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Smart and Sustainable Cities and Rural Regions: Data-Driven Approaches for Resilient and Sustainable Infrastructure

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 3163

Special Issue Editor

Dr. Jidong J. Yang

E-Mail Website
Guest Editor
College of Engineering, University of Georgia, Athens, GA 30602, USA
Interests: sustainable and resilient infrastructure systems; smart mobility systems; big data mining and analytics; deep learning methods and applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the role of advanced technologies and data-driven solutions in developing resilient, sustainable, and interconnected infrastructure across both urban and rural settings. As cities and rural regions evolve, their infrastructure needs are increasingly interlinked, requiring integrated approaches that support economic growth, environmental sustainability, and social well-being. Rapid urbanization, climate variability, and technological advancements are reshaping urban infrastructure. Rural areas, which are critical for food production, resource management, and cultural preservation, are modernizing to enhance their connectivity, service delivery, and resilience. Bridging urban and rural infrastructure gaps requires innovative, adaptable, and region-specific solutions.

Urban centers are leveraging smart technologies such as the Internet of Things (IoT), big data, and artificial intelligence to optimize infrastructure systems, including transportation networks, energy grids, and water management. These advancements enhance efficiency, reduce environmental impacts, and improve urban living conditions. Meanwhile, rural regions are embracing digital connectivity, precision agriculture, and decentralized energy solutions to enhance the reliability of their infrastructure and the accessibility of services. Challenges such as aging infrastructures, climate resilience, and equitable access to resources require coordinated strategies that align technological advancements with local needs.

This Special Issue will showcase case studies, methodologies, and policies that highlight how integrated, technology-driven infrastructure solutions can support both urban and rural regions. By fostering collaboration and leveraging smart systems, we can create more resilient, adaptive, and sustainable infrastructure that meets the needs of diverse communities and ensures long-term prosperity.

Dr. Jidong J. Yang
Guest Editor

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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • resilient infrastructure
  • data-driven solutions
  • digital transformation
  • Internet of Things (IoT)
  • sustainable development
  • big data analytics
  • rural connectivity
  • urban–rural integration

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Published Papers (2 papers)

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Research

26 pages, 1885 KB  
Article
Evaluation and Barrier Diagnosis of the “Smart-Resilience” of Urban Infrastructure in Kunming, China
by Meixin Hu and Chuanchen Bi
Sustainability 2026, 18(7), 3193; https://doi.org/10.3390/su18073193 - 24 Mar 2026
Viewed by 206
Abstract
Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in [...] Read more.
Due to the rapid process of urbanization and the threat of environmental hazards, the need to enhance the intelligence and resilience of urban infrastructure has emerged as a pre-eminent demand of sustainable urban development. This paper evaluates the smart-resilience of urban infrastructure in Kunming by creating a well-developed evaluation framework with reference to the DPSIR (Driving Force–Pressure–State–Impact–Response) model and using the Entropy Weight TOPSIS technique to measure infrastructure performance during the years 2020–2024. The study fills an existing gap in the literature regarding the integration of intelligence and resilience evaluation, as well as the dynamic obstacle diagnosis based on causal logic. It provides a transferable analytical framework and empirical evidence for the “smart-resilience” development of similar cities. The findings suggest that there is steady progress in infrastructure smart-resilience in Kunming, whereby the composite index grew from 0.330 to 0.597, which is equivalent to an average growth rate of about 16.0 per annum. In spite of this favorable tendency, there are a number of structural issues that remain unsolved. The driving force dimension is unstable with regard to long-term mechanisms of investment, and the responding dimension is lagging behind, indicating weaknesses in the governance capacity and inter-departmental coordination. Moreover, extreme weather events have become the major threat to infrastructure systems in the city, superseding traditional social and operational risks; consequently, the city has changed its risk profile. Obstacle factor analysis shows that state and response dimensions make up almost 60% of the total constraint level, which shows the significance of enhancing the effectiveness of management. The research findings are based on the proposal of specific policy actions, such as the creation of special infrastructure resilience funds, the enhancement of mechanisms relating to cross-departmental emergency responses, the implementation of risk-based engineering standards, and the creation of an integrated infrastructure data platform to facilitate efficient, resilient, and sustainable urban governance. Full article
(This article belongs to the Special Issue Smart and Sustainable Cities and Rural Regions: Data-Driven Approaches for Resilient and Sustainable Infrastructure)
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19 pages, 4400 KB  
Article
Smart Street Lighting Powered by Renewable Energy: A Multi-Criteria, Data-Driven Decision Framework
by Jiachen Bian and Jidong J. Yang
Sustainability 2025, 17(13), 5874; https://doi.org/10.3390/su17135874 - 26 Jun 2025
Cited by 3 | Viewed by 2449
Abstract
Renewable energy sources, such as solar and wind power, are gaining increasing global attention. To facilitate their integration into transportation infrastructure, this paper proposes a multi-criteria assessment framework for identifying the most suitable renewable energy sources for street lighting at any given location. [...] Read more.
Renewable energy sources, such as solar and wind power, are gaining increasing global attention. To facilitate their integration into transportation infrastructure, this paper proposes a multi-criteria assessment framework for identifying the most suitable renewable energy sources for street lighting at any given location. The framework evaluates three key metrics: cost–benefit, reliability, and power generation potential, using time-series weather data. To demonstrate its effectiveness, we apply the framework to data from Georgia, USA. The results show that the proposed approach effectively classifies locations into four categories: solar-recommended, wind-recommended, hybrid-recommended, and no recommendation. Specifically, wind energy is primarily recommended in the southeastern region near the coastline, while solar energy is favored in the northwestern region. A hybrid of both sources is mainly recommended along the coast and in transitional areas. In several isolated parts of the northwest, neither energy source is recommended due to unfavorable weather conditions influenced by the local terrain. Since processing long-term time-series data is computationally intensive and challenging during inference, we train machine learning models, including Multilayer Perceptron (MLP) and Extreme Gradient Boosting (XGBoost), using temporally aggregated features for efficient and rapid decision-making. The MLP model achieves an overall accuracy of 92.4%, while XGBoost further improves accuracy to 94.3%. This study provides a practical reference for regional energy infrastructure planning, promoting optimized renewable energy use in street lighting through a robust, data-driven evaluation framework. Full article
(This article belongs to the Special Issue Smart and Sustainable Cities and Rural Regions: Data-Driven Approaches for Resilient and Sustainable Infrastructure)
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