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Understanding the distribution of economic benefits from improving coastal and marine ecosystems

Author

Listed:
  • Kristine Pakalniete

    (AKTiiVS Ltd., Latvia)

  • Juris Aigars

    (Latvian Institute of Aquatic Ecology)

  • Mikołaj Czajkowski

    (Faculty of Economic Sciences, University of Warsaw)

  • Solvita Strake

    (Latvian Institute of Aquatic Ecology)

  • Ewa Zawojska

    (Faculty of Economic Sciences, University of Warsaw)

  • Nick Hanley

    (University of St Andrews, Department of Geography and Sustainable Development)

Abstract

The ecological status of coastal and marine waterbodies world-wide is threatened by multiple stressors, including nutrient inputs from various sources and increasing occurrences of invasive alien species. These stressors impact the environmental quality of the Baltic Sea. Each Baltic Sea country contributes to the stressors and, at the same time, is affected by their negative impacts on water quality. Understanding who benefits from improvements in coastal and marine waters is key to assessing public support for policies aimed at achieving such changes. We propose a new approach to account for variability in benefits related to differences in socio-demographics of respondents, by using a structural model of discrete choice. Our method (1) provides a convenient way of incorporating a wide range of socio-demographics as explanatory variables in conditional multinomial logit models without the risk of collinearity, and (2) is more statistically efficient than the alternative, typically used approaches. The new technique is applied in a study which examines the preferences of Latvian citizens towards improvements of the coastal and marine environment quality that could help the Baltic Sea waters of Latvia reach Good Environmental Status as required by the European Union's Marine Strategy Framework Directive. Applying the discrete choice experiment method, we find that overall, Latvians are willing to pay for reducing losses of biodiversity, for improving water quality for recreation by reduced eutrophication, and for reducing new occurrences of invasive alien species. A significant group within the sample seems not to value environmental improvements in the Baltic Sea, and, thus, is unwilling to support costly measures for achieving such improvements. The structural model of discrete choice reveals substantial heterogeneity among Latvians towards changes in the quality of coastal and marine waters of Latvia.

Suggested Citation

  • Kristine Pakalniete & Juris Aigars & Mikołaj Czajkowski & Solvita Strake & Ewa Zawojska & Nick Hanley, 2016. "Understanding the distribution of economic benefits from improving coastal and marine ecosystems," Working Papers 2016-26, Faculty of Economic Sciences, University of Warsaw.
  • Handle: RePEc:war:wpaper:2016-26
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    File URL: http://www.wne.uw.edu.pl/index.php/download_file/3071/
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    Cited by:

    1. Christine Bertram & Heini Ahtiainen & Jürgen Meyerhoff & Kristine Pakalniete & Eija Pouta & Katrin Rehdanz, 2020. "Contingent Behavior and Asymmetric Preferences for Baltic Sea Coastal Recreation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(1), pages 49-78, January.
    2. Bernadeta Gołębiowska & Anna Bartczak & Mikołaj Czajkowski, 2020. "Energy Demand Management and Social Norms," Energies, MDPI, vol. 13(15), pages 1-20, July.
    3. Boyce, Christopher & Czajkowski, Mikołaj & Hanley, Nick, 2019. "Personality and economic choices," Journal of Environmental Economics and Management, Elsevier, vol. 94(C), pages 82-100.
    4. Wiktor Budziński & Mikołaj Czajkowski, 2018. "Hybrid choice models vs. endogeneity of indicator variables: a Monte Carlo investigation," Working Papers 2018-21, Faculty of Economic Sciences, University of Warsaw.
    5. Mikołaj Czajkowski & Marianne Zandersen & Uzma Aslam & Ioannis Angelidis & Thomas Becker & Wiktor Budziński & Katarzyna Zagórska, 2018. "Recreational Value of the Baltic Sea: a Spatially Explicit Site Choice Model Accounting for Environmental Conditions," Working Papers 2018-11, Faculty of Economic Sciences, University of Warsaw.
    6. Bernadeta Gołębiowska & Anna Bartczak & Mikołaj Czajkowski, 2020. "Energy demand management and social norms – the case study in Poland," Working Papers 2020-25, Faculty of Economic Sciences, University of Warsaw.
    7. Castillo-Eguskitza, Nekane & Hoyos, David & Onaindia, Miren & Czajkowski, Mikolaj, 2019. "Unraveling local preferences and willingness to pay for different management scenarios: A choice experiment to biosphere reserve management," Land Use Policy, Elsevier, vol. 88(C).
    8. Faccioli, Michela & Czajkowski, Mikołaj & Glenk, Klaus & Martin-Ortega, Julia, 2020. "Environmental attitudes and place identity as determinants of preferences for ecosystem services," Ecological Economics, Elsevier, vol. 174(C).
    9. Kristīne Pakalniete & Heini Ahtiainen & Juris Aigars & Ingrīda Andersone & Aurelija Armoškaite & Henning Sten Hansen & Solvita Strāķe, 2021. "Economic Valuation of Ecosystem Service Benefits and Welfare Impacts of Offshore Marine Protected Areas: A Study from the Baltic Sea," Sustainability, MDPI, vol. 13(18), pages 1-30, September.
    10. Michela Faccioli & Mikołaj Czajkowski & Klaus Glenk & Julia Martin-Ortega, 2018. "Environmental attitudes and place identity as simultaneous determinants of preferences for environmental goods," Working Papers 2018-08, Faculty of Economic Sciences, University of Warsaw.
    11. Joanna Mazur & Katarzyna Śledziewska & Damian Zieba, 2018. "Regulation of Geo-blocking: does it address the problem of low intraEU iTrade?," Working Papers 2018-20, Faculty of Economic Sciences, University of Warsaw.

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    More about this item

    Keywords

    good environmental status; coastal and marine water quality; biodiversity; invasive alien species; eutrophication; discrete choice experiment; observed preference heterogeneity; socio-demographic characteristics; hybrid choice model;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water
    • Q26 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Recreational Aspects of Natural Resources
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics
    • R50 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - General

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