WITCH Model Description and Applications FEEM

1. WITCH Model Description and Applications FEEM

2. The WITCH Team: Andrea Bastianin Valentina Bosetti Carlo Carraro Enrica De Cian Alice Favero Emanuele Massetti Lea Nicita Elena Ricci Fabio Sferra Massimo Tavoni www.feem-web.it/witch 1

3. The WITCH Model: An Introduction 2

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. 3

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) 4

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 5

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 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) 6

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: (1) Electricity Generation Operation & Maintanance Energy R&Ds Final Good GDP (2) Net fuel expenditures CCS (Transport and storage costs) 7

9. Output and Climate Damage Gross output is produced combining the inputscapital, labour(=population) andenergy servicesusing a nested, Constant Elasticity Production Function GROSS GDP (3) Climate change damage is a non-linear function. Climate change impacts can be either positive or negative and they are region-specific (4) 8

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) 9

11. Production Tree and Energy Technologies 10

12. Electricity Production - 1 Electricity is obtained by combining in fixed proportions the installedpower generation capacity (K), operation and maintenanceequipment (O&M) andfuel resources consumption (X) (when needed) Power Plant Electricity Fuels Production function are characterized byregion-specific parametersthat 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 Operation and Maintenance 11

13. Electricity Production - 2 Electricity production is described by aLeontief production function (6) 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 oncumulated investments(I) andinvestments costs(SC) which are time and region-specific: (7) 12

14. 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-Doingvia experience curves in power plants investment cost (9) 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

15. Technical Change – Energy Efficiency Learning-By-Researching via energy R&Dincreasingenergy efficiency(Popp, 2004) (10) The R&D sector exhibitsintertemporal spilloversand the production of new "ideas" follows an innovation possibility frontier (Popp, 2002; Jones,1995): (11) 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

16. Technical Change – International Spillovers The R&D sector exhibits alsointernational knowledge spillovers: (13) 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) Absorptive capacity Distance from the frontier

17. Technical Change – Advanced Biofuels Learning-By-Researching via dedicated R&D decreasing the cost of the cellulosic biofuels, PADVBIO(n,t) (15) where h stands for the relationship between new knowledge and cost (16) 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

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) 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

19. Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 18

20. Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 19

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 20

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 21

23. 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 2100 550 2050 650 2030 2100 2050 2030 2100 2030 2050 22

24. Electricity Mix, 550 ppm CO2-eq 23

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. 24

26. Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 25

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 26

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 27

29. Investment in R&D with Breakthrough Technologies • 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

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 29

31. Induced Technical Change and GWP Losses Overestimated when there is no ITCin theEnergy Sector Understimatedwhen there is no ITCin theNon-Energy Sector. Underestimatedwhen there isno ITC Understimated when there is only exogenous crowding out of Non-Energy R&D With respect to a Full Induced Technical change (ITC) Scenario Gross World Product (GWP) lossesare: 30

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. 31

33. Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 32

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 33

35. Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 34

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 35

37. 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 36

38. International Policy Architectures - 3 NB All refer to CO2 only 37 37

39. Climate Effectiveness 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 38

40. Economic Efficiency 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 39 39

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 40

42. Cooperative CO2 Emissions Sensitivity to these assumptions is far greater in the cooperative case. Higher damage and especially low discounting drive emissions down 41

43. CBA: Free riding – the case of SSA • When Africaleaves 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

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 43

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. 44

46. Major Research Topics Mitigation options and costs Innovation Uncertainty International policy architectures and coalition theory Optimal balance between mitigation and adaptation 45

47. 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