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Explaining Technological Change of Wind Power in China and The Syracuse University SURFACE Dissertations - ALL SURFACE August 2016 Explaining Technological Change of Wind Power in China and the United States: Roles of Energy Policies, Technological Learning, and Collaboration Tian Tang Syracuse University Follow this and additional works at: https://surface.syr.edu/etd Part of the Social and Behavioral Sciences Commons Recommended Citation Tang, Tian, "Explaining Technological Change of Wind Power in China and the United States: Roles of Energy Policies, Technological Learning, and Collaboration" (2016). Dissertations - ALL. 659. https://surface.syr.edu/etd/659 This Dissertation is brought to you for free and open access by the SURFACE at SURFACE. It has been accepted for inclusion in Dissertations - ALL by an authorized administrator of SURFACE. For more information, please contact [email protected]. Abstract The following dissertation explains how technological change of wind power, in terms of cost reduction and performance improvement, is achieved in China and the US through energy policies, technological learning, and collaboration. The objective of this dissertation is to understand how energy policies affect key actors in the power sector to promote renewable energy and achieve cost reductions for climate change mitigation in different institutional arrangements. The dissertation consists of three essays. The first essay examines the learning processes and technological change of wind power in China. I integrate collaboration and technological learning theories to model how wind technologies are acquired and diffused among various wind project participants in China through the Clean Development Mechanism (CDM)—an international carbon trade program, and empirically test whether different learning channels lead to cost reduction of wind power. Using pooled cross-sectional data of Chinese CDM wind projects and spatial econometric models, I find that a wind project developer’s previous experience (learning-by-doing) and industrywide wind project experience (spillover effect) significantly reduce the costs of wind power. The spillover effect provides justification for subsidizing users of wind technologies so as to offset wind farm investors’ incentive to free-ride on knowledge spillovers from other wind energy investors. The CDM has played such a role in China. Most importantly, this essay provides the first empirical evidence of “learning-by-interacting”: CDM also drives wind power cost reduction and performance improvement by facilitating technology transfer through collaboration between foreign turbine manufacturers and local wind farm developers. The second essay extends this learning framework to the US wind power sector, where I examine how state energy policies, restructuring of the electricity market, and learning among actors in wind industry lead to performance improvement of wind farms. Unlike China, the restructuring of the US electricity market created heterogeneity in transmission network governance across regions. Thus, I add transmission network governance to my learning framework to test the impacts of different transmission network governance models. Using panel data of existing utility-scale wind farms in US during 2001-2012 and spatial models, I find that the performance of a wind project is improved through more collaboration among project participants (learning-by-interacting), and this improvement is even greater if the wind project is interconnected to a regional transmission network coordinated by an independent system operator or a regional transmission organization (ISO/RTO). In the third essay, I further explore how different transmission network governance models affect wind power integration through a comparative case study. I compare two regional transmission networks, which represent two major transmission network governance models in the US: the ISO/RTO-governance model and the non-RTO model. Using archival data and interviews with key network participants, I find that a centralized transmission network coordinated through an ISO/RTO is more effective in integrating wind power because it allows resource pooling and optimal allocating of the resources by the central network administrative agency (NAO). The case study also suggests an alternative path to improved network effectiveness for a less cohesive network, which is through more frequent resource exchange among subgroups within a large network. On top of that, this essay contributes to the network governance literature by providing empirical evidence on the coexistence of hierarchy, market, and collaboration in complex service delivery networks. These coordinating mechanisms complement each other to provide system flexibility and stability, particularly when the network operates in a turbulent environment with changes and uncertainties. EXPLAINING TECHNOLOGICAL CHANGE OF WIND POWER IN CHINA AND THE UNITED STATES: ROLES OF ENERGY POLICIES, TECHNOLOGICAL LEARNING, AND COLLABORATION by Tian Tang M.A., Tsinghua University, 2011 LL.B. and B.S., Tsinghua University, 2008 Dissertation Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Public Administration Syracuse University August 2016 Copyright © Tian Tang 2016 All Rights Reserved Acknowledgement I have always felt lucky for being a member of the Maxwell community. During the past five years, extraordinary people in this school have inspired me to start my academic journey in public administration, and have encouraged me to continue my career in public administration research and education. I am deeply grateful to my adviser, David Popp, for his gracious support and encouragement in every aspect of my intellectual growth and career development. I would like to thank Pete Wilcoxen for all the inspiring questions, helpful comments, and encouragement he has given me throughout the evolution of my dissertation. I also want to thank Ines Mergel, Stuart Bretschneider, and David Van Slyke for all the detailed feedback that helped me improve my dissertation at every stage. In addition, I am grateful to all the professors whom I have taken classes with. Without them, I would not have started my dissertation and my professional career in this field. I was also fortunate enough to participate in the Energy Technology Innovation Policy Group and the Sustainability Science Program at Harvard Kennedy School in 2014-2015. Thank you to Laura Diaz Anadon, Henry Lee, Bill Clark, and Nancy Dickson, for supporting my research with tremendous resources, pushing me beyond what I thought were my intellectual limits, and broadening my horizons. Thanks are also given to all my friends and colleagues at Syracuse, Harvard and beyond for their friendship and support to help me get through my PhD process smoother and smarter. Finally, I want to give very special thanks to my loving parents. Thank you for your unconditional love, support, and patience. v Table of Contents Chapter 1: Introduction ........................................................................................ 1 Chapter 2: The Learning Process and Technological Change in Wind Power: Evidence from China’s CDM Wind Projects ........................................... 5 2.1 Introduction ...................................................................................................................... 6 2.2 The CDM and Domestic Policies for Wind Power Development in China ..................... 8 2.2.1 Policy instruments and technological change in China’s wind industry .................. 8 2.2.2 Rationale for using CDM wind projects data ......................................................... 11 2.3 Theoretical Framework and Hypotheses ........................................................................ 12 2.3.1 Learning by doing (LBD) ....................................................................................... 14 2.3.2 Spillover effects ...................................................................................................... 14 2.3.3 Learning by searching (LBS) .................................................................................. 15 2.3.4 Learning by interacting (LBI) ................................................................................. 16 2.4 Data and Descriptive Statistics ....................................................................................... 18 2.4.1 Data ......................................................................................................................... 18 2.4.2 Key variables .......................................................................................................... 19 2.4.3 Descriptive statistics ............................................................................................... 25 2.5 Empirical Models and Results........................................................................................ 26 2.5.1 Effects of internal learning and learning-by-interacting ......................................... 26 2.5.2 Spillover effects ...................................................................................................... 31 2.6 Conclusion ...................................................................................................................... 33 2.6.1 Learning-by-doing, spillovers and demand-pull policy design .............................. 33 2.6.2 Learning-by-interacting and international collaboration regimes ........................... 34 References ................................................................................................................................
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