National Institute for Environmental Studies (NIES) Tomoko Hasegawa, Shinichiro Fujimori, Kiyoshi Takahashi and Toshihiko Masui

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1 National Institute for Environmental Studies (NIES) Tomoko Hasegawa, Shinichiro Fujimori, Kiyoshi Takahashi and Toshihiko Masui The 7th IAMC Park, Nov 17-19, 214 1

2 Introduc2on Background Climate change would increase the number of malnourished children. The climate impacts strongly depend on popula;on and GDP. Based on the SRES and CMIP3; Need updated. Only popula2on and GDP were considered; other socioeconomic indicators could be considered. A new interdisciplinary scenario framework (SSP+RCP) has recently been designed for climate change research. Scenarios for various fields (e.g. water use) have been developed based on SSPs. No scenarios for risk of hunger consistent with SSPs have been developed. 2

3 Introduc2on Objec;ves 1. Develop 21 st - century scenarios for the risk of hunger consistent with SSPs as a basement of climate impact research on agriculture 2. Iden;fy the elements strongly affec;ng future risk of hunger 7 socioeconomic indicators were considered: Demographic change, GDP, equality of food distribu;on, crop yields, irriga;on area, land produc;vity of livestock and wood products 3

4 Method Shared Socioeconomic Pathways Low popula;on growth; high economic growth; high human development; low environmental awareness. Low popula;on growth; high economic growth; high levels of educa;on and governance; globaliza;on, interna;onal coopera;on, technological development, and environmental awareness. Socioeconomic challenges for mitigation SSP challenges space SSP5 Conventional Development SSP1 Sustainability SSP2 Middle of the road SSP3 Fragmentation SSP4 Inequity Socioeconomic challenges for adaptation Based on O Neill et al. (214) Rapid popula;on growth; Moderate economic growth; low levels of educa;on and governance; Regionaliza;on; low environmental awareness. A mixed world, with rapid technological development in high income countries. In other regions, development proceeds slowly. Inequality remains high. 4

5 Method AIM/CGE (Computable General Equilibrium) Economic model Cover the en2re economic market Fundamental idea: supply = demand balanced by price mechanism Price Demand curve Supply curve Popula;on & income growth à Increase in food demand à Increase in food price à Producers: increase in produc;on (cropland expansion, yield growth) à Consumers: decrease in consump;on; shi_ to less expensive goods Quan;ty Domes;c distribu;on of food energy (FAO,28) 5

6 Method Parameters related to food and hunger Parameters: Popula;on, demographic change GDP Equality of food distribu;on Crop yield Irriga;on area Land produc;vity of livestock and wood products Income elas;city of food demand Price elas;city of land use change Price elas;city of trade AIM/CGE Endogenous variables: Food consump;on Meat consump;on Land use (Cropland, pasture, forest) Socioeconomic challenges for mitigation SSP challenges space SSP5 Conventional Development SSP1 Sustainability SSP2 Middle of the road SSP3 Fragmentation SSP4 Inequity Socioeconomic challenges for adaptation Each parameters were determined from the adapta2on viewpoint Optimistic Optimistic Risk of hunger Medium Pessimistic Opt. (High income) Med (Middle income) Pes. (low income) 6

7 Method Parameters related to food and hunger Parameters: Popula;on, demographic change GDP Equality of food distribu;on Crop yield Irriga;on area Land produc;vity of livestock and wood products Income elas;city of food demand Price elas;city of land use change Price elas;city of trade AIM/CGE Endogenous variables: Food consump;on Meat consump;on Land use (Cropland, pasture, forest) Three approaches for assuming parameters 1. Based on observed data (Stylized fact) 2. Based on future es;mates of exis2ng studies 3. Assumed in line with SSP storylines if neither were available. 7

8 Hasegawa et al. in review Method Comparison with observed data: the improved equality of food distribu;on with income growth Inequality Coefficient of variation (CV) of the domestic distribution of dietary energy consumption observed data (25) Optimistic Median Pessimistic y =.6894 x y =.6531 x (Observed data; R =.61) y =.469 x.54 Pessimis;c Intermediate Op;mis;c Equality GDP per capita [US$, 25] 8

9 Hasegawa et al. in review Comparison with observed data: increased meat consump;on with income growth Method Meat- based calorie intake [kcal/day/cap] observed data (198-29) Optimistic y = 6. ln x Medium Pessimistic y = 87.8 ln x 545.3(Observed data; R =.57) y = 13 ln x 833. Pessimis;c income [US$/person] 2 Intermediate Op;mis;c 9

10 Result 12 The 21 st - century Scenarios of risk of hunger using SSPs Land use change Land use [Mha] Carolie intake [kcal /person/day] Food consump;on per capita SSP1 SSP2 SSP3 SSP4 SSP Food crops Pasture Grassland Managed forest Primary forest SSP1 SSP2 SSP3 SSP4 SSP5 Population at risk of hunger [million] Popula;on at risk of hunger SSP1 SSP5 SSP2 SSP3 SSP Hasegawa et al. in review 1

11 Method Decomposi;on analysis What strongly affects future risk of hunger? Change in hunger risk was decomposed into three factors; Change in pop. at risk of hunger Inequality of = Popula;on Food + food + + Residual growth consump;on distribu;on *See discussion paper for more 11

12 Result Factors affec;ng future hunger risk (global, at 25 level) Change in population at risk of hunger [million] Change in population at risk of hunger [million] a) SSP d) SSP Change in population at risk of hunger [million] Change in population at risk of hunger [million] b) SSP e) SSP Change in population at risk of hunger [million] c) SSP residual per- capita calorie (domestic production) inequality of food distribution population net change Popula;on, inequality of food distribu;on causes large differences in hunger risk among SSPs Hasegawa et al. in review 12

13 Regional popula2on at risk of hunger and its factors (SSP3, 21) 21st- century risk of hunger differs among SSPs Regional distribu;on depends greatly on popula;on growth, equality in food distribu;on and increase in food consump;on Regions with greater popula;on growth face higher risk of hunger. Population at risk of hunger [million] SSP1 SSP5 SSP2 SSP3 SSP Change in popula2on at risk of hunger [million] The most pessimis2c scenario (SSP3) Rest of South America Southeast Asia Middle East China Brazil North Africa Rest of Asia, 16% India, 23% Rest of Africa, 39% Former Soviet Union residual trade per- capita calorie inequality of food distribu;on popula;on Hasegawa et al. in review (Rela;ve to 25)

14 Result & discussion Population at risk of hunger [million] A1 A2 B1 B Comparison of the popula;on at risk of hunger with exis;ng studies Lines: this study Dots: exis;ng studies SSP1 SSP5 SSP2 SSP SSP3 Pop. at risk of hunger in this study was lower than in exis;ng studies. Improvements in food distribu;on equality was considered in this study whereas it was not for exis;ng studies. à Inequality of food distribu2on influences long- term assessments of hunger risk. Hasegawa et al. in review 14

15 Conclusion Conclusion (1/2) We developed scenarios for hunger risk in the 21 st century using SSPs. Factors affec;ng future hunger risk were inves;gated. Risk of hunger without climate change in the 21 st century differed among SSPs Factors affec;ng the future risk of hunger were popula;on, inequality of food distribu;on, and per- capita food consump;on. Improving food access and consump;on in low- income households will be effec;ve to reduce future hunger risk. Inequality of food distribu;on greatly influences long- term assessments of hunger risk. 15

16 Conclusion (2/2) This study represents a star;ng point for research using SSPs. Climate impact studies an agriculture and food could expand by including addi;onal factors. More specific and tailor- made scenarios could be developed to help policy decision making e.g. by based on stakeholders opinions. 16