Using Individualised Choice Maps to Capture the Spatial Dimensions of Value Within Choice Experiments

Abstract

Understanding how the value of environmental goods and services is influenced by their location relative to where people live can help identify the economically optimal spatial distribution of conservation interventions across landscapes. However, capturing these spatial relationships within the confines of a stated preference study has proved challenging. We propose and implement a novel approach to incorporating space within the design and presentation of stated preference choice experiments (CE). Using an investigation of preferences concerning land use change in Great Britain, CE scenarios are presented through individually generated maps, tailored to each respondent's home location. Each choice situation is generated in real time and is underpinned by spatially tailored experimental designs that reflect current British land uses and incorporate locational attributes relating to physical and administrative dimensions of space. To the best of our knowledge, this represents the first CE study to integrate space into both the survey design and presentation of choice tasks in this way. Presented methodology provides means for testing how presentation of spatial information influence stated preferences. We contrast our spatially explicit (mapped) approach with a commonly applied tabular CE approach finding that the former exhibits a number of desirable characteristics relative to the latter.

Public preferences for post 2020 agri-environmental policy in the Czech Republic: A choice experiment approach

“…As our -and other studies' (e.g. Moran et al, 2007;Arriaza et al, 2008;Badura et al, 2019) results suggest, the European public supports policies that focuses on delivery of multiple public goods that agricultural lands can provide, such as landscape amenity, biodiversity protection, water and soil quality retention, and carbon sequestration. However, the majority of current CAP funding is made for mere land ownership rather than for delivery of such public goods.…”

“…These results may be country and/or context specific as some former studies revealed high relative welfare estimates for biodiversity and habitats (e.g. Christie and Rayment, 2012;Badura et al 2019), but not all (e.g. Liekens et al 2013).…”

“…Hynes et al, (2010) estimated the welfare values for an agri-environmental policy using both contingent and CE approaches. Badura et al (2019) explored spatial aspects of preferences for agro-environmental interventions in Great Britain and Liekens et al (2013) estimated a value function for agricultural land use change in Belgium. Many of the above-mentioned studies highlight the presence of heterogeneous preferences and segmentation of respondents with distinct preferences.…”

Scope Elasticity of Willingness to pay in Discrete Choice Experiments

“…Table 1 summarizes the review of ten articles from environmental economics. Only three of these include a scope discussion of the reported results (Layton and Brown 2000;Zhao et al 2013;Badura et al 2020). Four studies employ a restrictive functional form that imposes unitary scope elasticity (Adamowicz et al 1994;Zhao et al 2013;Meyerhoff et al 2016;and Ando et al 2020).…”

Spatial clustering of willingness to pay for ecosystem services

“…Within the choice modelling framework, spatial preference heterogeneity has been studied in four main ways: (i) using spatially explicit choice attributes; (ii) including spatial covariates in the choice model; (iii) applying geographically weighted (lagged) choice models; and (iv) developing a second-stage spatial analysis with individual-specific WTP estimates. The first approach uses spatially explicit choice cards or attributes that may impose an additional cognitive burden on the respondent (Badura et al, 2019;Brouwer et al, 2010;Horne et al, 2005;Johnston et al, 2002;Logar & Brouwer, 2018;Meyerhoff et al, 2014;Schaafsma et al, 2012). The second approach might increase the risk of potential multicollinearity and model overspecification by estimating the interaction effects of spatial covariates, such as respondent's location, distance to the environmental good and substitutes, or other spatial environmental data (Abildtrup et al, 2013;Bergmann et al, 2008;De Valck et al, 2014;Meyerhoff, 2013;Schaafsma et al, 2013).…”