The DOI matches its Crossref record; 12 of the 13 references it cites were found in a registry or an index.
Rebound Effects Caused by Artificial Intelligence and Automation in Private Life and Industry
Open accessPreprint — published as 10.3390/su17051988
Abstract
Many tasks in a modern household are performed by machines, e.g., a dishwasher or a vacuum cleaner, and in the near future most household tasks will be performed by smart service robots. This will relieve the residents, who in turn can enjoy their free time. This newly gained free time will turn out to cause the so-called spare time rebound effect due to more resource consumption. We roughly quantify this rebound effect and propose a CO2-budget model to reduce or even avoid it. In modern industry, automation and AI are taking over work from humans, leading to higher productivity of the company as a whole. This is the main reason for economic growth, which leads to environmental problems due to higher consumption of natural resources. We show that, even though the effects of automation at home and in the industry are different (free time versus higher productivity), in the end they both lead to more resource consumption and environmental pollution. We discuss possible solutions to this problem, such as carbon taxes, emissions trading systems, and a carbon budget.
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“Initial work on the environmental consequences of these shifts has proposed that AI-enabled automation in household tasks and in the workplace actually leads to greater resource consumption and higher negative environmental impacts overall [36].”
From From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate (2025) · cites this paper as [36]
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Cites
The future of employment: How susceptible are jobs to computerisation?
Frey, C. B., & Osborne, M. A. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.08.019 · Cited by 8,727
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Answers to common questions in Ecological Footprint accounting
Kitzes, J. A., & Wackernagel, M. (2008) · Ecological Indicators · doi:10.1016/j.ecolind.2008.09.014 · Cited by 195
Service Robots: Trends and Technology
Gonzalez-Aguirre, J. A., Osorio-Oliveros, R., Rodríguez-Hernández, K. L., Lizárraga-Iturralde, J., Morales-Menéndez, R., Ramírez-Mendoza, R. A., Ramírez-Moreno, M. A., & Lozoya-Santos, J. D. J. (2021) · Applied Sciences · doi:10.3390/app112210702 · Cited by 189
Robotics, Artificial Intelligence, and the Evolving Nature of Work
Webster, C., & Ivanov, S. H. (2019) · Springer eBooks · doi:10.1007/978-3-030-08277-2_8 · Cited by 183
Artificial intelligence and work: a critical review of recent research from the social sciences
Deranty, J.-P., & Corbin, T. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01496-x · Cited by 118
Robot adoption and green productivity: Curse or Boon
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Autonomous Robots for Services—State of the Art, Challenges, and Research Areas
Misaroş, M., Stan, O. P., Donca, I.-C., & Miclea, L. C. (2023) · Sensors · doi:10.3390/s23104962 · Cited by 39
The Jevons Paradox and the Myth of Resource Efficiency Improvements
Alcott, B., Giampietro, M., Mayumi, K. T., & Polimeni, J. M. (2012) · Routledge eBooks · doi:10.4324/9781849773102 · Cited by 39
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The efficiency of muscular exercise
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Research works printed but not linked: 3
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Cited by
Luccioni, A. S., Strubell, E., & Crawford, K. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732007 · Cited by 71
Oliveira, A. P., Carraquico, T., & Martínez-Pérez, C. (2026) · Sustainability · doi:10.3390/su18031359 · Cited by 32
Chouksey, A., Rajan, A. K., Gurjar, V., Tiwari, R., & Mishra, P. K. (2026) · Global Environmental Change Advances · doi:10.1016/j.gecadv.2025.100029 · Cited by 16
AI, climate change and justice: elements for a normative framework centring the Global South
Anand, P., & Coeckelbergh, M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00949-5 · Cited by 16
Huang, K., Lin, Z., Wang, Y., & Wang, X. (2026) · Comprehensive Reviews in Food Science and Food Safety · doi:10.1111/1541-4337.70486 · Cited by 12
Metadata
- Authors
- Ertel, W., & Bonenberger, C.
- Affiliations
- University of Applied Sciences Ravensburg-Weingarten (DE)
- Corresponding author
- Ertel, Wolfgang
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2024-09-06
- Volume
- 17
- Issue
- 5
- Pages
- 1988-1988
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4407977794
- Cited by
- 12
- References
- 13
- Citations by year
- 2026: 7 · 2025: 5
- FWCI
- 11.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Sustainability (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Energy, Environment, Economic Growth
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Keywords
- rebound effect, artificial intelligence, automation, home robots, leisure, resource consumption, environmental pollution, industrial productivity, economic growth, carbon tax, emissions trading, carbon balance
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