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环境可持续的智慧城市及其融合的人工智能、物联网和大数据技术与解决方案:广泛文献综述的综合方法

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

作者信息

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

机构信息

School 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.

Graduate 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.

出版信息

Energy Inform. 2023;6(1):9. doi: 10.1186/s42162-023-00259-2. Epub 2023 Apr 5.

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.

摘要

最近,人们通过应用人工智能、物联网和大数据等创新解决方案,为应对环境退化和气候变化挑战付出了巨大努力。鉴于这些先进技术的协同潜力,智慧城市正在接纳并利用它们的融合,试图朝着实现被称为“环境可持续智慧城市”的可持续发展目标的环境目标迈进。这种新的城市主义范式本身就代表了一个重大的研究空白。为了填补这一空白,本研究探讨了环境可持续智慧城市的关键研究趋势和驱动因素,并绘制了它们的主题演变图。此外,它还研究了其基础城市主义模式的碎片化、融合和转变,以及它们正在融合的人工智能、物联网和大数据技术及解决方案。它采用并结合了文献计量分析和证据综合方法。从科学网数据库中收集了总共2574份文献,并划分为三个子时期:1991 - 2015年、2016 - 2019年和2020 - 2021年。结果表明,环境可持续智慧城市是一个快速发展的趋势,在第二和第三时期显著升级——这得益于数字化和脱碳议程的加速推进——这要归功于新冠疫情和数据驱动技术的快速发展。分析还表明,虽然总体优先研究主题随时间动态变化——一些人工智能模型和技术以及环境可持续性领域比其他领域受到了更多关注。综合的证据表明,对智慧城市和可持续城市碎片化的批评日益增加、可持续发展目标议程的广泛传播以及先进信息通信技术的主导地位,都对环境可持续智慧城市的实现产生了重大影响,从而影响了智慧城市的格局和动态。研究还表明,人工智能、物联网和大数据技术的融合为应对环境可持续性挑战提供了新方法。然而,这些技术涉及环境成本,并带来伦理风险和监管难题。这些发现可以为学者和从业者提供有关智慧城市新兴数据驱动技术解决方案的信息,也有助于政策制定者设计和实施相应的环境政策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/762b/10074362/92fb6e5c1181/42162_2023_259_Fig1_HTML.jpg

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