IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/20783.html
   My bibliography  Save this paper

Inter-temporal R&D and Capital Investment Portfolios for the Electricity Industry’s Low Carbon Future

Author

Listed:
  • Nidhi R. Santen
  • Mort D. Webster
  • David Popp
  • Ignacio Pérez-Arriaga

Abstract

This paper explores cost-effective low-carbon R&D and capital investment portfolios for the electricity generation sector through 2060. We present a novel method for long-term planning by combining an economic model of endogenous non-linear technical change and a generation capacity planning model with key features of the electricity system. The model captures the complementary nature of technologies in the power sector; physical integration constraints; and the opportunity to build new knowledge capital as a non-linear function of R&D and accumulated knowledge, which reflects the diminishing marginal returns to research characteristic of the energy innovation process. We show portfolios for future scenarios with and without carbon emission limits, and demonstrate the importance of including various features by comparing results from a reference version of the model to results from alternative versions that omit these features. Our results caution that using economic frameworks that do not incorporate critical electricity and innovation system features may over- or under-estimate the value of emerging technologies, and therefore the cost-effectiveness of R&D opportunities.

Suggested Citation

  • Nidhi R. Santen & Mort D. Webster & David Popp & Ignacio Pérez-Arriaga, 2014. "Inter-temporal R&D and Capital Investment Portfolios for the Electricity Industry’s Low Carbon Future," NBER Working Papers 20783, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:20783
    Note: EEE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w20783.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Popp, David & Santen, Nidhi & Fisher-Vanden, Karen & Webster, Mort, 2013. "Technology variation vs. R&D uncertainty: What matters most for energy patent success?," Resource and Energy Economics, Elsevier, vol. 35(4), pages 505-533.
    2. Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
    3. Popp, David & Newell, Richard G. & Jaffe, Adam B., 2010. "Energy, the Environment, and Technological Change," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 873-937, Elsevier.
    4. Miketa, Asami & Schrattenholzer, Leo, 2004. "Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes; first results," Energy Policy, Elsevier, vol. 32(15), pages 1679-1692, October.
    5. Patrik Söderholm & Ger Klaassen, 2007. "Wind Power in Europe: A Simultaneous Innovation–Diffusion Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 36(2), pages 163-190, February.
    6. Socrates Kypreos & Leonardo Barreto & Pantelis Capros & Sabine Messner, 2000. "ERIS: A model prototype with endogenous technological change," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 347-397.
    7. Berglund, Christer & Soderholm, Patrik, 2006. "Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models," Energy Policy, Elsevier, vol. 34(12), pages 1344-1356, August.
    8. Hobbs, Benjamin F., 1995. "Optimization methods for electric utility resource planning," European Journal of Operational Research, Elsevier, vol. 83(1), pages 1-20, May.
    9. Clarke, Leon & Weyant, John & Birky, Alicia, 2006. "On the sources of technological change: Assessing the evidence," Energy Economics, Elsevier, vol. 28(5-6), pages 579-595, November.
    10. Popp, David, 2004. "ENTICE: endogenous technological change in the DICE model of global warming," Journal of Environmental Economics and Management, Elsevier, vol. 48(1), pages 742-768, July.
    11. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
    12. 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.
    13. Sabine Messner, 1997. "Endogenized technological learning in an energy systems model," Journal of Evolutionary Economics, Springer, vol. 7(3), pages 291-313.
    14. Rubin, Edward S & Taylor, Margaret R & Yeh, Sonia & Hounshell, David A, 2004. "Learning curves for environmental technology and their importance for climate policy analysis," Energy, Elsevier, vol. 29(9), pages 1551-1559.
    15. van der Zwaan, B. C. C. & Gerlagh, R. & G. & Klaassen & Schrattenholzer, L., 2002. "Endogenous technological change in climate change modelling," Energy Economics, Elsevier, vol. 24(1), pages 1-19, January.
    16. Jones, Charles I, 1995. "R&D-Based Models of Economic Growth," Journal of Political Economy, University of Chicago Press, vol. 103(4), pages 759-784, August.
    17. William D. Nordhaus, 2014. "The Perils of the Learning Model for Modeling Endogenous Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    18. David Popp, 2002. "Induced Innovation and Energy Prices," American Economic Review, American Economic Association, vol. 92(1), pages 160-180, March.
    19. Nikolaos Kouvaritakis & Antonio Soria & Stephane Isoard, 2000. "Modelling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 14(1/2/3/4), pages 104-115.
    20. Goulder, Lawrence H. & Mathai, Koshy, 2000. "Optimal CO2 Abatement in the Presence of Induced Technological Change," Journal of Environmental Economics and Management, Elsevier, vol. 39(1), pages 1-38, January.
    21. Goulder, Lawrence H. & Schneider, Stephen H., 1999. "Induced technological change and the attractiveness of CO2 abatement policies," Resource and Energy Economics, Elsevier, vol. 21(3-4), pages 211-253, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Han & Chen, Zhoupeng & Wu, Xingyi & Nie, Xin, 2019. "Can a carbon trading system promote the transformation of a low-carbon economy under the framework of the porter hypothesis? —Empirical analysis based on the PSM-DID method," Energy Policy, Elsevier, vol. 129(C), pages 930-938.

    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. Nidhi R. Santen & Mort D. Webster & David Popp & Ignacio Pérez-Arriaga, 2014. "Inter-temporal R&D and Capital Investment Portfolios for the Electricity Industry's Low Carbon Future," CESifo Working Paper Series 5139, CESifo.
    2. Nidhi R. Santen & Mort D. Webster & David Popp & Ignacio Pérez-Arriaga, 2017. "Inter-temporal R&D and capital investment portfolios for the electricity industrys low carbon future," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    3. Rubin, Edward S. & Azevedo, Inês M.L. & Jaramillo, Paulina & Yeh, Sonia, 2015. "A review of learning rates for electricity supply technologies," Energy Policy, Elsevier, vol. 86(C), pages 198-218.
    4. Mort Webster & Karen Fisher-Vanden & David Popp & Nidhi Santen, 2017. "Should We Give Up after Solyndra? Optimal Technology R&D Portfolios under Uncertainty," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(S1), pages 123-151.
    5. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    6. Sue Wing, Ian, 2006. "Representing induced technological change in models for climate policy analysis," Energy Economics, Elsevier, vol. 28(5-6), pages 539-562, November.
    7. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    8. Berglund, Christer & Soderholm, Patrik, 2006. "Modeling technical change in energy system analysis: analyzing the introduction of learning-by-doing in bottom-up energy models," Energy Policy, Elsevier, vol. 34(12), pages 1344-1356, August.
    9. Kalkuhl, Matthias & Edenhofer, Ottmar & Lessmann, Kai, 2012. "Learning or lock-in: Optimal technology policies to support mitigation," Resource and Energy Economics, Elsevier, vol. 34(1), pages 1-23.
    10. Gerlagh, Reyer, 2008. "A climate-change policy induced shift from innovations in carbon-energy production to carbon-energy savings," Energy Economics, Elsevier, vol. 30(2), pages 425-448, March.
    11. Gillingham, Kenneth & Newell, Richard G. & Pizer, William A., 2008. "Modeling endogenous technological change for climate policy analysis," Energy Economics, Elsevier, vol. 30(6), pages 2734-2753, November.
    12. Shiell, Leslie & Lyssenko, Nikita, 2014. "Climate policy and induced R&D: How great is the effect?," Energy Economics, Elsevier, vol. 46(C), pages 279-294.
    13. Popp, David & Newell, Richard G. & Jaffe, Adam B., 2010. "Energy, the Environment, and Technological Change," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 873-937, Elsevier.
    14. Acemoglu, Daron & Rafey, Will, 2023. "Mirage on the horizon: Geoengineering and carbon taxation without commitment," Journal of Public Economics, Elsevier, vol. 219(C).
    15. Enrica Cian & Valentina Bosetti & Massimo Tavoni, 2012. "Technology innovation and diffusion in “less than ideal” climate policies: An assessment with the WITCH model," Climatic Change, Springer, vol. 114(1), pages 121-143, September.
    16. Santen, Nidhi R. & Anadon, Laura Diaz, 2016. "Balancing solar PV deployment and RD&D: A comprehensive framework for managing innovation uncertainty in electricity technology investment planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 560-569.
    17. Carraro, Carlo & De Cian, Enrica & Nicita, Lea & Massetti, Emanuele & Verdolini, Elena, 2010. "Environmental Policy and Technical Change: A Survey," International Review of Environmental and Resource Economics, now publishers, vol. 4(2), pages 163-219, October.
    18. Popp, David, 2006. "ENTICE-BR: The effects of backstop technology R&D on climate policy models," Energy Economics, Elsevier, vol. 28(2), pages 188-222, March.
    19. Castrejon-Campos, Omar & Aye, Lu & Hui, Felix Kin Peng, 2022. "Effects of learning curve models on onshore wind and solar PV cost developments in the USA," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    20. Philippe Aghion & Antoine Dechezleprêtre & David Hémous & Ralf Martin & John Van Reenen, 2016. "Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 1-51.

    More about this item

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:20783. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.