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<p>This study investigates the determinants of carbon dioxide (CO<sub>2</sub>) emissions in eight South Asian countries over the period 2000–2023 using a comprehensive panel econometric framework. The analysis incorporates key structural drivers, including renewable energy consumption, renewable electricity output, fossil fuel consumption, foreign direct investment (FDI), technological development, access to clean fuels, population, and GDP per capita. To address cross-sectional dependence and slope heterogeneity, the study applies second-generation panel techniques, including the Augmented Mean Group (AMG), Common Correlated Effects Mean Group (CCEMG), and panel quantile regression. The empirical findings reveal substantial heterogeneity in the effects of explanatory variables across countries and emission distributions. Renewable energy consumption exhibits a statistically significant negative association with CO<sub>2</sub> emissions under the CCEMG estimator, indicating its role in mitigating environmental degradation. In contrast, most other variables, including FDI, fossil fuel consumption, technological indicators, and GDP per capita, do not demonstrate statistically robust effects across estimators. The quantile regression results further confirm that the impacts of key variables vary across different emission levels. Overall, the findings highlight the importance of renewable energy transition while underscoring the complex and heterogeneous nature of emission dynamics in South Asia.</p>

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Decoding CO2 emissions in South Asia: evidence from heterogeneous effects and dynamic panel estimation using AMG, CCEMG, and quantile regression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Masud Rana,
  • Krishno Karmakar,
  • Md. Kamruzzaman,
  • Md. Kamal Hossain,
  • Md. Abdullah Al Mamun,
  • Sutap Kumar Ghosh,
  • Mohammad Mahadi Hasan

摘要

This study investigates the determinants of carbon dioxide (CO2) emissions in eight South Asian countries over the period 2000–2023 using a comprehensive panel econometric framework. The analysis incorporates key structural drivers, including renewable energy consumption, renewable electricity output, fossil fuel consumption, foreign direct investment (FDI), technological development, access to clean fuels, population, and GDP per capita. To address cross-sectional dependence and slope heterogeneity, the study applies second-generation panel techniques, including the Augmented Mean Group (AMG), Common Correlated Effects Mean Group (CCEMG), and panel quantile regression. The empirical findings reveal substantial heterogeneity in the effects of explanatory variables across countries and emission distributions. Renewable energy consumption exhibits a statistically significant negative association with CO2 emissions under the CCEMG estimator, indicating its role in mitigating environmental degradation. In contrast, most other variables, including FDI, fossil fuel consumption, technological indicators, and GDP per capita, do not demonstrate statistically robust effects across estimators. The quantile regression results further confirm that the impacts of key variables vary across different emission levels. Overall, the findings highlight the importance of renewable energy transition while underscoring the complex and heterogeneous nature of emission dynamics in South Asia.