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[1] WANG Dawei, KUMAR Prashant, CAO Shijie,. Prediction of carbon emissions with historical data [J]. Journal of Southeast University (English Edition), 2026, 42 (1): 55-64. [doi:10.3969/j.issn.1003-7985.2026.01.005]
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Prediction of carbon emissions with historical data()
基于历史数据的碳排放预测

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
42
Issue:
2026 1
Page:
55-64
Research Field:
Environmental Science and Engineering
Publishing date:
2026-03-20

Info

Title:
Prediction of carbon emissions with historical data
基于历史数据的碳排放预测
Author(s):
WANG Dawei1,2,3, KUMAR Prashant1,4,5, CAO Shijie1,3,4
1.School of Architecture, Southeast University, Nanjing 210096, China
2.School of Architecture and Engineering, Jinling Institute of Technology, Nanjing 211169, China
3.Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, Nanjing 210096, China
4.Global Centre for Clean Air Research (GCARE), School of Engineering, Civil and Environmental, University of Surrey, Guildford GU2 7XH, UK
5.Institute for Sustainability, University of Surrey, Guildford GU2 7XH, UK
王大伟1,2,3, KUMAR Prashant1,4,5, 曹世杰1,3,4
1.东南大学建筑学院, 南京 210096
2.金陵科技学院建筑工程学院, 南京 211169
3.东南大学江苏省城市建成环境热污协控工程研究中心, 南京 210096
4.Global Centre for Clean Air Research (GCARE), School of Engineering, Civil and Environmental, University of Surrey, Guildford GU2 7XH, UK
5.Institute for Sustainability, University of Surrey, Guildford GU2 7XH, UK
Keywords:
carbon emissions;  historical data;  bootstrap;  assessment;  sustainable development
碳排放;  历史数据;  bootstrap;  评估;  可持续发展
PACS:
X24;X22
DOI:
10.3969/j.issn.1003-7985.2026.01.005
Abstract:
Reducing carbon emissions is fundamental to achieving carbon neutrality. Existing studies have typically estimated emissions by predicting fossil fuel consumption across sectors under different socioeconomic scenarios; however, uncertainties in future development often lead to deviations from these assumptions. To address this limitation, this study proposes a data-driven approach for evaluating national carbon emissions using historical data. Countries with similar energy consumption patterns were selected as reference samples, and their emission pathways were analyzed to predict future emissions for countries that have not yet reached their peak. Key indicators, including peak levels, timing, plateau duration, and post-peak decline rates, were identified. The results indicate that the trends in unpeaked economies can be effectively assessed based on the emission patterns of countries with comparable energy structures. Applying this framework to China suggests a carbon peak between 2027 and 2030, in the range of 14.207 to 16.234 Gt, followed by a gradual decline from 2031 to 2036. Compared with the average results of the existing studies, the predicted minimum and maximum emissions show error margins of 10.1% and 1.41%, respectively. This study proposes a top-down methodology that provides a transparent, reproducible, and empirical framework for forecasting carbon emission pathways, thereby offering a scientific basis for assessing countries that have not yet reached their emissions peak.
减少碳排放是实现碳中和的核心,但当前基于预设社会经济模型的排放预测常因现实发展的不确定性而产生偏差。为解决这一问题,本研究提出基于历史数据驱动的国家碳排放评估方法。选取能源结构相似的碳达峰国家作为参考样本,分析其排放轨迹,识别关键指标包括峰值排放水平、峰值时间、平台期持续时间及峰值后减排速率,评估未达峰国家碳排放。研究表明,根据具有相似能源结构的碳达峰国家碳排放模式可有效估算未达峰值国家的排放趋势。以中国为例,研究显示中国将在2027—2030年间达到碳排放峰值,峰值介于14.207~16.234 Gt之间。预计2031—2036年间碳排放从峰值波动阶段进入稳定下降阶段。将本研究估算的碳排放量与现有研究的平均值进行比较,最低碳排放量的误差为10.1%,而最高值的误差为1.41%。本研究提出一种自上而下的方法,为预测碳排放路径提供了透明、可重复且基于实证的框架,从而为评估尚未达到碳排放峰值的国家提供了科学依据。

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Memo

Memo:
Received: 2025-06-13; Revised: 2025-10-13.
Biographies: WANG Dawei (1984—), male, Ph.D.candidate; CAO Shijie (corresponding author), male, doctor, professor, shijie_cao@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China (No.52470211), Special Foundation of Jiangsu Province Science and Technology Plan (No.BZ2024017), RECLAIM Network Plus Project (No.EP/W034034/1).
Citation: WANG Dawei, KUMAR Prashant, CAO Shijie. Prediction of carbon emissions with historical data[J]. Journal of Southeast University (English Edition), 2026, 42(1): 55-64. DOI: 10. 3969/j. issn. 1003-7985. 2026. 01. 005.
Last Update: 2026-03-20