The Effects of Economic Cycle Shocks on Polluting Emissions in Developing Countries: Insights from a Panel Vector Autoregression Analysis

This study explores the dynamic relationship between polluting emissions and economic cycle shocks in developing countries using a panel vector autoregressive (PVAR) framework. Recognizing the limitations of prior models that focused primarily on causality between emissions and economic variables without forecasting capabilities, this research incorporates a PVAR methodology aligned with innovative local gray forecast models to generate dynamic forecasts and conduct structural analyses. Employing the PVAR model, impulse-response functions (IRFs) were analyzed to assess the impacts of economic shocks on pollution levels and the challenges these pose to both renewable and non-renewable energy sources. The analysis further involved the decomposition of variance among the variables. Key findings reveal that economic growth in these countries often correlates with increased use of carbon dioxide-emitting energies. However, the substitution of these energies with renewable sources is not only feasible but also pivotal for promoting environmental purification and sanitation through enhanced investments in renewable energies. Despite the theoretical potential for growth in the renewable sector, its actual development in these countries remains inadequate, and its contribution to fostering an ecological environment that supports economic growth is minimal. The study underscores the necessity of robust policies to facilitate ecological growth and the imperative of a shared commitment among nations to ensure the effectiveness of these policies.