Statistical Evaluation of Input-Side Metrics for Life Cycle Impact Assessment of Emerging Technologies

Abstract

Life cycle assessment is a popular approach for evaluating environmental impact of technologies. However, it is often difficult to apply, especially to emerging technologies due to the difficulty of finding accurate output-side emissions and impact data. Usually, input-side data are more readily available, even for emerging technologies, and may provide a reasonable proxy for predicting the environmental impact associated with emissions. In this paper, this relationship is explored by case studies and regression of life cycle impact with input-side quantities of cumulative mass, energy, and exergy. As a single quantity, this study indicates that ecological cumulative exergy consumption may be best at predicting environmental impact. This work also confirms that if the input variables are separated, then nonrenewable energy use dominates overall impact. However, nonrenewable minerals and some renewable resources are also highly correlated with impact, and nonrenewable energy is only good at prediction of impact due to emission of CO2, SO2, and NO2. These preliminary results suggest the promise of using input-side metrics to predict life cycle environmental impact, and identifies areas of future work.