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Carbon dioxide emissions from port container distribution: spatial characteristics and driving factors

Wang, L, Peng, C, Shi, W ORCID: 0000-0001-6551-0499 and Zhu, M 2020 , 'Carbon dioxide emissions from port container distribution: spatial characteristics and driving factors' , Transportation Research. Part D: Transport and Environment, vol. 82 , doi: 10.1016/j.trd.2020.102318.

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Abstract

Port carbon dioxide (CO2) emissions in China have become an ever-increasing public concern dueto their significant impacts on human health and the environment. However, existing studiesfocus mainly on CO2 emissions from vessels calling at the ports and cargo handling within theports, paying little attention to the inland distribution networks. To fill this gap, this paperproposes an easily implemented method for calculating CO2 emissions from port container distribution(PCD) and investigates their spatial characteristics and driving factors. By analyzing 30container ports in China, the main findings are as follows. First, road transportation is the majorcontributor of CO2 emissions from PCD due to the lack of rail and inland water transportation.Second, PCD carbon emissions exhibit significant local spatial clustering. That is, ports withsimilar geographical locations tend to present a similar pattern of PCD carbon emissions. Third,as suggested by the spatial Durbin model, PCD carbon emissions are negatively determined bylocal gross domestic product, number of port berths, but are positively determined by localtertiary industry value and highway freight volume, and waterway freight volume in both localand neighboring ports. These results provide empirical insights into cross-port collaboration inreducing PCD carbon emissions.

Item Type: Article
Authors/Creators:Wang, L and Peng, C and Shi, W and Zhu, M
Keywords: port carbon emissions, container distribution facilities, local Moran’s I, spatial Durbin model, green port
Journal or Publication Title: Transportation Research. Part D: Transport and Environment
Publisher: Elsevier Ltd
ISSN: 1361-9209
DOI / ID Number: 10.1016/j.trd.2020.102318
Copyright Information:

Copyright 2020 Elsevier Ltd.

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