Statistical analysis of empirical lifetime mileage data for automotive LCA

Abstract

Purpose

Innovative fuel efficiency technologies as for example electrification and lightweight materials typically lead to a shift of emissions from the use phase to the production phase of a vehicle. Whether the efforts of applying sophisticated fuel saving technologies pay back in environmental terms over the products’ lifetime depends directly on the lifetime mileage assumed. Currently, different “educated guess” values of industry associations and car manufacturers ranging from 150,000 to 300,000 km are applied in automotive life cycle assessment (LCA). This study improves the database for LCA practitioners in the automotive industry by statistically determining empirical lifetime mileage values for different types of passenger cars.

Methods

The analysis of different data sets of a sample of more than 800,000 vehicles in Germany is performed by using statistical techniques. Firstly, survival analysis is used to describe the typical lifetime of passenger cars by using the Weibull distribution. Secondly, regression analysis is used to describe the progression of mileage during the course of a car’s life. The analysis differentiates eight different segments from urban small cars (A000) over midsized cars (A00, A0, A, B, C, D) to ultra luxury cars (E) as well as Otto and diesel engines.

Results and discussions

The results show varying lifetimes and mileages of passenger cars depending on the engine type and vehicle segment. Many decisions on innovative automotive technologies are independent from the engine type. In this case, the recommendation of the authors is to differentiate between three different groups of segments (A00/A0, A/B, C) with mileages going from 170,000 over 200,000 to 230,000 km. There might even be cases where the usage of a segment-specific value is not recommended. In such cases, the average value of the obtained results of 200,000 km appears as feasible choice. Even though the primary data are from Germany only, it is assumed that the results obtained in this study can be used as a proxy estimate for the geographical area of Europe.

Conclusions

The results of this paper can be used as default values for LCA practitioners performing automotive LCAs as they provide evidence-based values of passenger cars’ lifetimes and mileages in a disaggregated way. The previously often used assumption of 150,000 km based on educated guess represents an underestimated value and should be replaced by the data obtained in this study. Using the proposed lifetime mileage data improves the validity and representativeness of automotive LCAs in Europe.