AbstractThe rapid urbanization and increasing challenges are faced by cities globally, including climate change, population growth, and resource constraints. Sustainable smart city (also referred to as “smart sustainable city”) can offer innovative solutions by integrating advanced technologies to build smarter, greener, and more livable urban environments with significant benefits. Using the Web of Science (WoS) database, this study examined: (i) the mainstream approaches and current research trends in the literature of sustainable smart city; (ii) the extent to which the research of sustainable smart city aligns with Sustainable Development Goals (SDGs); (iii) the current topics and collaboration patterns in sustainable smart city research; and (iv) the potential opportunities for future research on the sustainable smart city field. The findings indicated that research on sustainable smart city began in 2010 and gained significant momentum in 2013, with China leading, followed by Italy and Spain. Moreover, 59.00% of the selected publications on the research of sustainable smart city focus on SDG 11 (Sustainable Cities and Communities). Bibliometric analysis outcome revealed that artificial intelligence (AI), big data, machine learning, and deep learning are emerging research fields. The terms smart city, smart cities, and sustainability emerged as the top three co-occurring keywords with the highest link strength, followed by frequently co-occurring keywords such as AI, innovation, big data, urban governance, resilience, machine learning, and Internet of Things (IoT). The clustering results indicated that current studies explored the theoretical foundation, challenges, and future prospects of sustainable smart city, with an emphasis on sustainability. To further support urban sustainability and the attainment of SDGs, the future research of sustainable smart city should explore the application and implications of AI and big data on urban development including cybersecurity and governance challenges.
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KeywordsSustainable smart citySustainable Development Goals (SDGs)Bibliometric analysisCo-occurrence analysisCo-citation analysis
1. IntroductionA massive urbanization movement is taking place all over the world and the United Nations (UN) projects that by 2050, around 6×109 people or approximately 68.00% of the global population, will live in cities, mostly in developing countries (UN, 2024a). This substantial demographic shift has placed enormous strain on urban infrastructure and resources, making it challenging for cities to address sustainability issues and manage socio-economic, political, and environmental concerns (Mensah, 2019; Kaiser, 2023a). To address these challenges and meet public service demands, scholars have highlighted the importance of the integration of sustainable smart city concepts and innovations in urban development (Allam and Dhunny, 2019; Yigitcanlar et al., 2019; Wang et al., 2022; Yigitcanlar et al., 2024). Through the use of cutting-edge technologies and data analytics, sustainable smart city initiatives can optimize resource use, improve public services, strengthen urban resilience, and raise the general standard of living in urban areas (Kaiser, 2024).Although the implementation of sustainable smart city can improve urban life, the associated risks of these technologies such as urban equity and environmental justice were concerned (Sharifi et al., 2024). Therefore, scholars and policymakers underscored the dire need to ensure sustainable development principles in the implementation of sustainable smart city projects (Castelnovo et al., 2016; Hens et al., 2018; Lytras and Visvizi, 2018; Kaiser, 2024). Besides, since the adoption of the UN’s Sustainable Development Goals (SDGs), the integration of sustainable smart city concept has become more crucial (UN, 2024b). The World Commission on Environment and Development (1987) defined that “sustainable development refers to meeting present needs without compromising the ability of future generations to meet their own”. The fundamental objective of the sustainable smart city (also referred to as “smart sustainable city”) concept is to follow the principles of sustainable development and adopt smart technologies such as artificial intelligence (AI), big data, and Internet of Things (IoT) to enhance the well-being and livability of citizens (Allam and Dhunny, 2019; Yigitcanlar et al., 2024). Implementing sustainable smart city can improve energy management, ensure public safety, and lessen urban congestion (Ahad et al., 2020).Prior bibliometric studies offer valuable insights into various aspects of sustainable smart city, including definitions, challenges, and frameworks. For instance, Höjer and Wangel (2015) highlighted the importance of defining sustainable smart city to avoid misunderstandings. To address the ambiguity surrounding the concept, Huovila et al. (2019) identified key components for sustainable smart city and discussed the appropriate circumstances for their implementation. The implementation of sustainable smart city has also brought up challenges to governance, technology, and economy (Höjer and Wangel, 2015; Silvia et al., 2018). In addition to theoretical studies on the topic, several scholars have conducted distinct empirical analyses. For instance, Bibri and Krogstie (2017) provided a thorough analysis of the literature on sustainable smart city to discover current practices and study gaps, while Wu et al. (2022) used scientometric analysis to investigate the field of sustainable smart city for urban sustainability.Previous studies have addressed the different dimensions of sustainable smart city (Perera et al., 2017; Silva et al., 2018; Ahad et al., 2020). It is yet to explore the comprehensive bibliometric nature of the topic. This study aims to provide a clear distinction between smart city, sustainable city, and sustainable smart city, which has not been explored in prior studies. In addition, we examined the association between the literature of sustainable smart city and SDGs. To fill the knowledge gap in the current literature, this study addressed four key research questions: (i) what are the mainstream approaches and current research trends in the literature of sustainable smart city? (ii) to what extent does the research of sustainable smart city align with SDGs? (iii) which topics and collaboration patterns are most frequently discussed in the selected literature? and (iv) what potential opportunities exist for future research on the sustainable smart city field?
2. Literature review
2.1. Sustainable city, smart city, and sustainable smart city
Sustainable city concentrates on reducing environmental effects, preserving resources, and ensuring social equity to create sustainable and environmentally friendly urban areas (Sodiq et al., 2019). On the other hand, a smart city is a technologically advanced urban area that leverages information and communication technology (ICT) and other strategies to enhance the quality of life, improve urban services, and promote efficiency (Toh, 2022; Agboola et al., 2023). The concepts of sustainable city and smart city are integrated into the broader framework of sustainable smart city. The sustainable smart city concept not only speeds up services and enhances urban life by integrating cutting-edge technologies, but also ensures environmental protection and promotes social equity and cohesion (Silva et al., 2018; Yigitcanlar et al., 2019; Ahad et al., 2020). For instance, smart grids, smart transportation, smart healthcare, and smart infrastructure help to reduce carbon emissions, optimize resource use, and create safer environments for future generations. Although the concepts of sustainable city, smart city, and sustainable smart city are closely interconnected, they exhibit some subtle differences. Table 1 provides a detailed comparison of the three concepts, highlighting their unique characteristics.Table 1. Comparison of sustainable city, smart city, and sustainable smart city.CriteriaSustainable citySmart citySustainable smart citySourceDefinitionConcentrating on reducing environmental effects, preserving resources, and advancing social equity for a sustainable and eco-friendly urban environment.Advanced technology and data-driven approaches are utilized to optimize urban services, improve efficiency, and foster economic development.Incorporating both sustainability goals and advanced technologies to attain equitable urban development and simultaneously solve environmental, social, and economic concerns for improving citizens’ quality of life.Ahvenniemi et al. (2017); Bibri and Krogstie (2017); Haarstad (2017); Angelidou et al. (2018); Martin et al. (2018); Sodiq et al. (2019); Kaiser (2024)Core focus fieldAttaining SDGs, resource and ecological conservation, environmental sustainability, and social equity.Urban efficiency was powered by innovative and advanced technology and the collection and optimization of data.Incorporating environmental goals alongside technological advancements to achieve balanced urban development.Primary goalReducing environmental degradation and emissions, promoting green space and infrastructure, and supporting community well-being.Improving efficiency by utilizing ICT and innovation, enhancing urban services, and promoting economic growth.Utilizing digital tools to promote sustainable urban growth with an emphasis on ecological and social equity objectives.Main challengeConflicting with development, ensuring funding, maintaining a consistent policy commitment, and promoting social inclusion.Digital divide, potential corporate dominance, initial high cost, techno-centric focus, and limited citizen engagement.Keeping a balance between SDGs and technology adoption, while ensuring equitable urban benefits and co-benefits.ApproachPrioritizing policy-oriented frameworks and focusing on regulations and standards.Highly relying on data analytics, smart grids, IoT, and smart devices to manage urban functions.Integrating rules and regulations with data-driven insights to ensure sustainable urban development.Technological integrationPrimarily focusing on green technologies and sustainable infrastructure for sustainability.Intensive and extensive use of ICT, IoT, AI, and data analytics for urban management.High emphasis on using technology to attain sustainable and equitable urban outcomes.Environmental emphasisHighly emphasis on reducing carbon footprint, promoting green spaces, and conserving resources and biodiversity.Frequently ignoring environmental targets and focusing more on social and economic advancement.Emphasizing the need to bridge technology and environmental sustainability.Social equityAiming to improve the quality of life, reduce inequality, and strengthen social infrastructure.Concentrating mostly on affluent and educated demographics and potentially overlooking marginalized communities.Striving to integrate social inclusion by making technology accessible and beneficial to all.Assessment frameworkUrban sustainability frameworks focus on environmental and quality-of-life indicators.Smart city frameworks prioritize technology deployment, efficiency, and economic growth.Comprehensive frameworks assess both environmental and technological impacts.Resilienc to climate changeConcentrating on sustainable infrastructure to mitigate climate risks like flooding, global warming, and extreme weather events.Technology-driven resilience strategies including predictive analytics and early-warning systems.Combining green infrastructure and predictive technology for comprehensive climate resilience.Data privacy and securityLess focusing on data privacy and security, as data collection is minimal.Extensive data collection raises privacy and security concerns, with varying levels of privacy protection.Emphasizing on ethical data practices and balancing data utilization with strong privacy protections.Biodiversity conservationFocusing on preserving natural habitats and expanding green spaces.Less emphasis on biodiversity and more on urban efficiency.Integrating biodiversity conservation with technology to monitor and protect urban flora, fauna, and efficiency.Circular economy approachHighly emphasizing recycling, use of renewable energy, and sustainable consumption.Limited implementation of circular economy principles.Optimizing waste reduction through the integration of smart technology and the widespread adoption of circular economy practices.PartnershipPromoting local and global partnerships with environmental organizations.Focusing on high tech-industry partnerships and often neglecting non-profit engagement.Encouraging inclusive partnerships across public, private, and non-profit sectors for shared urban development goals.Note: SDGs, Sustainable Development Goals; ICT, information and communication technology; IoT, Internet of Things; AI, artificial intelligence.
2.2. Association between sustainable smart cities and SDGs
In 2015, the UN adopted 17 SDGs. The objective of SDG 11 (Sustainable Cities and Communities) is to make urban areas inclusive, safe, resilient, and sustainable by addressing challenges such as urban sprawl, urban poverty, environmental degradation, and limited access to essential services (Kaiser et al., 2024). This goal underscores the critical role of government initiatives and community engagement in achieving the sustainable development of urban areas through strategies like congestion pricing and the expansion of green spaces (UN, 2024b).
Furthermore, other SDGs are closely related to the sustainable smart city. For example, SDG 9 (Industry, Innovation and Infrastructure) is related to the sustainable smart city, which advocates for sustainable industrialization, resilient infrastructure, and fostering innovation. Similarly, SDG 7 (Affordable and Clean Energy) seeks to ensure universal access to affordable, reliable, and modern energy through the development of smart grids and smart homes in cities. Al-Raeei (2024) suggested that AI and machine learning techniques can predict patterns, trends, and anomalies in energy usage in real time by retrieving and analyzing data from sensors. By leveraging these technologies, cities can reduce energy waste, optimize resource management, and enhance urban sustainability efforts in line with SDG 7.
In the context of climate change and environmental management, the sustainable smart city is also associated with SDG 13 (Climate Action). A key challenge in achieving this goal is the unpredictability of natural disasters, which hinders effective disaster management (Kaiser, 2023b). However, advanced technologies such as AI can analyze large datasets, including weather patterns (e.g., rainfall trends), geological data (e.g., seismic activity), and demographic information (e.g., population density in vulnerable areas) to identify flood-prone zones, and earthquake risks and vulnerabilities. By modelling, AI can make predictions and notify authorities of possible hazards, enabling them to take preventative actions to lessen the effects of disasters as well as mitigate climate-related challenges (Son et al., 2023; Al-Raeei, 2024).
In summary, the study of sustainable smart city can achieve SDGs and enhance the quality of life and well-being through the integration of cutting-edge technologies. The implementation of sustainable smart city initiatives should not be limited to the adoption of technology alone; rather, their contributions must align with achieving broader objectives, such as social, economic, and environmental sustainability.
3. Research method
The bibliometric analysis is an effective method to examine and evaluate a large number of articles. This analysis allows us to understand the evolution of a specific field (Donthu et al., 2021; Long et al., 2022). Unlike a systematic review, this method can analyze large datasets by quantitative techniques, providing performance evidence, and generating a more reliable map of scientific advancement (Ellegaard, 2018; Deb and Sultana, 2024). We utilized this approach to examine the intellectual framework of sustainable smart city and SDGs in the selected literature to identify emerging trends, journal performances, collaboration patterns, and research components.
Science mapping is an essential approach to examining the interactions among research components, focusing on intellectual connections and structural relationships (Donthu et al., 2021). This approach helps identify the main themes within a topic and allows for the identification of current research gaps, thereby guiding future studies.
3.1. Paper selection
We utilized the Web of Science (WoS) database to retrieve relevant literature and understand the current status and future research potentials of sustainable smart city. The WoS database is a compilation of scholarly publications and an organized database with enhanced metadata and complete citation links designed to meet diverse information needs. As the largest citation index and abstract platform, the WoS database can offer extensive citation data across various academic disciplines (Birkle et al., 2020). Using the relevant keywords (Fig. 1), we identified 3234 documents, including published articles, book chapters, editorials, and conference papers, published from 1 January 2010 to 9 June 2024. We used the maximum number of keywords based on the published literature of sustainable smart city to ensure that no relevant publications are excluded. We only included peer-reviewed publications since they undergo a rigorous blind peer review process and follow stringent quality control measures (Mele and Belardinelli, 2019; Deb and Chen, 2024). After excluding non-English publications, we retrieved 1903 publications for our final analysis.
Fig. 1. Data extraction process.
3.2. Data analysis
The bibliometric analysis outcome was divided into two parts. The first part involves the descriptive analysis. We processed and categorized the data based on indicators extracted from the literature. The “Biblioshiny” default package from the R Studio was used. This tool is essential for uncovering relevant bibliometric data, such as annual publication trends, scientific production by country, document sources, author impact, and productivity.
In the second part, we used VOSviewer software to analyze the scientific evidence. VOSviewer software can facilitate the visualization of large datasets, making it easier to comprehend extensive data and offer three options for analysis: cited references, authors, and publication sources. Its user-friendly interface enhances the examination and interpretation of bibliometric maps, including bibliographic coupling, co-authorship analysis, co-citation analysis, and co-word analysis (Bhatnagar and Sharma, 2022).
4. Results and discussion
4.1. Descriptive statistics
This study assessed the impact of academic research on a specific topic by descriptive analysis (Donthu et al., 2021; Islam et al., 2022) including examining annual publication trends, geographic distribution, document sources, and highly cited papers to gain insights into the current state of sustainable smart city. Citations serve as a metric to measure influence, while the number of publications reflects productivity.
4.1.1. Trend analysis of publications
Figure 2 demonstrates the publication trends of the selected documents on sustainable smart city. The annual growth rate of publications on sustainable smart city remained relatively low until 2012. However, research in this field gained significant momentum beginning in 2013, with a notable peak in 2023. Though the first five months of 2024 suggested a possible decline in publication output, this trend might change by the end of this year. Scholars argued that the sharp increase in publications may be attributed to the adoption of SDGs and the incorporation of city-scale assessments in the 5th Assessment Report of the Intergovernmental Panel on Climate Change (Sharifi, 2021). Previous bibliometric studies on smart cities also found that research on smart cities started in 2010 and the topic gained significant attention from 2013 (Guo et al., 2019; Zhao et al., 2019; Bajdor and Starostka-Patyk, 2021).
Fig. 2. Number of publications related to sustainable smart city from January 2010 to June 2024.
4.1.2. Relationship between publications and Sustainable Development Goals (SDGs)
Scholars have highlighted the need for sustainable smart city initiatives to achieve SDGs through urban transformation (Sharifi et al., 2024). As expected, 59.00% of the selected publications on the research of sustainable smart city focused on SDG 11, 9.00% of the selected publications were related to SDG 13, while SDG 12 (Responsible Consumption and Production), SDG 7, and SDG 9 shared a similar percentage of documents, each accounting for 5.00% of the selected publications (Table 2). Despite the importance of collaboration and partnerships among agencies and countries in achieving SDGs, the research of sustainable smart city has largely overlooked SDG 17 (Partnerships for the Goal) and SDG 2 (Zero Hunger). Similarly, although sustainable smart city is vital to achieve SDG 10 (Reduce Inequality) and SDG 8 (Decent Work and Economic Growth) through technological advancements, scholars have paid less attention to these fields.Table 2. Number of publications related to SDGs in the selected publications during 2010–2024.SDGsNumber of publicationsPercentage (%)SDG 11: Sustainable Cities and Communities112459.07SDG 13: Climate Action1729.04SDG 12: Responsible Consumption and Production1035.41SDG 7: Affordable and Clean Energy965.05SDG 9: Industry, Innovation and Infrastructure934.89SDG 15: Life on Land512.68SDG 3: Good Health and Well-Being472.47SDG 4: Quality Education432.26SDG 6: Clean Water and Sanitation241.26SDG 1: No Poverty120.63SDG 2: Zero Hunger110.58SDG 8: Decent Work and Economic Growth100.53SDG 14: Life below Water30.16SDG 10: Reduced Inequalities20.11SDG 16: Peace, Justice and Strong Institutions20.11SDG 5: Gender Equality10.05
Fig. 3. Top 10 countries with the most publications related to sustainable smart city during 2010–2024.
Table 3 presents the performance of the top 10 journals in the research of sustainable smart city. The results showed that Sustainability leads in the total number of publications during 2010–2024, followed by Sustainable Cities and Society. Notably, Journal of Urban Technology had the highest number of citations despite fewer publications. Additionally, while Cities and Journal of Cleaner Production have published fewer documents, they had significant impacts in the field of sustainable smart city, with 3571 and 2218 citations, respectively. The top 10 journals accounted for 36.00% of the publications selected for this study, demonstrating their substantial impact on the study of sustainable smart city.Table 3. Performance of the top 10 journals related to sustainable smart city during 2010–2024.Journal nameNumber of publicationsPercentage (%)Total citationsSustainability28915.194205Sustainable Cities and Society864.523378Energies613.21930Smart Cities603.151101Cities542.843571Journal of Cleaner Production422.212218IEEE Access341.79731Journal of Urban Technology241.264385Sensors211.10465Applied Sciences191.0072
In addition to considering a country’s contributions and authors’ productivity, it is essential to recognize the significance of individual works. The number of citations a publication received serves as a key indicator of its impact and importance within the field. Publications with a high number of citations are generally regarded as more influential than those with few or no citations (Culnan, 1986; Shang and Jin, 2023).
Table 4 presents the top 10 cited articles, including the source journals, total citations, and years of publication. The results exhibited that the top 2 most cited articles were from Journal of Urban Technology, while the third article was from Urban Studies. Three of the top 10 articles were published in Cities. The article of “Smart cities in Europe” published in Journal of Urban Technology was the most cited, with 1733 citations. Specifically, Caragliu et al. (2011) used empirical data to operationalize smart cities and identified the potential of ICT for public services, multimodal accessibility, creative class, and educational attainment in the development of sustainable smart city.Table 4. Top 10 cited articles related to sustainable smart city during 2010–2024.Article titleAuthors and publication yearJournal nameTotal citationsAverage annual citationsSmart cities in EuropeCaragliu et al. (2011)Journal of Urban Technology1733124Smart cities: Definitions, dimensions, performance, and initiativesAlbino et al. (2015)Journal of Urban Technology1495150Smart mentality: The smart city as disciplinary strategyVanolo (2014)Urban Studies69063What are the differences between sustainable and smart cities?Ahvenniemi et al. (2017)Cities64280Towards an effective framework for building smart cities: Lessons from Seoul and San FranciscoLee et al. (2014)Technological Forecasting and Social Change45842Smart cities: A conjuncture of four forcesAngelidou (2015)Cities41341Introducing the “15-Minute City”: sustainability, resilience and place identity in future post-pandemic citiesMoreno et al. (2021)Smart Cities408102Applications of big data to smart citiesAl Nuaimi et al. (2015)Journal of Internet Services and Applications40741On big data, artificial intelligence, and smart citiesAllam and Dhunny (2019)Cities39766Programming environments: Environmentality and citizen sensing in the smart cityGabrys (2014)Environment and Planning D: Society and Space37434
The second most-cited article “Smart cities: definitions, dimensions, performance, and initiatives” has received 1495 citations (Albino et al., 2015). This article aimed to define “smart” in the context of cities, using various indicators such as smart economy, smart governance, and smart people and compared cities by examining initiatives in places like Seattle (the United States) and Quebec (Canada).
The third most-cited article, “Smart mentality: the smart city as disciplinary strategy”, critiqued the operationalization of the term “smart city” and explained how politicians use it for their agendas (Vanolo, 2014). More specifically, the author described the naturalization and depoliticization of the concept and explored how cities tackle environmental and developmental challenges.
Ahvenniemi et al. (2017) found that smart cities tend to focus more on social and economic aspects, whereas urban sustainability frameworks are more aligned with measuring environmental sustainability. Lee et al. (2014) also sought to find an effective framework for smart city, using Seoul (South Korea) and San Francisco (the United States) as case studies. Other studies have explored various aspects of sustainable smart city concept (Angelidou, 2015), the challenges and benefits of integrating big data into sustainable smart city applications (Al Nuaimi et al., 2015; Allam and Dhunny, 2019), and how to understand smart city concept.
4.2. Co-occurrence and co-citation analyses
4.2.1. Evolution of research topics
The advancement of scientific research can be traced through the keywords in published articles and the shifting focus areas in the literature. This process allows us to identify emerging research topics and their impacts over time. Keyword trend analysis reveals which subjects have received attention from the scientific community, highlighting their relevance and influence over time (Shang and Jin, 2023).
From Figure 4 we can see that, since 2021, deep learning, machine learning, and AI have emerged as primary focus fields among scholars. The future of urban development will be significantly impacted by these fields. The adoption of these advanced technologies can improve the quality of life and ensure safety, security, sustainability, and efficiency. For instance, Al-Raeei (2024) suggested that integrating AI into urbanization would facilitate the achievement of SDGs by reducing emissions from traffic, improving energy efficiency, and mitigating disaster impacts. Javed et al. (2022) argued that this technological integration is essential to create a true sustainable smart city in the future, which includes smart life (smart communication and smart transportation), smart citizens (empowered individuals actively participating in governance and innovation), smart environment (smart power management and smart waste management), and smart governance (smart services and smart public safety).
Fig. 4. Evolution of topics related to sustainable smart city during 2013–2024. Line indicates the duration of topic’s influence. AI, artificial intelligence. Both “AI” and “artificial intelligence” were regarded as emerging topics because publications used the abbreviated and full form of these two terms .