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25 pages, 6507 KiB  
Article
Sustainable Urban Heat Island Mitigation Through Machine Learning: Integrating Physical and Social Determinants for Evidence-Based Urban Policy
by Amatul Quadeer Syeda, Krystel K. Castillo-Villar and Adel Alaeddini
Sustainability 2025, 17(15), 7040; https://doi.org/10.3390/su17157040 - 3 Aug 2025
Viewed by 227
Abstract
Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to [...] Read more.
Urban heat islands (UHIs) are a growing sustainability challenge impacting public health, energy use, and climate resilience, especially in hot, arid cities like San Antonio, Texas, where land surface temperatures reach up to 47.63 °C. This study advances a data-driven, interdisciplinary approach to UHI mitigation by integrating Machine Learning (ML) with physical and socio-demographic data for sustainable urban planning. Using high-resolution spatial data across five functional zones (residential, commercial, industrial, official, and downtown), we apply three ML models, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM), to predict land surface temperature (LST). The models incorporate both environmental variables, such as imperviousness, Normalized Difference Vegetation Index (NDVI), building area, and solar influx, and social determinants, such as population density, income, education, and age distribution. SVM achieved the highest R2 (0.870), while RF yielded the lowest RMSE (0.488 °C), confirming robust predictive performance. Key predictors of elevated LST included imperviousness, building area, solar influx, and NDVI. Our results underscore the need for zone-specific strategies like more greenery, less impervious cover, and improved building design. These findings offer actionable insights for urban planners and policymakers seeking to develop equitable and sustainable UHI mitigation strategies aligned with climate adaptation and environmental justice goals. Full article
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14 pages, 2200 KiB  
Article
Tree Species as Metabolic Indicators: A Comparative Simulation in Amman, Jordan
by Anas Tuffaha and Ágnes Sallay
Land 2025, 14(8), 1566; https://doi.org/10.3390/land14081566 - 31 Jul 2025
Viewed by 324
Abstract
Urban metabolism frameworks offer insight into flows of energy, materials, and services in cities, yet tree species selection is seldom treated as a metabolic indicator. In Amman, Jordan, we integrate spatial metabolic metrics to critique monocultural greening policies and demonstrate how species choices [...] Read more.
Urban metabolism frameworks offer insight into flows of energy, materials, and services in cities, yet tree species selection is seldom treated as a metabolic indicator. In Amman, Jordan, we integrate spatial metabolic metrics to critique monocultural greening policies and demonstrate how species choices forecast long-term urban metabolic performance. Using ENVI-met 5.61 simulations, we compare Melia azedarach, Olea europaea, and Ceratonia siliqua, mainly assessing urban flow related elements like air temperature reduction, CO2 sequestration, and evapotranspiration alongside rooting depth, isoprene emissions, and biodiversity support. Melia delivers rapid cooling but shows other negatives like a low biodiversity value; Olea offers average cooling and sequestration but has allergenic pollen issues in people as a flow; Ceratonia provides scalable cooling, increased carbon uptake, and has a high ecological value. We propose a metabolic reframing of green infrastructure planning to choose urban species, guided by system feedback rather than aesthetics, to ensure long-term resilience in arid urban climates. Full article
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35 pages, 10235 KiB  
Article
GIS-Driven Spatial Planning for Resilient Communities: Walkability, Social Cohesion, and Green Infrastructure in Peri-Urban Jordan
by Sara Al-Zghoul and Majd Al-Homoud
Sustainability 2025, 17(14), 6637; https://doi.org/10.3390/su17146637 - 21 Jul 2025
Viewed by 445
Abstract
Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle [...] Read more.
Amman’s rapid population growth and sprawling urbanization have resulted in car-centric, fragmented neighborhoods that lack social cohesion and are vulnerable to the impacts of climate change. This study reframes walkability as a climate adaptation strategy, demonstrating how pedestrian-oriented spatial planning can reduce vehicle emissions, mitigate urban heat island effects, and enhance the resilience of green infrastructure in peri-urban contexts. Using Deir Ghbar, a rapidly developing marginal area on Amman’s western edge, as a case study, we combine objective walkability metrics (street connectivity and residential and retail density) with GIS-based spatial regression analysis to examine relationships with residents’ sense of community. Employing a quantitative, correlational research design, we assess walkability using a composite objective walkability index, calculated from the land-use mix, street connectivity, retail density, and residential density. Our results reveal that higher residential density and improved street connectivity significantly strengthen social cohesion, whereas low-density zones reinforce spatial and socioeconomic disparities. Furthermore, the findings highlight the potential of targeted green infrastructure interventions, such as continuous street tree canopies and permeable pavements, to enhance pedestrian comfort and urban ecological functions. By visualizing spatial patterns and correlating built-environment attributes with community outcomes, this research provides actionable insights for policymakers and urban planners. These strategies contribute directly to several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by fostering more inclusive, connected, and climate-resilient neighborhoods. Deir Ghbar emerges as a model for scalable, GIS-driven spatial planning in rural and marginal peri-urban areas throughout Jordan and similar regions facing accelerated urban transitions. By correlating walkability metrics with community outcomes, this study operationalizes SDGs 11 and 13, offering a replicable framework for climate-resilient urban planning in arid regions. Full article
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20 pages, 8104 KiB  
Article
Energy Consumption Analysis of Using Mashrabiya as a Retrofit Solution for a Residential Apartment in Al Ain Square, Al Ain, UAE
by Lindita Bande, Anwar Ahmad, Saada Al Mansoori, Waleed Ahmed, Amna Shibeika, Shama Anbrine and Abdul Rauf
Buildings 2025, 15(14), 2532; https://doi.org/10.3390/buildings15142532 - 18 Jul 2025
Viewed by 263
Abstract
The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to [...] Read more.
The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments. Full article
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27 pages, 3984 KiB  
Article
Spatial and Temporal Expansion of Photovoltaic Sites and Thermal Environmental Effects in Ningxia Based on Remote Sensing and Deep Learning
by Heao Xie, Peixian Li, Fang Shi, Chengting Han, Ximin Cui and Yuling Zhao
Remote Sens. 2025, 17(14), 2440; https://doi.org/10.3390/rs17142440 - 14 Jul 2025
Viewed by 265
Abstract
Ningxia has emerged as a strategic hub for China’s photovoltaic (PV) industry by leveraging abundant solar energy resources and geoclimatic advantages. This study analyzed the spatiotemporal expansion trends and microclimatic impacts of PV installations (2015–2024) using Gaofen-1 (GF-1) and Landsat8 satellite imagery with [...] Read more.
Ningxia has emerged as a strategic hub for China’s photovoltaic (PV) industry by leveraging abundant solar energy resources and geoclimatic advantages. This study analyzed the spatiotemporal expansion trends and microclimatic impacts of PV installations (2015–2024) using Gaofen-1 (GF-1) and Landsat8 satellite imagery with deep learning algorithms and multidimensional environmental metrics. Among semantic segmentation models, DeepLabV3+ had the best performance in PV extraction, and the Mean Intersection over Union, precision, and F1-score were 91.97%, 89.02%, 89.2%, and 89.11%, respectively, with accuracies close to 100% after manual correction. Subsequent land surface temperature inversion and spatial buffer analysis quantified the thermal environmental effects of PV installation. Localized cooling patterns may be influenced by albedo and vegetation dynamics, though further validation is needed. The total PV site area in Ningxia expanded from 59.62 km2 to 410.06 km2 between 2015 and 2024. Yinchuan and Wuzhong cities were primary growth hubs; Yinchuan alone added 99.98 km2 (2022–2023) through localized policy incentives. PV installations induced significant daytime cooling effects within 0–100 m buffers, reducing ambient temperatures by 0.19–1.35 °C on average. The most pronounced cooling occurred in western desert regions during winter (maximum temperature differential = 1.97 °C). Agricultural zones in central Ningxia exhibited weaker thermal modulation due to coupled vegetation–PV interactions. Policy-driven land use optimization was the dominant catalyst for PV proliferation. This study validates “remote sensing + deep learning” framework efficacy in renewable energy monitoring and provides empirical insights into eco-environmental impacts under “PV + ecological restoration” paradigms, offering critical data support for energy–ecology synergy planning in arid regions. Full article
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20 pages, 1902 KiB  
Article
Prediction Model of Household Carbon Emission in Old Residential Areas in Drought and Cold Regions Based on Gene Expression Programming
by Shiao Chen, Yaohui Gao, Zhaonian Dai and Wen Ren
Buildings 2025, 15(14), 2462; https://doi.org/10.3390/buildings15142462 - 14 Jul 2025
Viewed by 198
Abstract
To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in [...] Read more.
To support the national goals of carbon peaking and carbon neutrality, this study proposes a household carbon emission prediction model based on Gene Expression Programming (GEP) for low-carbon retrofitting of aging residential areas in arid-cold regions. Focusing on 15 typical aging communities in Kundulun District, Baotou City, a 17-dimensional dataset encompassing building characteristics, demographic structure, and energy consumption patterns was collected through field surveys. Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. Experimental results demonstrated that the model achieved an R2 value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. It provides a reliable tool for precision-based low-carbon retrofits in aging residential areas of arid-cold regions and offers a methodological advance for research on building carbon emission prediction driven by urban renewal. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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21 pages, 5361 KiB  
Article
Inversion of County-Level Farmland Soil Moisture Based on SHAP and Stacking Models
by Hui Zhan, Peng Guo, Jiaxin Hao, Jiali Li and Zixu Wang
Agriculture 2025, 15(14), 1506; https://doi.org/10.3390/agriculture15141506 - 13 Jul 2025
Viewed by 296
Abstract
Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 [...] Read more.
Accurate monitoring of soil moisture in arid agricultural regions is essential for improving crop production and the efficient management of water resources. This study focuses on Shihezi City in Xinjiang, China. We propose a novel method for soil moisture retrieval by integrating Sentinel-1 and Sentinel-2 remote sensing data. Dual-polarization parameters (VV + VH and VV × VH) were constructed and tested. Pearson correlation analysis showed that these polarization combinations carried the most useful information for soil moisture estimation. We then applied Shapley Additive exPlanations (SHAP) for feature selection, and a Stacking model was used to perform soil moisture inversion based on the selected features. SHAP values derived from the coupled support vector regression (SVR) and random forest regression (RFR) models were used to select an additional six key features for model construction. Building on this framework, a comparative analysis was conducted to evaluate the predictive performance of multivariate linear regression (MLR), RFR, SVR, and a Stacking model that integrates these three models. The results demonstrate that the Stacking model outperformed other approaches in soil moisture retrieval, achieving a higher R2 of 0.70 compared to 0.52, 0.61, and 0.62 for MLR, RFR, and SVR, respectively. This process concluded with the use of the Stacking model to generate a county-level farmland soil moisture distribution map, which provides an objective and practical approach to guide agricultural management and the optimized allocation of water resources in arid regions. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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26 pages, 1541 KiB  
Article
Projected Urban Air Pollution in Riyadh Using CMIP6 and Bayesian Modeling
by Khadeijah Yahya Faqeih, Mohamed Nejib El Melki, Somayah Moshrif Alamri, Afaf Rafi AlAmri, Maha Abdullah Aldubehi and Eman Rafi Alamery
Sustainability 2025, 17(14), 6288; https://doi.org/10.3390/su17146288 - 9 Jul 2025
Viewed by 554
Abstract
Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach [...] Read more.
Rapid urbanization and climate change pose significant challenges to air quality in arid metropolitan areas, with critical implications for public health and sustainable development. This study projects the evolution of air pollution in Riyadh, Saudi Arabia, through 2070 using an integrated modeling approach that combines CMIP6 climate projections with localized air quality data. We analyzed daily concentrations of major pollutants (SO2, NO2) across 15 strategically selected monitoring stations representing diverse urban environments, including traffic corridors, residential areas, healthcare facilities, and semi-natural zones. Climate data from two Earth System Models (CNRM-ESM2-1 and MPI-ESM1.2) were bias-corrected and integrated with historical pollution measurements (2000–2015) using hierarchical Bayesian statistical modeling under SSP2-4.5 and SSP5-8.5 emission scenarios. Our results revealed substantial deterioration in air quality, with projected increases of 80–130% for SO2 and 45–55% for NO2 concentrations by 2070 under high-emission scenarios. Spatial analysis demonstrated pronounced pollution gradients, with traffic corridors (Eastern Ring Road, Northern Ring Road, Southern Ring Road) and densely urbanized areas (King Fahad Road, Makkah Road) experiencing the most severe increases, exceeding WHO guidelines by factors of 2–3. Even semi-natural areas showed significant increases in pollution due to regional transport effects. The hierarchical Bayesian framework effectively quantified uncertainties while revealing consistent degradation trends across both climate models, with the MPI-ESM1.2 model showing a greater sensitivity to anthropogenic forcing. Future concentrations are projected to reach up to 70 μg m−3 for SO2 and exceed 100 μg m−3 for NO2 in heavily trafficked areas by 2070, representing 2–3 times the Traffic corridors showed concentration increases of 21–24% compared to historical baselines, with some stations (R5, R13, and R14) recording projected levels above 4.0 ppb for SO2 under the SSP5-8.5 scenario. These findings highlight the urgent need for comprehensive emission reduction strategies, accelerated renewable energy transition, and reformed urban planning approaches in rapidly developing arid cities. Full article
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27 pages, 18002 KiB  
Article
Quantifying Ecological Dynamics and Anthropogenic Dominance in Drylands: A Hybrid Modeling Framework Integrating MRSEI and SHAP-Based Explainable Machine Learning in Northwest China
by Beilei Zhang, Xin Yang, Mingqun Wang, Liangkai Cheng and Lina Hao
Remote Sens. 2025, 17(13), 2266; https://doi.org/10.3390/rs17132266 - 2 Jul 2025
Viewed by 384
Abstract
Arid and semi-arid regions serve as crucial ecological barriers in China, making the spatiotemporal evolution of their ecological environmental quality (EEQ) scientifically significant. This study developed a Modified Remote Sensing Ecological Index (MRSEI) by innovatively integrating the Comprehensive Salinity Indicator (CSI) into the [...] Read more.
Arid and semi-arid regions serve as crucial ecological barriers in China, making the spatiotemporal evolution of their ecological environmental quality (EEQ) scientifically significant. This study developed a Modified Remote Sensing Ecological Index (MRSEI) by innovatively integrating the Comprehensive Salinity Indicator (CSI) into the Remote Sensing Ecological Index (RSEI) and applied it to systematically evaluate the spatiotemporal evolution of EEQ (2014–2023) in Yinchuan City, a typical arid region of northwest China along the upper Yellow River. The study revealed the spatiotemporal evolution patterns through the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test, and adopted the Light Gradient Boosting Machine (LightGBM) combined with the Shapley Additive Explanation (SHAP) to quantify the contributions of ten natural and anthropogenic driving factors. The results suggest that (1) the MRSEI outperformed the RSEI, showing 0.41% higher entropy and 5.63% greater contrast, better characterizing the arid region’s heterogeneity. (2) The EEQ showed marked spatial heterogeneity. High-quality areas are concentrated in the Helan Mountains and the integrated urban/rural development demonstration zone, while the core functional zone of the provincial capital, the Helan Mountains ecological corridor, and the eastern eco-economic pilot zone showed lower EEQ. (3) A total of 87.92% of the area (7609.23 km2) remained stable with no significant changes. Notably, degraded areas (934.52 km2, 10.80%) exceeded improved zones (111.04 km2, 1.28%), demonstrating an overall ecological deterioration trend. (4) This study applied LightGBM with SHAP to analyze the driving factors of EEQ. The results demonstrated that Land Use/Land Cover (LULC) was the predominant driver, contributing 41.52%, followed by the Digital Elevation Model (DEM, 18.26%) and Net Primary Productivity (NPP, 12.63%). This study offers a novel framework for arid ecological monitoring, supporting evidence-based conservation and sustainable development in the Yellow River Basin. Full article
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25 pages, 20862 KiB  
Article
GIS-Based Multi-Criteria Analysis for Urban Afforestation Planning in Semi-Arid Cities
by Halil İbrahim Şenol, Abdurahman Yasin Yiğit and Ali Ulvi
Forests 2025, 16(7), 1064; https://doi.org/10.3390/f16071064 - 26 Jun 2025
Viewed by 452
Abstract
Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites [...] Read more.
Urban forests are very important for the environment and for people, especially in semi-arid cities where there is not much greenery. This makes heat stress worse and makes the city less livable. This paper presents a comprehensive geospatial methodology for selecting afforestation sites in the expanding semi-arid urban area of Şanlıurfa, Turkey, characterized by minimal forest cover, rapid urbanization, and extreme weather conditions. We identified nine ecological and infrastructure criteria using high-resolution Sentinel-2 images and features from the terrain. These criteria include slope, aspect, topography, land surface temperature (LST), solar radiation, flow accumulation, land cover, and proximity to roads and homes. After being normalized to make sure they were ecologically relevant and consistent, all of the datasets were put together into a GIS-based Multi-Criteria Decision Analysis (MCDA) tool. The Analytic Hierarchy Process (AHP) was then used to weight the criteria. A deep learning-based semantic segmentation model was used to create a thorough classification of land cover, primarily to exclude unsuitable areas such as dense urban fabric and water bodies. The final afforestation suitability map showed that 151.33 km2 was very suitable and 192.06 km2 was suitable, mostly in the northeastern and southeastern urban fringes. This was because the terrain and subclimatic conditions were good. The proposed methodology illustrates that urban green infrastructure planning can be effectively directed within climate adaptation frameworks through the integration of remote sensing and spatial decision-support tools, especially in ecologically sensitive and rapidly urbanizing areas. Full article
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32 pages, 1996 KiB  
Article
An Economic Valuation of Forest Carbon Sink in a Resource-Based City on the Loess Plateau
by Xinlei Liu, Ya Yang, Ping Shen and Xingyu Liu
Sustainability 2025, 17(13), 5786; https://doi.org/10.3390/su17135786 - 24 Jun 2025
Viewed by 425
Abstract
Forest carbon sink (FCS) is essential for achieving carbon neutrality and supporting sustainable development in ecologically fragile, resource-based cities such as those on the Loess Plateau. Despite the success of national afforestation programs, economic valuations of FCS at the city level remain limited. [...] Read more.
Forest carbon sink (FCS) is essential for achieving carbon neutrality and supporting sustainable development in ecologically fragile, resource-based cities such as those on the Loess Plateau. Despite the success of national afforestation programs, economic valuations of FCS at the city level remain limited. This study develops an integrated framework combining carbon stock estimation, regional carbon pricing, and net present value (NPV)-based valuation. Using Shenmu City in Shaanxi Province as a case study, forest carbon stocks from 2010 to 2023 are estimated based on the 2006 IPCC Guidelines. Future stocks (2024–2060) are projected using the GM (1,1) model. A dynamic pricing mechanism with a government-guaranteed floor price is applied under three offset scenarios (5%, 10%, 15%). The results show that Shenmu’s forest carbon stock could reach 20.67 million tonnes of CO2 by 2060, and under a 15% offset scenario, the peak NPV reaches CNY 4.02 billion. Higher offset ratios increase FCS value by 18–22%, reflecting the growing scarcity of carbon credits. The pricing model improves market stability and investor confidence. This study provides a replicable approach for carbon sink valuation in semi-arid areas and offers policy insights aligned with SDG 13 (Climate Action) and SDG 15 (Life on Land). Full article
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24 pages, 1368 KiB  
Article
Unveiling the Value of Green Amenities: A Mixed-Methods Analysis of Urban Greenspace Impact on Residential Property Prices Across Riyadh Neighborhoods
by Tahar Ledraa and Sami Abdullah Aldubikhi
Buildings 2025, 15(12), 2088; https://doi.org/10.3390/buildings15122088 - 17 Jun 2025
Viewed by 626
Abstract
The literature shows greenspaces generally increase nearby property values, but in Riyadh, this relationship is complex and understudied. Existing studies lack sector-specific analyses across Riyadh’s neighborhoods, overlook the impact of the Green Riyadh Project launched in 2019, and fail to address negative externalities [...] Read more.
The literature shows greenspaces generally increase nearby property values, but in Riyadh, this relationship is complex and understudied. Existing studies lack sector-specific analyses across Riyadh’s neighborhoods, overlook the impact of the Green Riyadh Project launched in 2019, and fail to address negative externalities associated with large greenspaces in an arid, privacy-conscious context. Such paradoxical impact of larger greenspaces bordering major roads at the neighborhood edge, unexpectedly reduce property values by 2–4% due to petty crime, congestion, poor upkeep, and privacy concerns, contrasting with 10–18% premiums for properties abutting greenspaces with restricted access in affluent neighborhoods. Global studies typically report positive greenspace effects, so negative impacts in specific Riyadh sectors are surprising. This highlights the city’s unique arid, cultural, and urban dynamics in addressing this research gap. The research uses purposive quota sampling of Riyadh neighborhood greenspaces and a mixed-methods approach of quantitative hedonic pricing analysis combined with qualitative semi-structured interviews with real estate agents. Findings underscore the need for tailored urban planning (e.g., mitigating petty crime, overcrowding, poor maintenance). This suggests the importance of integrating green infrastructure into urban planning, not only for its ecological and social benefits but also for its tangible positive impact on property values. Poor greenspace upkeep and safety concerns can reduce price premiums of abutting properties. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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17 pages, 2158 KiB  
Article
Study on the Impact of Large-Scale Photovoltaic Systems on Key Surface Parameters in Desert Area Regions of Xinjiang, China
by Junxia Jiang, Huan Du, Huihui Yin and Hongbo Su
Energies 2025, 18(12), 3170; https://doi.org/10.3390/en18123170 - 17 Jun 2025
Viewed by 360
Abstract
This study evaluated the effects of photovoltaic (PV) arrays on critical surface parameters through analysis of observational data collected from a utility-scale PV power station located in Wujiaqu City, Xinjiang, in 2021. The results reveal that: (1) Installation of PV panels reduces surface [...] Read more.
This study evaluated the effects of photovoltaic (PV) arrays on critical surface parameters through analysis of observational data collected from a utility-scale PV power station located in Wujiaqu City, Xinjiang, in 2021. The results reveal that: (1) Installation of PV panels reduces surface albedo, which is significantly altered by dust storm conditions; (2) the installation of PV arrays increases the aerodynamic and thermal roughness length by increasing the frictional velocity across the mixed underlying surface; (3) the overall transport coefficients within the PV plant are higher than that of the reference site, with greater diurnal variation than nocturnal variation. The overall transport coefficient is highest in the unstable stratification conditions and lowest under stable stratification conditions; and (4) soil thermal property parameters exhibit seasonal variations. Significant changes in thermal conductivity and specific heat capacity were observed during spring thaw, high and fluctuating diffusivity in summer, and low and stable values in winter. The findings demonstrate that installing PV arrays in arid regions modifies surface energy balance and heat transfer characteristics. This provides a basis for optimizing PV station layouts and conducting climate impact assessments. Full article
(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)
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24 pages, 6149 KiB  
Article
Assessing the Spatial Benefits of Green Roofs to Mitigate Urban Heat Island Effects in a Semi-Arid City: A Case Study in Granada, Spain
by Francisco Sánchez-Cordero, Leonardo Nanía, David Hidalgo-García and Sergio Ricardo López-Chacón
Remote Sens. 2025, 17(12), 2073; https://doi.org/10.3390/rs17122073 - 16 Jun 2025
Viewed by 873
Abstract
Studies show that Nature-Based Solutions can mitigate Urban Heat Island (UHI) effects by implementing green spaces. Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green [...] Read more.
Studies show that Nature-Based Solutions can mitigate Urban Heat Island (UHI) effects by implementing green spaces. Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green roofs in buildings by using a Random Forest algorithm and different remote sensing methods. To this aim, the city of Granada, Spain, was used as a case study. The city is classified into different Local Climate Zones (LCZs) to determine the area available for retrofitting GRs in built-up areas. A total of 14 Surface Temperature Collection 2 Level-2 images were acquired through Landsat 8–9, while 14 images for spectral indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Building Index (NDBI), and Proportion Vegetation (PV) were calculated from Sentinel-2 in dates coinciding or close to LST images. Additional factors were considered including the sky view factor (SVF) and water distance (WD). The results suggest that Granada has limited suitable areas for retrofitting GRs, and available areas can reduce LST with a moderate impact, at an average of 1.45 °C; however, vegetation plays an important role in decreasing LST. This study provides a methodological example to identify the benefits of implementing GRs in reducing LST in semi-arid cities and recommends a combination of strategies for LST mitigation. Full article
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30 pages, 6902 KiB  
Article
Impacts of Landscape Composition on Land Surface Temperature in Expanding Desert Cities: A Case Study in Arizona, USA
by Rifat Olgun, Nihat Karakuş, Serdar Selim, Tahsin Yilmaz, Reyhan Erdoğan, Meliha Aklıbaşında, Burçin Dönmez, Mert Çakır and Zeynep R. Ardahanlıoğlu
Land 2025, 14(6), 1274; https://doi.org/10.3390/land14061274 - 13 Jun 2025
Viewed by 801
Abstract
Surface urban heat island (SUHI) effects are intensifying in arid desert cities due to rapid urban expansion, limited vegetation, and increasing impervious and barren land surfaces. This leads to serious ecological and socio-environmental challenges in cities. This study investigates the relationship between landscape [...] Read more.
Surface urban heat island (SUHI) effects are intensifying in arid desert cities due to rapid urban expansion, limited vegetation, and increasing impervious and barren land surfaces. This leads to serious ecological and socio-environmental challenges in cities. This study investigates the relationship between landscape composition and land surface temperature (LST) in Phoenix and Tucson, two rapidly growing cities located in the Sonoran Desert of the southwestern United States. Landsat-9 OLI-2/TIRS-2 satellite imagery was used to derive the LST value and calculate spectral indices. A multi-resolution grid-based approach was applied to assess spatial correlations between land cover and mean LST across varying spatial scales. The strongest positive correlations were observed with barren land, followed by impervious surfaces, while green space showed a negative correlation. Furthermore, the Urban Thermal Field Variation Index (UTFVI) and the Ecological Evaluation Index (EEI) assessments indicated that over one-third of both cities are exposed to strong SUHI effects and poor ecological quality. The findings highlight the critical need for ecologically sensitive urban planning, emphasizing the importance of the morphological structure of cities, the necessity of planning holistic blue–green infrastructure systems, and the importance of reducing impervious surfaces to decrease LST, mitigate SUHI and SUHI impacts, and increase urban resilience in desert environments. These results provide evidence-based guidance for landscape planning and climate adaptation in hyper-arid urban environments. Full article
(This article belongs to the Section Land Planning and Landscape Architecture)
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