Removing Shadows from Consequential LCA through a Time-Dependent Modeling Approach: Policy-Making in the Road...

Area of Science:

• Environmental Science
• Engineering
• Sustainability

Background:

• Consequential life cycle assessment (CLCA) traditionally lacks dynamic accounting, hindering accurate analysis of emission flows over time.
• Policy-making for infrastructure, such as pavements, requires precise environmental impact assessments.

Purpose of the Study:

• To propose and validate a dynamic CLCA framework for assessing pavement environmental consequences.
• To quantify the discrepancies between static and dynamic CLCA for pavement materials.

Main Methods:

• Developed a dynamic CLCA framework incorporating time-dependent parameters for demand and technosphere matrices.
• Utilized Monte Carlo simulation to propagate uncertainty from life cycle inventory (LCI) to damage assessment.
• Compared results from dynamic CLCA with a static approach for asphalt versus concrete pavements.

Main Results:

• Static CLCA significantly underestimates environmental benefits: 7% (climate change), 17% (human health), and 77% (resources) for concrete over asphalt.
• Ecosystem quality impact shows a 114% divergence using a static framework.
• Neglecting temporal GHG emissions profiles leads to a 105% divergence in global warming potential (GWP) benefits.

Conclusions:

• Dynamic accounting in CLCA is crucial for accurate environmental impact assessment of pavements.
• Static CLCA methods lead to substantial underestimations of environmental benefits and policy-relevant divergences.
• Variability in monthly temperature and service life significantly influences damage category outcomes.