IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v34y2023i2p255-282.html
   My bibliography  Save this article

Dynamic assessment of agro-industrial sector efficiency and productivity changes among G20 nations

Author

Listed:
  • Ying Feng
  • Ching-Cheng Lu
  • I-Fang Lin
  • Jia-Yan Lin

Abstract

In this study, the Group of 20 (G20; excluding EU economies) were selected as the research objects, and the dynamic network slacks-based model (SBM) was used to evaluate the impact of carbon dioxide (CO 2 ) emissions and forested area on the efficiency and productivity of the industrial and agricultural sectors from 2011 to 2015. Empirical results showed that: (1) The efficiency of the industrial sector was superior to that of the agricultural sector among the G20 countries. Argentina, Australia, Indonesia, Saudi Arabia, South Africa, Turkey, the UK, and the US maintained the best industrial sector efficiency values, falling on the efficiency boundary, whereas Argentina, Brazil, Canada, France, Indonesia, South Korea, Russia, and the US had the best agricultural sector efficiency values. (2) Argentina, Indonesia, and the US had the best overall efficiency value of G20 countries. Saudi Arabia (0.0303), China (0.2721), and the UK (0.2809) had the lowest efficiency values. (3) Only France and Germany had higher than average total factor productivity, while Indonesia and Saudi Arabia had declining industrial and agricultural sector productivity. (4) The proportion of forested area (546.02%) was the most important variable to be improved due to the influence of desert topography, followed by the proportion of agricultural output values (60.86%) and the proportion of industrial output values (38.02%) in some countries.

Suggested Citation

  • Ying Feng & Ching-Cheng Lu & I-Fang Lin & Jia-Yan Lin, 2023. "Dynamic assessment of agro-industrial sector efficiency and productivity changes among G20 nations," Energy & Environment, , vol. 34(2), pages 255-282, March.
    • Handle: RePEc:sae:engenv:v:34:y:2023:i:2:p:255-282
    DOI: 10.1177/0958305X211056030
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X211056030
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0958305X211056030?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    3. Kaoru Tone & Miki Tsutsui, 2014. "Slacks-Based Network DEA," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 231-259, Springer.
    4. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    5. Junfei Chen & Tonghui Ding & Huimin Wang & Xiaoya Yu, 2019. "Research on Total Factor Productivity and Influential Factors of the Regional Water–Energy–Food Nexus: A Case Study on Inner Mongolia, China," IJERPH, MDPI, vol. 16(17), pages 1-21, August.
    6. Kats, Gregory H., 1990. "Slowing global warming and sustaining development : The promise of energy efficiency," Energy Policy, Elsevier, vol. 18(1), pages 25-33.
    7. Stabile, Marcelo C.C. & Guimarães, André L. & Silva, Daniel S. & Ribeiro, Vivian & Macedo, Marcia N. & Coe, Michael T. & Pinto, Erika & Moutinho, Paulo & Alencar, Ane, 2020. "Solving Brazil's land use puzzle: Increasing production and slowing Amazon deforestation," Land Use Policy, Elsevier, vol. 91(C).
    8. 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.
    9. Chaido Dritsaki & Melina Dritsaki, 2014. "Causal Relationship between Energy Consumption, Economic Growth and CO2 Emissions: A Dynamic Panel Data Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 4(2), pages 125-136.
    10. Yu, Hang & Zhang, Yahua & Zhang, Anming & Wang, Kun & Cui, Qiang, 2019. "A comparative study of airline efficiency in China and India: A dynamic network DEA approach," Research in Transportation Economics, Elsevier, vol. 76(C).
    11. 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.
    12. 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)

    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. 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.
    2. Sun Meng & Wei Zhou & Jin Chen & Cheng Zhang, 2018. "A synthesized data envelopment analysis model and its application in resource efficiency evaluation and dynamic trend analysis," Energy & Environment, , vol. 29(2), pages 260-280, March.
    3. Haifeng Huang & Tao Wang, 2017. "The Total-Factor Energy Efficiency of Regions in China: Based on Three-Stage SBM Model," Sustainability, MDPI, vol. 9(9), pages 1-20, September.
    4. Sheng-Hsiung Chiu & Tzu-Yu Lin & Hai-Lan Yang, 2020. "Measuring Energy Performance for Regional Sustainable Development in China: A New Framework based on a Dynamic Two-Stage SBM Approach," Sustainability, MDPI, vol. 12(7), pages 1-15, April.
    5. Kai He & Nan Zhu & Wu Jiang & Chuanjin Zhu, 2022. "Efficiency Evaluation of Chinese Provincial Industrial System Based on Network DEA Method," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    6. Del Barrio-Tellado, María José & Gómez-Vega, Mafalda & Herrero-Prieto, Luis César, 2023. "Performance of cultural heritage institutions: A regional perspective," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    7. 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.
    8. Roman Lacko & Zuzana Hajduová, 2018. "Determinants of Environmental Efficiency of the EU Countries Using Two-Step DEA Approach," Sustainability, MDPI, vol. 10(10), pages 1-13, September.
    9. Tajbakhsh, Alireza & Hassini, Elkafi, 2018. "Evaluating sustainability performance in fossil-fuel power plants using a two-stage data envelopment analysis," Energy Economics, Elsevier, vol. 74(C), pages 154-178.
    10. An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.
    11. Bhat, Javed Ahmad & Haider, Salman & Kamaiah, Bandi, 2018. "Interstate energy efficiency of Indian paper industry: A slack-based non-parametric approach," Energy, Elsevier, vol. 161(C), pages 284-298.
    12. Khalid Mehmood & Yaser Iftikhar & Shouming Chen & Shaheera Amin & Alia Manzoor & Jinlong Pan, 2020. "Analysis of Inter-Temporal Change in the Energy and CO 2 Emissions Efficiency of Economies: A Two Divisional Network DEA Approach," Energies, MDPI, vol. 13(13), pages 1-17, June.
    13. Zhang, Lin & Zhao, Linlin & Zha, Yong, 2021. "Efficiency evaluation of Chinese regional industrial systems using a dynamic two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
    14. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    15. Wen-Min Lu & Qian Long Kweh & Chung-Wei Wang, 2021. "Integration and application of rough sets and data envelopment analysis for assessments of the investment trusts industry," Annals of Operations Research, Springer, vol. 296(1), pages 163-194, January.
    16. 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).
    17. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    18. Khushalani, Jaya & Ozcan, Yasar A., 2017. "Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA)," Socio-Economic Planning Sciences, Elsevier, vol. 60(C), pages 15-23.
    19. Qiong Xia & Min Li & Huaqing Wu & Zhenggang Lu, 2016. "Does the Central Government’s Environmental Policy Work? Evidence from the Provincial-Level Environment Efficiency in China," Sustainability, MDPI, vol. 8(12), pages 1-17, December.
    20. Yang, Guo-liang & Fukuyama, Hirofumi & Chen, Kun, 2019. "Investigating the regional sustainable performance of the Chinese real estate industry: A slack-based DEA approach," Omega, Elsevier, vol. 84(C), pages 141-159.

    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:sae:engenv:v:34:y:2023:i:2:p:255-282. 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: SAGE Publications (email available below). General contact details of provider: .

    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.