Li Yanan, Zhang Xinsheng, Wang Minghu
School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
J Environ Manage. 2025 Feb;374:124035. doi: 10.1016/j.jenvman.2025.124035. Epub 2025 Jan 10.
Accurately predicting carbon prices is crucial for effective government decision-making and maintenance the stable operation of carbon markets. However, the instability and nonlinearity of carbon prices, driven by the complex interaction between economic, environmental, and political factors, often result in inaccurate predictions. To confront this challenge, this paper proposed a carbon price prediction model that integrates dual decomposition integration and error correction. Firstly, the variational mode decomposition optimized by the sparrow search algorithm (SVMD) is used to decompose carbon price series into intrinsic mode functions (IMFs). Secondly, a classification-prediction module is constructed to classify IMFs on complexity using fuzzy entropy. The long short-term memory networks optimized by the whale optimization algorithm (WLSTM) is employed to capture temporal dynamics and long-term dependencies within data. Conversely, lower complexity IMFs characterized by smoother trends and less erratic behavior are predicted using computationally efficient extreme learning machines (ELM). To further refine the prediction accuracy, ensemble empirical mode decomposition (EEMD) is introduced to decompose the initially predicted error series into IMFs and then predicted by classification-prediction module. Reconstruct the initial prediction IMFs and the error prediction IMFs to obtain the final prediction results. Finally, the proposed model was validated using real carbon price data from three Chinese carbon exchanges. Compared with the 15 comparison models, the performance indicators RMSE, MAE, MAPE, and R of the proposed model have promoted at least 19.89%, 25.11%, 25.01%, and 0.79% on average. These results underscore the effectiveness and superiority in predicting carbon prices, providing a robust tool for carbon market stakeholders and climate change policymakers.
准确预测碳价格对于政府有效决策和维持碳市场的稳定运行至关重要。然而，由于经济、环境和政治因素之间的复杂相互作用，碳价格的不稳定性和非线性往往导致预测不准确。为应对这一挑战，本文提出了一种集成对偶分解集成和误差校正的碳价格预测模型。首先，使用经麻雀搜索算法优化的变分模态分解（SVMD）将碳价格序列分解为固有模态函数（IMF）。其次，构建分类预测模块，利用模糊熵对IMF按复杂性进行分类。采用经鲸鱼优化算法优化的长短期记忆网络（WLSTM）来捕捉数据中的时间动态和长期依赖性。相反，对于趋势较平滑且行为较不不稳定的低复杂性IMF，则使用计算效率高的极限学习机（ELM）进行预测。为进一步提高预测精度，引入总体经验模态分解（EEMD）将初始预测误差序列分解为IMF，然后由分类预测模块进行预测。将初始预测IMF和误差预测IMF进行重构以获得最终预测结果。最后，利用中国三个碳交易所的实际碳价格数据对所提出的模型进行了验证。与15个对比模型相比，所提出模型的性能指标RMSE、MAE、MAPE和R平均提升了至少19.89%、25.11%、25.01%和0.79%。这些结果强调了该模型在预测碳价格方面的有效性和优越性，为碳市场利益相关者和气候变化政策制定者提供了一个强大的工具。
