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A novel ensemble approach for road traffic carbon emission prediction: a case in Canada

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成果类型:
期刊论文
作者:
Liu, Yongliang;Tang, Chunling;Zhou, Aiying;Yang, Kai
通讯作者:
Tang, CL
作者机构:
[Liu, Yongliang; Zhou, Aiying; Tang, Chunling] Cent South Univ Forestry & Technol, Sch Econ, Changsha 410004, Hunan, Peoples R China.
[Yang, Kai] Tsinghua Univ, Shenzhen Int Grad Sch, Shenzhen 518055, Guangdong, Peoples R China.
通讯机构:
[Tang, CL ] C
Cent South Univ Forestry & Technol, Sch Econ, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Stacking technology;X-MARL model;Ensemble learning;Road traffic;\(\hbox {CO}_{2}\) emission
期刊:
Environment, Development and Sustainability
ISSN:
1387-585X
年:
2024
页码:
1-37
基金类别:
This work was supported by the Philosophy and Social Science Foundation of Hunan Province (Grant number 22YBA107).
机构署名:
本校为第一且通讯机构
院系归属:
经济学院
摘要:
The "Annual Report 2021" from the United Nations Environment Programme (UNEP) highlights that the transportation sector is the fastest-growing greenhouse gas emissions sector, accounting for approximately 25% of energy-related emissions. What is even more concerning is that, at a time when carbon emissions need to be urgently reduced across various industries globally, carbon emissions from the transportation sector continue to rise. This is because the improvement in the efficiency of vehicle power combustion struggles to offset the increasing emissions resulting from the massive volume of tr...

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