Big Data in Green Regional Development
Xuandong Zhang, Yankui Su, Jinjiang Li, Daohan Zhang
🤖 gxceed AI 要約
日本語
本研究は、デジタルトレースデータ（携帯電話信号、POIチェックイン、交通データなど）を用いて低炭素都市化を予測する手法を提案。時空間グラフ畳み込みネットワークとLSTMを組み合わせたハイブリッドモデルを構築し、従来手法では捉えられなかった炭素排出要因を特定。ソーシャルメディアのデータが低炭素ガバナンスの先行指標となる可能性を示した。
English
This study proposes a method using digital trace data (mobile phone signals, POI check-ins, traffic data, etc.) to predict low-carbon urbanization. A hybrid model combining spatiotemporal graph convolutional network and LSTM identifies carbon emission drivers missed by traditional methods. It shows that unstructured data from social platforms can serve as leading indicators for low-carbon governance, opening new pathways for global urban transitions.
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
日本ではスマートシティやSociety5.0の推進に合わせ、デジタルデータを活用した都市の脱炭素化が重要視されている。本手法は自治体が持つ多様なデジタルトレースを統合し、低炭素施策の効果予測に応用可能で、日本の地域脱炭素ロードマップと親和性が高い。
In the global GX context
Globally, data-driven urban climate action is gaining traction under frameworks like C40 and ICLEI. This study provides a transferable methodology for cities to leverage existing digital footprints (e.g., shared mobility, energy use) for carbon reduction planning, supporting the UN's Sustainable Development Goal 11 and the Paris Agreement's local-level implementation.
👥 読者別の含意
🔬研究者:Provides a novel AI model integrating heterogeneous digital traces for urban carbon forecasting, useful for advancing ML methods in sustainability science.
🏢実務担当者:Urban planners can use the digital trace-based indicators to monitor and guide low-carbon initiatives, enabling real-time adjustments.
🏛政策担当者:City governments can adopt this framework to design data-informed climate policies and track progress toward net-zero targets.
📄 Abstract（原文）
This study explored whether digital trace data could effectively predict the process of low-carbon urbanization and promote data-driven decisions for sustainable urban development. The study integrated 12 types of digital traces, including mobile phone signaling, point of interest check-ins, traffic checkpoint records, shared bicycle trajectories, nighttime light remote sensing, and enterprise electricity consumption data. Meanwhile, it established a multidimensional indicator system based on population mobility, energy intensity, and spatial interaction. A hybrid model combining a spatiotemporal graph convolutional network and long short-term memory was proposed. The study demonstrated that digital traces could reveal carbon emission drivers that traditional methods failed to capture. Unstructured data, such as point of interest check-ins in social platforms and food delivery heatmaps, could serve as leading indicators for low-carbon governance. Therefore, this study opened new pathways for global urban low-carbon transitions based on digital footprints.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.4018/ijitsa.413113first seen 2026-06-23 06:18:43
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