References - WITCH
|Model Documentation - WITCH|
|Institution||Fondazione Eni Enrico Mattei (FEEM), Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)|
|Concept||Hybrid: Economic optimal growth model, including a bottom-up energy sector and a simple climate model, embedded in a `game theory` framework.|
|Solution method||Regional growth models solved by non-linear optimization and game theoretic setup solved by tatonnement algorithm (cooperative solution: Negishi welfare aggregation, non-cooperative solution: Nash equilibrium)|
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Barreto, L. and S. Kypreos (2004). Endogenizing R&D and market experience in the "bottom-up" energy-systems ERIS model, Technovation 2, 615-629.
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 on Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-down: 13-38.
Bosetti, V., C. Carraro, E. Massetti and M. Tavoni (2008). International energy R&D spillovers and the economics of greenhouse gas atmospheric stabilization,Energy Economics, 30 (6) Pages 2912-2929.
Criqui, P., G. Klassen and L. Schrattenholzer (2000). The efficiency of energy R&D expenditures. Economic modeling of environmental policy and endogenous technical change, Amsterdam, November 16-17, 2000.
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EPA Report 430-R-06-003, June 2006. http://www.epa.gov/climatechange/economics/mitigation.html.
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IEA (2005). Resources to Reserves ? Oil & Gas Technologies for the Energy Markets of the Future. OECD/IEA, Paris.
IPCC (2007) Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment.
IPCC, (2007b): Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA., XXX pp.
Jamasab, T. (2007). Technical change theory and learning curves: patterns of progress in electric generation technologies, The Energy Journal 28 (3).
Junginger, M., A. Faaij and W. C. Turkenburg (2005). Global experience curves for wind farms, Energy Policy 33: 133-150.
Kahouli-Brahmi, S. (2008). Technological learning in energy-environment-economy modelling: a survey, Energy Policy 36 : 138-162.
Klassen, G., A. Miketa, K. Larsen and T. Sundqvist (2005). The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom, Ecological Economics 54 (2-3): 227-240.
Kouvaritakis, N., A. Soria and S. Isoard (2000). Endogenous Learning in World Post-Kyoto Scenarios: Application of the POLES Model under Adaptive Expectations, International Journal of Global Energy Issues 14 (1-4): 228-248.
Kypreos, S. (2007). A MERGE model with endogenous technical change and the cost of carbon stabilisation, Energy Policy 35 : 5327-5336.
McDonald, A. and L. Schrattenholzer (2001). Learning rates for energy technologies, Energy Policy 29 (4): 255-261.
Nemet, G. F. (2006). Beyond the learning curve: factors influencing cost reductions in photovoltaics, Energy Policy 34 (17): 3218-3232.
Nemet, G. F. and D. M. Kammen (2007). U.S. energy research and development: declining investment, increasing need, and the feasibility of expansion, Energy Policy 35 (1): 746-755.
Schock, R. N., W. Fulkerson, M. L. Brown, R. L. San Martin, D. L. Greene and J. Edmonds (1999). How much is energy research & development worth as insurance? Annual Review of Energy and the Environment 24 : 487-512.
Söderholm, P. and G. Klassen (2007). Wind power in Europe: a simultaneous innovation-diffusion model, Environmental and Resource Economics 36 (2): 163-190.
Tavoni, M., B. Sohngen and V. Bosetti (2007), Forestry and the carbon market response to stabilise climate, Energy Policy, 35 : 5346--5353.
UN (2004), World Population to 2300, Report No. ST/ESA/SER.A/236, Department of Economic and Social Affairs, Population Division, New York