温室气体
生命周期评估
背景(考古学)
过程(计算)
运输工程
工程类
环境影响评价
土木工程
环境科学
计算机科学
生产(经济)
生物
操作系统
宏观经济学
古生物学
经济
生态学
作者
Darrell L. Cass,Amlan Mukherjee
出处
期刊:Journal of the Construction Division and Management
[American Society of Civil Engineers]
日期:2011-01-10
卷期号:137 (11): 1015-1025
被引量:163
标识
DOI:10.1061/(asce)co.1943-7862.0000349
摘要
Large quantities of greenhouse gases (GHG) are emitted in producing and acquiring materials for the construction, maintenance, and rehabilitation of highway infrastructure. The objective of this paper is to develop and illustrate a method that can be applied by state agencies to quantify the life-cycle emissions associated with different pavement designs. It applies existing life-cycle assessment (LCA) methods that integrate process-level construction data. The research emphasizes the construction phase and contributes a method that can be used to develop and analyze construction phase life-cycle inventories. It describes on-site collection of material and equipment usage data during construction and rehabilitation operations. Departing from traditional approaches that tend to use LCA as a way to compare alternative pavement materials or designs on the basis of estimated inventories, this paper proposes a shift to a context-sensitive process-based approach that uses actual observed construction data to calculate greenhouse gas emissions using a hybrid LCA. The goal is to support strategies that reduce long-term environmental impacts. A case study involving the rehabilitation of a concrete pavement was used to illustrate the proposed method. The key findings were as follows: total CO2 emissions are 787.19 and 1,383.28 MT per lane mile for Hybrid Models 1 and 2, respectively; the production of the materials, equipment, and fuel used to construct the project account for 90% and 94% of the total CO2 emissions throughout the construction phase for Hybrid Models 1 and 2, respectively; the equipment use and transportation impacts together only represent 6–10% of the total emission through the construction phase.
科研通智能强力驱动
Strongly Powered by AbleSci AI