Work

Seasonal Urban Carbon Emission Estimation Using Spatial Micro Big Data

Year: 2018

Type: article

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.

Source: Sustainability

DOI: 10.3390/su10124472

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

Institutions: National Institute for Environmental Studies, The Institute of Statistical Mathematics, Center for Spatial Information Science, The University of Tokyo, The University of Tokyo

Language: English

Topic: 3D Modeling in Geospatial Applications

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $2,896