Modelling the Economic Impact
of Climate Change:
Early Results,
Methodological Challenges
Roberto Roson
Università Ca’Foscari, ICTP and FEEM
EEE Seminar , Trieste, December 16th, 2003
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Motivation
•
•
To provide a basic overview of
the methodology
To illustrate and comment some
early simulation results
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Integrated Assessment
Models
Myth and Reality
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An Ideal IAM
Physical
Effects
Socio-Economic
System(s)
Climate
System(s)
Emissions
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Actual Approach #1: IPCC
• A series of socio-economic “scenarios”
(A1, A2, B1, B2)
• Forecasts of future climate consistent with
given benchmarks
• No feedback on the economy
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Actual Approach #2: RICE
• A series of parallel regional growth model
linked by the climate externality
• Emissions proportional to GDP
• Climate module translates emissions into
temperature changes
• Temperature affects productivity (potential
GDP)
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Inadequacy of Current IAM Models
•
•
the description of the world economic structure is
often too simplistic: limited number of industries
(sometimes only one good, available for both
consumption and investment), poor or absent
description of international trade and capital flows.
the multi-dimensional nature of the impact of the
climate change on the economic systems is
disregarded. This is usually accommodated by
specific ad-hoc relationships, making a certain
fraction of potential income “melting away” as
temperature increases
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The General Equilibrium Concept
p
S(p,…)
D(p,…)
q
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Are GE effects relevant in this
context?
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How the model works
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Simulation #1: Sea Level Rise
• Two scenarios: no protection/ full protection
• NP: reduction in the land stock
• FP: additional protective investment expenditure
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Early Results: Sea Level Rise
Tab. I: No protection scenario: main econo mi c indicators
Land lost
(% change
w.r.t.
baseline)
USA
EU
EEFSU
JPN
RoA1
EEx
CHIND
RoW
-0.055
-0.032
-0.018
-0.153
-0.006
-0.184
-0.083
-0.151
Value of land lost
1997
million
US$
102
187
611
20
221
15556
324
13897
% of
GDP
0.0002
0.0010
0.0100
0.0001
0.0030
0.1010
0.0030
0.0600
GDP
(% change
w.r.t.
baseline)
Household
private
expendit.
(% change
w.r.t.
baseline)
CO 2
Emiss ions
(% change
w.r.t.
baseline)
-0.002
-0.001
-0.002
-0.001
0.000
-0.021
-0.030
-0.017
-0.031
-0.026
-0.023
0.011
0.001
-0.028
0.012
-0.025
0.010
0.012
0.005
0.035
0.015
-0.008
-0.024
-0.012
Tab. IV: Total protection scena rio: main econo mi c indicators
Coasta l protection
expenditure
Region
USA
EU
EEFSU
JPN
RoA1
EEx
CHIND
RoW
1997
million
US$
% of
GDP
5153
11213
23076
7595
71496
363856
11747
38808
0.010
0.025
0.332
0.032
0.799
0.185
0.106
0.148
Investment
induced by
coastal
protection
(% change
w.r.t. baseline)
0.151
0.302
3.179
0.242
9.422
2.235
1.254
1.817
GDP
(% change
w.r.t.
baseline)
0.001
-0.022
0.049
-0.009
0.103
0.015
0.003
0.009
Household
private
expendit.
(% change
w.r.t.
baseline)
-0.31
-0.35
0.59
-1.08
0.90
-0.08
-1.13
-0.25
CO 2
Emiss ions
(% change
w.r.t.
baseline)
-0.069
-0.160
-0.133
-0.344
-0.130
-0.069
-0.116
-0.115
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Simulation #2: Health
• Two simultaneous effects
• Variations in labour stock/productivity
• Exogenous change in health services
expenditure (by the public sector)
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Early Results: Health
Change in Labour
Productivity
( % change)
Change in Health
Expenditure
( % change)
Change in value
of GDP
( % change)
CO2 Emissions
( % change)
USA
0.138182
-0.000635
0.089527
0.032077
EU
0.244331
-0.037549
0.132018
0.050688
EEFSU
0.744981
-0.041414
0.430204
0.196618
JPN
-0.101737
0.059357
-0.05519
-0.046133
0.38926
-0.054031
0.227172
0.092279
EEx
-0.754314
0.09854
-0.319031
-0.159689
CHIND
0.291378
0.966142
0.243019
0.067339
RoW
-0.612715
0.239236
-0.250661
-0.115078
2050
RoA1
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Simulation #3: Tourism
• Estimation of O/D matrices of tourists with and
without climate change
• Hypothesis: % change in the number of tourists
in a region = % change of tourism expenditure
• Apply exogenous variations in the consumption
demand of (1) recreational services and (2)
hotels and restaurants within the broader sector
“Trade”
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