A Holistic vs. an Attribute‐based Approach to Agri‐Environmental Policy Valuation: Do Welfare Estimates Differ?

Different economic valuation methodologies can be used to value the non‐market benefits of an agri‐environmental scheme. In particular, the non‐market value can be examined by assessing the public’s willingness to pay for the policy outputs as a whole or by modelling the preferences of society for the component attributes of the rural landscape that result from the implementation of the policy. In this article we examine whether the welfare values estimated for an agri‐environmental policy are significantly different between an holistic valuation methodology (using contingent valuation) and an attribute‐based valuation methodology (choice experiment). It is argued that the valuation methodology chosen should be based on whether or not the overall objective is the valuation of the agri‐environment policy package in its entirety or the valuation of each of the policy’s distinct environmental outputs.     

Using Geographically Weighted Choice Models to Account for the Spatial Heterogeneity of Preferences

In this paper, we investigate the use of geographically weighted choice models for modelling spatially clustered preferences. We argue that this is a useful way of generating highly‐detailed spatial maps of willingness to pay for environmental conservation, given the costs of collecting data. The data used in this study come from a discrete choice experiment survey of public preferences for the implementation of a new national forest management and protection programme in Poland. We combine these with high‐resolution spatial data related to local forest characteristics. Using locally estimated discrete choice models we obtain location‐specific estimates of willingness to pay (WTP). Variation in these estimates is explained by characteristics of the forests close to where respondents live. These results are compared with those obtained from a more typical, two stage procedure which uses Bayesian posterior means of the mixed logit model random parameters to calculate location‐specific estimates of WTP. We find that there are indeed strong spatial patterns to the benefits of changes to the management to national forests. People living in areas with more species‐rich forests and those living nearer bigger areas of mixed forests have significantly different WTP values than those living in other locations. This kind of information potentially enables a better distributional analysis of the gains and losses from changes to natural resource management, and better targeting of investments in forest quality.