Carbon dioxide emissions from port container distribution: Spatial characteristics and driving factors |
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| Affiliation: | 1. College of Transport & Communications, Shanghai Maritime University, Shanghai, China;2. Maritime and Logistics Management, Australian Maritime College, University of Tasmania, Launceston TAS 7250, Australia;1. Pot of Taichung, Taiwan International Ports Corporation, Taichung 43501, Taiwan, ROC;2. International School of Technology and Management, Feng Chia University, Taichung 40724, Taiwan, ROC;3. Department of Environmental Engineering and Science, Feng Chia University, Taichung 40724, Taiwan, ROC;1. NILU – Norwegian Institute for Air Research, Instituttveien 18, Kjeller 2027, Norway;2. PortsEYE AS, Instituttveien 18, Kjeller 2027, Norway;3. Port of Oslo, Akershusstranda 19, Shed 38, Oslo 0103, Norway;1. Institute of Information Systems, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg, Germany;2. State Key Laboratory of Coastal and Offshore Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, 116023 Dalian, Liaoning, China;1. IVL Swedish Environmental Research Institute, Aschebergsgatan 44, Gothenburg, Sweden;2. University of New South Wales, Sydney, NSW 2052, Australia;3. University of Marketing and Distribution Sciences, 3-1 Gakuen-Nishimachi, Nishi-ku, Kobe, Japan;4. University of Southern California, Los Angeles, CA 90089, USA;1. School of Economics & Management, Shanghai Maritime University, 1550 Haigang Avenue, Pudong, Shanghai 201306, PR China;2. College of Transport & Communications, Shanghai Maritime University, 1550 Haigang Avenue, Pudong, Shanghai 201306, PR China;3. Institute of Logistics Science & Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong, Shanghai 201306, PR China |
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| Abstract: | Port carbon dioxide (CO2) emissions in China have become an ever-increasing public concern due to their significant impacts on human health and the environment. However, existing studies focus mainly on CO2 emissions from vessels calling at the ports and cargo handling within the ports, paying little attention to the inland distribution networks. To fill this gap, this paper proposes an easily implemented method for calculating CO2 emissions from port container distribution (PCD) and investigates their spatial characteristics and driving factors. By analyzing 30 container ports in China, the main findings are as follows. First, road transportation is the major contributor 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 with similar 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 by local gross domestic product, number of port berths, but are positively determined by local tertiary industry value and highway freight volume, and waterway freight volume in both local and neighboring ports. These results provide empirical insights into cross-port collaboration in reducing PCD carbon emissions. |
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| Keywords: | Port carbon emissions Container distribution facilities local Moran’s I Spatial Durbin model Green port |
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