* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download to a pps diaporama
Climate change in Tuvalu wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climate engineering wikipedia , lookup
Climate governance wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change mitigation wikipedia , lookup
Atmospheric model wikipedia , lookup
Solar radiation management wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Energiewende in Germany wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Open energy system models wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Public opinion on global warming wikipedia , lookup
German Climate Action Plan 2050 wikipedia , lookup
Climate change adaptation wikipedia , lookup
Climate change in the United States wikipedia , lookup
Economics of global warming wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Climate change, industry and society wikipedia , lookup
General circulation model wikipedia , lookup
Low-carbon economy wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Climate change and poverty wikipedia , lookup
Economics of climate change mitigation wikipedia , lookup
Politics of global warming wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Mitigation of global warming in Australia wikipedia , lookup
WITCH Model Description and Applications FEEM The WITCH Team: Andrea Bastianin Valentina Bosetti Carlo Carraro Enrica De Cian Alice Favero Emanuele Massetti Lea Nicita Elena Ricci Fabio Sferra Massimo Tavoni 1 WITCH Model, Description and Applications www.feem-web.it/witch The WITCH Model: An Introduction 2 WITCH Model, Description and Applications 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) Economic Activity Game-theoretical set-up (free-riding incentives) temperature 3 Atmosphere Biosphere Bosetti V., E. De Cian, A. Sgobbi and M. Tavoni (2009). “The 2008 WITCH Model: New Model emissions Features and Baseline,” FEEM Working Paper October 2009.Deep Oceans Bosetti V., E. Massetti, M. Tavoni (2007). “The WITCH Model, Structure, Baseline, Solutions”, FEEM Working Paper 10.2007. Bosetti, V., C. Carraro, Energy Use M. Galeotti, E. Massetti and M. Tavoni (2006). “WITCH: A World Induced Technical Change Hybrid Model”, The Energy Journal, Special Issue. Hybrid Modeling of EnergyEnvironment Policies: Reconciling Bottom-up and Top-down, 13-38. WITCH Model, Description and Applications 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) 4 WITCH Model, Description and Applications Distinguishing Features 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 5 WITCH Model, Description and Applications Two possible regional aggregations World countries, aggregated into 12 regions 6 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) WITCH Model, Description and Applications United States (USA) Western EU countries (WEURO) Eastern EU countries (EEURO) Canada, Australia and New Zealand (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) 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): (1) W (n) L(n,t) log c(n,t) R(t) t 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: GDP (2) 7 Final Good Energy R&Ds Electricity Generation Operation & Maintanance C n , t Y n , t I n , t I n , t I n , t O& n , t C R & D , j j j j j j P n , t X n , t P n , t CCS n , t f CCS ff Net fuel expenditures WITCH Model, Description and Applications CCS (Transport and storage costs) Output and Climate Damage Gross output is produced combining the inputs capital, labour (=population) and energy services using a nested, Constant Elasticity Production Function GROSS GDP (3) Y n , t TFP n , t ( n ) K n , t L n , t ( 1 ( n )) E n , t n , t 1 ( n )( n ) C 1 / Climate change damage is a non-linear function. Climate change impacts can be either positive or negative and they are region-specific (4) 8 2 ( n , t ) 1 T ( t ) T ( t ) 1 , n 2 , n WITCH Model, Description and Applications 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) 9 n, t (1 (n)) ES n, t n, t f Pf n, t X f ,extr n, t Pfint t X f , netimp n, t PCCS n, t CCS n, t Ynet n, t TFPn, t (n) K C WITCH Model, Description and Applications 1 ( n ) n, t L (n) 1/ Production Tree and Energy Technologies 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 10 WITCH Model, Description and Applications 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 11 WITCH Model, Description and Applications 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 Electricity Production - 2 Electricity production is described by a Leontief production function (6) EL n , t min K n , t ; O& n , t ; X n , t j n , j j n , j j j j , EL 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 Power Generation capacity (Power Units) depends on cumulated investments (I) and investments costs (SC) which are time and region-specific: (7) I ( n , t ) j K n , t 1 K ( n , t ) 1 j j j SC ( n , t ) j WITCH Model, Description and Applications 12 Technical Change – Learning-By-Doing Endogenous Technical Change (ETC) accounts for the accumulation of both: • Experience (Learning-By-Doing) • R&D investment (Learning-By-Researching) Learning-By-Doing via experience curves in power plants investment cost (9) 2 j SC n , t B K n , t 1 j j j n tn l P og R 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 13 WITCH Model, Description and Applications Technical Change – Energy Efficiency Learning-By-Researching via energy R&D increasing energy efficiency (Popp, 2004) (10) ESn, t H HE(n, t ) EN EN(n, t ) 1/ The R&D sector exhibits intertemporal spillovers and the production of new "ideas" follows an innovation possibility frontier (Popp, 2002; Jones,1995): (11) Z n, t an I R&D (n, t ) b HE(n, t ) c 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: (12) 14 HE(n , t 1 ) HE ( n, t )(1 R & D ) Z n, t WITCH Model, Description and Applications Technical Change – International Spillovers The R&D sector exhibits also international knowledge spillovers: (13) Z n, t an I R&D (n, t ) b HE(n, t ) c SPILLn, t d 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) (14) HE ( n , t ) SPILL ( n , t ) ( HE ( n , t ) HE ( n , t )) HE ( n , t ) HI HI Absorptive capacity WITCH Model, Description and Applications Distance from the frontier Technical Change – Advanced Biofuels Learning-By-Researching via dedicated R&D decreasing the cost of the cellulosic biofuels, PADVBIO(n,t) (15) P n , t P n , 0 ( TOT ( n , t )) ADVBIO ADVBIO R & D , ADVB where stands for the relationship between new knowledge and cost t (16) TOT ( n , t ) K ( n , t 2 ) I ( n , ) R & D , ADV R & D , ADV BIO R & D , ADV BIO n t 1 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 16 WITCH Model, Description and Applications 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 c (17) b P R & D CC t, ec T t, ec T 2 t, ec T * P R & D CC t, ec 0 t, ec 0 t , ec 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) 17 WITCH Model, Description and Applications Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 18 WITCH Model, Description and Applications Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 19 WITCH Model, Description and Applications Mitigation Options, Technologies, Carbon Markets - 1 Major Areas of Research: 20 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 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 21 WITCH Model, Description and Applications Changes in Energy and Carbon Intensities Energy savings and efficiency should be pursued vigorously in the short term, but decarbonisation is essential from 2030 onwards already 100% Decarbonization 80% 2100 450 550 60% 2050 40% 2030 20% 0% 0% 2030 2030 2050 20% 40% 550 650 BAU 2100 past 30 yrs 2050 2100 60% 80% -20% Energy Intensity Improvement 22 WITCH Model, Description and Applications 100% Electricity Mix, 550 ppm CO2-eq World Electricity Generation Shares 1 0.9 0.8 0.7 Nuclear Hydroelectric Oil Gas IGCC+CCS Trad Coal Wind&Solar 0.6 0.5 0.4 0.3 0.2 0.1 0 2000 23 2020 WITCH Model, Description and Applications 2040 2060 2080 2100 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): 123137. Bosetti, V., C. Carraro and E. Massetti (2009). “Banking Permits: Economic Efficiency and Distributional Effects,” Journal of Policy Modeling, 31(3): 382403. 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. 24 WITCH Model, Description and Applications Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 25 WITCH Model, Description and Applications 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 26 WITCH Model, Description and Applications 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 redirects investments away from education toward R&D which instead is pollution-saving 27 WITCH Model, Description and Applications % of world GDP Investment in R&D with Breakthrough Technologies 0.14 Baseline 550ppm 0.12 550ppm w ith backstops 0.10 0.08 0.06 0.04 0.02 0.00 2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 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 28 WITCH Model, Description and Applications 500 450 550ppm 400 The price of carbon is much lower with breakthrough technologies 550ppm w ith backstops 350 300 250 Crucial role to decarbonize non-electric energy (transport) 200 150 100 50 0 2007 2012 2017 2022 2027 2032 2037 2042 And therefore the costs of stabilisation are much lower, especially in the long term 2047 2052 % change in GDP with respect to baseline $US/tCO2 eq Mitigation Costs with the Backstop Technologies 0.0 -1.0 -2.0 -3.0 -4.0 -5.0 -6.0 550ppm w ith backstops -7.0 29 550ppm WITCH Model, Description and Applications -8.0 2007 2012 2017 2022 2027 2032 2037 2042 2047 2052 2057 2062 2067 2072 2077 2082 Induced Technical Change and GWP Losses With respect to a Full Induced Technical change (ITC) Scenario Gross World Product (GWP) losses are: Overestimated when there is no ITC in the Energy Sector No ITC Energy 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 30 WITCH Model, Description and Applications Full ITC No ITC Exogenous Crowding-out No ITC Non-Energy 3.4% 3.5% 3.6% 3.7% 3.8% 3.9% 4.0% Discounted GWP Loss from Climate Policy (%) 4.1% 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): 123137. 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. 31 WITCH Model, Description and Applications Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 32 WITCH Model, Description and Applications 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 33 WITCH Model, Description and Applications Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 34 WITCH Model, Description and Applications 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 35 WITCH Model, Description and Applications 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 36 WITCH Model, Description and Applications International Policy Architectures - 3 Key feature Name CAT with redistribution Benchmark cap and trade Global carbon tax Global tax recycled domestically Carbon Tax REDD Inclusion of REDD Climate Clubs Scope Timing Universal Immediate Universal Immediate Cap and Trade Universal Immediate 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 NB All refer to CO2 only 37 Policy Instrument WITCH Model, Description and Applications Cap and Trade Climate Effectiveness °C above pre-industrial 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 U BA C AT w ith r b tri s i ed io ut n C e at lim ub Cl s R ED D rd Bu en S rin a h g ra G tio a du n l G a ob l rb ca on x ta D am n y ic Ta et g r s R &D C o iti al on 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 38 WITCH Model, Description and Applications Economic Efficiency R&D Coalition Dynamic Targets Global carbon tax Graduation Burden Sharing REDD Climate Clubs CAT with redistribution Change in GWP wrt BaU - Discounted at 5% 0.50% 0.00% -0.50% -1.00% -1.50% -2.00% 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 setups vary considerably. More stringent policy architectures imply a higher GWP loss 39 WITCH Model, Description and Applications 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 40 WITCH Model, Description and Applications Cooperative CO2 Emissions Sensitivity to these assumptions is far greater in the cooperative case. Higher damage and especially low discounting drive emissions down 41 WITCH Model, Description and Applications CBA: Free riding – the case of SSA USA Percentage Difference in Emissions Coalition w/o SSa comapred to the Grand Coalition 14% WEURO 300% EEURO AUCANZ 12% 250% JPNKOR TE 10% 200% 8% MENA SASIA 150% CHINA 6% 100% 4% SEASIA LAM 50% 2% 0% SSA (RH Axes) 0% 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 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) 42 WITCH Model, Description and Applications 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 43 WITCH Model, Description and Applications 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. 44 WITCH Model, Description and Applications Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 45 WITCH Model, Description and Applications Balancing Mitigation and Adaptation Policies Major 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 46 WITCH Model, Description and Applications