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The present study major aim was to examine the impact of globalization, economic growth, population growth, renewable energy usage and nuclear energy on CO2 emissions globally by taking the annual data varies from 1985 to 2020. Stationarity among study variables were tested via unit root testing, while nonlinear autoregressive distributed lag (NARDL) technique was used to demonstrate the linkages among variables with the estimation of long-run and short-run. Study results reveal that both in the short run and long run, negative globalization and economic growth shocks positively and negatively influence CO2 emissions, respectively. Besides, higher population growth is found to positively influence CO2 emissions while renewable energy consumption cannot influence the CO2 emission figures. Lastly, positive and negative shocks to alternative nuclear energy consumption are evidenced to negatively influence CO2 emissions both in the short run and long run. Hence, in line with these findings, several new policies and strategies are recommended for reducing carbon emissions globally. [ABSTRACT FROM AUTHOR]

Population growth has been a leading driver of global CO2 emissions over the last several decades. CO2 emission and greenhouse gas emissions are a key issue in the world that affects food production and also causes the climate change. The core purpose of this study was to inspect the influence of carbon dioxide emission to population growth, food production, economic growth, livestock and energy utilization in Pakistan. The STIRPAT (Stochastic Impact by Regression on Population, Affluence and Technology) model with the extension of an ARDL (Autoregressive Distributed Lag) method was utilized to demonstrate the linkage amid variables. Outcomes during short-run investigation reveal that variables population growth, economic growth, rural population growth, livestock production uncovered a productive association with CO2 emission. Furthermore, via long-run population growth, economic growth, rural population growth, livestock production and energy utilization have positive interaction with CO2 emission, while the variables food production and urban population growth demonstrated an adverse influence to CO2 emission during long- and short-run interaction. Similarly, the error correction model exposed that population growth, economic progress, livestock and energy utilization have constructive interaction to CO2 emission, while the variables food production, urban and rural population growth exposed an adverse impact to CO2 emission. On the basis of this analysis, we will address the strategic consequences. [ABSTRACT FROM AUTHOR]