Evaluating the economic and environmental impacts of road pavement using an integrated local sensitivity model
Globally, road pavement networks generate significant economic investment and environmental impact during the construction, maintenance, and operation of road infrastructure.
It is a sustainability requirement to reduce pavement life cycle costs and environmental impact for achieving cost efficiency and carbon neutrality in the near future. The Federal Highway Administration (FHWA) of the United States emphasised the urgency of road sustainability (FHWA, 2014), and called for optimising pavements systems for reducing both the long-term costs and environmental impacts at the same time.
However, this is far from a simple task because the current methods for assessing the life cycle economic and environmental impacts of pavements are completely independent. This leads to isolated considerations of economic and environmental impacts and the cost-effective and economically feasible measures for reducing pavement life cycle environmental impacts remain unclear.
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).
Photo credit: Jared Murray