デュアルカーボン目標下におけるデジタルツイン技術を用いた都市小規模緑地の高炭素吸収ランドスケープ設計
High-carbon sink landscape design of urban small and micro green spaces based on digital twin technology under the dual-carbon goals (原題)
Renjing Zhou
🤖 gxceed AI 要約
日本語
デジタルツインとLCAを統合し、都市の小規模緑地における動的炭素収支評価と高炭素吸収ランドスケープ最適化手法を開発した。長沙の街角緑地を対象に20年間のシミュレーションを行い、総合最適化シナリオで炭素排出を38.45%削減、吸収を33.78%増加させ、6年目にカーボンニュートラルを達成した。維持管理がライフサイクル排出の主要因となることを示し、精密な低炭素管理に定量根拠を提供する。
English
Integrating digital twin technology with life cycle assessment, this study develops a dynamic carbon-budget and high-sequestration landscape optimization method for urban small green spaces. Using a street-corner site in Changsha, a 20-year simulation showed the comprehensive scenario cut emissions 38.45% and raised sequestration 33.78%, reaching carbon balance in year six. Operation and maintenance emerged as the dominant emission source, offering quantitative support for refined low-carbon urban landscape 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
日本では都市緑地・公園の脱炭素化は自治体の実行計画やグリーンインフラ政策と連動しうるが、SSBJ・有報・TCFD開示への直接的な示唆は限定的。企業のScope算定や投資家対応よりも、自治体・建設業の低炭素設計実務に資する内容。
In the global GX context
Globally, this sits at the edge of disclosure scholarship: it contributes to urban carbon accounting and nature-based sequestration quantification, which increasingly feed into CSRD/ISSB-adjacent biodiversity and land-use metrics, but it does not engage TCFD/ISSB frameworks directly. Its value is methodological—dynamic, calibrated carbon budgeting for small-scale green infrastructure.
👥 読者別の含意
🔬研究者:動的炭素収支評価とデジタルツイン統合の手法論として、都市炭素会計研究に参考になる。
🏢実務担当者:自治体・建設・造園部門が小規模緑地の低炭素設計と維持管理最適化に活用できる。
🏛政策担当者:都市緑地政策やグリーンインフラ計画の定量評価手法として参考にできる。
📄 Abstract（原文）
Urban small and micro green spaces play an increasingly important role in enhancing urban carbon sequestration capacity and optimizing carbon budgets. To address the limitations of static assessment methods, this study developed a dynamic carbon-budget assessment and high-carbon-sequestration landscape optimization method by integrating digital twin (DT) technology with life cycle assessment. A street-corner green space in Changsha was selected as the study area. Multi-source spatial, ecological, environmental, material, and operation and maintenance data were integrated to construct a DT model with periodic data updating and parameter calibration. Based on this model, a 20-year carbon-budget simulation was conducted under a baseline scenario and four optimization scenarios involving plant configuration, low-carbon materials, operation and maintenance, and comprehensive optimization. The results showed that operation and maintenance gradually became the dominant source of carbon emissions over the life cycle. Under the comprehensive optimization scenario, total carbon emissions decreased from 150.01 t to 92.34 t, representing a reduction of 38.45%, while total carbon sequestration increased from 136.46 t to 182.57 t, representing an increase of 33.78%. The net carbon balance increased from − 13.55 t to 90.23 t, and carbon balance was achieved in the sixth year. The comprehensive optimization scenario achieved the highest average carbon-efficiency improvement rate of 39.64%, outperforming the individual optimization scenarios. The proposed method supported dynamic carbon-budget assessment through periodic state updating, model calibration, and iterative scenario optimization, providing quantitative support for high-carbon-sequestration landscape design and refined low-carbon management of urban small and micro green spaces.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s44274-026-01048-wfirst seen 2026-10-09 04:50:46
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