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2. geography
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23/10/2014
CGE MODEL WITH FORWARD LOOKING
EXPECTATIONS FOR CLIMATE CHANGE
ADAPTATION
EDIP-DP
ToPDad = tools & model for adaptation
research
EU FP7 project coordinated by VTT
Main objective: developing the next-generation tool set for
adaptation research
Focus areas: energy, tourism and transport
Methodologies: macro-econometric, CGE modelling, transport
network modelling & bottom-up investment analysis
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23/10/2014
Modeling impact of climate change
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Many models were developed in the last decades
Recursive or comparative dynamic CGE models with focus
on mitigation – ETS schemes, energy taxes, investments in
‘clean’ fuels & technology: GEM-E-3, EPPA, GTAP
Dynamic CGE models, based on optimal investment strategy:
DICE, RICE
Adaptation?
=Relatively new field of research with added complexity
Uncertainty on climate change impact and related damages +
impact of adaptation technology
Extension of DICE model: De Bruin K. et al (2009)
Examples using input-output methodology ARIO model:
Hallegatte S. et al (2011)
Generally: models are not really equipped for modeling
adaptation
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°C
Infrastructure with long lifetimes will
experience the climate conditions of 2030,
2050 and even 2100
In transport
Signaling ~ 3-10 year
Rolling stock ~ 30 years
Highways ~ 30 – 50 years
Waterway / Canals ~ +100 years
Damages
Resilience
Lifetime
2020
2050
2100
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What is your adaptation plan?
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EDIP
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EDIP = Economic model for analyzing the impact of EU policy on
Distribution of Income and Poverty
Computable general equilibrium model of 31 EU countries, based on
National account data & input-output data
Model integrates 59 sectors, based on NACE v2
Implemented in GAMS software
Data was recently updated, following outputs from other projects such
as ExioPoll and WIOD
Model has a detailed transport sector
EDIP can be coupled to the TREMOVE demand & emissions module for
transport
Applied in a number of EU FP7 projects: REFIT, IceWin, NEUJOBS and
now in ToPDad
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Methodology
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1) Develop a module for optimal investment in adaptation
applying the general ideas presented in the previous slides
2) Link the optimal investment module to the main CGE model
3) Apply a test case – assuming long-term climate damages for
the transport sector
4) Compare results of ‘Optimal’ – ‘No’ and ‘Reactive’ Adaptation
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Modeling an adaptation response (1)
Long term utility (U), represented by intertemporal iso-elasticity function,
with consumption ( ), discount rate (0 < < 1), intertemporal elasticity of
substitution ( > 0) until a time horizon (T).
=
1−
(1)
We assume that there is a production sector using capital and labour as
inputs (K & L), represented by a Constant Elasticity of Substitution (CES)
function.
,
=
.
/
.
+ (1 − )
/
.
(2)
Capital is subject to depreciation and investments ( ) are necessary for the
capital stock to grow. At the same time, capital may experience random
shocks ( ).
= 1−
+
+
(3)
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Modeling an adaptation response (2)
The investor can freely choose between both types of stocks,
with
a parameter for measuring the efficiency of capital stock
=
+
(4)
The adapted asset has a lower vulnerability for damages due to
extreme events ( ). We assume that possible damages are equal to
( . ), with −1 < < 0 and 0 ≤ ≤ 1
When
When
is equal to 0, the adapted stock provides perfect protection.
≤ , the long term efficiency of the ‘adapted’ asset is lower
than the vulnerable one
=
1+
&
≥
=
>0
1+
(8)
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Perfect adaptation to known shock
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2
3
4
1
2
3
4
Deceleration
Acceleration
Shock
Adjustment
+- no deviation from
long term capital stock
Adaptation decreases
after shock
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Reactive adaptation
Initial shock is 50% and takes place in period t=10. The
expectation for future damages is equal to 20% of the initial shock
(10%). In the next period the expectation decreases to 90% of
the previous period’s expectation. This continues for each period
without a shock.
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Combined response (expected + reactive)
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Linking optimal investment module to EDIP
Calibrate static version
of EDIP model
Calibrate perfect
foresight model
Choose and apply climate
change scenario
Long term growth rate
by economic sector
Capital cost, rate of
innovation, discount rate
Hazard rate, damage
intensity, climate
Dynamic reference
scenario of EDIP
Run dynamic model with perfect foresight
Choose active sectors,
growth scenario
Share of adapted stock,
given expected damage
Stochastic simulation of
shocks
Run sequential model, including adapted stock and stochastic shocks
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,
EDIP
Dynadapt
,
∗
,
The desired level of investments in the EDIP model, can be
derived from the desired capital stock in the next period ∗ .
This means that in EDIP, investments in period t (
) are
equal to (with s indicating sectors).
,
=
∗
,
− 1−
,
.
Investments in stock are linked in EDIP to savings, such that
,
=
−
−
+
+
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Results in a dynamic program with optimal
investments (Ramsey type)
3.5
Temperature profile until 2100
Θ=0.1 (hazard rate)
Ω=0.2 (damage inflation)
D0 = 5%
T* = 0.8 (hazard T)
T**= 1.5 (damage T)
3
Degree of warming
2.5
2
Δ= 0.05 (depreciation)
μ= 0/0.2 (price adapt)
1.5
1
0.5
0
2000
2020
2040
2060
2080
2100
2120
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Simulation with EDIP-DP
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Problem: Damages happen at discrete and unforeseen points in time > no smooth curve as in the pictures above
Solution: Simulate a dynamic non-homogeneous Poisson process that
uses the calculated probabilities as inputs and outputs discrete
‘disturbances’
EDIP models the full macro-economic impacts
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Actual shocks derived from heterogeneous
Poisson process (stochastic draw)
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Investment
1.025
Expected damages (in % of stock)
1.02
0.014
1.015
0.012
1.01
1.005
0.01
1
0.008
0.995
0.006
0.99
0.985
0.004
0.98
0.002
0.975
0.97
2010
2030
2050
2070
0
2010
2090
Total adapted stock (%)
2030
0.9
2070
2090
Capital stock
1.002
1
2050
1
0.8
0.7
0.998
0.6
0.5
0.996
0.4
0.994
0.3
0.2
0.992
0.1
0
2010
2030
2050
2070
2090
0.99
2010
2030
2050
2070
2090
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Disaggregated impact of shock on GDP
Impact of shock
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THANK YOU!
Questions?
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