基于因果推理的稳定时序模型，提升企业碳排放预测在政策与区域差异下的准确性。
Stable Time Series Prediction of Enterprise Carbon Emissions Based on Causal Inference
- 融合因果推断与稳定学习，识别对碳排放具有长期稳定影响的特征。
- 在跨区域、跨行业数据漂移下，预测误差显著降低，提升模型泛化能力。
- 适用于政策变动频繁或区域差异大的碳排放监管与低碳决策场景。
在碳达峰与碳中和目标背景下，企业碳排放趋势的准确预测是能源结构优化与低碳转型决策的重要基础。然而，不同地区、行业及企业间在能源结构、生产规模、政策强度与治理效能方面存在显著异质性，导致碳排放数据在时空维度上呈现明显分布漂移与非平稳性。这种跨区域、跨企业的数据漂移不仅影响碳排放报告的准确性，更严重削弱了预测模型对生产规划与碳配额交易决策的指导价值。为此，本文将因果推断与稳定学习方法结合，提出一种针对分布漂移环境的稳定时间序列预测机制。该机制整合企业级能源投入、资本投资、劳动力配置、碳定价、政府干预及政策执行强度等多维变量，构建风险一致性约束的稳定学习框架，从多元政策、区域与产业环境中提取对碳排放具有鲁棒性与长期稳定影响的因果特征。同时，通过自适应归一化与样本重加权策略，动态修正经济波动与政策变迁带来的时序非平稳性，最终提升模型在复杂环境中的泛化能力与可解释性。
原文摘要 · Abstract (English)
Against the backdrop of ongoing carbon peaking and carbon neutrality goals, accurate prediction of enterprise carbon emission trends constitutes an essential foundation for energy structure optimization and low-carbon transformation decision-making. Nevertheless, significant heterogeneity persists across regions, industries and individual enterprises regarding energy structure, production scale, policy intensity and governance efficacy, resulting in pronounced distribution shifts and non-stationarity in carbon emission data across both temporal and spatial dimensions. Such cross-regional and cross-enterprise data drift not only compromises the accuracy of carbon emission reporting but substantially undermines the guidance value of predictive models for production planning and carbon quota trading decisions. To address this critical challenge, we integrate causal inference perspectives with stable learning methodologies and time-series modelling, proposing a stable temporal prediction mechanism tailored to distribution shift environments. This mechanism incorporates enterprise-level energy inputs, capital investment, labour deployment, carbon pricing, governmental interventions and policy implementation intensity, constructing a risk consistency-constrained stable learning framework that extracts causal stable features (robust against external perturbations yet demonstrating long-term stable effects on carbon dioxide emissions) from multi-environment samples across diverse policies, regions and industrial sectors. Furthermore, through adaptive normalization and sample reweighting strategies, the approach dynamically rectifies temporal non-stationarity induced by economic fluctuations and policy transitions, ultimately enhancing model generalization capability and explainability in complex environments.
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