A Prediction Method of GHG Emissions for Urban Road Transportation Planning and Its Applications

Jing Gan, Linheng Li, Qiaojun Xiang and Bin Ran
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Jing Gan: School of Transportation, Southeast University, Nanjing 211189, China
Linheng Li: School of Transportation, Southeast University, Nanjing 211189, China
Qiaojun Xiang: School of Transportation, Southeast University, Nanjing 211189, China
Bin Ran: Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA

Sustainability, 2020, vol. 12, issue 24, 1-18

Abstract: The increasing vehicle usage has brought about a sharp increase in greenhouse gas (GHG) emissions of vehicles, which brings severe challenges to the sustainable development of road transportation in Chinese counties. Low-carbon transportation planning is an essential strategy for carbon control from the source of carbon emissions and is crucial to the full transition to a low-carbon future. For transportation planning designers, a quick and accurate estimation of carbon emissions under different transportation planning schemes is a prerequisite to determine the optimal low-carbon transportation development plan. To address this issue, a novel prediction method of hourly GHG emissions over the urban roads network was constructed in this paper. A case study was conducted in Changxing county, and the results indicate the effectiveness of our proposed method. Furthermore, we applied the same approach to 30 other counties in China to analyze the influencing factors of emissions from urban road networks in Chinese counties. The analysis results indicate that the urban road mileage and arterial road ratio are the two most important factors affecting road network GHG emissions in road traffic planning process. Moreover, the method was employed to derive peak hour emission coefficients that can be used to quickly estimate daily or annual GHG emissions. The peak hour emission of CO 2 , CH 4 , and N 2 O accounts for approximately 9–10%, 8.5–10.5%, 5.5–7.5% of daily emissions, respectively. It is expected that the findings from this study would be helpful for establishing effective carbon control strategies in the transportation planning stage to reduce road traffic GHG emissions in counties.

Keywords: low-carbon transportation planning; sustainable development; GHG emissions; smart cities; negative transport externalities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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