Abstract: 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.
Installed wind turbine capacity in China
Installed wind turbine capacity in ChinaHighlights
►
We model wind power price reductions in China between 2003-2007.
► Cumulative installed capacity and technology adoption were significant factors.
► China’s joint learning rates from LBD and LBS were 4.1%-4.3%.
► Inter-firm knowledge spillovers have had significant impacts on price.
► Wind farm economies of scale and turbine localization rate were also strong factors.
by Yueming Qiua, b
and Laura D. Anadonb,
a Stanford University, Atmosphere/Energy Program, The Jerry Yang and Akiko Yamazaki, Environment & Energy Building, 473 Via Ortega, Stanford, CA 94305, USA
b Harvard University, John F. Kennedy School of Government, Belfer Center for Science and International Affairs, 79 John F. Kennedy Street, Cambridge, MA 02138, USA
► Cumulative installed capacity and technology adoption were significant factors.
► China’s joint learning rates from LBD and LBS were 4.1%-4.3%.
► Inter-firm knowledge spillovers have had significant impacts on price.
► Wind farm economies of scale and turbine localization rate were also strong factors.
by Yueming Qiua, b
and Laura D. Anadonb, a Stanford University, Atmosphere/Energy Program, The Jerry Yang and Akiko Yamazaki, Environment & Energy Building, 473 Via Ortega, Stanford, CA 94305, USA
b Harvard University, John F. Kennedy School of Government, Belfer Center for Science and International Affairs, 79 John F. Kennedy Street, Cambridge, MA 02138, USA
Volume 34, Issue 3, May 2012, Pages 772–785
Keywords: Wind power; Learning curve; China; Technology adoption; Learning-by-doing; Learning-by-searching
No comments:
Post a Comment