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Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and...
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Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and Communities

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Socio-Economic and Political Issues".

Deadline for manuscript submissions: 15 October 2025 | Viewed by 2725

Special Issue Editors

Dr. Xiwei Shen

E-Mail Website
Guest Editor
School of Architecture, University of Nevada, Las Vegas, VA 89154, USA
Interests: landscape architecture; ecology restoration; VOC emission
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Zhang

E-Mail Website
Guest Editor
Department of Horticulture and Landscape Architecture, Oklahoma State University, Stillwater, OK 74077, USA
Interests: data science; social value of places; design theory
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Song

E-Mail Website
Guest Editor
Department of Landscape Architecture & Urban Planning, Texas A&M University, College Station, TX 77840, USA
Interests: data science; social value of places; design theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue dedicated to exploring the intersection of computational design and planning with socio-environmental sustainability. This Special Issue aims to delve into innovative approaches, methodologies, and technologies that leverage computational tools to foster sustainable landscapes and communities.

The sustainability of landscapes and communities is a pressing global concern, requiring a multidisciplinary approach that integrates environmental, social, and economic dimensions. Computational design and planning offer transformative potential in addressing these challenges by providing advanced tools for analysis, simulation, visualization, and decision-making.

This Special Issue seeks contributions that achieve the following:

  1. Advance Theoretical and Practical Knowledge: Papers that contribute to the theoretical foundations of computational design and planning in the context of socio-environmental sustainability. We encourage submissions that offer new insights, models, and frameworks.
  2. Showcase Innovative Applications: Papers that demonstrate the application of computational techniques in real-world scenarios. These should highlight successful implementations and lessons learned in enhancing sustainability.
  3. Develop New Tools and Techniques: Papers that introduce novel computational tools, algorithms, and methods aimed at improving the outcomes of sustainability. This includes advancements in Geographic Information Systems (GISs), remote sensing, environmental modeling, machine learning, and participatory planning platforms.
  4. Promote Interdisciplinary Collaboration: Papers that emphasize the importance of interdisciplinary approaches, involving collaboration between urban planners, landscape architect, environmental scientists, engineers, social scientists, and other relevant stakeholders.

We invite submissions focused on a wide range of topics, including, but not limited to, the following:

  • Computational models for sustainable land use and urban planning;
  • Environmental impact assessment using computational tools;
  • Simulation and visualization of socio-environmental dynamics;
  • Integration of big data and AI in sustainability planning;
  • Participatory planning and decision support systems;
  • Climate change adaptation and resilience planning;
  • Smart cities and sustainable infrastructure design;
  • Ecosystem services assessment and management;
  • Sustainable water resource management;
  • Green infrastructure and urban ecology.

Dr. Xiwei Shen
Dr. Bo Zhang
Dr. Yang Song
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Land 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 2600 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

  • socio-environmental sustainability
  • climate change adaption
  • environment impact assessment
  • smart cities
  • green infrastructure
  • environmental modeling

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

26 pages, 13129 KiB  
Article
Assessing Socio-Economic Vulnerabilities to Urban Heat: Correlations with Land Use and Urban Morphology in Melbourne, Australia
by Cheuk Yin Wai, Muhammad Atiq Ur Rehman Tariq, Nitin Muttil and Hing-Wah Chau
Land 2025, 14(5), 958; https://doi.org/10.3390/land14050958 (registering DOI) - 29 Apr 2025
Viewed by 33
Abstract
Modern cities are rapidly evolving in terms of urban morphology, driven by exponential population growth that accelerates the urbanisation process. The changes in land use have increased urban area and density, intensifying the urban heat island (UHI) effect, which poses one of the [...] Read more.
Modern cities are rapidly evolving in terms of urban morphology, driven by exponential population growth that accelerates the urbanisation process. The changes in land use have increased urban area and density, intensifying the urban heat island (UHI) effect, which poses one of the biggest threats to human health and well-being, especially in metropolitan regions. One of the most effective strategies to counter urban heat is the implementation of green infrastructure and the use of suitable building materials that help reduce heat stress. However, access to green spaces and the affordability of efficient building materials are not the same among citizens. This paper aims to identify the socio-economic characteristics of communities in Melbourne, Australia, that contribute to their vulnerability to urban heat under local conditions. This study employs remote sensing and geographical information systems (GIS) to conduct a macro-scale analysis, to investigate the correlation between urban heat patterns and socio-economic characteristics, taking into account factors such as vegetation cover, built-up areas, and land use types. The results from the satellite images and the geospatial data reveal that Deer Park, located in the western suburbs of Melbourne, has the highest land surface temperature (LST) at 32.54 °C, a UHI intensity of 1.84 °C, a normalised difference vegetation index (NDVI) of 0.11, and a normalised difference moisture index (NDMI) of −0.081. The LST and UHI intensity indicate a strong negative correlation with the NDVI (r = −0.42) and NDMI (r = −0.6). In contrast, the NDVI and NDMI have a positive correlation with the index of economic resources (IER) with r values of 0.29 and 0.24, indicating that the areas with better finance resources tend to have better vegetation coverage or plant health with less water stress, leading to lower LST and UHI intensity. This study helps to identify the most critical areas in the Greater Melbourne region that are vulnerable to the risk of urban heat and extreme heat events, providing insights for the local city councils to develop effective mitigation strategies and urban development policies that promote a more sustainable and liveable community. Full article
(This article belongs to the Special Issue Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and Communities)
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29 pages, 5041 KiB  
Article
Integrating Machine Learning, SHAP Interpretability, and Deep Learning Approaches in the Study of Environmental and Economic Factors: A Case Study of Residential Segregation in Las Vegas
by Jingyi Liu, Yuxuan Cai and Xiwei Shen
Land 2025, 14(5), 957; https://doi.org/10.3390/land14050957 (registering DOI) - 29 Apr 2025
Viewed by 44
Abstract
Over the past two decades, research on residential segregation and environmental justice has evolved from spatial assimilation models to include class theory and social stratification. This study leverages recent advances in machine learning to examine how environmental, economic, and demographic factors contribute to [...] Read more.
Over the past two decades, research on residential segregation and environmental justice has evolved from spatial assimilation models to include class theory and social stratification. This study leverages recent advances in machine learning to examine how environmental, economic, and demographic factors contribute to ethnic segregation, using Las Vegas as a case study with broader urban relevance. By integrating traditional econometric techniques with machine learning and deep learning models, the study investigates (1) the correlation between housing prices, environmental quality, and segregation; (2) the differentiated impacts on various ethnic groups; and (3) the comparative effectiveness of predictive models. Among the tested algorithms, LGBM (Light Gradient Boosting) delivered the highest predictive accuracy and robustness. To improve model transparency, the SHAP (SHapley Additive exPlanations) method was employed, identifying key variables influencing segregation outcomes. This interpretability framework helps clarify variable importance and interaction effects. The findings reveal that housing prices and poor environmental quality disproportionately affect minority populations, with distinct patterns across different ethnic groups, which may reinforce these groups’ spatial and economic marginalization. These effects contribute to persistent urban inequalities that manifest themselves in racial segregation and unequal environmental burdens. The methodology of this study is generalizable, offering a reproducible framework for future segregation studies in other cities and informing equitable urban planning and environmental policy. Full article
(This article belongs to the Special Issue Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and Communities)
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29 pages, 19581 KiB  
Article
Integrating Blue–Green Infrastructure with Gray Infrastructure for Climate-Resilient Surface Water Flood Management in the Plain River Networks
by Liqing Zhu, Chi Gao, Mianzhi Wu and Ruiming Zhu
Land 2025, 14(3), 634; https://doi.org/10.3390/land14030634 - 17 Mar 2025
Viewed by 509
Abstract
Along with the progression of globalized climate change, flooding has become a significant challenge in low-lying plain river network regions, where urban areas face increasing vulnerability to extreme climate events. This study explores climate-adaptive land use strategies by coupling blue–green infrastructure (BGI) with [...] Read more.
Along with the progression of globalized climate change, flooding has become a significant challenge in low-lying plain river network regions, where urban areas face increasing vulnerability to extreme climate events. This study explores climate-adaptive land use strategies by coupling blue–green infrastructure (BGI) with conventional gray infrastructure, forming blue–green–gray infrastructure (BGGI), to enhance flood resilience at localized and regional scales. By integrating nature-based solutions with engineered systems, this approach focuses on flood mitigation, environmental co-benefits, and adaptive land-use planning. Using the Minhang District in Shanghai as a case study, the research employs geospatial information system (GIS) analysis, hydrological modeling, and scenario-based assessments to evaluate the performance of BGGI systems under projected climate scenarios for the years 2030, 2050, and 2100. The results highlight that coupled BGGI systems significantly improve flood storage and retention capacity, mitigate risks, and provide ecological and social benefits. Water surface-to-catchment area ratios were optimized for primary and secondary catchment areas, with specific increases required in high-risk zones to meet future flood scenarios. Ecological zones exhibited greater adaptability, while urban and industrial areas required targeted interventions. Scenario-based modeling for 2030, 2050, and 2100 demonstrated the scalability, feasibility, and cost-effectiveness of BGI in adapting to climate-induced flooding. The findings contribute to the existing literature on urban flood management, offering a framework for climate-adaptive planning and resilience building with broader implications for sustainable urban development. This research supports the formulation of comprehensive flood management strategies that align with global sustainability objectives and urban resilience frameworks. Full article
(This article belongs to the Special Issue Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and Communities)
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32 pages, 7130 KiB  
Article
Investigation of Diverse Urban Carbon Emission Reduction Pathways in China: Based on the Technology–Organization–Environment Framework for Promoting Socio-Environmental Sustainability
by Haiyan Jiang, Jiaxi Lu, Ruidong Zhang and Xi Xiao
Land 2025, 14(2), 260; https://doi.org/10.3390/land14020260 - 26 Jan 2025
Viewed by 643
Abstract
In the context of global carbon emissions and climate change, identifying context-specific low-carbon pathways for urban areas is critical for achieving socio-environmental sustainability. This study applies the technology–organization–environment (TOE) framework to examine the driving mechanisms and the diversity in carbon reduction pathways across [...] Read more.
In the context of global carbon emissions and climate change, identifying context-specific low-carbon pathways for urban areas is critical for achieving socio-environmental sustainability. This study applies the technology–organization–environment (TOE) framework to examine the driving mechanisms and the diversity in carbon reduction pathways across 81 cities in China. Utilizing partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA), this research assesses the roles of technological, organizational, and environmental drivers in urban carbon reduction. Fuzzy-set qualitative comparative analysis (fsQCA) is employed to uncover distinct carbon reduction pathways and causal asymmetries between cities. The findings reveal that technological, organizational, and environmental factors significantly drive carbon reduction, with technological and organizational factors playing the central roles. Environmental factors exert primarily indirect effects, interacting with technological and organizational drivers. This study categorizes cities into three distinct carbon reduction models: cities with high carbon-neutral potential primarily leverage technological innovation and energy efficiency optimization; cities with moderate potential integrate technology and policy, emphasizing green landscape planning to achieve balanced development; and cities with lower carbon reduction potential are mainly policy-driven, constrained by technological and resource limitations. This study underscores the role of computational modeling in providing valuable insights for the development of context-tailored carbon reduction strategies. It highlights the synergetic interactions among technological, organizational, and environmental factors, offering essential guidance for advancing sustainable development planning and facilitating the low-carbon transition of cities and communities. Full article
(This article belongs to the Special Issue Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and Communities)
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28 pages, 38863 KiB  
Article
Exploring the Relationship Between Visual Perception of the Urban Riverfront Core Landscape Area and the Vitality of Riverfront Road: A Case Study of Guangzhou
by Shawei Zhang, Junwen Lu, Ran Guo and Yiding Yang
Land 2024, 13(12), 2142; https://doi.org/10.3390/land13122142 - 9 Dec 2024
Viewed by 1009
Abstract
The vitality of riverfront districts, as a crucial component of urban livability, is profoundly influenced by human visual perception of the surrounding environment. This study takes the Pearl River in Guangzhou as an example and explores the relationship between the visual perception of [...] Read more.
The vitality of riverfront districts, as a crucial component of urban livability, is profoundly influenced by human visual perception of the surrounding environment. This study takes the Pearl River in Guangzhou as an example and explores the relationship between the visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. Employing subjective environment perception prediction methods and analyzing the riverfront landscape pictures captured by the research team, we quantified six essential perceptual dimensions. Furthermore, we evaluated the vitality of Riverfront Road through a four-step process: 1. measuring key visual indices of Riverfront Road, including the green view index (GVI), water view index (WVI), sky view index (SVI), and building view index (BVI); 2. evaluating the proximity of cultural landmarks to Riverfront Road; 3. calculating the convenience of driving, buses, and subways for Riverfront Road with the network analysis method; 4. deriving the vitality value of Riverfront Road through the combination of hotspot data from Baidu. With the application of random forest and result comparisons, we obtained a comprehensive analysis of the correlation between visual perception of the urban riverfront core landscape area and the vitality of Riverfront Road. The results reveal the significant correlation between these two factors and highlight that visual perception of the old city landscape area is superior to that of the new city, although the cultural landmarks and transportation convenience play essential roles in the improvement of vitality in Riverfront Road. It is evident that relying solely on visual design may fail to prominently boost vitality. Overall, spatial design should adopt a multidimensional approach, integrating various factors such as transportation convenience, social interaction venues, cultural activities, etc., to create a cohesive vitality network. Full article
(This article belongs to the Special Issue Computational Design and Planning for Socio-Environmental Sustainability of Landscapes and Communities)
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