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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Ottmar Edenhofer & Brigitte Knopf & Terry Barker & Lavinia Baumstark & Elie Bellevrat & Bertrand Chateau & Patrick Criqui & Morna Isaac & Alban Kitous & Socrates Kypreos & Marian Leimbach & Kai Lessma, 2010.
"The economics of low stabilization: Model comparison of mitigation strategies and costs,"
- 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).
- 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 37-49.
- 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.
- 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.
- Gritsevskyi, Andrii & Nakicenovi, Nebojsa, 2000. "Modeling uncertainty of induced technological change," Energy Policy, Elsevier, vol. 28(13), pages 907-921, November.
- Blanford, Geoffrey J., 2009. "R&D investment strategy for climate change," Energy Economics, Elsevier, vol. 31(Supplemen), pages 27-36.
When requesting a correction, please mention this item's handle: RePEc:eee:eneeco:v:33:y:2011:i:4:p:619-631. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.