Circular Economy Policies and Sustainable Economic Growth in Emerging Markets: A Level-2 Wavelet Decomposition with Two-Way Panel Fixed Effects

This paper examines how circular economy (CE) policies relate to sustainable economic growth and environmental sustainability across eight major emerging economies between 2005 and 2023, with particular attention to how that relationship shifts across time horizons. Much of the existing literature has a temporal aggregation problem: short- and long-run dynamics are averaged together into a single coefficient, which can obscure the underlying relationship or, worse, produce a misleading sign. We address this by decomposing two central CE variables—renewable energy share and energy intensity—into short-, medium-, and long-term frequency components using a Level-2 discrete wavelet transform. Each component is then estimated via two-way fixed-effects panel regressions; given the constraints of a relatively small panel, we validate inference using a Wild Cluster Bootstrap procedure. Three findings stand out. First, although the growth elasticity of renewable energy rises across horizons—a descriptive pattern consistent with short-run adjustment costs giving way to longer-run sustainable gains—Wild Cluster Bootstrap (WCB) inference indicates that these structural coefficients are not statistically significant in this sample. Second, the energy intensity coefficient reverses the sign, moving from a weakly significant negative short-run effect (p < 0.10) to positive estimates in the longer run; these longer-run effects, however, fail to reach statistical significance, and so offer only suggestive theoretical alignment with the macroeconomic Jevons Rebound Effect rather than firm empirical confirmation of it. Third, Industry Value Added stands out as the one robust macroeconomic factor in the model, remaining a significant and positive driver of sustainable development at every frequency horizon. Taken together, these results suggest that while CE transitions display suggestive multi-horizon dynamics, industrial expansion remains the more statistically dependable engine of sustainable growth across these emerging markets. Although statistical significance is dependent on the sample size, the identified trajectories provide a useful structural diagnosis for policymakers who need to plan different investment horizons for the sustainable green transition.