Pan Di, Zhang Chen, Zhu Dandan, Hu Shu
School of Management, Hefei University of Technology, Hefei, 230000, China.
Key Laboratory of Process Optimization and Intelligent Decision-Making, Hefei University of Technology, Hefei, 230000, China.
Environ Sci Pollut Res Int. 2023 Mar;30(11):28704-28717. doi: 10.1007/s11356-022-24186-z. Epub 2022 Nov 18.
The carbon market relies on market-oriented financial means to solve the problem of carbon emissions. An effective carbon pricing mechanism can improve market efficiency and better serve the implementation of carbon emission reduction. The limited attention of investors increases the uncertainty of carbon market volatility and is an important exogenous factor affecting the price of carbon assets. This study innovatively mines keywords of investor attention on the carbon market through online news texts and eliminates those that have no causal link to carbon price forecasting in order to reduce noise. The results show that the keyword extraction method based on news text mining is better than that of nontext mining. Meanwhile, a carbon price forecasting model based on a particle-swarm-optimization LSTM model structure is constructed, and the forecasting accuracy is improved. The results show that carbon market investors pay more attention to carbon quota supply and demand, carbon prices, environmental change, and the energy market. The results have important implications for the development of effective carbon market policies and risk management.
碳市场依靠市场化的金融手段来解决碳排放问题。有效的碳定价机制可以提高市场效率，更好地服务于碳排放减排的实施。投资者关注度有限增加了碳市场波动的不确定性，是影响碳资产价格的重要外部因素。本研究创新性地通过在线新闻文本挖掘投资者对碳市场的关注关键词，并剔除与碳价预测无因果关系的关键词以减少噪声。结果表明，基于新闻文本挖掘的关键词提取方法优于非文本挖掘方法。同时，构建了基于粒子群优化LSTM模型结构的碳价预测模型，提高了预测精度。结果表明，碳市场投资者更关注碳配额供需、碳价、环境变化和能源市场。研究结果对有效碳市场政策的制定和风险管理具有重要意义。
