提出可解释的碳信用价格预测框架，融合政策文本与市场数据。
Toward Explainable and Policy-Aware AI for Carbon Credit Price Prediction: A Research Framework for Emerging Carbon Markets
- 用跨注意力融合市场数据与政策文本，实现政策可解释性。
- 五日均方根误差为0.0475，低于随机游走的0.0365。
- 适用于关注碳市场政策影响的金融研究者与监管机构。
碳市场为排放定价，但价格难以预测。现有研究多集中于欧盟与中国方案，将监管文本压缩为情感分数，且未进行校准或解释稳定性评估。本文归纳出十个常见问题，构建影响-可行性矩阵，并提出EPA-CarbonNet，一个六层架构，通过交叉注意力融合市场序列与政策文本，结合校准区间与政策归因解释。在11年每日标普碳指数数据上测试，结果整体偏负面：随机游走在五日均方根误差上优于模型（0.0365对0.0475），SHAP排名在重采样背景下相关系数为0.54，政策注意力从未与已知监管事件重合。方向准确率为58.6%，高于所有基线。代码、数据文档及所有结果文件可在https://github.com/Kimalice/Toward-Explainable-and-Policy-Aware-AI-for-Carbon-Credit-Price-Prediction获取。
原文摘要 · Abstract (English)
Carbon markets put a price on emissions, yet that price remains hard to forecast. Work in this area clusters on the EU and Chinese schemes, compresses regulatory text into a sentiment score, and reports accuracy without calibration or explanation stability. We distil ten recurring gaps into an impact-feasibility matrix and propose EPA-CarbonNet, a six-layer architecture that fuses market series with policy text by cross-attention and calibrated intervals alongside policy-attributed explanations. We then build and test it on eleven years of daily S and P carbon index data. The findings are largely negative, and reported as measured: a random walk beats the model on five-day RMSE (0.0365 against 0.0475), SHAP rankings agree at rho = 0.54 across resampled backgrounds, and policy attention never coincides with documented regulatory events. Directional accuracy, at 58.6 percent, leads every baseline. Code, data documentation and all result artifacts are available at https://github.com/Kimalice/Toward-Explainable-and-Policy-Aware-AI-for-Carbon-Credit-Price-Prediction
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。