中国林业经济

碳交易市场的价格变化作为衡量市场运行的核心指标,为碳排放权交易市场监管者和投资者提供决策依据与参考。受碳价非线性、高噪声和强波动特性的影响,碳排放权价格的预测研究面临较大挑战。基于上海碳市场2013—2023年的每日成交均价,构建了ARIMA-LSTM组合模型,首先运用ARIMA模型对数据进行预测,捕捉碳价的线性与周期性趋势。其次,将ARIMA模型对碳价线性预测产生的残差作为LSTM模型的输入,学习序列中的非线性部分。最后,将两种模型的结果进行加权组合,得到未来5天的预测数据。实验结果显示,ARIMA-LSTM组合模型在碳价预测精度和结果解释性上均优于单一的ARIMA或LSTM模型,能够更好地捕捉碳市场价格的波动趋势。为市场参与者了解碳排放权定价的内在机制提供了帮助,有助于推动我国碳市场可持续发展。

The price changes in the carbon trading market, as the core indicator for measuring the market operation, provide decision-making basis and reference for the regulators and investors of the carbon emission rights trading market. Affected by the nonlinear, high noise and strong fluctuation characteristics of carbon prices, the prediction research of carbon emission rights prices faces considerable challenges. Based on the daily average transaction prices of the Shanghai carbon market from₂₀₁₃ to_2023, this article constructs the ARIMA-LSTM combined model. Firstly, the ARIMA model is used to predict the data to capture the linear and cyclical trends of carbon prices. Secondly, the residuals generated by the linear prediction of carbon prices by the ARIMA model are taken as the input of the LSTM model to learn the nonlinear parts in the sequence. Finally, the results of the two models are weighted and combined to obtain the final prediction data. The experimental results show that the ARIMA-LSTM combined model is superior to the single ARIMA or LSTM models in both the prediction accuracy of carbon prices and the interpretability of the results, and can better capture the fluctuation trend of carbon market prices. This study would provide assistance for market participants to understand the internal mechanism of carbon emission rights pricing, and be conducive to promoting the sustainable development of China's carbon market.