Predicting GHG emissions from subway lines in the planning stage on a city level. (20th June 2020)

• Record Type: Journal Article
• Authors: Liu, Minghui; Jia, Siyi; Li, Pan; Liu, Xuan; Zhang, Yunling
• Is Part Of: Journal of cleaner production. Volume 259(2020)
• Publication Date: 2020-06-20
• Subjects: Urban rail transit -- GHG emissions -- Planning stage -- Artificial neuro network -- Carbon-efficiency; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.120823
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The construction of urban rail transit (URT) infrastructures requires extensive input of resources and energy, thus generating considerable greenhouse gas (GHG) emissions. Despite efforts to quantify and mitigate the carbon footprint (CFP) during the URT's construction stage, the magnitude of this CFP is largely dependent on design parameters, such as route selection and buried depth, that require confirmation during the planning stage. Therefore, the best means by which to control the CFP of URT construction is for government and design consultancies to have mitigation awareness in the determination of early proposal parameters. To achieve this goal, it is important to identify a link between the construction CFP and these parameters. In this paper, an artificial neuro network (ANN) model is established to predict the GHG emissions from the construction of the planning URT lines of the Fuzhou subway using training data from the in-service lines. GHG emissions from the construction of the planning lines amount to 6.46 Mt CO2.eq., averaging 53024.13 t CO2.eq. per unit line length. Buried depth is identified as the primary factor that determines the GHG intensity of URT stations and tunnel sections, and two simplified equations are derived to facilitate the estimation. In addition, the payback periods of the stations are evaluated. For lines 2, 4, 5, 6, it is estimated to require 16.52, 11.69, 21.60 and 12.67 years, respectively, before the initial construction CFPs are balanced by those mitigated by URT operation. Carbon-inefficient stations are attributed to either low service levels or overconstruction. Highlights: A prediction model based on ANN is constructed with characterized input variables of the planning Subway lines. Buried depth is identified as the major factor influencing the GHG emissions from subway construction. Payback periods of the planning stations are evaluated considering their serviceability.