Conference Agenda

This study introduces the basket-based choice experiment (BBCE) as suggested by Caputo and Lusk (2022) into the field of environmental economics. The application is a survey to assess garden owners’ preferences for installing of elements conducive to biodiversity conservation in private gardens. This application of the BBCE adds two new features to this approach. First, an experimental design is used to provide context variables for each basket-based choice to assess the extent to which policy levers set by local councils can affect how garden owners design their gardens. Second, the econometric model to analyse the resulting basket-based choice data is augmented by a latent class structure to accommodate the empirical finding that an overproportional share of respondents never chose to add any element to their garden (i.e., choose an empty basket). Results show that the policy instruments have mixed effects on the element-specific choice probabilities, with financial support exhibiting the strongest effect on the selection of elements. Furthermore, it is demonstrated how prediction can be used to assess the uptake of biodiversity friendly garden elements as a function of policy instruments and individual and garden characteristics.

Motivated reasoning may exacerbate the divergence of opinions over global challenges, such as climate change. As a result, politicians may adopt ineffective measures to combat these challenges either because they themselves have motivated beliefs or because they cater to constituencies with motivated beliefs. In an online experiment where a broad sample of the German population makes consequential decisions about abatement options, we analyze whether motivated reasoning among supporters of climate action favours ineffective measures. We find that individuals prefer a concrete over an abstract abatement measure, but are responsive to information that the abstract measure is more effective. There is no evidence for motivated reasoning. The results imply that support for effective climate policies can be increased through information provision.

Choice experiments (CEs) can be used to elicit preferences regarding potential changes in natural environments. Seasonal variation causes a multitude of changes in the appearance and function of natural landscapes, while human-nature interactions vary with seasonal factors such as weather or cultural events. We may, therefore, expect preferences to alter throughout the course of a year, and if these preferences are reflected in environmental valuations, this would have implications for how outcomes from CEs are interpreted for policy and decision-making. Existing test-retest studies indicate that preferences are consistent between years, yet few studies have examined within-year changes. Here, we determine if preferences for attributes of forest biodiversity are seasonally-variant by repeating the same CE over four seasons with participants (N = 7163) from across Great Britain. Using Poe tests, we found that willingness-to-pay (WTP) did not vary across seasons, while preference and scale parameters varied between seasons. Although participants’ choices were influenced by the season in which the surveys were delivered, our findings demonstrate the reliability of CEs to recover similar preferences for a seasonally variable good. Moreover, the stable WTP suggests that welfare calculations used in policy may be more valid, given their insensitivity to the seasonal timing of data collection.

In the last decades, stated preferences analyses proved to be useful to guide policy making, in particular when it results in the expression of individual preferences for different policy options. However, when it comes to spatial planning policies, the usefulness of results such as averaged individual or group preferences may be limited for decision-makers, since spatial heterogeneity of preferences is strong in most cases. In this paper, we propose two methods for spatializing the preferences derived from a DCE, that we call the “one-step” and the “two-step” methods. Both methods use the influence of socio-economic and spatial variables on the general preferences of the population to predict average marginal willingness to pay (mWTPs) for different attributes at small geographic scales. We aim to compare these methods to give guidelines and best practices to spatialize the results of DCEs. To do so, we employ Monte Carlo simulations, making different assumptions on the data generating process, to determine which method performs better in theory. Then, we apply the methods' comparison to the case of light pollution mitigation policies in Montpellier Metropolitan Area (France). Our results lead us to prefer the one-step method over the two-step method, although the latter shows practical aspects that can be of interest to applied researchers.