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Open AccessArticle

A Framework to Predict Consumption Sustainability Levels of Individuals

Information Management Institute, University of Neuchâtel, A.L. Breguet 2, CH-2000 Neuchâtel, Switzerland
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Sustainability 2020, 12(4), 1423; https://doi.org/10.3390/su12041423 (registering DOI)
Received: 10 December 2019 / Revised: 7 February 2020 / Accepted: 10 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Green Technology Innovation for Sustainability)
Innovative Information Systems services have the potential to promote more sustainable
behavior. For these so-called Green Information Systems (Green IS) to work well, they should
be tailored to individual behavior and attitudes. Although various theoretical models already
exist, there is currently no technological solution that automatically estimates individual’s current
sustainability levels related to their consumption behaviors in various consumption domains
(e.g., mobility and housing). The paper aims at addressing this gap and presents the design of
GREENPREDICT, a framework that enables to predict these levels based on multiple features, such as
demographic, socio-economic, psychological, and factual knowledge about energy information. To
do so, the paper presents and evaluates six different classifiers to predict acts of consumption on
the Swiss Household Energy Demand Survey (SHEDS) dataset containing survey answers of 2000
representative individuals living in Switzerland. The results highlight that the ensemble prediction
models (i.e., random forests and gradient boosting trees) and the multinomial logistic regression
model are the most accurate for the mobility and housing prediction tasks.
Keywords: sustainable consumption behavior; green technology; transitioning to sustainability; data analytics; decision making; green information systems sustainable consumption behavior; green technology; transitioning to sustainability; data analytics; decision making; green information systems
MDPI and ACS Style

Moro, A.; Holzer, A. A Framework to Predict Consumption Sustainability Levels of Individuals. Sustainability 2020, 12, 1423.

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