The CO2 Emission Forecasting in Asia in Context of Time-series and Machine Learning Approaches

Zhuoran Li

doi:10.1117/12.2672687

28 March 2023 The CO₂ emission forecasting in Asia in context of time-series and machine learning approaches

Zhuoran Li

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 125973E (2023) https://doi.org/10.1117/12.2672687
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

Contemporarily, Asia is the world's largest carbon emitter among other continents according to the recorded data, which plays an essential role in climate change. This paper introduces Asia's carbon dioxide emission effect and mainly focuses on the emission prediction for the future five years based on the time series data from 1950 to 2021. Some statistical models are implemented in forecasting, including ARIMA, SARIMA, and SARIMAX models. Besides, two general machine learning models, linear regression and random forest regression would also be applicable in this paper. According to the analysis, based on the model performance matrix, the study found that the SARIMA model is the optimal model to explain and forecast the data. Specifically, it has the lowest MSE, MRSE, and MAE values and the highest AIC and BIC scores. These results shed light on guiding further exploration of carbon dioxide emission prediction in the next five years in Asia.

Citation Download Citation

Zhuoran Li "The CO₂ emission forecasting in Asia in context of time-series and machine learning approaches", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 125973E (28 March 2023); https://doi.org/10.1117/12.2672687

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