Driving factors of CO2 emissions in South American countries: An application of Seemingly Unrelated Regression model
Received date: 2024-02-19
Revised date: 2024-06-12
Accepted date: 2024-11-14
Online published: 2024-12-19
Carbon emissions have become a critical concern in the global effort to combat climate change, with each country or region contributing differently based on its economic structures, energy sources, and industrial activities. The factors influencing carbon emissions vary across countries and sectors. This study examined the factors influencing CO2 emissions in the 7 South American countries including Argentina, Brazil, Chile, Colombia, Ecuador, Peru, and Venezuela. We used the Seemingly Unrelated Regression (SUR) model to analyse the relationship of CO2 emissions with gross domestic product (GDP), renewable energy use, urbanization, industrialization, international tourism, agricultural productivity, and forest area based on data from 2000 to 2022. According to the SUR model, we found that GDP and industrialization had a moderate positive effect on CO2 emissions, whereas renewable energy use had a moderate negative effect on CO2 emissions. International tourism generally had a positive impact on CO2 emissions, while forest area tended to decrease CO2 emissions. Different variables had different effects on CO2 emissions in the 7 South American countries. In Argentina and Venezuela, GDP, international tourism, and agricultural productivity significantly affected CO2 emissions. In Colombia, GDP and international tourism had a negative impact on CO2 emissions. In Brazil, CO2 emissions were primarily driven by GDP, while in Chile, Ecuador, and Peru, international tourism had a negative effect on CO2 emissions. Overall, this study highlights the importance of country-specific strategies for reducing CO2 emissions and emphasizes the varying roles of these driving factors in shaping environmental quality in the 7 South American countries.
Gadir BAYRAMLI , Turan KARIMLI . Driving factors of CO2 emissions in South American countries: An application of Seemingly Unrelated Regression model[J]. Regional Sustainability, 2024 , 5(4) : 100182 . DOI: 10.1016/j.regsus.2024.100182
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