School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China; Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China.
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Sci Total Environ. 2021 Nov 20;796:149110. doi: 10.1016/j.scitotenv.2021.149110. Epub 2021 Jul 15.
Reasonable carbon price can effectively promote the low-carbon transformation of economy. The future carbon price has an important guiding significance for enterprises and the country. However, the nonlinear and high noise characteristics inherent in carbon price make them challenging to predict accurately. A hybrid decomposition and integration prediction model is proposed using the Hodrick-Prescott filter, an improved grey model and an extreme learning machine to solve this problem. First, a large number of factors that influence carbon price are collected by meta-analysis. The final input is selected through a two-stage feature selection process. Second, the HP filter is used to decompose the input into long-term trends and short-term fluctuations predicted by the improved GM and ELM, respectively. Finally, the two prediction sequences are compared to obtain the final result. European Union Allowances futures price data are applied for empirical analysis. The results show that the prediction performance of this model is better than the other 10 benchmark models, the T-bill, Stoxx50, S&P clean energy index and Brent oil price in the financial and energy markets are helpful in the carbon price's prediction. T-bill affects carbon price frequently, Stoxx50 has a negative correlation with the carbon price in the influence period. Under normal circumstances, the S&P clean energy index is positively correlated with the carbon price. However, when the economic situation is depressed, resulting in a short-term negative correlation between them. In general, carbon market is significantly affected by cross spill over between different markets. The method not only improves the accuracy of carbon price forecast, but also the application of the improved GM explains the reasons for the change of carbon price, which is helpful to promote the realization of carbon neutralization by market-oriented means.
合理的碳价可以有效地促进经济的低碳转型。未来的碳价对企业和国家具有重要的指导意义。然而，碳价固有的非线性和高噪声特征使得它们难以准确预测。为了解决这个问题，提出了一种混合分解和集成预测模型，该模型使用 Hodrick-Prescott 滤波器、改进的灰色模型和极限学习机。首先，通过荟萃分析收集了大量影响碳价的因素，通过两阶段特征选择过程选择最终输入。其次，HP 滤波器用于将输入分解为长期趋势和短期波动，分别由改进的 GM 和 ELM 预测。最后，将两个预测序列进行比较以得到最终结果。应用欧洲联盟配额期货价格数据进行实证分析。结果表明，该模型的预测性能优于其他 10 个基准模型，金融和能源市场中的 T 型票据、Stoxx50、S&P 清洁能源指数和布伦特油价对碳价的预测具有帮助。T 型票据经常影响碳价，Stoxx50 在影响期内与碳价呈负相关。在正常情况下，S&P 清洁能源指数与碳价呈正相关，但当经济形势低迷时，两者之间存在短期负相关。总体而言，碳市场受到不同市场之间交叉溢出的显著影响。该方法不仅提高了碳价预测的准确性，而且改进的 GM 的应用解释了碳价变化的原因，这有助于通过市场化手段实现碳中和。
