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Economic Assessment of the Recreational Value of Ecosystems: Methodological Development and National and Local Application


We present a novel methodology for spatially sensitive prediction of outdoor recreation visits and values for different ecosystems. Data on outset and destination characteristics and locations are combined with survey information from over 40,000 households to yield a trip generation function (TGF) predicting visit numbers. A new meta-analysis (MA) of relevant literature is used to predict site specific per-visit values. Combining the TGF and MA models permits spatially explicit estimation of visit numbers and values under present and potential future land use. Applications to the various land use scenarios of the UK National Ecosystem Assessment, as well as to a single site, are presented.

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  1. The analyses and results reported in this paper are a subsequent development of the more preliminary analyses summarised in UK NEA (2011). Due to data limitations, we do not consider Northern Ireland in this study.

  2. In the interests of brevity, further details are presented in Sen et al. (2012).

  3. We use GeoConvert at MIMAS which queries the 2010 UK National Statistics Postcode Directory Crown Copyright 2006. Source: National Statistics/Ordnance Survey. Extracts are Crown Copyright and may only be reproduced by permission.

  4. Data were provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is copyright of the Crown.

  5. Linkage achieved using Casweb ( Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for Scotland.

  6. Source: the Experian Limited Demographic Data, ESRC/JISC Agreement.

  7. Crown Copyright/database right 2011. An Ordnance Survey/EDINA supplied service. Calculation of travel time is via cost weighted distance functions and details are provided in Sen et al. (2012).

  8. Zonal Statistics ++, a module of the ‘Hawths Tools’ plug-in for ArcGIS (Beyer 2004), is used to query the habitat types in the cells entirely within the search radius. These are converted into percentages of the total search area (1 km cells entirely within the search radius which is varied as described subsequently).

  9. Radii of 1, 2.5, 5 and 10 km are used for defining substitution availability measures around outset locations. The resultant measures are used within a variety of model specifications including travel time from the population-weighted centroid of each LSOA to the nearest substitute site and interactions between travel time and the proportion of the above circles taken up by substitutes. An AIC criterion comparison of different models indicates that a measure of the coverage of each land use/habitat type within a 10 km radius of the LSOA population weighted centroids provides the best fit to the MENE visitation data.

  10. Various functional forms were tested for the TGF, for example by including interactions between travel time and the various land use types. An AIC criterion comparison of different models indicates that the TGF in Table 1 provides the best fit to our data. Details regarding the alternative model specifications tested are presented in Sen et al. (2012). The site-level variance component is parameterised as the log of the variance [labelled in (\(\upsigma ^{2}_{u})\) in the table]. The standard deviation is also included in the table and is labelled as \(\upsigma _{u}\). When \(\upsigma _{u}\) is zero, the site-level variance component is no longer important and the panel Poisson estimator is similar to the pooled Poisson estimator. The likelihood-ratio test (included at the bottom of the table) formally compares the pooled Poisson estimator with the panel Poisson estimator. We find that \(\upsigma _{u}\) is significantly greater than zero, so that a panel Poisson estimator is preferred.

  11. References for the full set of studies used within the MA are given in Sen et al. (2012).

  12. The data on purchasing power parity indices were obtained from the Penn World Table, Version 6.3 available at and data on the gross domestic product deflator were obtained from the HM-Treasury web page at

  13. Each scenario was further varied according to whether a low or high greenhouse gas emission future was envisioned. For purposes of brevity, in the present paper we focus only on the high emissions variant of each scenario, with low emission variants presented in Bateman et al. (2011a).

  14. Further details in eftec (2011) with an earlier application given in Bateman et al. (2011a).

  15. Note that we hold the substitute coverage variables for the 151 LSOAs that intersect the \(5\,{\text{ km }} \times 5\,{\text{ km }}\) cell which contains the new woodland site constant at their baseline levels. This assumption is made to ensure that the new site does not act as a substitute for itself and this is crucial for local application of our methodology.


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We would like to thank the Monitor of the Engagement with the Natural Environment (MENE) teams at Natural England and their contractor TNS for sharing their data with us. We gratefully acknowledge comments from the UK Forestry Commission, Carlo Fezzi (UEA), Susana Mourato (LSE), other UK NEA Economics group members and Kenneth Small, David Brownstone and Priya Ranjan at the University of California, Irvine.

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Correspondence to Antara Sen.

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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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Sen, A., Harwood, A.R., Bateman, I.J. et al. Economic Assessment of the Recreational Value of Ecosystems: Methodological Development and National and Local Application. Environ Resource Econ 57, 233–249 (2014).

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  • Recreation
  • Recreational value
  • Ecosystem services
  • UK National Ecosystem Assessment
  • Meta-analysis
  • Spatially sensitive