References - WITCH

Model Documentation - WITCH
Model scope and methods - WITCH Model concept, solver and details - WITCH Temporal dimension - WITCH Spatial dimension - WITCH Policy - WITCH Socio-economic drivers - WITCH Population - WITCH Economic activity - WITCH Macro-economy - WITCH Technological change - WITCH Energy - WITCH Energy resource endowments - WITCH Energy conversion - WITCH Electricity - WITCH Heat - WITCH Grid, pipelines and other infrastructure - WITCH Energy end-use - WITCH Transport - WITCH Residential and commercial sectors - WITCH Industrial sector - WITCH Other end-use - WITCH Energy demand - WITCH Technological change in energy - WITCH Land-use - WITCH Agriculture - WITCH Forestry - WITCH Land-use change - WITCH Emissions - WITCH GHGs - WITCH Pollutants and non-GHG forcing agents - WITCH Carbon dioxide removal (CDR) options - WITCH Climate - WITCH Modelling of climate indicators - WITCH Climate damages, temperature changes - WITCH Non-climate sustainability dimension - WITCH Air pollution and health - WITCH Water - WITCH Appendices - WITCH Mathematical model description - WITCH Data - WITCH References - WITCH
Corresponding documentation
AIM-Hub BLUES C3IAM COFFEE-TEA DNE21+ GCAM GEM-E3 GRACE IFs IMACLIM IMAGE MESSAGE-GLOBIOM POLES PROMETHEUS REMIND-MAgPIE TIAM-UCL WITCH
Previous versions
Archive of WITCH version 4.8
Model information
Model link	https://www.witchmodel.org/ https://github.com/witch-team/witchmodel https://doc.witchmodel.org
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

Anderson, D. (2006). Costs and finance of carbon abatement in the energy sector, Paper for the Stern Review.

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.

Eliasch J. (2008) Climate Change: Financing Global Forests. The Eliasch Review, available at http://www.occ.gov.uk/activities/eliasch.htm

EIA (2008). Annual Energy Outlook. Energy Information Administration, Washington, DC.

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

EPA Report 430-R-06-003, June 2006. http://www.epa.gov/climatechange/economics/mitigation.html.

Farrell A.E. and Brandt, A.R.(2006). Risks of the oil transition, Environmental Research Letters, 1 (1).

IEA (2007). World Energy Outlook 2007. OECD/IEA, Paris.

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