NCCR-CLIMATE Phase I (WP4): Integrated Assessment Modelling (IAM)
- What is the portfolio of efficient technological and other options to mitigate climate change?
- Which policy mix will insure that the most efficient options are selected and promoted?
Specifically, significant advances have been made on the endogenization of technology learning, an important driving force in the evolution of global energy systems. The two main technology learning mechanisms, namely Research and Development (R&D) and experience gathered in the marketplace through demonstration and deployment (D&D), have been incorporated in the models in order to capture the early investments required for a technology to progress and achieve long-term competitiveness. Insights from our modelling exercises suggest that models with endogenized technology learning tend to produce lower estimates of the costs of climate-change mitigation policies than models where technological change is exogenous, in particular when higher learning rates are assumed for emerging low-carbon energy technologies. The use of experience curves in this kind of models draws attention to the fact that without accumulation of knowledge through R&D activities and market experience through demonstration and deployment (D&D) programs, cleaner and more efficient low-carbon energy technologies cannot develop and become competitive in the long term.
- S. Kypreos
Turton, H. and L. Barreto (2007). Automobile Technology, Hydrogen and Climate Change: A Long-term Modeling Analysis, International Journal of Alternative Propulsion, Vol. 1(4), pp. 397-426, DOI: 10.1504/IJAP.2007.013332
Bahn, O., L. Drouet, N. Edwards, A. Haurie, S. Kypreos, T. Stocker and J.P. Vial (2006). The Coupling of Optimal Economic and Climate Dynamics, Climatic Change (Special Issue of the Swiss NCCR-Climate Project), Vol. 79, pp. 103-119, DOI: 10.1007/978-1-4020-5714-4_6
Bürgenmeier, B., A. Baranzini, C. Ferrier, C. Germond-Duret, K. Ingold, S. Perret, P. Rafaj, S. Kypreos, A. Wokaun (2006). Economics of Climate Policy and collective Decision Making, Climatic Change (Special Issue of the Swiss NCCR-Climate Project), Vol. 79, pp. 143-162, DOI: 10.1007/s10584-006-9147-x
Rafaj, P., L. Barreto and S. Kypreos (2006). Combining Policy Instruments for Sustainable Energy Systems: An Analysis with the GMM Model, Environmental Modelling and Assessment, Vol. 11(4), pp. 227-295, DOI: 10.1007/s10666-005-9037-z
Turton, H. and L. Barreto (2006). Long-term Security of Energy Supply and Climate Change: A Bottom-Up Modelling Analysis, Energy Policy, Vol. 34(15), pp. 2232-2250, DOI: 10.1016/j.enpol.2005.03.016
Viguier, L., L. Barreto, A. Haurie, S. Kypreos and P. Rafaj (2006). Modelling Endogenous Learning and imperfect Competition Effects in Climate Change Economics, Climatic Change (Special Issue of the Swiss NCCR-Climate Project), Vol. 79, pp. 121-141, DOI: 10.1007/978-1-4020-5714-4_7
Kypreos S. (2005). Modeling experience curves in MERGE (model for evaluating regional and global effects), Energy, Vol. 30(14), pp. 2721-2737, DOI: 10.1016/j.energy.2004.07.006
Rafaj, P., S. Kypreos and L. Barreto (2005). Flexible Carbon Mitigation Policies: Analysis with a Global Multi-regional MARKAL Model. In Haurie, A., Viguier, L., (Editors), Coupling Climate and Economic Dynamics, Kluwer Academic Publishers. Dordrecht, the Netherlands. ISBN 1-4020-3424-5
Barreto, L. and S. Kypreos (2004). Emissions Trading and Technology Deployment in an Energy-Systems "Bottom-Up" Model with Technology Learning, European Journal of Operational Research, Vol. 158(1), pp. 243-261, DOI: 10.1016/S0377-2217(03)00350-3
Barreto, L. and S. Kypreos (2004). Endogenizing R&D and Market Experience in the "Bottom-Up" Energy-Systems ERIS Model, Technovation, Vol. 24(8), pp. 615-629, DOI: 10.1016/S0166-4972(02)00124-4
Bahn, O. and S. Kypreos (2003). Incorporating different endogenous learning formulations in MERGE, International Journal of Global Energy Issues, Vol. 19(4), pp. 333-358, External Link
Kypreos, S. and O. Bahn (2003). A MERGE model with endogenous technological progress, Environmental Modeling and Assessment, Vol. 8(3), pp. 249-259, DOI: 10.1023/A:1025551408939
Sager, J. (2003). An analysis with the CERT Model of the FSU Market Power in the Carbon Emissions Trading Market, Environmental Modeling and Assessment, Vol. 8(3), pp. 219-238, DOI: 10.1023/A:1025547308030
Krzyzanowski, D., S. Kypreos and L. Barreto (2007). Assessment of Market Penetration Potential of Hydrogen Fuel Cell Vehicles, EEM Working Paper, Paul Scherrer Institute
Rafaj, P., L. Barreto and S. Kypreos (2006). The Role of Non-CO2 Gases in Flexible Climate Policy: An Analysis with the Energy-Systems GMM Model, NCCR-Climate WP4 Research Paper Series, Download (0.4MB)
Krzyzanowski, D.A., S. Kypreos, L. Gutzwiller and L. Barreto (2005). Implications of Technology Learning in Energy-Economy Models of the Transport Sector, PSI Bericht No. 05-06, Paul Scherrer Institute Download (0.5MB)
Rafaj, P., S. Kypreos and L. Barreto (2004). Combined Policy Instruments for Sustainable Energy Systems, Annual PSI Report, Paul Scherrer Institute
Schulz, T.F., L. Barreto, S. Kypreos and A. Wokaun (2004). Some Insights of the 2000-Watt Society for Switzerland, Annual PSI Report, Paul Scherrer Institute