Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThe implementation of heating technologies based on renewable resources is an important part of Italy's energy policy. Yet, despite efforts to promote the uptake of such technologies, their diffusion is still limited while heating systems based on fossil fuels are still predominant. Theory suggests that beliefs and attitudes of individual consumers play a crucial role in the diffusion of innovative products. However, empirical studies corroborating such observations are still thin on the ground. We use a Choice Experiment and a Latent Class-Random Parameter model to analyze preferences of households in the Veneto region (North-East Italy) for key features of ambient heating systems. We evaluate the coherence of the underlying preference structure using as criteria psychological constructs from the Theory of Diffusion of Innovation by Rogers. Our results broadly support this theory by providing evidence of segmentation of the population consistent with the individuals' propensity to adopt innovations. We found that preferences for heating systems and respondents' willingness to pay for their key features vary across segments. These results enabled us to generate maps that show how willingness to pay estimates vary across the region and can guide local policy design aimed at stimulating adoption of sustainable solutions.

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractClimate change has increased the frequency and intensity of weather-related natural hazards everywhere. In particular, mountain areas with dense human settlements, such as the Italian Alps, stand to suffer the costliest consequences from landslides. Options for risk management policies are currently being debated among residents and decision makers. Preference analysis of residents for risk reduction programs is hence needed to inform the policy debate. We use discrete choice experiments to investigate the social demand for landslide protection projects. Given the importance of information in public good valuation via surveys, we explore the effect of specific visual information on the stability of preference estimates. In our survey, we elicit preferences before and after providing respondents with scientific-based information, based on visual simulations of possible events. This enables us to measure information effects. Choice data are used to estimate a Mixed Logit (MXL) model in WTP space to obtain robust estimates of marginal willingness-to-pay (mWTP) estimates and control for the effect of information. Mapping posterior individual specific mWTP estimates provide additional policy implications. Overall, we found the mWTP estimates to be dependent on information.

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The logit‐mixed logit (LML) model advances choice modeling by generalizing previous parametric and semi‐nonparametric specifications and allowing retrieval of flexible taste distributions. Using standard operating conditions in the field, we report results from Monte Carlo experiments designed to assess the finite sample bias‐variance tradeoff for the LML using as a benchmark conventional Mixed logit models (MXL) under asymmetric and multimodal taste distributions. The LML specification always outperforms the MXL in terms of bias, but when the variance around modes is high the mean squared error (MSE) is lower than that of MXL only at sample sizes larger than usual and with some nuances. D‐error minimizing experimental design predicated on multinomial logit significantly reduces MSE, but no clear winner is found between polynomial, step, and spline functions for the multidimensional grid function. Analysis of empirical data from a choice experiment on tap water shows that multimodality emerges only if higher number of node parameters are used in the LML.