Statistical Analysis of Carbon Dioxide Emission and Its Effect with Population in Different Countries
Authors/Creators
Description
This research addresses the critical global issue of rising carbon dioxide (CO2) emissions, a primary driver of global warming and the greenhouse effect. While complex problems often demand sophisticated analytical methods, statistical analysis provides a powerful pathway for distilling insights from vast and complex data sources. This paper employs statistical modeling to investigate and quantify the primary anthropogenic drivers of CO2 emissions. The analysis focuses on key contributing factors,
including population growth, industrial activity (fuel combustion in manufacturing and power generation), and deforestation.
By systematically evaluating these variables, the study aims to identify significant correlations and provide a data-driven
framework to inform targeted mitigation strategies and policy interventions.
Files
Statistical_analysis_of_carbon_dioxide_emission.pdf
Files
(4.3 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:117ce5a7728f8956c009a0eaa27c46af
|
4.3 MB | Preview Download |
Additional details
Software
- Programming language
- SAS
References
- M. J. Hossain, Dashboard Design of Carbon Dioxide Emission and Its Effect with Population in Different Countries, Zenodo, 2025. https://doi.org/10.5281/zenodo.17780793.
- T. J. Winn, Introduction to the SAS® Programming Language. Cary, NC, USA: SAS Institute, 2001.
- D. Iacobucci, S. Rom'an, S. Moon, and D. Rouzi'es, A Tutorial on What to Do with Skewness, Kurtosis, and Outliers: New Insights to Help Scholars Conduct and Defend Their Research, Psychology & Marketing, vol. 42, no. 5, pp. 1398–1414, 2025.
- R. Shi and S. A. Conrad, Correlation and Regression Analysis, Annals of Allergy, Asthma & Immunology, vol. 103, no. 4, pp. S34–S41, 2009.
- T. K. Kim, Understanding One-Way ANOVA Using Conceptual Figures, Korean Journal of Anesthesiology, vol. 70, no. 1, p. 22, 2017.