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Dataset for the life-cycle assessment of the Basque Y high-speed rail line in Spain
- Source :
- Data in Brief, Vol 54, Iss , Pp 110472- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- This dataset presents a detailed description of the data and information used in the life-cycle assessment (LCA) of the Basque Y HSR line, which is a high-performance line for mixed traffic still under construction in 2023 (190 km). The LCI data presented in this paper support the original research carried out on whether the construction of the Basque Y HSR line infrastructure is justified in terms of reducing environmental impacts and energy consumption [1].Life-cycle inventory (LCI) data related to the construction and maintenance phases of the infrastructure was collected using Google Earth tool following the information from stakeholder AHT gelditu [2], including the length of each item (bridges, tunnels, earthworks, railway tracks); and complemented with data obtained from the LCA carried out by Tuchschmid et al. [3]. LCI data associated with the operation phase of the infrastructure was built on passenger data for the years 2020, 2030, 2040 and 2049 available in ADIF [4], and freight data for the period of 2023–2050 available in the report by ADIF and the Basque Government [5]. Environmental impacts for transport modes were obtained from the ecoinvent v3.7 database [6,7] and processed with openLCA software [8]. Life-cycle impact assessment (LCIA) results gathered in the dataset include Global Warming (GWP100a), Cumulative Energy Demand and total emissions for PM10, SO2, NOX and NMVOC.Access to the explanation of these data allows any reader to reproduce the calculations of the main project and may be used as a baseline for future studies on transport economics too.
Details
- Language :
- English
- ISSN :
- 23523409
- Volume :
- 54
- Issue :
- 110472-
- Database :
- Directory of Open Access Journals
- Journal :
- Data in Brief
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3c3d71f068a046d9bd0fd23a7990216c
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.dib.2024.110472