Life cycle assessment and material flow analysis of road and rail infrastructure assets – A critical review

IF 4.9 Q2 ENGINEERING, ENVIRONMENTAL Cleaner Environmental Systems Pub Date : 2025-01-24 DOI:10.1016/j.cesys.2025.100259
Soroosh Ataee , André Stephan
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Abstract

Transport infrastructure assets are essential for society, but they also usually result in significant environmental effects. To assess and reduce these effects, various methods such as life cycle assessment (LCA) and material flow analysis (MFA) have been applied to transport infrastructure assets. This paper provides a comprehensive review of the current research on the application of LCA and MFA to transport infrastructure assets, focusing on energy and greenhouse gas emissions. Considering 640 publications, the paper identifies the aims and drivers of the studies, the scope and boundaries of transport infrastructure assets, tools and data sources used for LCA and MFA, and the key findings and limitations of previous studies and models. The paper also highlights the research gaps and future research directions, such as integrating LCA and MFA, expanding the scope of transport infrastructure assets considered, improving the data quality and availability, and addressing the uncertainties and trade-offs in the assessment. The paper aims to provide a critical overview of the state-of-the-art methods and knowledge on the environmental assessment of TIA, and to suggest ways to improve the performance of TIA in the future.
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公路和铁路基础设施资产的生命周期评估和物质流分析——一项关键性审查
交通基础设施资产对社会至关重要,但它们通常也会对环境产生重大影响。为了评估和减少这些影响,各种方法,如生命周期评估(LCA)和物料流分析(MFA)已应用于交通基础设施资产。本文对LCA和MFA在交通基础设施资产中的应用研究进行了全面综述,重点关注能源和温室气体排放。考虑到640份出版物,本文确定了研究的目标和驱动因素,交通基础设施资产的范围和边界,LCA和MFA使用的工具和数据来源,以及以前研究和模型的主要发现和局限性。本文还强调了研究差距和未来的研究方向,如整合LCA和MFA,扩大考虑的交通基础设施资产范围,提高数据质量和可用性,以及解决评估中的不确定性和权衡。本文旨在提供最先进的TIA环境评估方法和知识的关键概述,并提出在未来提高TIA性能的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cleaner Environmental Systems
Cleaner Environmental Systems Environmental Science-Environmental Science (miscellaneous)
CiteScore
7.80
自引率
0.00%
发文量
32
审稿时长
52 days
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