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Stochastic decision modeling for sustainable pavement designs

  • INPUT-OUTPUT AND HYBRID LCA
  • Published:
The International Journal of Life Cycle Assessment Aims and scope Submit manuscript

Abstract

Purpose

In the USA, several studies have been conducted to analyze the energy consumption and atmospheric emissions of Warm-mix Asphalt (WMA) pavements. However, the direct and indirect environmental, economic, and social impacts, termed as Triple-Bottom-Line (TBL), were not addressed sufficiently. Hence, the aim of this study is to develop TBL-oriented sustainability assessment model to evaluate the environmental and socio-economic impacts of pavements constructed with different types of WMA mixtures and compare them to a conventional Hot-mix Asphalt (HMA). The types of WMA technologies investigated in this research include Asphamin® WMA, Evotherm™ WMA, and Sasobit® WMA.

Methods

To achieve this goal, supply and use tables published by the U.S. Bureau of Economic Analysis were merged with 16 macro-level sustainability metrics. A hybrid TBL-LCA model was built to evaluate the life-cycle sustainability performance of using WMA technologies in construction of asphalt pavements. The impacts on the sustainability were calculated in terms of socio-economic (import, income, gross operating surplus, government tax, work-related injuries, and employment) and environmental (water withdrawal, energy use, carbon footprint, hazardous waste generation, toxic releases into air, and land use). A stochastic compromise programming model was then developed for finding the optimal allocation of different pavement types for the U.S. highways.

Results and discussion

WMAs did not perform better in terms of environmental impacts compared to HMA. Asphamin® WMA was found to have the highest environmental and socio-economic impacts compared to other pavement types. Material extractions and processing phase had the highest contribution to all environmental impact indicators that shows the importance of cleaner production strategies for pavement materials. Based on stochastic compromised programming results, in a balanced weighting situation, Sasobit® WMA had the highest percentage of allocation (61 %); while only socio-economic aspects matter, Asphamin® WMA had the largest share (57 %) among the asphalt pavements. The optimization results also supported the significance of an increased WMA use in the U.S. highways.

Conclusions

This research complemented previous LCA studies by evaluating pavements not only from environmental emissions and energy consumption standpoint, but also from socio-economic perspectives. Multi-objective optimization results also provided important insights for decision makers when finding the optimum allocation of pavement alternatives based on different environmental and socio-economic priorities. Consequently, this study aimed to increase awareness of the inherent benefits of economic input–output analysis and multi-criteria decision making through application to emerging sustainable pavement practices.

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Authors and Affiliations

  1. Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, 32816, USA

    Murat Kucukvar, Mehdi Noori & Omer Tatari

  2. Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND, 58102, USA

    Gokhan Egilmez

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  1. Murat Kucukvar
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  2. Mehdi Noori
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  3. Gokhan Egilmez
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  4. Omer Tatari
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Correspondence to Omer Tatari.

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Responsible editor: Marzia Traverso

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Kucukvar, M., Noori, M., Egilmez, G. et al. Stochastic decision modeling for sustainable pavement designs. Int J Life Cycle Assess 19, 1185–1199 (2014). https://doi.org/10.1007/s11367-014-0723-4

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