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The Responsiveness of Urban Water Demand to Working from Home Intensity

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  • Magnus Moglia

    (Centre for Urban Transitions, Swinburne University of Technology, Hawthorn 3122, Australia)

  • Christian Andi Nygaard

    (Centre for Urban Transitions, Swinburne University of Technology, Hawthorn 3122, Australia)

Abstract

Working from home (WFH) is now widespread around the world. Sustainability benefits can arise from WFH, but there remains limited evidence on resource use and its full sustainability implications. To provide some answers on this issue, we analyse water use data from Sydney, Australia, mapped against mobility changes during the natural experiment that COVID-19-related lockdowns represented. We use an auto-regressive distributed lag model to evaluate how variations in WFH influence the demand for water, after accounting for factors like temperature, rainfall, water restrictions, and so on. We find that in response to a 10% increase in WFH, single residential demand does not significantly change, whilst multi-dwelling demand increases 1%, industrial demand decreases 2%, commercial demand increases 3%, and miscellaneous demand increases 3%. Overall, sectoral changes balance each other out, leaving no significant change in aggregate demand. Our contribution is two-fold. First, we operationalise WFH by looking at the intensity of workplace mobility during the pandemic. Second, we establish disaggregated sectoral water consumption elasticities to WFH and show that aggregate water consumption patterns disguise sectoral changes that relate to where and when water is consumed. These results need to inform infrastructure and water supply–demand planning.

Suggested Citation

  • Magnus Moglia & Christian Andi Nygaard, 2024. "The Responsiveness of Urban Water Demand to Working from Home Intensity," Sustainability, MDPI, vol. 16(5), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1867-:d:1345173
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    References listed on IDEAS

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    1. Pesaran, M. Hashem, 2015. "Time Series and Panel Data Econometrics," OUP Catalogue, Oxford University Press, number 9780198759980.
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    3. Schleich, Joachim & Hillenbrand, Thomas, 2009. "Determinants of residential water demand in Germany," Ecological Economics, Elsevier, vol. 68(6), pages 1756-1769, April.
    4. Muhammad Al-Zahrani & Amin Abo-Monasar, 2015. "Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3651-3662, August.
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