Regression Models for Environmental Assessment in Aviation

Abstract

The increasing flight demand directly correlates with the environmental impacts of aviation. Therefore, assessing the environmental impacts for optimizing problematic processes is necessary to make aviation more sustainable. Life Cycle Assessment (LCA), based on ISO 14040/14044, provides a standardized framework, to assess the environmental impacts of different aircraft models. In this thesis, correlations for predicting environmental impacts with linear regression models are found based on specific aircraft parameters. This so-called High-Level LCA (HL-LCA) enables a quicker assessment of comprehensive environmental impacts based on predictions, while reducing the effort for future LCA of aircraft models. Therefore, detailed LCA studies are conducted for nine aircraft models. Each model has a specific Life Cycle Inventory data (LCI), covering production, maintenance, and flight operations. To enable a better adaption of an aircraft life cycle, the Life Cycle Impact Assessment (LCIA) results are combined with a discrete-event simulation tool. For this reason, the Life Cycle Cash Flow Environment (LYFE) developed at the German Aerospace Centre (DLR) is applied, to assess the environmental impacts of production, maintenance, and flight operations to represent the life cycle of an aircraft. For the HL-LCA, the environmental impact categories of the detailed LCA are used in regression models and defined as target variables. Then, aircraft parameters are used to predict the environmental impact categories. For the case study of the impact category Climate Change (CC), the regression models estimate impacts across the life cycle phases of production, maintenance, and flight operations. Thereby, the predictor variables cruise Mach number, wing area, average flight time, fuselage height, and fuselage width proved to be beneficial.