Evidence map›Paper›PMID 37032812›Full record

ReviewEnergy informatics2023

Environmentally sustainable smart cities and their converging AI, IoT, and big data technologies and solutions: an integrated approach to an extensive literature review.

Simon Elias Bibri, Alahi Alexandre, Ayyoob Sharifi, John Krogstie

Abstract readReview
In one paragraph

Review in Energy informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Review
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  6. Review
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  9. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Simon Elias BibriSchool of Architecture, Civil and Environmental Engineering, Civil Engineering Institute, Visual Intelligence for Transportation , Swiss Federal Institute of Technology in Lausanne (EPFL), GC C1 383 (Bâtiment GC), Station 18, 1015 Lausanne, Switzerland.
Alahi AlexandreSchool of Architecture, Civil and Environmental Engineering, Civil Engineering Institute, Visual Intelligence for Transportation , Swiss Federal Institute of Technology in Lausanne (EPFL), GC C1 383 (Bâtiment GC), Station 18, 1015 Lausanne, Switzerland.
Ayyoob SharifiGraduate School of Humanities and Social Science, Graduate School of Advanced Science and Engineering, Network for Education and Research on Peace and Sustainability (NERPS), Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima, 739-8530 Japan.
John KrogstieDepartment of Computer Science, Norwegian University of Science and Technology, Sem Saelands Veie 9, 7491 Trondheim, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed "environmentally sustainable smart cities." This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991-2015, 2016-2019, and 2020-2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods-due to the acceleration of the digitalization and decarbonization agendas-thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time-some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies.

Indexed as

AIAIoTBig dataClimate changeEnvironmentally sustainable smart citiesEnvironmental sustainabilityIoTSmart citiesSustainable cities

Identifiers

PMID37032812
PMCPMC10074362

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.