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Estimating Energy Efficiency and Energy Saving Potential in the Republic of Korea’s Offshore Fisheries

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  • Yonghan Jeon

    (Resources & Environmental Economics Institute, Pukyong National University, Busan 48547, Republic of Korea)

  • Jongoh Nam

    (Division of Marine & Fisheries Business and Economics, Pukyong National University, Busan 48513, Republic of Korea)

Abstract

The Republic of Korea’s government has established a carbon negativity policy to mitigate climate change in the fisheries sector. To achieve this objective, the government proposed enhancing energy efficiency in vessel fisheries, known for high carbon emissions. However, it was difficult to find research that investigated the energy consumption status of vessel fisheries. Thus, this study aims to calculate the offshore fisheries’ energy efficiency (EE) and to estimate the energy saving potential (ESP) needed in order to achieve efficient energy consumption. For this purpose, annual fisheries management surveys and data on the tax-free petroleum supply are employed. This study measures the EE and the ESP of offshore fisheries by year and fishing gear by employing the stochastic frontier analysis (SFA), which considers exogenous determinants of energy inefficiency. The analysis results show a decline in the EE over time and an increasing trend in the ESP. Notably, the trawl and fleet fisheries tend to have lower energy efficiency. Furthermore, the trawl and fleet fisheries were identified as having the highest ESP. Therefore, to utilize energy efficiently and reduce energy consumption in offshore fisheries, this study suggests scaling down fleet fisheries, developing energy saving fishing nets and eco-friendly fishing vessels, expanding modernization projects for fishing vessels, and revising the related acts.

Suggested Citation

  • Yonghan Jeon & Jongoh Nam, 2023. "Estimating Energy Efficiency and Energy Saving Potential in the Republic of Korea’s Offshore Fisheries," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:15026-:d:1262411
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    as
    1. Lv, Yulan & Chen, Wei & Cheng, Jianquan, 2020. "Effects of urbanization on energy efficiency in China: New evidence from short run and long run efficiency models," Energy Policy, Elsevier, vol. 147(C).
    2. Calvin Nsangou, Jean & Kenfack, Joseph & Nzotcha, Urbain & Tamo, Thomas Tatietse, 2020. "Assessment of the potential for electricity savings in households in Cameroon: A stochastic frontier approach," Energy, Elsevier, vol. 211(C).
    3. Massimo Filippini & Lester C. Hunt, 2011. "Energy Demand and Energy Efficiency in the OECD Countries: A Stochastic Demand Frontier Approach," The Energy Journal, , vol. 32(2), pages 59-80, April.
    4. Xu, Mengmeng & Tan, Ruipeng & He, Xinju, 2022. "How does economic agglomeration affect energy efficiency in China?: Evidence from endogenous stochastic frontier approach," Energy Economics, Elsevier, vol. 108(C).
    5. Filippini, Massimo & Hunt, Lester C., 2012. "US residential energy demand and energy efficiency: A stochastic demand frontier approach," Energy Economics, Elsevier, vol. 34(5), pages 1484-1491.
    6. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    7. Christensen, Laurits R & Jorgenson, Dale W & Lau, Lawrence J, 1973. "Transcendental Logarithmic Production Frontiers," The Review of Economics and Statistics, MIT Press, vol. 55(1), pages 28-45, February.
    8. Rolf Färe & Shawna Grosskopf & Dimitri Margaritis, 2006. "Productivity Growth and Convergence in the European Union," Journal of Productivity Analysis, Springer, vol. 25(1), pages 111-141, April.
    9. Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LP, vol. 13(4), pages 718-758, December.
    10. Filippini, Massimo & Hunt, Lester C. & Zorić, Jelena, 2014. "Impact of energy policy instruments on the estimated level of underlying energy efficiency in the EU residential sector," Energy Policy, Elsevier, vol. 69(C), pages 73-81.
    11. Haider, Salman & Mishra, Prajna Paramita, 2021. "Does innovative capability enhance the energy efficiency of Indian Iron and Steel firms? A Bayesian stochastic frontier analysis," Energy Economics, Elsevier, vol. 95(C).
    12. Zhang, H. & Fan, L.W. & Zhou, P., 2020. "Handling heterogeneity in frontier modeling of city-level energy efficiency: The case of China," Applied Energy, Elsevier, vol. 279(C).
    13. Kenneth Kigundu Macharia & Dianah Ngui & John Kamau Gathiaka, 2022. "Effects of Energy Efficiency on Firm Productivity in Kenya’s Manufacturing Sector," Journal of Sustainable Development, Canadian Center of Science and Education, vol. 15(3), pages 1-90, May.
    14. Maroula Khraiche & Levent Kutlu & Xi Mao, 2022. "Energy efficiencies of European countries," Applied Economics, Taylor & Francis Journals, vol. 54(23), pages 2694-2706, May.
    15. Boyd, Gale A. & Lee, Jonathan M., 2019. "Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis," Energy Economics, Elsevier, vol. 81(C), pages 159-174.
    16. Du, Kerui & Lin, Boqiang, 2017. "International comparison of total-factor energy productivity growth: A parametric Malmquist index approach," Energy, Elsevier, vol. 118(C), pages 481-488.
    17. Liu, Fengqin & Sim, Jae-yeon & Sun, Huaping & Edziah, Bless Kofi & Adom, Philip Kofi & Song, Shunfeng, 2023. "Assessing the role of economic globalization on energy efficiency: Evidence from a global perspective," China Economic Review, Elsevier, vol. 77(C).
    18. Macharia, Kenneth Kigundu & Gathiaka, John Kamau & Ngui, Dianah, 2022. "Energy efficiency in the Kenyan manufacturing sector," Energy Policy, Elsevier, vol. 161(C).
    19. Du, Minzhe & Wang, Bing & Zhang, Ning, 2018. "National research funding and energy efficiency: Evidence from the National Science Foundation of China," Energy Policy, Elsevier, vol. 120(C), pages 335-346.
    20. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    21. Wen-Ling Hsiao & Jin-Li Hu & Chan Hsiao & Ming-Chung Chang, 2018. "Energy Efficiency of the Baltic Sea Countries: An Application of Stochastic Frontier Analysis," Energies, MDPI, vol. 12(1), pages 1-11, December.
    22. Zhou, P. & Ang, B.W. & Zhou, D.Q., 2012. "Measuring economy-wide energy efficiency performance: A parametric frontier approach," Applied Energy, Elsevier, vol. 90(1), pages 196-200.
    23. Jianxu Liu & Heng Wang & Sanzidur Rahman & Songsak Sriboonchitta, 2021. "Energy Efficiency, Energy Conservation and Determinants in the Agricultural Sector in Emerging Economies," Agriculture, MDPI, vol. 11(8), pages 1-18, August.
    24. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    25. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    26. Seog-Chan Oh & Alfred J. Hildreth, 2014. "Estimating the Technical Improvement of Energy Efficiency in the Automotive Industry—Stochastic and Deterministic Frontier Benchmarking Approaches," Energies, MDPI, vol. 7(9), pages 1-27, September.
    27. Lin, Boqiang & Du, Kerui, 2014. "Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: An application to Chinese energy economy," Energy, Elsevier, vol. 76(C), pages 884-890.
    28. Cheilari, Anna & Guillen, Jordi & Damalas, Dimitrios & Barbas, Thomas, 2013. "Effects of the fuel price crisis on the energy efficiency and the economic performance of the European Union fishing fleets," Marine Policy, Elsevier, vol. 40(C), pages 18-24.
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