Impact of Urban Density on Carbon Emissions Using High-Resolution Spatial Carbon Mapping in South Korean Megacities

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.

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Publication Details

Journal
Journal of Urban Planning and Development
Published
2026-10-30
DOI
https://doi.org/10.1061/jupddm.upeng-6899
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
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article

Impact of Urban Density on Carbon Emissions Using High-Resolution Spatial Carbon Mapping in South Korean Megacities

Sung Moon Kwon, Solhee Kim, Jeongbae Jeon, 김태곤
4 links
Journal of Urban Planning and Development
Energy, Environment, Economic Growth
article

Impact of Urban Density on Carbon Emissions Using High-Resolution Spatial Carbon Mapping in South Korean Megacities

Sung Moon Kwon, Solhee Kim, Jeongbae Jeon, 김태곤
article en

Abstract

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.

Journal of Urban Planning and DevelopmentVol. 153(1)
Land and Geospatial Informatix Corporation (South Korea) (KR), Jeonbuk National University (KR), Daegu University (KR)
Sustainable cities and communities, Climate action
Openalex Percentile: Top 8%
Energy, Environment, Economic Growth
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