Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2018 | 27 | 3 |

Tytuł artykułu

Industrial CO2 emissions efficiency and its determinants in China: analyzing differences across regions and industry sectors


Warianty tytułu

Języki publikacji



This paper utilizes industrial CO₂ emissions efficiency as a measure of the low-carbon transformation index and used industrial provincial panel data during 1997-2014 and industrial panel data during 2000- 14 based on the modified Super-SBM model with undesirable outputs that measure carbon efficiency levels of different provinces and industrial sectors in China. Differences among sectors and provinces were calculated using the Dagum Gene coefficient and the subgroup decomposition method, and the determinants of carbon efficiency were explored by regression analysis. It turns out that industrial CO₂ emissions efficiency in China is generally low, and it has been steadily improving since 2003. Industrial carbon efficiency shows the unbalanced characteristics (high in eastern areas, low in western areas) and the value of the western regions was overtaken by the central region during the period of the 12th Five-Year Plan. From the perspective of industrial sectors, industrial CO₂ emissions efficiency of lightly polluted industries is significantly higher than that of moderately and heavily polluted industries. In addition, the carbon efficiency of technology-intensive industries and clean production industries as part of industries with light pollution is at an optimal level, while that of some resource-intensive industries and traditional manufacturing industries is relatively low. Both the regional and industrial sectors’ Dagum Gini coefficients of industrial carbon efficiency exhibit the tendency of down first, and then up and stable on the whole. The regional disequilibrium problem mainly arises from the gap between the eastern and western regions, and the inter-industry gap is primarily manifested between heavily polluted and lightly polluted industries. The relationship between scale effect and industrial carbon efficiency presents a “U”-type curve. Ownership structure, technological innovation, government environment, and openness degree can all have a positive effect on industrial carbon efficiency, while endowment structure and energy consumption structure exert markedly negative effects. However, effects of these factors differ among different areas and different sectors.

Słowa kluczowe








Opis fizyczny



  • Zhejiang Sci-Tech University, School of Economics and Management, 310018, Zhejiang, China
  • Zhejiang Sci-Tech University, Zhejiang Ecological Civilization Research Center, 310018, Zhejiang, China
  • Guangzhou University, School of Economics and Statistics, 510006, Guangzhou, China


  • 1. CETIN M., SEVIK H. Measuring the Impact of Selected Plants on Indoor CO₂ Concentrations. Pol J of Environ Stud., 25 (3), 973, 2016.
  • 2. CETIN M., SEVIK H., ISINKARALAR K. Changes in the particulate matter and CO₂ concentrations based on the time and weather conditions: The case of Kastamonu Oxidation Communications, 40 (1), 477, 2017.
  • 3. SEVIK H., CETIN M., BELKAYALI N. Effects of Forests on Amounts of CO₂: Case Study of Kastamonu and Ilgaz Mountain National Parks. Pol J of Environ Stud., 24 (1), 253, 2015.
  • 4. YAMAJI K., MATSUHASHI R., NAGATA Y., KAYA Y. A study on economic measures for CO₂ reduction in Japan. Energy Policy, 21 (2), 123, 1993.
  • 5. ZHANG Z., QU J., ZENG J. A quantitative comparison and analysis on the assessment indicators of greenhouse gases emission. Journal of Geographical Sciences, 18 (4), 387, 2008.
  • 6. RAMANATHAN R. Combining indicators of energy consumption and CO₂ emissions: a cross-country comparison. International Journal of Global Energy Issues, 17 (3), 214, 2002.
  • 7. ZHOU P., ANG B.W., HAN J.Y. Total factor carbon emission performance: a Malmquist index analysis. Energy Economics, 32 (1), 194, 2010.
  • 8. IFTIKHAR Y., HE W.J., WANG Z.H. Energy and CO₂ emissions efficiency of major economies: A non-parametric analysis. Journal of Cleaner Production, 139 (12), 779, 2016.
  • 9. ZHOU W., NIE M. Regional difference in the efficiency of industrial carbon emissions in China. The Journal of Quantitative & Technical Economics, 9, 58, 2012 [In Chinese].
  • 10. WANG B., YU L., YANG Y. Measuring and decomposing energy productivity of China’s industries under Carbon Emission Constraints. Journal of Financial Research, 10, 128, 2013 [In Chinese]
  • 11. MENG M., FU Y., WANG T., JING K. Analysis of Low-Carbon Economy Efficiency of Chinese Industrial Sectors Based on a RAM Model with Undesirable Outputs. Sustainability, 9 (3), 451, 2017.
  • 12. BERGER A.N., HUMPHERY D.B. Efficiency of financial institutions: International survey and directions for future research. Social Science Electronic Publishing, 98 (2), 175, 1997.
  • 13. BI G.B., SONG W., ZHOU P., LIANG L. Does environmental regulation affect energy efficiency in China’s thermal power generation? Empirical evidence from a slacks-based DEA model. Energy Policy, 66 (3), 537, 2014.
  • 14. Ma D. China’s Low Carbon Economic Growth Efficiency: an Analysis Involving Carbon Sink. Pol J of Environ Stud., 26 (3), 1147, 2017.
  • 15. WANG B., WU Y., YAN P. Environmental Efficiency and Environmental Total Factor Productivity Growth in China’s Regional Economies. Economic Research Journal, 5, 95, 2010 [In Chinese]
  • 16. TONE K. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130 (3), 498, 2001.
  • 17. TONE K. A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143 (1), 32, 2002.
  • 18. TONE K. Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. Tokyo: GRIPS Research Report Series, 2003.
  • 19. LI H., FANG K., YANG W., WANG D., HONG X. Regional environmental efficiency evaluation in China: Analysis based on the Super-SBM model with undesirable outputs. Mathematical & Computer Modelling, 58 (5-6), 1018, 2013.
  • 20. LI H., SHI J. Energy efficiency analysis on Chinese industrial sectors: an improved Super-SBM model with undesirable outputs. Journal of Cleaner Production, 65 (4), 97, 2014.
  • 21. Gómez-Calvet R., Conesa D., Gómez-Calvet A.R., Tortosaausina E. Extending the use of superefficiency under undesirable outputs: An application to energy efficiency in the European Union. working paper. on 2 January 2017).
  • 22. CHARNES A., COOPER W.W. Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9 (3-4), 181, 1963.
  • 23. ZHENG J., LIU X., BIGSTEN A. Ownership structure and determinants of technical efficiency: an application of data envelopment analysis to Chinese enterprises (1986-1990). Journal of Comparative Economics, 26 (3), 465, 1998.
  • 24. Dagum C. A new approach to the decomposition of the Gini income inequality ratio. Empirical Economics, 22 (4), 515, 1997.
  • 25. Dagum C. Decomposition and interpretation of Gini and the generalized entropy inequality measures. Statistica, 57 (3), 295, 1997.
  • 26. CHANG Y.T., ZHANG N., DANAO D., ZHANG N. Environmental efficiency analysis of transportation system in China: a non-radial DEA approach. Energy Policy, 58 (9), 277, 2013.
  • 27. LI H., FANG K., YANG W., WANG D., HONG X. Regional environmental efficiency evaluation in China: Analysis based on the Super-SBM model with undesirable outputs. Mathematical and Computer Modelling, 58 (5-6), 1018, 2013.
  • 28. CHENG S., LIU J., GONG Z. China’s industry economic growth effect measurement of energy saving and emission reduction and its determents. The Journal of World Economy, 39, 166, 2016 [In Chinese]
  • 29. KANEKO S., MANAGI S. Environmental productivity in China. Economics Bulletin, 17 (2), 1, 2004.
  • 30. WANG X., HAN L., Yin L. Environmental Efficiency and Its Determinants for Manufacturing in China. Sustainability, 9 (1), 47, 2016.
  • 31. IPCC. IPCC Guidelines for National Greenhouse Gas Inventories. (accessed on 17 December 2016).
  • 32. ZHAO X. Environmental Protection and Industrial International Competitiveness. China Social Sciences Academy Press. 2003.
  • 33. LI S., CHU S., SHEN C. Local Government Competition, Environmental Regulation and Regional Eco-Efficiency. The Journal of World Economy, 4, 88, 2014 [In Chinese].
  • 34. LI S. The Effects of Environmental Regulation on the Structure of Employment Skills: An Analysis Based on Industrial Dynamic Panel Data. Chinese Journal of Population Science, 5, 90, 2016 [In Chinese].

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.