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A Combinatorial Optimization Approach to Nonmarket Environmental Benefit Aggregation via Simulated Populations

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Listed:
  • Stephen Hynes
  • Nick Hanley
  • Cathal O’Donoghue

Abstract

This paper considers the use of a "combinatorial optimization" technique in the aggregation of environmental benefit values. Combinatorial optimization is used to statistically match population census data to a contingent valuation survey. The matched survey and census information is then used to produce regional and national total willingness-to-pay figures. These figures are then compared to figures derived using more standard approaches to calculating aggregate environment benefit values. The choice of aggregation approach is shown to have a major impact upon estimates of total benefits at a regional level, especially when the target population displays considerable heterogeneity across space.

Suggested Citation

  • Stephen Hynes & Nick Hanley & Cathal O’Donoghue, 2010. "A Combinatorial Optimization Approach to Nonmarket Environmental Benefit Aggregation via Simulated Populations," Land Economics, University of Wisconsin Press, vol. 86(2), pages 345-362.
    • Handle: RePEc:uwp:landec:v:86:y:2010:i:2:p:345-362
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    References listed on IDEAS

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    1. Bateman, Ian J. & Day, Brett H. & Georgiou, Stavros & Lake, Iain, 2006. "The aggregation of environmental benefit values: Welfare measures, distance decay and total WTP," Ecological Economics, Elsevier, vol. 60(2), pages 450-460, December.
    2. David J. Lewis & Andrew J. Plantinga, 2007. "Policies for Habitat Fragmentation: Combining Econometrics with GIS-Based Landscape Simulations," Land Economics, University of Wisconsin Press, vol. 83(2), pages 109-127.
    3. Rosenberger, Randall S. & Stanley, Tom D., 2006. "Measurement, generalization, and publication: Sources of error in benefit transfers and their management," Ecological Economics, Elsevier, vol. 60(2), pages 372-378, December.
    4. P Williamson & M Birkin & P H Rees, 1998. "The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records," Environment and Planning A, , vol. 30(5), pages 785-816, May.
    5. John B. Loomis, 1987. "Expanding Contingent Value Sample Estimates to Aggregate Benefit Estimates: Current Practices and Proposed Solutions," Land Economics, University of Wisconsin Press, vol. 63(4), pages 396-402.
    6. V. Kerry Smith, 1993. "Nonmarket Valuation of Environmental Resources: An Interpretive Appraisal," Land Economics, University of Wisconsin Press, vol. 69(1), pages 1-26.
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    Cited by:

    1. Cathal O'Donoghue & Thia Hennessy, 2014. "Chapter 03: The Agri-Food Sector," Chapters from Rural Economic Development in Ireland, in: Rural Economic Development in Ireland, edition 1, chapter 3, Rural Economy and Development Programme,Teagasc.
    2. Karyn Morrissey & Graham Clarke & Paul Williamson & Antoinette Daly & Cathal O'Donoghue, 2015. "Mental Illness in Ireland: Simulating its Geographical Prevalence and the Role of Access to Services," Environment and Planning B, , vol. 42(2), pages 338-353, April.
    3. Cathal O'Donoghue & Thia Hennessy, 2015. "Policy and Economic Change in the Agri-Food Sector in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 46(2), pages 315-337.
    4. Norton, Daniel & Hynes, Stephen, 2018. "Estimating the Benefits of the Marine Strategy Framework Directive in Atlantic Member States: A Spatial Value Transfer Approach," Ecological Economics, Elsevier, vol. 151(C), pages 82-94.
    5. Hynes, Stephen & O'Donoghue, Cathal, 2019. "Estimating the value of achieving good ecological status across Irish water catchments using value transfer," Working Papers 309538, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    6. Stefan Wimmer & Robert Finger, 2023. "A note on synthetic data for replication purposes in agricultural economics," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(1), pages 316-323, February.
    7. Wiktor Budziński & Danny Campbell & Mikołaj Czajkowski & Urška Demšar & Nick Hanley, 2018. "Using Geographically Weighted Choice Models to Account for the Spatial Heterogeneity of Preferences," Journal of Agricultural Economics, Wiley Blackwell, vol. 69(3), pages 606-626, September.
    8. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    9. Stephen Hynes & Daniel Norton & Nick Hanley, 2013. "Adjusting for Cultural Differences in International Benefit Transfer," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 56(4), pages 499-519, December.
    10. Hynes, S. & Ankamah-Yeboah, I. & O’Neill, S. & Needham, K. & Bich Xuan, B. & Armstrong, C., 2020. "Entropy balancing for causal effects in discrete choice analysis: The Blue Planet II effect," Working Papers 309500, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    11. Wiktor Budziński & Mikołaj Czajkowski, 2018. "A Monte Carlo investigation of the effects of spatial heterogeneity of preferences for discrete choice models," Working Papers 2018-24, Faculty of Economic Sciences, University of Warsaw.
    12. Wiktor Budziński & Mikołaj Czajkowski, 2021. "Accounting for Spatial Heterogeneity of Preferences in Discrete Choice Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 13(1), pages 1-24, March.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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