Drivers of Renewable Energy Adoption: Assessing the Role of Artificial Intelligence and Climate Finance in High-Income Economies

Abstract

Renewable energy adoption (RNE) has become a worldwide concern owing to its fundamental role in achieving environmental goals. The literature has suggested diverse factors that can influence RNE. However, the role of artificial intelligence (AI) and climate finance in shaping RNE has received little attention. This research examines the role of AI and climate finance in shaping renewable energy, utilizing panel data from 29 high-income countries from 2000 to 2020. The empirical analysis is conducted using panel data estimators such as fixed and effects models and the system generalized method of moments. Moreover, the method of moments quantile regression is used to assess the nonlinear effects of AI on RNE. The results are estimated using Stata software. The empirical outcomes indicate that AI exerts a positive influence on renewable energy. This finding implies that AI initiatives can trigger efforts toward the renewable energy transition. Moreover, the results demonstrate that the marginal effects of AI on RNE vary across different levels of AI. Similarly, climate finance also positively and significantly contributes to renewable energy. Finally, the empirical outcomes demonstrate that climate finance moderates the role of AI in RNE. Policymakers need to focus on AI integration in renewable energy systems by prioritizing climate finance availability in AI applications that support renewable energy development.