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Can regional integration narrow city-level energy efficiency gap in China?

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  • Kang, Jijun
  • Yu, Chenyang
  • Xue, Rui
  • Yang, Dong
  • Shan, Yuli

Abstract

Improving energy efficiency is essential for energy conservation, emissions reduction, and sustainable development. Prevalent huge efficiency gaps are not advantageous for the improvement of the region's overall energy efficiency. Although studies have analyzed the influencing factors of the regional energy efficiency gap, the impact of regional integration on the regional energy efficiency gap remains untested. This paper applies the extended stochastic frontier analysis (SFA) method that incorporates time-varying, time-invariant and city heterogeneous characteristics to estimate the city-level energy efficiency in China from 2005 to 2017. Building on the “center-periphery” framework, we further calculate the regional energy efficiency gap and investigate the impact of regional integration on the regional energy efficiency gap through the generalized moment method (GMM). The results show that 1) average city-level energy efficiency is 44.2%, ranging from 2.9% to 75.5%, indicating that China has a huge regional energy efficiency gap; 2) there is a U-shaped relationship between regional integration and the regional energy efficiency gap within city agglomeration. Improvement in regional integration can narrow the regional energy efficiency gap when the degree of regional integration is low, and expand the efficiency gap when regional integration level is high; 3) government intervention will smooth the impact of regional integration on the regional energy efficiency gap within city agglomeration. Practicable policies to mitigate the regional energy efficiency gap in China are suggested and applicable to other emerging economies, especially for those with a huge imbalance in regional energy efficiency.

Suggested Citation

  • Kang, Jijun & Yu, Chenyang & Xue, Rui & Yang, Dong & Shan, Yuli, 2022. "Can regional integration narrow city-level energy efficiency gap in China?," Energy Policy, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:enepol:v:163:y:2022:i:c:s0301421522000453
    DOI: 10.1016/j.enpol.2022.112820
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    as
    1. Fisher-Vanden, Karen & Jefferson, Gary H. & Liu, Hongmei & Tao, Quan, 2004. "What is driving China's decline in energy intensity?," Resource and Energy Economics, Elsevier, vol. 26(1), pages 77-97, March.
    2. Stuermer, Martin, 2017. "Industrialization and the demand for mineral commodities," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 16-27.
    3. Yin, Jianhua & Zheng, Mingzheng & Chen, Jian, 2015. "The effects of environmental regulation and technical progress on CO2 Kuznets curve: An evidence from China," Energy Policy, Elsevier, vol. 77(C), pages 97-108.
    4. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 59-80.
    5. Byrnes, Joel & Crase, Lin & Dollery, Brian & Villano, Renato, 2010. "The relative economic efficiency of urban water utilities in regional New South Wales and Victoria," Resource and Energy Economics, Elsevier, vol. 32(3), pages 439-455, August.
    6. Yao, Yao & Ivanovski, Kris & Inekwe, John & Smyth, Russell, 2019. "Human capital and energy consumption: Evidence from OECD countries," Energy Economics, Elsevier, vol. 84(C).
    7. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    8. Filippini, Massimo & Hunt, Lester C., 2015. "Measurement of energy efficiency based on economic foundations," Energy Economics, Elsevier, vol. 52(S1), pages 5-16.
    9. Chen, Xiude & Qin, Quande & Wei, Y.-M., 2016. "Energy productivity and Chinese local officials’ promotions: Evidence from provincial governors," Energy Policy, Elsevier, vol. 95(C), pages 103-112.
    10. 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.
    11. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    12. Subal C. Kumbhakar & Almas Heshmati, 1995. "Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976–88," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 660-674.
    13. Duranton, Gilles & Puga, Diego, 2005. "From sectoral to functional urban specialisation," Journal of Urban Economics, Elsevier, vol. 57(2), pages 343-370, March.
    14. Shahbaz, Muhammad & Mallick, Hrushikesh & Mahalik, Mantu Kumar & Sadorsky, Perry, 2016. "The role of globalization on the recent evolution of energy demand in India: Implications for sustainable development," Energy Economics, Elsevier, vol. 55(C), pages 52-68.
    15. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    16. Sa'ad, Suleiman, 2010. "Improved technical efficiency and exogenous factors in transportation demand for energy: An application of structural time series analysis to South Korean data," Energy, Elsevier, vol. 35(7), pages 2745-2751.
    17. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Mousazadeh, Hossein, 2013. "Reduction of CO2 emission by improving energy use efficiency of greenhouse cucumber production using DEA approach," Energy, Elsevier, vol. 55(C), pages 676-682.
    18. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    19. Commendatore, Pasquale & Kubin, Ingrid & Petraglia, Carmelo & Sushko, Iryna, 2014. "Regional integration, international liberalisation and the dynamics of industrial agglomeration," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 265-287.
    20. Padilla, Emilio & Duro, Juan Antonio, 2013. "Explanatory factors of CO2 per capita emission inequality in the European Union," Energy Policy, Elsevier, vol. 62(C), pages 1320-1328.
    21. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    22. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    23. Adriana Peluffo, 2013. "Regional integration and technology diffusion: The case of Uruguay," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 22(5), pages 786-816, August.
    24. Xiao, Hongwei & Ma, Zhongyu & Mi, Zhifu & Kelsey, John & Zheng, Jiali & Yin, Weihua & Yan, Min, 2018. "Spatio-temporal simulation of energy consumption in China's provinces based on satellite night-time light data," Applied Energy, Elsevier, vol. 231(C), pages 1070-1078.
    25. Jin, Gang & Shen, Kunrong & Li, Jian, 2020. "Interjurisdiction political competition and green total factor productivity in China: An inverted-U relationship," China Economic Review, Elsevier, vol. 61(C).
    26. Wang, Hung-Jen & Ho, Chia-Wen, 2010. "Estimating fixed-effect panel stochastic frontier models by model transformation," Journal of Econometrics, Elsevier, vol. 157(2), pages 286-296, August.
    27. Zaidi, Syed Anees Haider & Wei, Zixiang & Gedikli, Ayfer & Zafar, Muhammad Wasif & Hou, Fujun & Iftikhar, Yaser, 2019. "The impact of globalization, natural resources abundance, and human capital on financial development: Evidence from thirty-one OECD countries," Resources Policy, Elsevier, vol. 64(C).
    28. Zhao, Hongli & Lin, Boqiang, 2019. "Will agglomeration improve the energy efficiency in China’s textile industry: Evidence and policy implications," Applied Energy, Elsevier, vol. 237(C), pages 326-337.
    29. Kumbhakar, Subal C. & Wang, Hung-Jen, 2005. "Estimation of growth convergence using a stochastic production frontier approach," Economics Letters, Elsevier, vol. 88(3), pages 300-305, September.
    30. Ke, Shanzi, 2015. "Domestic Market Integration and Regional Economic Growth—China’s Recent Experience from 1995–2011," World Development, Elsevier, vol. 66(C), pages 588-597.
    31. Molinos-Senante, María & Hernández-Sancho, Francesc & Mocholí-Arce, Manuel & Sala-Garrido, Ramón, 2014. "Economic and environmental performance of wastewater treatment plants: Potential reductions in greenhouse gases emissions," Resource and Energy Economics, Elsevier, vol. 38(C), pages 125-140.
    32. Ya Chen & Wade D. Cook & Juan Du & Hanhui Hu & Joe Zhu, 2017. "Bounded and discrete data and Likert scales in data envelopment analysis: application to regional energy efficiency in China," Annals of Operations Research, Springer, vol. 255(1), pages 347-366, August.
    33. Sorrell, Steve, 2015. "Reducing energy demand: A review of issues, challenges and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 74-82.
    34. Jiang, Xuemei & Duan, Yuwan & Green, Christopher, 2017. "Regional disparity in energy intensity of China and the role of industrial and export structure," Resources, Conservation & Recycling, Elsevier, vol. 120(C), pages 209-218.
    35. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    36. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    37. Bertoldi, Paolo & Mosconi, Rocco, 2020. "Do energy efficiency policies save energy? A new approach based on energy policy indicators (in the EU Member States)," Energy Policy, Elsevier, vol. 139(C).
    38. Zhang, Yanfang & Zhang, Ming & Liu, Yue & Nie, Rui, 2017. "Enterprise investment, local government intervention and coal overcapacity: The case of China," Energy Policy, Elsevier, vol. 101(C), pages 162-169.
    39. Lee, Young Hoon, 2006. "A stochastic production frontier model with group-specific temporal variation in technical efficiency," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1616-1630, November.
    40. Fritsch, Markus, 2019. "On GMM estimation of linear dynamic panel data models," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-36-19, University of Passau, Faculty of Business and Economics.
    41. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514.
    42. Joanne Evans & Massimo Filippini & Lester C. Hunt, 2013. "The contribution of energy efficiency towards meeting CO2 targets," Chapters, in: Roger Fouquet (ed.), Handbook on Energy and Climate Change, chapter 8, pages 175-223, Edward Elgar Publishing.
    43. Qu, Chenyao & Shao, Jun & Shi, Zhenkai, 2020. "Does financial agglomeration promote the increase of energy efficiency in China?," Energy Policy, Elsevier, vol. 146(C).
    44. Jianhuan Huang & Yantuan Yu & Chunbo Ma, 2018. "Energy Efficiency Convergence in China: Catch-Up, Lock-In and Regulatory Uniformity," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(1), pages 107-130, May.
    45. Choi, Bongseok, 2020. "Productivity and misallocation of energy resources: Evidence from Korea’s manufacturing Sector," Resource and Energy Economics, Elsevier, vol. 61(C).
    46. Yu, Jihai & Zhou, Li-An & Zhu, Guozhong, 2016. "Strategic interaction in political competition: Evidence from spatial effects across Chinese cities," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 23-37.
    47. Elliott, Robert J.R. & Sun, Puyang & Zhu, Tong, 2017. "The direct and indirect effect of urbanization on energy intensity: A province-level study for China," Energy, Elsevier, vol. 123(C), pages 677-692.
    48. Xu, Xiaojing & Chen, Chien-fei, 2019. "Energy efficiency and energy justice for U.S. low-income households: An analysis of multifaceted challenges and potential," Energy Policy, Elsevier, vol. 128(C), pages 763-774.
    49. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    50. 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.
    51. Alcantara, Vicent & Duro, Juan Antonio, 2004. "Inequality of energy intensities across OECD countries: a note," Energy Policy, Elsevier, vol. 32(11), pages 1257-1260, July.
    52. Yong ZHAO & Ouge QI, 2017. "Would Functional Specialization of Space Narrow Down Regional Disparities? — An Empirical Analysis Based on Panel Data of Chinese Urban Agglomerations 2003–2011," Chinese Journal of Urban and Environmental Studies (CJUES), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-21, March.
    53. 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.
    54. Huntington, Hillard G., 1994. "Been top down so long it looks like bottom up to me," Energy Policy, Elsevier, vol. 22(10), pages 833-839, October.
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    Keywords

    Regional integration; Energy efficiency gap; Government intervention; Sustainable development;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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