Energy Infrastructure and Geohazard Risk in China's Urban Expansion: A Hybrid <scp>MCDA</scp> Framework Integrating Bayesian Networks for Sustainable Resource Management and Climate Adaptation
中国の都市拡大におけるエネルギーインフラと地質災害リスク：持続可能な資源管理と気候適応のためのベイジアンネットワークを統合したハイブリッドMCDAフレームワーク (AI 翻訳)
Yue Shen, Xingcheng Ge, Wang Yang, Zhiwei Guo, Aftab Ahmed Laghari
🤖 gxceed AI 要約
日本語
本研究は、中国の都市開発における持続可能性基準を優先順位付けするためのMCDAフレームワークを提案。デルファイ法、階層ベイズネットワーク、BWMを統合し、環境持続可能性とガバナンスが最も重要であることを示した。炭素排出削減、再生可能エネルギー統合、長期計画が主要サブ基準として特定され、中国の2060年カーボンニュートラル目標と整合する。
English
This study proposes an MCDA framework for prioritizing sustainability criteria in China's urban development, integrating Delphi, Bayesian networks, and BWM. It finds that environmental sustainability and governance are most critical, with carbon reduction, renewable energy, and long-term planning as key sub-criteria, aligning with China's 2060 carbon neutrality goal.
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
中国の都市計画に焦点を当てているが、提案されたMCDAフレームワークは日本の都市持続可能性評価にも応用可能。特に、政府のガバナンスと環境基準の連携は日本のGX政策（グリーン成長戦略など）と共通する課題を示唆。
In the global GX context
While focused on China, the MCDA framework offers a transferable methodology for integrating sustainability criteria into urban planning globally. Its emphasis on governance and carbon reduction echoes themes in global climate adaptation and urban resilience discourse.
👥 読者別の含意
🔬研究者:The hybrid MCDA-Bayesian network approach provides a robust method for modeling interdependencies among sustainability criteria, applicable to other case studies.
🏢実務担当者:Urban planners can adopt this framework to systematically prioritize environmental and governance criteria in development projects.
🏛政策担当者:The findings support alignment of urban development policies with national carbon neutrality targets, offering a replicable decision-support tool.
📄 Abstract（原文）
ABSTRACT This study proposes a comprehensive multi‐criteria decision analysis (MCDA) framework designed to prioritise sustainability criteria for urban development in China, addressing key environmental, economic, social, governance, technological and resilience‐related challenges. The approach combines expert knowledge with advanced analytical techniques, including the Delphi method to refine criteria, probability distribution modelling to quantify expert judgements, hierarchical Bayesian networks to capture interdependencies, and the best‐worst method (BWM) to determine the relative importance of criteria and sub‐criteria. The results indicate that environmental sustainability and governance are the most critical dimensions, with carbon emissions reduction, renewable energy integration, and long‐term planning identified as the most influential sub‐criteria. The probabilistic analysis revealed strong consensus among experts regarding environmental priorities, whereas opinions on technological integration were more varied. Furthermore, the Bayesian network analysis highlighted significant interconnections among criteria, particularly emphasising the central role of governance in facilitating sustainability outcomes. These findings are consistent with China's national objectives, including its commitment to carbon neutrality by 2060, and contribute to the broader global discourse on sustainable urbanisation. The study offers practical insights for policymakers and urban planners by providing a robust, adaptable, and replicable framework that can be applied to other developing contexts. By integrating expert‐driven approaches with probabilistic and decision‐making models, the research advances understanding of urban sustainability and supports the development of informed, balanced and strategic policy decisions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/gj.70322first seen 2026-05-21 04:55:02
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。