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Examining the environmental tax and the fishing grounds footprint nexus: Case of the African fishing industry

Author

Listed:
  • Muhamadu Awal Kindzeka Wirajing
  • Roger Tsafack Nanfosso
  • Armand Mboutchouang Kountchou

Abstract

The implementation of environmental stringent policies faces a trade‐off between ensuring livelihood sustainability and reducing global fishing footprints. This study investigates the impact of environmental tax on fishing footprints in 23 African countries from 2000 to 2018, using the smoothed instrumental‐variables quantile regression approach. The need to establish a sustainable fishing business in Africa, considering the growing fishing footprint, and investigate if environmental levies could help achieve this goal are the driving forces behind this study. The findings reveal that environmental tax reduces footprints in African fishing grounds up to the 0.75th quantile, beyond which its impact remains insignificant in its conditional distribution. More importantly, environmental tax stringency‐reducing impacts are only apparent in the coastal and middle‐income groups up to the 0.90th and 0.50th quantiles, respectively. Environmental tax appeared to only contribute to reducing fishing footprints beyond the 0.75th quantile with higher levels of pollution tax across its conditional distribution in landlocked countries, but remaining insignificant in the low‐income group. The insignificant impact on the low‐income group is due to relatively relaxed environmental regulations that are unable to address the ecological deficit. The findings further indicate that digital infrastructures and financial development can contribute to enhancing fishing grounds biocapacity, in promoting environmental awareness and ecofriendly strategies. These findings are significant because they show that, regardless of whether environmental standards are governed by common law or civil law legal systems, environmental taxes can be utilized to stop deleterious ecoenvironmental practices within the African fishing industry. The study recommends that policy makers adopt more stringent environmental regulations, particularly for the landlocked countries, whose limited fishery grounds biocapacity has dropped in recent years.

Suggested Citation

  • Muhamadu Awal Kindzeka Wirajing & Roger Tsafack Nanfosso & Armand Mboutchouang Kountchou, 2025. "Examining the environmental tax and the fishing grounds footprint nexus: Case of the African fishing industry," Natural Resources Forum, Blackwell Publishing, vol. 49(3), pages 2130-2154, August.
    • Handle: RePEc:wly:natres:v:49:y:2025:i:3:p:2130-2154
    DOI: 10.1111/1477-8947.12411
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    References listed on IDEAS

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    1. Galvao, Antonio F. & Kato, Kengo, 2016. "Smoothed quantile regression for panel data," Journal of Econometrics, Elsevier, vol. 193(1), pages 92-112.
    2. Geoffrey Heal & Wolfram Schlenker, 2019. "Coase, Hotelling and Pigou: The Incidence of a Carbon Tax and CO₂ Emissions," NBER Working Papers 26086, National Bureau of Economic Research, Inc.
    3. Favourate Y. Mpofu, 2022. "Green Taxes in Africa: Opportunities and Challenges for Environmental Protection, Sustainability, and the Attainment of Sustainable Development Goals," Sustainability, MDPI, vol. 14(16), pages 1-26, August.
    4. Lipford Jody W. & Yandle Bruce, 2011. "NAFTA, Environmental Kuznets Curves, and Mexico's Progress," Global Economy Journal, De Gruyter, vol. 10(4), pages 1-20, January.
    5. Liang Chen & Yulong Huo, 2021. "A simple estimator for quantile panel data models using smoothed quantile regressions," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 247-263.
    6. He, Xuming & Pan, Xiaoou & Tan, Kean Ming & Zhou, Wen-Xin, 2023. "Smoothed quantile regression with large-scale inference," Journal of Econometrics, Elsevier, vol. 232(2), pages 367-388.
    7. Kallbekken, Steffen & Kroll, Stephan & Cherry, Todd L., 2011. "Do you not like Pigou, or do you not understand him? Tax aversion and revenue recycling in the lab," Journal of Environmental Economics and Management, Elsevier, vol. 62(1), pages 53-64, July.
    8. Kaplan, David M. & Sun, Yixiao, 2017. "Smoothed Estimating Equations For Instrumental Variables Quantile Regression," Econometric Theory, Cambridge University Press, vol. 33(1), pages 105-157, February.
    9. González, Andrés & Teräsvirta, Timo & van Dijk, Dick & Yang, Yukai, 2005. "Panel Smooth Transition Regression Models," SSE/EFI Working Paper Series in Economics and Finance 604, Stockholm School of Economics, revised 11 Oct 2017.
    10. Lin, Boqiang & Li, Xuehui, 2011. "The effect of carbon tax on per capita CO2 emissions," Energy Policy, Elsevier, vol. 39(9), pages 5137-5146, September.
    11. Elahi, Ehsan & Khalid, Zainab, 2022. "Estimating smart energy inputs packages using hybrid optimisation technique to mitigate environmental emissions of commercial fish farms," Applied Energy, Elsevier, vol. 326(C).
    12. Gene M. Grossman & Alan B. Krueger, 1991. "Environmental Impacts of a North American Free Trade Agreement," NBER Working Papers 3914, National Bureau of Economic Research, Inc.
    13. Sami Ullah & Rundong Luo & Tomiwa Sunday Adebayo & Mustafa Tevfik Kartal, 2023. "Dynamics between environmental taxes and ecological sustainability: Evidence from top‐seven green economies by novel quantile approaches," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(2), pages 825-839, April.
    14. Pawson, M.G. & Glenn, H. & Padda, G., 2008. "The definition of marine recreational fishing in Europe," Marine Policy, Elsevier, vol. 32(3), pages 339-350, May.
    15. Partha Dasgupta & Geoffrey Heal & Joseph E. Stiflitx, 1980. "The Taxation of Exhaustible Resources," NBER Working Papers 0436, National Bureau of Economic Research, Inc.
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