IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v142y2018icp79-89.html
   My bibliography  Save this article

Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN

Author

Listed:
  • He, Yong
  • Liao, Nuo
  • Zhou, Ya

Abstract

Data envelopment analysis (DEA), rough set theory (RS) and fuzzy artificial neural network (FANN) are combined as DEA-RS-FANN procedure to explore the effects of influencing factors on energy efficiency in China's provincial industry sectors. The analysis begins with the DEA technique to evaluate energy efficiency in provincial industries, followed by fuzzy c-means (FCM) algorithm to classify energy efficiency and the influencing factors to three categories (low-, medium- and high-levels). This process facilitates the construction of the decision table from condition attribute (the influencing factors) to decision attribute (energy efficiency). Then significance analysis of attributes in RS theory is adopted to investigate the significance of the influencing factors and determine the primary factors. Finally, FANN is utilized to further analyze the marginal effect of primary factors on energy efficiency in three specific categories, comprising of those provinces with different levels of energy efficiency. The proposed method takes into consideration non-linear and lag effects between energy efficiency and the influencing factors, as well as the characteristics of the impreciseness and incompleteness of the statistical data, ultimately leading to more precise and reliable results, as compared to conventional methods.

Suggested Citation

  • He, Yong & Liao, Nuo & Zhou, Ya, 2018. "Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN," Energy, Elsevier, vol. 142(C), pages 79-89.
    • Handle: RePEc:eee:energy:v:142:y:2018:i:c:p:79-89
    DOI: 10.1016/j.energy.2017.10.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217316808
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.10.011?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. Norman, Jonathan B., 2017. "Measuring improvements in industrial energy efficiency: A decomposition analysis applied to the UK," Energy, Elsevier, vol. 137(C), pages 1144-1151.
    2. 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.
    3. Wu, Libo & Huo, Hong, 2014. "Energy efficiency achievements in China׳s industrial and transport sectors: How do they rate?," Energy Policy, Elsevier, vol. 73(C), pages 38-46.
    4. Patrik Thollander & Jenny Palm, 2015. "Industrial Energy Management Decision Making for Improved Energy Efficiency—Strategic System Perspectives and Situated Action in Combination," Energies, MDPI, vol. 8(6), pages 1-10, June.
    5. Yong He & Siwei Gao & Nuo Liao, 2016. "An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 399-423, October.
    6. 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.
    7. Wang, Ke & Wei, Yi-Ming, 2014. "China’s regional industrial energy efficiency and carbon emissions abatement costs," Applied Energy, Elsevier, vol. 130(C), pages 617-631.
    8. 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.
    9. 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.
    10. Palm, Jenny & Thollander, Patrik, 2010. "An interdisciplinary perspective on industrial energy efficiency," Applied Energy, Elsevier, vol. 87(10), pages 3255-3261, October.
    11. Ang, B.W. & Xu, X.Y., 2013. "Tracking industrial energy efficiency trends using index decomposition analysis," Energy Economics, Elsevier, vol. 40(C), pages 1014-1021.
    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. Sueyoshi, Toshiyuki & Qu, Jingjing & Li, Aijun & Liu, Xiaohong, 2021. "A new approach for evaluating technology inequality and diffusion barriers: The concept of efficiency Gini coefficient and its application in Chinese provinces," Energy, Elsevier, vol. 235(C).
    2. 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.
    3. Yasmeen, Rizwana & Zhaohui, Cui & Hassan Shah, Wasi Ul & Kamal, Muhammad Abdul & Khan, Anwar, 2022. "Exploring the role of biomass energy consumption, ecological footprint through FDI and technological innovation in B&R economies: A simultaneous equation approach," Energy, Elsevier, vol. 244(PA).
    4. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    5. Shah, Wasi Ul Hassan & Hao, Gang & Yan, Hong & Yasmeen, Rizwana & Padda, Ihtsham Ul Haq & Ullah, Assad, 2022. "The impact of trade, financial development and government integrity on energy efficiency: An analysis from G7-Countries," Energy, Elsevier, vol. 255(C).
    6. Shakila Aziz & Sheikh Morshed Jahan, 2023. "Eco-efficiency analysis of industrial sectors in Bangladesh using data envelopment analysis and malmquist productivity index," SN Business & Economics, Springer, vol. 3(2), pages 1-23, February.
    7. Geng, Zhiqiang & Zeng, Rongfu & Han, Yongming & Zhong, Yanhua & Fu, Hua, 2019. "Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries," Energy, Elsevier, vol. 179(C), pages 863-875.
    8. Lin Zhang and Philip Kofi Adom, 2018. "Energy Efficiency Transitions in China: How Persistent are the Movements to/from the Frontier?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 6).
    9. Zhao, Haoran & Guo, Sen & Zhao, Huiru, 2019. "Provincial energy efficiency of China quantified by three-stage data envelopment analysis," Energy, Elsevier, vol. 166(C), pages 96-107.
    10. Yao, Xing & Yasmeen, Rizwana & Hussain, Jamal & Hassan Shah, Wasi Ul, 2021. "The repercussions of financial development and corruption on energy efficiency and ecological footprint: Evidence from BRICS and next 11 countries," Energy, Elsevier, vol. 223(C).
    11. Cuijie Lu & Gaopeng Jiang & Xintong Zhang & Pan Li & Jie Li, 2023. "Evaluation of Energy Utilization Efficiency in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    12. Liu, Bin & Wang, Dedong & Xu, Youquan & Liu, Chunlu & Luther, Mark, 2018. "Vertical specialisation measurement of energy embodied in international trade of the construction industry," Energy, Elsevier, vol. 165(PB), pages 689-700.
    13. Yu, Yantuan & Tang, Kai, 2023. "Does financial inclusion improve energy efficiency?," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    14. 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.
    15. Liu, Bin & Gao, Qun & Liang, Lingfeng & Sun, Jide & Liu, Chunlu & Xu, Youquan, 2021. "Ecological relationships of global construction industries in sustainable economic and energy development," Energy, Elsevier, vol. 234(C).
    16. Yang, Sheng & Zhang, Lu & Xie, Nan & Gu, Zhaohui & Liu, Zhiqiang, 2021. "Thermodynamic analysis of a semi-lean solution process for energy saving via rectisol wash technology," Energy, Elsevier, vol. 226(C).
    17. Lin, Boqiang & Xu, Bin, 2018. "Growth of industrial CO2 emissions in Shanghai city: Evidence from a dynamic vector autoregression analysis," Energy, Elsevier, vol. 151(C), pages 167-177.
    18. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    19. Liu, Zhao & Zhang, Huan & Zhang, Yue-Jun & Zhu, Tian-Tian, 2020. "How does industrial policy affect the eco-efficiency of industrial sector? Evidence from China," Applied Energy, Elsevier, vol. 272(C).
    20. Liu, Fangmei & Li, Li & Ye, Bin & Qin, Quande, 2023. "A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency," Energy Economics, Elsevier, vol. 119(C).

    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. Liao, Nuo & He, Yong, 2018. "Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model," Energy, Elsevier, vol. 158(C), pages 782-795.
    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. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    4. Feng, Chao & Zhang, Hua & Huang, Jian-Bai, 2017. "The approach to realizing the potential of emissions reduction in China: An implication from data envelopment analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 859-872.
    5. 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.
    6. 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.
    7. Ke Wang & Xueying Yu, 2017. "Industrial Energy and Environment Efficiency of Chinese Cities: An Analysis Based on Range-Adjusted Measure," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1023-1042, July.
    8. Ning Zhang & Jong-Dae Kim, 2014. "Measuring sustainability by Energy Efficiency Analysis for Korean Power Companies: A Sequential Slacks-Based Efficiency Measure," Sustainability, MDPI, vol. 6(3), pages 1-13, March.
    9. Ying Li & Yung-Ho Chiu & Liang Chun Lu, 2018. "Regional Energy, CO 2 , and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach," Energies, MDPI, vol. 11(8), pages 1-20, August.
    10. Wang, Qunwei & Su, Bin & Sun, Jiasen & Zhou, Peng & Zhou, Dequn, 2015. "Measurement and decomposition of energy-saving and emissions reduction performance in Chinese cities," Applied Energy, Elsevier, vol. 151(C), pages 85-92.
    11. Zhang, Yue-Jun & Liu, Jing-Yue & Su, Bin, 2020. "Carbon congestion effects in China's industry: Evidence from provincial and sectoral levels," Energy Economics, Elsevier, vol. 86(C).
    12. Feng, Chao & Wang, Miao, 2018. "Analysis of energy efficiency in China's transportation sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 565-575.
    13. 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.
    14. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    15. Ying Li & Yung-ho Chiu & Tai-Yu Lin, 2019. "The Impact of Economic Growth and Air Pollution on Public Health in 31 Chinese Cities," IJERPH, MDPI, vol. 16(3), pages 1-26, January.
    16. 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.
    17. Fei, Rilong & Lin, Boqiang, 2016. "Energy efficiency and production technology heterogeneity in China's agricultural sector: A meta-frontier approach," Technological Forecasting and Social Change, Elsevier, vol. 109(C), pages 25-34.
    18. 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.
    19. Wu, Jie & Zhu, Qingyuan & Liang, Liang, 2016. "CO2 emissions and energy intensity reduction allocation over provincial industrial sectors in China," Applied Energy, Elsevier, vol. 166(C), pages 282-291.
    20. 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.

    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:energy:v:142:y:2018:i:c:p:79-89. 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.journals.elsevier.com/energy .

    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.