The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization
Using the bidding prices of participants in China's national wind project concession programs from 2003 to 2007, this paper built up a learning curve model to estimate the joint learning from learning-by-doing and learning-by-searching, with a novel knowledge stock metric based on technology adoption in China through both domestic technology development and international technology transfer. The paper describes, for the first time, the evolution of the price of wind power in China, and provides estimates of how technology adoption, experience in building wind farm projects, wind turbine manufacturing localization, and wind farm economies of scale have influenced the price of wind power. The learning curve model presented includes several important control variables, namely, wind resource indicators and steel prices. The results indicate that joint learning from technology adoption and learning-by-doing through cumulative installed capacity, wind turbine manufacturing localization, and wind farm economies of scale comprise the three most significant factors associated with reductions in the price of wind power in China during the period under consideration. The two types of learning investigated are associated with a 4.1%–4.3% price reduction per doubling of installed capacity, providing an estimate of the evolution of the price of wind power, a technology widely used in other markets, which in China has benefited from technology leapfrogging, established supply chains, and operational experience in other countries. Because of the change of bidding rules in 2007, our estimates can be interpreted as the lower bound of the true joint learning rates. Our model also indicates that most learning about the installation and operation of wind farms was common to the whole industry (i.e., we found little evidence for intra-firm learning). The policies that have contributed to the growth of the Chinese knowledge stock through the promotion of technology adoption are also discussed.
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- 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.
- Klaassen, Ger & Miketa, Asami & Larsen, Katarina & Sundqvist, Thomas, 2005. "The impact of R&D on innovation for wind energy in Denmark, Germany and the United Kingdom," Ecological Economics, Elsevier, vol. 54(2-3), pages 227-240, August.
- Nadiri, M Ishaq & Prucha, Ingmar R, 1996. "Estimation of the Depreciation Rate of Physical and R&D Capital in the U.S. Total Manufacturing Sector," Economic Inquiry, Western Economic Association International, vol. 34(1), pages 43-56, January.
- Hausman, Jerry, 2015.
"Specification tests in econometrics,"
Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
- Coulomb, L. & Neuhoff, K., 2006. "Learning curves and changing product attributes: the case of wind turbines," Cambridge Working Papers in Economics 0618, Faculty of Economics, University of Cambridge.
- Patrik Söderholm & Ger Klaassen, 2007. "Wind Power in Europe: A Simultaneous Innovation–Diffusion Model," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 36(2), pages 163-190, February.
- Nemet, Gregory F., 2009. "Interim monitoring of cost dynamics for publicly supported energy technologies," Energy Policy, Elsevier, vol. 37(3), pages 825-835, March.
- Junginger, M. & Faaij, A. & Turkenburg, W. C., 2005. "Global experience curves for wind farms," Energy Policy, Elsevier, vol. 33(2), pages 133-150, January.
- Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
- Grubler, Arnulf & Nakicenovic, Nebojsa & Victor, David G., 1999. "Dynamics of energy technologies and global change," Energy Policy, Elsevier, vol. 27(5), pages 247-280, May.
- Irwin, Douglas A & Klenow, Peter J, 1994. "Learning-by-Doing Spillovers in the Semiconductor Industry," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1200-1227, December.
- 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.
- Ibenholt, Karin, 2002. "Explaining learning curves for wind power," Energy Policy, Elsevier, vol. 30(13), pages 1181-1189, October.
- Kahouli-Brahmi, Sondes, 2008. "Technological learning in energy-environment-economy modelling: A survey," Energy Policy, Elsevier, vol. 36(1), pages 138-162, January.
- Arthur van Benthem & Kenneth Gillingham & James Sweeney, 2008. "Learning-by-Doing and the Optimal Solar Policy in California," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 131-152.
- Berry, David, 2009. "Innovation and the price of wind energy in the US," Energy Policy, Elsevier, vol. 37(11), pages 4493-4499, November.
- 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.
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