Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Thematic Session: Land use and ecosystem restoration in the Iberian Peninsula (Streaming) Location: B009 Session Chair: Maria L. Loureiro, Univgersity Santiago de Compostela Session Chair: Lígia Pinto, University of Minho | |
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Assessing Preferences for Restoration Actions after Wildfires 1: University Santiago de Compostela, Spain; 2: Universidade de Santiago de Compostela, Spain Wildfires severely disrupt ecosystem services, generating long-lasting environmental and socio-economic impacts that require effective post-fire restoration strategies. Understanding societal preferences for ecosystem services restoration is therefore essential to design policies that align restoration efforts with public values. This study investigates preferences and willingness to pay (WTP) for post-wildfire ecosystem services restoration actions in Spain, France, and Portugal using a standardized online survey incorporating a Discrete Choice Experiment (DCE). Results reveal significant cross-country differences in preferences and WTP, with respondents from countries more affected by wildfires (Portugal and Spain) expressing higher WTP for restoration and post-fire risk prevention measures, suggesting a potential role of local fire exposure and context in shaping public support for ecosystem services restoration policies. Given the reliance on online surveys, particular attention is paid to data quality as a secondary but critical methodological aspect. Multiple strategies—including paradata monitoring, device control, an oath of careful participation, and the exclusion of inattentive or speeding respondents—were applied to ensure robust estimation. A conservative data-cleaning approach shows that low-quality data tend to inflate mean WTP estimates and increase their variance, while cleaned datasets provide more precise and comparable estimates across countries, reducing confidence interval widths by approximately 10–20. | |