Life Cycle Assessment (LCA) is commonly used to measure the environmental and economic impacts of engineering projects and/or products. However, there is some uncertainty associated with any LCA study. The LCA inventory analysis generally relies on imperfect data in addition to further uncertainties created by the assessment process itself. It is necessary to measure the effects that data and process uncertainty have on the LCA result and to communicate the level of uncertainty to those making decisions based on the LCA. To accomplish this, a systematic and rigorous means to assess the overall uncertainty in LCA results is required.
This paper demonstrates the use of Monte Carlo Analysis to track and measure the propagation of uncertainty in LCA studies. The Monte Carlo technique basically consists of running repeated assessments using random input values chosen from a specified probable range. The effect of this input uncertainty can then be measured by variability of the assessment output. Additional measures are available to consider the systematic uncertainties resulting from boundary selection, aggregation of similar environmental impacts, etc. While measuring the overall LCA uncertainty, the Monte Carlo technique also measures the uncertainty contribution of each unit process within the system being considered. This allows the LCA practitioner to identify those factors contributing the largest amount of uncertainty and to streamline efforts to improve the LCA assessment.