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Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures
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Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.
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“The use of AI applications to enhance urban efficiencies involves struggles in accomplishing the transformative changes to smart cities due to “the short-sighted, technologically determined, and reductionist AI approaches being applied to complex…problems” (Yigitcanlar et al. 2021).”
From Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review (2023) · cites this paper as Yigitcanlar et al. 2021
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Stein, E. J., & Head, B. W. (2020) · Australian Journal of Public Administration · doi:10.1111/1467-8500.12416 · Cited by 9
There are 10 catastrophic threats facing humans right now, and coronavirus is only one of them
Hunter, A., & HEWSON, J. R. (2020) · doi:10.64628/aa.kwvqqgt46 · Cited by 9
How a Machine Learns and Fails
Pasquinelli, M. (2019) · media/rep/ · doi:10.25969/mediarep/13490 · Cited by 8
Robodebt was a fiasco with a cost we have yet to fully appreciate
Whiteford, P. (2020) · doi:10.64628/aa.amkuuhwva · Cited by 5
Strategies for transcending the chaos of societal disruptions
Camillus, J. C., Baker, J., Daunt, A. I., & Jang, J. (2020) · Vilakshan – XIMB Journal of Management · doi:10.1108/xjm-07-2020-0052 · Cited by 4
(2019) · Geopolitics History and International Relations · doi:10.22381/ghir11220198 · Cited by 4
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Flagged first, then by citations
Research works printed but not linked: 2
- Kirwan, C., and Fu, Z. (2020). Smart Cities and Artificial Intelligence: Convergent Systems for Planning, Design, and Operations, Elsevier.
- Jothiswaran, 2020, Jayaraman, R. Application of artificial intelligence in fisheries and aquaculture, Biot. Res. Today, 2, 499
Cited by
Sorted by citations
Artificial intelligence for waste management in smart cities: a review
Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P.-S. (2023) · Environmental Chemistry Letters · doi:10.1007/s10311-023-01604-3 · Cited by 629
Opportunities and Adoption Challenges of AI in the Construction Industry: A PRISMA Review
Regona, M., Yiğitcanlar, T., Xia, B., & Li, R. Y. M. (2022) · Journal of Open Innovation Technology Market and Complexity · doi:10.3390/joitmc8010045 · Cited by 696
Bibri, S. E., Alexandre, A., Sharifi, A., & Krogstie, J. (2023) · Energy Informatics · doi:10.1186/s42162-023-00259-2 · Cited by 299
Bradu, P., Biswas, A., Nair, C., Sreevalsakumar, S., Patil, M., Kannampuzha, S., Mukherjee, A. G., Wanjari, U. R., Renu, K., Vellingiri, B., et al. (2022) · Environmental Science and Pollution Research · doi:10.1007/s11356-022-20024-4 · Cited by 327
RetractedRegona, M., Yiğitcanlar, T., Hon, C. K. H., & Teo, M. M. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105499 · Cited by 322
Metadata
- Authors
- Yigitcanlar, T., Mehmood, R., & Corchado, J. M.
- Affiliations
- School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia
- King Abdulaziz University (SA)
- Universidad de Salamanca (ES)
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2021-08-10
- Volume
- 13
- Issue
- 16
- Pages
- 8952
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W3192495768
- Cited by
- 319
- References
- 119
- Citations by year
- 2026: 71 · 2025: 82 · 2024: 76 · 2023: 47 · 2022: 39 · 2021: 4
- FWCI
- 18.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Urban Studies › Smart Cities and Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- green AI, smart cities, urban sustainability, urban equity, AI efficiency, urban planning, urban future, technocentrism, urbanization
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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