IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i1d10.1007_s10668-022-02716-6.html
   My bibliography  Save this article

Spatial–temporal differentiation and influencing factors of marine fishery carbon emission efficiency in China

Author

Listed:
  • Yuan Gao

    (Liaoning Normal University)

  • Zhongwei Fu

    (Liaoning Normal University)

  • Jun Yang

    (Liaoning Normal University
    Northeastern University)

  • Miao Yu

    (Liaoning Normal University)

  • Wenhui Wang

    (Liaoning Normal University)

Abstract

Carbon reduction in marine fisheries plays a role in realizing the “dual carbon” goal. In this study, we measured the marine fishery carbon emission efficiency in the eastern coastal provinces of China from 2008 to 2020. We analyzed the spatial–temporal variation characteristics of marine fishery carbon emission efficiency based on a violin plot and standard deviation ellipse. Additionally, we used ridge regression and fuzzy-set qualitative comparative analysis to study the influencing factors and improvement paths of marine fishery carbon emission efficiency. The results demonstrate that the overall marine fishery carbon emission efficiency in coastal provinces increased from 2008 to 2020. The violin plot evolves from a "double peak" to a "single peak" and the carbon emission efficiency transitions from a low-value range to a high-value range. There is room to improve the carbon emission efficiency of provinces to different degrees. Additionally, the area of standard deviation ellipse of marine fishery carbon emission efficiency fluctuated from 1.46 million km2 in 2008 to 1.45 million km2 in 2020, and the spatial distribution range showed a concentrated trend. The spatial distribution center of carbon emission efficiency mainly moved northward before 2014 and southward after 2014. Regarding the influencing factors, the industrial structure did not pass the significance level, and the employment structure exhibited a negative correlation with the marine fishery carbon emission efficiency with a ridge regression coefficient of − 0.217. The level of economic development, carbon sink capacity, and educational level of technical extension personnel exhibited a positive correlation with the carbon emission efficiency, with ridge regression coefficients 0.034, 0.049, and 0.028, respectively. Furthermore, there were different paths for the low-carbon and green development of marine fisheries in coastal provinces. The individual and combined effects of influencing factors varied according to coastal provinces. Based on the influencing factors, three paths were determined. This study provides relevant recommendations to improve carbon emission efficiency and sustainably develop marine fisheries in China.

Suggested Citation

  • Yuan Gao & Zhongwei Fu & Jun Yang & Miao Yu & Wenhui Wang, 2024. "Spatial–temporal differentiation and influencing factors of marine fishery carbon emission efficiency in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 453-478, January.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:1:d:10.1007_s10668-022-02716-6
    DOI: 10.1007/s10668-022-02716-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02716-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-022-02716-6?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. 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.
    2. Fare, Rolf, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    3. Driscoll, John & Tyedmers, Peter, 2010. "Fuel use and greenhouse gas emission implications of fisheries management: the case of the new england atlantic herring fishery," Marine Policy, Elsevier, vol. 34(3), pages 353-359, May.
    4. Atakelty Hailu & Terrence S. Veeman, 2001. "Non-parametric Productivity Analysis with Undesirable Outputs: An Application to the Canadian Pulp and Paper Industry," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 605-616.
    5. Rebeca Fontanilla Andong & Edsel Sajor, 2017. "Urban sprawl, public transport, and increasing CO2 emissions: the case of Metro Manila, Philippines," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(1), pages 99-123, February.
    6. 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.
    7. Scheel, Holger, 2001. "Undesirable outputs in efficiency valuations," European Journal of Operational Research, Elsevier, vol. 132(2), pages 400-410, July.
    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. Halkos, George & Petrou, Kleoniki Natalia, 2019. "Treating undesirable outputs in DEA: A critical review," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 97-104.
    2. Ma-Lin Song & Yuan-Xiang Zhou & Rong-Rong Zhang & Ron Fisher, 2017. "Environmental efficiency evaluation with left–right fuzzy numbers," Operational Research, Springer, vol. 17(3), pages 697-714, October.
    3. Chiu, Yung-Ho & Lee, Jen-Hui & Lu, Ching-Cheng & Shyu, Ming-Kuang & Luo, Zhengying, 2012. "The technology gap and efficiency measure in WEC countries: Application of the hybrid meta frontier model," Energy Policy, Elsevier, vol. 51(C), pages 349-357.
    4. Xiang Ji & Jiasen Sun & Qunwei Wang & Qianqian Yuan, 2019. "Revealing Energy Over-Consumption and Pollutant Over-Emission Behind GDP: A New Multi-criteria Sustainable Measure," Computational Economics, Springer;Society for Computational Economics, vol. 54(4), pages 1391-1421, December.
    5. Halkos, George & Petrou, Kleoniki Natalia, 2018. "A critical review of the main methods to treat undesirable outputs in DEA," MPRA Paper 90374, University Library of Munich, Germany.
    6. Li, Mingquan & Wang, Qi, 2014. "International environmental efficiency differences and their determinants," Energy, Elsevier, vol. 78(C), pages 411-420.
    7. Afzalinejad, Mohammad, 2020. "Reverse efficiency measures for environmental assessment in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    8. César Salazar & Roberto Cárdenas-Retamal & Marcela Jaime, 2023. "Environmental efficiency in the salmon industry—an exploratory analysis around the 2007 ISA virus outbreak and subsequent regulations in Chile," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8107-8135, August.
    9. Chu, Junfei & Hou, Tianteng & Li, Feng & Yuan, Zhe, 2024. "Dynamic bargaining game DEA carbon emissions abatement allocation and the Nash equilibrium," Energy Economics, Elsevier, vol. 134(C).
    10. Adler, Nicole & Volta, Nicola, 2016. "Accounting for externalities and disposability: A directional economic environmental distance function," European Journal of Operational Research, Elsevier, vol. 250(1), pages 314-327.
    11. Berre, David & Boussemart, Jean-Philippe & Leleu, Hervé & Tillard, Emmanuel, 2013. "Economic value of greenhouse gases and nitrogen surpluses: Society vs farmers’ valuation," European Journal of Operational Research, Elsevier, vol. 226(2), pages 325-331.
    12. Zhou, P. & Ang, B.W. & Poh, K.L., 2008. "A survey of data envelopment analysis in energy and environmental studies," European Journal of Operational Research, Elsevier, vol. 189(1), pages 1-18, August.
    13. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2018. "Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach," European Journal of Operational Research, Elsevier, vol. 269(1), pages 35-50.
    14. Puggioni, Daniela & Stefanou, Spiro E., 2016. "The Value of Being Socially Responsible. A DEA Approach for Analyzing Efficiency and Recovering Shadow Prices of CSR Activities," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235723, Agricultural and Applied Economics Association.
    15. García-Alonso, Carlos R. & Salvador-Carulla, Luis & Fernández-Rodríguez, Vicente, 2015. "Evaluation of system efficiency using the Monte Carlo DEA: The case of small health areasAuthor-Name: Torres-Jiménez, Mercedes," European Journal of Operational Research, Elsevier, vol. 242(2), pages 525-535.
    16. Dakpo, Hervé K & Jeanneaux, Philippe & Latruffe, Laure, 2014. "Inclusion of undesirable outputs in production technology modeling: The case of greenhouse gas emissions in French meat sheep farming," Working Papers 207806, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    17. Cherchye, Laurens & Rock, Bram De & Walheer, Barnabé, 2015. "Multi-output efficiency with good and bad outputs," European Journal of Operational Research, Elsevier, vol. 240(3), pages 872-881.
    18. Ma-Lin Song & Ron Fisher & Jian-Lin Wang & Lian-Biao Cui, 2018. "Environmental performance evaluation with big data: theories and methods," Annals of Operations Research, Springer, vol. 270(1), pages 459-472, November.
    19. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.
    20. Fusco, Elisa & Vidoli, Francesco & Rogge, Nicky, 2020. "Spatial directional robust Benefit of the Doubt approach in presence of undesirable output: An application to Italian waste sector," Omega, Elsevier, vol. 94(C).

    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:spr:endesu:v:26:y:2024:i:1:d:10.1007_s10668-022-02716-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.