Research Information System
Energy Technology, vol.12, no.5, 2024 (SCI-Expanded)
In this study, given the critical role and importance of the energy transition, the effect of information and communication technologies (ICT) is researched and the levels of energy-related research and development (R&D) investments on the energy transition index (ETI), controlling for human capital (HC), energy consumption (EC), and income (gross domestic product [GDP]) are disaggregated. Therefore, in this study, the United States of America (USA) is focused on as the world's leading economy, a total of five machine learning (ML) algorithms are performed, and the data from 2000/Q1 to 2021/Q4 are used. In the outcomes, it is shown that: 1) the multivariate adaptive regression splines approach is the best estimation algorithm among the ML approaches based on the coefficient of determination (R2), which has 85% estimation capacity for ETI; 2) ICT and EC are the most important factors for ETI, followed by renewable energy R&D investments, energy efficiency R&D investments, HC, GDP, and nuclear energy R&D investments, in that order; and 3) R&D investments in carbon capture and storage has no significant effect on ETI. In the overall results of the study, it is suggested that technological progress is central to energy transition in the USA and that environmental policies implemented for energy transition should be closely linked to technological progress.