Cited by 586Smart Cities and TechnologiesInternet of Things and AIEnvironmental Quality and Pollution

The DOI matches its Crossref record; 295 of the 295 references it cites were found in a registry or an index; 4 of the 179 sources its claims cite were checked for support.

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

Open access
Bibri, Simon Elias, Krogstie, John, Kaboli, Amin, Alahi, Alexandre·2024·Environmental Science and Ecotechnology

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.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This comprehensive systematic review synthesizes 235 documents (2015–2023) to map how AI and AIoT solutions are being applied to smarter eco-cities for environmental sustainability. It finds strong potential across energy, water, waste, transport, biodiversity, climate adaptation, and disaster resilience, but also identifies major environmental, technical, ethical, and governance challenges.

Key findings

  • The review defines smarter eco-cities as an evolution of smart eco-cities that integrate AI and AIoT with environmental technologies and strategies to optimize sustainable systems and smart systems, producing combined effects greater than the sum of their separate effects.
  • It identifies AI/AIoT applications across energy conservation and renewable energy, sustainable transportation and traffic control, water resources conservation, waste management, biodiversity and ecosystem services, environmental monitoring and control, climate change adaptation and mitigation, and disaster resilience and management.
  • Reported benefits include optimized resource management, increased energy efficiency, enhanced waste management, improved transportation and mobility management, reduced environmental impacts, increased resilience, and enhanced decision-making in urban management and planning.
  • Challenges include environmental costs and high energy demand of AI/AIoT, e-waste and toxic pollution, privacy and cybersecurity risks, public trust and social acceptance issues, limited technical expertise, lack of robust regulatory frameworks, and ethical/societal concerns; the paper also cites estimates that global electricity demand for advanced ICT could rise to 20% compared with around 1% today, and that AI may advance 134 SDG targets while hindering 59.

Methods

  • A systematic literature review with nine stages, following PRISMA for search and selection, using SCOPUS and a broad search string; the search was conducted in late March 2023 and covered 2015–2023, returning 455 documents, reduced to 230 after excluding overlaps, then 12 excluded and 17 added via citation tracking, yielding a final database of 235 documents.
  • The synthesis used a unified evidence synthesis framework integrating configurative, aggregative, and narrative synthesis approaches, with deductive content analysis via a structured Excel spreadsheet and critical appraisal of selected studies.

Limitations

  • The paper states that the search strategy may have missed relevant studies due to database selection, search keywords, language restriction, and inclusion/exclusion criteria; reliance on peer-reviewed documents risks excluding grey literature and stakeholder input.
  • It also states publication bias (positive results more likely published), English-language bias, and potential funding-source bias, plus data extraction and synthesis challenges from heterogeneity in study designs and reporting formats.
  • The text makes evident that the review is a qualitative evidence synthesis rather than a quantitative meta-analysis, so it does not pool effect sizes or provide statistical estimates of AI/AIoT impacts.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.ese.2023.100330

Cites

295 of 295 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Graphical abstract
Graphical abstract CC BY · PubMed Central
Fig. 1 Smarter eco-cities and their underlying urbanism paradigms, environmental solutions, and data-driven technologies.
Fig. 1 Smarter eco-cities and their underlying urbanism paradigms, environmental solutions, and data-driven technologies.CC BY · PubMed Central
Fig. 2 The five pillars of an AI/AIoT-driven system: 1-sensing in charge of collecting raw data, 2-perceiving in charge of extracting semantically meaningful information from raw data, 3-learning in charge of learning to predict patterns, 4-visualizing in charge of communicating key insights, and 5-acting in charge of taking action to achieve a certain goal.
Fig. 2 The five pillars of an AI/AIoT-driven system: 1-sensing in charge of collecting raw data, 2-perceiving in charge of extracting semantically meaningful information from raw data, 3-learning in charge of learning to predict patterns, 4-visualizing in charge of communicating key insights, and 5-acting in charge of taking action to achieve a certain goal.CC BY · PubMed Central
Fig. 3 Flow diagram outlining the process of conducting the systematic review.
Fig. 3 Flow diagram outlining the process of conducting the systematic review.CC BY · PubMed Central
Fig. 4 The PRISMA flowchart for literature search and selection. Adapted from [[111], [112]].
Fig. 4 The PRISMA flowchart for literature search and selection. Adapted from [[111], [112]].CC BY · PubMed Central
Fig. 5 A framework for unified evidence synthesis and its characteristics.
Fig. 5 A framework for unified evidence synthesis and its characteristics.CC BY · PubMed Central
Fig. 6 An overview of the main conceptual categories identified and their relationships.
Fig. 6 An overview of the main conceptual categories identified and their relationships.CC BY · PubMed Central

Verification

Claims the paper makes

118 Own interpretation · 115 Not checked yet · 66 No basis given · 7 Own method · 3 Supported · 2 Own result · 1 Couldn’t confirm · accuracy not yet measured

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Show all 99 claims

  • It may state two separate claims as one. 99
  • Probably not a substantial claim. 167
  • The quote only partly states this claim. 1
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  • The paper relies on this without support.

    ICT enables the implementation of smart grids that use advanced sensors, communication networks, and data analytics to optimize energy production, consumption, and distribution.

    “ICT enables the implementation of smart grids, which use advanced sensors, communication networks, and data analytics to optimize energy production, consumption, and distribution.”
  • The paper relies on this without support.

    ICT tools and systems facilitate the monitoring, analyzing, and managing of natural resources and related infrastructure.

    “Moreover, ICT tools and systems facilitate the monitoring, analyzing, and managing of natural resources and related infrastructure.”
  • The paper relies on this without support.

    With sensors, remote sensing technologies, and data analytics, environmental parameters such as air quality, water quality, and waste management can be monitored and controlled in real time, enabling more effective environmental sustainability practices.

    “With sensors, remote sensing technologies, and data analytics, environmental parameters such as air quality, water quality, and waste management can be monitored and controlled in real time, enabling more effective environmental sustainability practices.”
  • The paper relies on this without support.

    ICT applications contribute to optimizing transport efficiency and managing mobility in smart cities.

    “Also, ICT applications contribute to optimizing transport efficiency and managing mobility in smart cities.”
  • The paper relies on this without support.

    ICT solutions are utilized to improve waste management processes.

    “Furthermore, ICT solutions are utilized to improve waste management processes.”
  • The paper relies on this without support.

    ICT has played a vital role in transforming cities into smart and sustainable environments by enabling key advancements to achieve greater environmental sustainability and resilience in urban areas.

    “ICT has played a vital role in transforming cities into smart and sustainable environments by enabling key advancements to achieve greater environmental sustainability and resilience in urban areas.”
  • The paper relies on this without support.

    Technological advancements provide the tools and capabilities for smart eco-cities to integrate and optimize various urban systems for sustainability.

    “Technological advancements provide the tools and capabilities for these cities to integrate and optimize various urban systems for sustainability.”
  • The paper relies on this without support.

    Environmental concerns drive the need for smart eco-cities to adopt smart and eco-friendly solutions.

    “Environmental concerns drive the need for these cities to adopt smart and eco-friendly solutions.”
  • The paper relies on this without support.

    The effective use of policy instruments is essential in harnessing the full potential of technological advancements and addressing environmental challenges to realize the vision of smart eco-cities.

    “The effective use of policy instruments is essential in harnessing the full potential of technological advancements and addressing environmental challenges to realize the vision of smart eco-cities.”
  • The paper relies on this without support.

    IoT devices equipped with sensors and actuators collect real-time data on environmental parameters.

    “IoT devices equipped with sensors and actuators collect real-time data on environmental parameters.”
  • The paper relies on this without support.

    IoT enables seamless data transmission to centralized platforms such as cloud computing.

    “IoT enables seamless data transmission to centralized platforms, such as cloud computing, ensuring that data from distributed sources is readily available for processing and analysis.”
  • The paper relies on this without support.

    AI algorithms process IoT-generated data to identify patterns, anomalies, and trends.

    “IoT generates vast amounts of data and AI algorithms, including ML and DL, process these data to identify patterns, anomalies, and trends, providing insights into environmental conditions.”
  • The paper relies on this without support.

    AI-driven predictive models utilize historical and real-time IoT-driven data to forecast environmental changes.

    “AI-driven predictive models utilize historical and real-time IoT-driven data to forecast environmental changes, which enables proactive decision-making and timely responses.”
  • The paper relies on this without support.

    IoT's integration with AI allows for autonomous control of systems and processes.

    “IoT's integration with AI allows for autonomous control of systems and processes.”
  • The paper relies on this without support.

    AI algorithms optimize resource allocation and utilization.

    “AI algorithms optimize resource allocation and utilization, ensuring minimal waste and maximum efficiency in such areas as energy consumption, water usage, and transportation.”
  • The paper relies on this without support.

    IoT-enabled devices can be remotely controlled and monitored.

    “IoT-enabled devices can be remotely controlled and monitored, reducing the need for physical interventions and minimizing human impact on sensitive ecosystems.”
  • The paper relies on this without support.

    Climate change has accelerated the need for proactive measures.

    “Climate change has accelerated the need for proactive measures, particularly in urban areas where high energy consumption contributes significantly to GHG emissions.”
  • The paper relies on this without support.

    High energy consumption in urban areas contributes significantly to GHG and CO2 emissions.

    “particularly in urban areas where high energy consumption contributes significantly to GHG emissions”
    “as cities contribute significantly to CO2 emissions through high energy consumption”
  • The paper relies on this without support.

    The operation of AI and AIoT applications requires a lot of energy to power IoT infrastructure, data processing platforms, cloud and edge computing, high-speed wireless networks, and large-scale AI systems.

    “The operation of AI and AIoT applications requires a lot of energy to power the IoT infrastructure, data processing platforms, cloud and edge computing, high-speed wireless networks, and large-scale AI systems.”
  • The paper relies on this without support.

    Blockchain provides a decentralized and transparent platform for securely recording and verifying transactions, data, and information.

    “It provides a decentralized and transparent platform for securely recording and verifying transactions, data, and information.”
  • The paper relies on this without support.

    AI and AIoT solutions can be implemented in combination with sustainable and eco-friendly design principles, energy-efficient policy instruments, and other relevant measures such that efficiency gains lead to reducing energy use and carbon footprint.

    “This, in turn, means that AI and AIoT solutions should be carefully implemented in combination with sustainable and eco-friendly design principles, energy-efficient policy instruments, and other relevant measures. This is to ensure that the efficiency gains enabled by AI and AIoT solutions lead to reducing energy use and carbon footprint.”
  • The paper relies on this without support.

    The transformative potential of AI and AIoT technologies in reshaping smarter eco-cities is real.

    “Considering the transformative potential of AI and AIoT technologies in reshaping smarter eco-cities, a comprehensive investigation is warranted to unravel the dimensions, opportunities, benefits, and challenges inherent in this emerging urban paradigm.”
  • The paper relies on this without support.

    AI and AIoT are disruptive technologies.

    “As disruptive technologies, AI and AIoT lay the foundational technological infrastructure essential for constructing the digital ecosystem of emerging smarter eco-cities to amplify and sustain their contributions to environmental sustainability goals.”
  • The paper relies on this without support.

    Smarter eco-cities are complex systems characterized by wicked problems.

    “However, these technologies cannot serve as a universal remedy or panacea for the wicked problems characterizing smarter eco-cities as complex systems.”
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Citations

Cites

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Show all 284 references

Research works printed but not linked: 14

  1. Yigitcanlar, 2021, Green artificial intelligence: toward an efficient, Sustain. Equit. Technol. Smart Cities Futures, 13, 8952
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  3. Martınez-Miranda, 2005, Aldea Emotions in human and artificial intelligence Computers in Human Behavior, 21, 323
  4. Mosavi, 2019, State of the art of machine learning models in energy systems, Syst. Rev., 12, 1301
  5. Chen, 2023, Artificial intelligence-based solutions for climate change: a review, Environ. Chem. Lett., 10.1007/s10311-023-01617-y · DOI
  6. Toppeta, 2010, vol. 5, 1
  7. Deren, 2015, Big data in smart cities, Sci. China Inf. Sci., 58, 1
  8. Nikitin, 2016, Data-driven cities: from concept to applied solutions, PricewaterhouseCoopers (PWC)
  9. Alsadi, 2018, Photovoltaic power systems optimization research status: a review of criteria, Constrain. Models Techn. Software Tools, 8, 1761
  10. Ivanova, 2023, Guidelines for the application of artificial intelligence in the study of the influence of climate change on transport infrastructure, vol. 1, 29
  11. Walsh, 2020, vol. 1, 1
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  13. Petit, 2018, Toward a critique of algorithmic reason. A state-of-the-art review of artificial intelligence, its influence on politics and its regulation, Quaderns del CAC, 44
  14. Sun, 2020, Applications of artificial intelligence for disaster management, Nat Hazards, 103, 2631, 10.1007/s11069-020-04124-3 · DOI · Paper page

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Metadata

Authors
Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A.
Affiliations
  • École Polytechnique Fédérale de Lausanne (CH)
  • Norwegian University of Science and Technology (NO)
Corresponding author
Bibri, Simon Elias
Venue
Environmental Science and Ecotechnology
Publisher
Elsevier BV
Published
2024-05
Volume
19
Pages
100330
ISSN
  • 2096-9643 (ISSN-L)
  • 2666-4984
OpenAlex
W4387767134
Cited by
536
References
284
Citations by year
2026: 99 · 2025: 215 · 2024: 212
FWCI
10.28
Type
journal article
Language
en
Open access
Locations
  • Environmental Science and Ecotechnology (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
Funders
  • Horizon 2020 European Innovation Council Fast Track to Innovation (101034260)
  • Horizon 2020
  • H2020 Marie Skłodowska-Curie Actions
Topics
  • Social Sciences › Urban Studies › Smart Cities and Technologies
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Physical Sciences › Environmental Science › Ecology › Environmental Quality and Pollution
Keywords
AIoT, environmental sustainability, smart cities, eco-urbanism, sustainable urban development, implementation barriers, urban environmental problems

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