We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

Valuing landslide risk reduction programs in the Italian Alps : the effect of visual information on preference stability.

Mattea, S. and Franceschinis, C. and Scarpa, R. and Thiene, M. (2016) 'Valuing landslide risk reduction programs in the Italian Alps : the effect of visual information on preference stability.', Land use policy., 59 . pp. 176-184.


Climate 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.

Item Type:Article
Full text:(AM) Accepted Manuscript
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
Download PDF
Publisher Web site:
Publisher statement:© 2016 This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Date accepted:26 August 2016
Date deposited:16 September 2016
Date of first online publication:08 September 2016
Date first made open access:08 September 2017

Save or Share this output

Look up in GoogleScholar