Prediction of carbon emission based on energy consumption 
structure by statistical forecasting

Xintong Zheng; Diyu Zhang; Junfu Su; Hao Chen

doi:10.1117/12.2628222

22 April 2022 Prediction of carbon emission based on energy consumption structure by statistical forecasting

Xintong Zheng, Diyu Zhang, Junfu Su, Hao Chen

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 1216317 (2022) https://doi.org/10.1117/12.2628222
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

The rapid increase in emissions of greenhouse gases such as carbon dioxide after the first Industrial Revolution has been pointed out as the leading cause of global climate deterioration. Global temperatures have risen by 0.6 degrees Celsius in the past 100 years. On this trajectory, global temperatures are projected to increase by 1.5 to 4.5 degrees Celsius by the middle of the 21st century. In the past one hundred years, global temperatures have risen by 0.6 degrees Celsius, and at this rate, they are expected to increase by 1.5 to 4.5 degrees Celsius by the middle of the 21st century. At the same time, sea levels are rising because of increased carbon dioxide. These changes are devastating for wildlife and negatively affect the human environment. The alarm bell to reduce carbon emissions has been sounded, and low carbon emissions are imminent. The following explains the current situation of global carbon emissions from the perspective of sustainable development by combining the structure of global energy consumption and factors affecting carbon emissions. Seven prediction models are established, and additional emission reduction measures are proposed.

Citation Download Citation

Xintong Zheng, Diyu Zhang, Junfu Su, and Hao Chen "Prediction of carbon emission based on energy consumption structure by statistical forecasting", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 1216317 (22 April 2022); https://doi.org/10.1117/12.2628222

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