AI 中文总结
本研究针对新兴碳市场碳信用价格预测的痛点，构建了可解释且感知政策的EPA-CarbonNet模型，经11年标普碳指数数据测试，其方向准确率优于所有基准，但五日RMSE不及随机游走模型。
AI 中文摘要
碳市场为碳排放定价，但该价格仍难以预测。现有研究多聚焦欧盟和中国碳市场方案，将监管文本压缩为情感得分，仅报告准确率而未进行校准或解释稳定性分析。我们提炼出十大共性缺口，构建了影响-可行性矩阵，并提出EPA-CarbonNet，这是一个六层架构，通过交叉注意力机制和校准区间融合市场序列与政策文本，同时生成归因于政策的解释。随后，我们利用11年的标普碳指数日度数据对该模型进行构建与测试。研究结果总体为负面，具体测量结果如下：随机游走模型在五日均方根误差（RMSE）上优于本模型（0.0365对比0.0475）；SHAP排名在重采样背景下的相关系数ρ=0.54；政策注意力从未与已记录的监管事件重合。而方向准确率达58.6%，优于所有基准模型。代码、数据文档及所有结果工件可在该https链接获取。
英文摘要
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
Comments7 pages, 4 figures, 7 tables
