This study employs the ARDL (Autoregressive Distributed Lag) framework to investigate the long-run and short-run relationships among key variables in a model. The findings from the ARDL analysis are presented, focusing on the estimated coefficients and their associated t-statistics. The long-run estimates reveal that changes in the lagged values of CO2, Transport services (% of commercial service exports), and Trade (% of GDP) significantly influence current CO2 emissions. However, the effect of the lagged value of Urban Population (% of total population) is found to be statistically insignificant. In the short run, the changes in Trade (% of GDP) and Urban Population (% of total population) do not exhibit significant relationships with changes in CO2 emissions. Nevertheless, the lagged change in Urban Population (% of total population) demonstrates a statistically significant impact on the current change in CO2 emissions. These findings provide insights into the complex dynamics between the variables and contribute to a better understanding of the factors affecting CO2 emissions and urbanization. They have implications for policymakers and researchers in formulating strategies to mitigate CO2 emissions and manage urban development sustainably.