Variable impact transportation (VIT) model for energy and environmental impact of hauling truck operation

Purpose: Fuel economy and emissions of heavy-duty trucks greatly vary based on vehicular/environmental conditions. Large-scale infrastructure construction projects require a large amount of material/equipment transportation. Single-parameter generic hauling models may not be the best option for an accurate estimation of hauling contribution in life cycle assessment (LCA) involving construction projects; therefore, more precise data and parameterized models are required to represent this contribution. This paper discusses key environmental/operational variables and their impact on transportation of materials and equipment; a variable-impact transportation (VIT) model accounting for these variables was developed to predict environmental impacts of hauling. Methods: The VIT model in the form of multi-nonlinear regression equations was developed based on simulations using the U.S. Environmental Protection Agency (EPA)’s Motor Vehicle Emission Simulator (MOVES) to compute all the impact categories in EPA’s TRACI 2.1 and energy consumption of transportation. Considering actual driving cycles of hauling trucks recorded during a pavement rehabilitation project, the corresponding environmental impacts were calculated, and sensitivity analyses were performed. In addition, an LCA case study based on historical pavement reconstruction projects in Illinois was conducted to analyze the contribution of transportation and variability of its impacts during the pavements’ life cycle. Results and discussion: The importance of vehicle driving cycles was realized from simulation results. The case study results showed that considering driving cycles using the VIT model could increase the contribution of hauling in total life cycle Global Warming Potential (GWP) and total life cycle GWP itself by 2–4 and 3–5%, respectively. In addition to GWP, ranges of other hauling-related impact categories including Smog, Ozone Depletion, Acidification, and Primary Energy Demand from fuel were presented based on the case study. Ozone Depletion ranged from 9 to 45%, and Smog ranged from 11 to 48% of the total relevant life cycle impacts. The GWP contribution of hauling in pavement LCA ranged between 5 and 32%. The results indicate that the contribution of hauling transportation can be significant in pavement LCA. Conclusions: For large-scale roadway infrastructure construction projects that need a massive amount of material transportation, high fidelity models and data should be used especially for comparative LCAs that can be used as part of decision making between alternatives. The VIT model provides a simple analytical platform to include the critical vehicular/operational variables without any dependence on an external software; the model can also be incorporated in those studies where some of the transportation activity data are available.