GIS and Geographically Weighted Regression in stated preferences analysis of the externalities produced by linear infrastructure

The paper uses Contingent Valuation to investigate the externalities from linear infrastructures, with a particular concern for their dependence on characteristics of the local context within which they are perceived. We employ Geographical Information Systems and a spatial econometric technique, the Geographic Weighted Regression, integrated in a dichotomous choice CV in order to improve both the sampling design and the econometric analysis of a CV survey. These tools are helpful when local factors with an important spatial variability may have a crucial explanatory role in the structure of individual preferences. The Geographic Weighted Regression is introduced, beside GIS, as a way to enhance the flexibility of a stated preference analysis, by fitting local changes and highlighting spatial non-stationarity in the relationships between estimated WTP and explanatory variables. This local approach is compared with a standard double bounded contingent valuation through an empirical study about high voltage transmission lines. The GWR methodology has not been applied before in environmental economics. The paper shows its significance in testing the consistency of the standard approach by monitoring the spatial patterns in the distribution of the WTP and the spatial stability of the parameters estimated in order to compute the conditional WTPs.