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Integrated modeling for sustainable fisheries in Morocco: a dynamic computable general equilibrium approach

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Abstract

This paper explores how public policies and economic conditions impact fisheries sustainability using an integrated approach. Combining a synthetic fisheries resource assessment model with a dynamic computable general equilibrium model (DCGEM), the research challenges the belief that overexploited fish stocks inevitably lead to extinction. It highlights the sensitivity of the fishing sector and its sustainability to changes in fishing effort and fluctuations in fuel prices, emphasizing the risks of overexploitation in the medium and long term, even under initially acceptable conditions. The key takeaway is the importance of maintaining balanced catch levels and fishing efforts below their recommended theoretical threshold to ensure sustainable fisheries. The study brings original insights, providing practical policy implications and advocating for continuous monitoring along with adaptive management strategies.

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Notes

  1. MEFISTO: MEditerranean FIsheries Simulation Tool.

  2. Refer to Kamili and Maynou (2011)’s study for a practical demonstration of the application of the MEFISTO model (Version 3.0; 2005).

  3. See”Framework Law No. 99-12 concerning the National Charter for the Environment and Sustainable Development. Official Bulletin of the Kingdom of Morocco No. 6240-18 Jumada I 1435 (20-3-2014); http://www.sgg.gov.ma/”.

  4. Refer to Doukkali and Kamili (2018) for more details on the Moroccan fisheries production system.

  5. Catchability (Q) can be interpreted as the probability that a unit of biomass will be caught when a unit of fishing effort is engaged (Laloë, 1990). It is influenced by two main groups of factors shown by Chadwick and O’Boyle (1990). The first group includes operation-related factors like fishing gear, boat characteristics, and fishermen’s skills, which contribute significantly to catchability variability. The second group consists of factors associated with the targeted species, such as their physiology (growth rate) and behavior, including daily and seasonal migration patterns.

  6. IFPRI: International Food Policy Research Institute.

    N.B. For consistency and ease of use, we have kept the main symbols used in the IFPRI static reference model using the GAMS software (General Algebraic Modeling System).

  7. The National Observatory of Human Development (https://www.ondh.ma/) predicts an annual Moroccan population growth rate of 1% between 2015 and 2050.

  8. Sub-sector growth is assumed to be in line with that of the main sectors with which they are associated.

    Government consumption expenditure is assumed to grow by 3%, and transfers to households by 5%.

  9. “Mer en chiffre 2018” in http://www.mpm.gov.ma/.

  10. \({CPUE}_{sp,t}={Q}_{sp,t}.{B}_{sp,t}=\frac{{Y}_{sp,t}}{{E}_{sp,t}}\)

  11. Octopus (Octopus vulgaris) Stock Evaluation Report, 2004: Findings from the Trawling Assessment Campaign aboard the Research Vessel ‘Charif Al Idrissi,’ conducted by the Moroccan National Institute of Fisheries Research, Department of Fisheries Resources.

  12. Pan et al. (2007) support the assumption that parameter (\({r}_{sp}\)) can be influenced by abiotic factors, which are strongly related to the overall health of marine ecosystems.

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  1. Africa Institute for Research in Economics and Social Sciences, FGSES-Mohammed VI Polytechnic University, Rabat, Morocco

    Abdelkabir Kamili

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  1. Abdelkabir Kamili
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The author conceived and designed the study, developed the model, collected, and prepared the data, conducted the simulations, analyzed the results, and wrote the manuscript.

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Correspondence to Abdelkabir Kamili.

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Appendix 1: Effects of changes in the model’s bioecological parameters

Appendix 1: Effects of changes in the model’s bioecological parameters

We have made modifications to the growth rate of sardines (\({r}_{sardine}\)) at various levels compared to the growth rates for reference state (\({r0}_{sardine}\)= 1.5). It is important to note that the maximum sustainable yield (MSY) parameter adjusts in different simulations as it depends on the growth rate. Figure 9 illustrates the MSY curve for the reference state and, for comparison purposes, the MSY curve for the simulation with \({r}_{sardine}=1.95\).

Assuming abiotic factors (e.g., water quality, phytoplankton and zooplankton abundance, temperature) affect bioecological parameters (such as \({r}_{sp}\)),Footnote 12 this scenario confirms the causal link between environmental degradation and resource depletion. Higher growth rates result in an augmentation of balanced catches. Conversely, to sustain biomass when growth rates are lower, balanced catches must be reduced.

Fig. 9
figure 9

Effects of changing the fish growth rate parameter (case of sardine \({r}_{sardine}\))

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Kamili, A. Integrated modeling for sustainable fisheries in Morocco: a dynamic computable general equilibrium approach. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04864-3

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