When will wind energy achieve grid parity in China? – Connecting technological learning and climate finance

China has adopted an ambitious plan for wind energy deployment. This paper uses the theory of the learning curve to investigate financing options to support grid parity for wind electricity. First, relying on a panel dataset consisting of information from 1207 wind projects in China’s thirty provinces over the period of 2004–2011, this study empirically estimates the learning rate of onshore wind technology to be around 4.4%. Given this low learning rate, achieving grid parity requires a policy of pricing carbon at 13€/ton CO2e in order to increase the cost of coal-generated electricity. Alternatively, a learning rate of 8.9% would be necessary in the absence of a carbon price. Second, this study assesses the evolution of additional capital subsidies in a dynamic framework of technological learning. The implicit average CO2 abatement cost derived from this learning investment is estimated to be around 16€/ton CO2e over the breakeven time period. The findings suggest that climate finance could be structured in a way to provide up-front financing to support this paradigm shift in energy transition.

[1]  P. Kort,et al.  Optimal Investment in Learning-Curve Technologies , 2011 .

[2]  Patrik Söderholm,et al.  Wind Power in Europe: A Simultaneous Innovation–Diffusion Model , 2007 .

[3]  F. Ferioli,et al.  Use and limitations of learning curves for energy technology policy: A component-learning hypothesis , 2009 .

[4]  K. Arrow.  The Economic Implications of Learning by Doing , 1962 .

[5]  Patrik Söderholm,et al.  Wind power learning rates: A conceptual review and meta-analysis☆ , 2012 .

[6]  David Infield,et al.  Renewable Energy Sources and Climate Change Mitigation: Wind Energy , 2011 .

[7]  P. Río,et al.  Analysing the determinants of on-shore wind capacity additions in the EU: An econometric study , 2012 .

[8]  A. Denny Ellerman,et al.  The cost of abating CO2 emissions by renewable energy incentives in Germany , 2013, 2013 10th International Conference on the European Energy Market (EEM).

[9]  J. Dutton,et al.  Treating Progress Functions as a Managerial Opportunity , 1984 .

[10]  Ryan Wiser,et al.  Understanding wind turbine price trends in the U.S. over the past decade , 2012 .

[11]  Laura Diaz Anadon,et al.  The price of wind power in China during its expansion: Technology adoption, learning-by-doing, economies of scale, and manufacturing localization , 2012 .

[12]  S. Kahouli-Brahmi,et al.  Testing for the presence of some features of increasing returns to adoption factors in energy system dynamics: An analysis via the learning curve approach , 2009 .

[13]  Yang Liu.  CDM and national policy: Synergy or conflict? Evidence from the wind power sector in China , 2015 .

[14]  E. Delarue,et al.  CO2 Abatement from RES Injections in the German Electricity Sector: Does a CO2 Price Help? , 2012 .

[15]  Edward S. Rubin,et al.  A review of uncertainties in technology experience curves , 2012 .

[16]  Flávio Sanson Fogliatto,et al.  Learning curve models and applications: Literature review and research directions , 2011 .

[17]  Lena Neij,et al.  Cost development of future technologies for power generation--A study based on experience curves and complementary bottom-up assessments , 2008 .

[18]  D. Kammen,et al.  Where, when and how much wind is available? A provincial-scale wind resource assessment for China , 2014 .

[19]  Patrik Söderholm,et al.  Technology learning in the presence of public R&D : the case of European wind power , 2010 .