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Decomposing TIAM-MACRO to Assess Climatic Change Mitigation

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Abstract

TIAM-MACRO (TM) is a mathematical programming growth model where the global multi-region bottom-up engineering model TIAM is linked with a top-down macroeconomic module MACRO to maximize an inter-temporal utility function for a single representative producer-consumer agent in each region. The size of TM is such that non-linear (NL) optimal solutions cannot be obtained even when the best available personal computers and solvers are used. Therefore, an alternative is proposed based on decomposition methods converting TM to a small-size NL macroeconomic model, called TIAM-MACRO Stand-Alone (TMSA), and where the energy model TIAM is substituted by appropriate quadratic cost-supply functions (QSF). The TIAM model and the TMSA are calibrated to the demands estimated with a scenario generator and are then solved iteratively. This report concentrates on the description and foundation of the algorithm and explains why an adjusted production function is needed to allow for sectoral income and price elasticities that reproduce/calibrate the baseline scenario. It is shown that the decomposed problem for a single region is calibrated and solved to exactly the same results as the original problem in 3 min of computer time instead of 2–3 h without decomposition. Also, for the first time, we are able to solve the global TM model with 15 regions in 1.5 h applying the approach based on TMSA (in Windows 7, 64-bit workstation, solution in a single thread).

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Notes

  1. The link of such models with TIMES goes in two directions; first, the CGE model is used to generate a consistent set of drivers which together with income and price elasticities define demands for energy services. Then, a TIMES solution returns final energy flows per industrial sectors, households, services, and transportation, over-writing the Leontief relations of the CGE model until all sectorial markets clear and equilibrium prices are defined.

References

  1. Wilkerson, T. J., Leibowicz, D. B., Turner, D. D., & Weyant, P. J. (2015). Comparison of integrated assessment models: carbon price impacts on U.S. energy. Energy Policy, 76, 18–31.

    Article  Google Scholar 

  2. Kriegler, E., Petermann, N., Krey, V., Schwanitz, V. J., Luderer, G., Ashina, S., Bosetti, V., Eom, J., Kitous, A., Méjean, A., Paroussos, L., Sano, F., Turton, H., Wilson, C., & Van Vuuren, D. P. (2014). Diagnostic indicators for integrated assessment models of climate policy. Technological Forecasting and Social Change. doi:10.1016/j. techfore.2013.09.020.

    Google Scholar 

  3. Ortiz, R. A., Markandya, A. (2009). Integrated impact assessment models with an emphasis on damage functions: a literature review. BC3 Working Paper Series 2009–06. Bilbao: Basque Centre for Climate Change (BC3). http://www.bc3research.org/lits_publications.html.

  4. Capros, P., Georgakopoulos, T., Van Regemorter, D., Proost, S., Schmidt, T., & Conrad, K. (1997). The GEM-E3 model for the European Union. Journal of Economic & Financial Modelling, 4(2&3), 51–160.

    Google Scholar 

  5. Arrow, K. J., & Debreu, G. (1954). Existence of an equilibrium for a competitive economy. Econometrica, 22, 265–290.

    Article  Google Scholar 

  6. Manne, A., & Wene, C.-O. (1992). MARKAL-MACRO: a linked model for energy-economy analysis. No. BNL–47161. Upton: Brookhaven National Lab.

    Google Scholar 

  7. Böhringer, C., & Rutherford, T. F. (2005). Integrating bottom-up into top-down: a mixed complementarity approach. ZEW Discussion Papers 05–28. ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.

  8. Labriet, M., Loulou, R., Vielle, M., & Drouet, L. (2008). Coupling TIAM and GEMINI-E3. Proceedings of the ETSAP Meeting, Sophia-Antipolis, December 17, 2008.

  9. Simões, S., Fortes, P., Seixas, J., van Regemorter, D., & Ferreira, F. (2013). Top-down and bottom-up modelling to support low carbon scenarios: climate policy, implications. Climate Policy Journal, 13(3), 1–20. doi:10.1080/14693062.2013.768919.

    Google Scholar 

  10. Fishbone, L. G., & Abilock, H. (1981). Markal, a linear-programming model for energy systems analysis: technical description of the BNL version. International Journal of Energy Research, 5(4), 353–375.

    Article  Google Scholar 

  11. Loulou, R., Remne, U., Kanudia, A., Lehtila, A., & Goldstein, G. (2005). Documentation for the TIMES Model (Part I, II and III), Energy Technology Systems Analysis Programme (ETSAP). http://www.etsap.org/tools.htm.

  12. SAGE (2003). Office of integrated analysis and forecasting, energy information administration. Washington, DC: U.S. Department of Energy. The System for Analysis of Global Energy markets (SAGE) model of the US-EIA, DOE/EIA-M072 (2003)/1, August 2003. http://www.eia.doe.gov/bookshelf/docs.html.

  13. Haurie, A., Kanudia, A., Loulou, R., van Regemorter, D., & Vaillancourt, K. (2004). The EFDA World Model. Final Report prepared for EFDA, ORDECSYS, HALOA/KANORS and KUL.

  14. Barreto, L. (2001). Technological Learning in Energy Optimisation Models and the Deployment of Emerging Technologies. Ph.D. Thesis No 14151. Swiss Federal Institute of Technology Zurich.

  15. Rafaj, P., Kypreos, S., & Barreto, L. (2005). Flexible carbon mitigation policies: analysis with a global multi-regional MARKAL model. In A. Haurie & L. Viguier (Eds.), Coupling climate and economic dynamics. Dordrecht: Kluwer Academic Publishers. ISBN 1-4020-3424-5.

    Google Scholar 

  16. Messner, S., & Schrattenholzer, L. (2000). MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively. Energy, 25(3), 267–282.

    Article  Google Scholar 

  17. Büeler, B. (1997). Solving an equilibrium model for trade of C02 emission permits. European Journal of Operational Research, 102(1997), 393–403.

    Article  Google Scholar 

  18. Bahn, O., Barreto, L., Büeler, B., & Kypreos, S. (1997). A Multi-regional MARKAL-MACRO Model to Study an International Market of CO2 Emission Permits, PSI Report No. 97–09, Paul Scherrer Institute, Villigen, Switzerland.

  19. Negishi, T. (1972). General equilibrium theory and international trade. Amsterdam: North-Holland Publishing Company.

    Google Scholar 

  20. Rutherford, T. (1999). Sequential joint maximization. In J. Weyant (Ed.), Energy and environmental policy modeling. Kluwer: International Series in Operations Research and Management Science.

    Google Scholar 

  21. Manne, A., Mendelsohn, R., & Richels, R. (1995). MERGE: a model for evaluating regional and global effects of GHG reduction policies. Energy Policy, 23(1), 17–34.

    Article  Google Scholar 

  22. Kypreos, S. (1996). The Markal-macro model and the climate change. PSI Report Nr 96–14, Paul Scherrer Institut (PSI), July 1996, Villigen, Switzerland.

  23. Remme, U., & Markus, B. (2006). Documentation of the TIMES-MACRO model. http://www.etsap.org.

  24. Kypreos, S. (2006). An algorithm to decompose the global MARKAL-MACRO (GMM) and TIMES models. working document for ETSAP. Switzerland: PSI.

    Google Scholar 

  25. Mousavi, B. (2014). Global decarburization pathways: contribution of different options in CO2 reduction: TIAM-MACRO, Decomposition analysis. http://www.iea-etsap.org/web/Copenhagen_Nov2014.asp.

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Acknowledgments

The IEA/ETSAP support for the initiation and completion of the project and the trust of GianCarlo Tosato, Project Head of ETSAP, for his never-ending hope concerning the final success of this work, are highly appreciated.

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Authors and Affiliations

  1. Energy Economics Group, Paul Scherrer Institut, Villigen, Switzerland

    Socrates Kypreos

  2. VTT Technical Research Centre of Finland, Espoo, Finland

    Antti Lehtila

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  1. Socrates Kypreos
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  2. Antti Lehtila
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Correspondence to Socrates Kypreos.

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Kypreos, S., Lehtila, A. Decomposing TIAM-MACRO to Assess Climatic Change Mitigation. Environ Model Assess 20, 571–581 (2015). https://doi.org/10.1007/s10666-015-9451-9

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