Towards Dynamic Carbon Management: A Structured Analytical Review and Multi-Scale Framework for Urban Carbon Accounting
動的炭素管理に向けて:都市炭素会計のための構造的分析レビューとマルチスケールフレームワーク (AI 翻訳)
Auwalu Faisal Koko, Akram Ahmed Noman Alabsi, Khaled Mohammed Alshareem, Ahmed Abdurabu Ali Al-Nehmi
🤖 gxceed AI 要約
日本語
本研究は都市炭素研究の構造的レビューを行い、建築・都市形態・自然ベース解決策を統合した動的炭素管理(DCM)フレームワークを提案。従来の静的インベントリから動的管理への転換を促す。
English
This paper conducts a structured review of urban carbon research and proposes a Dynamic Carbon Management (DCM) framework that integrates carbon flows across buildings, urban form, and nature-based solutions, shifting from static inventories to dynamic management.
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
日本でも都市のカーボンニュートラル目標やSSBJ開示が進む中、本フレームワークは都市計画と炭素管理の統合に示唆を与える。
In the global GX context
The DCM framework addresses the gap between static carbon inventories and dynamic management, relevant to global urban climate action and ISSB/TCFD-aligned city disclosures.
👥 読者別の含意
🔬研究者:Provides a theoretically grounded framework for advancing urban carbon accounting research, bridging scales and processes.
🏢実務担当者:Offers an operational logic for integrating carbon management into urban planning and design for decarbonization.
🏛政策担当者:Supports development of policies for city-level carbon neutrality by emphasizing dynamic multi-scale management.
📄 Abstract(原文)
As cities increasingly become central arenas for achieving climate mitigation and carbon neutrality targets, the capacity to actively manage urban carbon rather than merely quantify emissions has emerged as a critical challenge for urban planning, design, and environmental governance. Despite substantial advances in quantifying urban emissions, existing studies frequently exhibit fragmented treatment of urban carbon processes, limited coupling across spatial scales, and insufficient integration of carbon storage and sequestration mechanisms, particularly those associated with nature-based solutions. These limitations constrain the capacity of urban planning and design to engage with carbon as a dynamic and spatially embedded urban system. This study aims to address these challenges through a structured analytical review of urban carbon research, employing an adapted PRISMA-informed protocol combined with process- oriented and scale-sensitive coding to systematically examine how carbon generation, accounting, storage, sequestration, and mitigation are conceptualised and operationalised in the literature. Comparative synthesis of the reviewed literature reveals persistent methodological and conceptual patterns, including the dominance of emission-centric accounting logics, the marginalisation of ecological and biogenic carbon processes, weak cross-scale integration between buildings and urban systems, and a prevailing reliance on static representations of carbon dynamics. Building on this analytical synthesis, the paper introduces Dynamic Carbon Management (DCM) as a conceptual framework that reframes urban carbon as an integrated, multi-process, and multi-scale system requiring coordinated management rather than isolated accounting. DCM framework formally links carbon flows across buildings, urban form, and nature-based interventions, providing an operational logic for understanding interactions between carbon generation, mitigation, storage, and sequestration over time. By shifting the analytical focus from static inventories toward dynamic management perspectives, this study contributes a theoretically grounded framework that advances urban carbon research and establishes a foundation for future empirical validation, modelling efforts, and policy-oriented applications aimed at supporting more adaptive, integrated, and multi-scale urban decarbonisation strategies.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.53941/ubs.2026.100016first seen 2026-07-01 05:13:08 · last seen 2026-07-01 05:13:11
🔔 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。