Forecasting the effectiveness of public funding for research and development in the field of energy technologies in the context of climate policy targets pursuit

Abstract

Subject. This article deals with the issues related to public funding for research and development in the field of energy technologies. Objectives. The article aims to develop a logistic regression model, which, based on data on public funding for research and development in the field of various energy technologies, could help predict whether the carbon intensity of the country's economy will be reduced (yes / no) in the medium term (about 5 years). Conclusions and Relevance. Based on the developed logistic regression model, the article concludes that public financing of research and development of hydrocarbon technologies affects the dynamics of the country's carbon intensity negatively and increases the likelihood of an increase in carbon intensity compared to previous periods. The developed model can be used in practice to predict the effectiveness of financing innovations in the field of energy technologies in the context of climate policy. The used approach to forecasting, based on machine learning, seems to be promising, especially with the accumulation of sufficiently large amounts of statistical data on the structure of R&D financing and carbon intensity.