Contributions and Risks of Artificial Intelligence (AI) in Building Smarter Cities: Insights from a Systematic Review of the Literature

Artificial intelligence and sustainable development during the pandemic: An overview of the scientific debates

The current work aims to analyze the main themes related to artificial intelligence (AI) and sustainable development during the pandemic period. This study provides an overview of the specialized literature related to AI and sustainability from the beginning of the pandemic through 2023. The present paper analyses scientific literature emphasizing both artificial intelligence's positive and negative impacts on sustainable development objectives (SDGs). To conduct the research, we employed bibliometric analysis and text-mining techniques to identify the major themes in the literature indexed in the Web of Science and Scopus databases. Firstly, we used descriptive measures to identify the authors' impact, the article production by country, the main keywords used, and other descriptive data. We further used data reduction methods based on co-word analysis (such as multiple correspondence analysis) on authors' keywords to show patterns in the themes explored in the literature. Bibliometric analysis was supplemented by text mining using Latent Dirichlet allocation (LDA) and structural topic modeling on abstracts to provide a comprehensive view of scientific debates on AI and sustainable development. Our research has identified various themes in the literature related to AI and sustainable development. These themes include social sustainability, health-related issues, AI technologies for energy efficiency, sustainability in industry and innovation, IoT technologies for smart and sustainable cities, urban planning, technologies for education and knowledge production, and the impact of technologies on SDGs. We also found that there is a significant positivity bias in the literature when discussing the impact of AI on sustainable development. Despite acknowledging certain risks, the literature tends to focus on the potential benefits of AI across various sectors. In addition, the analysis shows a growing emphasis on energy efficiency, which is facilitated by the use of AI technologies. Our study contributes to a better understanding of current scholarly discussion trends and emerging scientific avenues regarding AI and sustainable development. It also highlights the areas where research is needed and the implications for practitioners and policymakers.

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

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.

Microwave pretreatment of wastewater sludge technology-a scientometric-based review

This manuscript presents a scientometric review of recent advances in microwave pretreatment processes for sewage sludge, systematically identifying existing gaps and prospects. For this purpose, 1763 papers on the application of microwave technology to sludge pretreatment were retrieved from the Web of Science (WoS) using relevant keywords. These publications were then analyzed using diverse scientometric indices. The results show that research in this field encompasses applications based on the non-thermal effects of microwaves, enhanced effectiveness of anaerobic digestion (AD), and the energy balance of this pretreatment system. Overcoming existing technical challenges, such as the cleavage of extracellular polymers, reducing microwave energy consumption, understanding the non-thermal effects of microwaves, promoting AD of sludge in combination with other chemical and physical methods, and expanding the application of the technology, are the main scientific focuses. Additionally, this paper thoroughly examines both the constraints and potential of microwave pretreatment technology for wastewater treatment.

Applications, challenges, and solutions of unmanned aerial vehicles in smart city using blockchain

Real-time data gathering, analysis, and reaction are made possible by this information and communication technology system. Data storage is also made possible by it. This is a good move since it enhances the administration and operation services essential to any city's efficient operation. The idea behind "smart cities" is that information and communication technology (ICTs) need to be included in a city's routine activities in order to gather, analyze, and store enormous amounts of data in real-time. This is helpful since it makes managing and governing urban areas easier. The "drone" or "uncrewed aerial vehicle" (UAV), which can carry out activities that ordinarily call for a human driver, serves as an example of this. UAVs could be used to integrate geospatial data, manage traffic, keep an eye on objects, and help in an emergency as part of a smart urban fabric. This study looks at the benefits and drawbacks of deploying UAVs in the conception, development, and management of smart cities. This article describes the importance and advantages of deploying UAVs in designing, developing, and maintaining in smart cities. This article overviews UAV uses types, applications, and challenges. Furthermore, we presented blockchain approaches for addressing the given problems for UAVs in smart research topics and recommendations for improving the security and privacy of UAVs in smart cities. Furthermore, we presented Blockchain approaches for addressing the given problems for UAVs in smart cities. Researcher and graduate students are audience of our article.

Grading by AI makes me feel fairer? How different evaluators affect college students' perception of fairness

Introduction

In the field of education, new technologies have enhanced the objectivity and scientificity of educational evaluation. However, concerns have been raised about the fairness of evaluators, such as artificial intelligence (AI) algorithms. This study aimed to assess college students' perceptions of fairness in educational evaluation scenarios through three studies using experimental vignettes.

Methods

Three studies were conducted involving 172 participants in Study 1, 149 in Study 2, and 145 in Study 3. Different evaluation contexts were used in each study to assess the influence of evaluators on students' perception of fairness. Information transparency and explanations for evaluation outcomes were also examined as potential moderators.

Results

Study 1 found that different evaluators could significantly influence the perception of fairness under three evaluation contexts. Students perceived AI algorithms as fairer evaluators than teachers. Study 2 revealed that information transparency was a mediator, indicating that students perceived higher fairness with AI algorithms due to increased transparency compared with teachers. Study 3 revealed that the explanation of evaluation outcomes moderated the effect of evaluator on students' perception of fairness. Specifically, when provided with explanations for evaluation results, the effect of evaluator on students' perception of fairness was lessened.

Discussion

This study emphasizes the importance of information transparency and comprehensive explanations in the evaluation process, which is more crucial than solely focusing on the type of evaluators. It also draws attention to potential risks like algorithmic hegemony and advocates for ethical considerations, including privacy regulations, in integrating new technologies into educational evaluation systems. Overall, this study provides valuable theoretical insights and practical guidance for conducting fairer educational evaluations in the era of new technologies.

Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review

The occurrence of disasters has the potential to impede the progress of sustainable urban development. For instance, it has the potential to result in significant human casualties and substantial economic repercussions. Sustainable cities, as outlined in the United Nations Sustainable Development Goal 12, prioritize the objective of disaster risk reduction. According to the Gesi Smarter 2030, the Internet of Things (IoT) assumes a pivotal role in the context of smart cities, particularly in domains including smart grids, smart waste management, and smart transportation. IoT has emerged as a crucial facilitator for the management of disasters, contributing to the development of cities that are both resilient and sustainable. This systematic literature analysis seeks to demonstrate the sensors utilized in IoT for the purpose of urban catastrophe management. The review encompasses both the pre-disaster and post-disaster stages, drawing from a total of 72 articles. During each stage, we presented the characteristics of sensors employed in IoT. Additionally, we engaged in a discourse regarding the various communication technologies and protocols that can be utilized for the purpose of transmitting the data obtained from sensors. Furthermore, we have demonstrated the methodology for analyzing and implementing the data within the application layer of IoT. In conclusion, this study addresses the existing research deficiencies within the literature and presents potential avenues for future exploration in the realm of IoT-enabled urban catastrophe management, drawing upon the findings of the evaluated publications.

On the post-pandemic travel boom: How capacity building and smart tourism technologies in rural areas can help - evidence from Iran

While there have been numerous studies investigating the impact of the COVID-19 pandemic on tourism, few research projects have examined the impact of the outbreak on using smart tourism technologies (STT), especially in developing countries. This study adopted thematic analysis, with data collected using in-person interviews. The participants for the study were selected using the snow-balling technique. We explored the process of developing smart technologies during the pandemic and its impact on smart rural tourism technology development upon travel restart. The subject was investigated by focusing on five selected villages in central Iran which have tourism dependent economies. Overall, the results indicated that the pandemic partially changed the government's resistance towards the fast development of smart technologies. Thus, the role of smart technologies in curbing the virus spread was officially recognized. This change of policy led to the implementation of Capacity Building (CB) programs to improve digital literacy and minimize the digital gap that exists between urban and rural areas in Iran. Implementing CB programs during the pandemic directly and indirectly contributed to the digitalization of rural tourism. Implementing such programs enhanced tourism stakeholders' individual and institutional capacity to gain access to and creatively use STT in rural area. The results of this study improve our understanding and knowledge of the impact of crises on the degree of acceptability and use of STT in traditional rural societies.

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

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.

Assessing the Role of AI-Based Smart Sensors in Smart Cities Using AHP and MOORA

We know that in today's advanced world, artificial intelligence (AI) and machine learning (ML)-grounded methodologies are playing a very optimistic role in performing difficult and time-consuming activities very conveniently and quickly. However, for the training and testing of these procedures, the main factor is the availability of a huge amount of data, called big data. With the emerging techniques of the Internet of Everything (IoE) and the Internet of Things (IoT), it is very feasible to collect a large volume of data with the help of smart and intelligent sensors. Based on these smart sensing devices, very innovative and intelligent hardware components can be made for prediction and recognition purposes. A detailed discussion was carried out on the development and employment of various detectors for providing people with effective services, especially in the case of smart cities. With these devices, a very healthy and intelligent environment can be created for people to live in safely and happily. With the use of modern technologies in integration with smart sensors, it is possible to use energy resources very productively. Smart vehicles can be developed to sense any emergency, to avoid injuries and fatal accidents. These sensors can be very helpful in management and monitoring activities for the enhancement of productivity. Several significant aspects are obtained from the available literature, and significant articles are selected from the literature to properly examine the uses of sensor technology for the development of smart infrastructure. The analytical hierarchy process (AHP) is used to give these attributes weights. Finally, the weights are used with the multi-objective optimization on the basis of ratio analysis (MOORA) technique to provide the different options in their order of importance.

Exploration of the intelligent-auxiliary design of architectural space using artificial intelligence model

In order to carry out a comprehensive design description of the specific architectural model of AI, the auxiliary model of AI and architectural spatial intelligence is deeply integrated, and flexible design is carried out according to the actual situation. AI assists in the generation of architectural intention and architectural form, mainly supporting academic and working theoretical models, promoting technological innovation, and thus improving the design efficiency of the architectural design industry. AI-aided architectural design enables every designer to achieve design freedom. At the same time, with the help of AI, architectural design can complete the corresponding work faster and more efficiently. With the help of AI technology, through the adjustment and optimization of keywords, AI automatically generates a batch of architectural space design schemes. Against this background, the auxiliary model of architectural space design is established through the literature research of the AI model, the architectural space intelligent auxiliary model, and the semantic network and the internal structure analysis of architectural space. Secondly, to ensure compliance with the three-dimensional characteristics of the architectural space from the data source, based on the analysis of the overall function and structure of space design, the intelligent design of the architectural space auxiliary by Deep Learning is carried out. Finally, it takes the 3D model selected in the UrbanScene3D data set as the research object, and the auxiliary performance of AI's architectural space intelligent model is tested. The research results show that with the increasing number of network nodes, the model fitting degree on the test data set and training data set is decreasing. The fitting curve of the comprehensive model shows that the intelligent design scheme of architectural space based on AI is superior to the traditional architectural design scheme. As the number of nodes in the network connection layer increases, the intelligent score of space temperature and humidity will continue to rise. The model can achieve the optimal intelligent auxiliary effect of architectural space. The research has practical application value for promoting the intelligent and digital transformation of architectural space design.

Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises

The purpose of the paper is to create a multidimensional talent management model with embedded aspects of artificial intelligence in the human resource processes to increase employees' engagement and performance of the enterprise. The research was implemented on a sample of 317 managers/owners in Slovenian enterprises. Multidimensional constructs of the model include several aspects of artificial intelligence implementation in the organization's activities related to human resource management in the field of talent management, especially in the process of acquiring and retaining talented employees, appropriate training and development of employees, organizational culture, leadership, and reducing the workload of employees, employee engagement and performance of the enterprise. The results show that AI supported acquiring and retaining a talented employees, AI supported appropriate training and development of employees, appropriate teams, AI supported organizational culture, AI supported leadership, reducing the workload of employees with AI have a positive effect on performance of the enterprise and employee engagement. The results will help managers or owners create a successful work environment by implementing artificial intelligence in the enterprise, leading to increased employee engagement and performance of the enterprise. Namely, our results contribute to the efficient implementation of artificial intelligence into an enterprise and give owners or top managers a broad insight into the various aspects that must be taken into account in business management in order to increase employee engagement and enterprise's competitive advantage.

Analysis of the implementation of urban computing in smart cities: A framework for the transformation of Saudi cities

Smart city development is gaining widespread acceptance as a means of mitigating urban development problems. However, the implementation of smart cities faces challenges, especially in developing countries. Urban computing is regarded as an enabler of innovation and smart city development. This study explores the adoption of urban computing to address the smart city and urban development problems of Saudi cities. Using a systematic review, this study highlights the trends and influential contributions of urban computing to smart city research. It identifies the urban computing framework and uses the framework to analyze the use of urban computing in Saudi cities to promote smart city development and sustainability. While Saudi Arabia has taken notable steps in urban computing, especially in providing services, further steps need to be taken to achieve the transformation into smart sustainable cities.

Drivers behind the public perception of artificial intelligence: insights from major Australian cities

Artificial intelligence (AI) is not only disrupting industries and businesses, particularly the ones have fallen behind the adoption, but also significantly impacting public life as well. This calls for government authorities pay attention to public opinions and sentiments towards AI. Nonetheless, there is limited knowledge on what the drivers behind the public perception of AI are. Bridging this gap is the rationale of this paper. As the methodological approach, the study conducts an online public perception survey with the residents of Sydney, Melbourne, and Brisbane, and explores the collected survey data through statistical analysis. The analysis reveals that: (a) the public is concerned of AI invading their privacy, but not much concerned of AI becoming more intelligent than humans; (b) the public trusts AI in their lifestyle, but the trust is lower for companies and government deploying AI; (c) the public appreciates the benefits of AI in urban services and disaster management; (d) depending on the local context, public perceptions vary; and (e) the drivers behind the public perception include gender, age, AI knowledge, and AI experience. The findings inform authorities in developing policies to minimise public concerns and maximise AI awareness.

Smart cities: reviewing the debate about their ethical implications

This paper considers a host of definitions and labels attached to the concept of smart cities to identify four dimensions that ground a review of ethical concerns emerging from the current debate. These are: (1) network infrastructure, with the corresponding concerns of control, surveillance, and data privacy and ownership; (2) post-political governance, embodied in the tensions between public and private decision-making and cities as post-political entities; (3) social inclusion, expressed in the aspects of citizen participation and inclusion, and inequality and discrimination; and (4) sustainability, with a specific focus on the environment as an element to protect but also as a strategic element for the future. Given the persisting disagreements around the definition of a smart city, the article identifies in these four dimensions a more stable reference framework within which ethical concerns can be clustered and discussed. Identifying these dimensions makes possible a review of the ethical implications of smart cities that is transversal to their different types and resilient towards the unsettled debate over their definition.

Analysis of the Effect of Classroom Reform of English Literature on the Theme of Environmental Protection in Universities Based on Artificial Intelligence Technology

Under the current theme of environmental protection, the effect of English literature classroom teaching reform cannot be analyzed qualitatively. Based on BP neural network, we present an analytical model of the impact of College English Literature Classroom Teaching Reform on environmental protection based on artificial intelligence technology. We analyze the teaching reform of English literature classrooms with the theme of environmental protection under artificial intelligence technology and explore the concept of "intelligent education" and the construction path of university English ecological teaching mode. Based on the in-depth excavation of teaching information, this paper analyzes the impact of college English literature classroom reform on the theme of environmental protection. Combined with BP neural network, a scientific analytic hierarchy process index system model is established, and the weight relationship of various reform influencing factors is given relatively objectively. The scores of the model based on the BP neural network under the six effect analysis indicators of teaching attitude, teaching tools, reform plan, reform teaching content, classroom organization, and reform rationality are 90.97, 86.3, 80.4, 95.7, 84.8, and 87.4, respectively. The results show that this model has a good ability for analysis and evaluation, which also provides some new ideas and entry points for the reform of the current English classroom teaching model. This study has a certain contribution to the breakthrough of college English teaching goals.

Greencoin as an AI-Based Solution Shaping Climate Awareness

Our research aim was to define possible AI-based solutions to be embedded in the Greencoin project, designed as a supportive tool for smart cities to achieve climate neutrality. We used Kamrowska-Załuska's approach for evaluating AI-based solutions' potential in urban planning. We narrowed down the research to the educational and economic aspects of smart cities. Furthermore, we used a systematic literature review. We propose solutions supporting the implementation process of net zero policies benefiting from single actions of urban dwellers based on the Greencoin project developed by us. By following smart city sectors, the paper introduces AI-based solutions which can enrich Greencoin by addressing the following needs: (1) shaping pro-environmental behaviors, (2) introducing instruments to reinforce the urban management process, (3) supporting bottom-up initiatives allowing to shape urban resilience, (4) enhancing smart mobility, (5) shaping local economies supporting urban circularity, and (6) allowing better communication with residents. Our research fills the gap in the limited group of studies focused on shaping climate awareness, enhancing smart governance, and supporting social participation and inclusion. It proves that AI-based educational tools can be supportive when implementing adaptation policies toward climate neutrality based on our proposed AI-based model shaping climate awareness.

Comprehensive Analysis of Network Slicing for the Developing Commercial Needs and Networking Challenges

Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance.

Design and Optimization of Aesthetic Education Teaching Information Platform Based on Big Data Analysis

In the process of promoting school aesthetic education, some schools have some problems, such as insufficient construction of campus aesthetic education environment, lack of aesthetic thinking in various disciplines, and so on. In view of these problems, combined with the concept of the flipped classroom and the characteristics of artificial intelligence task-driven teaching, taking PHP, HTML + CSS + JS, and other development technologies as the main development technologies, and relying on the flipped classroom teaching mode of network learning space, this paper constructs an artificial intelligence core course website as a teaching platform for graduate teaching and undergraduate extended learning. The platform seeks the optimal solution of multiple combination optimization based on a genetic algorithm effectively improves the teaching quality of artificial intelligence courses and students' learning efficiency.

Decision-Making Based on Network Analyses of New Infrastructure Layouts

New Infrastructure (NI) has gradually become a new driving force for regional economic growth and an important part of the construction of new urban infrastructure in many countries, including China. Compared with traditional infrastructures, these NIs have mutually supportive functions and complex interrelationships that create interconnected networks of resources, information, and other interactions during the construction of the NIs. Therefore, it is important to analyze such correlation networks and explore their formation mechanisms in order to develop more scientific and reasonable strategies for NI investment and construction. In this study, the interdependence between NIs in Chongqing was analyzed as an example. Social network analysis (SNA) was used for the overall characteristics of the interdependency network of the NIs and an exponential random graph model (ERGM) was used to reveal the formation mechanism of this network. The results showed that information infrastructure is the key node for enhancing the effectiveness of Chongqing’s NI needs and its government should play a coordinating role. The network of related relationships is characterized by “reciprocity” and “small group”. The aggregation of NIs with such characteristics can produce an agglomeration effect. So, in the planning of NIs, the coordination among management departments should be strengthened and project locations should be reasonably arranged according to the functional interactive characteristics of the projects.

Sustainable city planning and public administration assisted by green AI: attendant legal challenges under Japanese law

Purpose

The purpose of this paper is to present the legal conditions under which governments may use green artificial intelligence (AI) in city planning. Although Japan was one of the early countries to release its general AI principles, it has been relatively slow in establishing conditions where administrative agencies may use AI. Granted, there have been some recent scholarship that discusses the usage of AI in general under Japanese administrative law, but the use of green AI in city planning under Japanese law has not yet been discussed. Hence, this paper intends to focus on green AI in city planning and discuss the conditions for usage based on different categories of AI.

Design/methodology/approach

This paper conducts a legal analysis on the utilization of AI for the purpose of sustainable city planning and administration in Japan. The approach of this paper is to summarize the existing scholarship in Japanese administrative law and analyse the new elements in the new field of green AI in city planning. This paper is not a natural science paper. The social science method of jurisprudence is used. This paper cites only public sources, and no informal literature has been referenced.

Findings

This paper establishes the conditions where Japanese central and local government may use green AI in city planning from a legal viewpoint based on three categories. The categories are green AI usage in city planning concerning things, green AI usage in city planning concerning people and green AI usage in city planning concerning automated decision-making.

Research limitations

This research is limited to an analysis of Japanese law, which means that issues other than law are not included in this paper. Further, although general legal issues are discussed, this paper is intended to discuss Japanese law issues only, and foreign laws are not discussed. Therefore, this paper mostly cites Japanese language papers published in domestic journals.

Practical implications

The intended practical implication of this paper is to allow central and local governments to determine – based on the proposed categories – whether green AI can be used for city planning purposes and under which conditions. The authors hope that this will assist the Japanese government in establishing rules on the usage of AI by governmental agencies and allow for the greater actual usage by Japanese central and local governments of green AI in future city planning.

Social implications

As the theme of this paper deals with governmental use (and the function of a government is to serve society), the social implications at issue can be said to be equivalent to the practical implication.

Originality/value

There have been articles discussing Japanese administrative law restrictions on AI in general. However, as of now, to the best of the authors’ knowledge, there have been no articles published focusing on green AI used for city planning. The authors note that the green AI used for city planning would have different legal implications from AI’s usage by the government in general, such as the chatbot used by the agencies or lethal autonomous weapons by the military force. Therefore, this paper is original in focusing on green AI used for city planning.

A Review on the Adoption of AI, BC, and IoT in Sustainability Research

The rise of artificial intelligence (AI), blockchain (BC), and the internet of things (IoT) has had significant applications in the advancement of sustainability research. This review examines how these digital transformations drive natural and human systems, as well as which industry sectors have been applying them to advance sustainability. We adopted qualitative research methods, including a bibliometric analysis, in which we screened 960 publications to identify the leading sectors that apply AI/BC/IoT, and a content analysis to identify how each sector uses AI/BC/IoT to advance sustainability. We identified “smart city”, “energy system”, and “supply chain” as key leading sectors. Of these technologies, IoT received the most real-world applications in the “smart city” sector under the dimensions of “smart environment” and “smart mobility” and provided applications resolving energy consumption in the “energy system” sector. AI effectively resolved scheduling, prediction, and monitoring for both the “smart city” and “energy system” sectors. BC remained highly theoretical for “supply chain”, with limited applications. The technological integration of AI and IoT is a research trend for the “smart city” and “energy system” sectors, while BC and IoT is proposed for the “supply chain”. We observed a surge in AI/BC/IoT sustainability research since 2016 and a new research trend—technological integration—since 2020. Collectively, six of the United Nation’s seventeen sustainable development goals (i.e., 6, 7, 9, 11, 12, 13) have been the most widely involved with these technologies.

Building Information Modeling (BIM) Capabilities in the Operation and Maintenance Phase of Green Buildings: A Systematic Review

In recent years, green buildings have gradually become a worldwide trend. Compared with traditional buildings, green buildings have advanced requirements and standards in their operation and maintenance phase. In such a context, some studies proposed that building information modeling (BIM) is an effective method to improve green buildings’ operation and maintenance quality. The aim of this study is to perform a comprehensive review of the BIM capabilities in the operation and maintenance phase of green buildings through a systematic literature review. To achieve this aim, the PRISMA protocol was used to perform this systematic review. The whole systematic review was conducted between January 2022 and April 2022: 128 articles were included. In the process of study, Web of Science (WoS) and Scopus were adopted as bibliographic repositories. Through this study, it can be determined that BIM capabilities can be utilized in the facility management of the green building in the following aspects: safety and emergency management, maintenance and repair, energy management, security, retrofit and renovation, space management, and asset management. Secondly, these BIM capabilities were discussed, and the challenges and shortcomings of BIM capabilities in the operation and maintenance phase of green buildings were reviewed. Finally, a comprehensive overview of BIM capabilities in the facility management of green buildings was developed, and suggestions for future study were provided.

The Transition of Cities towards Innovations in Mobility: Searching for a Global Perspective

The growing concentration of the population in urban areas presents great challenges for sustainability. Within this process, mobility emerges as one of the main generators of externalities that hinder the achievement of the Sustainable Development Goals. The transition of cities towards innovations in sustainable mobility requires progress in different dimensions, whose interaction requires research. Likewise, it is necessary to establish whether the experiences developed between cities with different contexts can be extrapolated. Therefore, the purpose of this study was to identify how the conditions that determine a city’s readiness to implement urban mobility innovations could be combined. For this, qualitative comparative analysis was applied to a model developed using the multi-level perspective, analyzing 60 cities from different geographical areas and with a different gross domestic product per capita. The R package Set Methods was used. The explanation of the readiness of cities to implement mobility innovations is different to the explanation of the readiness negation. While readiness is explained by two solutions, in which only regime elements appear, the negation of readiness is explained by five possible solutions, showing the interaction between the landscape and regimen elements and enacting the negation of innovations as a necessary condition. The cluster analysis shows us that the results can be extrapolated between cities with different contexts.

Artificial intelligence in local governments: perceptions of city managers on prospects, constraints and choices

Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI is on the rise in urban policy circles, there is limited understanding and lack of empirical studies on the city manager perceptions concerning urban AI systems. Bridging this gap is the rationale of this study. The methodological approach adopted in this study is twofold. First, the study collects data through semi-structured interviews with city managers from Australia and the US. Then, the study analyses the data using the summative content analysis technique with two data analysis software. The analysis identifies the following themes and generates insights into local government services: AI adoption areas, cautionary areas, challenges, effects, impacts, knowledge basis, plans, preparedness, roadblocks, technologies, deployment timeframes, and usefulness. The study findings inform city managers in their efforts to deploy AI in their local government operations, and offer directions for prospective research.

Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education

Purpose

Artificial intelligence (AI) is one of the latest digital transformation (DT) technological trends the university library can use to provide library users with alternative educational services. AI can foster intelligent decisions for retrieving and sharing information for learning and research. However, extant literature confirms a low adoption rate by the university libraries in using AI to provide innovative alternative services, as this is missing in their strategic plan. The research develops (AI-LSICF) an artificial intelligence library services innovative conceptual framework to provide new insight into how AI technology can be used to deliver value-added innovative library services to achieve digital transformation. It will also encourage library and information professionals to adopt AI to complement effective service delivery.

Design/methodology/approach

This study adopts a qualitative content analysis to investigate extant literature on how AI adoption fosters innovative services in various organisations. The study also used content analysis to generate possible solutions to aid AI service innovation and delivery in university libraries.

Findings

This study uses its findings to develop an Artificial Intelligence Library Services Innovative Conceptual Framework (AI-LSICF) by integrating AI applications and functions into the digital transformation framework elements and discussed using a service innovation framework.

Research limitations/implications

In research, AI-LSICF helps increase an understanding of AI by presenting new insights into how the university library can leverage technology to actualise innovation in service provision to foster DT. This trail will be valuable to scholars and academics interested in addressing the application pathways of AI library service innovation, which is still under-explored in digital transformation.

Practical implications

In practice, AI-LSICF could reform the information industry from its traditional brands into a more applied and resolutely customer-driven organisation. This reformation will awaken awareness of how librarians and information professionals can leverage technology to catch up with digital transformation in this age of the fourth industrial revolution.

Social implications

The enlightenment of AI-LSICF will motivate library professionals to take advantage of AI's potential to enhance their current business model and achieve a unique competitive advantage within their community.

Originality/value

AI-LSICF development serves as a revelation, motivating university libraries and information professionals to consider AI in their strategic plan to enable technology to support university education. This act will enable alternative service delivery in the face of unforeseen circumstances like technological disruption and the present global COVID-19 pandemic that requires non-physical interaction.

Participatory Governance of Smart Cities: Insights from e-Participation of Putrajaya and Petaling Jaya, Malaysia

Participatory governance is widely viewed as an essential element of realizing planned smart cities. Nonetheless, the implementation of e-participation platforms, such as the websites and mobile applications of civic authorities, often offer ambiguous information on how public voices may influence e-decision-making. This study aims to examine the status of participatory governance from the angle of e-participation platforms and from the broader scope of linking e-platforms to a smart city blueprint. In order to achieve this aim, the study focuses on shedding light on the e-governance space given to smart city realization in a developing country context—i.e., Malaysia. The Putrajaya and Petaling Jaya smart cities of Malaysia were selected as the testbeds of the study, which used the multiple case study methodology and multiple data collection designs. The analyses were done through the qualitative observations and quantitative descriptive statistics. The results revealed that both of the investigated smart city cases remained limited in their provision of e-decision-making space. The inefficiency of implementing planned initiatives to link the city blueprints to e-platforms was also evidenced. The study evidenced that the political culture of e-decision-making is undersized in Malaysia, which hinders the achievement of e-democracy in the smart cities’ development. This study has contributed a case report on a developing country’s smart cities, covering the participatory issues from the angle of e-participation and e-platforms.

Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories

Natural hazard-related disasters are disruptive events with significant impact on people, communities, buildings, infrastructure, animals, agriculture, and environmental assets. The exponentially increasing anthropogenic activities on the planet have aggregated the climate change and consequently increased the frequency and severity of these natural hazard-related disasters, and consequential damages in cities. The digital technological advancements, such as monitoring systems based on fusion of sensors and machine learning, in early detection, warning and disaster response systems are being implemented as part of the disaster management practice in many countries and presented useful results. Along with these promising technologies, crowdsourced social media disaster big data analytics has also started to be utilized. This study aims to form an understanding of how social media analytics can be utilized to assist government authorities in estimating the damages linked to natural hazard-related disaster impacts on urban centers in the age of climate change. To this end, this study analyzes crowdsourced disaster big data from Twitter users in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method and conducts sentiment and content analyses of location-based Twitter messages (n = 131,673) from Australia. The study informs authorities on an innovative way to analyze the geographic distribution, occurrence frequency of various disasters and their damages based on the geo-tweets analysis.

Challenges and Opportunities for Public Participation in Urban and Regional Planning during the COVID-19 Pandemic—Lessons Learned for the Future

The COVID-19 pandemic has spurred significant changes in the fields of economic development, social issues, everyday life, etc. Activities that used to depend on face-to-face communication were firstly suspended and then shifted to new forms of communication. This includes the public participation process in urban and spatial planning. Therefore, this study explores the new domain developed in urban and spatial planning with regard to public participation and surmises future realms in the post-pandemic era. On the occasion of the virtual collaboration platform Cyber Agora organized by the ISOCARP (International Society of City and Regional Planners), chosen participants got together virtually to share, discuss, and compare their practical knowledge in public participation before and during COVID-19. In addition, they addressed the potential benefits of shifting from traditional to virtual participation and potential benefits in the post-COVID-19 era. Considering the collected data and understanding them in the light of the available literature, this study concludes that the application of a combined approach (using both traditional and virtual modes of participation) is recommended because it would enable a larger number and higher diversity of participants. The study also elaborates particular modes of virtual participation with the pros and cons of their use in a particular context.

Greening the Artificial Intelligence for a Sustainable Planet: An Editorial Commentary

Artificial intelligence (AI) is one of the most popular and promising technologies of our time [...]

Innovation Trajectories for a Society 5.0

Big Data, the Internet of Things, and robotic and augmented realities are just some of the technologies that belong to Industry 4.0. These technologies improve working conditions and increase productivity and the quality of industry production. However, they can also improve life and society as a whole. A new perspective is oriented towards social well-being and it is called Society 5.0. Industry 4.0 supports the transition to the new society, but other drivers are also needed. To guide the transition, it is necessary to identify the enabling factors that integrate Industry 4.0. A conceptual framework was developed in which these factors were identified through a literature review and the analytical hierarchy process (AHP) methodology. Furthermore, the way in which they relate was evaluated with the help of the interpretive structural modeling (ISM) methodology. The proposed framework fills a research gap, which has not yet consolidated a strategy that includes all aspects of Society 5.0. As a result, the main driver, in addition to technology, is international politics.

Moral Awareness of College Students Regarding Artificial Intelligence

To evaluate the moral awareness of college students regarding artificial intelligence (AI) systems, we have examined 467 surveys collected from 152 Japanese and 315 non-Japanese students in an international university in Japan. The students were asked to choose a most significant moral problem of AI applications in the future from a list of ten ethical issues and to write an essay about it. The results show that most of the students (n = 269, 58%) considered unemployment to be the major ethical issue related to AI. The second largest group of students (n = 54, 12%) was concerned with ethical issues related to emotional AI, including the impact of AI on human behavior and emotion and robots' rights and emotions. A relatively small number of students referred to the risk of social control by AI (6%), AI discrimination (6%), increasing inequality (5%), loss of privacy (4%), AI mistakes (3%), malicious AI (3%), and AI security breaches (3%). Calculation of the z score for two population proportions shows that Japanese students were much less concerned about AI control of society (- 3.1276, p < 0.01) than non-Japanese students, but more concerned about discrimination (2.2757, p < 0.05). Female students were less concerned about unemployment (- 2.6108, p < 0.01) than males, but more concerned about discrimination (2.4333, p < 0.05). The study concludes that the moral awareness of college students regarding AI technologies is quite limited and recommends including the ethics of AI in the curriculum.

Understanding and Acceptance of Smart City Policies: Practitioners’ Perspectives on the Malaysian Smart City Framework

Whilst a plethora of research exists on the smart cities and project performance evaluations, only few studies have focused on the smart city policy evaluation from the perspective of its acceptance by practitioners. This paper aims to generate insights by evaluating the smart city policy through a developing country case study—i.e., Malaysia. This study employed a questionnaire survey method for data collection and analyzed the data by using Fuzzy Delphi analysis. A group of 40 practitioners was gathered in a focus group discussion through purposive sampling. The main objectives of this survey were to identify the understanding and acceptance levels of the seven smart city domains and respective strategies that are outlined in the Malaysian Smart City Framework. The results disclosed that the practitioners possessed divergent levels of understanding and acceptance in terms of smart city domains. The study participant practitioners accepted all understanding and acceptance objectives of smart economy, living, people, and governance domains (expert agreement 75–92% and threshold d value 0.123–0.188), but rejected all objectives for both smart environment and digital infrastructure domains (expert agreement 55–74% and threshold d value 0.150–0.212). Along with this, acceptance of smart mobility was also rejected (expert agreement 56% and threshold d value 0.245). The findings reveal that considering all opinions expressing dissensus is essential when building more inclusive smart city strategies. This study contributes to the smart city discourse as being one of the first in capturing professional practitioners’ understanding and acceptance on a national level smart city policy by applying the Delphi method in the smart city context. Most importantly, the study informs urban policymakers on how to capture the voices and perspectives of the general public on national and local smart city strategy and initiatives.

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

Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.

Post-Materialist Values of Smart City Societies: International Comparison of Public Values for Good Enough Governance

This study aims to analyze the application of good enough governance in considering the citizens’ value propositions that shape smart city societies. This paper applied a quantitative method with cross-country comparisons. Survey data were derived from the World Values Survey. Malaysia was chosen as the main study area, and compared with Indonesia and other countries worldwide. The findings revealed that politics is the value of least concern across all samples. In terms of qualities for children to develop, respondents in both Malaysia and Indonesia were less concerned about imagination and unselfishness. As for materialist versus post-materialist, the ratios of Malaysia and Indonesia were slightly higher than the average; the post-materialist value of free speech was the lowest value chosen. In the long term, all countries are experiencing the trend of moving toward post-materialist societies. To be sustained under the Collective and Adaptive System of smart city societies, good enough governance in Malaysia and Indonesia should consider the cultural context of the Muslim majority, prioritize governance content that allows more space for political participation and free speech, and cultivate the imagination and unselfishness of children. The generated insights underline the critical role that smart societies play in establishing smart cities.

Financial Inclusion in Emerging Economies: The Application of Machine Learning and Artificial Intelligence in Credit Risk Assessment

In banking and finance, credit risk is among the important topics because the process of issuing a loan requires a lot of attention to assessing the possibilities of getting the loaned money back. At the same time in emerging markets, the underbanked individuals cannot access traditional forms of collateral or identification that is required by financial institutions for them to be granted loans. Using the literature review approach through documentary and conceptual analysis to investigate the impact of machine learning and artificial intelligence in credit risk assessment, this study discovered that artificial intelligence and machine learning have a strong impact on credit risk assessments using alternative data sources such as public data to deal with the problems of information asymmetry, adverse selection, and moral hazard. This allows lenders to do serious credit risk analysis, to assess the behaviour of the customer, and subsequently to verify the ability of the clients to repay the loans, permitting less privileged people to access credit. Therefore, this study recommends that financial institutions such as banks and credit lending institutions invest more in artificial intelligence and machine learning to ensure that financially excluded households can obtain credit.

An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review

Rapid advances that improve flood management have facilitated the disaster response by providing first aid services, finding safe routes, maintaining communication and developing flood maps. Different technologies such as image processing, satellite imagery, synthetic imagery and integrated approaches have been extensively analysed in the literature for disaster operations. There is a need to review cutting-edge technologies for flood management. This paper presents a review of the latest advancements in the flood management domain based on image processing, artificial intelligence and integrated approaches with a focus on post-disaster. It answers the following research questions: (1) What are the latest developments in image processing for flood management in a post-disaster scenario? (2) What are the latest techniques for flood management based on artificial intelligence in a post-disaster scenario? (3) What are the existing gaps in the selected technologies for post-disaster? (4) How can the authorities improve the existing post-disaster management operation with cutting-edge technologies? A novel framework has been proposed to optimise flood management with the application of a holistic approach.

Achieving Operational Excellence Through Artificial Intelligence: Driving Forces and Barriers

This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence (AI). Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to machines mimicking human behavior in terms of cognitive functions. The evolution of new technological procedures and advancements in producing intelligence for machines creates a positive impact on decisions, operations, strategies, and management incorporated in the production process of goods and services. Businesses develop various methods and solutions to extract meaningful information, such as big data, automatic production capabilities, and systematization for business improvement. The progress in organizational competitiveness is apparent through improvements in firm's decisions, resulting in increased operational efficiencies. Innovation with AI has enabled small businesses to reduce operating expenses and increase revenues. The focused literature review reveals the driving forces for achieving operational excellence through AI are improvement in computing abilities of machines, development of data-based AI, advancements in deep learning, cloud computing, data management, and integration of AI in operations. The barriers are mainly cultural constraints, fear of the unknown, lack of employee skills, and strategic planning for adopting AI. The current paper presents an analysis of articles focused on AI adoption in production and operations. We selected articles published between 2015 and 2020. Our study contributes to the literature reviews on operational excellence, artificial intelligence, driving forces for AI, and AI barriers in achieving operational excellence.

Smart and Resilient Urban Futures for Sustainability in the Post COVID-19 Era: A Review of Policy Responses on Urban Mobility

The COVID-19 pandemic has put lifestyles in question, changed daily routines, and limited citizen freedoms that seemed inalienable before. A human activity that has been greatly affected since the beginning of the health crisis is mobility. Focusing on mobility, we aim to discuss the transformational impact that the pandemic brought to this specific urban domain, especially with regards to the promotion of sustainability, the smart growth agenda, and the acceleration towards the smart city paradigm. We collect 60 initial policy responses related to urban mobility from cities around the world and analyze them based on the challenge they aim to address, the exact principles of smart growth and sustainable mobility that they encapsulate, as well as the level of ICT penetration. Our findings suggest that emerging strategies, although mainly temporary, are transformational, in line with the principles of smart growth and sustainable development. Most policy responses adopted during the first months of the pandemic, however, fail to leverage advancements made in the field of smart cities, and to adopt off-the-shelf solutions such as monitoring, alerting, and operations management.

Smart Fishery: A Systematic Review and Research Agenda for Sustainable Fisheries in the Age of AI

Applications of artificial intelligence (AI) technologies for improving the sustainability of the smart fishery have become widespread. While sustainability is often claimed to be the desired outcome of AI applications, there is as yet little evidence on how AI contributes to the sustainable fishery. The purpose of this paper is to perform a systematic review of the literature on the smart fishery and to identify upcoming themes for future research on the sustainable fishery in the Age of AI. The findings of the review reveal that scholarly attention in AI-inspired fishery literature focuses mostly on automation of fishery resources monitoring, mainly detection, identification, and classification. Some papers list marine health and primary production which are vital dimensions for Large Marine Ecosystems to recycle nutrients to sustain anticipated production levels. Very few reviewed articles refer to assessing individual needs, particularly fishers, from AI deployment in fisheries and policy response from governments. We call for future AI for sustainable fishery studies on how fishers perceive AI needs, and how governments possess a tangible strategy or depth of understanding on the regulation of AI concerning smart fishery systems and research on resilience-enhancing policies to promote the value and potentials of the AI-inspired smart fishery in different locations.

Digital Technologies for Urban Metabolism Efficiency: Lessons from Urban Agenda Partnership on Circular Economy

Digital technologies engaged in urban metabolism for efficiency provide policymakers, urban managers, and planners with useful instruments to collect, monitor, analyze, and evaluate the circularity of environmental, social, and economic resources to improve their effectiveness and quality. At present, the digital technology-based approach is strategic for circular cities engaged in the development of smart and sustainable actions in the fields of mobility, energy, environment, waste, telecommunications, and security. Through the ‘Circular Resource Efficiency Management Framework’ developed by the European Commission, this paper generates insights into the digitalization practices of the circularity of urban metabolism by analyzing the initiatives implemented by the municipalities of Kaunas, Flanders region, Porto, Prato, The Hague, and Oslo, which constitute the Partnership on Circular Economy (PCE) of the Urban Agenda of the European Union. The results of the analysis provide a wide range of practices such as real-time monitoring stations for water and energy consumption, digital cameras for controlling vehicle flows, web platforms for sharing goods and services, and tracking sensors for public transport, which aim to optimize the efficiency of the circularity of urban metabolic flows. This study increases the understanding and awareness of digital technologies in this paradigm shift.

Artificial Intelligence in the Industry 4.0, and Its Impact on Poverty, Innovation, Infrastructure Development, and the Sustainable Development Goals: Lessons from Emerging Economies?

Artificial intelligence in the fourth industrial revolution is beginning to live up to its promises of delivering real value necessitated by the availability of relevant data, computational ability, and algorithms. Therefore, this study sought to investigate the influence of artificial intelligence on the attainment of Sustainable Development Goals with a direct focus on poverty reduction, goal one, industry, innovation, and infrastructure development goal 9, in emerging economies. Using content analysis, the result pointed to the fact that artificial intelligence has a strong influence on the attainment of Sustainable Development Goals particularly on poverty reduction, improvement of the certainty and reliability of infrastructure like transport making economic growth and development possible in emerging economies. The results revealed that Artificial intelligence is making poverty reduction possible through improving the collection of poverty-related data through poverty maps, revolutionizing agriculture education and the finance sector through financial inclusion. The study also discovered that AI is also assisting a lot in education, and the financial sector allowing the previously excluded individuals to be able to participate in the mainstream economy. Therefore, it is important that governments in emerging economies need to invest more in the use of AI and increase the research related to it so that the Sustainable Development Goals (SDGs) related to innovation, infrastructure development, poverty reduction are attained.

Smart City Ontologies and Their Applications: A Systematic Literature Review

The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems.

Disruption Potential Assessment of the Power-to-Methane Technology

Power-to-methane (P2M) technology is expected to have a great impact on the future of the global energy sector. Despite the growing amount of related research, its potential disruptive impact has not been assessed yet. This could significantly influence investment decisions regarding the implementation of the P2M technology. Based on a two-year-long empirical research, the paper focuses on exploring the P2M technology deployment potential in different commercial environments. Results are interpreted within the theoretical framework of disruptiveness. It is concluded that P2M has unique attributes because of renewable gas production, grid balancing, and combined long-term energy storage with decarbonization, which represent substantial innovation. Nevertheless, empirical data suggest that the largest P2M plants can be deployed at industrial facilities where CO2 can be sourced from flue gas. Therefore, a significant decrease of carbon capture technology related costs could enable the disruption potential of the P2M technology in the future, along with further growth of renewable energy production, decarbonization incentives, and significant support of the regulatory environment.

Justice in (English) Water Infrastructure: A Systematic Review

This paper reports on a systematic review of the literature around governance and water infrastructure in England to analyse data on the application, or absence, of justice themes. It finds that, unlike in other sectors, justice thinking is far from embedded in the water sector here and whilst there are signs of a discussion there is a lack of sophistication and coherence around the debate. More positively, the research suggests that the concept of justice can be used as a tool or framework to help air and address these complex issues and in doing so is an advance on the concept of sustainability. By exploring the issues in this way, the study reveals a wealth of opportunities to use justice-thinking to improve infrastructure decision making. It is suggested a justice approach is the next step as our thinking matures beyond sustainability, improving the decisions we make for people and planet.

Technology and Entrepreneurial Marketing Decisions During COVID-19

The prime concern of this study is to explore how technology influences entrepreneurial marketing decisions during the world pandemic (COVID-19). The study uses a sample of 127 SMEs from Bangladesh. Data were collected by sending out questionnaires electronically and by mail. Smart PLS (SEM) 3.0 was used to analyse the data following the quantitative method. The study reveals positive and significant relationships between entrepreneurial opportunity recognition, opportunity development and opportunity exploitation with their entrepreneurial marketing decisions. It also claims that entrepreneurial passion mediates the relationship between entrepreneurial opportunity recognition and opportunity development with their entrepreneurial marketing decisions. However, entrepreneurial passion does not mediate the relationship between entrepreneurial opportunity exploitation and entrepreneurial marketing decisions. The study offers researchers a broader and more wide-ranging view of the importance of artificial intelligence in small firms. Researchers, educators and practitioners will benefit from the findings. The analyses are more complex and varied than the methodologies used in most of the limited previous research.

The Evolution of City-as-a-Platform: Smart Urban Development Governance with Collective Knowledge-Based Platform Urbanism

Since the advent of the second digital revolution, the exponential advancement of technology is shaping a world with new social, economic, political, technological, and legal circumstances. The consequential disruptions force governments and societies to seek ways for their cities to become more humane, ethical, inclusive, intelligent, and sustainable. In recent years, the concept of City-as-a-Platform was coined with the hope of providing an innovative approach for addressing the aforementioned disruptions. Today, this concept is rapidly gaining popularity, as more and more platform thinking applications become available to the city context—so-called platform urbanism. These platforms used for identifying and addressing various urbanization problems with the assistance of open data, participatory innovation opportunity, and collective knowledge. With these developments in mind, this study aims to tackle the question of “How can platform urbanism support local governance efforts in the development of smarter cities?” Through an integrative review of journal articles published during the last decade, the evolution of City-as-a-Platform was analyzed. The findings revealed the prospects and constraints for the realization of transformative and disruptive impacts on the government and society through the platform urbanism, along with disclosing the opportunities and challenges for smarter urban development governance with collective knowledge through platform urbanism.