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Seasonal Urban Carbon Emission Estimation Using Spatial Micro Big Data

descriptionPublicationkeyboard_double_arrow_right Article , Journal 28 Nov 2018Publisher:MDPI AGJournal:Sustainability, volume 10, page 4,472 (eissn: 2071-1050,

Authors: Yoshiki Yamagata; Takahiro Yoshida; Daisuke Murakami; Tomoko Matsui; Yuki Akiyama;

doi: 10.3390/su10124472

Seasonal Urban Carbon Emission Estimation Using Spatial Micro Big Data

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Abstract

The objective of this study is to map direct and indirect seasonal urban carbon emissions using spatial micro Big Data, regarding building and transportation energy-use activities in Sumida, Tokyo. Building emissions were estimated by considering the number of stories, composition of use (e.g., residence and retail), and other factors associated with individual buildings. Transportation emissions were estimated through dynamic transportation behaviour modelling, which was obtained using person-trip surveys. Spatial seasonal emissions were evaluated and visualized using three-dimensional (3D) mapping. The results suggest the usefulness of spatial micro Big Data for seasonal urban carbon emission mapping; a process which combines both the building and transportation sectors for the first time with 3D mapping, to detect emission hot spots and to support community-level carbon management in the future.

Related Organizations

University of Tokyo
Japan
National Institute for Environmental Studies
Japan
The Institute of Statistical Mathematics
Japan
National Institute for Environmental Studies
Japan

Keywords

Environmental effects of industries and plants, TJ807-830, TD194-195, Renewable energy sources, Environmental sciences, carbon mapping, urban sustainability, micro geodata, person-trip survey, GE1-350, mobile GPS data, individual buildings

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	13
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%