초록 열기/닫기 버튼
This study suggests an integrated approach to simultaneously analyze public preferences for multiple policies in a policy category. It is often necessary to understand public preference structure for a certain policy category in order to design overall policy direction. To achieve this, a data classification method is developed to classify various policy alternatives. The multivariate probit model is used to analyze these classified data. Empirical analyses are conducted for three renewable energy policies: the Renewable Portfolio Standard, Renewable Fuel Standard, and Renewable Heat Obligations. The selected policies represent a strong regulatory component and serve as quantitative policies in the electric power, transport, and heating sectors, respectively. The results show that the public is sensitive to increased energy prices in general, because they assign great importance to the price attribute. Moreover, the public’s average preferences for renewable energy policies can change according to the type of RHO. While the public’s level of knowledge about renewable energy policies has a positive effect on their choice of eco-friendly policies, their attitude toward environmental protection has no bearing on the same. Thus, in order to ease public resistance incurred by possible increases in energy prices, governments should map out efficient strategies to improve the public’s knowledge of renewable energy policies. In conclusion, the proposed methodology in this study allows one to analyze public preferences for a superordinate policy category simultaneously. The framework of this study can be generally applied to any public policy. Notably, the proposed integrated data classification method can be applied to any category of policies/ products having common attributes.
