Renewable energy conversion: A spatial analysis on selected countries

Spatial analysis methods deal with the relations between cities, countries or regions that make up the analysis units by weighting them according to their geographical distances from each other. The basic logic is Tobler's (1970) "Everything is interrelated; but things that are close are more related than things that are distance". Accordingly, similar values of a variable usually occur in close locations, and this creates spatial clustering. In this study, the factors affecting the adoption of the Feed-in-Tariff (FIT) incentive policy, which is the most widely applied among the regulatory policies in order to encourage the use of renewable energy resources by using the spatial probit method, were examined and the degree of spatial effect was calculated. By this means, how important it is for countries to be close to each other when adopting this policy, in other words, the value of neighborhood and intergroup interaction has been emphasized. Annual data for the period 1980-2018 obtained from the USA Energy Information Administration and International Renewable Energy Agency databases of 54 selected developed and developing countries were used in the study. The findings show that the total carbon dioxide emissions from energy consumption, the per capita gross domestic product and the participation of countries in the Kyoto Protocol have a positive and significant effect on the adoption of the renewable energy incentive policy.