Stable Time Series Prediction of Enterprise Carbon Emissions Based on Causal Inference
基于因果推断视角的企业碳排放稳定时间序列预测AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要核心是非平稳碳排放序列在分布漂移下的预测与泛化；因果视角的表述尚未明确对应处理效应识别。
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基于因果推断视角的企业碳排放稳定时间序列预测
- 研究问题
- 面对企业碳排放数据在时间、地区和行业之间的分布漂移，如何提取稳定特征并提高未来排放预测的泛化能力？
- 方法与思路
- 作者结合稳定学习与时间序列建模，在多环境样本中设置风险一致性约束，并通过自适应归一化和样本重加权处理非平稳性。
- 材料陈述的贡献
- 摘要提出面向分布变化的稳定预测框架，称其可提取具有长期稳定作用的特征，改善复杂环境中的泛化与可解释性。
- 阅读关注点
- 核对环境划分、风险一致性约束及重加权规则，尤其检查因果稳定特征的依据和跨时间、跨企业预测的验证设计。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未获核实；摘要未提供数据规模、比较结果或因果识别条件，改善程度无法判断。
原文摘要
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.