Smart Cities and E-Government: Leveraging Big Data for Urban Development

A special issue of Urban Science (ISSN 2413-8851). This special issue belongs to the section "Intelligent Cities and Technology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1872

Special Issue Editors


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Guest Editor
Faculty of Public Administration, National University of Political Studies and Public Administration, Bucharest, Romania
Interests: applied artificial intelligence; e-government; smart cities; information society; IT&C
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Public Administration, National University of Political Studies and Public Administration, Bucharest, Romania
Interests: IT&C; e-government; smart cities; sustainability; social change
School of Management, Beijing Institute of Technology, Research Center for Technological Innovation, Tsinghua University, Beijing 100081, China
Interests: technological innovation management; context-driven innovation; commercialization and governance of AI and digital technologies

Special Issue Information

Dear Colleagues,

The Special Issue, entitled “Smart Cities and E-Government: Leveraging Big Data for Urban Development”, focuses on examining longitudinal trends and evaluating the sustained impacts of AI and digital reforms in urban governance, both at local and global scales.

The scope of this Special Issue includes comprehensive analyses and original studies that address the pivotal dimensions of smart city development. Contributions are encouraged in fields such as e-government, e-democracy, social innovation, context-driven innovation, urban planning, and sustainable urban and rural development. Specifically, this Issue aims to highlight context-driven innovation and progress across core smart city contexts, focusing on governance, environment, technology, economy, human capital, mobility, quality of life, and education. Additionally, it encourages explorations into emerging trends and the role of young leaders and technologies such as AI and LLMs shaping smarter states.

We seek to document advancements, critically analyze policy impacts, and offer insights into successful practices and future pathways toward achieving sustainable, inclusive, and technologically empowered urban environments.

Overall, this Issue intends to enrich the current body of literature by providing robust longitudinal studies, case studies, and other empirical studies with mixed methods, which are often lacking in existing research on digital reforms and smart city implementations. It will critically complement prevailing academic and policy discourses by offering comparative insights, innovative approaches, and empirical evidence from diverse contexts. By engaging both academic scholars and practitioners from local governments, we aim to bridge theoretical frameworks with practical applications, thus contributing significantly to the scholarly understanding and practical governance of smart cities.

Contributions should focus on, but are not limited to, the following topics of interest:

  1. Longitudinal impact assessments of AI, Big Data, and LLMs in e-government and e-democracy.
  2. Context-driven social innovation and citizen co-creation in smart city governance.
  3. Comparative policy analysis of digital reforms between EU cities and emerging economies.
  4. Smart governance models that integrate environmental sustainability, mobility, and quality-of-life metrics.
  5. Data ethics, privacy, and trust frameworks in AI-enabled urban services.
  6. Role of young leaders, start-ups, and open-data ecosystems in scaling smart city solutions.
  7. Mixed-method case studies on smart urban planning, rural–urban digital divides, and inclusive development.
  8. Digital platforms for participatory budgeting, predictive urban analytics, and real-time policy feedback loops.

Dr. Catalin Vrabie
Dr. Luminita Movanu
Dr. Ximing Yin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Urban Science is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • context-driven innovation
  • smart cities
  • digital reforms
  • e-government
  • e-democracy
  • urban innovation
  • smart governance
  • sustainable urban development
  • smart technology
  • urban planning
  • social innovation

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Published Papers (2 papers)

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Research

15 pages, 3485 KB  
Article
Added Value for Urban Heat Island Quantification from Machine Learning Downscaling of Air Temperatures
by Hjalte Jomo Danielsen Sørup, Maria Castro, Kasper Stener Hintz, Rune Magnus Koktvedgaard Zeitzen, Peter Thejll, Quentin Paletta, Mark R. Payne, Inês Girão and Ana Oliveira
Urban Sci. 2026, 10(3), 171; https://doi.org/10.3390/urbansci10030171 - 20 Mar 2026
Viewed by 586
Abstract
The urban heat island effect is well recognized and has been quantified using ground observations within and outside urban areas. Earth Observation has further revealed small-scale local spatial differences, especially in urban surface temperatures, that have been shown to be highly correlated with [...] Read more.
The urban heat island effect is well recognized and has been quantified using ground observations within and outside urban areas. Earth Observation has further revealed small-scale local spatial differences, especially in urban surface temperatures, that have been shown to be highly correlated with differences in the urban fabric. However, surface temperatures do not directly translate to human-experienced temperatures, and hence high-resolution air temperature data is of high relevance. However, air temperature is not easily measured from space, and seldom do ground measurements allow for small-scale differences to be quantified to a satisfactory degree. In the present study, we assessed the added value of an air temperature product downscaled using machine learning compared to the high-resolution reanalysis model that formed its foundation. The downscaled product was developed using satellite data, local observations from privately owned weather stations, and high-resolution reanalysis. The comparison focused on Denmark’s four largest urban areas and examined the two data product’s ability to describe the urban heat island effect at the city scale as well as intra-city differences in air temperatures. Both data products show similar urban heat island effects at the city scale, while the downscaled product shows greater intra-city variance in air temperature, with patterns that are somewhat correlated with both urban density and urban green spaces. Generally, the downscaling product offers city planners a better data basis for evaluating where to prioritize contingency and mitigation measures within the urban space. Full article
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32 pages, 366 KB  
Article
Digital Public Service Maturity and Municipal Governance Performance: A City-Level Diagnostic Framework for Armenia
by Khoren Mkhitaryan, Gagik Aslanyan, Anna Sanamyan, Armenuhi Ordyan and Hayk Harutyunyan
Urban Sci. 2026, 10(3), 167; https://doi.org/10.3390/urbansci10030167 - 18 Mar 2026
Viewed by 682
Abstract
Digital transformation of municipal public services remains uneven in transition economies, while national e-government indices often obscure substantial disparities across cities. This study develops a composite E-Government Maturity and Performance Index (EGMPI) to evaluate eleven Armenian municipalities across four governance dimensions: digital service [...] Read more.
Digital transformation of municipal public services remains uneven in transition economies, while national e-government indices often obscure substantial disparities across cities. This study develops a composite E-Government Maturity and Performance Index (EGMPI) to evaluate eleven Armenian municipalities across four governance dimensions: digital service availability, administrative efficiency, transparency and accountability, and citizen interaction and participation. Using publicly available data for Q1 2024, the analysis reveals pronounced metropolitan concentration, with large cities significantly outperforming smaller municipalities. Although performance correlates with population size and fiscal capacity, institutional and managerial factors strongly mediate outcomes, as comparable municipalities display substantial differences in service maturity. Results further show that local governments tend to prioritize transparency measures over functional efficiency, indicating symbolic digitalization rather than substantive service transformation. Unlike national e-government indices, this study provides a city-level diagnostic framework enabling intra-country performance comparison and actionable municipal policy design. Based on the findings, a multi-level policy roadmap is proposed, including shared national platforms, regional digital hubs, targeted capacity building, and leapfrogging strategies for low-maturity cities. The proposed governance-oriented framework offers a replicable tool for transition economies and demonstrates that effective digital transformation depends primarily on institutional coordination and citizen-centric management rather than financial resources alone. Full article
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