[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/ags/feemcl/230681.html
   My bibliography  Save this paper

Sensitivity to Energy Technology Costs: A Multi-model Comparison Analysis

Author

Listed:
  • Bosetti, Valentina
  • Marangoni, Giacomo
  • Borgonovo, Emanuele
  • Diaz Anadon, Laura
  • Barron, Robert
  • McJeon, Haewon C.
  • Politis, Savvas
  • Friley, Paul
Abstract
In the present paper we use the output of multiple expert elicitation surveys on the future cost of key low-carbon technologies and use it as input of three Integrated Assessment models, GCAM, MARKAL_US and WITCH. By means of a large set of simulations we aim to assess the implications of these subjective distributions of technological costs over key model outputs. We are able to detect what sources of technology uncertainty are more influential, how this differs across models, and whether and how results are affected by the time horizon, the metric considered or the stringency of the climate policy. In unconstrained emission scenarios, within the range of future technology performances considered in the present analysis, the cost of nuclear energy is shown to dominate all others in affecting future emissions. Climate-constrained scenarios, stress the relevance, in addition to that of nuclear energy, of biofuels, as they represent the main source of decarbonization of the transportation sector and bioenergy, since the latter can be coupled with Carbon Capture and Storage (CCS) to produce negative emissions.

Suggested Citation

  • Bosetti, Valentina & Marangoni, Giacomo & Borgonovo, Emanuele & Diaz Anadon, Laura & Barron, Robert & McJeon, Haewon C. & Politis, Savvas & Friley, Paul, 2016. "Sensitivity to Energy Technology Costs: A Multi-model Comparison Analysis," Climate Change and Sustainable Development 230681, Fondazione Eni Enrico Mattei (FEEM).
    • Handle: RePEc:ags:feemcl:230681
    DOI: 10.22004/ag.econ.230681
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/230681/files/NDL2015-098.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.230681?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Baker, Erin & Olaleye, Olaitan & Reis, Lara Aleluia, 2015. "Decision Frameworks and the Investment in R&D," Climate Change and Sustainable Development 204431, Fondazione Eni Enrico Mattei (FEEM).
    2. Kriegler, Elmar & Petermann, Nils & Krey, Volker & Schwanitz, Valeria Jana & Luderer, Gunnar & Ashina, Shuichi & Bosetti, Valentina & Eom, Jiyong & Kitous, Alban & Méjean, Aurélie & Paroussos, Leonida, 2015. "Diagnostic indicators for integrated assessment models of climate policy," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 45-61.
    3. Baker, Erin & Bosetti, Valentina & Anadon, Laura Diaz & Henrion, Max & Aleluia Reis, Lara, 2015. "Future costs of key low-carbon energy technologies: Harmonization and aggregation of energy technology expert elicitation data," Energy Policy, Elsevier, vol. 80(C), pages 219-232.
    4. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    5. Edmonds Jae & Reilly John, 1983. "Global Energy and C02 to the Year 2050," The Energy Journal, , vol. 4(3), pages 21-48, July.
    6. Edmonds, Jae & Reilly, John, 1983. "Global energy production and use to the year 2050," Energy, Elsevier, vol. 8(6), pages 419-432.
    7. Beccacece, F. & Borgonovo, E., 2011. "Functional ANOVA, ultramodularity and monotonicity: Applications in multiattribute utility theory," European Journal of Operational Research, Elsevier, vol. 210(2), pages 326-335, April.
    8. Peter W. Glynn & Donald L. Iglehart, 1989. "Importance Sampling for Stochastic Simulations," Management Science, INFORMS, vol. 35(11), pages 1367-1392, November.
    9. Baker, Erin & Olaleye, Olaitan & Aleluia Reis, Lara, 2015. "Decision frameworks and the investment in R&D," Energy Policy, Elsevier, vol. 80(C), pages 275-285.
    10. Elmar Kriegler & John Weyant & Geoffrey Blanford & Volker Krey & Leon Clarke & Jae Edmonds & Allen Fawcett & Gunnar Luderer & Keywan Riahi & Richard Richels & Steven Rose & Massimo Tavoni & Detlef Vuu, 2014. "The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies," Climatic Change, Springer, vol. 123(3), pages 353-367, April.
    11. Erin Baker & Olaitan Olaleye & Lara Aleluia Reis, 2015. "Decision Frameworks and the Investment in R&D," Working Papers 2015.42, Fondazione Eni Enrico Mattei.
    12. Manel Baucells & Emanuele Borgonovo, 2013. "Invariant Probabilistic Sensitivity Analysis," Management Science, INFORMS, vol. 59(11), pages 2536-2549, November.
    13. McJeon, Haewon C. & Clarke, Leon & Kyle, Page & Wise, Marshall & Hackbarth, Andrew & Bryant, Benjamin P. & Lempert, Robert J., 2011. "Technology interactions among low-carbon energy technologies: What can we learn from a large number of scenarios?," Energy Economics, Elsevier, vol. 33(4), pages 619-631, July.
    14. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Borgonovo, Emanuele & Plischke, Elmar, 2016. "Sensitivity analysis: A review of recent advances," European Journal of Operational Research, Elsevier, vol. 248(3), pages 869-887.
    2. Baker, Erin & Bosetti, Valentina & Salo, Ahti, 2020. "Robust portfolio decision analysis: An application to the energy research and development portfolio problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1107-1120.
    3. Milford, James & Henrion, Max & Hunter, Chad & Newes, Emily & Hughes, Caroline & Baldwin, Samuel F., 2022. "Energy sector portfolio analysis with uncertainty," Applied Energy, Elsevier, vol. 306(PA).
    4. Kenneth Gillingham & William D. Nordhaus & David Anthoff & Geoffrey Blanford & Valentina Bosetti & Peter Christensen & Haewon McJeon & John Reilly & Paul Sztorc, 2015. "Modeling Uncertainty in Climate Change: A Multi-Model Comparison," NBER Working Papers 21637, National Bureau of Economic Research, Inc.
    5. S. Cucurachi & E. Borgonovo & R. Heijungs, 2016. "A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment," Risk Analysis, John Wiley & Sons, vol. 36(2), pages 357-377, February.
    6. Plischke, Elmar & Borgonovo, Emanuele, 2019. "Copula theory and probabilistic sensitivity analysis: Is there a connection?," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1046-1059.
    7. Stefano Cucurachi & Carlos Felipe Blanco & Bernhard Steubing & Reinout Heijungs, 2022. "Implementation of uncertainty analysis and moment‐independent global sensitivity analysis for full‐scale life cycle assessment models," Journal of Industrial Ecology, Yale University, vol. 26(2), pages 374-391, April.
    8. Bistline, John E., 2016. "Energy technology R&D portfolio management: Modeling uncertain returns and market diffusion," Applied Energy, Elsevier, vol. 183(C), pages 1181-1196.
    9. Emanuele Borgonovo & Gordon B. Hazen & Elmar Plischke, 2016. "A Common Rationale for Global Sensitivity Measures and Their Estimation," Risk Analysis, John Wiley & Sons, vol. 36(10), pages 1871-1895, October.
    10. Liu, Xing & Ferrario, Elisa & Zio, Enrico, 2019. "Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 423-434.
    11. Di Maio, Francesco & Nicola, Giancarlo & Borgonovo, Emanuele & Zio, Enrico, 2016. "Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant," Reliability Engineering and System Safety, Elsevier, vol. 151(C), pages 12-19.
    12. Cheng, Lei & Lu, Zhenzhou & Zhang, Leigang, 2015. "Application of Rejection Sampling based methodology to variance based parametric sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 9-18.
    13. Tatsuya Sakurahara & Seyed Reihani & Ernie Kee & Zahra Mohaghegh, 2020. "Global importance measure methodology for integrated probabilistic risk assessment," Journal of Risk and Reliability, , vol. 234(2), pages 377-396, April.
    14. Kucherenko, Sergei & Song, Shufang & Wang, Lu, 2019. "Quantile based global sensitivity measures," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 35-48.
    15. van der Zwaan, Bob & Kober, Tom & Calderon, Silvia & Clarke, Leon & Daenzer, Katie & Kitous, Alban & Labriet, Maryse & Lucena, André F.P. & Octaviano, Claudia & Di Sbroiavacca, Nicolas, 2016. "Energy technology roll-out for climate change mitigation: A multi-model study for Latin America," Energy Economics, Elsevier, vol. 56(C), pages 526-542.
    16. Tobias Fissler & Silvana M. Pesenti, 2022. "Sensitivity Measures Based on Scoring Functions," Papers 2203.00460, arXiv.org, revised Jul 2022.
    17. Tianyang Wang & James S. Dyer & Warren J. Hahn, 2017. "Sensitivity analysis of decision making under dependent uncertainties using copulas," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 117-139, November.
    18. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    19. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    20. Laura Diaz Anadon & Erin Baker & Valentina Bosetti & Lara Aleluia Reis, 2016. "Expert views - and disagreements - about the potential of energy technology R&D," Climatic Change, Springer, vol. 136(3), pages 677-691, June.

    More about this item

    Keywords

    Research and Development/Tech Change/Emerging Technologies;

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:feemcl:230681. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/feemmit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.