Quantification of technological learning by R&D and its application for renewable energy technologies

Abstract

The paper examines the effects of research and development (R&D) on the capital cost reductions and the introduction of renewable energy technologies, namely solar photovoltaics (PV) and wind technologies. The proposed model in the study deals with the dynamics of technological learning. Functional form is a new kind of three-factor learning curve as a function of the cumulative capacity and the knowledge stock accumulated by public and private R&D expenditures. An econometric analysis is used to identify the influence of the knowledge stock on the capital costs of renewable energy technologies. Moreover, the study clarifies the relationship between the cost reductions and the market penetration. If the expenditures for public and private R&D in 2010 are fixed until 2050, then the model predicts that the capital costs of solar PV systems in 2050 become $1,750 /kW. Sensitivities of the annual R&D growth rate for the technologies are tested. The model also provides important results that the increase by four times of R&D budgets is necessary in order to reach the cost reduction targets by the Japanese government. The proposed methodology herein is helpful for decision makers to forecast how the costs of renewable energy technologies will change, and thereby providing the basis for R&D planning.