Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

S. Bibri Ayyoob Sharifi Alahi Alexandre + 1 lainnya

5 April 2023

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.

E. Papageorgiou S. Trang Ilja Nastjuk

28 November 2022

In the last two decades, the concept of smart cities has attracted significant research and policy attention. Despite its extensive discussion in literature, the term smart city is a fuzzy concept (Albino et al., 2015; Angelidou, 2014; Anthopoulos, 2015). It commonly refers to environments in which information and communication technologies (ICTs) are utilized to offer innovative services to citizens in order to enhance their well-being and to stimulate sustainable economic growth (Yigitcanlar et al., 2018). According to Giffinger et al. (2007), the key defining characteristics of smart cities include smart economy, smart people, smart governance, smart mobility, smart environment, and smart living, addressing key topics such as economic competitiveness, educational level of citizens, quality of social interactions, flexibility of labor market, governmental strategies, innovative transportation systems, sustainable resource management, or public safety. However, since the introduction of the term smart cities in the ’90 s, numerous perspectives on smart cities have emerged (e.g., Chourabi et al., 2012; Dameri & Cocchia, 2013; Hosseini et al., 2018; Yigitcanlar et al., 2018). One predominant perspective relates to the role of smart ICTs to improve the quality of citizens’ life (e.g., Bifulco et al., 2016; Dameri, 2017; Ferro et al., 2013; Gade, 2019; Van Dinh et al., 2020). Smart ICTs are wireless, embedded in objects, and record the environment using sensors (Yigitcanlar & Lee, 2014). They provide the critical infrastructure for more intelligent and interconnected solutions in areas such as healthcare, real estate, utilities, transportation, public safety, and administration (Washburn et al., 2009). In the energy grid domain, for example, smart ICTs help collect and share consumption data to optimize energy management (Farmanbar et al., 2019). In the transportation domain, smart ICTs enable safe, socially inclusive, and sustainable multi-modal transportation networks, which allow citizens to travel with ease (Herrenkind et al., 2019; Lembcke et al., 2021; Nastjuk et al., 2020; Nikitas et al., 2017; Rocha et al., 2020; Trang et al., 2015). In the building domain, smart ICTs can help to establish so-called “zero energy buildings” by significantly reducing the energy demand during the lifecycle of residential and commercial buildings (Kylili & Fokaides, 2015). In the healthcare domain, smart wearable devices can, for example, cater for remote diagnosis, medical prescriptions, and treatment of patients (Ghazal et al., 2021) or allow for the effective monitoring of public health (Trang et al., 2020). In the education domain, smart ICTs promote a more engaged learning experience in which learners can “learn at anytime, anywhere, in any way and at any pace” (Liu et al., 2017, p. 33). The importance of ICTs as a key driver for smart cities varies in the aforementioned application fields. In domains such as energy or transportation management, smart ICTs are essential enablers and require big data processing capabilities, while in domains such as education or public administration, smart ICTs have a more limited role where processing large volumes of data in real time is usually not required (Neirotti et al., 2014). Apart of the relevance of ICTs to envision smart cities, a significant body of literature has argued extensively about This article is part of the Topical Collection on Smart Cities Smart governance models for future cities.

Athar Mansoor Khansa Rasheed Umme Ammara + 2 lainnya

8 Juni 2022

Modern cities are complex adaptive systems in which there is a lot of dependency and interaction between the various stakeholders, components, and subsystems. The use of digital Information and Communications Technology (ICT) has opened up the vision of smart cities in which the city dwellers can have a better quality of life and the city can be better organized and managed. The deployment of ICT solutions, however, does not automatically or invariably improve the quality of living of the citizens. Analyzing cities as complex systems with various interacting sub-systems can help us understand urban dynamics and the fate of smart cities. We will be able to analyze various policy interventions and ascertain their effectiveness and anticipate potential unintended consequences. In this paper, we discuss how smart cities can be viewed through the lens of systems thinking and complex systems and provide a comprehensive review of related techniques and methods. Along with highlighting the science of cities in light of historic urban modeling and urban dynamics, we focus on shedding light on the smart city complex systems. Finally, we will describe the various challenges of smart cities, discuss the limitations of existing models, and identify promising future directions of work.

Makó Csaba Amir H. Mosavi Mehdi Sookhak + 7 lainnya

2022

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of smart city. Application domains include the essential aspect of the smart cities including the energy, healthcare, transportation, security, and pollution. The methodology presents the state-of-the-art, taxonomy, evaluation and model performance. The study concludes that the hybrid models and ensembles are the best performers since they exhibit both high accuracy and not-costly complexity. On the other hand, the deep learning (DL) techniques had higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference is negligible, it can be concluded that using either of them is appropriate. The study’s findings identify the pros and cons of the methods in each application for future researchers, practitioners, and policy-makers for the right problem within the context of smart cities.

Z. Allam B. Feizizadeh Ayyoob Sharifi + 1 lainnya

25 Juni 2021

The concept of smart cities has gained significant momentum in science and policy circles over the past decade. This study aims to provide an overview of the structure and trends in the literature on smart cities. Bibliometric analysis and science mapping techniques using VOSviewer and CiteSpace are used to identify the thematic focus of over 5000 articles indexed in the Web of Science since 1991. In addition to providing insights into the thematic evolution of the field, the three-decade study period is divided into two sub-periods (1991–2015 and 2016–2021). While splitting the dataset into more sub-periods would have been desirable, we decided to only examine two sub-periods as only very few papers have been published until 2010. The annual number of publications has progressively increased since then, with a surge in the annual number of publications observable from 2015 onwards. The thematic analysis showed that the intellectual base of the field has been very limited during the first period, but has expanded significantly since 2015. Over time, some thematic evolutions, such as further attention to linkages to climate change and resilience, and more emphasis on security and privacy issues, have been made. The thematic analysis shows that existing research on smart cities is dominated by either conceptual issues or underlying technical aspects. It is, therefore, essential to do more research on the implementation of smart cities and actual and/or potential contributions of smart cities to solving societal issues. In addition to elaborating on thematic focus, the study also highlights major authors, journals, references, countries, and institutions that have contributed to the development of the smart cities literature.