References - WITCH

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Model Documentation - WITCH

Corresponding documentation
Previous versions
Model information
Model link
Institution European Institute on Economics and the Environment (RFF-CMCC EIEE), Italy, http://www.eiee.org.
Solution concept General equilibrium (closed economy)
Solution method Optimization
Anticipation

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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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EIA (2008b). International Energy Outlook. Energy Information Administration, Washington, DC. ENERDATA (2008). Energy Statistics.

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Herrero, Mario, Petr Havlik, J McIntire, Amanda Palazzo, and Hugo Valin. 2014. “African Livestock Futures: Realizing the Potential of Livestock for Food Security, Poverty Reduction and the Environment in Sub-Saharan Africa.”

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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.

Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley. 2011. “Emulating Coupled Atmosphere-Ocean and Carbon Cycle Models with a Simpler Model, Magicc6: Part I – Model Description and Calibration.” Atmospheric Chemistry and Physics 11: 1417–56.

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.

Nordhaus, William D., and Zili Yang. 1996. “A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies.” The American Economic Review 86 (4): 741–65.

Nordhaus, William, and Paul Sztorc. 2013. User’s Manual for Dice-2013R.

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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