Exploring CO2 emission in developing countries by PCA and time series analysis

Xiaolei Cheng

doi:10.1117/12.2672670

28 March 2023 Exploring CO₂ emission in developing countries by PCA and time series analysis

Xiaolei Cheng

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 1259718 (2023) https://doi.org/10.1117/12.2672670
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

Carbon dioxide emission are major driver of global warming and climate change, hence one should urgently reduce carbon dioxide emission to avoid the impacts from climate change. With such a worldwide problem, the regulation and generalization have become a problem. When comparing each countries carbon dioxide emission data, mankind has missed lots of omitted variables including countries’ economics status, countries’ location etc. As the world enters the era of big data analysis, this study will mainly discuss why some developing countries cannot reduce carbon dioxide emission at the same rate as those developed countries. From the dataset, the research used 12 major carbon dioxide countries all over the world including both developing and developed countries. The research first performed explanatory data analysis to check if there is any association between carbon dioxide emission, GDP per capital, and life expectancy in all 12 countries. Then, the PCA classification is adopted to classify all the selected 12 countries, and one may see all the developing countries are classified in similar area. In the end, the study performed a SARIMA time series analysis on China’s future carbon dioxide emission, and the result showed a decline trend of China’s carbon dioxide emission in the future. This research will correct the misleading idea on simply compare with countries precisely carbon dioxide emission and help to understand the tradeoffs that many developing countries face during reduction of carbon dioxide.

Citation Download Citation

Xiaolei Cheng "Exploring CO₂ emission in developing countries by PCA and time series analysis", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 1259718 (28 March 2023); https://doi.org/10.1117/12.2672670

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KEYWORDS

Climate change

Principal component analysis

Analytical research

Data analysis

Carbon dioxide

Mathematical modeling

Time series analysis

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