Panel Paper: The Learning Process and Technological Change in US Wind Industry

Thursday, November 6, 2014 : 1:20 PM
Apache (Convention Center)

*Names in bold indicate Presenter

Tian Tang, Florida State University
In response to global climate change and energy security concerns, increasing the share of renewable energy in the electricity supply has been proposed as a promising solution in the United States. Among all types of renewable energy technologies, the US has experienced tremendous technological change in wind power over the past two decades and has become the largest wind power country in the world in terms of annual wind power generation since 2008.

In this research, I am going to examine the determinants of technological change of wind power in US from a learning perspective. Based on technological learning theories and network theories, this research will use a unbalanced panel data of 764 wind projects from 1985 to 2012 to test the impacts of four learning channels – learning through R&D in wind turbine manufacturing (learning-by-searching), learning from manufacturer and wind power generator’s previous experience of installation and operation (learning-by-doing), learning from the experience of other firms (knowledge spillover), and learning through the network among wind turbine manufacturer, wind power generators, and transmission system operators (learning-by-interacting) –on technological change measured as the improvement of a wind project’s capacity factor. [1] In addition, I am going to control for the institutional context that affects the learning among different actors in the wind industry, including state renewable energy policies and electricity market structure.

This proposed research will be of interest to utility regulators and other policymakers for energy technology policies. By focusing on the roles of various actors in the US wind industry, this research will increase the understanding of the learning process in the US wind industry. As a result, it will help policy-makers better target policies to different learning channels so as to improve the performance of wind farms and drive down the price of wind power.

Besides its potential policy implications, this proposed research will also contribute to the literature on state renewable energy policies and technological learning. Most existing empirical studies on state renewable energy polices focus on testing whether a particular policy has led to the diffusion of wind power in the form of installed capacity or share of electricity generation. The only one that examines the impacts of renewable energy polices on the technological change of wind power is the work of Nemet (2012), which looks at the learning-by-doing (LBD) effects in wind projects within California. With a more representative sample of wind projects in US, my proposed research will provide a cross-state analysis for the technological change in wind power, and test the impacts of different channels of learning in addition to the LBD.

[1] Capacity factor is the ratio of the actual electricity produced by a wind farm in a given period, to its potential output if it was operated at full nameplate capacity for the entire period, which captures the productivity of a wind farm.