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How does economic agglomeration affect energy efficiency in China?: Evidence from endogenous stochastic frontier approach

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  • Xu, Mengmeng
  • Tan, Ruipeng
  • He, Xinju

Abstract

The energy consumption and economic agglomeration degree in China have both grown rapidly in the past several years. It is expected that China will complete its urbanization process in the near future in which the energy demand may surge, so energy conservation will become extremely important. Thus, investigating how the degree of economic agglomeration influences energy efficiency is imperative. However, because of reverse causality, the endogeneity problem of the degree of economic agglomeration makes it difficult to identify the effect of economic agglomeration on energy efficiency. This paper adopts a novel stochastic frontier methodology to examine the relationship between them on the condition of addressing endogeneity well. Using the data from Chinese cities and adopting different instrumental variables, we find that the economic agglomeration degree has an inverted U-shaped impact on energy efficiency. When the economic agglomeration degree is below the critical point, its increase can improve the energy efficiency. After crossing the critical point, its increase will decrease the energy efficiency. Our findings are robust to concerns such as measurement of economic agglomeration degree, instrumental variable adequacy, and production function form in the model. We show that failure to address endogeneity will underestimate energy efficiency and obtain a biased conclusion about the relationship between the degree of economic agglomeration and energy efficiency. Our findings demonstrate that an agglomeration development strategy can be adopted to increase energy efficiency and conserve energy in current China.

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  • Xu, Mengmeng & Tan, Ruipeng & He, Xinju, 2022. "How does economic agglomeration affect energy efficiency in China?: Evidence from endogenous stochastic frontier approach," Energy Economics, Elsevier, vol. 108(C).
  • Handle: RePEc:eee:eneeco:v:108:y:2022:i:c:s0140988322000822
    DOI: 10.1016/j.eneco.2022.105901
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    References listed on IDEAS

    as
    1. Ciccone, Antonio, 2002. "Agglomeration effects in Europe," European Economic Review, Elsevier, vol. 46(2), pages 213-227, February.
    2. Lin, Boqiang & Zhu, Junpeng, 2021. "Impact of China's new-type urbanization on energy intensity: A city-level analysis," Energy Economics, Elsevier, vol. 99(C).
    3. Liu, Shuchang & Xiao, Wu & Li, Linlin & Ye, Yanmei & Song, Xiaoli, 2020. "Urban land use efficiency and improvement potential in China: A stochastic frontier analysis," Land Use Policy, Elsevier, vol. 99(C).
    4. Haider, Salman & Danish, Mohd Shadab & Sharma, Ruchi, 2019. "Assessing energy efficiency of Indian paper industry and influencing factors: A slack-based firm-level analysis," Energy Economics, Elsevier, vol. 81(C), pages 454-464.
    5. Bhat, Javed Ahmad & Haider, Salman & Kamaiah, Bandi, 2018. "Interstate energy efficiency of Indian paper industry: A slack-based non-parametric approach," Energy, Elsevier, vol. 161(C), pages 284-298.
    6. C. Cindy Fan & Allen J. Scott, 2003. "Industrial Agglomeration and Development: A Survey of Spatial Economic Issues in East Asia and a Statistical Analysis of Chinese Regions," Economic Geography, Taylor & Francis Journals, vol. 79(3), pages 295-319, July.
    7. Gale A. Boyd, 2008. "Estimating Plant Level Energy Efficiency with a Stochastic Frontier," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 23-44.
    8. Wei, Xinyang & Tong, Qing & Magill, Iain & Vithayasrichareon, Peerapat & Betz, Regina, 2020. "Evaluation of potential co-benefits of air pollution control and climate mitigation policies for China's electricity sector," Energy Economics, Elsevier, vol. 92(C).
    9. Brülhart, Marius & Mathys, Nicole A., 2008. "Sectoral agglomeration economies in a panel of European regions," Regional Science and Urban Economics, Elsevier, vol. 38(4), pages 348-362, July.
    10. Wahl, Fabian, 2016. "Does medieval trade still matter? Historical trade centers, agglomeration and contemporary economic development," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 50-60.
    11. Mustafa U. Karakaplan & Levent Kutlu, 2017. "Endogeneity in panel stochastic frontier models: an application to the Japanese cotton spinning industry," Applied Economics, Taylor & Francis Journals, vol. 49(59), pages 5935-5939, December.
    12. He, Jie, 2006. "Pollution haven hypothesis and environmental impacts of foreign direct investment: The case of industrial emission of sulfur dioxide (SO2) in Chinese provinces," Ecological Economics, Elsevier, vol. 60(1), pages 228-245, November.
    13. Timothy J. Bartik, 1991. "Who Benefits from State and Local Economic Development Policies?," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number wbsle, November.
    14. Tan, Xiujie & Choi, Yongrok & Wang, Banban & Huang, Xiaoqi, 2020. "Does China's carbon regulatory policy improve total factor carbon efficiency? A fixed-effect panel stochastic frontier analysis," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    15. Yuan, Huaxi & Feng, Yidai & Lee, Chien-Chiang & Cen, Yan, 2020. "How does manufacturing agglomeration affect green economic efficiency?," Energy Economics, Elsevier, vol. 92(C).
    16. 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.
    17. Amsler, Christine & Prokhorov, Artem & Schmidt, Peter, 2016. "Endogeneity in stochastic frontier models," Journal of Econometrics, Elsevier, vol. 190(2), pages 280-288.
    18. Wang, Ailun & Hu, Shuo & Lin, Boqiang, 2021. "Can environmental regulation solve pollution problems? Theoretical model and empirical research based on the skill premium," Energy Economics, Elsevier, vol. 94(C).
    19. Ciccone, Antonio & Hall, Robert E, 1996. "Productivity and the Density of Economic Activity," American Economic Review, American Economic Association, vol. 86(1), pages 54-70, March.
    20. Zheng Song & Kjetil Storesletten & Fabrizio Zilibotti, 2011. "Growing Like China," American Economic Review, American Economic Association, vol. 101(1), pages 196-233, February.
    21. Liang, Wenquan & Lu, Ming, 2019. "Growth led by human capital in big cities: Exploring complementarities and spatial agglomeration of the workforce with various skills," China Economic Review, Elsevier, vol. 57(C).
    22. Yali Liu & Ming Lu & Kuanhu Xiang, 2018. "Balance through Agglomeration: A Race between Geography and Policy in China's Regional Development," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 26(6), pages 72-96, November.
    23. Paul Goldsmith-Pinkham & Isaac Sorkin & Henry Swift, 2020. "Bartik Instruments: What, When, Why, and How," American Economic Review, American Economic Association, vol. 110(8), pages 2586-2624, August.
    24. Taskin, Fatma & Zaim, Osman, 2001. "The role of international trade on environmental efficiency: a DEA approach," Economic Modelling, Elsevier, vol. 18(1), pages 1-17, January.
    25. 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.
    26. Léopold Simar, 2003. "Detecting Outliers in Frontier Models: A Simple Approach," Journal of Productivity Analysis, Springer, vol. 20(3), pages 391-424, November.
    27. Qu, Chenyao & Shao, Jun & Shi, Zhenkai, 2020. "Does financial agglomeration promote the increase of energy efficiency in China?," Energy Policy, Elsevier, vol. 146(C).
    28. Tran, Kien C. & Tsionas, Efthymios G., 2015. "Endogeneity in stochastic frontier models: Copula approach without external instruments," Economics Letters, Elsevier, vol. 133(C), pages 85-88.
    29. Verhoef, Erik T. & Nijkamp, Peter, 2002. "Externalities in urban sustainability: Environmental versus localization-type agglomeration externalities in a general spatial equilibrium model of a single-sector monocentric industrial city," Ecological Economics, Elsevier, vol. 40(2), pages 157-179, February.
    30. Zeng, Dao-Zhi & Zhao, Laixun, 2009. "Pollution havens and industrial agglomeration," Journal of Environmental Economics and Management, Elsevier, vol. 58(2), pages 141-153, September.
    31. Moon, Hana & Min, Daiki, 2017. "Assessing energy efficiency and the related policy implications for energy-intensive firms in Korea: DEA approach," Energy, Elsevier, vol. 133(C), pages 23-34.
    32. Lin, Boqiang & Du, Kerui, 2015. "Modeling the dynamics of carbon emission performance in China: A parametric Malmquist index approach," Energy Economics, Elsevier, vol. 49(C), pages 550-557.
    33. Reifschneider, David & Stevenson, Rodney, 1991. "Systematic Departures from the Frontier: A Framework for the Analysis of Firm Inefficiency," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(3), pages 715-723, August.
    34. Kutlu, Levent, 2010. "Battese-coelli estimator with endogenous regressors," Economics Letters, Elsevier, vol. 109(2), pages 79-81, November.
    35. Mustafa U. Karakaplan, 2017. "Fitting endogenous stochastic frontier models in Stata," Stata Journal, StataCorp LP, vol. 17(1), pages 39-55, March.
    36. Li, Xiaoyan & Xu, Hengzhou, 2020. "The Energy-conservation and Emission-reduction Paths of Industrial sectors: Evidence from Chinas 35 industrial sectors," Energy Economics, Elsevier, vol. 86(C).
    37. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, November.
    38. Haider, Salman & Mishra, Prajna Paramita, 2021. "Does innovative capability enhance the energy efficiency of Indian Iron and Steel firms? A Bayesian stochastic frontier analysis," Energy Economics, Elsevier, vol. 95(C).
    39. Du, Kerui & Lin, Boqiang, 2017. "International comparison of total-factor energy productivity growth: A parametric Malmquist index approach," Energy, Elsevier, vol. 118(C), pages 481-488.
    40. Tao, Jin & Ho, Chun-Yu & Luo, Shougui & Sheng, Yue, 2019. "Agglomeration economies in creative industries," Regional Science and Urban Economics, Elsevier, vol. 77(C), pages 141-154.
    41. Mustafa U. Karakaplan & Levent Kutlu, 2019. "School district consolidation policies: endogenous cost inefficiency and saving reversals," Empirical Economics, Springer, vol. 56(5), pages 1729-1768, May.
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