Stable Time Series Prediction of Enterprise Carbon Emissions Based on Causal Inference
因果推論に基づく企業炭素排出量の安定した時系列予測 (AI 翻訳)
Zitao Hong, Zhen Peng, Xueping Liu
🤖 gxceed AI 要約
日本語
企業の炭素排出量予測は地域・業種間の不均一性により困難。本研究は因果推論と安定学習を組み合わせ、分布シフトに頑健な予測メカニズムを提案。政策や経済変動に対応した一般化能力の高いモデルを開発する。
English
This study addresses the challenge of predicting enterprise carbon emissions amid distribution shifts across regions, industries, and policies. By integrating causal inference with stable learning, it proposes a time-series prediction mechanism that extracts robust causal features and adapts to non-stationarity, improving model generalization and explainability for carbon management decisions.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の企業は炭素排出量開示や排出権取引において予測モデルの精度向上が求められている。本研究の因果推論に基づく安定予測手法は、日本企業のエネルギー構造や政策変化に対応した排出量予測に応用可能であり、SSBJ下での開示充実にも寄与し得る。
In the global GX context
This paper presents a novel stable learning framework for enterprise carbon emission prediction that addresses real-world distribution shifts. Its causal feature extraction and adaptive normalization are relevant for global corporate carbon accounting and policy design, especially as firms face heterogeneous regulations and strive for accurate Scope 1 reporting.
👥 読者別の含意
🔬研究者:The paper introduces a causal inference-based stable learning approach for time-series prediction that can be built upon for carbon emission forecasting in heterogeneous settings.
🏢実務担当者:The proposed model can help enterprises improve carbon emission reporting and decision-making for production planning and carbon trading by accounting for regional and policy variations.
🏛政策担当者:The framework's ability to handle policy transitions and regional heterogeneity can inform carbon pricing impact assessments and regulatory adjustments.
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.48550/arxiv.2602.00775first seen 2026-05-05 22:54:32
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