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A Framework to Predict Consumption Sustainability Levels of Individuals

Author

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  • Arielle Moro

    (Information Management Institute, University of Neuchâtel, A.L. Breguet 2, CH-2000 Neuchâtel, Switzerland)

  • Adrian Holzer

    (Information Management Institute, University of Neuchâtel, A.L. Breguet 2, CH-2000 Neuchâtel, Switzerland)

Abstract

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 G REEN P REDICT , 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.

Suggested Citation

  • Arielle Moro & Adrian Holzer, 2020. "A Framework to Predict Consumption Sustainability Levels of Individuals," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1423-:d:320849
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    References listed on IDEAS

    as
    1. Alenka Baggia & Matjaž Maletič & Anja Žnidaršič & Alenka Brezavšček, 2019. "Drivers and Outcomes of Green IS Adoption in Small and Medium-Sized Enterprises," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    2. Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
    3. Zhaojun Yang & Jun Sun & Yali Zhang & Ying Wang, 2017. "Green, Green, It’s Green: A Triad Model of Technology, Culture, and Innovation for Corporate Sustainability," Sustainability, MDPI, vol. 9(8), pages 1-23, August.
    4. Büchs, Milena & Bahaj, AbuBakr S. & Blunden, Luke & Bourikas, Leonidas & Falkingham, Jane & James, Patrick & Kamanda, Mamusu & Wu, Yue, 2018. "Promoting low carbon behaviours through personalised information? Long-term evaluation of a carbon calculator interview," Energy Policy, Elsevier, vol. 120(C), pages 284-293.
    5. Sylvain Weber & Paul Burger & Mehdi Farsi & Adan L. Martinez-Cruz & Michael Puntiroli & Iljana Schubert & Benjamin Volland, 2017. "Swiss Household Energy Demand Survey (SHEDS): Objectives, design, and implementation," IRENE Working Papers 17-14, IRENE Institute of Economic Research.
    6. Sonja Maria Geiger & Daniel Fischer & Ulf Schrader, 2018. "Measuring What Matters in Sustainable Consumption: An Integrative Framework for the Selection of Relevant Behaviors," Sustainable Development, John Wiley & Sons, Ltd., vol. 26(1), pages 18-33, January.
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    1. Angélica Pigola & Priscila Rezende da Costa & Luísa Cagica Carvalho & Luciano Ferreira da Silva & Cláudia Terezinha Kniess & Emerson Antonio Maccari, 2021. "Artificial Intelligence-Driven Digital Technologies to the Implementation of the Sustainable Development Goals: A Perspective from Brazil and Portugal," Sustainability, MDPI, vol. 13(24), pages 1-28, December.

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