Leveraging Deep Learning for Carbon Market Price Forecasting and Risk Evaluation in Green Finance Under Climate Change

Carbon markets play an important role in combating climate change, and accurate carbon price forecasts and risk assessments are essential for effective policymaking and green finance. However, existing models face challenges in dealing with the complexity of carbon markets, especially in handling the integration of price data with structured and unstructured data such as policy documents. To overcome these problems, this paper proposes an innovative hybrid model, combining an autoencoder, bidirectional encoder representations from transformers (BERT) model, and Monte Carlos tree search (MCTS), called AutoBERT-MCTS. An autoencoder is used to extract market data features, BERT handles policy texts, and MCTS optimizes investment strategies in uncertain environments. The model effectively integrates multi-source data and optimizes the strategies, which is a powerful tool in the field of green finance. By enhancing carbon market prediction and risk management capabilities, this paper provides valuable insights for policy and investment decisions in climate change.