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Economic Analysis for the UK National Ecosystem Assessment: Synthesis and Scenario Valuation of Changes in Ecosystem Services

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Abstract

We combine natural science modelling and valuation techniques to present economic analyses of a variety of land use change scenarios generated for the UK National Ecosystem Assessment. Specifically, the agricultural, greenhouse gas, recreational and urban greenspace impacts of the envisioned land use changes are valued. Particular attention is given to the incorporation of spatial variation in the natural environment and to addressing issues such as biodiversity impacts where reliable values are not available. Results show that the incorporation of ecosystem services and their values within analyses can substantially change decisions.

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Notes

  1. To our knowledge this is the earliest source of this phrase.

  2. Accepting that, given the limits of economic, social and natural scientific knowledge, no analysis using any methodology can ever be perfect.

  3. Note that, although the pollination example is often cited, as here, as a use value, strictly speaking it occupies a lower, more supportive position in the ecosystem service hierarchy. As highlighted by Mace et al. (2012), pollination services are inputs to the production of goods, rather than goods in themselves. Therefore our example is in fact somewhat erroneous although production function methods could be applied to identify the input value of pollination services.

  4. Where levels of depleting pressures (e.g. pollution inputs) need to be reduced well below those which cause the threshold effect before reversibility begins to operate (see Tett et al. 2007).

  5. Typically reversibility refers to natural processes of restoration. However, as a subset of this we can also identify economic non-reversibility where the costs associated with moving to a situation where such restoration can occur and assessed (either correctly or not) as prohibitive (Bateman et al. 2011).

  6. Although, as discussed in our concluding section, we recognise that this assessment is incomplete. As such it is the methodology developed in this paper which, we feel, constitutes its contribution.

  7. While this list is more comprehensive than that considered in many assessments of projects related to land use change (e.g. the CAP example discussed previously), we acknowledge that it is not comprehensive. In particular one substantial omission concerns the impacts of land use change upon the water environment. This particular issue is a focus of attention for the ongoing second phase of the UK-NEA.

  8. Note that, while much of the data used in this analysis is collected for all of the UK, data gaps meant that our analysis had to be restricted to Great Britain (i.e. Northern Ireland is omitted from the analysis reported here).

  9. FGM is defined as the value of output minus the cost of variable inputs (Nix 2009), i.e. it ignores fixed costs as these tend not to vary over the relatively short periods for which farms make output decisions. Ideally our CBA would employ profit estimates (i.e. including fixed costs) and this is the focus of ongoing work under the second phase of the UK-NEA using data obtained from the UK Farm Business Survey. FGM estimates were obtained from Nix (2009) and Fezzi et al. (2010).

  10. Our earlier caution regarding the over-interpretation of the absolute value of estimates applies here. Readers are also reminded of the changes in population between scenarios.

  11. Note that, as predicted changes in agricultural land use themselves incorporate expected change in climate variables there is an important feedback element incorporated within this analysis.

  12. Although in a full analysis these would have to be set against the housing benefits generated by such development.

  13. Furthermore, asking survey respondents to express their preferences for biodiversity conservation in a unit (money) which some individuals may see as incommensurate with species existence clearly raises the potential for protest responses (Jorgensen et al. 1999).

  14. Although it should be noted that the last of these three has recently been repealed.

  15. Note that in this paper we adopt a constraint against extinctions irrespective of where they occur in Great Britain. Arguably, individuals might be prepared to countenance a looser requirement that a policy can be sanctioned provided that species are conserved in at least one area within the country. The spatially explicit nature of the methodology developed here is readily suitable to applying such a constraint.

  16. Regional variations in bird diversity were controlled for by including the 100 km Ordnance Survey grid square in which each BBS square is located within the analysis. A regional bias in survey effort across the UK towards highly populated areas was accounted for by weighting regions with lower survey effort more highly.

  17. Defined as a group in terms of the common foods they consume; in this case primarily seeds and invertebrates.

  18. In comparison to interim results given in reports to the UK-NEA project the present analysis adjusts for double counting (see discussion) and utilises and standardises a larger dataset.

  19. To avoid these observations influencing the prediction of visits the trip generation function (Sen et al. this issue) was re-estimated omitting these trips. Full details of this adjustment are given in Sen et al. (2012).

  20. These will of course mean that per capita values need not perfectly follow the pattern of results shown in Table 2. However, the differences due to these changes are relatively minor (as indicated by the figures presented in Table 1).

  21. Note that this is not always the case across all scenarios with the NS scenario revealing a win-win outcome although its impact upon urban greenspace makes this unattractive overall.

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Acknowledgments

This work was funded by the Social and Environmental Economic Research (SEER) into Multi-Objective Land Use Decision Making project (which in turn is funded by the Economic and Social Research Council (ESRC); Funder Ref: RES-060-25-0063) and by the UK-NEA (which is in turn funded by the UK Department of Food and Rural Affairs (Defra), the devolved administrations of Scotland, Wales and Northern Ireland, the Natural Environment Research Council (NERC) and the Economic and Social Research Council (ESRC)).

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Authors and Affiliations

  1. Centre for Social and Economic Research on the Global Environment (CSERGE), School of Environmental Sciences, University of East Anglia (UEA), Norwich Research Park, Norwich, NR4 7TJ, UK

    Ian J. Bateman, Amii R. Harwood, Barnaby Andrews, Andrew Crowe, Steve Dugdale, Carlo Fezzi, Jo Foden, Paul Munday & Antara Sen

  2. School of Business, Economics and Social Sciences, University of Hamburg, Hamburg, Germany

    Grischa Perino

  3. FuturES Research Center, Leuphana Universität Lüneburg, Lüneburg, Germany

    David J. Abson

  4. School of Earth and Environment, University of Leeds, Leeds, UK

    David J. Abson & Mette Termansen

  5. Centre for Environment, Fisheries and Aquaculture Science (Cefas), Lowestoft, UK

    Jo Foden

  6. UNE Business School, University of New England, Armidale, NSW, Australia

    David Hadley

  7. School of Geosciences, University of Edinburgh, Edinburgh, UK

    James Paterson

  8. Centre for Environmental Management (CEM), School of Geography, University of Nottingham, Nottingham, UK

    Roy Haines-Young & Gavin Siriwardena

  9. British Trust for Ornithology, Thetford, UK

    Mark Hulme

  10. Department of Land Economy, University of Cambridge, Cambridge, UK

    Andreas Kontoleon & Unai Pascual

  11. Basque Centre for Climate Change (BC3), Bilbao, Spain

    Unai Pascual

  12. School of Economics, University of East Anglia, Norwich, UK

    Grischa Perino

  13. Department of Environmental Science, Aarhus University, Aarhus, Denmark

    Mette Termansen

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Bateman, I.J., Harwood, A.R., Abson, D.J. et al. Economic Analysis for the UK National Ecosystem Assessment: Synthesis and Scenario Valuation of Changes in Ecosystem Services. Environ Resource Econ 57, 273–297 (2014). https://doi.org/10.1007/s10640-013-9662-y

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  • DOI: https://doi.org/10.1007/s10640-013-9662-y

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