Energy Transition and Carbon Emissions in NICs: Evidence from the CS-ARDL Approach and Dynamic ARDL Simulation

Newly Industrialized Countries (NICs) represent the most dynamic economic clusters globally and drive the marginal growth of global carbon emissions, rendering their carbon mitigation performance pivotal to delivering the climate targets pledged under the Paris Agreement. Grounded in the Environmental Kuznets Curve (EKC) hypothesis, this study applies the cross-sectional augmented autoregressive distributed lag (CS-ARDL) framework to the panel data from NICs for the 1990–2022 period and incorporates dynamic autoregressive distributed lag simulation methods to investigate the dynamic relationship between energy transition and CO2 emissions across these economies. The CS-ARDL estimation results suggest that the conventional inverted U-shaped Environmental Kuznets Curve (EKC) hypothesis is invalid under the conditions of cross-sectional dependence and slope heterogeneity for NICs. Per capita GDP growth is found to exert a significant negative impact on CO2 emissions in these economies. In both the long and short run, rising per capita non-renewable energy consumption substantially increases CO2 emissions, whereas a higher share of renewable energy in total final energy consumption yields a significant CO2 mitigation effect. Estimations conducted via the Augmented Mean Group (AMG), Common Correlation Effect Mean Group (CCEMG), and Dynamic Common Correlation Effect Mean Group (DCCEMG) estimators yield consistent findings with the CS-ARDL model. Dynamic ARDL simulation results reveal that positive shocks to per capita non-renewable energy consumption and renewable energy share in total final energy consumption induce heterogeneous dynamic response trajectories of CO2 emissions across countries. Nevertheless, rising per capita non-renewable energy consumption triggers an overall sustained growth in CO2 emissions, while a higher proportion of renewable energy achieves long-term mitigation of CO2 emissions.