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Article

Spatial clustering of willingness to pay for ecosystem services

Details

Citation

Toledo‐Gallegos VM, Long J, Campbell D, B?rger T & Hanley N (2021) Spatial clustering of willingness to pay for ecosystem services. Journal of Agricultural Economics, 72 (3), pp. 673-697. https://doi.org/10.1111/1477-9552.12428

Abstract
Variations of willingness to pay (WTP) in geographical space have been characterised by the presence of localised patches of higher and lower values. However, to date, spatial valuation studies have not explored whether the distribution of hot (cold) spots of WTP is particular to each environmental good or if it follows similar patterns to other, comparable, environmental goods. We address this question by contrasting the spatial patterns of hot (cold) clusters of WTP for improvements in several ecosystem services. We geocoded individual‐specific WTP estimates derived from a discrete choice experiment exploring preferences for ecosystem service improvements for three different catchment areas in Scotland comprising urban, agricultural, riverine and estuarine ecosystems. The local Moran's I statistic was used to find statistically significant local clusters and identify hot spots and cold spots. Finally, Multi‐type Ripley's K and L functions were used to contrast the spatial patterns of local clusters of WTP among ecosystem services, and across case studies. Our results show that hotspots of WTP for environmental improvements tend to occur close to each other in space, regardless of the ecosystem service or the area under consideration. Our findings suggest that households sort themselves according to their preferences for estuarine ecosystem services.

Keywords
catchment area; discrete choice experiment; ecosystem services; estuaries; Ripley's K function; spatial autocorrelation; spatial sorting; willingness to pay

Journal
Journal of Agricultural Economics: Volume 72, Issue 3

StatusPublished
Publication date30/09/2021
Publication date online08/04/2021
Date accepted by journal22/01/2021
URL
ISSN0021-857X
eISSN1477-9552

People (1)

Professor Danny Campbell

Professor Danny Campbell

Professor, Economics

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