From Artificial Intelligence to Circularity: How Finance, Governance, and Risk Shape Circular Economy Performance in G10 Countries

ABSTRACT The present research focuses on evaluating the determinants of the performance of the circular economy in G10 countries (Belgium, Canada, France, Germany, Italy, Japan, the Netherlands, Sweden, Switzerland, the United Kingdom, and the United States) throughout the years 2000–2023 by incorporating financial, technological, institutional, macroeconomic, and risk‐related aspects in one unified panel structure. The performance of a circular economy is assessed through a composite index named the Circular Economy Index (CEI), and Public Green Innovation Finance (GF), AI‐related industrial automation intensity (AI), Economic Risk (ER), Financial Risk (FR), Government Effectiveness (GOVS), Economic Growth (GDP), Geopolitical Risk (GR), and Environmental Technology (ET) are used as the explanatory variables. The analysis uses genuine panel econometric methods that consider cross‐sectional dependence, slope heterogeneity, mixed orders of integration, and long‐run cointegration, such as fixed‐effects regression with Driscoll–Kraay standard errors and Panel FMOLS estimation. The findings indicate that AI and GDP have a strong and positive impact on CEI, which demonstrates the significance of automation intensity and the size of an economy in improving the performance of the circular economy in developed economies. Conversely, FR and GOVS are found to have a strong and negative correlation with CEI, showing that financial instability and forms of government could be a barrier to circular transformation. GF is statistically significant in the baseline model and selectively in FMOLS, but the other estimations (GR, ER, ET) are only significant in the baseline estimations and much less robust when other specifications are used. In general, according to the results, technological capability and economic growth are the key success factors of the circular economy performance in G10 countries, whereas financial risk and institutional orientation are the key constraining factors. The findings justify the policy actions that are in line with the Sustainable Development Goals, especially SDGs 8, 9, 12, 13, and 16.