Quantifying Public Acceptance of Innovation Policy: A Demand-Oriented Analysis for Renewable Energy Policy

Abstract

Innovation policies are considered as key to encouraging innovative activity, which may serve as essential and valid means to survive and adapt to our current fast-changing society. To date, innovation policies have mostly focused on supply-side measures by creating and diffusing new technologies. However, since demand also plays a crucial role by being one of the primary sources of innovation, the importance of demand-oriented innovation policies has received much attention recently. Public acceptance is a very important consideration from the perspective of demand-oriented innovation policies, because innovation policies may face social resistance despite their obvious advantages and usefulness. The purpose of this dissertation is twofold. The first is to quantitatively analyze public preferences for an innovation policy and to forecast the level of public acceptance according to variations in policy attribute levels. To achieve this, stated preference data obtained from choice experiments are analyzed using a mixed logit model, one of the discrete choice models (DCMs). The second is to suggest 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 (MVP) model, which is also a DCM, is used to analyze these classified data. Empirical analyses are conducted for three renewable energy policies: the Renewable Portfolio Standard (RPS), Renewable Fuel Standard (RFS), and two different types of Renewable Heat Obligations (RHOs), namely RHO schemes aimed at either heat suppliers or building owners. The selected policies represent a strong regulatory component and serve as quantitative policies in the electric power, transport, and heating sectors, respectively. The results of the mixed logit model show that the public assigns great importance to the price attribute, which is critical to maintain relatively high public acceptance. In the case of the RPS, public acceptance will be maintained at above 89.5% if the increase in electricity bills is limited to under 6%. Public acceptance of the RFS varies from 91.2-48.8% when the price of transportation fuels is increased by 0-45%. In case of the RHO for heat suppliers, an increase of 0-30% in heating expenses decreases public acceptance from 99.9-60.3%. Other important attributes having substantial influence on public acceptance of renewable energy policies are new job creation in the RPS, stability of the heat supply in the RHO for heat suppliers, and government subsidy in the RHO for building owners. In the case of the RFS, attributes other than increased fuel price have little effect on public acceptance. The results of the MVP model show that the public is sensitive to increased energy prices in general, because they assign great importance to the price attribute. Moreover, the publics average preferences for renewable energy policies can change according to the type of RHO. While the publics 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 publics knowledge of renewable energy policies. In conclusion, the proposed methodology in this dissertation allows one to not only analyze public acceptance of an innovation policy more quantitatively but also to analyze public preferences for a superordinate policy category simultaneously. The framework of this research can be generally applied to any public innovation policy. Notably, the proposed integrated data classification method can be applied to any category of policies/products having common attributes.