IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v140y2020ics0301421520301580.html
   My bibliography  Save this article

Energy eco-efficiency: Is there any spatial correlation between different regions?

Author

Listed:
  • Peng, Benhong
  • Wang, Yuanyuan
  • Wei, Guo

Abstract

This study explores a spatial correlation network of regional energy eco-efficiency to examine the existence of such a correlation from the new perspective of networking. Firstly, by utilizing the panel data of 13 prefecture-level cities in Jiangsu Province from 2008 to 2017, a super efficiency Slack Based Model (SBM) was developed to calculate the energy eco-efficiency of 13 cities. Then, the spatial correlation network and its characteristics of energy eco-efficiency between prefecture-level cities are elaborated by using Vector Autoregression (VAR) granger causality test and social network analyses. The results show that: 1) there is spatial heterogeneity of energy eco-efficiency for the study period, and the gap in the South of the province and the Central and North is widening. 2) The spatial correlation network of energy eco-efficiency is unevenly distributed, only Nanjing is at the center of the network. 3) The energy eco-efficiency can be divided into four plates, but the spillover effect between plates is relatively low. The study confirms that there is a significant spatial correlation network of regional energy eco-efficiency. To boost the energy eco-efficiency in the whole region, it is necessary not only to specifically strengthen the export of technology and experience from advanced regions, but also to fully understand the role and maximize the advantages of each plate.

Suggested Citation

  • Peng, Benhong & Wang, Yuanyuan & Wei, Guo, 2020. "Energy eco-efficiency: Is there any spatial correlation between different regions?," Energy Policy, Elsevier, vol. 140(C).
  • Handle: RePEc:eee:enepol:v:140:y:2020:i:c:s0301421520301580
    DOI: 10.1016/j.enpol.2020.111404
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421520301580
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.enpol.2020.111404?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Gengyuan & Yang, Zhifeng & Fath, Brian D. & Shi, Lei & Ulgiati, Sergio, 2017. "Time and space model of urban pollution migration: Economy-energy-environment nexus network," Applied Energy, Elsevier, vol. 186(P2), pages 96-114.
    2. Wang, Yongpei & Li, Jun, 2019. "Spatial spillover effect of non-fossil fuel power generation on carbon dioxide emissions across China's provinces," Renewable Energy, Elsevier, vol. 136(C), pages 317-330.
    3. Wenwen Li & Wenping Wang & Yu Wang & Yingbo Qin, 2017. "Industrial structure, technological progress and CO2 emissions in China: Analysis based on the STIRPAT framework," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1545-1564, September.
    4. Jie Wu & Qingyuan Zhu & Pengzhen Yin & Malin Song, 2017. "Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices," Operational Research, Springer, vol. 17(3), pages 715-735, October.
    5. K Tone, 2002. "A strange case of the cost and allocative efficiencies in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1225-1231, November.
    6. Honma, Satoshi & Hu, Jin-Li, 2008. "Total-factor energy efficiency of regions in Japan," Energy Policy, Elsevier, vol. 36(2), pages 821-833, February.
    7. Morton, Craig & Wilson, Charlie & Anable, Jillian, 2018. "The diffusion of domestic energy efficiency policies: A spatial perspective," Energy Policy, Elsevier, vol. 114(C), pages 77-88.
    8. Rashidi, Kamran & Farzipoor Saen, Reza, 2015. "Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement," Energy Economics, Elsevier, vol. 50(C), pages 18-26.
    9. Yeh, Tsai-lien & Chen, Tser-yieth & Lai, Pei-ying, 2010. "A comparative study of energy utilization efficiency between Taiwan and China," Energy Policy, Elsevier, vol. 38(5), pages 2386-2394, May.
    10. Zeppini, Paolo & van den Bergh, Jeroen C.J.M., 2020. "Global competition dynamics of fossil fuels and renewable energy under climate policies and peak oil: A behavioural model," Energy Policy, Elsevier, vol. 136(C).
    11. Zhang, Xing-Ping & Cheng, Xiao-Mei & Yuan, Jia-Hai & Gao, Xiao-Jun, 2011. "Total-factor energy efficiency in developing countries," Energy Policy, Elsevier, vol. 39(2), pages 644-650, February.
    12. Shi, Guang-Ming & Bi, Jun & Wang, Jin-Nan, 2010. "Chinese regional industrial energy efficiency evaluation based on a DEA model of fixing non-energy inputs," Energy Policy, Elsevier, vol. 38(10), pages 6172-6179, October.
    13. Fang, Chin-Yi & Hu, Jin-Li & Lou, Tze-Kai, 2013. "Environment-adjusted total-factor energy efficiency of Taiwan's service sectors," Energy Policy, Elsevier, vol. 63(C), pages 1160-1168.
    14. 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.
    15. Yantuan Yu & Hui Hu & Yun Zhang & Zhujia Yin, 2019. "Metafrontier Eco-Efficiency and Its Convergence Analysis for China: A Multidimensional Heterogeneity Perspective," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(7), pages 1531-1549, May.
    16. Mandal, Sabuj Kumar, 2010. "Do undesirable output and environmental regulation matter in energy efficiency analysis? Evidence from Indian Cement Industry," Energy Policy, Elsevier, vol. 38(10), pages 6076-6083, October.
    17. Gross, Christian, 2012. "Explaining the (non-) causality between energy and economic growth in the U.S.—A multivariate sectoral analysis," Energy Economics, Elsevier, vol. 34(2), pages 489-499.
    18. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    19. Yao, Xin & Zhou, Hongchen & Zhang, Aizhen & Li, Aijun, 2015. "Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis," Energy Policy, Elsevier, vol. 84(C), pages 142-154.
    20. Qian, Xianhang & Wang, Ying & Zhang, Guangli, 2018. "The spatial correlation network of capital flows in China: Evidence from China's High-Value Payment System," China Economic Review, Elsevier, vol. 50(C), pages 175-186.
    21. 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.
    22. Jie Wu & Beibei Xiong & Qingxian An & Jiasen Sun & Huaqing Wu, 2017. "Total-factor energy efficiency evaluation of Chinese industry by using two-stage DEA model with shared inputs," Annals of Operations Research, Springer, vol. 255(1), pages 257-276, August.
    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. Yang Zhou & Hankun Wang & Zuqiang Wang & Xiang Dai, 2022. "The Improvement Path for Regionally Coordinated Green Development: Evidence from Social Network Analysis," IJERPH, MDPI, vol. 19(18), pages 1-14, September.
    2. Wang, Chaofan & Zhao, Yujia & Strezov, Vladimir & Shuai, Chuanmin & Cheng, Xin & Shuai, Jing, 2023. "Spatial correlation analysis of comprehensive efficiency of the photovoltaic poverty alleviation policy - Evidence from 110 counties in China," Energy, Elsevier, vol. 282(C).
    3. Mingze Du & Weijiang Liu & Yizhe Hao, 2021. "Spatial Correlation of Air Pollution and Its Causes in Northeast China," IJERPH, MDPI, vol. 18(20), pages 1-15, October.
    4. Luping Zhang & Yingying Zhu & Liwei Fan, 2021. "Temporal-Spatial Structure and Influencing Factors of Urban Energy Efficiency in China’s Agglomeration Areas," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
    5. Mrówczyńska, M. & Skiba, M. & Sztubecka, M. & Bazan-Krzywoszańska, A. & Kazak, J.K. & Gajownik, P., 2021. "Scenarios as a tool supporting decisions in urban energy policy: The analysis using fuzzy logic, multi-criteria analysis and GIS tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    6. Yiyang Sun & Guolin Hou, 2021. "Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration," IJERPH, MDPI, vol. 18(5), pages 1-29, March.
    7. Guo, Qiu-tong & Dong, Yong & Feng, Biao & Zhang, Hao, 2023. "Can green finance development promote total-factor energy efficiency? Empirical evidence from China based on a spatial Durbin model," Energy Policy, Elsevier, vol. 177(C).
    8. Wu, Junnian & Li, Xue & Jin, Rong, 2022. "The response of the industrial system to the interrelationship approaching to carbon neutrality of carbon sources and sinks from carbon metabolism: Coal chemical case study," Energy, Elsevier, vol. 261(PB).
    9. Jingbin Wang & Huiling Qiao & Jing Liu & Bo Li, 2022. "Does the Establishment of National New Areas Improve Urban Ecological Efficiency? Empirical Evidence Based on Staggered DID Model," IJERPH, MDPI, vol. 19(20), pages 1-21, October.
    10. Liu, Haomin & Zhang, Zaixu & Zhang, Tao & Wang, Liyang, 2020. "Revisiting China’s provincial energy efficiency and its influencing factors," Energy, Elsevier, vol. 208(C).
    11. Tang, Chang & Xue, Yan & Wu, Haitao & Irfan, Muhammad & Hao, Yu, 2022. "How does telecommunications infrastructure affect eco-efficiency? Evidence from a quasi-natural experiment in China," Technology in Society, Elsevier, vol. 69(C).
    12. Bing Xia & Suocheng Dong & Yu Li & Zehong Li & Dongqi Sun & Wenbiao Zhang & Wenlong Li, 2021. "Evolution Characters and Influencing Factors of Regional Eco-Efficiency in a Developing Country: Evidence from Mongolia," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
    13. Meixia Wang & Qingyun Zheng & Yunxia Wang, 2023. "Spatial Correlation Network and Driving Factors of Urban Energy Eco-Efficiency from the Perspective of Human Well-Being: A Case Study of Shaanxi Province, China," IJERPH, MDPI, vol. 20(6), pages 1-20, March.
    14. Ahlrichs, Jakob & Wenninger, Simon & Wiethe, Christian & Häckel, Björn, 2022. "Impact of socio-economic factors on local energetic retrofitting needs - A data analytics approach," Energy Policy, Elsevier, vol. 160(C).
    15. Ye Tan & Pingan Xiang & Shuning Liu & Liang Yu, 2023. "Evaluating provincial tourism competitiveness in China: an empirical application based on the EM-MGM-SNA model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 506-527, January.
    16. Rui Wang & Bing Xia & Suocheng Dong & Yu Li & Zehong Li & Duoxun Ba & Wenbiao Zhang, 2020. "Research on the Spatial Differentiation and Driving Forces of Eco-Efficiency of Regional Tourism in China," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
    17. Song, Malin & Xie, Qianjiao & Shen, Zhiyang, 2021. "Impact of green credit on high-efficiency utilization of energy in China considering environmental constraints," Energy Policy, Elsevier, vol. 153(C).
    18. Yahong Liu & Hailian Sun & Lei Shi & Huimin Wang & Zhai Xiu & Xiao Qiu & Hong Chang & Yu Xie & Yang Wang & Chengjie Wang, 2021. "Spatial-Temporal Changes and Driving Factors of Land-Use Eco-Efficiency Incorporating Ecosystem Services in China," Sustainability, MDPI, vol. 13(2), pages 1-15, January.

    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. 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.
    2. Weibin Lin & Bin Chen & Lina Xie & Haoran Pan, 2015. "Estimating Energy Consumption of Transport Modes in China Using DEA," Sustainability, MDPI, vol. 7(4), pages 1-15, April.
    3. Cheng, Zhonghua & Liu, Jun & Li, Lianshui & Gu, Xinbei, 2020. "Research on meta-frontier total-factor energy efficiency and its spatial convergence in Chinese provinces," Energy Economics, Elsevier, vol. 86(C).
    4. Wu, F. & Fan, L.W. & Zhou, P. & Zhou, D.Q., 2012. "Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis," Energy Policy, Elsevier, vol. 49(C), pages 164-172.
    5. Du, Minzhe & Wang, Bing & Zhang, Ning, 2018. "National research funding and energy efficiency: Evidence from the National Science Foundation of China," Energy Policy, Elsevier, vol. 120(C), pages 335-346.
    6. Bian, Yiwen & Hu, Miao & Wang, Yousen & Xu, Hao, 2016. "Energy efficiency analysis of the economic system in China during 1986–2012: A parallel slacks-based measure approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 990-998.
    7. Tao Xu & Jianxin You & Hui Li & Luning Shao, 2020. "Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review," Energies, MDPI, vol. 13(14), pages 1-20, July.
    8. 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).
    9. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2013. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis," Applied Energy, Elsevier, vol. 104(C), pages 105-116.
    10. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    11. Demiral, Elif E. & Sağlam, Ümit, 2021. "Eco-efficiency and Eco-productivity assessments of the states in the United States: A two-stage Non-parametric analysis," Applied Energy, Elsevier, vol. 303(C).
    12. Ouyang, Wendi & Yang, Jian-bo, 2020. "The network energy and environment efficiency analysis of 27 OECD countries: A multiplicative network DEA model," Energy, Elsevier, vol. 197(C).
    13. Shuangjie Li & Li Li & Liming Wang, 2020. "2030 Target for Energy Efficiency and Emission Reduction in the EU Paper Industry," Energies, MDPI, vol. 14(1), pages 1-17, December.
    14. Georgia Makridou, Kostas Andriosopoulos, Michael Doumpos, and Constantin Zopounidis, 2015. "A Two-stage approach for energy efficiency analysis in European Union countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    15. Iftikhar, Yaser & Wang, Zhaohua & Zhang, Bin & Wang, Bo, 2018. "Energy and CO2 emissions efficiency of major economies: A network DEA approach," Energy, Elsevier, vol. 147(C), pages 197-207.
    16. Li, Nan & Jiang, Yuqing & Mu, Hailin & Yu, Zhixin, 2018. "Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA)," Energy, Elsevier, vol. 164(C), pages 1145-1160.
    17. Wang, Zhaohua & Feng, Chao, 2015. "Sources of production inefficiency and productivity growth in China: A global data envelopment analysis," Energy Economics, Elsevier, vol. 49(C), pages 380-389.
    18. Zhou, D.Q. & Wu, F. & Zhou, X. & Zhou, P., 2016. "Output-specific energy efficiency assessment: A data envelopment analysis approach," Applied Energy, Elsevier, vol. 177(C), pages 117-126.
    19. Ying Li & Yung-ho Chiu & Liang Chun Lu, 2019. "New Energy Development and Pollution Emissions in China," IJERPH, MDPI, vol. 16(10), pages 1-24, May.
    20. Kounetas, Konstantinos & Stergiou, Eirini, 2019. "Technology heterogeneity in European industries' energy efficiency performance. The role of climate, greenhouse gases, path dependence and energy mix," MPRA Paper 92314, University Library of Munich, Germany.

    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:eee:enepol:v:140:y:2020:i:c:s0301421520301580. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    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.