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National Institute for Environmental Studies (NIES)
Tomoko Hasegawa, Shinichiro Fujimori, Kiyoshi Takahashi and Toshihiko Masui
1
The 7th IAMC meeting @College Park, Nov 17-19, 2014
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
Introduc2on
Objec;ves
1. Develop 21st-‐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
Introduc2on
Optimistic Pessimistic
Medium
Optimistic
Opt.(High income)
Med (Middle income)
Pes.(low income)
SSP5Conventional Development
SSP3Fragmentation
SSP2Middle of the road
SSP1Sustainability
SSP4Inequity
Socioe
cono
mic challenges fo
r mitigatio
n
Socioeconomic challenges for adaptation
SSP challenges space Risk of hunger
Shared Socioeconomic Pathways
4
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.
Rapid popula;on growth; Moderate economic growth; low levels of educa;on and
governance; Regionaliza;on;
low environmental awareness.
Low popula;on 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 • Cover the en2re economic market • Fundamental idea: • supply = demand • balanced by price mechanism 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
AIM/CGE (Computable General Equilibrium)
Price
Quan;ty
Supply curve Demand curve
5
Domes;c distribu;on of food energy (FAO,2008)
Method
Parameters related to food and hunger
6
Method
AIM/CGE
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
Endogenous variables: Food consump;on Meat consump;on Land use (Cropland, pasture, forest)
Optimistic Pessimistic
Medium
Optimistic
Opt.(High income)
Med (Middle income)
Pes.(low income)
SSP5Conventional Development
SSP3Fragmentation
SSP2Middle of the road
SSP1Sustainability
SSP4Inequity
Socioe
cono
mic challenges fo
r mitigatio
n
Socioeconomic challenges for adaptation
SSP challenges space Risk of hunger
Each parameters were determined from the adapta2on viewpoint
7
AIM/CGE
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
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.
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$, 2005]
observed data
Optimistic
Median
Pessimistic
0.134
0.109 2
0.054
0.6894
0.6531 (Observeddata; 0.61)
0.4609
y x
y x R
y x
−
−
−
= ⋅
= ⋅ =
= ⋅
Comparison with observed data: the improved equality of food distribu;on with income growth
8
Intermediate
Pessimis;c
Op;mis;c
Equality
Inequality (2005)
Method
Hasegawa et al. in review
0
100
200
300
400
500
600
700
800
0 20000 40000 60000 80000
Meat-‐ba
sed calorie
intake [k
cal/da
y/cap]
income [US$/person]
observed dataOptimisticMediumPessimistic
2
60.0 ln 399.8
87.8 ln 545.3(Observeddata; 0.57)130 ln 833.0
y x
y x Ry x
= ⋅ −
= ⋅ − =
= ⋅ −
Comparison with observed data: increased meat consump;on with income growth
9
(1980 -‐ 2009)
Intermediate
Pessimis;c
Op;mis;c
Method
Hasegawa et al. in review
05001000150020002500300035004000
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 consump;on 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
0100200300400500600700800900
2005
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Popu
latio
n at risk of h
unger
[million]
SSP1SSP2SSP3SSP4SSP5
Land use change
Popula;on at risk of hunger
Result
0100200300400500600700800900
2005
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Popu
latio
n at risk of h
unger
[million]
SSP1SSP2SSP3SSP4SSP5
SSP3
SSP4
SSP2 SSP5
SSP1
Hasegawa et al. in review
Decomposi;on analysis What strongly affects future risk of hunger?
• Change in hunger risk was decomposed into three factors;
11
Change in pop. at risk of hunger
= + + +Residual
Popula;on growth
Inequality of food distribu;on
Food consump;on
*See discussion paper for more detail @hjp://www.nies.go.jp/social/dp/dpindex.html.
Method
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Factors affec;ng future hunger risk (global, at 2005 level)
• Popula;on, inequality of food distribu;on 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 2095
Change 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) SSP5
-‐1500
-‐1000
-‐500
0
500
1000
SSP1 SSP2 SSP3 SSP4 SSP5
Change in pop
ulation at risk of
hunger [m
illion]
residual
per-‐capita calorie(domestic production)inequality of food distribution
population
net change
f) 2100
Result
Hasegawa et al. in review
• 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.
Regional popula2on at risk of hunger and its factors (SSP3, 2100)
Hasegawa et al. in review
The most pessimis2c 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]
SSP1SSP2SSP3SSP4SSP5
SSP3
SSP4
SSP2
SSP5 SSP1
-‐500
-‐250
0
250
500 Ch
ange in pop
ula2
on at risk of
hunger [m
illion]
residual
trade
per-‐capita calorie
inequality of food distribu;on
popula;on
(Rela;ve to 2005)
0
200
400
600
800
1000
1200
1990 2020 2050 2080
Popu
latio
n at risk of h
unger [million]
SSP1SSP2SSP3SSP4SSP5Alexandratos and Bruinsma, 2012 medArnell et al., 2002 highArnell et al., 2002 lowArnell et al., 2002 medParry et al., 2004 A1Parry et al., 2004 A2Parry et al., 2004 B1Parry et al., 2004 B2Parry et al., 2005 highParry et al., 2005 lowParry et al., 2005 medSchmidhuber and Tubiello, 2007 (AEZ+BLS) A1Schmidhuber and Tubiello, 2007 (AEZ+BLS) A2Schmidhuber and Tubiello, 2007 (AEZ+BLS) B1Schmidhuber and Tubiello, 2007 (AEZ+BLS) B2Schmidhuber and Tubiello, 2007 (DESSAT+BLS) A1Schmidhuber and Tubiello, 2007 (DESSAT+BLS) A2Schmidhuber and Tubiello, 2007 (DESSAT+BLS) B1Schmidhuber and Tubiello, 2007 (DESSAT+BLS) B2
• Lines: this study • Dots: exis;ng studies
SSP3
SSP4
SSP2 SSP5
SSP1
Comparison of the popula;on at risk of hunger with exis;ng studies
14
• 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.
Result & discussion
Hasegawa et al. in review
Conclusion (1/2) We developed scenarios for hunger risk in the 21st century using SSPs. Factors affec;ng future hunger risk were inves;gated. • Risk of hunger without climate change in the 21st 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
Conclusion
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
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