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Sustainability
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AI-Driven Innovations in Urban Resilience and Climate Adaptation
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AI-Driven Innovations in Urban Resilience and Climate Adaptation

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2489

Special Issue Editors

Dr. Suwan Shen

E-Mail Website
Guest Editor
Department of Urban and Regional and Planning, University of Hawaii at Manoa, Saunders Hall 107, 2424 Maile Way, Honolulu, HI 96822, USA
Interests: climate vulnerability; community resilience; adaptation planning; GIS and GeoAI
Dr. Yuqin Jiang

E-Mail Website
Guest Editor
Geography and Environment, University of Hawaii at Manoa, Saunders Hall 419, 2424 Maile Way, Honolulu, HI 96822, USA
Interests: geographic information science; high-performance computing; CyberGIS

Special Issue Information

Dear Colleagues,

Climate change poses a global challenge, threatening urban environments worldwide with increased risks such as extreme weather events and rising sea levels. As the impacts of climate change continue to challenge urban environments globally, there is a pressing demand for innovative approaches to enhance urban resilience. This Special Issue, "AI-Driven Innovations in Urban Resilience and Climate Adaptation", aims to showcase cutting-edge research that employs geospatial artificial intelligence (GeoAI) as a pivotal tool in enhancing the adaptability and resilience of urban areas. With its sophisticated capabilities in handling complex spatial datasets, GeoAI provides essential tools for improving predictions, planning, and response strategies against climate-related challenges.

We seek contributions that utilize GeoAI to predict, analyze, and enhance urban resilience against extreme weather events, sea-level rise, and other climate-related risks. These contributions would highlight the transformative potential of GeoAI in making urban areas more robust and prepared for future challenges. We invite researchers and practitioners to submit papers demonstrating how GeoAI drives innovations in urban resilience and climate adaptation. Submissions may cover applications in disaster risk management, urban infrastructure adaptation, and socio-economic resilience in the face of climate change. We encourage submissions that explore novel applications of GeoAI in urban settings, discuss methodological advancements in spatial analysis related to climate adaptation, and offer theoretical contributions that bridge gaps in the existing literature on urban resilience.

The scope of the Special Issue includes, but is not limited to, the following topics:                                  

  • GeoAI for enhanced prediction and management of climate risks.
  • GeoAI applications for urban resilience and vulnerability assessment.
  • GeoAI-supported designs and solutions for adaptive urban infrastructure systems.
  • Advancements in GeoAI methodologies for mapping and mitigating climate risks.
  • Integration of GeoAI in urban planning for climate change adaptation strategies.
  • Case studies on the successful implementation of GeoAI in urban resilience initiatives.
  • Theoretical advancements in GeoAI that contribute to a better understanding of climate impacts and adaptation in urban settings.
  • Development of GeoAI tools and technologies that aid in resilience planning and disaster response.

We invite you to share your research and advancements in this critical area. Together, we can forge a path toward more resilient urban environments equipped to handle the challenges of tomorrow.

Dr. Suwan Shen
Dr. Yuqin Jiang
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. 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

  • GeoAI
  • urban resilience
  • climate adaptation
  • spatial analysis
  • infrastructure resilience
  • disaster response
  • sustainable development
  • smart cities
  • resilience planning
  • climate risk management

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Published Papers (1 paper)

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Research

26 pages, 6809 KB  
Article
Intra-Urban CO2 Spatiotemporal Patterns and Driving Factors Using Multi-Source Data and AI Methods: A Case Study of Shanghai, China
by Leyi Pan, Qingyan Fu, Fan Yang, Yuchen Shao and Chao Liu
Sustainability 2025, 17(23), 10794; https://doi.org/10.3390/su172310794 - 2 Dec 2025
Viewed by 1226
Abstract
Cities are major sources of anthropogenic carbon dioxide (CO2) emissions, making the study of intra-urban CO2 concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city [...] Read more.
Cities are major sources of anthropogenic carbon dioxide (CO2) emissions, making the study of intra-urban CO2 concentration patterns an emerging research priority. However, limited data availability and the complexity of urban environments have impeded detailed spatiotemporal analyses at the city scale. To address these challenges, an analysis supported by multi-source data and GeoAI methods is carried out to examine the spatial distribution, vertical variation, temporal dynamics, and driving factors of CO2 concentrations in urban areas. We combined OCO-2 satellite-derived XCO2 data (2014–2024) with ground-based measurements from the Shanghai Tower (August 2024 to March 2025), alongside meteorological and socioeconomic variables. The analysis employed spatial interpolation (inverse distance weighting), nonparametric testing (Mann–Whitney U test), time series decomposition, ordinary least squares (OLS) regression, and machine learning techniques including random forest and SHAP (SHapley Additive exPlanations) analysis. Results reveal that CO2 concentrations are significantly higher in central urban districts compared to suburban areas, with notable spatial heterogeneity. Elevated levels were detected near ports and ferry routes, with airports and industrial emissions identified as principal contributors. Vertically, CO2 concentrations decline with increasing altitude but exhibit a peak at mid-level heights. Temporally, a pronounced seasonal pattern was observed, characterized by higher concentrations in winter and lower levels in summer. Both OLS regression and machine learning models highlight proximity to emission sources, wind speed, and temperature as key determinants of spatial CO2 variability, with these factors collectively explaining 67% of the variance in OLS models. This study demonstrates how multi-source data and advanced methods can capture the spatial, vertical, and seasonal dynamics and driving factors of urban CO2 concentrations, offering insights for policy, planning, and mitigation. Full article
(This article belongs to the Special Issue AI-Driven Innovations in Urban Resilience and Climate Adaptation)
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