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Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts

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  • Andreas Froemelt
  • René Buffat
  • Stefanie Hellweg

Abstract

As major drivers of economy, households induce a large share of worldwide environmental impacts. The variability of local consumption patterns and associated environmental impacts needs to be quantified as an important starting point to devise targeted measures aimed at reducing household environmental footprints. The goal of this article is the development and appraisal of a comprehensive regionalized bottom‐up model that assesses realistic environmental profiles for individual households in a specific region. For this purpose, a physically based building energy model, the results of an agent‐based transport simulation, and a data‐driven household consumption model were interlinked within a new probability‐based classification framework and applied to the case of Switzerland. The resulting model predicts the demands in about 400 different consumption areas for each Swiss household by considering its particular circumstances and produces a realistic picture of variability in household environmental footprints. An analysis of the model results on a municipal level reveals per‐capita income, population density, buildings' age, and household structure as possible drivers of municipal carbon footprints. While higher‐emission municipalities are located in rural areas and tend to show higher shares of older buildings, lower‐emission communities have larger proportions of families and can be found in highly populated regions by trend. However, the opposing effects of various variables observed in this analysis confirm the importance of a model that is able to capture regional distinctions. The overall model constitutes a comprehensive information base supporting policymakers in understanding consumption patterns in their region and deriving environmental strategies tailored to their specific population.

Suggested Citation

  • Andreas Froemelt & René Buffat & Stefanie Hellweg, 2020. "Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 639-652, June.
    • Handle: RePEc:bla:inecol:v:24:y:2020:i:3:p:639-652
    DOI: 10.1111/jiec.12969
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    Cited by:

    1. Viktoras Kulionis & Andreas Froemelt & Stephan Pfister, 2024. "Multiscale Orientation Values for Biodiversity, Climate and Water: A Scientific Input for Science- Based Targets," Papers 2403.11680, arXiv.org.
    2. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    3. Gilang Hardadi & Alexander Buchholz & Stefan Pauliuk, 2021. "Implications of the distribution of German household environmental footprints across income groups for integrating environmental and social policy design," Journal of Industrial Ecology, Yale University, vol. 25(1), pages 95-113, February.
    4. Franco Donati & Sébastien M. R. Dente & Chen Li & Xaysackda Vilaysouk & Andreas Froemelt & Rohit Nishant & Gang Liu & Arnold Tukker & Seiji Hashimoto, 2022. "The future of artificial intelligence in the context of industrial ecology," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1175-1181, August.

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