CO₂ emissions in G20 economies: A dynamic panel analysis of economic and energy-sector drivers

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Type of the article: Research Article

Abstract
Mitigating the effects of climate change has emerged as a crucial global need, with carbon dioxide emissions serving as the principal driver of greenhouse gas accumulation. This paper analyzes the factors influencing CO₂ emissions in G20 countries from 2000 to 2021, emphasizing the effects of renewable energy consumption, trade openness, economic growth, and energy intensity. The study utilizes advanced dynamic panel econometric techniques, namely, the Augmented Mean Group (AMG) Estimator and the Common Correlated Effects Mean Group (CCEMG) Estimator, which address cross-sectional dependence and parameter heterogeneity among nations. The analysis indicates that the use of renewable energy noticeably decreases CO₂ emissions, with elasticity values between –0.15 and –0.16. The effect is especially significant in lower-income G20 countries and during the post-2005 era. Economic growth indicates a strong positive correlation with CO₂ emissions, characterized by elasticity values ranging from 0.83 to 0.89, whereas energy intensity also displays positive effects with coefficients between 0.69 and 0.82. Trade openness exhibits insignificant statistical effects in both models. The heterogeneity study reveals that the emission-reduction potential of renewable energy is significantly greater in emerging nations than in advanced economies, with coefficients of –0.25 and –0.08, respectively. The results highlight the essential role of renewable energy transitions and enhancements in energy efficiency for meeting climate goals, especially when aligned with specific policies for various income levels and timeframes within the G20 context.

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    • Table 1. Descriptive statistics
    • Table 2. Correlation matrix
    • Table 3. Cross-sectional dependence test results
    • Table 4. Panel unit root test results
    • Table 5. Panel cointegration test results
    • Table 6. Long-run estimation results
    • Table 7. Short-run estimation results
    • Table 8. CCEMG estimation results by time period
    • Table 9. CCEMG estimation results by income group
    • Conceptualization
      Nuriddin Shanyazov, Alibek Rajabov, Manzura Masharipova, Javohir Babajanov
    • Formal Analysis
      Nuriddin Shanyazov, Alibek Rajabov, Manzura Masharipova, Dilshodbek Saidov, Javohir Babajanov
    • Funding acquisition
      Nuriddin Shanyazov, Alibek Rajabov, Manzura Masharipova, Sadokat Rakhimova, Javohir Babajanov
    • Investigation
      Nuriddin Shanyazov, Alibek Rajabov, Manzura Masharipova, Sadokat Rakhimova, Dilshodbek Saidov, Javohir Babajanov
    • Methodology
      Nuriddin Shanyazov, Alibek Rajabov
    • Project administration
      Nuriddin Shanyazov, Alibek Rajabov
    • Resources
      Nuriddin Shanyazov, Alibek Rajabov, Manzura Masharipova, Sadokat Rakhimova, Dilshodbek Saidov, Javohir Babajanov
    • Software
      Nuriddin Shanyazov, Alibek Rajabov
    • Supervision
      Nuriddin Shanyazov, Alibek Rajabov
    • Validation
      Nuriddin Shanyazov, Alibek Rajabov
    • Visualization
      Nuriddin Shanyazov, Alibek Rajabov, Sadokat Rakhimova, Dilshodbek Saidov, Javohir Babajanov
    • Writing – original draft
      Nuriddin Shanyazov, Alibek Rajabov
    • Writing – review & editing
      Nuriddin Shanyazov
    • Data curation
      Manzura Masharipova, Dilshodbek Saidov, Javohir Babajanov