Evaluating the economic and environmental impacts of road pavement using an integrated local sensitivity model

Yaning Qiao, Zhiyue Wang, Fanran Meng, Tony Parry, Jonathan Cullen, Shu Liu

Research output: Journal PublicationArticlepeer-review

3 Citations (Scopus)

Abstract

Assessing the life cycle economic and environmental impacts of road pavements is critical for achieving long-term sustainability in transport systems. Life cycle cost analysis (LCCA) and life cycle assessment (LCA) methods have been widely applied to the assessment of road pavements, however, there is still a need to identify which approaches boost both economic and environmental benefits, simultaneously. This study develops an integrated LCCA-LCA method, which identifies and merges common inputs from LCCA and LCA models. A local sensitivity analysis is conducted to identify the most influential inputs in the integrated LCCA-LCA model, and to identify more effective measures to reduce both the costs and environmental impacts of pavements. The results show that only 10 inputs, of the 568 studied, affect both the LCCA and LCA results significantly. Important inputs leading to reduced environmental pollution with less life cycle cost include: traffic volume, road surface area, heavy-duty vehicle ratio, traffic speed, and other design parameters (standard normal deviates, combined standard error, asphalt layer thickness, base layer thickness, layer structural coefficients, and drainage coefficients).

Original languageEnglish
Article number133615
JournalJournal of Cleaner Production
Volume371
DOIs
Publication statusPublished - 15 Oct 2022

Keywords

  • Asphalt pavements
  • Life cycle assessment
  • Life cycle cost analysis
  • Sensitivity analysis

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Environmental Science
  • Strategy and Management
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Evaluating the economic and environmental impacts of road pavement using an integrated local sensitivity model'. Together they form a unique fingerprint.

Cite this