Je okoljska Kuznetsova krivulja še aktualna v današnjem času?

This study investigated the presence of EKC-like relationships between various socioeconomic variables and air pollution indicators across 151 Chinese cities, analysed by quadratic regression models and geographic weighted regression (GWR) analysis. The results present critical insights into the applicability and limitations of the EKC. Only Air Quality Index, Nitrogen Dioxide (NO2), and Fine Particulate Matter (PM2.5) show statistically significant correlations with one socioeconomic variable, respectively, in an EKC-like pattern which is meaningful in reality. GWR coefficients serve as a diagnostic tool to identify those burdened cities where stricter emissions standards, greener industrial practices, or economic restructuring should be prioritized. The spatial dependencies challengethe EKC’s assumption of isolated environmental-economic dynamics. Stricter environmental regulations in developed areas often lead to the displacement of polluting activities to regions with laxer standards. Policy efforts in tackling air pollution should focus on directly reducing emissions through localized, technology-based interventions rather than relying on economic growth to eventually improve air quality. Spatially targeted policies informed by city-specific patterns are essential, as pollution outcomes are shaped by regional industrial structures, population density, and cross-boundary spillover effects.