J Edward Taylor Winter 2018 Carbon Dioxide (CO2) Emissions

J Edward Taylorwinter 2018carbon Dioxide Co2 Emissions Are Widely

J. Edward Taylor Winter 2018 examines the drivers behind countries’ CO₂ emissions, specifically analyzing whether income or population has a greater influence. Using cross-sectional data from 2010, including CO₂ emissions, gross national income (GNI), and population, the study employs statistical models—simple and multiple linear regressions—to explore these relationships.

The initial hypothesis posits that higher income levels lead to increased CO₂ emissions, justified by economic theories suggesting that as countries become wealthier, their consumption and industrial activities expand, resulting in more carbon emissions. This model expresses emissions (Y) as a function of GNI (X₁): Y = β₀ + β₁X₁ + ε, where β₀ is the intercept, β₁ the slope representing the change in emissions per unit change in income, and ε the error term.

Using regression analysis in Excel, the estimated parameters were obtained: β₀ ≈ 12,187, indicating the baseline CO₂ emissions when income is zero, and β₁ ≈ 0.443, implying that a one-million US dollar increase in GNI corresponds to approximately 0.44 kilotons increase in CO₂ emissions. The elasticity of emissions with respect to GNI, evaluated at mean values, is about 0.93, indicating that a 1% increase in income results in roughly a 0.93% rise in CO₂.

Adding population (X₂) into the model, justified by the theory that larger populations directly contribute to higher emissions through increased activity, the multiple regression equation becomes: Y = β₀ + β₁X₁ + β₂X₂ + ε. Empirical results reveal that population significantly influences emissions, with β₂ ≈ 3,127.37 kilotons per million people. The elasticity of CO₂ with respect to population is approximately 0.68, meaning a 1% growth in population correlates with a 0.68% increase in emissions.

Inclusion of population reduces the elasticity estimate with respect to income from 0.93 to about 0.62, suggesting that population accounts for some of the variation previously attributed solely to income. This indicates that while income remains a substantial driver, population has a more pronounced effect on CO₂ emissions.

Based on these findings, the primary driver of countries’ carbon footprints appears to be population rather than income, as evidenced by the higher elasticity coefficient and the greater impact observed in the multiple regression model. This conclusion underscores that policies targeting population control or management could be crucial in efforts to reduce global carbon emissions.

The regression analyses also shed light on how income and population explain differences in emissions between specific countries such as China and the United States. The model explains approximately 16% of the variation in their CO₂ emissions, indicating other factors also play significant roles. When comparing the explanatory power of the models, the R-squared increases from 0.61 in the simple regression to 0.86 in the multiple regression, illustrating that incorporating both variables provides a more accurate prediction of emissions.

In conclusion, the empirical evidence from the regression models affirms the vital roles of both income and population in determining national CO₂ emissions, with population exerting a somewhat stronger influence. This analysis highlights the importance of integrating demographic considerations into climate policy frameworks aimed at mitigating carbon footprints globally.

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