Optimization of Regional CO2 Emission Prediction Model and Discussion on Management Response Strategies

DOI: https://doi.org/10.62517/jbdc.202501310

Author(s)

Weijia Kang1, Xinyue Wang2, Xin Liang1,*, Yuan Qian3

Affiliation(s)

1Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan, Hubei, China 2Central& Southern China Municipal Engineering Design and Research Institute Co., Ltd., Wuhan, Hubei, China 3Unit 32680 of the Chinese People's Liberation Army, Shenyang, Liaoning, China *Corresponding Author

Abstract

Against the backdrop of global climate change and CO2 emissions, the formulation and path planning of regional dual CO2 targets have become important issues in today's society. This article first establishes a comprehensive analysis model that includes multiple indicators such as economy, population, and energy consumption to evaluate the current status of CO2 emissions. A CO2 emission prediction model was designed for different scenarios using ARIMA time series and polynomial regression analysis. The BP neural network model was used to modify and tune the model parameters, and strategies and measures were proposed to achieve CO2 peak and CO2 neutrality goals. The advantages and disadvantages of the above measures were evaluated. The results of this study can provide important references for regional planners and decision-makers on emission reduction pathways and management to achieve sustainable CO2 reduction goals.

Keywords

Autoregressive Integrated Moving Average Model Time Series; Multiple regression Modelling; Neural Networks; CO2 Neutrality; CO2 Emission Reduction

References

[1] Zhang Junli, Liu Liping. An overview of the application of regional CO2 emission prediction models in China. Journal of Environmental Science, 2019: 15-21. [2] Yutao M, Kai G, Ronghua D. Prediction of regional carbon emissions using deep learning and mathematical–statistical model.Journal of Ambient Intelligence and Smart Environments, 2023, 15(4): 401-417. [3] Panda A, Mahapatra S, K.R. G A, et al. Lowering carbon emissions from a zinc oxide rotary kiln using event-scheduling observer-based economic model predictive controller. Chemical Engineering Research and Design, 2024, 207420-438. [4] Hassan M, Khosravi K, Farooque A A, et al. Prediction of carbon dioxide emissions from Atlantic Canadian potato fields using advanced hybridized machine learning algorithms – Nexus of field data and modelling. Smart Agricultural Technology, 2024, 9100559-100559. [5] Kumar S, Muhuri K P. A concise review towards a novel target specific multi-source unsupervised transfer learning technique for GDP estimation using CO2 emission data. Artificial Intelligence Review, 2024, 57(12): 329-329. [6] Kohút R, Klaučo M, Kvasnica M. Unified carbon emissions and market prices forecasts of the power grid. Applied Energy, 2025, 377(PC): 124527-124527. [7] Agaj T, Budka A, Janicka E, et al. Using ARIMA and ETS models for forecasting water level changes for sustainable environmental management. Scientific reports, 2024, 14 (1): 22444. [8] Szostek K, Mazur D, Drałus G, et al. Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production. Energies, 2024, 17(19): 4803-4803. [9] Fang Hui. Empirical study on CO2 emission and economic growth based on environmental EKC theory. Modern Marketing (Lower Decade), 2019: 20-21. [10] TIAN Ze, ZHANG Hongyang, NU Wenjie. CO2 emission peak prediction and emission reduction strategy in the Yangtze River Economic Belt. Resources and Industry, 2021: 97-105. [11] Xu Lijie. Research on the influence of environmental regulation on regional CO2 emissions in China. Journal of Zhengzhou Institute of Aviation Industry Management, 2020: 68-77. [12] Fan, S., & Hyndman, R. J. (2011). The price elasticity of electricity demand in South Australia. energy policy, 39(6), 3709-3719.2024011722001462391452011876 [13] Zhang, Y. J., & Da, Y. B. (2015). The decomposition of energy-related CO2 emission and its decoupling with economic growth in China. Renewable and Sustainable Energy Reviews, 41, 1255-1266. [14] DAI Yong, CHEN Xiang, WANG Min. Study on the Kuznets effect of CO2 emissions: the case of Jiangsu Province. Statistics and Decision Making, 2020: 4. [15] Bagrecha C, Singh K, Sharma G, et al. Forecasting silver prices: a univariate ARIMA approach and a proposed model for future direction. Mineral Economics, 2024, (prepublish): 1-11. [16] Yasmin S, Moniruzzaman M. Forecasting of area, production, and yield of jute in Bangladesh using Box-Jenkins ARIMA model. Journal of Agriculture and Food Research, 2024, 16101203. [17] D.K. D, P.K. S, R. A, et al. Rainfall probability distribution and forecasting monthly rainfall of navsari using ARIMA model. Indian Journal of Agricultural Research, 2022, 56 (1): 47-56. [18] Feng Zongxian, Gao Win. Exploration of regional CO2 emission drivers, emission reduction contributions and potentials in China. Journal of Beijing Institute of Technology (Social Science Edition), 2019: 13-20. [19] WU Lijun, WANG Qianyuan, TIAN Qibo. Research on global CO2 emission right allocation and emission reduction sharing - based on the perspective of environmental constraints and fairness perception. China Environmental Management, 2021: 11. [20] Chen Shasha. Research on the prediction of CO2 emission in a district based on combined prediction model. China New Technology and New Products, 2022:3. [21] Cairone S, Hasan W S, Choo H K, et al. Revolutionizing wastewater treatment toward circular economy and carbon neutrality goals: Pioneering sustainable and efficient solutions for automation and advanced process control with smart and cutting-edge technologies. Journal of Water Process Engineering, 2024, 63105486. [22] Ji P, Lin H, Li S, et al. Technical system and prospects for precise methane extraction in the entire life cycle of coal mining under the goal of “carbon peak and carbon neutrality". Geoenergy Science and Engineering, 2024, 238212855. [23] Bhatia M, Meenakshi N, Kaur P, et al. Digital technologies and carbon neutrality goals: An in-depth investigation of drivers, barriers, and risk mitigation strategies. Journal of Cleaner Production, 2024, 451141946.