This paper analyses the convergence process of environmental fiscal pressure within the framework of the incipient process of economic globalisation and fiscal harmonisation. On the basis of data obtained from Eurostat for the 1987-2008 period, and taking the first fifteen members of the European Union as a reference, we have applied beta, sigma, and gamma-convergence tools to cross-section data. The results obtained have allowed us to verify the existence of convergence in environmental fiscal pressure, except in Denmark and the Low Countries, whose performance is quite different to the rest of the countries due to their high levels of environmental fiscal pressure.

Environmental fiscal effort, when compared with environmental fiscal pressure, represents a far more precise parameter when measuring a country’s environmental sacrifice given that it introduces GDP per capita as an analysis variable. This paper focuses on the analysis of convergence in environmental fiscal effort amongst the EU-15 Member States between 1987 and 2008, employing, to this end, the techniques of sigma, beta and gamma convergence. On a complementary level, a spatial autocorrelation study is carried out in order determine whether or not the geographical proximity of countries has any bearing on similar fiscal effort.

The aim of this paper is to identify the main factors that determine territorial differences between two large neighbouring provinces (Granada and Almería) that each occupy both sides of the Sierra Nevada mountain range in southern Spain. Our analysis produced a prediction model for riverside woodland cover, one of the variables that most strongly characterizes the differences between the two provinces. A general linear model was constructed for spatial analysis (spherical model with REML estimation), taking into account the location of the municipalities (longitude, latitude, and elevation) and other factors related to soil type, vegetation, or woodland cover. Interpolation of spatial data was performed by ordinary kriging. The predictions obtained over a grid of locations enabled patterns in each region to be derived and visualised, with very clear results. The pattern of riverside woodland cover is highly differentiated between the two provinces, despite their presenting adjoining areas.