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A Bidirectional Coupling Study on Carbon Emission Forecasting and Energy Planning for Zero-Carbon Industrial Parks: A Case Study of a Chemical Park in Northern China

ゼロカーボン工業団地の炭素排出予測とエネルギー計画に関する双方向連成研究：中国北部の化学工業団地を事例として (AI 翻訳)

Meirong Li, Jing Guo, Dengyi Chen, Xiaoxiao Zhou, Jie Chen, Haoran Leng

Springer Link (Chiba Institute of Technology)📚 査読済 / ジャーナル2026-07-27#炭素会計Origin: CN経営インパクト: コスト削減対象セクター: manufacturing
DOI: 10.1051/e3sconf/202672801023/pdf
原典: https://doi.org/10.1051/e3sconf/202672801023/pdf
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🤖 gxceed AI 要約

日本語

本論文は、拡張Kaya恒等式に基づく炭素排出予測モデルを開発し、中国北部の化学工業団地を対象に実証分析を行った。モデルは5つのマクロパラメータに分解し、高い解釈可能性と政策適合性を示した。ゼロカーボン工業団地のエネルギー計画への明確な意思決定支援を提供する。

English

This paper develops a top-down carbon emission forecasting model based on an extended Kaya identity, applied to a chemical industrial park in Northern China. The model decomposes emissions into five macro parameters, offering strong interpretability and policy alignment. It provides clear decision support for zero-carbon energy planning in industrial parks.

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

本論文の拡張Kaya恒等式による予測手法は、日本の工業団地や産業集積地の脱炭素計画にも応用可能。ただし、日本のSSBJや有報への直接的な関連は薄いが、地域レベルの排出量予測とエネルギー計画の連携に示唆を与える。

In the global GX context

This paper offers an interpretable carbon emission forecasting model for industrial parks, contributing to the global challenge of decarbonizing industrial clusters. The Kaya identity approach is adaptable across regions and supports alignment with national climate targets, such as China's dual-carbon goals, and can inform similar efforts elsewhere.

👥 読者別の含意

🔬研究者:This paper provides a forecasting model with strong interpretability for industrial park emissions, useful for researchers developing regional carbon accounting tools.

🏢実務担当者:Corporate sustainability teams in industrial park management can use this model for energy planning and emission reduction target setting.

🏛政策担当者:Policymakers can adopt the model to evaluate decarbonization pathways for industrial zones.

📄 Abstract（原文）

Industrial parks are a major source of carbon emissions in China, and their zero-carbon transition is critical for achieving the dual-carbon goals. However, existing carbon emission forecasting models of parks commonly suffer from a lack of interpretability, making it difficult to establish clear links between predictions and specific mitigation measures. This paper develops a top-down carbon emission forecasting model based on an extended Kaya identity, decomposing park carbon emissions into five macro-level parameters: park area, land use type, output value per unit area, energy intensity, and carbon intensity. A chemical park in Northern China is selected as an empirical case for validation. The results demonstrate that the proposed model exhibits strong interpretability and policy alignment, providing clear decision-making support for zero-carbon energy planning in industrial parks.

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