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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1497941

Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-Research on ARIMA-BP Neural Network Algorithm

Provisionally accepted
Sanglin Zhao Sanglin Zhao 1*Zhetong Li Zhetong Li 1*Bingkun Yuan Bingkun Yuan 2*Chao Wang Chao Wang 1*Hao Deng Hao Deng 1*Jiaang Tong Jiaang Tong 3*Xing You Xing You 1*
  • 1 Hunan University of Finance and Economics, Changsha, China
  • 2 Central South University, Changsha, Hunan Province, China
  • 3 Fudan University, Shanghai, Shanghai Municipality, China

The final, formatted version of the article will be published soon.

    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 factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper calculates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 based on ARIMA model and BP neural network model, and uses ArcGIS and standard elliptic difference to visually analyze the spatial and temporal evolution characteristics, and further uses LMDI model to decompose the driving factors affecting carbon emissions.

    Keywords: carbon emissions, ARIMA-BP model, LMDI decomposition, Temporal and spatial evolution, Standard elliptic difference

    Received: 19 Sep 2024; Accepted: 05 Nov 2024.

    Copyright: © 2024 Zhao, Li, Yuan, Wang, Deng, Tong and You. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Sanglin Zhao, Hunan University of Finance and Economics, Changsha, China
    Zhetong Li, Hunan University of Finance and Economics, Changsha, China
    Bingkun Yuan, Central South University, Changsha, 130012, Hunan Province, China
    Chao Wang, Hunan University of Finance and Economics, Changsha, China
    Hao Deng, Hunan University of Finance and Economics, Changsha, China
    Jiaang Tong, Fudan University, Shanghai, 200433, Shanghai Municipality, China
    Xing You, Hunan University of Finance and Economics, Changsha, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.