As concerns around climate change and global warming intensify, extreme weather events such as heavy rain, blizzards, and smog-induced haze have greatly impacted the commuting travel mode selection of urban residents. Such behavioral shifts have in turn led to changes of the carbon emissions generated from these residents. This paper constructs a “extreme weather ( W)–travel behavior ( B)–carbon emissions ( C)” research framework. Using a multiple logistic regression model, the transportation mode shift model, and the econometric model of urban resident’s travel behavior under the influence of extreme weather conditions were constructed. The marginal effects of weather on residents’ commuter behavior, through the use of transportation type and distance of travel were also obtained. The study found that the overall carbon dioxide emission levels of daily commuting has gradually decreased due to the influence of extreme weather. However, as some travelers still adopted high-emission commuting modes through the use of taxis or ride-sharing services, there was still a slight increase in CCDE levels in certain extreme weather contexts. In particular, when haze was prevalent, vehicle restriction policies only reduced CCDE by 2.18%, while the remaining 77.83% of total CCDE remaining unchanged. This research provides a key reference point for governmental departments in urban transportation management and environmental protection to formulate policies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.