State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang, Jiangxi 330013, China.
School of Information Engineering, East China University of Technology, Nanchang, Jiangxi 330013, China.
Comput Intell Neurosci. 2022 Jul 7;2022:6822467. doi: 10.1155/2022/6822467. eCollection 2022.
Since China's reform and opening up, the social economy has achieved rapid development, followed by a sharp increase in carbon dioxide (CO) emissions. Therefore, at the 75th United Nations General Assembly, China proposed to achieve carbon peaking by 2030 and carbon neutrality by 2060. The research work on advance forecasting of CO emissions is essential to achieve the above-mentioned carbon peaking and carbon neutrality goals in China. In order to achieve accurate prediction of CO emissions, this study establishes a hybrid intelligent algorithm model suitable for CO emissions prediction based on China's CO emissions and related socioeconomic indicator data from 1971 to 2017. The hyperparameters of Least Squares Support Vector Regression (LSSVR) are optimized by the Adaptive Artificial Bee Colony (AABC) algorithm to build a high-performance hybrid intelligence model. The research results show that the hybrid intelligent algorithm model designed in this paper has stronger robustness and accuracy with relative error almost within ±5% in the advance prediction of CO emissions. The modeling scheme proposed in this study can not only provide strong support for the Chinese government and industry departments to formulate policies related to the carbon peaking and carbon neutrality goals, but also can be extended to the research of other socioeconomic-related issues.
自改革开放以来，中国社会经济快速发展，随之而来的是二氧化碳（CO）排放量的急剧增加。因此，在第 75 届联合国大会上，中国提出了力争 2030 年前实现碳达峰、2060 年前实现碳中和的目标。开展 CO 排放提前预测的研究工作，对于实现中国碳达峰、碳中和目标至关重要。为了实现 CO 排放的准确预测，本研究基于中国 1971 年至 2017 年的 CO 排放及相关社会经济指标数据，建立了一种适用于 CO 排放预测的混合智能算法模型。采用自适应人工蜂群（AABC）算法优化最小二乘支持向量回归（LSSVR）的超参数，构建高性能混合智能模型。研究结果表明，该文设计的混合智能算法模型在 CO 排放提前预测中具有更强的稳健性和准确性，相对误差几乎都在±5%以内。本研究提出的建模方案不仅可为中国政府和行业部门制定与碳达峰、碳中和目标相关的政策提供有力支持，还可推广应用于其他与社会经济相关的问题研究。
