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This study employs a robust Ordinary Least Squares (OLS) framework to investigate the relationship between Gross Domestic Product (GDP) and CO2 emissions from 1990 to 2022. Two models are estimated: Model 1 regresses total CO2 emissions on GDP, while Model 2 examines individual CO2 sources (cement, coal, flaring, gas, and oil) as functions of GDP. The analysis includes exploratory data visualization (pairwise scatterplot matrices and time series plots), diagnostic tests for autocorrelation and heteroskedasticity, and robust standard errors to ensure reliable inference. Results reveal significant positive associations between GDP and cement and coal emissions, a negative association with oil emissions, and no significant relationship with gas emissions. Autocorrelation is detected, suggesting the need for time series adjustments. This comprehensive methodology provides a robust foundation for understanding the economic-environmental nexus

  • Read count 24
  • Date of publication 10-07-2025
  • Main LanguageIngliz
  • Pages58-70
English

This study employs a robust Ordinary Least Squares (OLS) framework to investigate the relationship between Gross Domestic Product (GDP) and CO2 emissions from 1990 to 2022. Two models are estimated: Model 1 regresses total CO2 emissions on GDP, while Model 2 examines individual CO2 sources (cement, coal, flaring, gas, and oil) as functions of GDP. The analysis includes exploratory data visualization (pairwise scatterplot matrices and time series plots), diagnostic tests for autocorrelation and heteroskedasticity, and robust standard errors to ensure reliable inference. Results reveal significant positive associations between GDP and cement and coal emissions, a negative association with oil emissions, and no significant relationship with gas emissions. Autocorrelation is detected, suggesting the need for time series adjustments. This comprehensive methodology provides a robust foundation for understanding the economic-environmental nexus

Name of reference
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