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National Institute for Environmental Studies (NIES)Tomoko Hasegawa, Shinichiro Fujimori,Kiyoshi Takahashi and Toshihiko Masui
1The 20th AIM international workshop,
Tsukuba, January 23-24, 2015
Hasegawa, T., Fujimori, S., Takahashi, K., Masui, T., 2015. Scenarios for the risk of hunger in the twenty-first century using Shared Socioeconomic Pathways. Environmental Research Letters 10, 014010.
Article URLhttp://iopscience.iop.org/1748-9326/10/1/014010
Background• Climate change would increase the number of malnourished children.
• The climate impacts strongly depend on population and GDP.– Based on the SRES and CMIP3; Need updated.– Only population 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
Introduction
Objectives
1. Develop 21st‐century scenarios for the risk of hunger consistent with SSPs as a baseline of climate impact research on agriculture
2. Identify the elements strongly affecting future risk of hunger
• 7 socioeconomic indicators were considered:– Population, demographic change, GDP, equality of food distribution, crop yields, irrigation area, land productivity of livestock and wood products
3
Introduction
Shared Socioeconomic Pathways
4
Low population growth; high economic growth;high levels of education
and governance; globalization, international cooperation, technological
development, and environmental awareness.
Rapid population growth; Moderate economic growth; low levels of education and
governance; Regionalization;low environmental
awareness.
Low population growth; high economic growth;
high human development; low environmental awareness.
Method
Based on O’Neill et al. (2014)
A mixed world, with rapid technological
development in high income countries. In other regions, development proceeds
slowly. Inequality remains high.
• Economic model• Fundamental idea: • supply = demand, • balanced by price mechanism
Population & income growthIncrease in food demandIncrease in food priceProducers: increase in production (cropland expansion, yield growth)Consumers: decrease in consumption; shift to less expensive goods
AIM/CGE (Computable General Equilibrium)
Price
Quantity
Supply curveDemand curve
5
Domestic distribution of food energy(FAO,2008)
Method
Parameters related to food and hunger
6
Method
AIM/CGE
Parameters:Population, demographic changeGDPEquality of food distributionCrop yieldIrrigation areaLand productivity of livestock and wood productsIncome elasticity of food demandPrice elasticity of land use changePrice elasticity of trade
Endogenous variables:Food consumptionMeat consumptionLand use (Cropland, pasture, forest)
Each parameters were determined from the adaptation viewpoint
Hasegawa et al. 2015
7
AIM/CGE
Parameters:Population, demographic changeGDPEquality of food distributionCrop yieldIrrigation areaLand productivity of livestock and wood productsIncome elasticity of food demandPrice elasticity of land use changePrice elasticity of trade
Endogenous variables:Food consumptionMeat consumptionLand use (Cropland, pasture, forest)
Three approaches for assuming parameters1. Based on observed data
(Stylized fact)2. Based on existing studies3. Assumed in line with SSP
storylines if neither were available.
Method
Parameters related to food and hunger
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0 20000 40000 60000 80000
Coefficient of v
ariatio
n (CV) of the
dom
estic
distrib
ution of dietary ene
rgy consum
ption
GDP per capita [US$, 2005]
observed data
Optimistic
Median
Pessimistic
0.134
0.109 2
0.054
0.6894
0.6531 (Observed data; 0.61)
0.4609
y x
y x R
y x
−
−
−
= ⋅
= ⋅ =
= ⋅
Comparison with observed data:the improved equality of food distribution with income growth
8
Intermediate
Pessimistic
Optimistic
Equality
Inequality(2005)
Method
Hasegawa et al. 2015
0
100
200
300
400
500
600
700
800
0 20000 40000 60000 80000
Meat‐ba
sed calorie
intake
[kcal/da
y/cap]
income [US$/person]
observed dataOptimisticMediumPessimistic
2
60.0 ln 399.8
87.8 ln 545.3(Observed data; 0.57)130 ln 833.0
y x
y x Ry x
= ⋅ −
= ⋅ − == ⋅ −
Comparison with observed data:increased meat consumption with income growth
9
(1980 ‐ 2009)
Intermediate
Pessimistic
Optimistic
Method
Hasegawa et al. 2015
0500
1000150020002500300035004000
2005
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Carolie intake [kcal /pe
rson
/day]
The 21st‐century Scenarios of risk of hunger using SSPs
10
Food consumption per capita
0
2000
4000
6000
8000
10000
12000
SSP1 SSP2 SSP3 SSP4 SSP5
2005 2100
Land
use [M
ha] Food crops
Pasture
Grassland
Managed forest
Primary forest
SSP1SSP2SSP3SSP4SSP5
Land use change
Population at risk of hunger
Results
0100200300400500600700800900
2005
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Popu
latio
n at risk of h
unger
[million]
SSP3
SSP4
SSP2
SSP5
SSP1
Hasegawa et al. 2015
Decomposition analysisWhat strongly affects future risk of hunger?
• Change in hunger risk was decomposed into three factors;
11
Change in pop. at risk of hunger
= + + +Residual Population growth
Inequality of food distribution
Food consumption
*See discussion paper for more detail @http://www.nies.go.jp/social/dp/dpindex.html.
Method
12
Factors affecting future hunger risk(global, at 2005 level)
• Population, inequality of food distribution causes large differences in hunger risk among SSPs
‐1500
‐1000
‐500
0
500
1000
2005 2020 2035 2050 2065 2080 2095
Change in pop
ulation at risk of
hunger [m
illion]
a) SSP1
‐1500
‐1000
‐500
0
500
1000
2005 2020 2035 2050 2065 2080 2095Ch
ange in pop
ulation at risk of
hunger [m
illion]
b) SSP2
‐1500
‐1000
‐500
0
500
1000
2005 2020 2035 2050 2065 2080 2095
Change in pop
ulation at risk of
hunger [m
illion]
c) SSP3
‐1500
‐1000
‐500
0
500
1000
2005 2020 2035 2050 2065 2080 2095
Change in pop
ulation at risk of
hunger [m
illion]
d) SSP4
‐1500
‐1000
‐500
0
500
1000
2005 2020 2035 2050 2065 2080 2095
Change in pop
ulation at risk of
hunger [m
illion]
e) SSP5residual
per‐capita calorie(domestic production)inequality of food distribution
population
net change
Result
Hasegawa et al. 2015
• 21st‐century risk of hunger differs among SSPs• Regional distribution depends greatly on population growth, equality in food
distribution and increase in food consumption• Regions with greater population
growth face higher risk of hunger.
Regional population at risk of hunger and its factors (SSP3, 2100)
Hasegawa et al. in review
The most pessimistic scenario (SSP3)
Rest of Africa, 39%
India, 23%
Rest of Asia, 16%Middle
East
Southeast Asia
Rest of South America
China Brazil North Africa Former
Soviet Union
0100200300400500600700800900
2005
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Popu
latio
n at risk of h
unger
[million]
SSP3
SSP4
SSP2
SSP5SSP1
‐500
‐250
0
250
500
Chan
ge in
pop
ulation at risk of
hunger [m
illion]
residual
trade
per‐capita calorie
inequality of fooddistribution
population
(Relative to 2005)
Comparison of the population at risk of hunger with existing studies
14
• Pop. at risk of hunger in this study was lower than in existing studies.
• Improvements in food distribution equality was considered in this study whereas it was not for existing studies.
• Inequality of food distribution influences long‐term assessments of hunger risk.
0
200
400
600
800
1000
1200
1990 2020 2050 2080
Popu
latio
n at risk of h
unger [million]
A1A2B1B2
• Lines: this study• Dots: existing studies
Result & discussion
SSP3
SSP4
SSP2
SSP5
SSP1
Hasegawa et al. 2015
ConclusionWe developed scenarios for hunger risk in the 21stcentury using SSPs. Factors affecting future hunger risk were investigated.
• Risk of hunger without climate change in the 21stcentury differed among SSPs
• Factors influencing the future reduction of hunger risk were population, inequality of food distribution, and per‐capita food consumption.
• Inequality of food distribution greatly influences long‐term assessments of hunger risk.
15
Conclusion
Thank you for your attention!
16
AcknowledgmentsThis research was supported by the Environment Research andTechnology Development Fund (2–1402) of the Ministry of theEnvironment, Japan, and the climate change research program of theNational Institute for Environmental Studies.
17
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