The immediate need to mitigate greenhouse gas (GHG) emissions due to the urgent threat of climate change emphasizes the importance of investigating the Environmental Kuznets Curve (EKC) hypothesis. This thesis aims to assess the validity of the EKC hypothesis by focusing on total GHG emissions per capita and real GDP per capita along with Energy and Circular Economy (CE) indicators. The data was constructed from the Material Flow Database of Eurostat and Emissions Database for Global Atmospheric Research (EDGAR) emissions database for 27 EU countries, ranging from the year 2000 to 2022.
Using panel data, this thesis studies the EKC relationship considering the CE indicators such as Domestic Material Consumption (DMC), Resource Productivity (REP), Material Import Dependency (MID), and Energy Intensity (EIN) as control variables in the short and long-term using Pooled Mean Group regression.
The main results show that, in the long-run, DMC, REP, and EIN exacerbate GHG emissions and GDP growth decreases GHG emissions. MID and EIN have a significant positive effect on GHG emissions in the short run. Fixed effects model with cluster robust standard errors, suggests the role of EIN in mitigating GHG emissions. The findings also reveal that GDP per capita, DMC, and REP have a positive effect on the GHG emissions and the growth in GDP per capita has a negative effect on the GHG emissions. The sensitivity analysis conducted with additional variables, such as Municipal Waste Generation and Recycling rate of Municipal waste confirmed the same results.