Impacts of flexible pavement design and management decisions on life cycle energy consumption and carbon footprint
PurposeThe study aims to develop a methodological framework to estimate life cycle energy consumption and greenhouse gas (GHG) emissions related to pavement design and management decisions. Another objective is to apply the framework to the design and management of flexible highway pavement in Hong Kong. Traditionally, pavement design and management decisions are solely based on economic considerations. This study quantifies the relationships between such decisions and the environmental impacts, thereby helping highway agencies understand the environmental implications of their decisions and make more balanced decisions to improve highway sustainability.Methods(1) A methodological framework is developed by integrating the mechanistic empirical pavement design guide (ME PDG) and life cycle assessment (LCA) methods. (2) The calculation processes for the detailed components in the framework are proposed by synthesizing existing models, data, and tools. (3) In applying the framework to pavement design and management in Hong Kong, a large number of simulations are conducted to generate pavement performance data at different combinations of pavement thickness, roughness trigger value, and traffic levels. (4) GHG emissions and energy consumption are calculated for each simulation scenario, and the results are used to build statistical regression models. (5) The simulation and calculation results are also analyzed to gain additional insights on the environmental impacts of pavement design and management decisions.Results and conclusions(1) The developed framework that integrates ME PDG and LCA methods is useful to assess pavement related life cycle energy consumption and GHG emissions. (2) The developed regression models can well capture the trends of life cycle energy consumption and GHG emissions at different traffic levels, using asphalt concrete (AC) layer thickness and roughness trigger value as independent variables. (3) Material production, road use, and congestion due to road closure dominate pavement related life cycle energy use and GHG emissions. (4) Optimum pavement thickness and international roughness index (IRI) trigger values exist, and they vary with traffic levels.
The International Journal of Life Cycle Assessment