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Spatial Pattern Analysis of CO2 Emission in Seoul Metropolitan City Based on a Geographically Weighted Regression
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 Title & Authors
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;
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Effort to reduce energy consumptions or CO2 emissions is global trend. To follow this trend, spatial studies related to characteristics affecting energy consumptions or CO2 emissions have been conducted, but only with the focus on spatial dependence, not on spatial heterogeneity. The aim of this study is to investigate spatial heterogeneity patterns of CO2 emission based on socio-economic factors, land-use characteristics and traffic infrastructure of Seoul city. Geographically Weighted Regression (GWR) analysis was performed with 423 administrative district data in Seoul. The results suggest that population and employment densities, road density and railway length in most districts are found to have positive impact on the CO2 emissions. Residential and green area densities also have the highest positive impact on CO2 emissions in most districts of Gangnam-gu. The resulting model can be used to identify the spatial patterns of CO2 emissions at district level in Seoul. Eventually it can contribute to local energy policy and planning of metropolitan area.
CO2 Emission;Energy Consumptions;Seoul Metropolitan City;Spatial Heterogeneity;Geographically Weighted Regression;
 Cited by
Anselin, L. (1995), Local indicators of spatial association-LISA, Geographical Analysis, 27(2), 93-115.

Anselin, L. (1988), Spatial econometrics : Methods and models, Kluwer Academic, Dordrecht, Netherland.

Brunsdon, C., Fotheringham, A. S., and Charlton, M. E. (1996), Geographically weighted regression : A method for exploring spatial nonstationarity, Geographical Analysis, 28(4), 281-298.

Burnett, J. W., Bergstrom, J. C., and Dorfman, J. H. (2013), A spatial panel data approach to estimating US state-level energy emissions, Energy Economics, 40, 396-404. crossref(new window)

Cheng, Y., Wang, Z., Ye, X., and Wei, Y. D. (2014), Spatiotemporal dynamics of carbon intensity from energy consumption in China, Journal of Geographical Sciences, 24(4), 631-650. crossref(new window)

Choi, H. S. and Sohn, S. Y. (2015), Influence of interests in geographical indication on the prediction of price change of agricultural product : case of apples, Journal of Korean Institute of Industrial Engineers, 41(4), 359-367. crossref(new window)

De Smith, M. J., Goodchild, M. F., and Longley, P. (2007), Geospatial analysis : a comprehensive guide to principles, techniques and software tools, Troubador Publishing, Leicester, UK.

Desipri, K., Legaki, N. Z., and Assimakopoulos, V. (2014), Determinants of domestic electricity consumption and energy behavior : A Greek case study, The 5th International Conference on IEEE in Information, Intelligence, Systems and Applications, IEEE, 144-149.

Glasure, Y. U. and Lee, A. R. (1998), Cointegration, error-correction, and the relationship between GDP and energy : The case of South Korea and Singapore, Resource and Energy Economics, 20(1), 17-25. crossref(new window)

Griffin, A. (2014), Domestic energy consumption and social living standards : A GIS analysis within the Greater London authority area, LUMAGIS Thesis, Lund University, Lund, Sweden.

Hurvich, C. M., Simonoff, J. S., and Tsai, C. L. (1998), Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion, Journal of the Royal Statistical Society : Series B (Statistical Methodology), 60(2), 271-293. crossref(new window)

IPCC (2007), Climate change 2007 : The physical science basis, Cambridge University Press, 141.

Jiang, L., Ji, M., and Bai, L. (2015), Characterizing China's energy consumption with selective economic factors and energy-resource endowment : A spatial econometric approach, Frontiers of Earth Science, 9(2), 355-368. crossref(new window)

Kang, C. D. (2011), Analysis on energy consumption and its policy implication in Seoul with spatial econometrics : focusing on electricity and gas consumption, Seoul Studies, 12(4), 1-22.

Kiehl, J. T. and Trenberth, K. E. (1997), Earth's annual global mean energy budget, Bulletin of the American Meteorological Society, 78(2), 197-208. crossref(new window)

Kim, I. H., Oh, K. S., and Jeong, S. H. (2011), Carbon emission model development using urban planning criteria : focusing on the case of Seoul, Journal of Korea Spatial Information Society, 19(6), 12.

Kim, Y. G., An, Q., and Kim, S. W. (2015), The research of difference between public and private section : sort by region in China, Journal of the Korean Operations Research and Management Science Society, 40(1), 139-154. crossref(new window)

Lee, W. S. and Sohn, S. Y. (2015), Topic model analysis of research trend on spatial big data, Journal of Korean Institute of Industrial Engineers, 41(1), 64-73. crossref(new window)

Li, Q. and Racine, J. (2004), Cross-validated local linear nonparametric regression, Statistica Sinica, 14(2), 485-512.

Nam, K. C., Lim, U., Kim, H. S., and Lee, J. S. (2010), Building carbon emission model for the change of urban spatial structure and growth management using system dynamics approach, Journal of Korea Regional Science Association, 26(3), 99-114.

Narayan, P. K. and Smyth, R. (2005), Electricity consumption, employment and real income in Australia evidence from multivariate granger causality tests, Energy Policy, 33(9), 1109-1116. crossref(new window)

Osland, L. (2010), An application of spatial econometrics in relation to hedonic house price modeling, Journal of Real Estate Research, 32(3), 289-320.

Parajuli, R., Ostergaard, P. A., Dalgaard, T., and Pokharel, G. R. (2014), Energy consumption projection of Nepal : An econometric approach, Renewable Energy, 63, 432-444. crossref(new window)

Seoul : Seoul Metropolitan Government (2010), Cartography of Climate and Energy of Seoul-The Third Final Report, Seoul.

Sovacool, B. K. and Brown, M. A. (2010), Twelve metropolitan carbon footprints : A preliminary comparative global assessment, Energy policy, 38(9), 4856-4869. crossref(new window)

Tewathia, N. (2014), Determinants of the household electricity consumption : A case study of delhi, International Journal of Energy Economics and Policy, 4(3), 337-348.

Tian, W., Song, J., and Li, Z. (2014), Spatial regression analysis of domestic energy in urban areas, Energy, 76, 629-640. crossref(new window)

Videras, J. (2014), Exploring spatial patterns of carbon emissions in the USA : a geographically weighted regression approach, Population and Environment, 36(2), 137-154. crossref(new window)

Wang, S., Fang, C., Ma, H., Wang, Y., and Qin, J. (2014), Spatial differences and multi-mechanism of carbon footprint based on GWR model in provincial China, Journal of Geographical Sciences, 24(4), 612-630. crossref(new window)

Yang, H. Y. (2000), A note on the causal relationship between energy and GDP in Taiwan, Energy Economics, 22(3), 309-317. crossref(new window)

Yu, H. (2012), The influential factors of China's regional energy intensity and its spatial linkages : 1988-2007, Energy Policy, 45, 583-593. crossref(new window)

Zhao, X., Burnett, J., and Fletcher, J. J. (2014), Spatial analysis of China province-level CO2 emission intensity, Renewable and Sustainable Energy Reviews, 33, 1-10. crossref(new window)