IDEAS home Printed from https://ideas.repec.org/a/eee/resene/v35y2013i4p505-533.html
   My bibliography  Save this article

Technology variation vs. R&D uncertainty: What matters most for energy patent success?

Author

Listed:
  • Popp, David
  • Santen, Nidhi
  • Fisher-Vanden, Karen
  • Webster, Mort

Abstract

R&D is an uncertain activity with highly skewed outcomes. Nonetheless, most recent empirical studies and modeling estimates of the potential of technological change focus on the average returns to research and development (R&D) for a composite technology and contain little or no information about the distribution of returns to R&D – which could be important for capturing the range of costs associated with climate change mitigation policies – by individual technologies. Through an empirical study of patent citation data, this paper adds to the literature on the outcomes of energy R&D by focusing on the behavior of the most successful innovations for six energy technologies, allowing us to determine whether uncertainty or differences in technologies matter most for success. We highlight two key results. First, we compare the results from an aggregate analysis of six energy technologies to technology-by-technology results. Our results show that existing work that assumes diminishing returns but assumes one generic technology is too simplistic and misses important differences between more successful and less successful technologies. Second, we use quantile regression techniques to learn more about patents that have a high positive error term in our regressions – that is, patents that receive many more citations than predicted based on observable characteristics. We find that differences across technologies, rather than differences across quantiles within technologies, are more important. The value of successful technologies persists longer than those of less successful technologies, providing evidence that success is the culmination of several advances building upon one another, rather than resulting from one single breakthrough. Diminishing returns to research activities appear most problematic during rapid increases of research investment, such as experienced by solar energy in the 1970s.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:resene:v:35:y:2013:i:4:p:505-533
    DOI: 10.1016/j.reseneeco.2013.05.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0928765513000304
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    2. 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.
    3. Jaffe, Adam B & Fogarty, Michael S & Banks, Bruce A, 1998. "Evidence from Patents and Patent Citations on the Impact of NASA and Other Federal Labs on Commercial Innovation," Journal of Industrial Economics, Wiley Blackwell, vol. 46(2), pages 183-205, June.
    4. Valentina Bosetti & Carlo Carraro & Marzio Galeotti & Emanuele Massetti & Massimo Tavoni, 2006. "WITCH. A World Induced Technical Change Hybrid Model," Working Papers 2006_46, Department of Economics, University of Venice "Ca' Foscari".
    5. Popp David & Juhl Ted & Johnson Daniel K.N., 2004. "Time In Purgatory: Examining the Grant Lag for U.S. Patent Applications," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 4(1), pages 1-45, November.
    6. Zvi Griliches, 1998. "Patent Statistics as Economic Indicators: A Survey," NBER Chapters,in: R&D and Productivity: The Econometric Evidence, pages 287-343 National Bureau of Economic Research, Inc.
    7. Blanford, Geoffrey J., 2009. "R&D investment strategy for climate change," Energy Economics, Elsevier, vol. 31(Supplemen), pages 27-36.
    8. Baker, Erin & Adu-Bonnah, Kwame, 2008. "Investment in risky R&D programs in the face of climate uncertainty," Energy Economics, Elsevier, vol. 30(2), pages 465-486, March.
    9. Stijn Kelchtermans & Reinhilde Veugelers, 2011. "The great divide in scientific productivity: why the average scientist does not exist," Industrial and Corporate Change, Oxford University Press, vol. 20(1), pages 295-336, February.
    10. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, Oxford University Press, vol. 108(3), pages 577-598.
    11. repec:fth:harver:1473 is not listed on IDEAS
    12. Bye, Brita & Jacobsen, Karl, 2011. "Restricted carbon emissions and directed R&D support; an applied general equilibrium analysis," Energy Economics, Elsevier, vol. 33(3), pages 543-555, May.
    13. David Popp, 2002. "Induced Innovation and Energy Prices," American Economic Review, American Economic Association, vol. 92(1), pages 160-180, March.
    14. Pakes, Ariel S, 1986. "Patents as Options: Some Estimates of the Value of Holding European Patent Stocks," Econometrica, Econometric Society, vol. 54(4), pages 755-784, July.
    15. Bosetti, Valentina & Tavoni, Massimo, 2009. "Uncertain R&D, backstop technology and GHGs stabilization," Energy Economics, Elsevier, vol. 31(Supplemen), pages 18-26.
    16. Alfonso Miranda, 2008. "Planned fertility and family background: a quantile regression for counts analysis," Journal of Population Economics, Springer;European Society for Population Economics, vol. 21(1), pages 67-81, January.
    17. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    18. 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.
    19. Ricardo J. Caballero & Adam B. Jaffe, 1993. "How High are the Giants' Shoulders: An Empirical Assessment of Knowledge Spillovers and Creative Destruction in a Model of Economic Growth," NBER Chapters,in: NBER Macroeconomics Annual 1993, Volume 8, pages 15-86 National Bureau of Economic Research, Inc.
    20. 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.
    21. Baker, Erin & Solak, Senay, 2011. "Climate change and optimal energy technology R&D policy," European Journal of Operational Research, Elsevier, vol. 213(2), pages 442-454, September.
    22. Otto, Vincent M. & Löschel, Andreas & Reilly, John, 2008. "Directed technical change and differentiation of climate policy," Energy Economics, Elsevier, vol. 30(6), pages 2855-2878, November.
    23. Malte Schwoon & Richard S.J. Tol, 2006. "Optimal CO2-abatement with Socio-economic Inertia and Induced Technological Change," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 25-60.
    24. 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.
    25. Winkelmann, Rainer, 2006. "Reforming health care: Evidence from quantile regressions for counts," Journal of Health Economics, Elsevier, vol. 25(1), pages 131-145, January.
    26. 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.
    27. Jean O. Lanjouw & Mark Schankerman, 2004. "Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators," Economic Journal, Royal Economic Society, vol. 114(495), pages 441-465, April.
    28. David Popp, 2006. "They Don'T Invent Them Like They Used To: An Examination Of Energy Patent Citations Over Time," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 15(8), pages 753-776.
    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. Jin, Wei & Zhang, ZhongXiang, 2016. "On the mechanism of international technology diffusion for energy technological progress," Resource and Energy Economics, Elsevier, vol. 46(C), pages 39-61.
    2. 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.
    3. Clément Bonnet, 2016. "Measuring Knowledge with Patent Data: an Application to Low Carbon Energy Technologies," EconomiX Working Papers 2016-37, University of Paris Nanterre, EconomiX.
    4. repec:aen:journl:ej38-6-santen is not listed on IDEAS
    5. repec:eee:respol:v:46:y:2017:i:9:p:1580-1594 is not listed on IDEAS
    6. 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 Group Munich.
    7. Clément Bonnet, 2017. "Measuring Inventive Performance with Patent Data: an Application to Low Carbon Energy Technologies," Working Papers 1709, Chaire Economie du climat.
    8. David Popp, 2016. "From Science to Technology: The Value of Knowledge From Different Energy Research Institutions," NBER Working Papers 22573, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Technological change; Energy efficiency; Alternative energy; R&D; Patents; Climate change; Uncertainty;

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • 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
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • 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:eee:resene:v:35:y:2013:i:4:p:505-533. 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). General contact details of provider: http://www.elsevier.com/locate/inca/505569 .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.