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The WITCH 2016 Model - Documentation and Implementation of the Shared Socioeconomic Pathways

Listed author(s):
  • Emmerling, Johannes
  • Drouet, Laurent Drouet
  • Reis, Lara Aleluia
  • Bevione, Michela
  • Berger, Loic
  • Bosetti, Valentina
  • Carrara, Samuel
  • De Cian, Enrica
  • De Maere D'Aertrycke, Gauthier
  • Longden, Tom
  • Malpede, Maurizio
  • Marangoni, Giacomo
  • Sferra, Fabio
  • Tavoni, Massimo
  • Witajewski-Baltvilks, Jan
  • Havlik, Petr

This paper describes the WITCH - World Induced Technical Change Hybrid - model in its structure, calibration, and the implementation of the SSP/RCP scenario implementation. The WITCH model is a regionally disaggregated hard-linked model based on a Ramsey type optimal growth model and a detailed bottom-up energy sector model. A particular focus of the model is the modeling or technical change and RnD investments and the analysis of cooperative and non-cooperative climate policies. Moreover, the WITCH 2016 version now includes land-use change modeling based on the GLOBIOM model, and air pollutants, as well as detailed modeling of the transport sector and the possibility for stochastic modeling. This version has been also used to implement the Shared Socioeconomic Pathways (SSPs) set of scenarios and RCP based climate policies to provide a new set of climate scenarios. In this paper, we describe in detail the mathematical formulation of the WITCH model, the solution method and calibration, as well as the implementation of the five SSP scenarios. This report therefore provides detailed information for interested users of the model, and for understanding the implementation of the different “worlds" of the SSP.

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File URL: http://purl.umn.edu/240748
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Paper provided by Fondazione Eni Enrico Mattei (FEEM) in its series MITP: Mitigation, Innovation,and Transformation Pathways with number 240748.

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Date of creation: 04 Jul 2016
Handle: RePEc:ags:feemmi:240748
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  1. Longden, Thomas, 2014. "Travel intensity and climate policy: The influence of different mobility futures on the diffusion of battery integrated vehicles," Energy Policy, Elsevier, vol. 72(C), pages 219-234.
  2. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
  3. Bosetti, Valentina & Longden, Thomas, 2013. "Light duty vehicle transportation and global climate policy: The importance of electric drive vehicles," Energy Policy, Elsevier, vol. 58(C), pages 209-219.
  4. Nordhaus, William D & Yang, Zili, 1996. "A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies," American Economic Review, American Economic Association, vol. 86(4), pages 741-765, September.
  5. Shardul Agrawala & Francesco Bosello & Carlo Carraro & Kelly de Bruin & Enrica De Cian & Rob Dellink & Elisa Lanzi, 2010. "Plan or React? Analysis of Adaptation Costs and Benefits Using Integrated Assessment Models," OECD Environment Working Papers 23, OECD Publishing.
  6. Pietzcker, Robert Carl & Stetter, Daniel & Manger, Susanne & Luderer, Gunnar, 2014. "Using the sun to decarbonize the power sector: The economic potential of photovoltaics and concentrating solar power," Applied Energy, Elsevier, vol. 135(C), pages 704-720.
  7. Valentina Bosetti & Emanuele Massetti & Massimo Tavoni, 2007. "The WITCH Model. Structure, Baseline, Solutions," Working Papers 2007.10, Fondazione Eni Enrico Mattei.
  8. Webster, Mort & Paltsev, Sergey & Reilly, John, 2008. "Autonomous efficiency improvement or income elasticity of energy demand: Does it matter?," Energy Economics, Elsevier, vol. 30(6), pages 2785-2798, November.
  9. Emanuele Massetti & Fabio Sferra, 2010. "A Numerical Analysis of Optimal Extraction and Trade of Oil under Climate Policy," Working Papers 2010.113, Fondazione Eni Enrico Mattei.
  10. Brian Blankespoor & Susmita Dasgupta & Benoit Laplante & David Wheeler, 2010. "The Economics of Adaptation to Extreme Weather Events in Developing Countries," Working Papers 199, Center for Global Development.
  11. Söderholm, Patrik & Sundqvist, Thomas, 2007. "Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies," Renewable Energy, Elsevier, vol. 32(15), pages 2559-2578.
  12. 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.
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