ROADWAYS AND INFRASTRUCTURE Variable impact transportation (VIT) model for energy and environmental impact of hauling truck operation Seunggu Kang 1 & Mojtaba Ziyadi 2 & Hasan Ozer 1 & Imad L. Al-Qadi 1 Received: 17 May 2018 /Accepted: 6 November 2018 /Published online: 3 December 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract 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. Keywords Hauling . Truckemission . Greenhouse gas . Pavement life cycle assessment . Truck fuel consumption . HDVemission 1 Introduction Medium- and heavy-duty trucks (gross vehicle weight rating (GVWR) > 8500 lb.) account for only 4% of registered vehi- cles on the road in the USA but are responsible for one quarter of the on-road greenhouse gas (GHG) emissions, making it the second largest source of GHG in the transportation sector in 2010 (The White House 2014). The transportation sector itself is the largest contributor of GHG emissions in the USA in Responsible editor: Omer Tatari * Hasan Ozer hozer2@illinois.edu 1 Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 N Mathews MC-250, Urbana, IL 61801, USA 2 SmartDrive Systems, 4790 Eastgate Mall, San Diego, CA 92121, USA The International Journal of Life Cycle Assessment (2019) 24:1154–1168 https://doi.org/10.1007/s11367-018-1554-5