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Green Finance

2019, Volume 1, Issue 2: 110-129. doi: 10.3934/GF.2019.2.110
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Research article

Sustainable financing of marine protected areas: the case of the Martinique regional marine reserve of “Le Prêcheur”

Running title: Marine protected Areas sustainable financing: the case of the Martinique Regional Marine Reserve
  • 1.
    Centre for Blue Governance, Business School, University of Portsmouth, Portsmouth, United Kingdom
  • 2.
    Université des Antilles, Martinique, France
  • 3.
    Agrocampus Ouest, 65 rue de Saint-Brieuc, 35000 Rennes, France
  • 4.
    NovaBlue Environment, Martinique, France
  • Received: 13 March 2019 Accepted: 11 April 2019 Published: 23 April 2019

  • JEL Codes: Q51, Q57

  • Marine Protected Areas require sustainable funding for their effectiveness. This article aims to provide an analysis of the feasibility of financial possibilities for the support of a Regional Marine Protected Area in Martinique. It provides a valuation of the funding alternatives. The concept of payment for ecosystem services is introduced to seek the benefits that fishermen can get from their involvement in the management of the protected areas.

    Citation: Pierre Failler, Claire Montocchio, Adeline Borot de Battisti , Thomas Binet, Jean-Philippe Maréchal, MyriamThirot. Sustainable financing of marine protected areas: the case of the Martinique regional marine reserve of “Le Prêcheur”[J]. Green Finance, 2019, 1(2): 110-129. doi: 10.3934/GF.2019.2.110

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  • Marine Protected Areas require sustainable funding for their effectiveness. This article aims to provide an analysis of the feasibility of financial possibilities for the support of a Regional Marine Protected Area in Martinique. It provides a valuation of the funding alternatives. The concept of payment for ecosystem services is introduced to seek the benefits that fishermen can get from their involvement in the management of the protected areas.




    [1] Badré M, Duranthon JP (2010) Mission sur l'évolution de l'organisation des opérateurs publics en matière de protection de la nature, Ministère de l'écologie, de l'énergie, du développement durable et de la mer.
    [2] Binet T, de Battistia AB, Failler P, et al. (2013) Valeur économique totale des récifs coralliens, mangroves et herbiers de la future aire marine régionale du Prêcheur (Martinique) et implications pour la gestion. Études Caribéennes 26.
    [3] Borot de Battisti A, Binet T, Failler P (2012) Création de la réserve marine régionale du Prêcheur : Étude de faisabilité de la mise en place de taxes pour financement partiel de la gestion de la réserve. Prêcheur, Rapport pour le Conseil Régional de la Martinique, Fort-de-France, Martinique 22.
    [4] Chen JL, Chen JY, Chuang CT, et al. (2014) Developing a co-management financing mechanism to enhance the financial sustainability of marine protected areas in Taiwan. Mar Pol 48: 126–133. doi: 10.1016/j.marpol.2014.03.022
    [5] Failler P, Binet T, de Battisti AB (2011) Création de la réserve marine régionale du Prêcheur: Diagnostic socio-économique des écosystèmes marins et côtiers. Prêcheur, Rapport pour le Conseil Régional de la Martinique, Fort-de-France, Martinique 1.
    [6] Failler P (2016) Synthesis report of the work package 3-CARIPES; Analysis of users' capacity and willingness to pay for conservation efforts made by fishermen.
    [7] Failler P, Pètre É, Binet T, et al. (2015) Valuation of marine and coastal ecosystem services as a tool for conservation: The case of Martinique in the Caribbean. Ecosyst Serv 11: 67–75. doi: 10.1016/j.ecoser.2014.10.011
    [8] Failler P, Pètre É, Borot de Battisti A, et al. (2016) Les touristes sont-ils plus exigeants que les résidents en matière de protection du littoral? Le cas de la Martinique. Études caribéennes, 33–34.
    [9] Froger G, Méral P, Coq JFL, et al. (2012) Regards croisés de l'économie sur les services écosystémiques et environnementaux. VertigO-la revue électronique en sciences de l'environnement 12.
    [10] IUCN (2010) Analyse des mécanismes de financement des aires marines protégées d'Afrique de l'Ouest-Proposition de renforcement de leur durabilité, TOME 3 : Le financement durable des aires protégées terrestres et marine : principes, mécanismes et expériences, The Environment and Development Group, Oxford, UK
    [11] Le Grenelle de la Mer, n.d. Droits d'usage des mers, financement, fiscalité. Rapport final du Comité opérationnel n°5. Available from: http://www.legrenelle-environnement.gouv.fr/IMG/pdf/G5.pdf)
    [12] Meral P (2010) Les services environnementaux en économie: revue de la littérature. Programme SERENA, document de travail 2010–05.
    [13] Muradian R, Corbera E, Pascual U, et al. (2010) Reconciling theory and practice: An alternative conceptual framework for understanding payments for environmental services. Ecol Econ 69: 1202–1208. doi: 10.1016/j.ecolecon.2009.11.006
    [14] Pelosse H, Painault P, Laurent W, et al. (2011) La fiscalité et la mise en œuvre de la nouvelle stratégie nationale pour la biodiversité 2010–2020, Rapport pour l'Inspection générale des finances et le Conseil général de l'environnement et du développement durable.
    [15] Pagiola S (2008) Can payments for environmental services help protect coastal and marine areas. Environ Matters, 14–17.
    [16] Reid-Grant K, Bhat MG (2009) Financing marine protected areas in Jamaica: An exploratory study. Mar Pol 33: 128–136. doi: 10.1016/j.marpol.2008.05.004
    [17] République Française (2011) Evaluation des voies et moyens-Tome I, Recettes. Projet de loi de finances pour 2012. Available from: http://www.performance-publique.budget.gouv.fr/farandole/2012/pap/pdf/VMT1-2012.pdf.
    [18] Semaphores (2010) Etude sur les budgets de fonctionnement des Parcs naturels régionaux de France, Fédération des Parcs Naturels Régionaux de France.
    [19] Thirot M, Failler P, Daniel J (2017) Des savoirs locaux aux experts naturalistes. La reconnaissance des savoirs de pêcheurs artisanaux de Martinique. Revue d'ethoécologi 11: 1–19.
    [20] Thur SM (2010) User fees as sustainable financing mechanisms for marine protected areas: An application to the Bonaire National Marine Park. Mar pol 34: 63–69. doi: 10.1016/j.marpol.2009.04.008
    [21] Voltaire L, Nassiri A, Bailly D, et al. (2011) Effet d'une taxe et d'un droit d'entrée sur les consentements à payer des touristes pour de nouvelles réserves naturelles dans le golfe du Morbihan. Revue d'Etudes en Agriculture et Environnement-Review of agricultural environ stud 92: 183–209.
    [22] Vourc'h A, Natali JM (2000) Sites naturels: contribution du tourisme à leur gestion et à leur entretien: guide de savoir-faire, Agence française de l'ingénierie touristique.
    [23] Wunder S (2005) Payments for environmental services: some nuts and bolts. CIFOR Occasional paper, N. 42.

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