An empirical study of the relationship between the export dynamics on wind energy technology markets and the public policy
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This article deals with the question of how public policy affects export of wind energy technologies, using panel data from 18 OECD countries over the period 1992-2008. We carry out panel unit-root and cointegration tests, taking into account the results of the normality and structural breaks tests for each individual time series and of tests for the presence of cross-sectional dependence in the panel. Time series data on public policies and exports are integrated and cointegrated. The dynamic OLS results indicate that in the long run, a 1% increase in public R&D expenditures increases exports by 0.722%. The Blundell–Bond system generalized methods of moments (GMM) is employed to conduct a panel causality test in a vector error-correction mechanism (VECM) setting. Evidence of a short-term linear causal relationship running from the contribution of wind energy to the total energy supply (used as a proxy variable for feet-in-tariffs) to exports is uncovered. The joint test results of the study show that exports have a positive effect on GDP and the contribution of wind energy to the total energy supply and that there is bidirectional Granger causality between export and GDP. We suggest some policy implications based on the results of this study.
Abstract
This article deals with the question of how public policy affects export of wind energy technologies, using panel data from 18 OECD countries over the period 1992-2008. We carry out panel unit-root and cointegration tests, taking into account the results of the normality and structural breaks tests for each individual time series and of tests for the presence of cross-sectional dependence in the panel. Time series data on public policies and exports are integrated and cointegrated. The dynamic OLS results indicate that in the long run, a 1% increase in public R&D expenditures increases exports by 0.722%. The Blundell–Bond system generalized methods of moments (GMM) is employed to conduct a panel causality test in a vector error-correction mechanism (VECM) setting. Evidence of a short-term linear causal relationship running from the contribution of wind energy to the total energy supply (used as a proxy variable for feet-in-tariffs) to exports is uncovered. The joint test results of the study show that exports have a positive effect on GDP and the contribution of wind energy to the total energy supply and that there is bidirectional Granger causality between export and GDP. We suggest some policy implications based on the results of this study.
참고문헌 (40)
- An, G., "The export technology content, learning by doing and specialization in foreign trade", Journal of International Economics, 64, pp.465~483 (2004)
- Arellano, M., "Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations", Review of Economic Studies, 58, pp.277~297 (1991)
- Arent, D. J., "The status and prospects of renewable energy for combating global warming", Energy Economics, 33, pp.584~593 (2011)
- Cointegration in panel data with breaks and cross-section dependence, "Banerjee, A", European Central Bank, ~, pp.Working Paper 591 (2006)
- Banerjee, A., "Some cautions of the use of panel methods for integrated series of macro-economic data", Econometrics Journal, 7, pp.322~340 (2004)
- Breitung, J., "The econometrics of panel data: Fundamentals and recent developments in theory and practice", pp.297~322 (2008), Kluwer Academic Publishers
- Breitung, J., "The local power of some unit root tests for panel data", Advances in Econometrics, 15, pp.161~177 (2001)
- Engle, R., "Cointegration and error correction: representation, estimation, and testing", Econometrica, 55, pp.251~276 (1987)
- Frees, E. W., "Assessing cross-sectional correlation in panel data", Journal of Econometrics, 69, pp.393~414 (1995)
- Gross, R., "Progress in renewable energy", Environmental International, 29, pp.105~122 (2003)
- Haas, R., "Efficiency and effectiveness of promotion systems for electricity generation from renewable energy sources – Lessons from EU countries", Energy, 36, pp.2186~2193 (2011)
- Hansen, L., "Large sample properties of Generalized Method of Moment estimators", Econometrica, 50, pp.1029~1054 (1982)
- Holtz-Eakin, D., "Estimating vector autoregressions with panel data", Econometrica, 56, pp.1371~1395 (1988)
- IEA., "Renewable Information 2004" (2004), OECD Publications
- World energy outlook 2012, "IEA", International Energy Agency, ~ (2012)
- Im, K. S., "Testing for unit roots in heterogeneous panels", Journal of Econometrica, 115, pp.53~74 (2003)
- Trade flows, barriers and market drivers in renewable energy supply goods: The need to level the playing field, "Jha, V.", International Centre for Trade and Sustainable Development, ~, pp.ICTSD Trade and Environment Issue Paper 10 (2009)
- Kao, C., "On the estimation and inference of a cointegrated regression in panel data", Advances in Econometrics, 15, pp.179~222 (2000)
- Lewis, J. I., "Fostering a renewable energy technology industry: An international comparison of wind industry policy support mechanisms", Energy Policy, 35, pp.1844~1857 (2007)
- Lewis, J. I., "Building a national wind turbine industry: Experience from China, India and South Korea", International Journal of Technology and Globalisation, 5, pp.281~305 (2011)
- Liu, J., "Understanding China’s renewable energy technology exports", Energy Policy, 52, pp.417~428 (2013)
- Lund, P. D., "Effects of energy policies on industry expansion in renewable energy", Renewable Energy, 34, pp.53~64 (2009)
- Implementing the EU Renewables Directive, "Neuhoff, K.", University of Cambridge, ~, pp.Cambridge Working Paper in Economics 0913 (2009)
- Pedroni, P., "Critical values of cointegration tests in heterogeneous panels with multiple regressors", Oxford Bulletin of Economics and Statistics, 61, pp.653~678 (1999)
- Pedroni P., "Fully modified OLS for the heterogeneous cointegrated panels", Advances in Econometrics, 15, pp.93~130 (2000)
- Pedroni, P., "Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis", Econometric Theory, 20, pp.597~625 (2004)
- General diagnostic tests for cross section dependence in panels, "Pesaran, M. H.", University of Cambridge, ~, pp.Cambridge Working Papers in Economics No. 0435 (2004)
- Resch, G., "Potentials and prospects for renewable energies at global scale", Energy Policy, 36, pp.4048~4056 (2008)
- Roodman, D., "How to xtabond2: An introduction to difference and system GMM in Stata", The Stata Journal, 9, pp.86~136 (2009)
- Ru, P., "Behind the development of technology: The transition of innovation modes in China’s wind turbine manufacturing industry", Energy Policy, 43, pp.58~69 (2012)
- Saidur, R., "A review on global wind energy policy", Renewable and Sustainable Energy Reviews, 14, pp.1744~1762 (2010)
- Saikkonen, P., "Asymptotically efficient estimation of cointegration regressions", Econometric Theory, 12, pp.1~21 (1991)
- Sargan, J., "The estimation of economic relationships using instrumental variables", Econometrica, 26, pp.393~415 (1958)
- Silverman, M., "The future of renewable energy industry", The Electricity Journal, 8, pp.12~31 (1995)
- Sung, B., "Causality between public policies and exports of renewable energy technologies", Energy Policy, 55, pp.95~104 (2013)
- UNDP., "World Energy Assessment: Energy and the challenge of sustainability" (2000), \
- Wang, Z., "China’s wind power industry: Policy support, technological achievements, and emerging challenges", Energy Policy, 51, pp.80~88 (2012)
- Westerlund, J., "Testing for error correction in panel data", Oxford Bulletin of Economics and Statistics, 69, pp.709~748 (2007)
- Zhao, Z. -Y., "The impact of international forces on the Chinese wind power industry", Renewable and Sustainable Energy Reviews, 24, pp.131~141 (2013)
- Zhang, S., "International competitiveness of China’s wind turbine manufacturing industry and implications for future development", Renewable and Sustainable Energy Reviews, 16, pp.3903~3909 (2012)