Ministry of Agriculture and Forestry, No:161, 06800, Çankaya, Ankara, Turkey.
J Environ Manage. 2023 Sep 15;342:118061. doi: 10.1016/j.jenvman.2023.118061. Epub 2023 May 16.
Importance of the carbon trading has been escalating expeditiously not only because of the environmentalist purposes to mitigate the adverse effects of climate change but also the increasing diversification benefits of the carbon emission contracts due to the low correlation between the emission, equity, and commodity markets. In accordance with the promptly rising significance of accurate carbon price prediction, this paper develops and compares 48 hybrid machine learning models by using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Permutation Entropy (PE), and multiple types of Machine Learning (ML) models optimized by Genetic Algorithm (GA). The outcomes of this study present the performances of the implemented models at different levels of mode decomposition and the impact of genetic algorithm optimization by comparing the key performance indicators that the CEEMDAN-VMD-BPNN-GA optimized double decomposition hybrid model outperforms the others with a striking R value of 0.993, RMSE of 0.0103, MAE of 0.0097, and MAPE of 1.61%.
重要性的碳交易一直在迅速升级不仅因为环保主义者的目的减轻气候变化的不利影响,而且由于排放、股票和商品市场之间的低相关性不断增加的多元化效益的碳排放合同。根据准确的碳价格预测迅速上升的意义,本文开发和比较 48 个混合机器学习模型使用完全集成经验模态分解自适应噪声(CEEMDAN)、变分模态分解(VMD)、排列熵(PE)和多种类型的机器学习(ML)模型优化遗传算法(GA)。本研究的结果呈现的执行模型在不同层次的模式分解和遗传算法优化的影响通过比较关键绩效指标的 CEEMDAN-VMD-BPNN-GA 优化双分解混合模型优于其他与引人注目的 R 值 0.993, RMSE 的 0.0103, MAE 的 0.0097 和 MAPE 的 1.61%。
