School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province 116025, China.
Institute of Systems Engineering, Macau University of Science and Technology, Taipa Street, Macau, China.
J Environ Manage. 2022 Jan 15;302(Pt A):113951. doi: 10.1016/j.jenvman.2021.113951. Epub 2021 Oct 20.
Carbon emissions play a crucial role in inducing global warming and climate change. Accurate and stable carbon emissions forecasting is beneficial for formulating emissions reduction schemes and achieving carbon neutrality as early as possible. Although previous studies have concentrated on employing one or several methods for carbon emissions forecasting, the improvement in forecasting performance is limited because they ignore the importance of objectively selecting the models and the necessity of interval forecasting. In this paper, a novel ensemble prediction system, composed of data decomposition, model selection, phase space reconstruction, ensemble point prediction, and interval prediction, is proposed to conduct both point and interval predictions, which has been proven to be effective in prompting carbon emissions forecasting accuracy and stability. According to the empirical results, the mean MAPE results of our proposed forecasting strategy in point prediction are 1.1102% (in Dataset A) and 1.1382% (in Dataset B), and the mean CWC values in the interval forecasting are 0.3512 and 0.1572, respectively. Thus, the proposed forecasting system improves the forecasting performance relative to other models considerably, which can provide meaningful references for policymakers.
碳排放在引发全球变暖及气候变化方面起着至关重要的作用。准确且稳定的碳排放预测有利于制定减排方案，尽早实现碳中和。尽管先前的研究集中于采用一种或多种方法进行碳排放预测，但由于它们忽略了客观选择模型的重要性和区间预测的必要性，因此预测性能的提升较为有限。在本文中，提出了一种新颖的集成预测系统，由数据分解、模型选择、相空间重构、集成点预测和区间预测组成，用于进行点预测和区间预测，事实证明，该系统在提高碳排放预测准确性和稳定性方面非常有效。根据经验结果，我们提出的预测策略在点预测中的平均 MAPE 结果分别为 1.1102%（在数据集 A 中）和 1.1382%（在数据集 B 中），在区间预测中的平均 CWC 值分别为 0.3512 和 0.1572。因此，与其他模型相比，所提出的预测系统大大提高了预测性能，可为政策制定者提供有意义的参考。
