Articles article
Forecast data of provincial carbon emissions in China from 2025 to 2035: based on ARIMA-BP model
Abstract
China is an important contributor to global carbon emissions. Accurately estimating carbon emissions is crucial for reducing carbon emissions in accordance with the United Nations Sustainable Development Goals and China's carbon neutrality strategy. This study selected energy consumption data from Chinese provinces from 2000 to 2021, calculated carbon emissions using the carbon emission factor method, and then predicted carbon emissions data for 2022-2035 using the ARIMA-BP model. This dataset can be used to describe the spatiotemporal evolution trend of carbon emissions in China. This study can provide policy basis and wisdom for China's carbon reduction efforts.
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References
[1]Xuyi Liu,Wentian Cui &Shun Zhang.(2025).Better e-commerce less carbon emissions in China?.Energy134820-134820.
[2]Congqi Wang,Haslindar Ibrahim,Fanghua Wu &Wenting Chang.(2025).Spatial and temporal evolution patterns and spatial spillover effects of carbon emissions in China in the context of digital economy.Journal of Environmental Management123811-123811.
[3]Zhao S, Li Z, Deng H, You X, Tong J, Yuan B and Zeng Z (2024) Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm. Front. Environ. Sci. 12:1497941. doi: 10.3389/fenvs.2024.1497941
[4]Caiping Zhang, Falong Liu, Dawei Wu,Deming Tan & Linping Niu.(2025).The impact of the carbon emissions trading scheme on corporate strategic deviance in China.Technological Forecasting & Social Change123952-123952.
[5]Feng Yi,Ziheng Niu & Shunbin Zhong.(2025).Does information and communication technology reduce carbon emissions in China? Evidence from the quasi-natural experiment of the “Broadband China” pilot policy.Journal of Environmental Planning and Management(1),1-27.
[6]Chenglin He,Huiming Duan & Yongshan Liu.(2025).A neural network grey model based on dynamical system characteristics and its application in predicting carbon emissions and energy consumption in China.Expert Systems With Applications126101-126101.
[7]TingtingLi, XiangruiMeng,LiukaiWang & YuGong.(2024).Will China's Carbon Emission Trading Regulation Really Decrease the Green Investment of Firms? A Microperspective on Corporate Governance.Business Strategy and the Environment(2),2222-2238.
[8]Meilian Liu, Mei Zhan,Yiping Liu & Ming Zhao.(2024).Impact of FDI and foreign trade openness on carbon emissions in China: evidence from threshold regression model.Applied Economics(58),8332-8345.
[9]Xiaoping Zhou, Ying Liang, Li Li, Duo Chai, Xiaokun Gu,Lan Yang & Jinlong Duan.(2024).Analysis of spatial and temporal characteristics and influence mechanisms of carbon emissions in China's, 1997–2017.Journal of Cleaner Production144411-144411.
[10]Jikun Yao.(2024).A Fusion Method Integrated Econometrics and Deep Learning to Improve the Interpretability of Prediction: Evidence From Chinese Carbon Emissions Forecast Based on OLS-CNN Model.Computational Economics(prepublish),1-20.
[11]Zhi Lin Lu, Li Li Wang, Xue Peng Guo,Jun Pang & Jia Jia Huan.(2024).Decoupling effect and influencing factors of carbon emissions in China: Based on production, consumption, and income responsibilities.Advances in Climate Change Research(6),1177-1188.
[12]Mei Chang,Zeshui Xu & Xunjie Gou.(2024).Does advanced human capital structure improve carbon emission performance in China? Empirical research from a spatial spillover perspective.Journal of Cleaner Production144152-144152.
[13]Zhao, S., Cao, J., Lu, K., & Steve, J. (2025). Research on Olympic medal prediction based on GA-BP and logistic regression model. F1000Research, 14, 245.
[14]Zhao, S., Deng, H., Cao, J., & Gustaf, M. (2025). Digital transformation of construction enterprises and carbon emission reduction: evidence from listed companies. Frontiers in Environmental Science, 13, 1570182.
[15]Qian, Z., Zhao, S., Deng, H., & Gustaf, M. (2025). Commentary: The synergistic effect of digital transformation technology and supply chain finance: empirical evidence from 500 listed companies. Frontiers in Physics, 13, 1694604.
[16]Zhao, S., & Cao, J. (2024). Measurement of coupling development level of new infrastructure investment and digital transformation and its temporal and spatial evolution trend. arXiv preprint arXiv:2412.19861.
[17]Zhao, S., Li, Z., Deng, H., You, X., Tong, J., Yuan, B., & Zeng, Z. (2025). Corrigendum: Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm. Frontiers in Environmental Science, 13, 1592508.
[18]Shi, M., Qian, Z., & Zhao, S. (2026). Green finance, green technology innovation and carbon emission reduction. Discover Sustainability.