Abstract：
Carbon price is a pivotal element in the carbon trading sector. Accurate estimation of carbon price can offer precise guidance for the carbon market participants. This study introduces a novel prediction model encompassing both point and interval prediction for the carbon price. Firstly, to distill the volatility traits inherent in carbon price, the successive variational mode decomposition is utilized to adaptively decompose the carbon price into regular sequences. Secondly, to obtain the optimal input variables, the partial autocorrelation function and random forest are employed to filter the influencing factors and historical carbon price. Then, to avoid single model constraint, a combination model of categorical boosting and kernel extreme learning machine optimized by the sparrow search algorithm is employed for the point prediction, and the shapley additive explanation is employed to elucidate the model prediction process. Finally, to provide more efficient information, the adaptive bandwidth kernel density estimation is applied to the interval prediction. The data from Hubei carbon market is adopted as a case study, and the results indicate that the mean absolute error, mean absolute percentage error, root mean square error and R2 of the proposed model are 0.1022, 0.0022, 0.1262 and 0.9921, respectively. The historical carbon price, Brent crude oil futures settlement price and European Union allowance futures carbon price have a positive impact on carbon price, and Hushen 300 has a negative impact on carbon price. Compared with the constant kernel density estimation, the proposed model achieves higher interval coverage probability and lower interval width. Thus, the application of the hybrid model can promote the operational efficiency of the carbon market and facilitate the implementation of carbon emission reduction policies.
摘要：
碳价格是碳交易领域的关键要素。碳价格的准确估算可以为碳市场参与者提供准确的指导。本研究引入了一种新颖的预测模型，该模型包含碳价格的点和区间预测。首先,为了提炼出碳价固有的波动性特征，利用连续变分模态分解将碳价自适应分解为规则序列。其次,为了获得最佳输入变量，利用偏自相关函数和随机森林对影响因素和历史碳价格进行筛选。然后，为了避免单一模型约束，采用麻雀搜索算法优化的分类提升和核极限学习机的组合模型进行点预测，并采用shapley加性解释来阐明模型预测过程。最后,为了提供更有效的信息，将自适应带宽核密度估计应用于区间预测。以湖北碳市场数据为例，结果表明，平均绝对误差，平均绝对百分比误差，模型的均方根误差和R2分别为0.1022、0.0022、0.1262和0.9921。历史碳价格，布伦特原油期货结算价和欧盟配额期货碳价格对碳价格有正向影响,和沪深300对碳价有负面影响。与常数核密度估计相比，该模型实现了更高的区间覆盖概率和更低的区间宽度。因此，混合模式的应用可以促进碳市场的运行效率，促进碳减排政策的实施。
