IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i7p1039-d105432.html
   My bibliography  Save this article

Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

Author

Listed:
  • Jin-Peng Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Qian-Ru Yang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Lin He

    (Economy and Technology Research Institute, State Grid Xin Jiang Electric Power Corporation, Wulumuqi 830011, China)

Abstract

Under the background of a new round of power market reform, realizing the goals of energy saving and emission reduction, reducing the coal consumption and ensuring the sustainable development are the key issues for thermal power industry. With the biggest economy and energy consumption scales in the world, China should promote the energy efficiency of thermal power industry to solve these problems. Therefore, from multiple perspectives, the factors influential to the energy efficiency of thermal power industry were identified. Based on the economic, social and environmental factors, a combination model with Data Envelopment Analysis (DEA) and Malmquist index was constructed to evaluate the total-factor energy efficiency (TFEE) in thermal power industry. With the empirical studies from national and provincial levels, the TFEE index can be factorized into the technical efficiency index (TECH), the technical progress index (TPCH), the pure efficiency index (PECH) and the scale efficiency index (SECH). The analysis showed that the TFEE was mainly determined by TECH and PECH. Meanwhile, by panel data regression model, unit coal consumption, talents and government supervision were selected as important indexes to have positive effects on TFEE in thermal power industry. In addition, the negative indexes, such as energy price and installed capacity, were also analyzed to control their undesired effects. Finally, considering the analysis results, measures for improving energy efficiency of thermal power industry were discussed widely, such as strengthening technology research and design (R&D), enforcing pollutant and emission reduction, distributing capital and labor rationally and improving the government supervision. Relative study results and suggestions can provide references for Chinese government and enterprises to enhance the energy efficiency level.

Suggested Citation

  • Jin-Peng Liu & Qian-Ru Yang & Lin He, 2017. "Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques," Energies, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1039-:d:105432
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/7/1039/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/7/1039/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jinpeng Liu & Yun Long & Xiaohua Song, 2017. "A Study on the Conduction Mechanism and Evaluation of the Comprehensive Efficiency of Photovoltaic Power Generation in China," Energies, MDPI, vol. 10(5), pages 1-22, May.
    2. Barros, Carlos Pestana, 2008. "Efficiency analysis of hydroelectric generating plants: A case study for Portugal," Energy Economics, Elsevier, vol. 30(1), pages 59-75, January.
    3. Shuai, Chenyang & Shen, Liyin & Jiao, Liudan & Wu, Ya & Tan, Yongtao, 2017. "Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011," Applied Energy, Elsevier, vol. 187(C), pages 310-325.
    4. Yao, Xin & Guo, Chengwen & Shao, Shuai & Jiang, Zhujun, 2016. "Total-factor CO2 emission performance of China’s provincial industrial sector: A meta-frontier non-radial Malmquist index approach," Applied Energy, Elsevier, vol. 184(C), pages 1142-1153.
    5. Andrew N. Kleit & Dek Terrell, 2001. "Measuring Potential Efficiency Gains From Deregulation Of Electricity Generation: A Bayesian Approach," The Review of Economics and Statistics, MIT Press, vol. 83(3), pages 523-530, August.
    6. See, Kok Fong & Coelli, Tim, 2012. "An analysis of factors that influence the technical efficiency of Malaysian thermal power plants," Energy Economics, Elsevier, vol. 34(3), pages 677-685.
    7. Fare, R. & Grosskopf, S. & Logan, J., 1985. "The relative performance of publicly-owned and privately-owned electric utilities," Journal of Public Economics, Elsevier, vol. 26(1), pages 89-106, February.
    8. Olatubi, Williams O. & Dismukes, David E., 2000. "A data envelopment analysis of the levels and determinants of coal-fired electric power generation performance," Utilities Policy, Elsevier, vol. 9(2), pages 47-59, June.
    9. Shumin Jiang & Jingtao Guo & Chen Yang & Zhanwen Ding & Lixin Tian, 2017. "Analysis of the Relative Price in China’s Energy Market for Reducing the Emissions from Consumption," Energies, MDPI, vol. 10(5), pages 1-13, May.
    10. Guo, Xiaoying & Lu, Ching-Cheng & Lee, Jen-Hui & Chiu, Yung-Ho, 2017. "Applying the dynamic DEA model to evaluate the energy efficiency of OECD countries and China," Energy, Elsevier, vol. 134(C), pages 392-399.
    11. Fukuyama, Hirofumi & Weber, William L., 2009. "A directional slacks-based measure of technical inefficiency," Socio-Economic Planning Sciences, Elsevier, vol. 43(4), pages 274-287, December.
    12. Ping Wang & Bangzhu Zhu & Xueping Tao & Rui Xie, 2017. "Measuring regional energy efficiencies in China: a meta-frontier SBM-Undesirable approach," 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. 85(2), pages 793-809, January.
    13. Pun-Lee Lam & Alice Shiu, 2004. "Efficiency and Productivity of China's Thermal Power Generation," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 24(1), pages 73-93, February.
    14. Vaninsky, Alexander, 2006. "Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis," Energy Economics, Elsevier, vol. 28(3), pages 326-338, May.
    15. L. Dean Hiebert, 2002. "The Determinants of the Cost Efficiency of Electric Generating Plants: A Stochastic Frontier Approach," Southern Economic Journal, John Wiley & Sons, vol. 68(4), pages 935-946, April.
    16. Jiro Nemoto & Mika Goto, 2003. "Measurement of Dynamic Efficiency in Production: An Application of Data Envelopment Analysis to Japanese Electric Utilities," Journal of Productivity Analysis, Springer, vol. 19(2), pages 191-210, April.
    17. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    18. Welch, Eric & Barnum, Darold, 2009. "Joint environmental and cost efficiency analysis of electricity generation," Ecological Economics, Elsevier, vol. 68(8-9), pages 2336-2343, June.
    19. Xin Yan & Jianping Ge, 2017. "The Economy-Carbon Nexus in China: A Multi-Regional Input-Output Analysis of the Influence of Sectoral and Regional Development," Energies, MDPI, vol. 10(1), pages 1-28, January.
    20. Nelson, Randy A & Wohar, Mark E, 1983. "Regulation, Scale Economies, and Productivity in Steam-Electric Generation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(1), pages 57-79, February.
    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.
    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. Thiago Gomes Leal Ganhadeiro & Eliane Da Silva Christo & Lidia Angulo Meza & Kelly Alonso Costa & Danilo Pinto Moreira de Souza, 2018. "Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps," Energies, MDPI, vol. 11(10), pages 1-14, October.
    2. Zhang, Ruchuan & Wei, Qian & Li, Aijun & Ren, LiYing, 2022. "Measuring efficiency and technology inequality of China's electricity generation and transmission system: A new approach of network Data Envelopment Analysis prospect cross-efficiency models," Energy, Elsevier, vol. 246(C).
    3. 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.
    4. Benito, Bernardino & Guillamón, María-Dolores & Martínez-Córdoba, Pedro-José, 2020. "Determinants of efficiency improvement in the Spanish public lighting sector," Utilities Policy, Elsevier, vol. 64(C).
    5. Reema Gh. Alajmi, 2024. "Total-Factor Energy Efficiency (TFEE) and CO 2 Emissions for GCC Countries," Sustainability, MDPI, vol. 16(2), pages 1-14, January.
    6. Yulin Lu & Chengyu Li & Min-Jae Lee, 2023. "A Study on the Measurement and Influences of Energy Green Efficiency: Based on Panel Data from 30 Provinces in China," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    7. Toshiyuki Sueyoshi & Mika Goto, 2021. "Performance Assessment of Japanese Electricity and Gas Companies during 2002–2018: Three DEA Approaches," Energies, MDPI, vol. 14(6), pages 1-18, March.
    8. Hu, Jun & Xia, Chengyi & Li, Huijia & Zhu, Peican & Xiong, Wenjun, 2020. "Properties and structural analyses of USA’s regional electricity market: A visibility graph network approach," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    9. Lijun Zeng & Laijun Zhao & Qin Wang & Bingcheng Wang & Yuan Ma & Wei Cui & Yujing Xie, 2018. "Modeling Interprovincial Cooperative Energy Saving in China: An Electricity Utilization Perspective," Energies, MDPI, vol. 11(1), pages 1-25, January.
    10. Chu, Junfei & Shao, Caifeng & Emrouznejad, Ali & Wu, Jie & Yuan, Zhe, 2021. "Performance evaluation of organizations considering economic incentives for emission reduction: A carbon emission permit trading approach," Energy Economics, Elsevier, vol. 101(C).
    11. Wei Wei & Shuangying Ding & Silin Zheng & Jingjing Ma & Tong Niu & Jinkai Li, 2021. "Environmental Efficiency Evaluation of China’s Power Industry Based on the Two-Stage Network Slack-Based Measure Model," IJERPH, MDPI, vol. 18(23), pages 1-21, November.
    12. Chang, Ming-Chung, 2020. "An application of total-factor energy efficiency under the metafrontier framework," Energy Policy, Elsevier, vol. 142(C).
    13. Han, Yongming & Lou, Xiaoyi & Feng, Mingfei & Geng, Zhiqiang & Chen, Liangchao & Ping, Weiying & Lu, Gang, 2022. "Energy consumption analysis and saving of buildings based on static and dynamic input-output models," Energy, Elsevier, vol. 239(PC).
    14. Lubing Xie & Xiaoming Rui & Shuai Li & Xiaozhao Fan & Ruijing Shi & Guohua Li, 2018. "A Critical Analysis on Influential Factors on Power Energy Resources in China," Modern Applied Science, Canadian Center of Science and Education, vol. 12(2), pages 1-1, February.
    15. Tien-Chin Wang & Su-Yuan Tsai, 2018. "Solar Panel Supplier Selection for the Photovoltaic System Design by Using Fuzzy Multi-Criteria Decision Making (MCDM) Approaches," Energies, MDPI, vol. 11(8), pages 1-22, July.
    16. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(C).
    17. Chia-Nan Wang & Van Thanh Nguyen & Hoang Tuyet Nhi Thai & Duy Hung Duong, 2018. "Multi-Criteria Decision Making (MCDM) Approaches for Solar Power Plant Location Selection in Viet Nam," Energies, MDPI, vol. 11(6), pages 1-27, June.
    18. Chiang-Ping Chen & Ming-Chung Chang & Wei-Che Tsai, 2021. "Dynamic Energy Efficiency, Energy Decoupling Rate, and Decarbonization: Evidence from ASEAN+6," SAGE Open, , vol. 11(3), pages 21582440211, September.

    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. Gharneh, Naser Shams & Nabavieh, Alireza & Gholamiangonabadi, Davoud & Alimoradi, Mohammad, 2014. "Productivity change and its determinants: Application of the Malmquist index with bootstrapping in Iranian steam power plants," Utilities Policy, Elsevier, vol. 31(C), pages 114-120.
    2. See, Kok Fong & Coelli, Tim, 2014. "Total factor productivity analysis of a single vertically integrated electricity utility in Malaysia using a Törnqvist index method," Utilities Policy, Elsevier, vol. 28(C), pages 62-72.
    3. See, Kok Fong & Coelli, Tim, 2012. "An analysis of factors that influence the technical efficiency of Malaysian thermal power plants," Energy Economics, Elsevier, vol. 34(3), pages 677-685.
    4. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.
    5. Ghosh, Ranjan & Kathuria, Vinish, 2016. "The effect of regulatory governance on efficiency of thermal power generation in India: A stochastic frontier analysis," Energy Policy, Elsevier, vol. 89(C), pages 11-24.
    6. 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.
    7. Seifert, Stefan & Cullmann, Astrid & von Hirschhausen, Christian, 2016. "Technical efficiency and CO2 reduction potentials — An analysis of the German electricity and heat generating sector," Energy Economics, Elsevier, vol. 56(C), pages 9-19.
    8. 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.
    9. Barros, Carlos Pestana & Chen, Zhongfei & Managi, Shunsuke & Antunes, Olinda Sequeira, 2013. "Examining the cost efficiency of Chinese hydroelectric companies using a finite mixture model," Energy Economics, Elsevier, vol. 36(C), pages 511-517.
    10. Xiangyu Teng & Danting Lu & Yung-ho Chiu, 2019. "Emission Reduction and Energy Performance Improvement with Different Regional Treatment Intensity in China," Energies, MDPI, vol. 12(2), pages 1-18, January.
    11. Stefan Seifert & Astrid Cullmann & Christian von Hirschhausen, 2014. "Technical Efficiency and CO2 Reduction Potentials: An Analysis of the German Electricity Generating Sector," Discussion Papers of DIW Berlin 1426, DIW Berlin, German Institute for Economic Research.
    12. Barros, Carlos Pestana & Managi, Shunsuke, 2009. "Regulation, pollution and heterogeneity in Japanese steam power generation companies," Energy Policy, Elsevier, vol. 37(8), pages 3109-3114, August.
    13. Assaf, A. George & Barros, Carlos Pestana & Managi, Shunsuke, 2011. "Cost efficiency of Japanese steam power generation companies: A Bayesian comparison of random and fixed frontier models," Applied Energy, Elsevier, vol. 88(4), pages 1441-1446, April.
    14. Shrivastava, Naveen & Sharma, Seema & Chauhan, Kavita, 2012. "Efficiency assessment and benchmarking of thermal power plants in India," Energy Policy, Elsevier, vol. 40(C), pages 159-176.
    15. Yu, Bolin & Fang, Debin & Meng, Jingxuan, 2021. "Analysis of the generation efficiency of disaggregated renewable energy and its spatial heterogeneity influencing factors: A case study of China," Energy, Elsevier, vol. 234(C).
    16. Li, Hong-Zhou & Tian, Xian-Liang & Zou, Tao, 2015. "Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function," Applied Energy, Elsevier, vol. 151(C), pages 296-305.
    17. 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.
    18. Liu, C.H. & Lin, Sue J. & Lewis, Charles, 2010. "Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis," Energy Policy, Elsevier, vol. 38(2), pages 1049-1058, February.
    19. Zhang, Ning & Zhao, Yu & Wang, Na, 2022. "Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets," Energy Economics, Elsevier, vol. 112(C).
    20. Fang-Rong Ren & Ze Tian & Yu-Ting Shen & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Energy, CO 2 , and AQI Efficiency and Improvement of the Yangtze River Economic Belt," Energies, MDPI, vol. 12(4), pages 1-17, February.

    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:10:y:2017:i:7:p:1039-:d:105432. 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.