A Satellite-Driven Model for Monitoring Urban Material Metabolism, Embodied Emissions, and Carbonation
衛星画像駆動型モデルによる都市の物質代謝、体化排出、炭酸化のモニタリング (AI 翻訳)
Yu Nie, Ting Mao, Yupeng Liu, Yinhuan Chen, Yingziwei Liu, Wei-Qiang Chen
🤖 gxceed AI 要約
日本語
本研究は機械学習と衛星画像を用いて、中国・厦門市の30年にわたる都市代謝（建築ストックの増加、材流入・廃棄フロー、体化排出、セメント炭酸化による炭素吸収）をボトムアップで定量化した。建築物の高さ推定R²=0.81、建設・解体年のF1スコア0.89・0.81と高精度で、移転可能なフレームワークを提供する。
English
This study uses machine learning on satellite imagery to reconstruct Xiamen, China's 30-year urban metabolism: building stock growth, material flows, embodied emissions, and cement carbonation offsetting 6.6% of lifecycle emissions. It achieves high accuracy (height R²=0.81, construction/demolition F1=0.89/0.81) and offers a transferable framework for urban carbon accounting.
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
日本ではSSBJや有報でのGHG開示が進む中、都市スケールのカーボンアカウンティング手法は自治体や不動産セクターのScope 3対応にも応用可能。特に建築物のライフサイクル排出と炭酸化吸収の定量化は、日本のカーボンニュートラル計画における都市緑化政策と統合できる。
In the global GX context
Globally, the framework aligns with emerging urban carbon accounting standards (e.g., GPC, C40) and supports net-zero transitions. The integration of remote sensing AI with LCA provides a scalable method for cities to track embodied emissions and carbon sinks, relevant for ISSB climate disclosures and urban climate action plans.
👥 読者別の含意
🔬研究者:Demonstrates a transferable AI+GIS+LCA framework for urban metabolism studies, with high potential for adaptation to other cities.
🏢実務担当者:Urban planners and sustainability officers can use this approach to quantify building-stock emissions and carbonation sinks, informing low-carbon urban development.
🏛政策担当者:Provides evidence for integrating satellite-based monitoring into national GHG inventories and urban climate policy, particularly for embodied emissions and cement carbonation.
📄 Abstract（原文）
Urban systems are central to global material consumption and carbon emissions. However, systematically understanding urban metabolism remains a challenge due to the reliance on aggregated, top-down data which fails to capture fine-scale urban dynamics. To address this challenge, we developed an integrated, bottom-up framework and reconstructed the 30-year metabolic history of Xiamen, China. Our approach leverages a custom machine learning workflow on multitemporal, open-source satellite imagery to create a dynamic 3D Building Dynamics (3D-BD) model. This model accurately maps key building attributes (footprint, height, type, and construction/demolition year), achieving an R2 of 0.81 for height estimation and F1-scores of 0.89 and 0.81 for detecting construction and demolition years, respectively. This high-resolution database then drives a GIS-MFA and LCA model to quantify material stocks, flows, embodied emissions across key lifecycle stages, and the often-overlooked carbon sink from cement carbonation. Results for Xiamen revealed a 5-fold increase in building stock, with material inflows peaking in the early 2000s while demolition outflows steadily rise, signaling a shift to urban renewal. Over the study period, cement carbonation provided a significant, distributed carbon sink, offsetting an estimated 6.6% of the building stock's total lifecycle embodied emissions. This transferable framework provides a more complete accounting of the urban carbon budget, offering a powerful tool for guiding sustainable planning and net-zero transitions.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.1021/acs.est.5c03058first seen 2026-06-29 04:47:24
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