Modeling the Land-Use-Driven Energy Consumption Nexus in Shaanxi Province, China: A Digital Approach Integrating Machine Learning and Spatial Simulation
中国陕西省における土地利用主導のエネルギー消費ネクサスのモデル化：機械学習と空間シミュレーションを統合したデジタルアプローチ (AI 翻訳)
Li Liu, Xiaohu Yang
🤖 gxceed AI 要約
日本語
本研究は、ランダムフォレスト（R^2=0.95/0.91）と空間シミュレーション（PLUSモデル、産業用地精度82.5%）を用いて、中国陕西省（2005-2030年）の土地利用とエネルギー消費の関係を定量化した。結果、社会経済要因が土地利用拡大を主導し、都市部の94.2%増加とエネルギー消費の3倍増が予測される。また、石炭依存度は低下するが、西安と榆林で消費の70%を占めるなど地域格差が顕著であることを示した。
English
This study integrates Random Forest (R²=0.95/0.91 for training/testing) and PLUS simulation (82.5% accuracy for industrial land) to quantify land-use-energy dynamics in Shaanxi, China (2005-2030). Key findings: socioeconomic factors drive land expansion; urban built-up areas are projected to grow by 94.2%, tripling energy consumption; coal dependency declines from 78% to 62%, but Xi'an and Yulin account for 70% of provincial consumption by 2030. Results highlight the need for land-use-based energy constraints.
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、土地利用とエネルギー消費の関係は、都市計画やカーボンニュートラル戦略において重要であり、特に地方都市のエネルギー管理に応用可能。本研究の空間シミュレーション手法は、日本の地域エネルギー計画やSSBJを補完するエビデンス基盤として参考になる。
In the global GX context
This paper demonstrates a spatial explicit methodology linking land-use structure to energy demand, relevant for global regions pursuing integrated land-energy planning. The machine learning and simulation framework can inform energy transition strategies, complementing disclosure frameworks like TCFD/ISSB by providing scenario-based spatial insights.
👥 読者別の含意
🔬研究者:The integration of ML and spatial simulation for land-use-energy modeling offers a methodological template applicable to other regions.
🏢実務担当者:Spatial path-dependency risks identified in Xi'an and Yulin can guide urban planners and energy companies in prioritizing interventions.
🏛政策担当者:The evidence base supports shifting from general energy policies to spatially differentiated, land-use-based energy constraints.
📄 Abstract（原文）
Within the context of regional energy governance, land use has emerged as a critical regulatory interface for managing energy demand. Clarifying the land-use–energy nexus is a technical prerequisite for evidence-based and spatially explicit energy planning. This study develops a digital modeling framework that integrates machine learning (Random Forest, achieving R2 = 0.95/0.91 for training/testing) and spatial simulation (Patch-generating Land Use Simulation model, with 82.5% accuracy for industrial land) to quantify land-use-driven energy dynamics in Shaanxi Province, China (2005–2030). Key findings reveal: (1) socioeconomic factors dominate land-use expansion, with service industries (14.8–22.4%) and infrastructure (13.5–18.9%) acting as primary drivers, leading to a projected 94.2% growth in urban built-up areas and a tripling of total energy consumption; (2) structural transitions indicate a declining industrial energy share (from 68% to 54%) and reduced coal dependency (from 78% to 62%), though with significant regional disparities; (3) spatial analysis identifies critical energy path-dependency risks in Xi’an City and Yulin City, which are projected to account for 70% of provincial consumption by 2030. These results demonstrate that land-use structure constitutes a direct physical interface linking regional development with energy demand trajectories. The findings underscore the necessity of transitioning from generalized energy policies toward data-driven, land-use-based energy constraints, providing a digital evidentiary base for more precise and stable regional energy governance.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18083709first seen 2026-05-17 07:00:15 · last seen 2026-05-20 05:15:35
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