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Managing Marine Mammals and Fisheries: A Calibrated Programming Model for the Seal-Fishery Interaction in Sweden

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  • Torbjörn Jansson

    (Swedish University of Agricultural Sciences)

  • Staffan Waldo

    (Swedish University of Agricultural Sciences)

Abstract

This paper develops a model based on the concept of Positive Mathematical Programming (PMP) that is useful for ex-ante analyses of how policy measures affect commercial fisheries. PMP models are frequently used in agriculture, but rarely for analyzing fisheries. Fisheries often face a large set of constraints such as effort regulations and catch quotas of which some might be binding and others not. An econometric approach is developed for calibrating models with both binding and non-binding constraints. The interaction between seals and Swedish fisheries is used as an empirical application. Seal interaction is modeled as seals predating fish from passive gear (nets and hooks), which is primarily an issue for the coastal fishery. The model contains 24 fleet segments involved in 247 different fishing activities in 2012. The results show that if no further management action is taken, fisheries using passive gear will reduce their activities from about 46 000 days at sea per year to about 41 000 and reducing their economic performance from losses of about 2 million Euros to about 3.3 million. The impact from seals can be reduced by reducing the seal population or providing economic compensation.

Suggested Citation

  • Torbjörn Jansson & Staffan Waldo, 2022. "Managing Marine Mammals and Fisheries: A Calibrated Programming Model for the Seal-Fishery Interaction in Sweden," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 81(3), pages 501-530, March.
    • Handle: RePEc:kap:enreec:v:81:y:2022:i:3:d:10.1007_s10640-021-00637-y
    DOI: 10.1007/s10640-021-00637-y
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    References listed on IDEAS

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    1. Basnet, Shyam Kumar & Jansson , Torbjorn & Heckelei, Thomas, 2021. "A Bayesian econometrics and risk programming approach for analysing the impact of decoupled payments in the European Union," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 65(03), January.
    2. Arata, Linda & Donati, Michele & Sckokai, Paolo & Arfini, Filippo, 2017. "Incorporating risk in a positive mathematical programming framework: a dual approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 61(2), April.
    3. Jonasson, Lars & Apland, Jeffrey, 1997. "Frontier Technology and Inefficiencies in Programming Sector Models: An Application to Swedish Agriculture," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 24(1), pages 109-131.
    4. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(1), pages 27-50, March.
    5. Heckelei, Thomas & Britz, Wolfgang & Zhang, Yinan, 2012. "Positive Mathematical Programming Approaches – Recent Developments in Literature and Applied Modelling," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(1), pages 1-16, April.
    6. Ottmar Röhm & Stephan Dabbert, 2003. "Integrating Agri-Environmental Programs into Regional Production Models: An Extension of Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 254-265.
    7. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
    8. Jonathan R. Sweeney & Richard E. Howitt & Hing Ling Chan & Minling Pan & PingSun Leung, 2017. "How do fishery policies affect Hawaii's longline fishing industry? Calibrating a positive mathematical programming model," Papers 1707.03960, arXiv.org.
    9. Bauer, Siegfried & Kasnakoglu, Haluk, 1990. "Non-linear programming models for sector and policy analysis : Experiences with the Turkish agricultural sector model," Economic Modelling, Elsevier, vol. 7(3), pages 275-290, July.
    10. Hans Frost & Peder Andersen & Ayoe Hoff, 2013. "Management of Complex Fisheries: Lessons Learned from a Simulation Model," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 61(2), pages 283-307, June.
    11. Cronin, M. & Jessopp, M. & Houle, J. & Reid, D., 2014. "Fishery-seal interactions in Irish waters: Current perspectives and future research priorities," Marine Policy, Elsevier, vol. 44(C), pages 120-130.
    12. Prellezo, Raúl & Accadia, Paolo & Andersen, Jesper L. & Andersen, Bo S. & Buisman, Erik & Little, Alyson & Nielsen, J. Rasmus & Poos, Jan Jaap & Powell, Jeff & Röckmann, Christine, 2012. "A review of EU bio-economic models for fisheries: The value of a diversity of models," Marine Policy, Elsevier, vol. 36(2), pages 423-431.
    13. Pierre Mérel & Santiago Bucaram, 2010. "Exact calibration of programming models of agricultural supply against exogenous supply elasticities," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 37(3), pages 395-418, September.
    14. David Finnoff & John Tschirhart, 2003. "Protecting an Endangered Species While Harvesting Its Prey in a General Equilibrium Ecosystem Model," Land Economics, University of Wisconsin Press, vol. 79(2), pages 160-180.
    15. Jansson, Torbjörn & Heckelei, Thomas, 2009. "A new estimator for trade costs and its small sample properties," Economic Modelling, Elsevier, vol. 26(2), pages 489-498, March.
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