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Does industrial agglomeration improve effective energy service: An empirical study of China’s iron and steel industry

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  • Wu, Rongxin
  • Lin, Boqiang

Abstract

The iron and steel industry is the critical sector for energy consumption and CO2 emissions in China. From the perspective of industrial agglomeration, this paper explores how much energy is transformed into effective work from 1997 to 2016. The location entropy index is constructed to represent industrial agglomeration, and the DEA model is introduced to capture the iron and steel industry's effective energy service. Then, we apply the panel threshold model to identify the nonlinear relationship between agglomeration and effective energy service. The empirical results show that the iron and steel industry exhibits agglomeration characteristics, with the average location entropy index exceeding 1. The effective energy service experiences a rise and then a fall over the sample period. Besides, industrial agglomeration promotes effective energy service by infrastructure sharing, knowledge spillovers and internal market competition. With the economic growth, the positive influence of agglomeration on effective energy service is increasing. Based on the results, we put forward suggestions to improve the iron and steel industry's effective energy service.

Suggested Citation

  • Wu, Rongxin & Lin, Boqiang, 2021. "Does industrial agglomeration improve effective energy service: An empirical study of China’s iron and steel industry," Applied Energy, Elsevier, vol. 295(C).
  • Handle: RePEc:eee:appene:v:295:y:2021:i:c:s0306261921005213
    DOI: 10.1016/j.apenergy.2021.117066
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    as
    1. Li, Jianglong & Liu, Hongxun & Du, Kerui, 2019. "Does market-oriented reform increase energy rebound effect? Evidence from China's regional development," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    2. Wang, Ke & Lu, Bin & Wei, Yi-Ming, 2013. "China’s regional energy and environmental efficiency: A Range-Adjusted Measure based analysis," Applied Energy, Elsevier, vol. 112(C), pages 1403-1415.
    3. Helmut Lütkepohl & Fang Xu, 2012. "The role of the log transformation in forecasting economic variables," Empirical Economics, Springer, vol. 42(3), pages 619-638, June.
    4. Cravioto, Jordi & Yamasue, Eiji & Okumura, Hideyuki & Ishihara, Keiichi N., 2014. "Energy service satisfaction in two Mexican communities: A study on demographic, household, equipment and energy related predictors," Energy Policy, Elsevier, vol. 73(C), pages 110-126.
    5. Shanzi Ke & Yufeng Yu, 2014. "The pathways from industrial agglomeration to TFP growth – the experience of Chinese cities for 2001–2010," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 19(2), pages 310-332, April.
    6. Haas, Reinhard & Nakicenovic, Nebojsa & Ajanovic, Amela & Faber, Thomas & Kranzl, Lukas & Müller, Andreas & Resch, Gustav, 2008. "Towards sustainability of energy systems: A primer on how to apply the concept of energy services to identify necessary trends and policies," Energy Policy, Elsevier, vol. 36(11), pages 4012-4021, November.
    7. Chang, C-L. & Khamkaew, T. & McAleer, M.J., 2009. "A Panel Threshold Model of Tourism Specialization and Economic Development," Econometric Institute Research Papers EI 2009-40, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Pierre-Philippe Combes & Thierry Mayer & Jacques-François Thisse, 2008. "Economic Geography: The Integration of Regions and Nations," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00311000, HAL.
    9. Michael Greenstone & Richard Hornbeck & Enrico Moretti, 2010. "Identifying Agglomeration Spillovers: Evidence from Winners and Losers of Large Plant Openings," Journal of Political Economy, University of Chicago Press, vol. 118(3), pages 536-598, June.
    10. Nissing, Christian & von Blottnitz, Harro, 2010. "An economic model for energisation and its integration into the urban energy planning process," Energy Policy, Elsevier, vol. 38(5), pages 2370-2378, May.
    11. Ellison, Glenn & Glaeser, Edward L, 1997. "Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach," Journal of Political Economy, University of Chicago Press, vol. 105(5), pages 889-927, October.
    12. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
    13. Cerina, Fabio & Mureddu, Francesco, 2014. "Is agglomeration really good for growth? Global efficiency, interregional equity and uneven growth," Journal of Urban Economics, Elsevier, vol. 84(C), pages 9-22.
    14. Zhang, Ning & Zhou, P. & Choi, Yongrok, 2013. "Energy efficiency, CO2 emission performance and technology gaps in fossil fuel electricity generation in Korea: A meta-frontier non-radial directional distance functionanalysis," Energy Policy, Elsevier, vol. 56(C), pages 653-662.
    15. Sovacool, Benjamin K., 2011. "Conceptualizing urban household energy use: Climbing the "Energy Services Ladder"," Energy Policy, Elsevier, vol. 39(3), pages 1659-1668, March.
    16. 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.
    17. Wang, Zhaohua & He, Weijun & Wang, Bo, 2017. "Performance and reduction potential of energy and CO2 emissions among the APEC's members with considering the return to scale," Energy, Elsevier, vol. 138(C), pages 552-562.
    18. Lin, Boqiang & Chen, Yu, 2020. "Transportation infrastructure and efficient energy services: A perspective of China's manufacturing industry," Energy Economics, Elsevier, vol. 89(C).
    19. Haas, Reinhard, 1997. "Energy efficiency indicators in the residential sector : What do we know and what has to be ensured?," Energy Policy, Elsevier, vol. 25(7-9), pages 789-802.
    20. Sorrell, Steve & Dimitropoulos, John, 2008. "The rebound effect: Microeconomic definitions, limitations and extensions," Ecological Economics, Elsevier, vol. 65(3), pages 636-649, April.
    21. 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.
    22. Akihiro Otsuka & Mika Goto & Toshiyuki Sueyoshi, 2010. "Industrial agglomeration effects in Japan: Productive efficiency, market access, and public fiscal transfer," Papers in Regional Science, Wiley Blackwell, vol. 89(4), pages 819-840, November.
    23. Ayres, Robert U. & Warr, Benjamin, 2005. "Accounting for growth: the role of physical work," Structural Change and Economic Dynamics, Elsevier, vol. 16(2), pages 181-209, June.
    24. An, Runying & Yu, Biying & Li, Ru & Wei, Yi-Ming, 2018. "Potential of energy savings and CO2 emission reduction in China’s iron and steel industry," Applied Energy, Elsevier, vol. 226(C), pages 862-880.
    25. Liu, Hongxun & Du, Kerui & Li, Jianglong, 2019. "An improved approach to estimate direct rebound effect by incorporating energy efficiency: A revisit of China's industrial energy demand," Energy Economics, Elsevier, vol. 80(C), pages 720-730.
    26. Brülhart, Marius & Sbergami, Federica, 2009. "Agglomeration and growth: Cross-country evidence," Journal of Urban Economics, Elsevier, vol. 65(1), pages 48-63, January.
    27. Lin, Hui-Lin & Li, Hsiao-Yun & Yang, Chih-Hai, 2011. "Agglomeration and productivity: Firm-level evidence from China's textile industry," China Economic Review, Elsevier, vol. 22(3), pages 313-329, September.
    28. Ming Lei & Zihan Yin, 2016. "An Analysis of Energy and Environment Efficiency of China's Iron and Steel Industry," Frontiers of Economics in China-Selected Publications from Chinese Universities, Higher Education Press, vol. 11(1), pages 123-141, March.
    29. Guo, Z.C. & Fu, Z.X., 2010. "Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China," Energy, Elsevier, vol. 35(11), pages 4356-4360.
    30. Akihiro Otsuka & Mika Goto & Toshiyuki Sueyoshi, 2014. "Energy efficiency and agglomeration economies: the case of Japanese manufacturing industries," Regional Science Policy & Practice, Wiley Blackwell, vol. 6(2), pages 195-212, June.
    31. Day, Rosie & Walker, Gordon & Simcock, Neil, 2016. "Conceptualising energy use and energy poverty using a capabilities framework," Energy Policy, Elsevier, vol. 93(C), pages 255-264.
    32. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    33. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    34. Delgado, Mercedes & Porter, Michael E. & Stern, Scott, 2014. "Clusters, convergence, and economic performance," Research Policy, Elsevier, vol. 43(10), pages 1785-1799.
    35. Cullen, Jonathan M. & Allwood, Julian M., 2010. "The efficient use of energy: Tracing the global flow of energy from fuel to service," Energy Policy, Elsevier, vol. 38(1), pages 75-81, January.
    36. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    37. Hu, Cui & Xu, Zhaoyuan & Yashiro, Naomitsu, 2015. "Agglomeration and productivity in China: Firm level evidence," China Economic Review, Elsevier, vol. 33(C), pages 50-66.
    38. Wahyu Widodo & Ruhul Salim & Harry Bloch, 2015. "The effects of agglomeration economies on technical efficiency of manufacturing firms: evidence from Indonesia," Applied Economics, Taylor & Francis Journals, vol. 47(31), pages 3258-3275, July.
    39. Hasanbeigi, Ali & Morrow, William & Sathaye, Jayant & Masanet, Eric & Xu, Tengfang, 2013. "A bottom-up model to estimate the energy efficiency improvement and CO2 emission reduction potentials in the Chinese iron and steel industry," Energy, Elsevier, vol. 50(C), pages 315-325.
    40. Ma, Linwei & Allwood, Julian M. & Cullen, Jonathan M. & Li, Zheng, 2012. "The use of energy in China: Tracing the flow of energy from primary source to demand drivers," Energy, Elsevier, vol. 40(1), pages 174-188.
    41. Brand-Correa, Lina I. & Steinberger, Julia K., 2017. "A Framework for Decoupling Human Need Satisfaction From Energy Use," Ecological Economics, Elsevier, vol. 141(C), pages 43-52.
    42. Kirsi Mukkala, 2004. "Agglomeration economies in the finnish manufacturing sector," Applied Economics, Taylor & Francis Journals, vol. 36(21), pages 2419-2427.
    43. Hunt, Lester C. & Ryan, David L., 2015. "Economic modelling of energy services: Rectifying misspecified energy demand functions," Energy Economics, Elsevier, vol. 50(C), pages 273-285.
    44. Patterson, Murray G, 1996. "What is energy efficiency? : Concepts, indicators and methodological issues," Energy Policy, Elsevier, vol. 24(5), pages 377-390, May.
    45. Sorrell, Steve & Dimitropoulos, John & Sommerville, Matt, 2009. "Empirical estimates of the direct rebound effect: A review," Energy Policy, Elsevier, vol. 37(4), pages 1356-1371, April.
    46. Ouyang, Xiaoling & Gao, Beiying & Du, Kerui & Du, Gang, 2018. "Industrial sectors' energy rebound effect: An empirical study of Yangtze River Delta urban agglomeration," Energy, Elsevier, vol. 145(C), pages 408-416.
    47. Xu, Tengfang & Karali, Nihan & Sathaye, Jayant, 2014. "Undertaking high impact strategies: The role of national efficiency measures in long-term energy and emission reduction in steel making," Applied Energy, Elsevier, vol. 122(C), pages 179-188.
    48. 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.
    49. Kaygusuz, Kamil, 2012. "Energy for sustainable development: A case of developing countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1116-1126.
    50. Li, Dongya & Lu, Yi & Wu, Mingqin, 2012. "Industrial agglomeration and firm size: Evidence from China," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 135-143.
    51. 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.
    52. Andrew Crawley & Malcolm Beynon & Max Munday, 2013. "Making Location Quotients More Relevant as a Policy Aid in Regional Spatial Analysis," Urban Studies, Urban Studies Journal Limited, vol. 50(9), pages 1854-1869, July.
    53. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.
    54. Lin, Boqiang & Li, Jianglong, 2014. "The rebound effect for heavy industry: Empirical evidence from China," Energy Policy, Elsevier, vol. 74(C), pages 589-599.
    55. Lin, Boqiang & Du, Kerui, 2014. "Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy," Energy, Elsevier, vol. 76(C), pages 884-890.
    56. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "Measuring environmental performance under different environmental DEA technologies," Energy Economics, Elsevier, vol. 30(1), pages 1-14, January.
    57. Ang, B.W., 2006. "Monitoring changes in economy-wide energy efficiency: From energy-GDP ratio to composite efficiency index," Energy Policy, Elsevier, vol. 34(5), pages 574-582, March.
    58. Chen, Wenying & Yin, Xiang & Ma, Ding, 2014. "A bottom-up analysis of China’s iron and steel industrial energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 136(C), pages 1174-1183.
    59. Fujimori, S. & Kainuma, M. & Masui, T. & Hasegawa, T. & Dai, H., 2014. "The effectiveness of energy service demand reduction: A scenario analysis of global climate change mitigation," Energy Policy, Elsevier, vol. 75(C), pages 379-391.
    60. Azari, Mehdi & Kim, Hakkon & Kim, Jun Yeup & Ryu, Doojin, 2016. "The effect of agglomeration on the productivity of urban manufacturing sectors in a leading emerging economy," Economic Systems, Elsevier, vol. 40(3), pages 422-432.
    61. Lin, Boqiang & Du, Kerui, 2015. "Measuring energy rebound effect in the Chinese economy: An economic accounting approach," Energy Economics, Elsevier, vol. 50(C), pages 96-104.
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