| Summary: | The application of Life Cycle Assessment to road pavements has been evolving over the last years, receiving a growing interest from the academic sector and from governmental and non-governmental institutions and organizations. However, the complete introduction of this approach in the asset management decision making process is not possible yet, due to an incomplete understanding of the impact of some relevant phases and components of a road pavement LCA, such as the work zone impact during maintenance events and the rolling resistance in the use phase. The first one refers to the additional congestion and traffic delay in an area of a trafficway interested by construction and maintenance activities. The road pavement rolling resistance is the energy loss due the pavement-vehicle interaction (PVI) and it is affected by the tire properties and by the pavement surface condition. The introduction of the Carbon Footprint/LCA approach in highway asset management, as a decision making tool, requires a deep understanding of all the phases of the life cycle of a road and of the impact of the selected methods and assumed parameters to model them. This thesis provides a review of the main models used to describe the influence on the vehicle fuel consumption - in terms of CO2 emissions - of the work zone during maintenance activities and the rolling resistance during the use phase and investigates the potential impact of these models and of some input parameters on the LCA results. The study was applied on two different UK road sections, characterized by different traffic volume, maintenance activities and design. The impact of the work zone during maintenance activities was explored, comparing the CO2 emissions obtained from two generally applied models in Life Cycle Assessment studies (LCAs) with different level of sophistication: the microsimulation model Aimsun and the macroscopic analytical/deterministic method described in the Highway Capacity Manual (HCM), which is based on the Demand-Capacity (D-C) model and the queue theory. In these models, the traffic volume, the Traffic Management (TM) strategy, the Emission Factor (EF) model and the network boundary are input variables that potentially generate uncertainty in the results and their impact was investigated. The impact of the rolling resistance, due to the pavement surface properties, was assessed with two different models provided in literature and a sensitivity test was performed on some significant input variables, namely the pavement deterioration, the traffic growth and the selected EF. The results obtained in this research have shown that the models adopted to estimate the vehicle emissions for both the work zone impact and the rolling resistance components have a significant influence on the LCA results. Therefore, the selection of the model to assess the impact of these components need to be accurate and appropriate. To assess the work zone impact during maintenance events, the selection of the traffic and emission models should be based on the study objectives and on the available resources. The assessment of the impact of the rolling resistance on the vehicle emissions requires the development of models to estimate the deterioration rate of the pavement surface properties over time and models to link them to the rolling resistance energy loss and to the vehicle emissions. Although currently there are few models available in literature, they are affected by site specific elements and are not suitable for all geographical locations. In the UK, there is currently a lack of general pavement deterioration models able to predict the change of unevenness and texture depth over time and the relationship between them and the rolling resistance and the fuel consumption. This must be corrected before pavement LCA studies can be extended to the use phase. The selected model is not the only source of uncertainty in the assessment of these components. In fact, the analysis of the work zone impact and of the rolling resistance requires several methodological assumptions that, as shown in this study, can have a relevant impact on the results, generating a high level of uncertainty. The results obtained from the work zone impact analysis are sensitive to all the input variables taken into account in this study: the traffic growth, the TM strategy adopted, the EF model and the extent of the road network assumed to be impacted by the work zone. For the rolling resistance, if the deterioration rate of the pavement surface properties is a significantly sensitive parameter, the traffic growth and the EF/fuel efficiency predictions, combined to predict future vehicle emissions, have a relatively small effect because they cancel out to a large extent. However, changes in predicted future traffic levels or EF could change this result and should be kept under review. These research outcomes highlight the importance of incorporating uncertainty into pavement LCA. The reliability and accuracy of an LCA is affected by the reliability of the methodologies and models adopted. LCA results should not be presented as ’single figure’ absolute values, but rather considering a range of values to reflect the uncertainties and variability that lie behind them.
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