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The 2008 WITCH Model: New Model Features and Baseline

Author

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  • Bosetti, Valentina
  • De Cian, Enrica
  • Sgobbi, Alessandra
  • Tavoni, Massimo
Abstract
WITCH is an energy-economy-climate model developed by the climate change group at FEEM. The model has been extensively used in the past 3 years for the economic analysis of climate change policies. WITCH is a hybrid top-down economic model with a representation of the energy sector of medium complexity. Two distinguishing features of the WITCH model are the representation of endogenous technological change and the game–theoretic set-up. Technological change is driven by innovation and diffusion processes, both of which feature international spillovers. World countries are grouped in 12 regions which interact with each other in a setting of strategic interdependence. This paper describes the updating of the base year data to 2005 and some new features: the inclusion of non-CO2 greenhouse gases and abatement options, the new specification of low carbon technologies and the inclusion of reducing emissions from deforestation and degradation.

Suggested Citation

  • Bosetti, Valentina & De Cian, Enrica & Sgobbi, Alessandra & Tavoni, Massimo, 2009. "The 2008 WITCH Model: New Model Features and Baseline," Sustainable Development Papers 55284, Fondazione Eni Enrico Mattei (FEEM).
  • Handle: RePEc:ags:feemdp:55284
    DOI: 10.22004/ag.econ.55284
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    References listed on IDEAS

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    1. Bosetti, Valentina & Carraro, Carlo & Massetti, Emanuele & Tavoni, Massimo, 2008. "International energy R&D spillovers and the economics of greenhouse gas atmospheric stabilization," Energy Economics, Elsevier, vol. 30(6), pages 2912-2929, November.
    2. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    3. Valentina Bosetti & Carlo Carraro & Marzio Galeotti & Emanuele Massetti & Massimo Tavoni, 2006. "WITCH. A World Induced Technical Change Hybrid Model," Working Papers 2006_46, Department of Economics, University of Venice "Ca' Foscari".
    4. Tavoni, Massimo & Sohngen, Brent & Bosetti, Valentina, 2007. "Forestry and the carbon market response to stabilize climate," Energy Policy, Elsevier, vol. 35(11), pages 5346-5353, November.
    5. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, , vol. 28(3), pages 51-72, July.
    6. Nemet, Gregory F. & Kammen, Daniel M., 2007. "U.S. energy research and development: Declining investment, increasing need, and the feasibility of expansion," Energy Policy, Elsevier, vol. 35(1), pages 746-755, January.
    7. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard & Claude Thonet, 2000. "Endogenous learning in world post-Kyoto scenarios: application of the POLES model under adaptive expectations," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 222-248.
    8. Kypreos, Socrates, 2007. "A MERGE model with endogenous technological change and the cost of carbon stabilization," Energy Policy, Elsevier, vol. 35(11), pages 5327-5336, November.
    9. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    10. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    11. Valentina Bosetti & David Tomberlin, 2004. "Fondazione Eni Enrico Mattei," Working Papers 2004.102, Fondazione Eni Enrico Mattei.
    12. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    13. Valentina Bosetti & Ruben Lubowski & Alexander Golub & Anil Markandya, 2009. "Linking Reduced Deforestation and a Global Carbon Market: Impacts on Costs, Financial Flows, and Technological Innovation," Working Papers 2009.56, Fondazione Eni Enrico Mattei.
    14. Valentina Bosetti & Carlo Carraro & Marzio Galeotti & Emanuele Massetti & Massimo Tavoni, 2006. "A World Induced Technical Change Hybrid Model," The Energy Journal, , vol. 27(2_suppl), pages 13-37, June.
    15. Klaassen, Ger & Miketa, Asami & Larsen, Katarina & Sundqvist, Thomas, 2005. "The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom," Ecological Economics, Elsevier, vol. 54(2-3), pages 227-240, August.
    16. Patrik Söderholm & Ger Klaassen, 2007. "Wind Power in Europe: A Simultaneous Innovation–Diffusion Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 36(2), pages 163-190, February.
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    More about this item

    Keywords

    Environmental Economics and Policy;

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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