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<p>This study assesses the temporal variability of municipal CO<sub>2eq</sub> emissions in Brazil between 1999 and 2021, analyzing their relationships with socioeconomic and demographic factors, including GDP per capita, urbanization index (UI), and population density (PD). The methodological approach integrated statistical techniques encompassing Spearman correlation, temporal autocorrelation analyses, and statistical modeling using multiple linear regression (MLR), generalized linear model (GLM), quantile regression, and linear mixed model (LMM) with adjustment for temporal heteroscedasticity. The results evidenced significant positive associations between UI (0.68), GDP per capita (0.52), and CO<sub>2eq</sub> emissions, indicating that more urbanized and economically developed municipalities have higher emission levels, possibly due to intensive energy consumption and natural and economic resources. It was also verified that municipalities with high PD exhibit higher emissions (correlation of 0.6), although not necessarily with high GDP per capita (correlation of −&#xa0;0.02). The inclusion of temporal dependence and variability among municipalities revealed that municipal emissions have become more heterogeneous over the years, indicating structural changes in local conditions. Therefore, the relevance of integrated and differentiated approaches for effective environmental policies is evidenced, considering the Brazilian context's socioeconomic, demographic, and temporal factors, thus contributing to mitigation strategies aligned with international climate discussions.</p>

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Temporal Dynamics of CO2eq Emissions in Brazilian Municipalities and Their Relationships with Socioeconomic Indicators

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Nícholas de Paula Nicomedes,
  • Arthur Pereira dos Santos,
  • Darllan Collins da Cunha e Silva

摘要

This study assesses the temporal variability of municipal CO2eq emissions in Brazil between 1999 and 2021, analyzing their relationships with socioeconomic and demographic factors, including GDP per capita, urbanization index (UI), and population density (PD). The methodological approach integrated statistical techniques encompassing Spearman correlation, temporal autocorrelation analyses, and statistical modeling using multiple linear regression (MLR), generalized linear model (GLM), quantile regression, and linear mixed model (LMM) with adjustment for temporal heteroscedasticity. The results evidenced significant positive associations between UI (0.68), GDP per capita (0.52), and CO2eq emissions, indicating that more urbanized and economically developed municipalities have higher emission levels, possibly due to intensive energy consumption and natural and economic resources. It was also verified that municipalities with high PD exhibit higher emissions (correlation of 0.6), although not necessarily with high GDP per capita (correlation of − 0.02). The inclusion of temporal dependence and variability among municipalities revealed that municipal emissions have become more heterogeneous over the years, indicating structural changes in local conditions. Therefore, the relevance of integrated and differentiated approaches for effective environmental policies is evidenced, considering the Brazilian context's socioeconomic, demographic, and temporal factors, thus contributing to mitigation strategies aligned with international climate discussions.