A material footprint model for green information systems – using statistical learning to identify the predictors of natural resource use. Issue 1 (1st January 2019)

• Record Type: Journal Article
• Authors: Buhl, Johannes; Liedtke, Christa; Teubler, Jens; Schuster, Sebastian; Bienge, Katrin
• Editors: Aziz, Hamidi Abdul
• Is Part Of: Cogent engineering. Volume 6:Issue 1(2019)
• Publication Date: 2019-01-01
• Subjects: sustainable consumption -- machine learning -- data mining -- online survey -- feedback; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2019.1616655
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Green Information Systems in general, and footprint calculators in particular, are promising feedback tools to assist people in adopting sustainable behaviour. Therefore, a Material Footprint model for use in an online footprint calculator was developed by identifying the most important predictors of the Material Footprint of the calculator's users. By means of statistical learning, the analysis revealed that 22 of the 95 predictors identified accounted for 74% of the variance in Material Footprints. Ten predictors out of the 95, mainly from the mobility domain, were capable of showing a prediction accuracy of 61%. The authors conclude that 22 predictors from the areas of mobility, housing and nutrition, as well as sociodemographic information, accurately predict a person's Material Footprint. The short and concise Material Footprint model may help developers and researchers to enhance their information systems with additional items while ensuring the data quality of such applications.