• CO 2 compensation behaviour is examined for the first time in a car sharing setting. • We analyse the dataset of a German car sharer by using machine learning. • Price, mileage, age, education level and place of residence are statistically significant predictors for voluntary carbon offsetting. • Gender is not a statistically significant predictor for voluntary carbon offsetting. • We provide insights into politics and business based on a precisely defined target group. Sharing seems a key feature of transforming linear consumption to a more environmentally friendly system. This is especially applicable to car sharing. The aim of this study is to find out which factors influence environmentally friendly behaviour and how strongly. 13,629 journeys of a German car sharing provider specialised in the transport of goods and larger groups of people are evaluated. The focus is on the possibility for customers to offset their carbon footprint by voluntarily making their journeys climate neutral. Considering socio-economic characteristics, a Light Gradient Boosting Machine (LightGBM) model is applied to analyse variables which influence environmentally friendly behaviour. Age, place of residence, mileage driven, and education level have a statistically significant influence in predicting whether a customer will voluntarily offset CO 2 or not, in contrast to gender. These findings have societal and political implications which could be used for future policy making.