Artificial Intelligence, Circular Economy, and Green Finance: Policy Insights From the <scp>G7</scp> Economies

ABSTRACT The goal of sustainable development in sophisticated economies is to incorporate financial, technological, and institutional processes that enhance low‐carbon changes as well as maintain systemic sustainability. This paper examines trends in the role of green finance in the G7 based on the impacts of artificial intelligence (AI), macro‐financial risks, effectiveness of governance, geopolitical instability, the performance of environmental technology (ET), and circular economy (CE). The material loop closure is reflected in a novel circular economy index, which is generated using a principal component analysis of waste treatment and recycling indicators. The analysis assumes that the year‐to‐year data of 1995–2022 are used to estimate the heterogeneity, long‐run dynamics of green finance using the method of moments quantile regression (MMQR) and fully modified ordinary least squares (FMOLS) to model the heterogeneity and dynamics. The proxy variables used by the researcher to measure green finance dynamics are the public energy RD&amp;D budgets as a share of GDP. The results indicate that financial risk mitigation (FRISK), the ET, and the performance of the CE contribute greatly to green finance at lower quantiles. It is worth noting that an increase in the CE index by 1% corresponds with a 0.047% to 0.058% rise in the amount of green finance, and a rise in the FRISK by 1% results in the maximum 0.24% change in the 90th percentile of green finance. On the other hand, the geopolitical (GRISK) and economic risks (ERISK) have all negative effects, regardless of quantiles, and diminish the green finance by up to 0.34% per 1% point increase in GRISK. There is an asymmetric effect of AI, which compensates for a more significant effect of decreasing green finance in low‐quantile settings by 0.019% but in high‐performing economies, there are weak or neutral effects, indicating reduced returns or systemic orthogamy in well‐established institutional settings. The interaction models show that economic growth is not sufficient to stop the negative impacts of macro‐financial instability on sustainability financing, but financial stability can conditionally enhance innovation effectiveness. The theoretical basis of the study is the ecological modernization theory, which focuses on the fundamental role of CE and AI trajectories in stimulating financial modernization and green innovation in the context of systemic risk. The recommendations of the policy include strong financial architecture, customized AI‐governing interplay, customized adjustment risk control, and broadened investment into circular infrastructure. The paper offers context‐sensitive information to G7 policy‐makers to guide green transformations in the face of volatile economic and technology uncertainties.