School of Mathematics and Statistics, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China.
Graduate School, Changchun University of Technology, No. 2055 Yan'an Street, Chaoyang District, Changchun, 130012, China.
Environ Sci Pollut Res Int. 2023 Jan;30(3):5730-5748. doi: 10.1007/s11356-022-22302-7. Epub 2022 Aug 18.
Global warming has constituted a major global problem. Carbon dioxide emissions from the burning of fossil fuels are the main cause of global warming. Therefore, carbon dioxide emission forecasting has attracted widespread attention. Aiming at the problem of carbon dioxide emissions forecasting, this paper proposes a new hybrid forecasting model of carbon dioxide emissions, which combines the marine predator algorithm (MPA) and multi-kernel support vector regression. For further strengthening the prediction accuracy, a novel variant of MPA is proposed, called EGMPA, which introduces the elite opposition-based learning strategy and the golden sine algorithm into MPA. Algorithm test results show that EGMPA can effectively improve the convergence speed and optimization accuracy. The carbon dioxide emission data of China from 1965 to 2020 are taken as the research objects. Root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed model. The proposed multi-kernel support vector regression model is used to forecast China's carbon dioxide emissions during the "14th Five-Year Plan" period. The results show that the proposed model has RMSE of 37.43 Mt, MAE of 30.63 Mt, and MAPE of 0.32%, which significantly improves the prediction accuracy and can accurately and effectively predict China's carbon dioxide emissions. During the "14th Five-Year Plan" period, China's carbon dioxide emissions will continue to show an increasing trend, but the growth rate will slow down significantly.
全球变暖已经构成了一个重大的全球问题。化石燃料燃烧产生的二氧化碳排放是全球变暖的主要原因。因此，二氧化碳排放预测引起了广泛关注。针对二氧化碳排放预测问题，本文提出了一种新的二氧化碳排放混合预测模型，该模型结合了海洋捕食者算法（MPA）和多核支持向量回归。为了进一步提高预测精度，提出了一种新的 MPA 变体，称为 EGMPA，它将精英反对学习策略和黄金正弦算法引入 MPA 中。算法测试结果表明，EGMPA 可以有效地提高收敛速度和优化精度。以 1965 年至 2020 年中国的二氧化碳排放数据为研究对象，采用均方根误差（RMSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）来评估所提出模型的性能。采用所提出的多核支持向量回归模型对中国“十四五”期间的二氧化碳排放量进行预测。结果表明，所提出的模型具有 37.43Mt 的 RMSE、30.63Mt 的 MAE 和 0.32%的 MAPE，显著提高了预测精度，可以准确有效地预测中国的二氧化碳排放量。在“十四五”期间，中国的二氧化碳排放量将继续呈上升趋势，但增长率将显著放缓。
