SoCoCA final workshop
Using climate projections for
analyses of economic impacts
and their social implications
Asbjørn Aaheim, Anton Orlov, Taoyuan Wei
CICERO
Forskningsparken, 19 – 20 Mars 2013
Levels of modeling, information and knowledge
Information: Global patterns and
long-term trends
Decisions: Position in negotiations
Information: Country aggregates for
socioeconomic and climate indicators
Decisions: Regulations, incentives
Information: Learning from others
and personal experiences
Decisions: Actions
Global - GCM
National - RegCM
Local – RegCM
Hydrological
Economic sectors and impacts of climate change in GRACE
A model for Global Responses to Anthropogenically driven Changes in the Environment
Sector
Specific impacts
Agriculture
Productivity of soil
Forestry
Growth of biomass
Fisheries
Size of of stocks
General impacts
Crude oil
Coal
Demand
Refined oil
Demand
Electricity
Demand
Gas
Demand
Iron and steel
Capital stock:
 Sea-level rise
 Extreme events
Labour supply:
 Health effects
Non-metallic minerals
Other manufacturing
Air transport
Tourist demand
Sea transport
Tourist demand
Other transport
Tourist demand
Other services
Tourist demand
Direct impacts in agriculture +2.5 oC global mean
Impact functions for
agriculture by world region
 Calibrated by estimates of direct
impacts in region at an increase in
global mean temperature of +2.5 °C
0,0800
0,0600
0,0400
Rate of change
 Based on the same functional
relationship as for Europe
0,1000
0,0200
0,0000
-0,0200
-0,0400
-0,0600
-0,0800
-0,1000
EU WEU EEU FSU MEA AFR SAS EAS PAS PAO NAM LAM
(ref)
0,300
WEU
0,200
EEU
0,100
FSU
0,000
MEA
AFR
-0,100
Calibration
 Calculate the EU impact with the
regional climate reference
SAS
-0,200
EAS
PAS
-0,300
PAO
-0,400
NAM
LAM
-0,500
0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,0 5,5 6,0
 Scale each parameter linearly to the
«observed» regional impact
 Keep the signs of dT and dP as in EU
The global scenarios: CO2 emissions in the RCP scenarios
35
30
PgC/year
25
20
MiniCAM - RCP 4.5
MESSAGE - RCP 8.5
15
10
5
0
2000
2005
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Temperature change (°C) and percent change in annual
precipitation at RCP4.5 and RCP8.5
Forested land
Populated areas
8,0
8,0
8,0
7,0
7,0
7,0
6,0
6,0
6,0
5,0
5,0
4,0
RCP8.5
5,0
4,0
3,0
3,0
2,0
2,0
1,0
1,0
0,0
0,0
RCP4.5
Celcius
RCP4.5
Celcius
Celcius
Crop land
RCP4.5
4,0
RCP8.5
RCP8.5
3,0
2,0
1,0
WEU EEU FSU MEA AFR SAS EAS PAS PAO NAM LAM
WEU EEU FSU MEA AFR SAS EAS PAS PAO NAM LAM
0,0
Axis Title
Forested land
Populated areas
20,0
20,0
10,0
10,0
10,0
0,0
0,0
0,0
RCP4.5
-10,0
RCP8.5
-20,0
-30,0
-40,0
WEU EEU FSU MEA AFR SAS EAS PAS PAO NAM LAM
RCP4.5
-10,0
RCP8.5
Percent
20,0
Percent
Percent
Crop land
WEU EEU FSU MEA AFR SAS EAS PAS PAO NAM LAM
RCP8.5
-20,0
-20,0
-30,0
-30,0
-40,0
RCP4.5
-10,0
-40,0
WEU EEU FSU MEA AFR SAS EAS PAS PAO NAM LAM
WEU EEU FSU MEA AFR SAS EAS PAS PAO NAM LAM
Annual increase in permit prices: 12 – 18 percent
2098
2095
2092
2089
2086
2083
2080
2077
2074
2071
2068
2065
2062
2059
2056
2053
2050
2047
2044
2041
2038
2035
2032
2029
2026
2023
2020
2017
2014
2011
2008
2005
Global costs of cutting emissions - 1000 US$/tC
2,5
2
1,5
1
0,5
0
Economic impacts of climate change. 1000 trill. US$ PPP.
1,00
1,00
0,00
0,00
AFR
-1,00
LAM
MEA
-2,00
PAO
-1,00
WEU
AFR
-2,00
PAS
-3,00
SAS
-4,00
EAS
FSU
-5,00
LAM
-3,00
MEA
-4,00
PAS
SAS
-5,00
EEU
-6,00
-7,00
NAM
PAO
-6,00
EAS
WEU
-7,00
FSU
EEU
-9,00
-9,00
2004
2009
2014
2019
2024
2029
2034
2039
2044
2049
2054
2059
2064
2069
2074
2079
2084
2089
2094
2099
-8,00
RCP8.5
2004
2008
2012
2016
2020
2024
2028
2032
2036
2040
2044
2048
2052
2056
2060
2064
2068
2072
2076
2080
2084
2088
2092
2096
2100
NAM
-8,00
RCP4.5
2100
2097
2094
2091
2088
2085
2082
2079
2076
2073
2070
2067
2064
2061
2058
2055
2052
2049
2046
2043
2040
2037
2034
2031
2028
2025
2022
2019
2016
2013
2010
2007
2004
Trill. US$
Change in global value added (GDP) by reducing emissions
from RCP8.5 path to RCP4.5 path
1,00
0,00
-1,00
-2,00
-3,00
-4,00
-5,00
-6,00
-7,00
-8,00
-9,00
Sub-Saharian Africa
Temperature increase by emission scenaiero and land
category in Sub-Saharian Africa (from 2006)
6
5
RCP4.5
3
RCP8.5
2
1
0
Crop
Forest
Population
Increase in precipitation in by emission scenario and
land category in Sub-Saharian Africa (from 2006)
8%
7%
6%
5%
Percent
+°C
4
4%
3%
RCP4.5
2%
RCP8.5
1%
0%
-1%
-2%
Crop
Forest
Population
2004
2007
2010
2013
2016
2019
2022
2025
2028
2031
2034
2037
2040
2043
2046
2049
2052
2055
2058
2061
2064
2067
2070
2073
2076
2079
2082
2085
2088
2091
2094
2097
2100
Trillion US$ (2004)
GDP for Africa
14
12
10
8
RCP8.5_CC
6
RCP4.5_CC
4
2
0
Costs of reducing emission pathway from RCP8.5 to RCP4.5 for Africa south of Sahara
Change in value added by sector
1400,0
1200,0
1000,0
Trill. US$
800,0
600,0
400,0
200,0
0,0
-200,0
-400,0
Alternative measures for the impacts of changing path from RCP4.5
to RCP8.5 on agriculture in 2100
20,0
15,0
10,0
5,0
Percent change
0,0
-5,0
Direct impacts
Volumes
-10,0
Value added
-15,0
-20,0
-25,0
-30,0
-35,0
WEU
CEE
FSU
MEA
AFR
SAS
EAS
PAS
PAO
NAM
LAM
From national economies to livelihood of small-holders
 Most small farmers produce what they need, not what they
can sell in markets
 Income from work off the farm may help farmers improve their
standards of living
In the tems of an economist: the budget constraint
changes from:
Income from production = total consumption and savings
to:
Food consumed from own farm + income from sales + wages
= Total consumption of food + consumption of other goods
The size of farms matters
Approximation of farms by size in Malawi
0,09
0,08
0,07
Frequency
0,06
0,05
0,04
0,03
0,02
0,01
0
0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1,0 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2,0 2,1 2,2 2,3 2,4 2,5 2,6 2,7 2,8 2,9 3,0 3,1 3,2 3,3 3,4 3,5 3,6 3,7 3,8 3,9
Farm size (ha)
Small:
Poor:
Consumption Consumption
constrained by
subject to
nutrition limit yield on own farm
Large:
Income from
production =
expenditures
Preliminary comparisons
Climate impacts (RCP8.5 vs RCP 4.5 for Africa south of Sahara):
 Productivity of soil in arable land: Down 17.5 percent
 Price of agricultural products: Up 14.9 percent
Wage effect:
 Climate impacts plus 5 % reduction of expected income from other sources
Case
Small farms
Poor farms
Largest farm
People - change
from base
Largest farm
People - change
from base
Base
2.93 ha
-
0.855
-
Climate impacts
1.95 ha
-1 196 000
0.865
78 000
Wage effect
1.75 ha
-2 546 000
0.875
156 000
Yield
0,7000
0,6000
0,4000
Base
0,3000
impacts
Wage effect
0,2000
0,1000
Percent work on farm
90,0
80,0
70,0
60,0
50,0
Base
40,0
impacts
30,0
Wage effect
20,0
10,0
ha
3,5
3,3
3,1
2,9
2,7
2,5
2,3
2,1
1,9
1,7
1,5
1,3
0,0
1,1
ha
100,0
0,9
0,0000
0,9
1,05
1,2
1,35
1,5
1,65
1,8
1,95
2,1
2,25
2,4
2,55
2,7
2,85
3
3,15
3,3
3,45
MKW
0,5000
Share of food provided from own farm
0,8
0,7
0,6
Base
0,4
Impacts
Wage effect
0,3
0,2
0,1
ha
3,5
3,4
3,3
3,2
3,1
3
2,9
2,8
2,7
2,6
2,5
2,4
2,3
2,2
2,1
2
1,9
1,8
1,7
1,6
1,5
1,4
1,3
1,2
1,1
1
0
0,9
Share
0,5
Consumption of food
0,5900
0,5400
0,4400
Base
0,3900
impacts
Wage effect
0,3400
0,2900
Consumption of other goods
0,2400
0,9
1,05
1,2
1,35
1,5
1,65
1,8
1,95
2,1
2,25
2,4
2,55
2,7
2,85
3
3,15
3,3
3,45
0,1300
ha
0,1250
0,1200
0,1150
0,1100
0,1050
Base
0,1000
impacts
0,0950
Wage effect
0,0900
0,0850
0,0800
0,9
1,05
1,2
1,35
1,5
1,65
1,8
1,95
2,1
2,25
2,4
2,55
2,7
2,85
3
3,15
3,3
3,45
MkW
MKW
0,4900
ha
That’s all for now
Thank you!