic models.Te full exen of auonomous adapaion opions is likelyslighly underesimaed.

ables 1–3 summarize he projeced changes in crop yields for he major crops in each of he subregions in hehree counries analyzed. Tese simulaions do no includehe beneficial effec of CO

2ferilizaion on crop yields. CO

2



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TABLE 1 Brazil: Projected annual crop yield changes (percentage per year), 2000–2050

Region

Maize, cassava,

and sugarcane Rice

Soybeans

and cotton WheatBrazil –0.39 –0.14 –0.27 –0.28

Central tropical subhumid –0.41 0.04 –0.36 –0.28

Eastern semiarid –0.34 –0.21 –0.20 –0.43

Northern tropical humid –0.37 –0.25 –0.20 –0.07

Southern humid –0.21 –0.31 0.06 0.00

Climate scenario: HADGEM2

Brazil –0.46 –0.26 –0.31 –0.56

Central tropical subhumid –0.33 –0.20 –0.26 –0.56



Southern humid –0.74 –0.40 –0.22 0.00

Climate scenario: IPSL

Brazil –0.57 –0.22 –0.40 –0.36

Central tropical subhumid –0.63 –0.10 –0.59 –0.36



Southern humid –0.61 –0.36 0.15 0.00

Climate scenario: MIROC

Brazil –0.45 –0.17 –0.32 –0.51

Central tropical subhumid –0.31 0.00 –0.11 –0.51

Eastern semiarid –0.47 –0.22 –0.35 –1.11Northern tropical humid –0.67 –0.28 –0.47 –0.46

Southern humid –0.65 –0.36 –0.15 0.00

Source: Authors’ calculations based on Decision Support System for Agrotechnology Transfer.

Note: GFDL = Earth System Model (ESM) designed and maintained by the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamic Laboratory,

HADGEM2 = Hadley Centre’s Global Environment Model (version 2), IPSL = Institut Pierre Simon Laplace’s ESM, and MIROC = Model for Interdisciplinary Research on

Climate.

ferilizaion was no included in his sudy for wo reasons.Firs, much uncerainy surrounds he exen o which hepoenial benefis of CO

2 may acually be achieved, as i

will depend on soil consrains (Reich and Hobbie 2013;

Norby e al. 2010) and fuure managemen pracices andadapaion (Müller e al. 2010). CO

2 ferilizaion may

increase growh (biomass) bu diminish nuriional qualiyin some crops such as cassava (Gleadow e al. 2009), andhe lifespan of cerain arboreal species (Bugmann and Bigler2011), which could have major unexpeced consequenceson arboreal planaion crops like coffee and cacao. In addi-ion o his uncerainy, he purpose of choosing a perfec

miigaion scenario and RCP 8.5 was o presen an envelopeof poenial climae resuls. Applying CO

2 ferilizaion in

DSSA would only decrease he climae possibiliy space being esed.

One of Brazil’s mos imporan agriculural producs issoybeans, conribuing abou 22 percen and 16 percen oregional agriculural GDP in he cenral ropical subhumidand souhern humid regions, respecively. According o heDSSA model, soybean producion in hese regions would be adversely affeced in all climae scenarios, bu especiallyin he we IPSL scenario (able 1).

MODELING SUITE 9


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In Mexico, maize is he main agriculural produc in he wes arid, cenral high arid, and souhern humid regions,conribuing beween 14 and 22 percen of regional agricul-

ural GDP. However, maize yields vary significanly acrossregions in hree of he four climae scenarios. Only for he very dry IPSL scenario do we see subsanial drops in maize yields in all pars of Mexico (able 2).

In Peru, poaoes conribue more han 10 percen o ag-riculural GDP in he highlands, and here all models predic very subsanial increases in yields due o higher empera-ures and more precipiaion (able 3).

Te yield changes modeled for all of hese counries are wihin he diverse range of effecs seen across he globe. Forexample, he annual climae change effec on soybeans rang-

es from –0.5 o +0.3 percen, while he whole world averageis around –0.2 percen. Brazil, a abou –0.3 percen per year, is slighly harder hi. Looking a he mos imporancrops for he oher counries in his sudy, we find ha someare in he middle of he pack while ohers even benefi fromclimae change. Across he world, he maize siuaion ranges beween –0.8 percen and no change, wih –0.5 percen fora ypical worldwide value, meaning ha Mexico’s roughly–0.3 percen per year is on he beter side of ypical. Te

TABLE 2 Mexico: Projected annual crop yield changes (percentage per year), 2000–2050 Region Maize and sugarcane Potatoes Sorghum Beans and alfalfa Wheat

Climate scenario: GFDL

Mexico –0.22 –0.10 0.01 –0.37 –0.04

West arid –0.58 –0.41 –0.09 –0.06 0.18

North and east arid –0.04 0.11 0.11 –0.46 0.12

Central high arid –0.02 0.08 –0.01 –0.28 –0.24

Southern humid –0.45 –0.13 –0.23 –0.29 –0.40


Mexico –0.33 0.08 –0.23 –0.37 –0.16

West arid –0.71 –0.02 –0.30 –0.38 0.08

North and east arid –0.55 0.14 –0.30 –0.37 0.10

Central high arid 0.02 0.17 –0.10 –0.36 –0.39

Southern humid –0.73 –0.03 –0.37 –0.38 –0.38


Mexico –0.59 –0.32 –0.09 –0.46 –0.07

West arid –0.78 –0.94 –0.30 –0.12 0.19

North and east arid –0.44 –0.07 0.30 –0.55 0.21

Central high arid –0.38 –0.02 –0.30 –0.36 –0.33

Southern humid –0.87 –0.08 –0.59 –0.38 –0.30


Mexico –0.32 –0.12 –0.22 –0.33 –0.04

West arid –0.60 –0.47 –0.20 –0.17 0.15

North and east arid –0.17 0.01 –0.38 –0.33 0.12

Central high arid –0.01 0.10 –0.03 –0.33 –0.22

Southern humid –0.73 –0.09 –0.37 –0.37 –0.33

Source: Authors’ calculations based on Decision Support System for Agrotechnology Transfer.

Note: GFDL = Earth System Model (ESM) designed and maintained by the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamic Laboratory,

HADGEM2 = Hadley Centre’s Global Environment Model (version 2), IPSL = Institut Pierre Simon Laplace’s ESM, and MIROC = Model for Interdisciplinary Research on

Climate.



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saring poin is also imporan. As already menioned, ohe exen ha he relaively coarse climae daa can repre-sen he opographic variabiliy in Peru, he high elevaions(and concomian lower emperaures) help o explain haa emperaure increase could be beneficial in large pars ofhe counry. Te effec of climae change on poaoes range beween abou –0.6 and +0.2 percen around he world andhe overall picure is abou –0.2 percen per year. Peru’s +1.0

percen is hen a he very high end of benefiting from hechanging climae. Overall, he modeled responses for hesehree counries are no ouliers in he global conex, al-hough hey do reflec he diversiy of circumsances foundon our plane.

Te IMPAC Global Agricultural Economy Model

Te counry-level DCGE models need guidance on he world prices a naional borders. Te world prices can be

deermined using a global model of agriculure, in his caseIMPAC.

IMPAC was developed a IFPRI in he early 1990s asa parial equilibrium compuer simulaion model focusedon global agriculure. Over ime his rade model has beenexpanded o answer a growing se of ex ane research ques-ions, such as he effecs of climae change and waer avail-abiliy on agriculure and food securiy globally. o respondo his growing demand, IMPAC has been redesigned asa modular nework of linked economic, waer, and cropmodels. A he core of his nework are he original parialequilibrium rade model and a suie of waer models.

Te rade model is a sysem of equaions offering a

mehodology for analyzing baseline and alernaive sce-narios for global food demand, supply, rade, income, andpopulaion. IMPAC simulaes agriculural markes in 159geopoliical regions (Brazil, Egyp, France, and so forh). Wihin each region supply, demand, and prices for agricul-ural commodiies are deermined, wih all regions linkedhrough rade. World agriculural commodiy prices are de-ermined annually a levels ha clear inernaional markes.o simulae he effecs of climae change and he availabiliyof waer, a more disaggregaed level is required. IMPACuses he food producion uni, which is he inersecion of

he 159 geopoliical regions wih 126 hydrological basins,giving 320 subnaional unis and allowing for he modelingof waer basin managemen and is effecs on agriculure.Te inegraion of a global economic model and a series of waer models, allows for he addiional modeling of changesin availabiliy of waer for irrigaion, and he effecs ha his would have on agriculural produciviy. IMPAC simu-laes 62 agriculural commodiy markes, which represenshe majoriy of food and cash crops. For more informa-ion abou he IMPAC model, please see Rosegran andIMPAC Developmen eam (2012) and Robinson e al.

(2015).Figure 5 describes he links beween he differen mod-

els ha consiue he IMPAC sysem of models. All mod-els excep he climae models (ESMs) are run by IFPRI.

Te five climae fuures were run agains he samesocioeconomic scenario, which was drawn from IPCC’sFih Assessmen Repor. Te socioeconomic scenario was defined as Shared Socioeconomic Pahway Number 2, which is characerized by economic developmen ha more

TABLE 3 Peru: Projected annual crop yieldchanges (percentage per year), 2000–2050

Region MaizePotatoes and

root crops Cotton Cereals

Climate scenario: GFDL

Peru –0.18 0.61 –0.12 –0.03

Coastal –0.41 0.65 –0.12 –0.16

Interior 0.01 0.53 –0.01 0.08


Peru –0.27 1.03 –0.16 –0.05

Coastal –0.58 1.15 –0.17 –0.21

Interior –0.02 0.75 0.00 0.10


Peru –0.25 1.03 –0.12 –0.06

Coastal –0.52 1.17 –0.12 –0.20

Interior –0.02 0.70 –0.18 0.05


Peru –0.25 1.10 –0.13 –0.01

Coastal –0.50 1.27 –0.14 –0.15

Interior –0.05 0.71 –0.10 0.10

Source: Authors’ calculations based on Decision Support System for Agrotech-

nology Transfer.

Note: GFDL = Earth System Model (ESM) designed and maintained by the

National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamic

Laboratory, HADGEM2 = Hadley Centre’s Global Environment Model (version 2),IPSL = Institut Pierre Simon Laplace’s ESM, and MIROC = Model for Interdisci-

plinary Research on Climate.

MODELING SUITE 11


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or less follows hisorical rends and a medium populaiongrowh projecion (O’Neill e al. 2014). Tis socioeconom-ic scenario is analogous o, alhough no exacly he same as,IPCC’s FourhAssessmen Repor (AR4) Medium-Mediumsocioeconomic scenario. able 4 summarizes he GDP andpopulaion growh rae assumpions ha define SharedSocioeconomic Pahway Number 2.

In general, during he nex 40 years, prices of majoragriculural producs are projeced o increase due o heincreased demand of a larger and richer world populaion wihou equivalen growh in agriculural produciviy.In addiion o hese increases, climae change is expecedo affec world prices due o changes in supply. Te priceindexes in Figure 6 show how world prices for key producsare expeced o develop under differen climae scenarios

and a perfec miigaion scenario according o he IMPACmodel.

Dynamic Computable General Equilibrium (DCGE)Models

Climae change affecs world prices and local agriculuralproducion (Figure 6), wih diverse implicaions for he

hree economies analyzed. Moreover, spaial variaion in lo-cal climae change impacs wihin counries means ha sucheffecs can vary across subnaional regions. We hereforedevelop economywide models for differen agroecologicalzones (AEZs) (Figure 7 and able 5) o capure he majorlinkages beween climae change, producion, and house-holds. Te recursive DCGE models used in his repor areconsruced o be consisen wih neoclassical general equi-librium heory and follow he model described in Diao and

FIGURE 5 The Impact System of Models

Source: Authors.

Note: DSSAT = Decision Support System for Agrotechnology, IMPACT = International Model for Policy Analysis of Agricultural Commodities and Trade.

Hydrology—

water basin management

and stress models

Outputs:

Yields

Production

Water

demand

trends

Climate

models

Crop models

(DSSAT)

IMPACT global

economic model

Macroeconomic

trends

Consumption

Trade

Harvested area

Commodity prices



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Turlow (2012). Recen applicaions of his model invesigae climae change impacs for Syria, unisia, and Yemen(see Verner and Breisinger 2013; Breisinger e al. 2013; Wiebel e al. 2015). In he following, we provide a sum-mary of he key model feaures, wih a focus on parameersha are mos relevan for racking climae change impacs.Te full se of model equaions can be found in Box A1 anda deailed descripion of he model in Diao and Turlow(2012).

o model he impac of local climae change, yieldchanges derived from he DSSA models direcly ener heproducion funcions of he DCGE models. More specifi-cally and consisen wih he above menioned lieraureon he economics of climae change, we use he oal facorproduciviy (FP) parameer o exogenously imposeclimae change-induced crop yield changes on he model.11

Reducions in yields end o hen ranslae ino reducedsecoral producion, which exers upward pressure on

domesic prices hereby inducing producers o shi heirsupply oward domesic markes while consumers shi heirdemand oward foreign markes. o model he impac ofglobal climae change on global food commodiy markes,projecions from he IMPAC model are inroduced asexogenous changes in world marke prices. For example, ifinernaional prices rise due o climae change, hen produc-ers increase supply o inernaional markes o maximizerevenues and reduce sales o domesic markes, while

consumers lower heir demand for impored goods andincrease i for domesically produced goods in an atemp ominimize coss of consumpion.

Besides hese direc effecs deermining he iniialdomesic oupu price response in he affeced agriculuralmarkes, indirec impacs or economywide effecs deer-mine he final resource shis and final impacs on secoralincome or value added as well as facor income and house-hold income. In he DCGE model, each counry faces

TABLE 4 Summary of GDP, population, and GDP per capita assumptions for SharedSocioeconomic Pathway Number 2, by region

GDP (Billions of USD) Population (millions) Per-capita GDP (USD per

2010 2050Annual

growth (%)2010 2050

Annual

growth (%)2010 2050

Annual

growth (%)

Africa andMiddle East 6,255 32,593 3.4 1,321 2,508 1.3 4,737 12,996 2.0

East Asia,

Southeast Asia,

and Oceania 19,277 80,411 2.9 2,216 2,337 0.1 8,699 34,408 2.8

South Asia 4,420 32,574 4.1 1,598 2,296 0.7 2,767 14,184 3.3

Former SovietUnion 2,855 8,984 2.3 279 277 0.0 10,234 32,402 2.3

Europe 14,629 27,784 1.3 537 577 0.1 27,228 48,146 1.1

Latin Americaand Caribbean 5,899 19,278 2.4 590 746 0.5 10,007 25,852 1.9

North America 14,289 29,929 1.5 344 450 0.5 41,490 66,526 0.9

World 67,624 231,553 2.5 6,884 9,191 0.6 9,823 25,192 1.9

Source: Authors’ compilation.

Note: GDP = gross domestic product; Shared Socioeconomic Pathway No. 2 = . For GDP, all local currency converted to US dollars at 2010 prices average

purchasing power parity exchange rates.

MODELING SUITE 13


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FIGURE 6 Global food price scenarios by climate model, 2010–2050


Note: GFDL = Earth System Model (ESM) designed and maintained by the National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamic

Laboratory, HADGEM2 = Hadley Centre’s Global Environment Model (version 2), IPSL = Institut Pierre Simon Laplace’s ESM, and MIROC = Model for Interdis-ciplinary Research on Climate.

220

180

140

1002010 2020 2030

Year

Sorghum

P r i c e i

n d e x

2040 2050

HADGEM2

MIROC

GFDL

IPSL

NoCC

220

180

140

1002010 2020 2030

Year

Potatoes

P r i c e i

n d e x

2040 2050

HADGEM2MIROC

GFDL

IPSL

NoCC

220

180

140

1002010 2020 2030

Year

Subtropical and tropical fruits

P r i c e i

n d e

x

2040 2050

HADGEM2

MIROC

GFDLIPSL

NoCC

220

180

140

1002010 2020 2030

Year

Coffee

P r i c

e i

n d e x

2040 2050

HADGEM2

MIROC

GFDL

IPSL

NoCC

220

180

140

1002010 2020 2030

Year

Vegetables

P r i c e i

n d e x

2040 2050

HADGEM2

MIROC

GFDL

IPSL

NoCC

220

180

140

1002010 2020 2030

Year

Soybeans

P r i c e i

n d e x

2040 2050

HADGEM2

MIROC

GFDL

IPSL

NoCC

HADGEM2

MIROC

GFDL

IPSL

NoCC

220

180

140

1002010 2020 2030

Year

Cotton

P r i c e i

n d e

x

2040 2050

HADGEM2

MIROC

GFDLIPSL

NoCC

220

180

140

1002010 2020 2030

Year

Sugarcane

P r i c

e i

n d e x

2040 2050



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FIGURE 7 Agroecological zones of Brazil, Mexico, and Peru


perfecly elasic world demand curves for is expors a fixed world prices assuming ha all counries receive he same

prices for similar goods and services. However, his “smallcounry assumpion” is relaxed by he fac ha he resuls ofhe IMPAC model ake ino accoun world marke price variaions resuling from producers ha are posiively ornegaively affeced by climae change. Tis is imporan forcounries like Brazil, which is a major exporer of soybeansand coffee.

Te model disinguishes beween various insiu-ions, including enerprises, he governmen, and differenhousehold groups. Households and enerprises receiveincome in paymen for he producers’ use of heir facors of

producion. Insiuions pay direc axes and save accordingo heir respecive marginal saving propensiies. Enerprisespay heir remaining incomes o households in he formof dividends. Households use heir incomes o consumecommodiies according o fixed budge shares as derivedfrom a Cobb-Douglas uiliy funcion. Te governmenreceives revenue from aciviy axes, sales axes, direc axes,and impor ariffs and hen makes ransfers o households,enerprises, and he res of he world. Te governmen

also purchases commodiies (acually remuneraion forhe provision of public goods) in he form of governmen

consumpion expendiures, and he governmen saves heremaining income (wih recurren budge deficis repre-sening negaive savings). All savings from households,enerprises, governmen, and he res of he world (foreignsavings) are colleced in a savings pool from which inves-men is financed.

Te model includes hree macroeconomic accouns:governmen balance, a curren accoun, and a savings-invesmen accoun. o balance he macro accouns, i isnecessary o specify a se of macro-closure rules, whichprovide a mechanism hrough which balance is achieved.

A savings-driven invesmen macro closure is assumedsuch ha invesmen is endogenously deermined by hesum of privae, public, and foreign savings. Privae savingsare assumed o be fixed proporions of ne enerprise andhousehold income. In he governmen accoun, he fiscal balance and herefore public savings are endogenous, wihgovernmen demand fixed and all ax raes held consan, soha governmen savings or dis-savings depend on he levelof economic aciviy. Finally, for he curren accoun, boh

Brazil Mexico Peru

West aridNorth and east arid

Central high arid

Southern humid

Coast

Inland

Northern tropical humidCentral tropical subhumid

Southern humid

Eastern semiarid

MODELING SUITE 15


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foreign savings in foreign currency erms and he nominalexchange rae are assumed o be fixed, while he domesicconsumer price index adjuss o reach overall equilibrium.

Several labor and capial caegories in he model are dif-fereniaed according o counry characerisics (able 5). All ypes of labor are assumed o be fully employed and mobile across secors. Capial is assumed o be secor specific wihin periods bu mobile over ime. New capial from pasinvesmen is allocaed o secors according o profi raedifferenials under a “puty-clay” specificaion. Tis meansha once capial socks have been invesed, i is difficul oransfer hem o oher uses. In agriculure, culivaed landin each AEZ is assumed o be mobile and can be reallocaed

across crops in response o shocks.12

Tus, changes in cropproducion could resul from changes in yields, inensifi-caion, and land use paterns. In he Peruvian model, allfacors, including labor ypes and capial, are inersecorallymobile bu inerregionally immobile (able 5).

Long-run secoral facor produciviy growh is speci-fied exogenously. Wihin he DCGE model, he decisionsof consumers, producers, and invesors change in responseo changes in economic condiions driven by differen sesof climae oucomes and marke oucomes. Te modelallows a degree of endogenous adapaion wihin periods,

wih changes in labor and land allocaion across secors andcrops in response o shocks.Te DCGE models for Brazil, Mexico, and Peru are

specifically buil o capure he economic and disribuionaleffecs of climae change in hese hree Lain Americancounries. Since global and local climae change affecs dif-feren crops differenly, he model capures boh he secoraland he spaial heerogeneiy of crop producion and islinkages o oher secors such as food processing, manufac-uring, and services.

Wiebel e al. (2015) provides a more deailed descrip-

ion of he essenial feaures of he DCGE model and adiscussion abou how srucural feaures and crucial param-eers affec he direc and indirec effecs of world markeprice changes and agriculural yield changes on resourceallocaion as well as funcional and socioeconomic incomedisribuion for he case of unisia.

GENDER AND RESILIENCE

A firs glance, climae change and all is associaed ef-

fecs, such as emperaure increases, changes in rainfall,sea level increase, and glacier rerea, do no seem o affec women and men differenly. However, here are complexand dynamic links beween gender relaions and climaechange (erry 2009). Te lieraure generally finds harural women in developing counries are among he mos vulnerable groups (for example, Lambrou and Piana 2006) because hey oen are responsible for he mos climae-sensiive aciviies, such as waer and fuelwood collecionand someimes agriculure (Byrne and Baden 1995; Denon2009; Scheurlen 2015); alhough men oen are mosly

responsible for agriculural aciviies. Moreover, here aregendered differences in access o waer, land, and resources(Sachs 1996; UN Women Wach 2009; Ringler e al. 2014).One of he reasons women are le wih he more climae-sensiive aciviies is gendered differences in labor markeaccess (Buechler 2009) and mobiliy (UN Women Wach2009).

Research on mobiliy and migraion paterns suggessha in imes of disaser and sress, such as hose ha migharise from climae change, men end more oen o migraefrom rural areas han women and girls. Te later end o

say behind. Te resuling increased work burdens, specifi-cally relaed o agriculure, may make i difficul for womenand girls o coninue heir exising income-generaingaciviies, le alone ake on addiional work (Denon 2009).

Te fac ha women and girls are oen responsiblefor mos of he unpaid care asks around he householdalso means heir lives are direcly affeced by he changes brough abou by climae change. Tey oen have o walkfarher o find increasingly scarce food, fuel, and waer as well as care for family members who are suscepible o hehealh risks linked o climae change. For example, climae

change is expeced o cause more exreme precipiaion pa-erns, wih more droughs and more floods (IPCC 2013),and in boh cases i is ypically women who have o workharder o obain waer during droughs and deal wih he in-creased disease burden caused by floods (Denon 2009). Asa resul, women and girls find hemselves wih less ime foreducaion, income-generaing aciviies, and paricipaion incommuniy decisionmaking processes, furher enrenchingunequal gender relaions (Skinner 2011). Tere also may be



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gendered differences in spending paterns. Households haspend a large share of heir income on food may be paricu-larly vulnerable o food price increases brough abou byclimae change (FAO 2011).

However, gender roles and relaions are highly conexspecific and herefore mus be sudied and addressed basedon local conexs (Verner 2012).

o complemen he simulaions by a direc vulnerabiliy

analysis of household survey daa from he hree counries, we use he vulnerabiliy indicaors proposed by a recenIner-American Developmen Bank sudy (Andersen e al.2014). Tis repor argues ha alhough vulnerabiliy is acomplex concep, i can be quanified and analyzed a hehousehold level using jus wo main indicaors: (1) percapia household income and (2) household income diver-sificaion. Te mos vulnerable households are hose hasimulaneously have low levels of income and low levels ofdiversificaion because any shock could hreaen heir enireincome base. In conras, households wih high and well-

diversified incomes will be much beter able o wihsandany adverse shock.

Andersen and Cardona (2013) develop a simpleypology of vulnerabiliy based on hese wo indicaors. Ahousehold ha has a per capia income below he naionalpovery line and a diversificaion index (DI) less han 0.5 isclassified as highly vulnerable, while a household ha has aper capia income above he povery line and a DI greaerhan 0.5 is classified as highly resilien (Figure 8).

Andersen and Cardona (2013) argue ha since diversi-ficaion is he opposie of income concenraion; a simpleand logical way of consrucing a DI is simply 1 minus he widely used Herfindahl-Hirschman Index of Concenraion:

DI = 1 –∑i=1

pi

(1)

where N is he oal number of income sources and pi

represens he income proporion of he ih income source.Te value of he index is 0 when here is complee special-izaion (100 percen of oal household income comes fromone source only) and approaches 1 as he number of incomesources increases and no single source dominaes householdincomes.

Te advanage of using he DI insead of jus he numberof livelihood sources is ha he DI is no very sensiive ohe grouping of small income sources wih bigger ones. Forexample, if a household had hree sources, conribuing 90percen, 9 percen, and 1 percen, respecively, he DI would

be 0.1818. If we lump ogeher he las wo sources, heindex changes only marginally o 0.1800. Tis is a reducionof less han 1 percen in he index, whereas he reducionin number of livelihood sources would be 33 percen. Tispropery of robusness o alernaive classificaions is impor-an as we will necessarily have o make some assumpionsabou how o classify and group differen income sourcesogeher (Andersen and Cardona 2013). In principle, oneshould define sources so ha here is very low correlaion

TABLE 5 Country model characteristicsCountry Sectors and commodities Agroecological zones (AEZs) Factors Households

Brazil 37 sectors, 19 nonagriculture, 18agriculture, all differentiated by

4 AEZs

4 AEZs: northern tropical humid (4.1% oftotal agricultural GDP), central tropical

subhumid (34.3%), southern humid (47.5%),eastern semiarid (14.1%)

10 labor types by wage level, mobileacross sectors and AEZs; sector-

specific capital; 4 land by AEZ

40 household typesby AEZ and income

deciles

Mexico 39 sectors, 20 nonagriculture,

19 agriculture differentiated by4 AEZs

4 AEZs: west arid (36.2%), north and east

arid (18.6%), central high arid (21.8%),southern humid (23.4%)

6 labor types by skill category and

male/female, mobile across sectorsand AEZs; sector-specific capital; 4

land by AEZ

80 household types

by AEZ, gender, andincome deciles

Peru 36 sectors, 21 nonagriculture, 15

agriculture, all differentiated by

2 AEZs

2 AEZs: Coast (31.8%) and Inland (68.2%) 3 labor types by skill category, mobile

across sectors; sector-specific capital;

2 land by AEZ

4 household types,

rural and urban by AEZ

Source: Country social accounting matrixes.

Note: AEZ = agroecological zone, GDP = gross domestic product.

N 2

MODELING SUITE 17


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across saes o naure. Tus, i boh he husband and he wie are engaged in subsisence agriculure, ha would

coun as only one income source because adverse climaicor marke condiions would affec boh in a similar way. Ihey also had some catle, hey would coun as an addiionalincome source, as catle and agriculural produciviy areno srongly correlaed. Indeed, catle are ofen used as aninsurance mechanism in Lain America. In pracice, heexac classificaion o sources will depend on he amouno deail available in he household surveys o each counry.Tus, while he index can be compared across groups wihinhe same counry, i is more difficul o compare acrosscounries.

Since hese wo indicaors can be calculaed or ev-ery household in each o he counries analyzed, we cancompare he probabiliy o alling ino he highly vulnerablecorner or any group o ineres, including emale- and male-headed households.

Te mehodology, however, does no permi he analysiso inrahousehold gender differences in vulnerabiliy. Tis

is a poenial weakness, as here may well be inrahouseholddifferences ha he household-level analysis canno capure(IADS 2014). Tere are muliple gender-specific vulnerabil-iies relaed o differing amilial and communiy obligaionsand lie cycle evens (or example, childbirh, childcare,lower social saus, lower access o and conrol over asses,and mobiliy consrains), and hese shape opporuniies ormen, women, and children o build resilience and consrainhe coping sraegies hey employ o manage risk (Holmesand Jones 2013).

INTERTEMPORAL DISCOUNT FACTORAND REPORTING OF RESULTS

o summarize he resuls o annual climae change impacsover he nex 40 years, we calculae he ne presen value(NPV) o he impacs and compare hem o he NPV o herelevan variable in he perec miigaion scenario. Tis will

give us an average measure o impac during he40 years, bu he impacs will ypically be smallerhan average in he beginning o he period, whenhe climae has no ye changed much. Impacs

will be larger han average by he end, when he

ull climae changes illusraed in Figures 3 and 4have occurred.

In order o calculae he NPV figures o allrelevan variables, we apply a very low iner-emporal discoun rae o 0.5 percen. Using adeliberaely low discoun rae is jusified based ona wors-case hypohesis. Te findings presenedin his repor show ha he climae-change effecsare small using a low discoun rae. Tey would beeven smaller i a higher discoun rae were used. Inoher words, he overall conclusion o his repor

holds even i oher (higher or lower) discounraes were used.

Tree indicaors are used o assess he impacso climae change on oal GDP, agriculural GDP,and households’ welare. Te hree indicaors arehe ollowing:

FIGURE 8 Four main vulnerability types

Cutoff:National poverty line

C: Poor but resilient

A: Highly vulnerable

B: Highly but resilient

A: Rich but vulnerable

Cutoff:

DI = 0.5

Per capita household income

D i v e r s i fi c a t i o n i n d e x

Source: Andersen and Cardona (2013).

Note: DI = Diversification Index.



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Accumulated Absolute Value of Losses

Indicaor 1 was calculaed following hree seps. Firs,

simulaion oupus of he model presen figures for all hree variables expressed in consan-value local currency for allscenarios. Tese figures were convered ino consan-valueUS dollars by dividing he whole series by he exchangerae in he base year. Second, aggregaed ne presen valuesof he variables were calculaed for each of he scenarios byadding up he sring of discouned values of he variablefor he whole ime horizon of he simulaion. Finally, heindicaor was calculaed as he deviaions of he aggregaeddiscouned values for each of he scenarios from hose cor-responding o he no climae change scenario.

I 1 = Σ VJ

i – Σ Vncc

i ,

where

I 1 = indicator 1,

VJ i = value of variable V in climate change scenario j

and in year i, and

Vncci = value of variable V in no climate change sce-

nario and in year i.

Percentage of Accumulated GDP in No ClimateChange Scenario

Indicaor 2 was calculaed by dividing indicaor I1 by he

aggregaed sring of discouned values of he variable in heno climae change scenario:

I 2 = ( Σ VJi – Σ Vncc

i) / Σ Vncc

i.

Percentage of GDP in Base Year

Indicaor 3 was calculaed by dividing indicaor I1 by he value of he variable in he base year (2009).

I 3 = ( Σ VJ

i – Σ Vncc

i) / V

BY ,

where

V BY

= value of the variable in the base year.

When using hese indicaors, i is imporan o bear in mindseveral caveas o avoid poenial misinerpreaions:

1. Indicaor 1 does no provide a clear idea of he size of heimpacs, since we canno ell by jus looking a his indi-caor wheher he impacs are large or small. I is alwaysimporan o compare i wih reference o he value of an-oher known variable. Tis shorcoming can be solved byexpressing his indicaor in per capia erms. Te posiiveaspec of his indicaor is ha is meaning can be easilyundersood. However, his indicaor is very sensiive ohe choice of discoun rae.

2. Indicaor 2 does provide a reference o assess wheherhe effecs are large or small as i is expressed as a percen-

age. I hus provides a more precise idea of he size of heeffecs as i compares an aggregaed sring of discounedGDP values of impacs over he long erm wih an ag-gregaed sring of discouned GDP values over he sameime horizon. Tis indicaor is less sensiive o he choiceof discoun rae, because he sring values of boh, he nu-meraor and denominaor of he indicaor, are discounedusing he same rae. However, he resul of comparingwo NPVs is pah-dependen. Since GDP is assumed oincrease a a fixed rae, and he impacs follow a differenpah over ime, concenraed a he far end of he period

near 2050, he choice of discoun rae does influence heindicaor value. However, he sensiiviy of he indicaoro he choice of discoun rae ends o be smaller, whichleads he impacs of climae change o be smaller. Ta ishe case presened in his repor.

3. Indicaor 3, like indicaor 2, provides a reference o assesshe magniude of he impacs. However, his indicaorends o provide an arificially enlarged magniude ofhe impacs since i compares an aggregaed sring ofdiscouned values of impacs over he long erm wih he value of he variable in one single year. Terefore, whilehe meaning of his indicaor can be easily inerpreedand undersood, he indicaor ends o be very sensiiveo he choice of discoun rae.

ables A6 hrough A8 provide an overview of resulsfor each counry and each indicaor. Troughou mos ofhe counry secions in he main ex, we discuss he resulsusing indicaor 2.

MODELING SUITE 19


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Socioeconomic Impacts ofClimate Change in Brazil

B ’ leading exporer of soy and coffee. I can herefore expec imporan impacs from bohglobal price changes and local yield changes. Previous sudies on he impacs of climae changeon agriculure in Brazil have found poenially severe impacs, wih farms losing on average 23percen of heir land values by 2060 in he wors climae scenario (Seo and Mendelsohn 2008).

Seo and Mendelsohn’s (2008) sudy on he economic coss and opporuniies of climae changein Brazil esimaes ha excep for sugarcane, all crops are adversely affeced by a reducion in

low-risk producion areas, especially soybeans (by 30 o 34 percen), maize (by 15 percen), andcoffee (by 17 o 18 percen). Crop yields would fall in paricular for saple crops in he norheasregion (Margulis and Burle Schmid Dubeux 2011).

However, alhough absolue agriculural producionis large in Brazil, agriculure conribues only abou 5.5percen o oal GDP (able 6), so he effec on he overalleconomy is sill bound o be relaively small. Moreover,mos of Brazil’s producion and consumpion of agriculural

goods is direced oward domesic markes. Direc impacsof global price changes will herefore be concenraed ona few expor-oriened agriculural subsecors (such as soybeans, coffee, and maize) and impor-subsiuing subsec-ors (whea), ogeher making up 30 percen of agriculural value-added, while all oher agriculural secors largely pro-duce for he domesic marke and herefore are no direclyaffeced by world marke price changes.13 Ye domesicagriculural subsecors and foresry compee wih direcly

affeced subsecors for agriculural land as well as oher pri-mary and secondary inpus such as labor and capial and areherefore likely o be impaced. In addiion, nonagriculuralsecors also compee wih agriculure for labor, capial, andinermediae inpus. Since Brazil is locaed in he ropics

and spans he equaor, he climae is already hoter hanideal for almos all crops. So, as indicaed in able 1, furheremperaure increases are expeced o have a negaive effecon crop yields almos everywhere. Again he direc impac isconcenraed on a few agriculural subsecors (maize, whea,rice, soybeans, cassava, coton, and sugarcane) making up40 percen of agriculural value-added. I is imporan onoe ha rice, cassava, coton and sugarcane are exclusivelysold in domesic markes. Moreover, hese agriculural

20


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goods do no face compeiion from abroad; hey are purelynonradable. For such crops wih weak or no links o iner-

naional markes, produciviy declines induced by climaechange may be more han offse by rising domesic prices.In his case, farmers may have incenives o shi resourcesoward raher han away from some srongly affeced crops.

In his secion we will firs analyze he effecs of changesin global agriculural prices due o global climae change,and hen we will analyze he effecs of changes in crop yieldsdue o local climae change. Aer presening he combined

effec of he wo ypes of impacs, we will presen a gender-differeniaed analysis of vulnerabiliy.

IMPACTS OF GLOBAL AGRICULTURAL

COMMODITY PRICE CHANGES ON

THE BRAZILIAN ECONOMY AND

HOUSEHOLDS

Brazil’s demand for agriculural goods is srongly biasedoward domesic markes, wih impored goods making up jus 3.6 percen of oal absorpion of agriculural goods and1.9 percen of he oal impor bill (able 6). Whea is he

TABLE 6 Brazil: Agricultural value-added by region and agricultural trade orientation, 2008

Northern

tropicalhumid

Central tropicalsubhumid Southern humid Eastern semiarid All regions

Agricultural

goods

Billions

of reaisPercent

Billions

of reaisPercent

Billions

of reaisPercent

Billions

of reaisPercent

Billions

of reaisPercent

EXP-

shr

EXP-

OUTshr

IMP-

shr

IMP-

DEM-

shr

Maize 0.18 3.23 4.27 9.06 6.55 10.05 0.93 4.80 11.93 8.69 0.59 10.44 0.07 1.56

Wheat 0.08 0.17 1.74 2.67 1.82 1.33 0.08 10.56 1.00 61.58

Rice 0.18 3.23 0.85 1.80 3.01 4.62 0.19 0.98 4.23 3.08

Soybeans 0.09 1.62 10.10 21.43 10.49 16.10 1.39 7.18 22.07 16.08 3.83 38.46

Cassava 0.55 9.87 0.72 1.53 0.76 1.17 0.57 2.94 2.60 1.89

Tobacco 2.78 4.27 0.05 0.26 2.83 2.06 0.02 1.72

Citrus fruits 0.03 0.54 0.28 0.59 2.94 4.51 0.26 1.34 3.51 2.56 0.03 1.95

Cotton 1.55 3.29 0.16 0.25 0.79 4.08 2.50 1.82

Coffee 4.63 9.82 0.84 1.29 0.34 1.76 5.81 4.23 1.61 64.47

Sugarcane 0.06 1.08 1.82 3.86 6.67 10.24

climate change impacts and household resilience: prospects for 2050 in brazil, mexico, and peru

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