Multiple Regression Models for Statistical Study of the Dependence of Innovative Activities Related to Renewable Energy on SocioEconomic Factors

Abstract

A statistical study was conducted to explore how socio-economic factors influence innovative activities in the renewable energy sector based on the development of two multiple regression models. The study focuses on the Republic of Belarus, employing data from the World Bank and National Statistical Committee of the Republic of Belarus. Multiple socioeconomic indicators were analyzed, including gross national income, renewable energy consumption, social contributions, and inflation rate. The first model considered the number of patent applications as the dependent variable, while the second focused on the share of shipped innovative products. Factors that have the greatest impact on innovation activity were identified. The determination coefficients for the multiple regression models were 0.863 and 0.907, respectively. Based on the developed models, we found dependencies, which show the ways to increase the level of innovation activities given the identified significant factors. The findings support the importance of renewable energy as a driver of innovation and highlight the need for strategic investment in energy and social infrastructure. The study also emphasizes the potential for integrating geographic information systems to optimize energy planning and support innovation development within the broader context of sustainable economic growth.