Government R&D investment decision-making in the energy sector: LCOE foresight model reveals what regression analysis cannot

Abstract

For governments that prioritize R&D investment, future decision-making depends on performance-based budgeting. Governments evaluate outputs and outcomes of R&D programs regularly and budget for next year on the basis of program assessment. However, existing assessment methodology disregards long-term technology development; in sectors such as the energy sector, it takes a long time for technologies to progress from R&D to commercialization. This paper is a comparative analysis of existing R&D assessment models and the new foresight model developed from the point of view of government. A regression analysis is conducted using probit and ordinary least squares (OLS) models to analyze the performance of projects completed based on past R&D investment. The foresight model, which is based on the levelized cost of electricity (LCOE), is discussed in comparison. Results of the regression analysis show that government investment in market expansion of renewable energy technologies is minimal in Korea. In contrast, the LCOE foresight model results show that renewable energy technologies are appropriate targets for government R&D investment. The foresight model should be utilized for government R&D decision-making in the energy sector because it brings to light hidden information, including learning rates and technology dynamics, which remains unaddressed when analyzing using existing R&D assessment models.