Estimating the value of natural resources under legal constraints: an application to marine resources in Sicily

Abstract

In this article, we use the Contingent Evaluation methodology to develop an economic evaluation of natural resources in a protected marine area of Sicily. Assuming a nonnormal distribution for the ML estimation, the article shows that a variant of the stochastic utility model appears to capture well the dependence of the willingness to pay (WTP) on the socioeconomic characteristics of a sample of stakeholders of the natural resources in question. The estimates obtained are consistent and robust across different policy measures, no embedding or sequencing effects emerge and option values also appear to have been elicited in a consistent way. Once these values are added to the basic WTP, the income elasticities estimated fall in the range reported by other studies.

Acknowledgements

This work is part of the research carried out in cooperation with CNR-IAMC of Mazara del Vallo and Castellammare del Golfo, financially supported by Regione Sicilia on ‘Contingent evaluation of fish resources in the Gulf of Castellammare’. The authors wish to gratefully acknowledge the valuable comments of participants at the Seminar held in Balestrate on July 2007.

Notes

² In 1981, the ‘Zingaro Natural Park’ was established by a regional law (98/81) as a strictly protected area, in 1990, another regional law (25/90) had banned the trail fishing in a limited area of the Gulf, in 2001 (decree 1339), the stopping of fishing for some months a year was introduced.

¹ Where you recall Marco Ventura's affiliation, or wherever you want The opinions expressed by the author do not necessarily reflect the position of the Institute.

³ This procedure suffers from two weaknesses: first, the alternative hypothesis does not allow to specify an alternative distribution, e.g. the normal, but it simply tests that the difference between the estimated parameters and the theoretical is nonsignificant, second, having arranged the WTP by classes one needs to take the central value of any class (or any other value within each class) to proxy a continuous distribution function and carry out the test. For a general description of empirical distribution function testing see D'Agostino and Stephens (1986).