Environmental damage evaluation in a willingness-to-accept scenario: A latent-class approach based on familiarity
Andrea Tabi and
Salvador del Saz-Salazar
Ecological Economics, 2015, vol. 116, issue C, 280-288
Abstract:
In this paper we report on the results of the application of a latent class model that was designed to identify and characterize unobserved preference heterogeneity in the context of a willingness-to-accept (WTA) framework involving negative environmental externalities stemming from the expansion of the Port of Valencia. We investigated the hypothesis that respondents with greater familiarity with the targeted good and any related environmental damage would demand more compensation; that is, they would have a significantly higher WTA. Based on respondents' familiarity with the Port of Valencia and their pre-existing knowledge about the negative consequences of its potential expansion three clusters based on six indicators are identified. Results show that, contrary to what might be expected, familiarity with a public good does not in all cases have a significant effect on stated WTA.
Keywords: Latent class cluster model; Contingent valuation; Willingness to accept; Negative externalities; Familiarity (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0921800915002256
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolec:v:116:y:2015:i:c:p:280-288
DOI: 10.1016/j.ecolecon.2015.05.010
Access Statistics for this article
Ecological Economics is currently edited by C. J. Cleveland
More articles in Ecological Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().