Peak prediction and emission-reduction potential analysis for industrial carbon emissions in Western China

ZOU Yan; LI Jiajia; WANG Shuping

doi:10.13205/j.hjgc.202505022

Volume 43 Issue 5

May 2025

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ZOU Yan, LI Jiajia, WANG Shuping. Peak prediction and emission-reduction potential analysis for industrial carbon emissions in Western China[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(5): 199-206. doi: 10.13205/j.hjgc.202505022

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ZOU Yan, LI Jiajia, WANG Shuping. Peak prediction and emission-reduction potential analysis for industrial carbon emissions in Western China[J]. ENVIRONMENTAL ENGINEERING , 2025, 43(5): 199-206. doi: 10.13205/j.hjgc.202505022

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Peak prediction and emission-reduction potential analysis for industrial carbon emissions in Western China

doi: 10.13205/j.hjgc.202505022

School of Economics and Management, Chongqing Normal University, Chongqing 401331, China

Received Date: 2024-06-17
Accepted Date: 2024-08-15
Rev Recd Date: 2024-07-23

Available Online: 2025-09-11

Abstract

Abstract

As a critical region for China's industrial development， accurately predicting the industrial carbon peak in Western China is essential for formulating effective emission reduction policies tailored to local characteristics. This paper proposes a carbon emission accounting system aligned with China's specific conditions to reasonably estimate industrial carbon emissions in 11 major western regions. Based on this， a combined forecasting model was established using particle swarm optimization （PSO） to optimize wavelet neural networks （WNN） for predicting the total industrial carbon emissions in the western region from 2020 to 2030. Furthermore， the final prediction results were analyzed to assess the western region's industrial carbon peak situation and reduction potential. The findings indicated that：1） under the natural peak scenario， eight administrative regions of Guangxi， Yunnan， Shaanxi， Gansu， Xinjiang， Chongqing， Qinghai， and Ningxia were expected to reach their peak before 2030， while three administrative regions of Inner Mongolia， Sichuan， and Guizhou might have some difficulties in achieving this carbon peak target. 2） in terms of carbon emission intensity reduction， the industrial sectors in the western region， except Inner Mongolia， were capable of reaching a reduction in carbon emission intensity by 60% to 65% compared to their 2005 levels. Notably， the carbon emissions in Guangxi， Chongqing， Shaanxi， Guizhou， and Yunnan were projected to decrease by more than 90%. 3） regarding emission reduction potential， the western region showed substantial overall potential， with Inner Mongolia， Qinghai， and Ningxia identified as the three regions with the highest emission reduction potential. Based on the characteristics of the industrial sectors in the western region， this paper proposed recommendations for adjusting the focus of emission reduction efforts， optimizing the energy structure， and developing dynamic peak control strategies. The primary recommendations are as follows： 1） classified regulation based on carbon emission trends across western administrative regions： administrative regions with a relatively strong foundation in carbon reduction may warrant a moderate relaxation of regulatory measures， provided that industrial carbon emissions continue to decline without rebounding. For regions that have transitioned from the growth phase to the reduction phase of carbon emissions， sustained regulation of industrial carbon reduction is essential to further decrease emissions and ensure the achievement of carbon peak targets. Conversely， regions with a weaker foundation in carbon reduction require prioritized monitoring of industrial carbon emissions. Regulatory efforts in these areas should be continuously adjusted according to their specific carbon emission levels to enhance the effectiveness of reduction measures. 2） focus on regions with high carbon reduction potential： regions with significant carbon reduction potential should be prioritized for increased policy interventions， and this can be achieved by advancing technological capabilities， promoting clean energy development， and optimizing industrial structures.
- industrial carbon emissions,
- wavelet neural network,
- the western region,
- prediction of carbon peaking,
- emission reduction potential analysis

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References

[1]	HUO T F，LI X H，CAI W J，et al. Exploring the impact of urbanization on urban building carbon emissions in China:Evidence from a provincial panel data model[J]. Sustainable Cities and Society，2020，56.
[2]	HU J B，ZHAO K，YANG Y H. Prediction and influencing factors of China's industrial carbon emission peak:an empirical analysis based on the BP-LSTM neural network model[J]. Guizhou Social Sciences，2021（9）:135-146. 胡剑波，赵魁，杨苑翰. 中国工业碳排放达峰预测及控制因素研究:基于BP-LSTM神经网络模型的实证分析[J]. 贵州社会科学，2021（9）:135-146.
[3]	LIU T W，BIAN X Y，WU S，et al. Overview and prospect of carbon emission accounting in electric power systems[J]. Power System Protection and Control，2024，52（4）:176-187. 刘天蔚，边晓燕，吴珊，等. 电力系统碳排放核算综述与展望[J]. 电力系统保护与控制，2024，52（4）:176-187.
[4]	CRESPO A M F，WANG C，CRESPO T M F，et al. Learning framework for carbon emissions predictions incorporating a RReliefF driven features selection and an iterative neural network architecture improvement[J]. SN Applied Sciences，2021，3（4）:1-14.
[5]	ZHAO J J，KOU L，WANG H T，et al. Carbon emission prediction model and analysis in the yellow river basin based on a machine learning method[J]. Sustainability，2022，14（10）:6098.
[6]	LIU Z，WANG Z L，YUAN C J. Impact of independent technological innovation on industrial carbon emissions and trend prediction from the perspective of structure[J]. China Population，Resources and Environment，2022，32（7）:12-21. 刘朝，王梓林，原慈佳. 结构视域下自主技术创新对工业碳排放的影响及趋势预测[J]. 中国人口·资源与环境，2022，32（7）:12-21.
[7]	LIAN Y Q，SUN D H，SHI S X. Carbon peak prediction in fujian province based on combined STIRPAT and CNN-LSTM models[J/OL]. Environmental Science:1-15.（2024-04-28）[ 2024-08-01] https://link.cnki.net/urlid/11.1895.X.20240428.1322.002. 连艳琼，苏墩煌，施生旭. 基于STIRPAT和CNN-LSTM组合模型的福建省碳达峰预测[J/OL]. 环境科学:1-15.（2024-04-28）[ 2024-08-01] https://link.cnki.net/urlid/11.1895.X.20240428.1322.002.
[8]	LIU H T，HU D W. Construction and analysis of machine learning based transportation carbon emission prediction model[J]. Environmental Science，2024，45（6）:3421-3432. 刘慧甜，胡大伟. 基于机器学习的交通碳排放预测模型构建与分析[J]. 环境科学，2024，45（6）:3421-3432.
[9]	ZHANG G M，WANG T，LOU Y Y，et al. Research on China’s provincial carbon emission peak path based on a LSTM Neural network approach[J/OL]. Chinese Journal of Management Science:1-12.（2023-11-15）[ 2024-08-01] https://doi.org/10.16381/j.cnki.issn1003-207x.2022.0097. 章高敏，王腾，娄渊雨，等. 基于LSTM神经网络的中国省级碳达峰路径分析[J/OL]. 中国管理科学:1-12.（2023-11-15）[ 2024-08-01] https://doi.org/10.16381/j.cnki.issn1003-207x.2022.0097.
[10]	SUN Y Z，SHU Q，JING C R，et al. Regional transport carbon emission forecasting and peak carbon pathway planning in China[J/OL]. Environmental Science:1-18.（2024-07-15）[ 2024-08-01] https://doi.org/10.13227/j.hjkx.202403152. 宋永朝，舒秦，金程容，等. 中国区域交通碳排放预测与碳达峰路径规划[J/OL]. 环境科学:1-18.（2024-07-15）[ 2024-08-01] https://doi.org/10.13227/j.hjkx.202403152.
[11]	WANG Q R，WANG J J，ZHU C F，et al. Carbon emission prediction of transportation industry based on VMD and SSA-LSSVM[J]. Environmental Engineering，2023，41（10）:124-132. 王庆荣，王俊杰，朱昌锋，等. 融合VMD和SSA-LSSVM的交通运输业碳排放预测研究[J]. 环境工程，2023，41（10）:124-132.
[12]	NIE W G，AO O，DUAN H M. A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application[J]. Environmental Science and Pollution Research，2023，30（8）:20704-20720.
[13]	HE X Q，SONG Y X，YU F M，et al. Applications of fractional order logistic grey models for carbon emission forecasting[J]. Fractal and Fractional，2024，8（3）:123.
[14]	DUAN H M，WANG D，PANG X Y，et al. A novel forecasting approach based on multi-kernel nonlinear multivariable grey model:a case report[J]. Journal of Cleaner Production，2020，260:121169.
[15]	LIU J B，YUAN X Y，LEE C C. Prediction of carbon emissions in China's construction industry using an improved grey prediction model[J]. Science of the Total Environment，2024，938:167892..
[16]	ZHANG Y D，LI X，ZHANG Y W. A novel integrated optimization model for carbon emission prediction:a case study on the group of 20[J]. Journal of Environmental Management，2023，344:118439.
[17]	ZHANG X S，WEI Z Z，CHEN Z Z，et al. Prediction of industrial carbon emissions in Shaanxi province based on LASSO-GWO-KELM model[J]. Environmental Engineering，2023，41（10）:141-149. 张新生，魏志臻，陈章政，等. 基于LASSO-GWO-KELM的工业碳排放预测方法研究[J]. 环境工程，2023，41（10）:141-149.
[18]	WEN S J，LIU Y Y，TANG F，et al. Driving forces and mitigation potential of CO₂ emissions for ship transportation in Guangdong Province[J]. Environmental Science，2024，45（1）:115-122. 翁淑娟，刘颍颖，唐凤，等. 广东省船舶二氧化碳排放驱动因素与减排潜力[J]. 环境科学，2024，45（1）:115-122.
[19]	LEI Y T，ZHANG X，SUN J J. Estimation and prediction of carbon emission reduction potential in China's manufacturing sector[J]. Statistics and decision，2023，39（4）:168-173. 雷玉桃，张萱，孙菁靖. 中国制造业部门碳减排潜力估算及预测[J]. 统计与决策，2023，39（4）:168-173.
[20]	GAO G L，WEN Y，WANG L，et al. Study on carbon peak of urban clusters based on analysis of influencing factors of carbon emissions[J]. Economic Management，2023，45（2）:39-58. 高国力，文扬，王丽，等. 基于碳排放影响因素的城市群碳达峰研究[J]. 经济管理，2023，45（2）:39-58.
[21]	GAO Y，FU Z W，ZHANG Z M. Analysis and prediction of carbon emission reduction potential of marine fishery in China[J]. Journal of Guangdong Ocean University，2022，42（3）:39-44. 高源，付忠伟，张兆敏. 中国海洋渔业碳排放减排潜力及预测[J]. 广东海洋大学学报，2022，42（3）:39-44.
[22]	ZHANG Q H A B. Wavelet networks[J]. IEEE Trans on Neural Networks，1992，3（5）:889-898.
[23]	Kreinovich V，Sirisaengtaksin O，Cabrera S. Wavelet neural networks are asymptotically optimal approximators for functions of one variable[J]. Proceeding of IEEE ICNN，1994，1:299-304.
[24]	ZHANG H，WANG X M，CAO J R，et al. A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series[J]. Applied Intelligence，2018，48（10）:3827-3838.
[25]	YIN L S，TANG S Q，LI S，et al. Traffic flow prediction based on hybrid model of auto-regressive integrated moving average and genetic particle swarm optimization wavelet neural network[J]. Journal of Electronics& Information Technology，2019，41（9）:2273-2279. 殷礼胜，唐圣期，李胜，等. 基于整合移动平均自回归和遗传粒子群优化小波神经网络组合模型的交通流预测[J]. 电子与信息学报，2019，41（9）:2273-2279.
[26]	CHEN Z M，WU S M，MA W B，et al. Driving forces of carbon dioxide emission for China’s cities:empirical analysis based on extended STIRPAT Mode[J]. China Population，Resource and Environment，2018，28（10）:45-54. 陈占明，吴施美，马文博，等. 中国地级以上城市二氧化碳排放的影响因素分析:基于扩展的STIRPAT模型[J]. 中国人口·资源与环境，2018，28（10）:45-54.
[27]	JIN P H，ZHAO C Y，AI Y L，et al. Fault detection of photovoltaic array based on particle swarm optimization wavelet neural network[J]. Engineering Journal of Wuhan University，2021，54（9）:860-865. 荆鹏辉，韩朝阳，艾永乐，等. 基于粒子群优化小波神经网络的光伏阵列故障检测[J]. 武汉大学学报（工学版），2021，54（9）:860-865.
[28]	LIANG C H，LIU X B，GU Z P，et al. Multi wavelet neighbor coefficient method for hybrid particle swarm optimization and its application[J]. Computer Integrated Manufacturing Systems，2022，28（3）:843-852. 梁春辉，刘晓波，辜振谱，等. 混合粒子群优化的多小波相邻系数法及其应用[J]. 计算机集成制造系统，2022，28（3）:843-852.
[29]	LI M S，ZHANG J H，LUO H J，et al. Sulphur dioxide reduction and potential in China[J]. Chinese Geographical Science，2011，31（9）:1065-1071. 李名升，张建辉，罗海江，等. 中国二氧化硫减排分析及减排潜力[J]. 地理科学，2011，31（9）:1065-1071.
[30]	LI Z X，SUN M. Dynamic effects of high-tech products import and innovation on productivity[J]. Statistics and Decision，2017（14）:101-104. 李志学，孙敏. 我国各省区碳退耦指数与减排潜力的测算[J]. 统计与决策，2017（14）:101-104.