Spatial Pattern Analysis of CO2 Emission in Seoul Metropolitan City Based on a Geographically Weighted Regression

Kim, Dong Ha; Kang, Ki Yeon; Sohn, So Young

doi:10.7232/jkiie.2016.42.2.096

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…By contrast, variables mentioned as significant or important are considered comprehensively in the present study. Among the previous studies on Seoul's energy consumption, the geographically weighted regression (GWR) model was used to analyze energy consumption in only one study [29]. However, even in this study, carbon dioxide emissions were used to measure energy consumption, which is an indirect method.…”

Section: Introductionmentioning

confidence: 97%

Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression

Kim

2022

Buildings

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The recent increase in energy consumption worldwide has accelerated global warming. Thus, developed countries are aiming to reduce energy consumption in cities and promote eco-friendly policies. Buildings account for most of the energy used in a city. Therefore, it is necessary to identify the factors that affect electrical energy consumption in urban buildings. In this study, we use multiscale geographically weighted regression (MGWR) to analyze these urban characteristic factors at the global and local scales in Seoul, Korea. It is found that population and household characteristics, outdoor temperature, green and water areas, building area according to building usage, and construction age significantly affect the electrical energy consumption of buildings. In addition, the influences of these variables change with the region. Variables with different coefficients by region are winter temperature, green and water area, and households with three or more persons. The results confirm that even within a city, the influence of the aforementioned factors varies in terms of spatial distribution and patterns. This study is significant as it carried out basic research for energy consumption reduction in buildings by deriving related influencing factors.

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Section: Introductionmentioning

confidence: 97%

Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression

Kim

2022

Buildings

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County-level land use carbon emissions in China: Spatiotemporal patterns and impact factors

Liu,

Hu,

et al. 2024

Sustainable Cities and Society

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Analysis of spatial and temporal evolution and driving factors of carbon emission in Shandong Province: based on the perspective of land use

Wu,

2024

Environ Sci Eur

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Spatial Pattern Analysis of CO2 Emission in Seoul Metropolitan City Based on a Geographically Weighted Regression

Cited by 4 publications

References 23 publications

Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression

Analyzing Electricity Consumption Factors of Buildings in Seoul, Korea Using Multiscale Geographically Weighted Regression

County-level land use carbon emissions in China: Spatiotemporal patterns and impact factors

Analysis of spatial and temporal evolution and driving factors of carbon emission in Shandong Province: based on the perspective of land use

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