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WITCH Model Description and Applications
FEEM
WITCH Model, Description and Applications 2
The WITCH Team:
Andrea Bastianin
Valentina Bosetti
Carlo Carraro
Enrica De Cian
Alice Favero
Emanuele Massetti
Lea Nicita
Elena Ricci
Fabio Sferra
Massimo Tavoniwww.feem-web.it/witch
WITCH Model, Description and Applications 3
The WITCH Model: An Introduction
WITCH Model, Description and Applications 4
The WITCH Model
WITCH: World Induced Technical Change Hybrid model
Hybrid I.A.M.: Economy: Ramsey-type optimal growth (inter-temporal) Energy: Energy sector detail (technology portfolio) Climate: Damage feedback (global variable)
12 Regions (“where” issues) Intertemporal (“when” issues) Game-theoretical set-up (free-riding incentives)
Bosetti V., E. De Cian, A. Sgobbi and M. Tavoni (2009). “The 2008 WITCH Model: New Model Features and Baseline,” FEEM Working Paper October 2009.
Bosetti V., E. Massetti, M. Tavoni (2007). “The WITCH Model, Structure, Baseline, Solutions”, FEEM Working Paper 10.2007.
Bosetti, V., C. Carraro, M. Galeotti, E. Massetti and M. Tavoni (2006). “WITCH: A World Induced Technical Change Hybrid Model”, The Energy Journal, Special Issue. Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-down, 13-38.
Economic Activity
Energy Use
emissions
AtmosphereBiosphere
Deep Oceans
temperatureEconomic Activity
Energy Use
emissions
AtmosphereBiosphere
Deep Oceans
temperatureEconomic Activity
Energy Use
emissions
AtmosphereBiosphere
Deep Oceans
temperature
WITCH Model, Description and Applications 5
The WITCH model - http://www.feem-web.it/witch/
A hybrid energy-economy-climate model Scale: global, with the world divided in 12 regions Economy: top-down intertemporal optimal growth model,
dynamic, perfect foresight Energy: bottom-up description of technological options:
Electric and Non Electric energy use Six fuel types specified (oil, gas, Coal, Uranium, traditional
and advanced biofuels) Seven technologies for electricity generation
Endogenous technical change – Learning-By-Doing and Learning-By-Researching
Climate: damage feedback via temperature change Strategic: non cooperative interactions between region with
externalities (environmental, price of exhaustible resources, technological spillovers, and trade of emission permits)
WITCH Model, Description and Applications 6
Bottom-up characterisation of the energy sector
Detailed representation of technological change
Learning-By-Doing in W&S
Energy intensity R&D
Breakthrough Technologies (two factors learning curves)
Several channels of interactions among regions
Technological spillovers
Environmental externality
Exhaustible common resources (coal, natural gas and uranium)
Trade of emission permits
Trade of oil
Game-theoretic set-up makes it possible to model strategic behaviour (open loop
Nash game) and to describe cooperative and non-cooperative solutions
Distinguishing Features
WITCH Model, Description and Applications 7
Two possible regional aggregations
United States (USA) Western EU countries
(WEURO) Eastern EU countries
(EEURO) Canada, Japan and New
Zealand (CAJANZ) Korea, Australia and South Africa
(KOSAU) Non-EU Eastern European
countries, including Russia (TE) Latin America, Mexico and
Caribbean (LAM) Middle East and North
Africa (MENA) South Asia, including India
(SASIA) China, including Taiwan
(CHINA) Sub‑Saharan Africa
excluding South Africa (SSA) South East Asia (EASIA)
World countries, aggregated into 12 regions
United States (USA) Western EU countries
(WEURO) Eastern EU countries
(EEURO) Canada, Australia and
NewZealand (AUCANZ)
Korea, Japan (JPNKOR) Non-EU Eastern
European countries, including Russia (TE)
Latin America, Mexico and Caribbean (LAM)
Middle East and North Africa (MENA)
South Asia, including India (SASIA)
China, including Taiwan (CHINA)
Sub‑Saharan Africa including South Africa (SSA)
South East Asia (EASIA)
WITCH Model, Description and Applications 8
The Objective Function and Budget Constraint
For each region (n) forward-looking central planner maximizes present value of (log) per capita consumption (5-yr time steps):
choosing the optimal path of investment variables simultaneously and strategically with respect to the other decision makers
Consumption of the single final good obeys to the economy budget constraint:
W (n) L (n, t) log c(n, t) R(t)t
(1)
(2)
tnCCStnPtnXtnP
tnO&MtnItnItnItnYtnC
CCSf ff
j j jjj jDRC
,,,,
,,,,,, ,&
GDPFinal Good
EnergyR&Ds
ElectricityGeneration
Operation & Maintanance
Net fuel expenditures
CCS (Transport and storage costs)
WITCH Model, Description and Applications 9
Output and Climate Damage
Gross output is produced combining the inputs capital, labour (=population) and energy services using a nested, Constant Elasticity Production Function
(3) tntnESntnLtnKntnTFPtnY nnC ,,))(1(,,)(,,
/1)()(1
2,2,1 )()(1),( tTtTtn nn (4)
GROSS GDP
Climate change damage is a non-linear function. Climate change impacts can be either positive or negative and they are region-specific
WITCH Model, Description and Applications 10
Output and Climate Damage
Net output is obtained after subtracting expenditure for fossil fuels, which is considered as a net loss for the economy
CCS is the amount of CO2 captured from the atmosphere and PCCS the corresponding costs that the economy has to pay to external suppliers of CCS know-how
(5)
tnCCStnP
tnXtPtnXtnP
tn
tnESntnLtnKntnTFPtnYnet
CCS
f netimpffextrff
nnC
,,
,,,
,
,))(1(,,)(,,
,int
,
/1)()(1
WITCH Model, Description and Applications
Production Tree and Energy Technologies
11
Production nest and the elasticity of substitution
Legenda: KL= Capital-labour aggregate; K = Capital invested in the production of final good; L = Labour; ES = Energy services; HE = Energy R&D capital; EN = Energy; EL = Electric energy; NEL = Non-electric energy; OGB = Oil, Backstop, Gas and Biofuel nest; ELFF = Fossil fuel electricity nest; W&S= Wind and Solar; ELj = Electricity generated with technology j (IGCC plus CCS, Oil, Coal, Gas, Backstop, Nuclear, Wind plus Solar); TradBiom= Traditional Biomass; TradBio= Traditional Biofuels; AdvBio= Advanced Biofuels
WITCH Model, Description and Applications 12
Electricity Production - 1
Electricity is obtained by combining in fixed proportions the installed power generation capacity (K), operation and maintenance equipment (O&M) and fuel resources consumption (X) (when needed)
Power Plant
Fuels
Operation and
Maintenance
Electricity
Production function are characterized by region-specific parameters that account for the technical features of each power production technology, such as the low utilisation factor of renewables, the higher costs of running and maintaining IGCC-CCS and nuclear plants
WITCH Model, Description and Applications
13
tnXtnO&MtnKtnEL ELjjjjnjjnj ,;,;,min, ,,,
Electricity production is described by a Leontief production function
),(
),(1),(1,
tnSC
tnItnKtnK
j
jjjj
Power Generation capacity (Power Units) depends on cumulated investments (I) and investments costs (SC) which are time and region-specific:
(6)
(7)
Electricity Production - 2
μ translates power capacity into electricity generation
Τ differentiates O&M over technologies
ζ yields the quantity of fuels needed to generate 1 KwH of electricity
WITCH Model, Description and Applications 14
Technical Change – Learning-By-Doing
Learning-By-Doing via experience curves in power plants investment cost
Endogenous Technical Change (ETC) accounts for the accumulation of both:
• Experience (Learning-By-Doing)• R&D investment (Learning-By-Researching)
nt n
PRjjj
jtnKBtnSC 2log1,,
World learning, assuming full technology spillover: investments in additional capacity by virtuous regions drive down investment costs worldwide, with benefits also for the non investing regions
(9)
WITCH Model, Description and Applications 15
Technical Change – Energy Efficiency
/1
),(),(, tnENtnHEtnES ENH
cbDR tnHEtnIn a tnZ ),(),(, &
tnZtn HE) tHE(n DR ,)1)(,(1, &
The R&D sector exhibits intertemporal spillovers and the production of new "ideas" follows an innovation possibility frontier (Popp, 2002; Jones,1995):
The flow of new ideas adds to the previously cumulated stock and generates the total amount of knowledge available to country n at time t:
Learning-By-Researching via energy R&D increasing energy efficiency (Popp, 2004)
(10)
(11)
(12)
WITCH Model, Description and Applications
Technical Change – International Spillovers
)),(),((),(
),(),( tnHEtnHE
tnHE
tnHEtnSPILL
HIHI
dcbDR tnSPILLtnHEtnIn a tnZ ,),(),(, &
The R&D sector exhibits also international knowledge spillovers:
(13)
(14)
The contribution of foreign knowledge to the production of new domestic ideas depends on the interaction between two terms: the first describes the absorptive capacity whereas the second captures the distance from the technology frontier, which is represented by the stock of knowledge in rich countries (USA, WEURO, EEURO, CAJANZ and KOSAU)
Absorptive capacity Distance from the frontier
WITCH Model, Description and Applications 17
Technical Change – Advanced Biofuels
Learning-By-Researching via dedicated R&D decreasing the cost of the cellulosic
biofuels, PADVBIO(n,t)
)),((0,, ,& tnTOTnPtnP ADVBIODRADVBIOADVBIO
t
tADVBIODR
nADVBIODRADVBIODR nItnKtnTOT
1,&,&,& ),()2,(),(
(15)
(16)
where stands for the relationship between new knowledge and cost
The stock of world R&D (ΣK) accumulates with the perpetual rule and it will influence other regions with a 10-year (2model periods) delay. The time lag is meant to account for the advantage of first movers in innovation
WITCH Model, Description and Applications 18
Technical Change – Breakthrough Technologies
Learning-By-Doing and Learning-By-Researching via cumulative capacity and dedicated R&D
decreasing the cost of breakthrough technologies, following a two factors learning curve
(17)
b
tec
Ttec
c
tec
Ttec
tec
Ttec
CC
CC
DR
DR
P
P
0,
,
0,
2,
0,
, *&
&
where the R&D stock (R&D tec) accumulates with the perpetual rule and it is also augmented by the stock of R&D accumulated in other regions through a spillover effect, SPILL, similarly to energy efficiency R&D
Two breakthrough technologies: one as substitute for nuclear in power generation and one as substitute for oil in the non-electric sector (transport)
WITCH Model, Description and Applications 19
Major Research Topics
Mitigation options and costs
Innovation
Uncertainty
International policy architectures and coalition theory
Optimal balance between mitigation and adaptation
WITCH Model, Description and Applications 20
Major Research Topics
Mitigation options and costs
Innovation
Uncertainty
International policy architectures and coalition theory
Optimal balance between mitigation and adaptation
WITCH Model, Description and Applications 21
Mitigation Options, Technologies, Carbon Markets - 1
Major Areas of Research: Optimal investments in energy technologies Optimal investments in R&D Climate policy costs: global and distribution Climate policy costs with limits on the penetration of carbon free
technologies Modeling backstop technologies Investments in electricity grids International trade of oil Financing climate policy Carbon markets
WITCH Model, Description and Applications 22
Mitigation Options, Technologies, Carbon Markets - 2
Key Findings: First energy efficiency, then decarbonization Climate policy costs are moderate for a 650 ppm CO2-eq Climate policy costs increase but are still reasonable for a 550 ppm
CO2-eq scenario No silver bullet. Complex portfolio mix with: nuclear, renewables,
coal with ccs Stringent climate policy is unfeasible with delayed (2030) or
incomplete action (China, India) Modeling international trade of oil tilts distribution of costs towards
oil exporting countries
WITCH Model, Description and Applications 23
-20%
0%
20%
40%
60%
80%
100%
0% 20% 40% 60% 80% 100%
Energy Intensity Improvement
Decarb
on
izati
on
450
550
BAU
past 30 yrs
Energy savings and efficiency should be pursued vigorously in the short term, but decarbonisation is essential from 2030 onwards already
2030
20502100
2030
2050
2100
2100
2050
2030
550
650
Changes in Energy and Carbon Intensities
WITCH Model, Description and Applications 24
World Electricity Generation Shares
2000 2020 2040 2060 2080 21000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
NuclearHydroelectricOilGasIGCC+CCSTrad CoalWind&Solar
Electricity Mix, 550 ppm CO2-eq
WITCH Model, Description and Applications 25
Mitigation Options, Technologies, Carbon Markets - 3
References: Bosetti, V., C. Carraro, E. Massetti, A. Sgobbi and M. Tavoni (2009). “Optimal
Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations,” Resource and Energy Economics, 31(2): 123-137.
Bosetti, V., C. Carraro and E. Massetti (2009). “Banking Permits: Economic Efficiency and Distributional Effects,” Journal of Policy Modeling, 31(3): 382-403.
De Cian, E. and M. Tavoni (2009). “Sharing the burden to 2050: what role for an international carbon market?” Fondazione Eni Enrico Mattei, July 2009, mimeo.
Bastianin, A., A. Favero and E. Massetti (2009). “Investing in a Low-Carbon World,” Fondazione Eni Enrico Mattei, July 2009, mimeo.
Massetti, E. and F. Sferra (2009). “A Numerical Analysis of Optimal Extraction and Trade of Oil Under Climate Policy and R&D Policy,” Fondazione Eni Enrico Mattei, July 2009, mimeo.
Tavoni, M., B. Sohngen and V. Bosetti (2008). "Forestry and the Carbon Market Response to Stabilize Climate", Energy Policy, 35: 5346-5353.
WITCH Model, Description and Applications 26
Major Research Topics
Mitigation options and costs
Innovation
Uncertainty
International policy architectures and coalition theory
Optimal balance between mitigation and adaptation
WITCH Model, Description and Applications 27
Innovation - 1
Major Areas of Research: Directed technical change Human capital accumulation International knowledge spillovers Intersectoral knowledge spillovers Two factors learning curves for backstop technologies
WITCH Model, Description and Applications 28
Innovation - 2
Key Findings: Sharp increment of energy R&D (four-fold) is needed R&D investments in backstop technologies play a key role when
there are constraints to the development of nuclear and/or renewables
Modeling international disembodied R&D spillovers does not change mitigation policy costs
Intersectoral R&D spillovers might have a greater influence With directed technical change, overall R&D investments
decline with climate policy, and GDP losses increase Human capital is pollution-using (due to the complementarity
between labor and energy) and therefore climate policy re-directs investments away from education toward R&D which instead is pollution-saving
WITCH Model, Description and Applications 29
Investment in R&D with Breakthrough Technologies
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
2007 2012 2017 2022 2027 2032 2037 2042 2047 2052
% o
f w
orl
d G
DP
Baseline
550ppm
550ppm w ith backstops
Breakthrough technologies can only become available with substantial investments in R&D Energy R&D expenditures increase up to 0.12% of GDP, vs. 0.02% in the BAU
WITCH Model, Description and Applications 30
Mitigation Costs with the Backstop Technologies
The price of carbon is much lower with breakthrough technologies
Crucial role to decarbonize non-electric energy (transport)
And therefore the costs of stabilisation are much lower, especially in the long term
0
50
100
150
200
250
300
350
400
450
500
2007 2012 2017 2022 2027 2032 2037 2042 2047 2052
$U
S/t
CO 2
eq
550ppm
550ppm w ithbackstops
-8.0
-7.0
-6.0
-5.0
-4.0
-3.0
-2.0
-1.0
0.0
2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072 2077 2082
% c
han
ge in
GD
P w
ith
resp
ect
to b
aselin
e
550ppm w ith backstops
550ppm
WITCH Model, Description and Applications 31
Induced Technical Change and GWP Losses
Overestimated when there is no ITC in the Energy Sector
Understimated when there is no ITC in the Non-Energy Sector.
Underestimated when there is no ITC
Understimated when there is only exogenous crowding out of Non-Energy R&D 3.4% 3.5% 3.6% 3.7% 3.8% 3.9% 4.0% 4.1%
No ITC Non-Energy
ExogenousCrowding-out
No ITC
Full ITC
No ITC Energy
Discounted GWP Loss from Climate Policy (%)
With respect to a Full Induced Technical change (ITC) Scenario Gross World Product (GWP) losses are:
WITCH Model, Description and Applications 32
Innovation - 3
References: Carraro, C., E. Massetti and L. Nicita (2009). “How Does Climate Policy Affect
Technical Change? An Analysis of the Direction and Pace of Technical Progress in a Climate-Economy Model.” The Energy Journal, Forthcoming.
Bosetti, V., C. Carraro and M.Tavoni (2009). “Climate Policy after 2012. Technology, Timing, Participation,” CESifo Economic Studies, Forthcoming.
Bosetti, V., C. Carraro, E. Massetti, A. Sgobbi and M. Tavoni (2009). “Optimal Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations,” Resource and Energy Economics, 31(2): 123-137.
Bosetti, V., C. Carraro, R. Duval, A. Sgobbi and M. Tavoni (2009). “The Role of R&D and Technology Diffusion in Climate Change Mitigation: New Perspectives using the WITCH Model.” OECD Working Paper No. 664, February.
Carraro, C., E. Massetti and L. Nicita (2009). “Optimal R&D Investments and the Cost of GHG Stabilization when Knowledge Spills across Sectors.” Fondazione Eni Enrico Mattei, July 2009, mimeo.
Carraro, C., E. De Cian and M. Tavoni (2009). “Human Capital Formation and Global Warming Mitigation: Evidence from an Integrated Assessment Model.” Fondazione Eni Enrico Mattei, July 2009, mimeo.
WITCH Model, Description and Applications 33
Major Research Topics
Mitigation options and costs
Innovation
Uncertainty
International policy architectures and coalition theory
Optimal balance between mitigation and adaptation
WITCH Model, Description and Applications 34
Uncertainty
Major Areas of Research: Stochastic WITCH Analysis of optimal investment trajectories under uncertainty Uncertainty on R&D productivity Policy uncertainty
Key Findings: Modeling innovation in a backstop technology as an uncertain process
leads to higher optimal levels of R&D investments Uncertainty on the stringency of the mitigation target leads to high
mitigation activity if a stringent target has the chance to come into force
References: Bosetti, V. and M. Tavoni (2009), "Uncertain R&D, backstop technology and
GHGs stabilization", Energy Economics, 31(1): S18-S26. Bosetti, V., C. Carraro, A. Sgobbi, and M.Tavoni (2009) "Delayed Action and
Uncertain Targets. How Much Will Climate Policy Cost?" Climatic Change, Forthcoming
WITCH Model, Description and Applications 35
Major Research Topics
Mitigation options and costs
Innovation
Uncertainty
International policy architectures and coalition theory
Optimal balance between mitigation and adaptation
WITCH Model, Description and Applications 36
International Policy Architectures - 1
Major Areas of Research: International climate policy architectures (Harvard Project on
International Climate Agreements) Stabilization costs, investments and innovation with different
degrees of cooperation Delayed participation of developing countries Optimal climate policy of high income countries in face of
delayed participation from low income countries The incentives to participate in and the stability of climate
coalitions
WITCH Model, Description and Applications 3737
International Policy Architectures - 2
• Global coalition with CAT and transfers
• Global coalition with carbon tax recycled domestically
• Global coalition with REDD
• Climate Clubs (sub-coalitions)
• Dynamic coalitions: incremental participation based on
Burden sharing rules
Graduation
Dynamic targets
• R&D and Technology coalition
WITCH Model, Description and Applications 3838
Name Key feature Policy
Instrument Scope Timing
CAT with redistribution
Benchmark cap and trade Cap and Trade Universal Immediate
Global carbon tax Global tax recycled domestically Carbon Tax Universal Immediate
REDD Inclusion of REDD Cap and Trade Universal Immediate
Climate Clubs Clubs of countries
Cap and Trade and R&D
Partial
Incremental
Burden Sharing Delayed participation of DCs. Cap and Trade Universal Incremental
Graduation Bottom up targets Cap and Trade Partial Incremental
Dynamic Targets Political feasibility Cap and Trade Universal Incremental
R&D Coalition R&D cooperation R&D Universal Immediate
International Policy Architectures - 3
NB All refer to CO2 only
WITCH Model, Description and Applications 3939
Climate Effectiveness
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
°C a
bo
ve p
re-i
nd
ust
ria
l
None of the policy architectures is able to keep temperature change below the 2°C threshold. A target between 2.5 and 3°C seems more feasible
WITCH Model, Description and Applications 4040
Economic Efficiency
Change in GWP wrt BaU - Discounted at 5%
-2.00%
-1.50%
-1.00%
-0.50%
0.00%
0.50%
CA
T w
ith
red
istr
ibu
tio
n
Clim
ate
Clu
bs
RE
DD
Bu
rde
n
Sh
arin
g
Gra
du
atio
n
Glo
ba
l ca
rbo
n
tax
Dyn
am
ic
Ta
rge
ts
R&
D C
oa
litio
n
While temperature change varies less across the eight architectures for agreement because of the inertia in the climate system, the economic costs of the different set-ups vary considerably. More stringent policy architectures imply a higher GWP loss
WITCH Model, Description and Applications 41
Non-Cooperative CO2 Emissions
The non-cooperative solution, defined also as the baseline, it best represents the strategic nature of international relations. Little variations are observed in a non-cooperative setting, reflecting the inability of individual regions to internalise the environmental externality
WITCH Model, Description and Applications 42
Cooperative CO2 Emissions
Sensitivity to these assumptions is far greater in the cooperative case. Higher damage and especially low discounting drive emissions down
WITCH Model, Description and Applications 43
CBA: Free riding – the case of SSA
Percentage Difference in EmissionsCoalition w/o SSa comapred to the Grand Coalition
0%
2%
4%
6%
8%
10%
12%
14%
2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
0%
50%
100%
150%
200%
250%
300%
USA
WEURO
EEURO
AUCANZ
JPNKOR
TE
MENA
SASIA
CHINA
SEASIA
LAM
SSA (RH Axes)
When Africa leaves the grand coalition• members emit more because they do not internalize the high negative impact of climate change on Africa (damage effect)• Africa emits more (free riding effect), but less than in the BaU (technology spillovers)
WITCH Model, Description and Applications 44
International Policy Architectures - 4
Major Findings: Delayed and fragmented participation of developing countries into
international climate agreements would raise the global policy costs considerably for serious stabilization targets
An international carbon market has the potential to alleviate such detrimental effects, but might involve large financial transfers
An agreement that envisions future commitments for some key emerging economies might represent a win-win strategy, since the optimal investment behavior is to anticipate climate policy
This is especially relevant for China, whose recent and foreseeable trends of investments in innovation are not incompatible with the adoption of domestic emission reduction obligations in 2030
In cost-benefit setting, only the Grand Coalition finds profitable to achieve the 550 ppm CO2-eq target, under very special condition
The Grand Coalition is neither stable nor potentially stable
WITCH Model, Description and Applications 45
International Policy Architectures - 5
References: Bosetti, V., C. Carraro, E. De Cian, R. Duval, E. Massetti and M. Tavoni (2009),
"The Incentives to Participate in and the Stability of International Climate Coalitions: a Game Theoretic Approach Using the WITCH Model," OECD Economics Department Working Papers No. 702, June 2009.
Bosetti, V., C. Carraro and M.Tavoni (2009), " Climate Policy After 2012. Technology, Timing, Participation,” CESifo Economic Studies, Forthcoming.
Bosetti, V., C. Carraro and M.Tavoni (2009) " Climate Change Mitigation Strategies in Fast-Growing Countries: The Benefits of Early Action”, Energy Economics, Forthcoming.
Bosetti, V., C. Carraro, A. Sgobbi, and M. Tavoni (2008). “Modelling Economic Impacts of Alternative International Climate Policy Architectures: A Quantitative and Comparative Assessment of Architectures for Agreement”, in Aldy and Stavins, eds, Post-Kyoto International Climate Policy: Implementing Architectures for Agreement Cambridge University Press, in press.
Bosetti, V., C. Carraro and M. Tavoni (2008), "Delayed Participation of Developing Countries to Climate Agreements: Should Action in the EU and US be Postponed?", FEEM Working Paper N.70-2008.
WITCH Model, Description and Applications 46
Major Research Topics
Mitigation options and costs
Innovation
Uncertainty
International policy architectures and coalition theory
Optimal balance between mitigation and adaptation
WITCH Model, Description and Applications 47
Balancing Mitigation and Adaptation PoliciesMajor Areas of Research:
Optimal mix of mitigation and adaptation policies Optimal investments in different adaptation forms
Key Findings: The introduction of adaptation decreases the need to mitigate and vice-versa Joint implementation of mitigation and adaptation in a cost-benefit framework
suggests that both policies are required Proactive adaptation is the first measure to be adopted. Reactive measures
prevail afterwards, when the damage is higher, and in non-OECD regions Developed countries are likely to experience minor aggregate
damages/benefits. Policy to control damages should focus on developing countries
References: Bosello, F., C. Carraro and E. De Cian (2009). “An Analysis of Adaptation as a
Response to Climate Change.” Copenhagen Consensus Center, July 2009 Bosello, F., C. Carraro and E. De Cian (2009). “Adaptation, Mitigation and Innovation: A
Comprehensive Approach to Climate Policy .” Fondazione Eni Enrico Mattei, September 2009, mimeo
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