Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/115507
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor | Research Institute for Sustainable Urban Development | en_US |
| dc.contributor | Research Institute for Land and Space | en_US |
| dc.creator | Yu, X | en_US |
| dc.creator | Wong, MS | en_US |
| dc.creator | Qin, K | en_US |
| dc.creator | Zhu, R | en_US |
| dc.creator | You, L | en_US |
| dc.creator | Wei, J | en_US |
| dc.date.accessioned | 2025-10-02T06:14:27Z | - |
| dc.date.available | 2025-10-02T06:14:27Z | - |
| dc.identifier.issn | 0301-4797 | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/115507 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Academic Press | en_US |
| dc.subject | Air pollution | en_US |
| dc.subject | Electric vehicle | en_US |
| dc.subject | EV charging demand | en_US |
| dc.subject | Random forest model | en_US |
| dc.subject | Scenario analysis | en_US |
| dc.title | Electric vehicle attributed future air pollution alleviation : A case study in Guangdong province, China | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 391 | en_US |
| dc.identifier.doi | 10.1016/j.jenvman.2025.126442 | en_US |
| dcterms.abstract | Electric vehicles (EVs) are advocated to combat the effects of tailpipe emissions. This study synergizes EV charging consumption and charging stations from six cities in Guangdong (GD) province, China, to reveal the potential impacts of EVs on four relevant air pollutants (PM2.5, NO2, SO2, CO) based on a data-driven attention-based Random Forest model and scenario analysis. Measurements from traffic-affected air pollution monitoring stations show that NO2 concentrations have a higher mean decrease trend (−2.39 year−1) in the PRD region after EV adoption, followed by PM2.5 (−0.29 year−1). In contrast, the environmental benefits of EVs for SO2 and CO are relatively lower, with decreasing trends of −0.12 year−1 and -0.013 year−1, respectively. Pronounced alleviations of these four air pollutants were presented for most districts in other cities under the assumption of conducting comparative EV policy, with mean reductions of −1.86 μg/m3, -1.08 μg/m3, -0.17 μg/m3 and -0.01 mg/m3 (by 7.8 %, 4.9 %, 1.9 % and 1.4 % with the reference of average values in 2023) for PM2.5, NO2, SO2 and CO, respectively. Moreover, the concentrations tend to decline as the increase in EV charging consumption and the number of EV charging stations. Results show that a 30 % increase in both EV charging consumption and stations results in a further decline in PM2.5 (−0.46 μg/m3), NO2 (−0.37 μg/m3), SO2 (−0.048 μg/m3), and CO (−0.0043 mg/m3) in Guang Dong (GD) province. To the best of our knowledge, it is the first time to assess environmental benefits of EVs with the involvement of actual EV charging demand and charging stations. | en_US |
| dcterms.accessRights | embargoed access | en_US |
| dcterms.bibliographicCitation | Journal of environmental management, Sept. 2025, v. 391, 126442 | en_US |
| dcterms.isPartOf | Journal of environmental management | en_US |
| dcterms.issued | 2025-09 | - |
| dc.identifier.scopus | 2-s2.0-105009459458 | - |
| dc.identifier.eissn | 1095-8630 | en_US |
| dc.identifier.artn | 126442 | en_US |
| dc.description.validate | 202510 bcwc | en_US |
| dc.description.oa | Not applicable | en_US |
| dc.identifier.SubFormID | G000176/2025-07 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | This project is substantially funded by the General Research Fund (Grant No. 15603923 and 15609421), and the Collaborative Research Fund (Grant No. C5062\u201321GF) and Young Collaborative Research Fund (Grant No. C6003\u201322Y) from the Research Grants Council , Hong Kong, China. The authors acknowledge the funding support (Grant No. BBG2 and CD81) from the Research Institute for Sustainable Urban Development, Research Institute of Land and Space, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China. We also thank the China Environmental Monitoring Center, the research teams of LandScan, OpenStreetMap, ERA-5, MODIS, Shuttle Radar Topography Mission, the Resource and Environmental Science Data Platform and Global Power Plant Database for providing high-quality data to make this study possible. | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.date.embargo | 2027-09-30 | en_US |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
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