Search Results (800)

The spatiotemporal evolution of the regional water use structure holds significant theoretical value for optimizing regional water resource allocation, adjusting industrial structures, and achieving sustainable water resource development. Shandong Province, located at the lowest reach of the Yellow River Basin in China, is a major economic, agricultural, and populous province, as well as a region with one of the most prominent water supply–demand imbalances in the country. As a result, exploring how water use patterns change over time and space in this region has become crucial. Using analytical methods like the Lorenz curve, Gini coefficient, cluster analysis, and spatial statistics, we examine shifts in Shandong’s water use structure from 2001 to 2023. We find that while agriculture remained the largest water consumer over this period, industrial, household, and ecological water use steadily increased, signaling a move toward more balanced resource distribution. Across Shandong’s 16 regions (cities), the water use patterns varied considerably, particularly in terms of agriculture, industry, and ecological needs. Among these, agricultural, industrial, and domestic water use were distributed relatively evenly, whereas ecological water use showed greater regional disparities. These results may have the potential to guide policymakers in refining water allocation strategies, improving industrial planning, and boosting the water use efficiency in Shandong and the country ore broadly. Full article

This study investigates the factors influencing school trip mode choice among senior high school students in the Chiang Rai urban area, Chiang Rai, Thailand, under normal and adverse weather conditions. Utilizing data from 472 students across six extra-large urban schools, a Multinomial Logit (MNL) regression model was applied to examine the effects of socio-demographic attributes, household vehicle ownership, travel distance, and spatial variables on mode selection. The results revealed notable modal shifts during adverse weather, with motorcycle usage decreasing and private vehicle reliance increasing, while school bus usage remained stable, highlighting its role as a resilient transport option. Car ownership emerged as a strong enabler of modal flexibility, whereas students with limited access to private transport demonstrated reduced adaptability. Additionally, increased waiting and travel times during adverse conditions underscored infrastructure and service vulnerabilities, particularly for mid-distance travelers. The findings suggest an urgent need for transport policies that promote inclusive and climate-resilient mobility systems, particularly in the context of Chiang Rai, including expanded school bus services, improved first-mile connectivity, and enhanced pedestrian infrastructure. This study contributes to the literature by addressing environmental variability in school travel behavior and offers actionable insights for sustainable transport planning in secondary cities and border regions. Full article

Urban vegetation monitoring is essential for sustainable city planning but is often constrained by the high cost and limited frequency of field-based inventories. This study evaluates the use of the Normalized Difference Vegetation Index (NDVI), derived from Sino-Brazilian CBERS-4A satellite imagery, as a spatially explicit and low-cost proxy for urban tree census data. CBERS-4A provides medium-resolution multispectral data freely accessible across South America, yet remains underutilized in urban environmental applications. Focusing on Aracaju, a metropolitan region in northeastern Brazil, we compared NDVI-based classification results with official municipal tree census data from 2022. The analysis revealed a strong spatial correlation, supporting the use of NDVI as a reliable indicator of canopy presence at the urban block scale. In addition to mapping vegetation distribution, the NDVI results identified areas with insufficient canopy coverage, directly informing urban greening priorities. By validating remote sensing data against field inventories, this study demonstrates how CBERS-4A imagery and vegetation indices can support municipal tree management and serve as scalable tools for environmental planning and policy. Full article

Over the last decade, the scientific community has increasingly focused on forest-based practices for human well-being (FBPW), a term that includes all forest activities (e.g., forest bathing, forest therapy, social outdoor initiatives) important for improving people’s health and emotional status. This paper aims to develop a quantitative literature review on FBPW based on big data analysis (text mining on Scopus title and abstract) and PRISMA evaluation. The two techniques facilitate investigations across different geographic areas (major areas and geographical regions) and allow a focus on various topics. The results of text mining highlight the prominence of publications on FBPW for the improvement of human health in East Asia (e.g., Japan and South Korea). Furthermore, some specific themes developed by the literature for each geographical area emerge: urban green areas, cities, and parks in Africa; sustainable forest management and planning in the Americas; empirical studies on physiological and psychological effects of FBPW in Asia; and forest management and FBPW in Europe. PRISMA indicates a gap in studies focused on the reciprocal influences of forest variables and well-being responses. An investigation of the main physiological indicators applied in the scientific literature for the theme is also developed. The main strengths and weaknesses of the method are discussed, with suggestions for potential future lines of research. Full article

The development of urban underground space is a necessary way to realize the sustainable development of the city, and it is also an essential means to solve urban environmental problems such as traffic congestion and resource shortage. Scientific suitability evaluation is the prerequisite for the rational planning and development of underground space. Previous studies have encountered problems such as an imperfect index system, a single weighting method, and loss of membership degrees in fuzzy evaluation, which have led to unreasonable evaluation results. Taking the northern coastal cities of Weifang as the research area, the evaluation index system is established, and the index weights are calculated by the improved structural CRITIC. An improved fuzzy mathematical evaluation model based on the weighted summation method is proposed to carry out the suitability evaluation of underground space development in the research area. The results show that: (1) The proposed method of combination weight and improved fuzzy mathematics evaluation takes into account the scientific weight and avoids the subjective bias, and also corrects the issue of membership degree loss in the membership matrix of comprehensive evaluation. (2) When the area of the grid unit is 0.02% of the area of the research area, the size of the evaluation unit is more reasonable. (3) The area that is very suitable for underground space development accounts for 8.69%, and the more suitable area accounts for 25.55%, mainly located in the northwest and central–southern regions of the research area. It can provide a reference for the suitability evaluation of underground space development. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

The growing mismatch between ecosystem service (ES) supply and demand underscores the importance of thoroughly understanding their spatiotemporal patterns and key drivers to promote ecological civilization and sustainable development at the regional level in China. This study investigates six key ES indicators across mainland China—habitat quality (HQ), carbon sequestration (CS), water yield (WY), sediment delivery ratio (SDR), food production (FP), and nutrient delivery ratio (NDR)—by integrating a suite of analytical approaches. These include a spatiotemporal analysis of trade-offs and synergies in supply, demand, and their ratios; self-organizing maps (SOM) for bundle identification; and interpretable machine learning models. While prior research studies have typically examined ES at a single spatial scale, focusing on supply-side bundles or associated drivers, they have often overlooked demand dynamics and cross-scale interactions. In contrast, this study integrates SOM and SHAP-based machine learning into a dual-scale framework (grid and city levels), enabling more precise identification of scale-dependent drivers and a deeper understanding of the complex interrelationships between ES supply, demand, and their spatial mismatches. The results reveal pronounced spatiotemporal heterogeneity in ES supply and demand at both grid and city scales. Overall, the supply services display a spatial pattern of higher values in the east and south, and lower values in the west and north. High-value areas for multiple demand services are concentrated in the densely populated eastern regions. The grid scale better captures spatial clustering, enhancing the detection of trade-offs and synergies. For instance, the correlation between HQ and NDR supply increased from 0.62 (grid scale) to 0.92 (city scale), while the correlation between HQ and SDR demand decreased from −0.03 to −0.58, indicating that upscaling may highlight broader synergistic or conflicting trends missed at finer resolutions. In the spatiotemporal interaction network of supply–demand ratios, CS, WY, FP, and NDR persistently show low values (below −0.5) in western and northern regions, indicating ongoing mismatches and uneven development. Driver analysis demonstrates scale-dependent effects: at the grid scale, HQ and FP are predominantly influenced by socioeconomic factors, SDR and WY by ecological variables, and CS and NDR by climatic conditions. At the city level, socioeconomic drivers dominate most services. Based on these findings, nine distinct supply–demand bundles were identified at both scales. The largest bundle at the grid scale (B3) occupies 29.1% of the study area, while the largest city-scale bundle (B8) covers 26.5%. This study deepens the understanding of trade-offs, synergies, and driving mechanisms of ecosystem services across multiple spatial scales; reveals scale-sensitive patterns of spatial mismatch; and provides scientific support for tiered ecological compensation, integrated regional planning, and sustainable development strategies. Full article

Accurate dengue forecasting is essential for sustainable public health planning, especially in tropical regions where the disease remains a persistent threat. This study evaluates the predictive performance of seven modeling approaches—Seasonal Autoregressive Integrated Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM (CNN–LSTM), and a Bayesian spatiotemporal model—using monthly dengue incidence data from 2009 to 2023 in Bandung City, Indonesia. Model performance was assessed using MAE, sMAPE, RMSE, and Pearson’s correlation (R). Among all models, the Bayesian spatiotemporal model achieved the best performance, with the lowest MAE (5.543), sMAPE (62.137), and RMSE (7.482), and the highest R (0.723). While SARIMA and XGBoost showed signs of overfitting, the Bayesian model not only delivered more accurate forecasts but also produced spatial risk estimates and identified high-risk hotspots via exceedance probabilities. These features make it particularly valuable for developing early warning systems and guiding targeted public health interventions, supporting the broader goals of sustainable disease management. Full article

How to promote sustainable development and deal with the actual development demands in economic transformation through land-use planning is crucial for local governments. The urban sustainable development mainly relies on creativity and talents in the digital era, and talents are increasingly attracted by local people-oriented land use. However, the current planning ideology remains at meeting corporate and people’s basic needs rather than specific needs of talents, especially the increasingly emerging digital creatives. To promote the talent agglomeration and sustainable development through land planning, this paper uses micro-celebrities on Bilibili, an influential creative content creation platform among young people in China, as an example to study the geographical distribution of digital creative talents and its relationship with urban amenities by constructing an index system of urban amenities, comprising natural, leisure, infrastructure, and social and institutional amenities. The concept of borrowed amenities is introduced to examine the effects of amenities of surrounding cities. This study demonstrates that micro-celebrities show a stronger preference for amenities compared with other skilled talents. Meanwhile, social and institutional amenities are most crucial. Furthermore, urban leisure represented by green spaces and consumption spaces is also attractive. At the regional scale, with prefecture-level cities as units, the local talents agglomeration is also influenced by the borrowed amenities in the context of regional integration. It indicates that the local land use should consider the characteristics of the surrounding cities. This study provides strategic inspiration that a happy and sustainable city should first be people-oriented and provide sufficient space for consumption, entertainment, and interaction. Full article

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

Clarifying the coordination and impediments of social, economic, and spatial connection in rural areas is essential for advancing rural revitalization, urban-rural integration, and regional coordinated development. Utilizing the 24 counties and districts in western Hunan as case studies, we developed an evaluation index system for sustainable rural development across three dimensions: social, economic, and spatial. We employed the coupling model, coordination model, and obstacle factor model to investigate the comprehensive development level, coupling and coordination status, and obstacle factors of the villages in the study area at three temporal points: 2002, 2012, and 2022. The findings indicate the following: (1) The degree of rural development in western Hunan has escalated swiftly throughout the study period, transitioning from relative homogeneity to a heterogeneous developmental landscape, accompanied by issues such as inadequate development and regional polarization. (2) The overall rural social, economic, and spatial indices are low, and the degree of coupling has increased variably across different study periods; the average coordination degree has gradually improved over time, yet the level of coordination remains low, and spatial development is unbalanced. (3) The criterion-level impediments hindering the sustainable development of rural society, economy, and space are, in descending order, social factors, spatial factors, and economic factors. The urbanization rate, total fixed investment rate, and arable land change rate are the primary impediments in most counties and cities. The study’s findings will inform the planning of rural development in ethnic regions, promote sustainable social and spatial advancement in the countryside, and serve as a reference for rural revitalization efforts. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

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

With the development of urban scale and economic growth, the challenges posed by limited resources and insufficient environmental carrying capacity become increasingly severe, making the sustainable improvement of production efficiency an urgent requirement. Based on panel data for cities in the Dianzhong Urban Agglomeration and the Chengdu–Chongqing Economic Circle in Southwest China (2012–2021), this study elucidates the positive effect of urban compactness on green total factor productivity (GTFP). By constructing a composite index to measure urban compactness and employing an SBM model to quantify GTFP, we find that a 1% increase in urban compactness leads to a 0.65% increase in GTFP. A mediating-effect analysis reveals that green technological innovation serves as a significant mediator, with a mediating effect value of 0.363. Heterogeneity analysis uncovers differing mechanisms of influence: urban compactness exerts a positive effect in regions with higher levels of economic development, while its impact is not significant in regions with lower economic development, indicating that the effect of compactness varies with economic context; the impact of urban compactness on GTFP is statistically insignificant in regions with higher tertiary sector shares (p > 0.1), whereas it exhibits a highly significant positive effect in regions with lower tertiary sector presence (β = 1.49, p < 0.01). These results collectively demonstrate that the influence of urban compactness on GTFP varies significantly with industrial structure composition. Threshold-effect analysis further shows that there is a threshold in the proportion of industrial output value, beyond which the influence of compactness on GTFP becomes even stronger. Our research quantitatively explores both linear and nonlinear relationships between urban compactness and GTFP, clarifying the linkage between urban spatial dynamics and green production efficiency, and provides empirical evidence and scholarly support for urban planning and economic development. Full article

The Eastern Huang–Huai region of China is a representative mining area with a high groundwater level. High-intensity underground mining activities have not only induced land cover and land use changes (LUCC) but also significantly changed the watershed hydrological behavior. This study integrated the land use prediction model PLUS and the hydrological simulation model MIKE 21. Taking the Bahe River Watershed in Huaibei City, China, as an example, it simulated the hydrological response trends of the watershed in 2037 under different land use scenarios. The results demonstrate the following: (1) The land use predictions for each scenario exhibit significant variation. In the maximum subsidence scenario, the expansion of water areas is most pronounced. In the planning scenario, the increase in construction land is notable. Across all scenarios, the area of cultivated land decreases. (2) In the maximum subsidence scenario, the area of high-intensity waterlogging is the greatest, accounting for 31.35% of the total area of the watershed; in the planning scenario, the proportion of high-intensity waterlogged is the least, at 19.10%. (3) In the maximum subsidence scenario, owing to the water storage effect of the subsidence depression, the flood peak is conspicuously delayed and attains the maximum value of 192.3 m³/s. In the planning scenario, the land reclamation rate and ecological restoration rate of subsidence area are the highest, while the regional water storage capacity is the lowest. As a result, the total cumulative runoff is the greatest, and the peak flood value is reduced. The influence of different degrees of subsidence on the watershed hydrological behavior varies, and the coal mining subsidence area has the potential to regulate and store runoff and perform hydrological regulation. The results reveal the mechanism through which different land use scenarios influence hydrological processes, which provides a scientific basis for the territorial space planning and sustainable development of coal mining subsidence areas. Full article