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

Deadline for manuscript submissions: closed (28 October 2025) | Viewed by 10059

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Special Issue Editors

Dr. Kangjae Lee

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Guest Editor

Department of Location-Based Information System, Kyungpook National University, Daegu, Republic of Korea
Interests: GIS; machine learning; algorithm; mobility
Special Issues, Collections and Topics in MDPI journals

Dr. Jeon-Young Kang

E-Mail Website
Guest Editor

Department of Geography, Kyung Hee University, Seoul, Republic of Korea
Interests: geospatial data science; agent-based modelling (ABM); CyberGIS; advanced geocomputation; health geography; spatial accessibility to urban infrastructure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

One of the sustainability development goals (SDGs) of the United Nations (UN), SDG3 aims to “ensure healthy lives and promote well-being for all at all ages”. Some built environments, including green spaces, support active lifestyles and contribute to promoting people’s physical and mental health. The UN highlighted parks, green spaces and waterways as solutions to the effects of unsustainable urbanization on health and well-being. Cities are recognizing that the increasing stream of data and information can support rapid advances in human health and built environments. Data-driven approaches with statistical methods, machine learning or deep learning techniques, and geospatial analytics can help gain insights into the interaction between built environments and humans.

This Special Issue will include new conceptual tools and theoretical frameworks that work toward understanding the association between human health and built environments. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Community health;
Green and blue space and human health;
Health inequalities;
Urban planning for resilience and health;
Land use/cover and human health;
Environmental health;
Built environments and physical activity;
Urban environments and active travel modes;
Sensor data and data analytics;
Geospatial data analytics;
Remote sensing and health.

We look forward to receiving your contributions.

Sincerely,

Dr. Kangjae Lee
Dr. Jeon-Young Kang
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

statistical methods
machine learning
deep learning
explainable artificial intelligence
built environment
green space
physical health
mental health
environmental health

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Published Papers (5 papers)

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Research

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24 pages, 11762 KB

Open AccessArticle

Assessment of the Impact of Land Use/Land Cover Changes on Carbon Emissions Using Remote Sensing and Deep Learning: A Case Study of the Kağıthane Basin

by Bülent Kocaman and Hayrullah Ağaçcıoğlu

Sustainability 2025, 17(23), 10690; https://doi.org/10.3390/su172310690 - 28 Nov 2025

Viewed by 96

Abstract

This study investigates the spatiotemporal changes in land use and land cover (LULC) in the Kağıthane basin, Istanbul, a region experiencing rapid urban growth, to assess its environmental sustainability. Sentinel-1 and Sentinel-2 satellite images processed on the Google Earth Engine (GEE) platform were used for 2017, 2020, and 2023. Optical data from Sentinel-2, after atmospheric and geometric corrections, combined with co- and cross-polarized radar backscatter from Sentinel-1, supported land cover classification. Additionally, 14 spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Urban Index (UI), enhanced discrimination between classes. To estimate LULC projections for 2035, 2050, 2065, 2080, and 2095, the Modules for Land Use Change Evaluation (MOLUSCE) model was used, which integrates artificial neural networks with a cellular automata framework. Six driving variables, roads, streams, topographic parameters (elevation, slope, and aspect), and population density, were incorporated into multiple scenarios. Model performance was evaluated using overall accuracy, Kappa statistics, and confusion matrices, yielding strong results (91.88% accuracy; Kappa = 0.84). The simulations indicate a significant decline in forest cover and barren lands, while vegetation and built-up areas are projected to grow steadily, raising concerns about long-term urban sustainability. Water bodies are projected to remain relatively stable. Under these changes, future direct carbon emissions were estimated using carbon emission coefficients by land class. Indirect carbon emissions were estimated based on natural gas and electricity consumption data. Considering both direct and indirect emissions, the results indicate a decrease in carbon emissions from 2023 to 2035, followed by an increase of up to 13% between 2035 and 2095. These findings emphasize the importance of combining multi-sensor remote sensing data with spatially explicit modeling to accurately assess land use changes in rapidly urbanizing basins. The study emphasizes the critical need to adopt sustainability measures that address changes in carbon emissions and guide future urban planning towards a more sustainable path. Full article

(This article belongs to the Special Issue Geographic Information Engineering and Geoenvironmental Sustainability)

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27 pages, 12600 KB

Open AccessArticle

Exploring the Complex Relationships Between Influential Factors of Urban Land Development Patterns and Urban Thermal Environment: A Study on Downtown Shanghai

by Hao-Rong Yang, Yan-He Li, Wen-Jia Wu, Ai-Lian Zhao and Hao Zhang

Sustainability 2025, 17(19), 8547; https://doi.org/10.3390/su17198547 - 23 Sep 2025

Viewed by 654

Abstract

The rapid urbanization process has exacerbated the urban heat island (UHI) effect in megacities like Shanghai. Urban green infrastructure (UGI) plays a crucial role in mitigating the UHI effect; however, its cooling capacity is subject to various urban land development patterns. This study examined 39 typical locations in downtown Shanghai to measure how urban land development patterns affect the UGI’s cooling capacity. Using a data-driven framework, we identified 12 key influencing factors and explored 4 interactions for building three regression models: multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR). For each of these models, we considered two variations: a basic model neglecting interactions and an enhanced model including interactions. Results showed that all enhanced models outperformed their basic counterparts. On average, the enhanced models increased their predictive power by 14.59% for training data and 32.15% for testing data. Additionally, among the three enhanced models, the SVR-enhanced models show the best performance, followed by the PLSR-enhanced models. Their mean predictive power increased by 8.33−37.43% for training data and 31.77−43.558% for testing data vs. the MLR-enhanced models. Overall, our findings revealed that impervious surfaces contribute positively to urban warming, while UGI acts as a negative contributor. Moreover, we highlighted how urban land development metrics, particularly the UGI’s percentage and spatial arrangements in relation to adjacent buildings, significantly affect the thermal environment. The findings can offer valuable insights for urban planners and decision-makers involved in managing UGI and developing strategies for UHI mitigation and urban climate adaptation. Full article

(This article belongs to the Special Issue Geographic Information Engineering and Geoenvironmental Sustainability)

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34 pages, 8025 KB

Open AccessArticle

Impact of Urban Green Space Patterns on Carbon Emissions: A Gray BP Neural Network and Geo-Detector Analysis

by Yao Xiong, Yiyan Sun and Yunfeng Yang

Sustainability 2025, 17(16), 7245; https://doi.org/10.3390/su17167245 - 11 Aug 2025

Cited by 4 | Viewed by 1601

Abstract

Rapid urbanization has altered the land use pattern, reducing urban green space and increasing carbon emissions, and it is critical to scientifically examine the interaction mechanism between green space and carbon emissions in order to drive low-carbon urban development. Using Nanjing as an example, this study examined the spatiotemporal evolution characteristics of urban green space patterns and carbon emissions between 2000 and 2020. Carbon emissions at the city and county levels were estimated with great precision using a gray BP neural network model and a downscaling decomposition method. Using urban green space landscape pattern indices and geographic detectors, significant driving factors were discovered and their impact on carbon emissions examined. The results show the following: (1) Carbon emissions are mostly influenced by socioeconomic factors, and the gray BP neural network model (R² = 0.9619, MAPE = 1.68%) can predict outcomes accurately. (2) Between 2000 and 2020, Nanjing’s overall carbon emissions increased by 118.9%, demonstrating a “core–periphery” pattern of spatial divergence, with significant emissions from industrial districts and emission reductions in the central urban region. (3) The urban green space exhibits “quantity decreasing and quality increasing” characteristics, with the total area falling by 4.84% but the structure optimized to form a networked pattern with huge ecological patches as the backbone. (4) The primary drivers are the LPI, COHESION, and AI. This study reveals the complex relationship mechanism between the spatial configuration of urban green space and carbon emissions and, based on the results, proposes a green space optimization framework with three dimensions, protection of core ecological patches, enhancement of connectivity through ecological corridors, and implementation of low-carbon maintenance measures, which will provide a scientific basis for the planning of urban green space and the construction of low-carbon cities in the Yangtze River Delta region. Full article

(This article belongs to the Special Issue Geographic Information Engineering and Geoenvironmental Sustainability)

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13 pages, 1798 KB

Open AccessReview

Daily Green Exposure, Mobility, and Health: A Scoping Review

by Tong Liu, Winifred E. Newman and Matthew H. E. M. Browning

Sustainability 2024, 16(8), 3412; https://doi.org/10.3390/su16083412 - 19 Apr 2024

Cited by 4 | Viewed by 1688

Abstract

Mounting evidence suggests urban greenery promotes physical activity and human health. However, scholars have differing views on defining or measuring the terms related to green mobility behavior (MB). Therefore, evaluating how green MB impacts health is challenging. After an initial review of the literature on mobility, greenness, and health, we proposed “daily greenness exposure” (DGE) to define people’s exposure to natural/green settings. This approach lets us review and compare general and emerging measures of greenery exposure and differentiate study outcomes in MB and health. We identified 20 relevant Web of Science Core Collection studies during a scoping review completed in November 2021. Three types of DGE assessments were observed: ecological momentary, effect, and spatiotemporal. Four relationships were noted between DGE, MB, and health: moderation, mediation, independence, and undifferentiated. Incorporating these assessments and DGE modeling relationships contributes to better analysis and communication of environmental factors promoting health to environmental designers and policymakers. Full article

(This article belongs to the Special Issue Geographic Information Engineering and Geoenvironmental Sustainability)

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22 pages, 7829 KB

Open AccessReview

Exploring GIS Techniques in Sea Level Change Studies: A Comprehensive Review

by Justine Sarrau, Khaula Alkaabi and Saif Obaid Bin Hdhaiba

Sustainability 2024, 16(7), 2861; https://doi.org/10.3390/su16072861 - 29 Mar 2024

Cited by 5 | Viewed by 5403

Abstract

Sea level change, a consequence of climate change, poses a global threat with escalating impacts on coastal regions. Since 1880, global mean sea level has risen by 8–9 inches (21–24 cm), reaching a record high in 2021. Projections by NOAA suggest an additional 10–12-inch increase by 2050. This paper explores research methodologies for studying sea level change, focusing on Geographic Information System (GIS) techniques. GIS has become a powerful tool in sea level change research, allowing the integration of spatial data, coastal process modeling, and impact assessment. This paper sets the link with sustainability and reviews key factors influencing sea level change, such as thermal expansion and ice-mass loss, and examines how GIS is applied. It also highlights the importance of using different scenarios, like Representative Concentration Pathways (RCP), for accurate predictions. The paper discusses data sources, index variables like the Coastal Vulnerability Index, and GIS solutions for modeling sea level rise impacts. By synthesizing findings from previous research, it contributes to a better understanding of GIS methodologies in sea level change studies. This knowledge aids policymakers and researchers in developing strategies to address sea level change challenges and enhance coastal resilience. Furthermore, global analysis highlights the pivotal roles of the United States and China in sea level change (SLC) and GIS research. In the Gulf Cooperation Council (GCC) region, rising temperatures have substantial impacts on local sea levels and extreme weather events, particularly affecting vulnerable coastal areas. Full article

(This article belongs to the Special Issue Geographic Information Engineering and Geoenvironmental Sustainability)

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