Technology interactions among low-carbon energy technologies: What can we learn from a large number of scenarios?
Advanced low-carbon energy technologies can substantially reduce the cost of stabilizing atmospheric carbon dioxide concentrations. Understanding the interactions between these technologies and their impact on the costs of stabilization can help inform energy policy decisions. Many previous studies have addressed this challenge by exploring a small number of representative scenarios that represent particular combinations of future technology developments. This paper uses a combinatorial approach in which scenarios are created for all combinations of the technology development assumptions that underlie a smaller, representative set of scenarios. We estimate stabilization costs for 768 runs of the Global Change Assessment Model (GCAM), based on 384 different combinations of assumptions about the future performance of technologies and two stabilization goals. Graphical depiction of the distribution of stabilization costs provides first-order insights about the full data set and individual technologies. We apply a formal scenario discovery method to obtain more nuanced insights about the combinations of technology assumptions most strongly associated with high-cost outcomes. Many of the fundamental insights from traditional representative scenario analysis still hold under this comprehensive combinatorial analysis. For example, the importance of carbon capture and storage (CCS) and the substitution effect among supply technologies are consistently demonstrated. The results also provide more clarity regarding insights not easily demonstrated through representative scenario analysis. For example, they show more clearly how certain supply technologies can provide a hedge against high stabilization costs, and that aggregate end-use efficiency improvements deliver relatively consistent stabilization cost reductions. Furthermore, the results indicate that a lack of CCS options combined with lower technological advances in the buildings sector or the transportation sector is the most powerful predictor of high-cost scenarios.
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- Baker, Erin & Chon, Haewon & Keisler, Jeffrey, 2009. "Advanced solar R&D: Combining economic analysis with expert elicitations to inform climate policy," Energy Economics, Elsevier, vol. 31(Supplemen), pages S37-S49.
- Blanford, Geoffrey J., 2009. "R&D investment strategy for climate change," Energy Economics, Elsevier, vol. 31(Supplemen), pages S27-S36.
- Ottmar Edenhofer , Brigitte Knopf, Terry Barker, Lavinia Baumstark, Elie Bellevrat, Bertrand Chateau, Patrick Criqui, Morna Isaac, Alban Kitous, Socrates Kypreos, Marian Leimbach, Kai Lessmann, Bertra, 2010. "The Economics of Low Stabilization: Model Comparison of Mitigation Strategies and Costs," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
- Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
- Robert J. Lempert & David G. Groves & Steven W. Popper & Steve C. Bankes, 2006. "A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios," Management Science, INFORMS, vol. 52(4), pages 514-528, April.
- Scott, Michael J. & Sands, Ronald D. & Edmonds, Jae & Liebetrau, Albert M. & Engel, David W., 1999. "Uncertainty in integrated assessment models: modeling with MiniCAM 1.0," Energy Policy, Elsevier, vol. 27(14), pages 855-879, December.
- Pugh, Graham & Clarke, Leon & Marlay, Robert & Kyle, Page & Wise, Marshall & McJeon, Haewon & Chan, Gabriel, 2011. "Energy R&D portfolio analysis based on climate change mitigation," Energy Economics, Elsevier, vol. 33(4), pages 634-643, July.
- J. M. Reilly & J. A. Edmonds & R. H. Gardner & A. L. Brenkerf, 1987. "Uncertainty Analysis of the IEA/ORAU CO2 Emissions Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 1-29.
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