Hybrid method of mapping urban residential carbon emissions with high-spatial resolution
A case study of Suzhou, China
Bibliographic Data
| ID | 21246942 |
|---|---|
| Authors | Junyang Gao (0000-0002-7177-4965, Huazhong University of Science and Technology), Helin Liu (0000-0002-1837-8381, Huazhong University of Science and Technology, corresponding author), Yongwei Tang (Huazhong University of Science and Technology), Mei Luo (0000-0003-1596-418X, Huazhong University of Science and Technology) |
| Year | 2024 |
| Volume | 51 |
| Issue | 1 |
| Pages | 75-88 |
| Publication date | 2024-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/23998083231167167 |
| OpenAlex | W4365448760 |
| Language | EN |
| References cited | 14 |
Implementing carbon mitigation through urban spatial optimisation is a possible solution for alleviating global warming. However, the relationship between urban carbon emissions and urban spatial structure has not been well clarified, as adequate mapping of high-spatial-resolution urban carbon emissions from different sectors (particularly residential sectors), a precondition to solving the problem, has yet to be achieved. This study proposes a hybrid method of mapping the spatial distribution of urban residential carbon emissions on a 1 km × 1 km scale using multi-source data and exemplifies it via a case study of the Chinese city of Suzhou. The purpose of using this method is to differentiate residential carbon emissions by commuter population and home-based population, as the time they spend at home differs. The mobile signalling data of Suzhou were used to identify commuter and home-based populations. The number and spatial distribution of these two groups were then calibrated by referring to land use and O-D data. Using calibrated data, the proportion of electricity consumed by the two groups in different residential districts across the city was calculated. Total urban residential carbon emissions were then proportionally allocated to 1 km × 1 km grids. By validating estimated data against the data from the Statistical Yearbook, we found that the proximity level is higher than 93%. Furthermore, comparing these outcomes against the results estimated by using NTL data and the size of the identified population as the proxy data, it was observed that the results estimated by the hybrid method are of higher accuracy and stability
China · Civil engineering · Environmental health · Geography · Greenhouse gas · Population · Remote sensing · Spatial analysis · Spatial distribution · Statistics · Urban planning · Air Quality and Health Impacts · Engineering · Environmental Science · Impact of Light on Environment and Health · Mathematics · Urban Transport and Accessibility
| Citation velocity | historical |
|---|---|
| Highly cited | No |