Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf

Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf

<p>China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving...

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Bibliographic Details
Main Author: Sanglin Zhao (20915732) (author)
Other Authors: Zhetong Li (20915735) (author), Hao Deng (409186) (author), Xing You (11352431) (author), Jiaang Tong (20915738) (author), Bingkun Yuan (20915741) (author), Zihao Zeng (14076333) (author)
Published: 2025
Subjects:
Environmental Science
carbon emissions
ARIMA-BP model
LMDI decomposition
temporal and spatial evolution
standard elliptic difference
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