Research on the Influencing Factors of Carbon Emissions in the Construction Industry of Hunan Province and Peak Prediction
湖南省建築産業の炭素排出影響要因とピーク予測に関する研究 (AI 翻訳)
Linghong Zeng, Yuhang He, Haidong Wang
🤖 gxceed AI 要約
日本語
本研究は、2005～2022年の湖南省建設業の炭素排出データを用い、排出係数法、STIRPATモデル、CNN-LSTM-Attentionハイブリッド深層学習モデルを組み合わせて排出要因分析とピーク予測を行った。結果、間接排出が主要であり、低炭素シナリオが2030年までの業界ピーク達成に最適と結論付けた。
English
Using 2005–2022 carbon emission data from Hunan's construction sector, this study applies the emission coefficient method, STIRPAT model with ridge regression, and a CNN-LSTM-Attention deep learning model to analyze driving factors and predict peak emissions. Findings show indirect emissions dominate and a low-carbon scenario is optimal for achieving the 2030 peak target.
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
中国湖南省の建設業に特化した事例だが、カーボンアカウンティング手法やAI予測モデルは日本の建設業や自治体の脱炭素計画にも応用可能。特にSSBJ基準に基づく排出量算定や地域別ピーク予測の参考になる。
In the global GX context
While focused on a Chinese province, the methodological blend of STIRPAT and deep learning offers a replicable framework for regional construction sector decarbonization planning. This is relevant as global disclosure frameworks like ISSB and TCFD increasingly demand forward-looking emission scenarios.
👥 読者別の含意
🔬研究者:The hybrid CNN-LSTM-Attention model for emission prediction and the STIRPAT-based factor decomposition provide a robust methodological template for regional carbon accounting studies.
🏢実務担当者:Construction firms can use the identified driving factors and scenario analysis to prioritize emission reduction levers and align with local government peak targets.
🏛政策担当者:The low-carbon scenario analysis offers evidence for designing effective carbon peak implementation plans at the provincial level, applicable to other regions with similar development stages.
📄 Abstract（原文）
In accordance with the national strategy of “carbon peaking by 2030 and carbon neutrality by 2060” and Hunan Province’s target of achieving carbon peaking in the construction sector by 2030, this study uses carbon emission data from Hunan’s construction sector for the period 2005–2022 as a research sample to conduct research on carbon emission accounting, analysis of influencing factors, and peak prediction. The carbon emission coefficient method was employed to calculate industry-wide carbon emissions. Using the STIRPAT model combined with ridge regression, we identified and quantified the driving factors of carbon emissions. A CNN-LSTM-Attention hybrid deep learning model was constructed, and three development scenarios—high-carbon, baseline, and low-carbon—were established to simulate the evolution of carbon emissions in Hunan’s construction industry from 2023 to 2040. The results indicate that carbon emissions from Hunan’s construction industry showed an overall upward trend during the study period, with indirect emissions constituting the primary component. Through variable optimization, the core positive drivers and negative restraints of carbon emissions in the construction industry were identified. The constructed hybrid model demonstrated excellent fitting performance, with prediction accuracy significantly higher than that of traditional machine learning and single deep learning models. Carbon emission trends varied significantly across different development scenarios, with the low-carbon development scenario identified as the optimal path for achieving the industry’s carbon peak target. These findings provide a theoretical basis and data support for the low-carbon transition of Hunan Province’s construction sector, as well as for the formulation and optimization of carbon peaking implementation plans.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.3390/buildings16091816first seen 2026-05-16 04:40:10 · last seen 2026-05-16 04:40:15
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