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The 2008 WITCH Model: New Model Features and Baseline

Author

Listed:
  • Enrica De Cian

    (Fondazione Eni Enrico Mattei)

  • Valentina Bosetti

    (Fondazione Eni Enrico Mattei, PEI Princeton University and CMCC)

  • Alessandra Sgobbi

    (Fondazione Eni Enrico Mattei and European Commission)

  • Massimo Tavoni

    (Fondazione Eni Enrico Mattei, PEI Princeton University and CMCC)

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

  • Enrica De Cian & Valentina Bosetti & Alessandra Sgobbi & Massimo Tavoni, 2009. "The 2008 WITCH Model: New Model Features and Baseline," Working Papers 2009.85, Fondazione Eni Enrico Mattei.
  • Handle: RePEc:fem:femwpa:2009.85
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    File URL: https://www.feem.it/m/publications_pages/NDL2009-085.pdf
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    References listed on IDEAS

    as
    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. 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.
    4. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    5. Valentina Bosetti & David Tomberlin, 2004. "Fondazione Eni Enrico Mattei," Working Papers 2004.102, Fondazione Eni Enrico Mattei.
    6. 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.
    7. 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.
    8. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    9. 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.
    10. 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.
    11. Nemet, Gregory F., 2006. "Beyond the learning curve: factors influencing cost reductions in photovoltaics," Energy Policy, Elsevier, vol. 34(17), pages 3218-3232, November.
    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, Carlo Carraro, Marzio Galeotti, Emanuele Massetti, Massimo Tavoni, 2006. "A World induced Technical Change Hybrid Model," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 13-38.
    14. 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.
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    More about this item

    Keywords

    Climate Policy; Hybrid Modelling; Integrated Assessment; Technological Change;
    All these keywords.

    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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