Decoding the impact of urban form on energy efficiency in shrinking cities: dynamic evolution, spatiotemporal transitions, and SHAP analysis
縮小都市における都市形状がエネルギー効率に与える影響の解読：動的進化、時空間遷移、およびSHAP分析 (AI 翻訳)
Xiaofeng Ran, Jiarui Yuan, Shuyue Jiang, Rui Ding
🤖 gxceed AI 要約
日本語
中国の159の縮小都市を対象に、エネルギー効率（EE）の動的進化と時空間遷移を分析。ランダムフォレストとSHAPを用いて都市形状がEEに与える影響メカニズムを解明した。結果、EE改善率はS字カーブを描き、地域格差が顕著で、パッチ距離などの指標が主要因であることを示した。
English
This study analyzes the dynamic evolution and spatiotemporal transitions of energy efficiency (EE) in 159 shrinking cities in China using exploratory spatiotemporal data analysis. It employs Random Forest and SHAP to reveal the impact mechanisms of urban form on EE. Findings show an S-shaped growth curve, regional disparities, and that urban form indicators like patch distance and fragmentation are key determinants.
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
日本では地方都市の縮小が進んでおり、本研究成果は都市計画やエネルギー効率向上策に示唆を与える。特に、縮小都市の都市形状最適化による省エネルギーの可能性を実証データで示した点は、日本のGX実践にも応用可能。
In the global GX context
This paper contributes to global GX scholarship by providing empirical evidence from China's shrinking cities on how urban form influences energy efficiency. The use of SHAP and Random Forest offers a methodological framework applicable to other contexts, supporting urban planners and policymakers in designing energy-efficient spatial strategies.
👥 読者別の含意
🔬研究者:Provides a novel application of SHAP and Random Forest to urban energy efficiency, offering methodological insights for spatiotemporal analysis.
🏢実務担当者:Urban planners can use findings on patch distance and fragmentation to inform zoning and infrastructure decisions that improve energy efficiency in shrinking cities.
🏛政策担当者:Policy implications include targeting regional disparities and promoting urban form adjustments to enhance energy efficiency in declining areas.
📄 Abstract（原文）
Analyzing the evolutionary patterns and influencing factors of energy efficiency (EE) is of significant importance for ensuring energy security and addressing urban sustainable development. This study, based on data from 159 shrinking cities (SC) in China, employs exploratory spatiotemporal data analysis to investigate the dynamic evolution and spatialtemporal transitions of EE. Additionally, it integrates SHapley Additive exPlanations (SHAP) and Random Forest (RF) to explore the impact mechanisms of urban form (UF) on EE. The findings reveal that the rate of EE improvement initially accelerates and then decelerates, following an “S-shaped growth curve”. Significant regional disparities exist, with the lowest levels observed in northeast China. The transition capability of EE is relatively weak, characterized by strong stability in local spatial transfer directions and the presence of path dependency. Explainable machine learning identifies that UF indicators such as patch distance, patch perimeter-area ratio, patch fragmentation, and total patch area are the primary determinants of EE. Among these, the positive effect of patch distance is dominant, exhibiting a spatial differentiation pattern where its intensity decreases from the southwest to the northeast. This research provides multiple perspectives for optimizing urban spatial construction, reducing energy waste, and enhancing overall efficiency.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1057/s41599-026-07528-xfirst seen 2026-06-11 05:37:12
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