Choice experiment, multiple programmes contingent valuation and landscape preferences: How can we support the land use decision making process?

The promotion of cost–benefit analysis for social and environmental policy choices is much debated. Then, the conception of a valuation method for the non-market benefit of these policies is still of a main research interest. Among direct valuation methods, the choice experiment method (CEM) becomes a highly attractive valuation procedure, in giving at the same time the economic value of the impacts of the different components of the policy as well as the global impact of a policy package. However, the main-effect designed protocol aiming to limit the cognitive burden of scenarios’ evaluation disregards the important question of the existence of substitution or complementary relation between these programmes for their main beneficiaries. Furthermore, the independent valuation and summation strategy developed in CEM opens to overvaluation or under valuation of the willingness to pay values. The aim of this article is to conduct testing of the additivity bias with CEM results. The distribution of the willingness to pay values for possible combinations of landscape programmes aiming to maintain two agricultural landscape attributes as well as one moorland attribute inferred from the earlier CEM survey is compared to results obtained with the sequential contingent valuation procedure for multidimensional policy suggested by Hoehn (1991). Our empirical results suggest that the additivity bias is not statistically significant in our case, even if specific relation between landscape attributes due to what we call the composition effect is of concern for the population we interviewed. Furthermore, landscape preferences derived from our empirical investigation support the need for more integration of agricultural issues with the local land-use issues.

► We revisit use of choice experiment method to study landscape preferences.
► We compare it with a sequential valuation procedure (MPCV).
► We implement external and internal testing of additivity bias.
► Interaction effects between agricultural and non agricultural landscape attributes are analysed.
► Our results confirm the need to integrate agricultural issues to land use policy.