IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i2p504-d483154.html
   My bibliography  Save this article

Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China

Author

Listed:
  • Jinping Zhang

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Qiuru Lu

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

  • Li Guan

    (College of Applied Sciences, Beijing University of Technology, Beijing 100124, China)

  • Xiaoying Wang

    (School of Mathematics and Physics, North China Electric Power University, Beijing 102206, China)

Abstract

This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating M o r a n ’ s I and local indicators of spatial association (LISA) of energy efficiency of regions in mainland China, we found that energy efficiency shows obvious spatial autocorrelation and spatial clustering phenomena. Secondly, we established the spatial quantile autoregression (SQAR) model, in which the energy efficiency is the response variable with seven influence factors. The seven factors include industrial structure, resource endowment, level of economic development etc. Based on the provincial panel data (1998–2016) of mainland China (data source: China Statistical Yearbook, Statistical Yearbook of provinces), the findings indicate that level of economic development and industrial structure have a significant role in promoting energy efficient. Resource endowment, government intervention and energy efficiency show a negative correlation. However, the negative effect of government intervention is weakened with the increase of energy efficiency. Lastly, we compare the results of SQAR with that of ordinary spatial autoregression (SAR). The empirical result shows that the SQAR model is superior to SAR model in influencing factors analysis of energy efficiency.

Suggested Citation

  • Jinping Zhang & Qiuru Lu & Li Guan & Xiaoying Wang, 2021. "Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China," Energies, MDPI, vol. 14(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:504-:d:483154
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/2/504/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/2/504/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Ke & Lin, Boqiang, 2018. "How to promote energy efficiency through technological progress in China?," Energy, Elsevier, vol. 143(C), pages 812-821.
    2. Makridou, Georgia & Andriosopoulos, Kostas & Doumpos, Michael & Zopounidis, Constantin, 2016. "Measuring the efficiency of energy-intensive industries across European countries," Energy Policy, Elsevier, vol. 88(C), pages 573-583.
    3. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    4. 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.
    5. Miketa, Asami, 2001. "Analysis of energy intensity developments in manufacturing sectors in industrialized and developing countries," Energy Policy, Elsevier, vol. 29(10), pages 769-775, August.
    6. Zheng, Jiali & Mi, Zhifu & Coffman, D'Maris & Milcheva, Stanimira & Shan, Yuli & Guan, Dabo & Wang, Shouyang, 2019. "Regional development and carbon emissions in China," Energy Economics, Elsevier, vol. 81(C), pages 25-36.
    7. Ayres, Robert U. & Turton, Hal & Casten, Tom, 2007. "Energy efficiency, sustainability and economic growth," Energy, Elsevier, vol. 32(5), pages 634-648.
    8. Yu, Xuewei & Moreno-Cruz, Juan & Crittenden, John C., 2015. "Regional energy rebound effect: The impact of economy-wide and sector level energy efficiency improvement in Georgia, USA," Energy Policy, Elsevier, vol. 87(C), pages 250-259.
    9. Lei Jiang & Henk Folmer & Minhe Ji & Jianjun Tang, 2017. "Energy efficiency in the Chinese provinces: a fixed effects stochastic frontier spatial Durbin error panel analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(2), pages 301-319, March.
    10. Chernozhukov, Victor & Hansen, Christian, 2008. "Instrumental variable quantile regression: A robust inference approach," Journal of Econometrics, Elsevier, vol. 142(1), pages 379-398, January.
    11. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    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 Liu & Ruochan Xiong & Shigong Lv & Da Gao, 2022. "The Impact of Digital Finance on Green Total Factor Energy Efficiency: Evidence at China’s City Level," Energies, MDPI, vol. 15(15), pages 1-17, July.
    2. Li, Mengna & Pan, Xiongfeng & Yuan, Sai, 2022. "Do the national industrial relocation demonstration zones have higher regional energy efficiency?," Applied Energy, Elsevier, vol. 306(PA).

    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. Galina Besstremyannaya, 2015. "Heterogeneous effect of residency matching and prospective payment on labor returns and hospital scale economies," Discussion Papers 15-001, Stanford Institute for Economic Policy Research.
    2. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, Center for Economic and Financial Research (CEFIR).
    3. Galina Besstremyannaya, 2014. "The efficiency of labor matching and remuneration reforms: a panel data quantile regression approach with endogenous treatment variables," Working Papers w0206, New Economic School (NES).
    4. Paul Contoyannis & Jinhu Li, 2017. "The dynamics of adolescent depression: an instrumental variable quantile regression with fixed effects approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 907-922, June.
    5. Denis Chetverikov & Bradley Larsen & Christopher Palmer, 2016. "IV Quantile Regression for Group‐Level Treatments, With an Application to the Distributional Effects of Trade," Econometrica, Econometric Society, vol. 84, pages 809-833, March.
    6. Fenfen Wei & Nanping Feng & Kevin H. Zhang, 2017. "Innovation Capability and Innovation Talents: Evidence from China Based on a Quantile Regression Approach," Sustainability, MDPI, vol. 9(7), pages 1-15, July.
    7. David Powell & Joachim Wagner, 2021. "The Exporter Productivity Premium Along the Productivity Distribution: Evidence from Quantile Regression with Nonadditive Firm Fixed Effects," World Scientific Book Chapters, in: Joachim Wagner (ed.), MICROECONOMETRIC STUDIES OF FIRMS’ IMPORTS AND EXPORTS Advanced Methods of Analysis and Evidence from German Enterprises, chapter 9, pages 121-149, World Scientific Publishing Co. Pte. Ltd..
    8. Chang, Huibin & Indra, Debarshi & Maiti, Abhradeep, 2023. "Metropolitan area heterogeneity and the impact of road infrastructure improvements on VMT," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    9. Arribas, Iván & Peiró-Palomino, Jesús & Tortosa-Ausina, Emili, 2020. "Is full banking integration desirable?," Journal of Banking & Finance, Elsevier, vol. 112(C).
    10. Harding, Matthew & Lamarche, Carlos, 2014. "Estimating and testing a quantile regression model with interactive effects," Journal of Econometrics, Elsevier, vol. 178(P1), pages 101-113.
    11. Li Tao & Yuanjie Zhang & Maozai Tian, 2019. "Quantile Regression for Dynamic Panel Data Using Hausman–Taylor Instrumental Variables," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1033-1069, March.
    12. José Manuel Pastor & Jose M. Pavía & Lorenzo Serrano & Emili Tortosa-Ausina, 2017. "Rich regions, poor regions and bank branch deregulation in Spain," Regional Studies, Taylor & Francis Journals, vol. 51(11), pages 1678-1694, November.
    13. Lamarche, Carlos, 2011. "Measuring the incentives to learn in Colombia using new quantile regression approaches," Journal of Development Economics, Elsevier, vol. 96(2), pages 278-288, November.
    14. Karadima, Maria & Louri, Helen, 2020. "Non-performing loans in the euro area: Does bank market power matter?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    15. Michel Cândido de Souza & Lízia de Figueiredo & Mauro Sayar Ferreira, 2021. "The asymmetric evolution of economic institutions: evidence from dynamic panel quantile regression with iv and fixed effects," Textos para Discussão Cedeplar-UFMG 631, Cedeplar, Universidade Federal de Minas Gerais.
    16. Ali Aghamohammadi, 2018. "Bayesian analysis of dynamic panel data by penalized quantile regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(1), pages 91-108, March.
    17. David Powell & Joachim Wagner, 2011. "The Exporter Productivity Premium along the Productivity Distribution Evidence from Unconditional Quantile Regression with Firm Fixed Effects," Working Papers WR-837, RAND Corporation.
    18. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    19. Galvao Jr., Antonio F., 2011. "Quantile regression for dynamic panel data with fixed effects," Journal of Econometrics, Elsevier, vol. 164(1), pages 142-157, September.
    20. Philip Kostov & Julie Le Gallo, 2018. "What role for human capital in the growth process: new evidence from endogenous latent factor panel quantile regressions," Scottish Journal of Political Economy, Scottish Economic Society, vol. 65(5), pages 501-527, November.

    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:2:p:504-:d:483154. 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.