Bayesian hierarchical and PVAR assessment of policy uncertainty, green finance, innovation, and circular economy outcomes in OECD economies

This paper examines the impacts of policy uncertainty, green finance, green innovation, and environmental policy stringency on the performance of the circular economy in OECD countries. The circular economy is developed based on indicators of resource productivity, waste recovery, and clean energy, using a circular economy index constructed through a transparent normalization process and principal component analysis as the weighting mechanism. Our results analyze a cohort of 16 OECD economies over the 2000–2022 period and incorporate macroeconomic, financial, and technological variables, along with regulatory variables, in a single empirical model. It employs a methodological approach that uses Bayesian hierarchical panel models, as well as typical correlated-effects estimators, to explain cross-sectional dependence, heterogeneous slopes, and persistence. Thereafter, a Bayesian panel vector autoregression is used to examine the feedback process among circular performance, green finance, innovation, and policy uncertainty. The design corrects for bias caused by unobserved common shocks and identification problems, and provides distribution-based findings on marginal effects and adjustment paths. The empirical evidence demonstrates that the higher the policy uncertainty, the lower the performance of the circular economy, whereas the higher the scores for green finance and green innovation, the higher the performance of the circular economy. The stringency of environmental policy increases these effects and alters the transmission of uncertainty. The terms of interaction imply that plausible regulatory regimes, as well as well-developed green finance systems, mitigate the negative effect of uncertainty and strengthen the positive role of innovation.