ÌÇÐÄvlog¹ÙÍø

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.