Global Persistence in CO₂ Emissions: Evidence from Panel Data Analysis Across Countries
DOI:
https://doi.org/10.58574/jaa.2026.v5.i1.02Keywords:
Temporal Dependence, Spatial Dependence, Generalised Least SquaresAbstract
This study examines different factors that affect emissions of carbon dioxide (CO2) from the power sector. A panel dataset of countries has been used, ranging from 2016 to 2023. Given the global nature of emissions, both temporal and spatial dependence are assessed before model estimation. The evidence from autocorrelation analysis indicates strong temporal persistence in emissions. Accordingly, a pooled linear panel regression model that has a first-order autoregressive error structure has been selected. The model is estimated using Generalised Least Squares (GLS). The results show that economic growth, measured by GDP, has a non-linear relationship with CO2 emissions. But population density does not exhibit a statistically significant association. The findings also suggest that emission dynamics are primarily driven by temporal processes. This highlights the importance of accounting for temporal dependence in global emission analyses.
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Copyright (c) 2026 Shreyasi Chatterjee , Dr. Surajit Sengupta, Dr. Suman Guha
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