Evidence map›Paper›PMID 38021367›Full record

ReviewEnvironmental science and ecotechnology2024

Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review.

Simon Elias Bibri, John Krogstie, Amin Kaboli, Alexandre Alahi

Abstract readReview
In one paragraph

Review in Environmental science and ecotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Review
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  14. Emerging technologies for the management of diabetic foot ulceration: a review.Frontiers in clinical diabetes and healthcare · 2024
    Review
  15. Neurosustainability.Frontiers in human neuroscience · 2024
    Article
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 (ENAC), Civil Engineering Institute (IIC), Visual Intelligence for Transportation (VITA), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
John KrogstieDepartment of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
Amin KaboliSchool of Engineering, Institute of Mechanical Engineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.
Alexandre AlahiSchool of Architecture, Civil and Environmental Engineering (ENAC), Civil Engineering Institute (IIC), Visual Intelligence for Transportation (VITA), Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent advancements made in the realms of Artificial Intelligence (AI) and Artificial Intelligence of Things (AIoT) have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities. These strides have, in turn, impacted smart eco-cities, catalyzing ongoing improvements and driving solutions to address complex environmental challenges. This aligns with the visionary concept of smarter eco-cities, an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies. However, there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions. To bridge this gap, this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leading-edge AI and AIoT solutions for environmental sustainability. To ensure thoroughness, the study employs a unified evidence synthesis framework integrating aggregative, configurative, and narrative synthesis approaches. At the core of this study lie these subsequent research inquiries: What are the foundational underpinnings of emerging smarter eco-cities, and how do they intricately interrelate, particularly urbanism paradigms, environmental solutions, and data-driven technologies? What are the key drivers and enablers propelling the materialization of smarter eco-cities? What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities? In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices, and what potential benefits and opportunities do they offer for smarter eco-cities? What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities? The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices, as well as the formidable nature of the challenges they pose. Beyond theoretical enrichment, these findings offer invaluable insights and new perspectives poised to empower policymakers, practitioners, and researchers to advance the integration of eco-urbanism and AI- and AIoT-driven urbanism. Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions, stakeholders gain the necessary groundwork for making well-informed decisions, implementing effective strategies, and designing policies that prioritize environmental well-being.

Indexed as

Artificial intelligenceArtificial intelligence of thingsClimate changeEnvironmental sustainabilityMachine learningSmart citiesSmart eco-citiesSmarter eco-cities

Identifiers

PMID38021367
PMCPMC10656232

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.