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Energy Efficiency of the Baltic Sea Countries: An Application of Stochastic Frontier Analysis

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  • Wen-Ling Hsiao

    (Department of Management Science, National Chiao Tung University, Hsinchu City 300, Taiwan)

  • Jin-Li Hu

    (Institute of Business and Management, National Chiao Tung University, Taipei City 100, Taiwan)

  • Chan Hsiao

    (Department of Management Science, National Chiao Tung University, Hsinchu City 300, Taiwan)

  • Ming-Chung Chang

    (Department of Marketing, Kainan University, Taoyuan City 33857, Taiwan)

Abstract

Using the stochastic frontier analysis (SFA) model, this research measures total-factor energy efficiency (TFEE) and disaggregate input efficiency for 10 countries across the Baltic Sea from 2004 to 2014. Real capital, labor, energy use, and carbon dioxide (CO 2 ) are input variables, real gross domestic product (GDP) is the output variable, and renewable energy consumption and urban population are the environmental variables. The results provide not only the TFEE scores, in which statistical noise is considered, but also the determinants of inefficiency, which show the following. (i) Norway, Sweden, Finland, and Latvia perform better with respect to energy efficiency than other countries in the Baltic Sea Region. (ii) Interestingly, the average energy use efficiency scores from 2004 to 2014 in the 10 Baltic countries exhibit a gradual upward trend except for 2009. (iii) For the inefficiency estimates, higher renewable energy consumption and urban population correspond to higher TFEE scores.

Suggested Citation

  • Wen-Ling Hsiao & Jin-Li Hu & Chan Hsiao & Ming-Chung Chang, 2018. "Energy Efficiency of the Baltic Sea Countries: An Application of Stochastic Frontier Analysis," Energies, MDPI, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:104-:d:193913
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    as
    1. Nair, Gireesh & Gustavsson, Leif & Mahapatra, Krushna, 2010. "Factors influencing energy efficiency investments in existing Swedish residential buildings," Energy Policy, Elsevier, vol. 38(6), pages 2956-2963, June.
    2. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    3. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    4. Jin-Li Hu & Chih-Hai Yang & Chiang-Ping Chen, 2014. "R&D Efficiency And The National Innovation System: An International Comparison Using The Distance Function Approach," Bulletin of Economic Research, Wiley Blackwell, vol. 66(1), pages 55-71, January.
    5. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    6. Filis, George & Degiannakis, Stavros & Floros, Christos, 2011. "Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 152-164, June.
    7. Wang, Qiang & Wu, Shi-dai & Zeng, Yue-e & Wu, Bo-wei, 2016. "Exploring the relationship between urbanization, energy consumption, and CO2 emissions in different provinces of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1563-1579.
    8. Graus, W.H.J. & Voogt, M. & Worrell, E., 2007. "International comparison of energy efficiency of fossil power generation," Energy Policy, Elsevier, vol. 35(7), pages 3936-3951, July.
    9. Streimikiene, Dalia & Ciegis, Remigijus & Grundey, Dainora, 2007. "Energy indicators for sustainable development in Baltic States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(5), pages 877-893, June.
    10. Geller, Howard & Harrington, Philip & Rosenfeld, Arthur H. & Tanishima, Satoshi & Unander, Fridtjof, 2006. "Polices for increasing energy efficiency: Thirty years of experience in OECD countries," Energy Policy, Elsevier, vol. 34(5), pages 556-573, March.
    11. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    12. Wang, Qiang, 2014. "Effects of urbanisation on energy consumption in China," Energy Policy, Elsevier, vol. 65(C), pages 332-339.
    13. Lin, Boqiang & Du, Kerui, 2013. "Technology gap and China's regional energy efficiency: A parametric metafrontier approach," Energy Economics, Elsevier, vol. 40(C), pages 529-536.
    14. Baumol, William J, 1986. "Productivity Growth, Convergence, and Welfare: What the Long-run Data Show," American Economic Review, American Economic Association, vol. 76(5), pages 1072-1085, December.
    15. Honma, Satoshi & Hu, Jin-Li, 2014. "A panel data parametric frontier technique for measuring total-factor energy efficiency: An application to Japanese regions," Energy, Elsevier, vol. 78(C), pages 732-739.
    16. Chien, Taichen & Hu, Jin-Li, 2008. "Renewable energy: An efficient mechanism to improve GDP," Energy Policy, Elsevier, vol. 36(8), pages 3035-3042, August.
    17. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    18. Roos, Inge & Soosaar, Sulev & Volkova, Anna & Streimikene, Dalia, 2012. "Greenhouse gas emission reduction perspectives in the Baltic States in frames of EU energy and climate policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2133-2146.
    19. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    20. Jin-Li Hu & Tzu-Pu Chang, 2016. "Total-Factor Energy Efficiency and Its Extensions: Introduction, Computation and Application," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 45-69, Springer.
    21. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    22. Gary Koop, 1998. "Carbon dioxide emissions and economic growth: A structural approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 489-515.
    23. Soytas, Ugur & Sari, Ramazan, 2003. "Energy consumption and GDP: causality relationship in G-7 countries and emerging markets," Energy Economics, Elsevier, vol. 25(1), pages 33-37, January.
    24. Zhang, Xing-Ping & Cheng, Xiao-Mei & Yuan, Jia-Hai & Gao, Xiao-Jun, 2011. "Total-factor energy efficiency in developing countries," Energy Policy, Elsevier, vol. 39(2), pages 644-650, February.
    25. Song, Ma-Lin & Zhang, Lin-Ling & Liu, Wei & Fisher, Ron, 2013. "Bootstrap-DEA analysis of BRICS’ energy efficiency based on small sample data," Applied Energy, Elsevier, vol. 112(C), pages 1049-1055.
    26. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    27. Renn, Ortwin & Marshall, Jonathan Paul, 2016. "Coal, nuclear and renewable energy policies in Germany: From the 1950s to the “Energiewende”," Energy Policy, Elsevier, vol. 99(C), pages 224-232.
    28. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    29. Klevas, Valentinas & Streimikiene, Dalia & Grikstaite, Ramute, 2007. "Sustainable energy in Baltic States," Energy Policy, Elsevier, vol. 35(1), pages 76-90, January.
    30. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    31. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    32. Yanrui Wu, 2004. "Openness, productivity and growth in the APEC economies," Empirical Economics, Springer, vol. 29(3), pages 593-604, September.
    33. Guo, Xiao-Dan & Zhu, Lei & Fan, Ying & Xie, Bai-Chen, 2011. "Evaluation of potential reductions in carbon emissions in Chinese provinces based on environmental DEA," Energy Policy, Elsevier, vol. 39(5), pages 2352-2360, May.
    34. Richmond, J, 1974. "Estimating the Efficiency of Production," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(2), pages 515-521, June.
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