IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i5p861-d99167.html
   My bibliography  Save this article

Learning of Power Technologies in China: Staged Dynamic Two-Factor Modeling and Empirical Evidence

Author

Listed:
  • Yan Xu

    (School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China)

  • Jiahai Yuan

    (.School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Jianxiu Wang

    (School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China)

Abstract

Cost evolution has an important influence on the commercialization and large-scale application of power technology. Many researchers have analyzed the quantitative relationship between the cost of power technology and its influencing factors while establishing various forms of technical learning curve models. In this paper, we focus on the positive effects of the policy on research and development (R&D) learning by summarizing and comparing four energy technology cost models based on learning curves. We explore the influencing factors and dynamic change paths of power technology costs. The paper establishes a multi-stage dynamic two-factor learning curve model based on cumulative R&D investment and the installed capacity. This work presents the structural changes of the influencing factors at various stages. Causality analysis and econometric estimation of learning curves are performed on wind power and other power technologies. The conclusion demonstrates that a “learn by researching” approach had led to cost reduction of wind power to date, but, in the long term, the effect of “learn by doing” is greater than that of “learn by researching” when R&D learning is saturated. Finally, the paper forecasts the learning rates and the cost trends of the main power technologies in China. The work presented in this study has implications on power technology development and energy policy in China.

Suggested Citation

  • Yan Xu & Jiahai Yuan & Jianxiu Wang, 2017. "Learning of Power Technologies in China: Staged Dynamic Two-Factor Modeling and Empirical Evidence," Sustainability, MDPI, vol. 9(5), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:861-:d:99167
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/5/861/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/5/861/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gert Jan Kramer & Martin Haigh, 2009. "No quick switch to low-carbon energy," Nature, Nature, vol. 462(7273), pages 568-569, December.
    2. Yeh, Sonia & Rubin, Edward S., 2007. "A centurial history of technological change and learning curves for pulverized coal-fired utility boilers," Energy, Elsevier, vol. 32(10), pages 1996-2005.
    3. Jean Tirole, 1988. "The Theory of Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262200716, December.
    4. Geroski, P. A., 2000. "Models of technology diffusion," Research Policy, Elsevier, vol. 29(4-5), pages 603-625, April.
    5. David, Paul A. & Hall, Bronwyn H. & Toole, Andrew A., 2000. "Is public R&D a complement or substitute for private R&D? A review of the econometric evidence," Research Policy, Elsevier, vol. 29(4-5), pages 497-529, April.
    6. Dale W. Jorgenson & Kevin J. Stiroh, 2000. "Raising the Speed Limit: U.S. Economic Growth in the Information Age," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 31(1), pages 125-236.
    7. Neij, L, 1999. "Cost dynamics of wind power," Energy, Elsevier, vol. 24(5), pages 375-389.
    8. Reinganum, Jennifer F., 1989. "The timing of innovation: Research, development, and diffusion," Handbook of Industrial Organization, in: R. Schmalensee & R. Willig (ed.), Handbook of Industrial Organization, edition 1, volume 1, chapter 14, pages 849-908, Elsevier.
    9. Griliches, Zvi, 1998. "R&D and Productivity," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226308869, November.
    10. Paul M. Romer, 1994. "The Origins of Endogenous Growth," Journal of Economic Perspectives, American Economic Association, vol. 8(1), pages 3-22, Winter.
    11. F. M. Scherer, 1986. "Innovation and Growth: Schumpeterian Perspectives," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262691027, December.
    12. Gene M. Grossman & Elhanan Helpman, 1994. "Endogenous Innovation in the Theory of Growth," Journal of Economic Perspectives, American Economic Association, vol. 8(1), pages 23-44, Winter.
    13. McDonald, Alan & Schrattenholzer, Leo, 2001. "Learning rates for energy technologies," Energy Policy, Elsevier, vol. 29(4), pages 255-261, March.
    14. Neij, Lena, 2008. "Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments," Energy Policy, Elsevier, vol. 36(6), pages 2200-2211, June.
    15. Zvi Griliches, 1998. "R&D and Productivity: The Econometric Evidence," NBER Books, National Bureau of Economic Research, Inc, number gril98-1, July.
    16. Neij, Lena, 1997. "Use of experience curves to analyse the prospects for diffusion and adoption of renewable energy technology," Energy Policy, Elsevier, vol. 25(13), pages 1099-1107, November.
    17. Isoard, Stephane & Soria, Antonio, 2001. "Technical change dynamics: evidence from the emerging renewable energy technologies," Energy Economics, Elsevier, vol. 23(6), pages 619-636, November.
    18. 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.
    19. repec:fth:harver:1487 is not listed on IDEAS
    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. He Huang & DaPeng Liang & Liang Liang & Zhen Tong, 2019. "Research on China’s Power Sustainable Transition Under Progressively Levelized Power Generation Cost Based on a Dynamic Integrated Generation–Transmission Planning Model," Sustainability, MDPI, vol. 11(8), pages 1-21, April.
    2. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    3. Yi Zhou & Alun Gu, 2019. "Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    4. Yan Xu & Junjie Kang & Jiahai Yuan, 2018. "The Prospective of Nuclear Power in China," Sustainability, MDPI, vol. 10(6), pages 1-21, June.
    5. Gurkan Calmasur & Meryem Emre Aysin, 2020. "Regional Technological Learning in Turkish Cement Industry," Eurasian Journal of Economics and Finance, Eurasian Publications, vol. 8(4), pages 204-216.

    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. Jaffe, Adam B. & Newell, Richard G. & Stavins, Robert N., 2003. "Chapter 11 Technological change and the environment," Handbook of Environmental Economics, in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 1, chapter 11, pages 461-516, Elsevier.
    2. Stavins, Robert & Jaffe, Adam & Newell, Richard, 2000. "Technological Change and the Environment," Working Paper Series rwp00-002, Harvard University, John F. Kennedy School of Government.
    3. Adam Jaffe & Richard Newell & Robert Stavins, 2002. "Environmental Policy and Technological Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 22(1), pages 41-70, June.
    4. Lindman, Åsa & Söderholm, Patrik, 2012. "Wind power learning rates: A conceptual review and meta-analysis," Energy Economics, Elsevier, vol. 34(3), pages 754-761.
    5. Grafström, Jonas & Lindman, Åsa, 2017. "Invention, innovation and diffusion in the European wind power sector," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 179-191.
    6. Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
    7. Samadi, Sascha, 2018. "The experience curve theory and its application in the field of electricity generation technologies – A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2346-2364.
    8. Neij, Lena, 2008. "Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments," Energy Policy, Elsevier, vol. 36(6), pages 2200-2211, June.
    9. Kahouli-Brahmi, Sondes, 2009. "Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach," Ecological Economics, Elsevier, vol. 68(4), pages 1195-1212, February.
    10. Reinhard Haas & Marlene Sayer & Amela Ajanovic & Hans Auer, 2023. "Technological learning: Lessons learned on energy technologies," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 12(2), March.
    11. Kahouli, Sondès, 2011. "Effects of technological learning and uranium price on nuclear cost: Preliminary insights from a multiple factors learning curve and uranium market modeling," Energy Economics, Elsevier, vol. 33(5), pages 840-852, September.
    12. Olivia-Roxana Alecsoiu & Maria-Felicia Chirculescu & Andreea Panagoret, 2022. "Financial Performance under the Impact of the Decision to Implement an Organizational Change," Sustainability, MDPI, vol. 14(21), pages 1-25, October.
    13. Fatma M. Utku-İsmihan, 2019. "Knowledge, technological convergence and economic growth: a dynamic panel data analysis of Middle East and North Africa and Latin America," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 713-733, March.
    14. Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
    15. Yu, C.F. & van Sark, W.G.J.H.M. & Alsema, E.A., 2011. "Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 324-337, January.
    16. Cong, Rong-Gang & Shen, Shaochuan, 2014. "How to Develop Renewable Power in China? A Cost-Effective Perspective," MPRA Paper 112209, University Library of Munich, Germany.
    17. 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.
    18. Dosi, Giovanni & Nelson, Richard R., 2010. "Technical Change and Industrial Dynamics as Evolutionary Processes," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 1, chapter 0, pages 51-127, Elsevier.
    19. Odam, Neil & de Vries, Frans P., 2020. "Innovation modelling and multi-factor learning in wind energy technology," Energy Economics, Elsevier, vol. 85(C).
    20. Lehmann, Paul & Gawel, Erik, 2013. "Why should support schemes for renewable electricity complement the EU emissions trading scheme?," Energy Policy, Elsevier, vol. 52(C), pages 597-607.

    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:gam:jsusta:v:9:y:2017:i:5:p:861-:d:99167. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.