IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i14p4151-d591686.html
   My bibliography  Save this article

Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry

Author

Listed:
  • Hongli Liu

    (School of Earth Resources, China University of Geosciences, Wuhan 430074, China
    Environment and Sustainability Institute, University of Exeter, Penryn TR10 9FE, Cornwall, UK)

  • Xiaoyu Yan

    (Environment and Sustainability Institute, University of Exeter, Penryn TR10 9FE, Cornwall, UK)

  • Jinhua Cheng

    (School of Economics and Management, China University of Geosciences, Wuhan 430074, China)

  • Jun Zhang

    (School of Earth Resources, China University of Geosciences, Wuhan 430074, China)

  • Yan Bu

    (School of Economics and Management, Dalian University of Technology, Dalian 116024, China)

Abstract

The new energy industry (NEI) is key to achieving a clean and low-carbon economy. Improving its technical efficiency, a factor reflecting the ability of an enterprise or industry to produce maximum economic outputs from a given set of inputs and production technologies, is vital for the healthy development of the NEI. Nevertheless, due to the fragmentation of industry data, it is still difficult to accurately measure the technical efficiency of China’s NEI and understand the driving factors behind it. Based on the panel data derived from 17,457 observations on new energy enterprises in 29 Chinese provinces during 1998 and 2013 (latest data available), this paper uses data envelopment analysis (DEA) and geographically and temporally weighted regression (GTWR) for the first time to investigate the spatiotemporal characteristics and driving factors of the technical efficiency of China’s NEI. The results show that the technical efficiency of China’s NEI was relatively low and increased modestly from 0.44 in 1998 to 0.52 in 2013. Exploring the reasons from the perspective of spatiotemporal heterogeneity, we find that enterprise scale and technological progress are the major driving factors for increasing NEI’s technical efficiency. However, the role of economic development in improving efficiency has gradually disappeared. Moreover, the negative effect of state-owned enterprises on efficiency becomes increasingly obvious. The effect of new energy resources is negligible. Our main contribution is the technical efficiency of China’s NEI which is measured at the provincial level and its main driving factors are explored by considering spatiotemporal heterogeneity. Accordingly, we put forward some specific recommendations to improve the technical efficiency of China’s NEI.

Suggested Citation

  • Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4151-:d:591686
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/14/4151/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/14/4151/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinyue Yan & Ying Yang & Pietro Elia Campana & Jijiang He, 2019. "City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China," Nature Energy, Nature, vol. 4(8), pages 709-717, August.
    2. Lin, Boqiang & Chen, Yufang, 2019. "Does electricity price matter for innovation in renewable energy technologies in China?," Energy Economics, Elsevier, vol. 78(C), pages 259-266.
    3. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.
    4. Dariush Khezrimotlagh & Yao Chen, 2018. "Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 217-234, Springer.
    5. He, Gang & Kammen, Daniel M., 2016. "Where, when and how much solar is available? A provincial-scale solar resource assessment for China," Renewable Energy, Elsevier, vol. 85(C), pages 74-82.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    8. Xu, Xin-long & Chen, Hsing Hung, 2018. "Examining the efficiency of biomass energy: Evidence from the Chinese recycling industry," Energy Policy, Elsevier, vol. 119(C), pages 77-86.
    9. Ryna Yiyun Cui & Nathan Hultman & Diyang Cui & Haewon McJeon & Sha Yu & Morgan R. Edwards & Arijit Sen & Kaihui Song & Christina Bowman & Leon Clarke & Junjie Kang & Jiehong Lou & Fuqiang Yang & Jiaha, 2021. "A plant-by-plant strategy for high-ambition coal power phaseout in China," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    10. Dong, Liang & Liang, Hanwei & Gao, Zhiqiu & Luo, Xiao & Ren, Jingzheng, 2016. "Spatial distribution of China׳s renewable energy industry: Regional features and implications for a harmonious development future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1521-1531.
    11. Zhang, Sufang & Andrews-Speed, Philip & Zhao, Xiaoli & He, Yongxiu, 2013. "Interactions between renewable energy policy and renewable energy industrial policy: A critical analysis of China's policy approach to renewable energies," Energy Policy, Elsevier, vol. 62(C), pages 342-353.
    12. Walheer, Barnabé & He, Ming, 2020. "Technical efficiency and technology gap of the manufacturing industry in China: Does firm ownership matter?," World Development, Elsevier, vol. 127(C).
    13. Pulina, Manuela & Detotto, Claudio & Paba, Antonello, 2010. "An investigation into the relationship between size and efficiency of the Italian hospitality sector: A window DEA approach," European Journal of Operational Research, Elsevier, vol. 204(3), pages 613-620, August.
    14. Banker, Rajiv & Natarajan, Ram & Zhang, Daqun, 2019. "Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using Data Envelopment Analysis: Second stage OLS versus bootstrap approaches," European Journal of Operational Research, Elsevier, vol. 278(2), pages 368-384.
    15. Xin-gang, Zhao & Zhen, Wei, 2019. "The technical efficiency of China's wind power list enterprises: An estimation based on DEA method and micro-data," Renewable Energy, Elsevier, vol. 133(C), pages 470-479.
    16. Liu, Yuanxin & Ren, Lingzhi & Li, Yanbin & Zhao, Xin-gang, 2015. "The industrial performance of wind power industry in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 644-655.
    17. Jia Liu & Jizu Li & Xilong Yao, 2019. "The Economic Effects of the Development of the Renewable Energy Industry in China," Energies, MDPI, vol. 12(9), pages 1-18, May.
    18. Zou, Hongyang & Du, Huibin & Brown, Marilyn A. & Mao, Guozhu, 2017. "Large-scale PV power generation in China: A grid parity and techno-economic analysis," Energy, Elsevier, vol. 134(C), pages 256-268.
    19. William Roy & Anne Yvrande-Billon, 2007. "Ownership, Contractual Practices and Technical Efficiency: The Case of Urban Public Transport in France," Post-Print halshs-00107375, HAL.
    20. Lin, Boqiang & Xu, Bin, 2018. "How to promote the growth of new energy industry at different stages?," Energy Policy, Elsevier, vol. 118(C), pages 390-403.
    21. Qingyou Yan & Youwei Wan & Jingye Yuan & Jieting Yin & Tomas Baležentis & Dalia Streimikiene, 2017. "Economic and Technical Efficiency of the Biomass Industry in China: A Network Data Envelopment Analysis Model Involving Externalities," Energies, MDPI, vol. 10(9), pages 1-19, September.
    22. Manuel Llorca & José Baños & José Somoza & Pelayo Arbués, 2017. "A Stochastic Frontier Analysis Approach for Estimating Energy Demand and Efficiency in the Transport Sector of Latin America and the Caribbean," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    23. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    24. William Roy & Anne Yvrande-Billon, 2007. "Ownership, Contractual Practices and Technical Efficiency: The Case of Urban Public Transport in France," Journal of Transport Economics and Policy, University of Bath, vol. 41(2), pages 257-282, May.
    25. Zhang, Lingxian & Wang, Jieqiong & Wen, Haojie & Fu, Zetian & Li, Xinxing, 2016. "Operating performance, industry agglomeration and its spatial characteristics of Chinese photovoltaic industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 373-386.
    26. Lin, Boqiang & Moubarak, Mohamed, 2014. "Renewable energy consumption – Economic growth nexus for China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 111-117.
    27. Zeng, Shihong & Jiang, Chunxia & Ma, Chen & Su, Bin, 2018. "Investment efficiency of the new energy industry in China," Energy Economics, Elsevier, vol. 70(C), pages 536-544.
    28. He, Gang & Kammen, Daniel M., 2014. "Where, when and how much wind is available? A provincial-scale wind resource assessment for China," Energy Policy, Elsevier, vol. 74(C), pages 116-122.
    29. Bai, Chong-En & Li, David D. & Tao, Zhigang & Wang, Yijiang, 2000. "A Multitask Theory of State Enterprise Reform," Journal of Comparative Economics, Elsevier, vol. 28(4), pages 716-738, December.
    30. Xu, Bin & Lin, Boqiang, 2018. "Do we really understand the development of China's new energy industry?," Energy Economics, Elsevier, vol. 74(C), pages 733-745.
    31. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    32. Anita Engels, 2018. "Understanding how China is championing climate change mitigation," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-6, December.
    33. Gary Jefferson & Thomas Rawski & Yifan Zhang, 2008. "Productivity growth and convergence across China's industrial economy," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 6(2), pages 121-140.
    34. Shayegh, Soheil & Sanchez, Daniel L. & Caldeira, Ken, 2017. "Evaluating relative benefits of different types of R&D for clean energy technologies," Energy Policy, Elsevier, vol. 107(C), pages 532-538.
    35. Xu, Xinpeng & Sheng, Yu, 2012. "Productivity Spillovers from Foreign Direct Investment: Firm-Level Evidence from China," World Development, Elsevier, vol. 40(1), pages 62-74.
    36. William Roy & Anne Yvrande-Billon, 2007. "Ownership, Contractual Practices and Technical Efficiency: The Case of Urban Public Transport in France," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00107375, HAL.
    37. Subodh Kumar & R. Robert Russell, 2002. "Technological Change, Technological Catch-up, and Capital Deepening: Relative Contributions to Growth and Convergence," American Economic Review, American Economic Association, vol. 92(3), pages 527-548, June.
    38. Alsaleh, Mohd & Abdul-Rahim, A.S. & Mohd-Shahwahid, H.O., 2017. "Determinants of technical efficiency in the bioenergy industry in the EU28 region," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1331-1349.
    39. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    40. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    41. Zhang, Anming & Zhang, Yimin & Zhao, Ronald, 2003. "A study of the R&D efficiency and productivity of Chinese firms," Journal of Comparative Economics, Elsevier, vol. 31(3), pages 444-464, September.
    42. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    43. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Talat S. Genc & Stephen Kosempel, 2023. "Energy Transition and the Economy: A Review Article," Energies, MDPI, vol. 16(7), pages 1-26, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hosseini, Keyvan & Stefaniec, Agnieszka, 2019. "Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure," Energy, Elsevier, vol. 189(C).
    2. Abbas Mardani & Dalia Streimikiene & Tomas Balezentis & Muhamad Zameri Mat Saman & Khalil Md Nor & Seyed Meysam Khoshnava, 2018. "Data Envelopment Analysis in Energy and Environmental Economics: An Overview of the State-of-the-Art and Recent Development Trends," Energies, MDPI, vol. 11(8), pages 1-21, August.
    3. Hao Zhang & Xinyue Wang & Letao Chen & Yujia Luo & Sujie Peng, 2022. "Evaluation of the Operational Efficiency and Energy Efficiency of Rail Transit in China’s Megacities Using a DEA Model," Energies, MDPI, vol. 15(20), pages 1-16, October.
    4. Laura Calzada-Infante & Ana María López-Narbona & Alberto Núñez-Elvira & Javier Orozco-Messana, 2020. "Assessing the Efficiency of Sustainable Cities Using an Empirical Approach," Sustainability, MDPI, vol. 12(7), pages 1-13, March.
    5. Jindal, Abhinav & Nilakantan, Rahul, 2021. "Falling efficiency levels of Indian coal-fired power plants: A slacks-based analysis," Energy Economics, Elsevier, vol. 93(C).
    6. Liang-Han Ma & Jin-Chi Hsieh & Yung-Ho Chiu, 2020. "Comparing regional differences in global energy performance," Energy & Environment, , vol. 31(6), pages 943-960, September.
    7. Walheer, Barnabé & He, Ming, 2020. "Technical efficiency and technology gap of the manufacturing industry in China: Does firm ownership matter?," World Development, Elsevier, vol. 127(C).
    8. Lin, Boqiang & Chen, Yufang, 2019. "Impacts of policies on innovation in wind power technologies in China," Applied Energy, Elsevier, vol. 247(C), pages 682-691.
    9. Shih-Heng Yu, 2019. "Benchmarking and Performance Evaluation Towards the Sustainable Development of Regions in Taiwan: A Minimum Distance-Based Measure with Undesirable Outputs in Additive DEA," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(3), pages 1323-1348, August.
    10. Kai Xu & Bart Bossink & Qiang Chen, 2019. "Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs," Sustainability, MDPI, vol. 12(1), pages 1-21, December.
    11. Haitao Li & Jie Xiong & Jianhui Xie & Zhongbao Zhou & Jinlong Zhang, 2019. "A Unified Approach to Efficiency Decomposition for a Two-Stage Network DEA Model with Application of Performance Evaluation in Banks and Sustainable Product Design," Sustainability, MDPI, vol. 11(16), pages 1-18, August.
    12. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    13. Wang, Kai-Hua & Su, Chi-Wei & Lobonţ, Oana-Ramona & Moldovan, Nicoleta-Claudia, 2020. "Chinese renewable energy industries’ boom and recession: Evidence from bubble detection procedure," Energy Policy, Elsevier, vol. 138(C).
    14. Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    15. Corrado Lo Storto, 2018. "A Nonparametric Economic Analysis of the US Natural Gas Transmission Infrastructure: Efficiency, Trade-Offs and Emerging Industry Configurations," Energies, MDPI, vol. 11(3), pages 1-24, February.
    16. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1, January.
    17. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    18. Karlaftis, Matthew G. & Tsamboulas, Dimitrios, 2012. "Efficiency measurement in public transport: Are findings specification sensitive?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(2), pages 392-402.
    19. Muhammet Enis Bulak & Murat Kucukvar, 2022. "How ecoefficient is European food consumption? A frontier‐based multiregional input–output analysis," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 817-832, October.
    20. Alizadeh, Reza & Gharizadeh Beiragh, Ramin & Soltanisehat, Leili & Soltanzadeh, Elham & Lund, Peter D., 2020. "Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach," Energy Economics, Elsevier, vol. 91(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4151-:d:591686. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.