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

被引:58
|
作者
Bibri, Simon Elias [1 ]
Krogstie, John [2 ]
Kaboli, Amin [3 ]
Alahi, Alexandre [1 ]
机构
[1] Swiss Fed Inst Technol Lausanne EPFL, Civil Engn Inst IIC, Sch Architecture Civil & Environm Engn ENAC, Visual Intelligence Transportat VITA, Lausanne, Switzerland
[2] Norwegian Univ Sci & Technol NTNU, Dept Comp Sci, Trondheim, Norway
[3] Swiss Fed Inst Technol Lausanne EPFL, Inst Mech Engn, Sch Engn, Lausanne, Switzerland
关键词
Smarter eco-cities; Smart eco-cities; Smart cities; Artificial intelligence; Artificial intelligence of things; Machine learning; Environmental sustainability; Climate change; SUPPORT VECTOR REGRESSION; ENERGY USE PREDICTION; BIG DATA; COMPUTATIONAL INTELLIGENCE; DECISION-MAKING; MACHINE; MODELS; CHALLENGES; CITY; FUTURE;
D O I
10.1016/j.ese.2023.100330
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 leadingedge 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. (c) 2023 The Authors. Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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页数:31
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