Role of Uncertainty Assessment in LCA of Pavements

Life Cycle Assessment (LCA) is a tool to appraise the environmental impact of a pavement during its service life. Typically, the LCA assessor has to make some assumptions and judgments, employ data from different sources, use analytical tools and models, etc., in order to provide information to the transportation officials for decision making. Since assumptions and data from different sources have inherent uncertainty, it is essential to explicitly report the underlying uncertainties and their consequences in LCA outputs. This study recommends an approach for performing uncertainty assessment in pavement LCA. The first step of the recommended approach is to define a clear objective for the need of the assessment. Then establish the scope of assessment such that it is technically feasible and satisfies the assessment objective. Prioritizing the phases in LCA that influence the output and concentrating only on significant phases makes the uncertainty assessment viable. The next step is classifying the uncertainties that prevailed in prioritized phases. Classification supports in selecting the proper methods and techniques that can apprehend the uncertainty. Finally, present the uncertainty results clearly and effectively to the transportation officials. The recommended approach is demonstrated through an example. within a population either caused by random variation or bias. The chances are high that every LCA assessor encounters certain type of uncertainty. Knowing the fact that uncertainty subsists in LCA, the questions that arise are: 1) what is the credibility of the LCA results? (Allaire and Willcox 2014), 2) which part of the LCA processes leads to primary uncertainty? (Hung and Ma 2009) , 3) How to present the uncertainty in LCA that can be easily comprehended by the policy makers? A systematic uncertainty assessment answers to the above questions to a good extent. According to Baker and Lepech (2009), uncertainty assessment supports: a) better decision-making b) increases transparency in the data c) increases the quality of analysis, and d) helps in planning information gathering exercise(s). May and Brennan (2003), and Helton et al. (2006) described that the uncertainty analysis could provide a possible range of outcomes rather than single outcome based on the individual data uncertainties. It is important to understand various chief sources of uncertainty and the best techniques to handle them. 2.1 Sources of Uncertainty in LCA Baker and Lepech (2009) classified the uncertainties as below:  Database uncertainty (missing or unrepresentative data)  Model uncertainty  Statistical measurement error (data based on a limited set of sample)  Uncertainty in preferences (uncertainty due to analyst’s choices)  Uncertainty in a future physical system, about the currently designed system (the degree to which the future conditions change in which present design will be subjected) One can further classify these uncertainty sources, for example, database uncertainty can be due to outdated data, spatial or technological variability of available data, inconsistency between various available data sources, etc. 2.2 Handling Uncertainty Typically, the objective of uncertainty assessment can be either reducing the uncertainty or communicating the possible impacts of uncertainties on LCA outputs. If the focus is reducing the uncertainty, Heijungs and Huijbregts (2004) clearly mentioned three approaches: 1) Scientific Approach: Performing thorough research while developing the characterization models, increasing the data size, etc. 2) Constructivist Approach: Involving stakeholders, discussing and finally deciding on or voting for a consensus characterization factors. 3) Legal Approach: Relying on authoritative bodies reports, like ISO or the US EPA. Out of the three ways, the first two are not practical at the level of an analyst because it needs the involvement of state or governmental bodies. Therefore, the analysts often attempt in using the data from authoritative bodies like the US EPA, the FHWA, etc. Additionally, some researchers have suggested handling uncertainty by using local databases containing site specific data (Cellura et al. 2011). However, most attention in the published literature of uncertainty assessment in LCA of pavements is given in communicating the possible outcomes due to the variability in inputs or data quality. There are various analytical tools such as a first order approximation of the Taylor expansion of the underlying model, fuzzy set methods, neural networks, etc., but the principal tool employed is Monte Carlo Simulation (MCS) (Hung and Ma 2009) (Noshadravan et al. 2013) (Allaire and Willcox 2014) (Yu et al. 2016) followed by sensitivity analysis. In MCS, the input parameter is expressed in the form of a probability distribution. Multiple simulations can be performed by varying the input parameters (as per probability distributions), and outputs can be collected for each simulation. From the pool of outputs obtained, statistical properties can be evaluated, and the possible outcomes are presented. The principal challenge in using MCS is assigning a probability distribution and defining statistical input parameters. Most common type of probability distributions employed are the normal, lognormal, uniform, and triangular (May and Brennan 2003), (Heijungs and Huijbregts 2004). In general, if the experimental data is not available, the LCA analyst assumes the probability distribution and statistical parameters (variance, standard deviation, etc.) for performing MCS. In some cases, if the input LCA data is available from numerous sources or from different time periods etc., then the analyst can characterize the quality of the available data and develop the statistical parameters for performing simulations. The data quality indicators (DQI’s) approach developed by Weidema and Wesnes (1996) is a well-known method for data characterization. The data is characterized based on five parameters that are reliability, completeness, temporal correlation, geographical correlation, and technological correlation. A numerical score is assigned to each parameter, and an aggregated DQI score is estimated at the end. Data quality score can be converted into a probability distribution function or as an additional variance in the data. For example, Yu et al. (2016) employed DQI score to develop a modified beta distribution based on the works of Canter et al. (2002). Noshadravan et al. (2013) used DQI’s scores to calculate the additional variance to the data as per the procedure presented in Weidema et al. (2013). Similar to MCS, sensitivity analysis, scenario analysis are other tools that can be used to include uncertainty assessment in LCA. Previous studies employed uncertainty propagation tools for a particular input parameter or a single phase of LCA. For example, Yu et al.(2016) worked on the albedo effect of pavements, another study by Yu et al.(2016) presented a procedure to evaluate the data quality of inputs for asphalt layers in pavement construction, Noshadravan et al. (2013) accounted for the uncertainty in the prediction of pavement roughness. There is a lack of guidance for performing the overall uncertainty assessment for LCA of pavements. The efforts from the previous researchers are commendable; the employed methodologies and findings from their studies can be implemented in performing a comprehensive and systematic uncertainty assessment of LCA of pavements.