高解像度空間炭素マッピングを用いた韓国大都市における都市密度の炭素排出への影響
Impact of Urban Density on Carbon Emissions Using High-Resolution Spatial Carbon Mapping in South Korean Megacities (原題)
Jeongbae Jeon, Sung Moon Kwon, 김태곤, Solhee Kim
🤖 gxceed AI 要約
日本語
韓国の大都市圏を対象に、国土交通省の炭素空間情報システムの高解像度排出データを用い、1,133の行政単位で都市密度と炭素排出の関係を空間計量分析した研究。排出量には有意な空間クラスタリング（Global Moran's I=0.2942）が確認され、就業者密度が排出を最も強く押し上げる一方、人口密度・コンパクト性・公共交通利用が排出を抑制することが示された。工業地域は排出を増やし住宅地域は減らす傾向があり、コンパクトシティ理論を実証的に支持する。
English
Using high-resolution carbon emission data across 1,133 administrative units in South Korean megacities, this study applies spatial econometrics to test how urban density shapes emissions. Emissions cluster significantly (Moran's I=0.2942); worker density raises emissions while population density, compactness, and public transit use lower them. Industrial land increases and residential land reduces emissions, empirically supporting compact-city theory and arguing for regionally coordinated, spatially aware carbon policy.
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
日本でもコンパクトシティ政策や都市計画と脱炭素の統合が議論されており、自治体の区域を越えた広域調整の必要性を示す実証として参考になる。ただし炭素開示制度との直接の接続は薄い。
In the global GX context
Adds city-level empirical evidence to the global debate on urban form and decarbonization, relevant to spatial approaches in climate-risk and land-use disclosure, though it does not engage TCFD/ISSB frameworks directly.
👥 読者別の含意
🔬研究者:空間計量モデルで都市形態と排出の因果的関連を検証する手法例として有用。
🏢実務担当者:都市開発・不動産の立地戦略でコンパクト性と公共交通接続の排出効果を参照できる。
🏛政策担当者:自治体単位ではなく広域の空間相互依存を考慮した都市計画・脱炭素政策の設計根拠になる。
📄 Abstract（原文）
Abstract This study examines the relationship between urban density and carbon emissions in metropolitan areas in South Korea using spatial econometric analysis. Utilizing high-resolution carbon emission data from the Ministry of Land, Infrastructure, and Transport's Carbon Spatial Information System, we analyzed 1,133 administrative units to investigate the spatial patterns and determinants of carbon emissions. This study employs exploratory spatial data analysis and spatial regression models to account for spatial autocorrelation effects. The main results revealed significant spatial clustering of carbon emissions, with a Global Moran's I coefficient of 0.2942 ( p < 0.001), indicating that similar emission levels tend to cluster spatially rather than distribute randomly. Local Indicators of Spatial Association analysis identified distinct high-emission clusters in southwestern and southeastern Gyeonggi State, contrasting with low-emission clusters in northern regions. The spatial error model (coefficient of determination, R 2 = 0.477) demonstrated superior explanatory power compared to the spatial lag model ( R 2 = 0.456) and the ordinary least squares model ( R 2 = 0.403). Key findings indicate that worker density represents the strongest positive predictor of emissions, while population density, compactness density, and public transit usage significantly reduce emissions. Living population density showed positive associations with emissions. Land-use analysis revealed that industrial areas increase emissions, while residential areas reduce them. These findings provide empirical support for the compact city theory, demonstrating that compact, high-density development can reduce carbon emissions despite increased economic activity concentration. The results suggest that carbon reduction policies should adopt regional approaches considering spatial interdependencies rather than fragmented municipal strategies, emphasizing integrated urban planning combining compactness-oriented development, public transportation enhancement, and sustainable land-use patterns.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1061/jupddm.upeng-6899first seen 2026-11-01 05:00:58 · last seen 2026-11-06 04:56:59
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