Search Results (102)

Rapid and unplanned urbanization in Dhaka is reshaping land use, intensifying peripheral expansion, and increasing pressure on urban and ecological resources. Understanding these growth dynamics is essential for effective urban growth boundary delineation and sustainable planning, yet integrated assessments of historical and future urban growth remain limited. The current study evaluates spatiotemporal urban expansion from 2010 to 2025, delineates the urban growth boundary using a morphological framework, and simulates future growth for 2030 through a coupled cellular automaton-neural network model. Multi-temporal Landsat imagery (2010, 2015, 2020, and 2025) was classified in Google Earth Engine using supervised Maximum Likelihood Classification. Urban growth patterns were quantified using the urban expansion intensity index (UEII), annual urban expansion rate (AUER), and landscape expansion index (LEI). The urban largest continuous patch index (ULCPI) approach was applied to extract functional urban boundaries. Model performance was validated using the Chi-square (χ²) goodness-of-fit test. Results show a substantial increase in built-up land from 115.85 km² to 171.42 km² between 2010 and 2025, accompanied by a decline of approximately 60 km² in urban green spaces. LEI results demonstrate a transition from compact infilling growth (2010–2015) to dominant edge and outlying expansion (2015–2020), indicating progressive peri-urbanization. The urban largest continuous patch (ULCP) nearly doubled from 78.58 km² to 152.13 km² over the same period, accentuating rapid spatial consolidation. The 2030 projection anticipates continued corridor-oriented expansion, particularly toward the northern and eastern peripheries, with predictive agreement from the CA–ANN model (χ² = 0.03 < 7.8). The study identifies a clear transition from monocentric compactness to polycentric expansion, emphasizing the necessity for enforceable growth containment, transit-oriented development, and ecologically responsive planning strategies to ensure long-term urban sustainability. Full article

Understanding land-use carbon emissions (LUCE) requires accounting for both spatial heterogeneity and intercity linkages, yet most studies treat cities as isolated units, overlooking network-level interactions and their implications for regional ecological support and functional zoning. Using the Beijing–Tianjin–Hebei urban agglomeration as a case study, we estimated LUCE from 2005 to 2020 via the carbon emission coefficient method, constructed potential intercity association networks using a gravity model and social network analysis (SNA), and integrated ecological support capacity (ESC) with network structural roles to develop a two-dimensional carbon balance zoning framework. Results indicate that LUCE exhibited an overall increasing trend with a stable south-high and north-low spatial pattern. The association network remained connected and exhibited a persistent core–periphery structure, with Beijing and Tianjin occupying core positions and the network showing increasing concentration after 2010. ESC displayed an opposite spatial gradient to LUCE, indicating stronger ecological support in northern and northwestern cities and weaker support in central-southern areas. Integrating ESC and network structural roles identified four carbon balance functional zones. The integrated framework provides a basis for differentiated low-carbon land-use management and collaborative carbon governance in urban agglomerations. Full article

Industrial land allocation spatial morphology (ILASM) is crucial for economic development and the advancement of new-type industrialization. However, existing studies lack a comprehensive understanding of the evolutionary characteristics of the spatial morphology of industrial land allocation, let alone its driving factors and the spatial heterogeneity of their effects. Therefore, this paper classifies industrial land allocation spatial morphology into traditional industrial land allocation spatial morphology (TILASM) and high-tech industrial land allocation spatial morphology (HILASM), then evaluates and identifies their characteristics based on the precise geographic coordinates of each industrial land parcel between 2007 and 2024 in the Yangtze River Delta (YRD). Subsequently, the Random Forest Regression model and Multi-Scale Geographically Weighted Regression model are integrated to systematically investigate the driving factors and spatiotemporal patterns of ILASM, including both TILASM and HILASM. The results show that from 2007 to 2024, different types of ILASM exhibited distinct spatiotemporal evolutionary characteristics. Specifically, first, in terms of the evolution of spatial distribution direction, overall industrial land allocation exhibited a pronounced agglomeration pattern, extending from the western (slightly northern) part of the region to the eastern (slightly southern) part. Moreover, the evolutionary direction of traditional industrial land allocation was consistent with that of overall industrial land allocation. However, high-tech industrial land allocation exhibited an agglomeration trend extending from west (slightly south) to east (slightly north). Second, in terms of evolution of agglomeration pattern, the spatial distribution of TILASM evolved from three-core dispersed configuration to a multi-core linkage, before reverting to a multi-core dispersed state; by contrast, both ILASM and HILASM exhibited a spatial pattern that progressed from dispersion to contiguous agglomeration. Third, the spatial distribution characteristics of different types of ILASM were shaped by the combined influence of natural conditions, economic development, social environment, innovation environment and infrastructure. However, the dominant driving factors differed among them. Specifically, the number of foreign-invested enterprises exhibited a negative influence on ILASM, while having positive effects on both TILASM and HILASM. The effects of patent applications, population density and internet penetration rate on ILASM; foreign-invested level and slope proportion on TILASM; as well as road density, labor quality and foreign invested level on HILASM all exhibited U-shaped relationships. Moreover, the influences of opening-up level and per capital road area on ILASM and HILASM displayed relatively complex N-shaped relationships. Finally, the effects of these crucial drivers displayed significant spatial non-stationarity and certain gradient effects, manifesting in southern–northern, western–eastern and core–periphery spatial differentiation patterns. Overall, this study provides scientific evidence and practical references for optimizing the spatial allocation of industrial land. Full article

In this study, the InVEST model, GTWR, and the Geodetector were comprehensively employed to systematically assess the spatiotemporal dynamics of habitat quality in Wuyishan National Park and to reveal its response patterns to changes in precipitation and temperature. Throughout the study period, the habitat quality of Wuyishan National Park remained relatively high overall. Temporally, habitat quality displayed a phased pattern of an initial decline followed by an increase; nevertheless, an overall slight downward trend was observed, with the mean value decreasing slightly from 0.934 in 2000 to 0.927 in 2020. Spatially, a “core-periphery” differentiation was observed; spatial heterogeneity was identified in the response of habitat quality to climatic factors. A positive, promoting effect on habitat quality was primarily exerted by precipitation, although localized negative stress effects were observed along the park’s northern and southern edges; a negative, constraining effect was predominantly exerted by temperature, whereas a positive driving effect was observed in the southeastern scenic area. Bivariate enhancement effects were identified among the factor interactions. A scientific basis is provided by these findings for Wuyishan National Park to implement tiered and zoned management and to formulate differentiated climate adaptation strategies, thus enhancing ecosystem resilience and sustainable development capacity. Full article

Ecological civilization education has become an important component of environmental education and sustainable development strategies in China. Field-based learning in real ecological environments is essential for helping students understand ecological processes and human–environment relationships. However, limited attention has been paid to whether schools can spatially access ecological learning sites within feasible travel-time ranges, especially in plateau and mountainous regions where transportation constraints are strong. From the perspective of ecosystem service flow, forest parks can be regarded as supply sites of ecological learning services, secondary schools as demand sites, and road networks as the pathways through which such services are transformed into actual educational opportunities. To address this gap, this study evaluates the accessibility of forest-park-based ecological learning resources for secondary schools in the Tibet Autonomous Region, China. Taking 11 national forest parks and 137 secondary schools as research objects, a network-based travel-time model was constructed using road network data, and 1 h, 3 h, and 6 h service areas were generated to assess forest park service capacity and school accessibility. The results show that forest park educational service capacity is highly uneven. Nimu, Jiedexiu, and Caina Forest Parks have the highest service capacity, each covering more than 60 secondary schools within 6 h, while nearly half of the parks show relatively limited coverage. School accessibility also presents a clear core–periphery pattern, with high-accessibility schools concentrated around Lhasa and nearby transport corridors, whereas many schools in western and northern Tibet remain outside the 6 h service range. Based on the accessibility levels, differentiated field education strategies are proposed, including short field observation, one-day field trips, cross-regional fieldwork, and virtual or school-based alternatives. This study provides a spatial analytical framework for understanding the flow of ecological learning services from forest parks to schools and offers evidence for optimizing geography field education and improving equitable access to ecological civilization education in mountainous regions. Full article

Housing financialization has transformed residential property from a basic social necessity into a speculative asset, intensifying socio-spatial inequalities in the Global South. In Iran, a bank-dominated financial regime and high spatial inequality severely restrict access to housing finance, particularly for low-income households and climate-induced migrants in mid-sized cities. While the spatial consequences of financialization have been extensively studied in major metropolises, neighborhood-scale exclusion mechanisms in secondary cities remain significantly understudied. This study addresses this gap by employing a hybrid methodology that integrates the Bayesian Best–Worst Method (BWM) with Geographic Information Systems (GISs) and Fuzzy Overlay analysis to quantify and spatially visualize barriers to housing finance in Birjand, Iran. Eleven indicators were weighted through probabilistic expert elicitation (n = 10) using Markov Chain Monte Carlo (MCMC) sampling, with excellent convergence confirmed by Gelman–Rubin diagnostics (R-hat ≈ 1.00). Results identify land use as the dominant barrier (posterior mean = 0.256; 95% CrI [0.244, 0.270]), followed by homeownership rate and education level (≈0.132 each). Spatial modeling reveals a pronounced north–south monetary–spatial divide: southern districts concentrate financial services and formal tenure, while northern peripheries, predominantly inhabited by low-income and climate-migrant populations, constitute financial deserts. These findings demonstrate that spatial barriers to housing finance are structurally embedded in land-use classification and asset-based gatekeeping. The study offers a replicable probabilistic–spatial framework for diagnosing financial exclusion in Global South mid-sized cities and provides evidence-based guidance for spatially targeted policy interventions aligned with SDGs 1, 10, and 11. Full article

This study examines spatial inequalities in job accessibility in Porto Alegre, Brazil, through a multimodal perspective that incorporates walking, cycling, and public transport within the 15-minute city framework. Accessibility was assessed using cumulative opportunity measures with a 15-minute travel threshold, combining open data on employment, transport networks, and demographics into hexagonal grids. Travel-time matrices were generated using the R5 routing engine via the r5r package in R, accounting for realistic multimodal routing conditions, including transfers and waiting times. The resulting accessibility measures were then integrated with socioeconomic and racial indicators for subsequent analysis. The analysis identified four accessibility profiles structured by a center–periphery divide: a low-accessibility southern periphery, a transitional northern belt with strong transit but limited active mobility, a central corridor with consistently high multimodal access, and a hyper-accessible core with exceptional walking and cycling opportunities. Income-elasticity analysis revealed stronger associations between income and active mobility accessibility than with public transport, highlighting how proximity to jobs privileges wealthier, centrally located households. High-skilled employment was concentrated in central areas, reinforcing cumulative advantages and deepening inequalities for low-income and racially marginalized populations. The findings underscore the urgency of integrated mobility and land-use policies to expand active and public transport infrastructure and decentralize employment opportunities, ensuring equitable access to jobs. Full article

Regional carbon balance emerges from the complex interplay between land-use dynamics, spatial economic activities, and ecological processes as a socio-ecological system cannot be captured by any single analytical lens. This study develops an integrated assessment workflow that links three components: (i) coefficient-based carbon emission and sequestration accounting by land-use type; (ii) intra-provincial spatial-interaction analysis operationalized through two complementary tools—the Ecological Support Coefficient (ESC) and Economic Contribution Coefficient (ECC), which characterize the local economy–ecology relationship within each city, and a gravity-based emission-linkage model that uses GDP, population, emissions, and inter-city distance to characterize the network structure of inter-city emission attraction; and (iii) the intPLUS model, which combines random-forest-derived transition probabilities with patch-generation rules to simulate multi-scenario land-use trajectories. The framework is applied to Jiangsu Province, China, across 13 prefecture-level cities, using 1995–2020 historical data and three 2030 scenarios. Model performance is validated against observed 2020 land use, with an overall Kappa coefficient of 0.82. The results reveal a stable “high-south–low-north” gradient in emissions, a contrasting “high-ECC/low-ESC” versus “low-ECC/high-ESC” combining pattern across southern and northern Jiangsu, and a hierarchical core–periphery emission-linkage network anchored on the southern metropolitan cluster. Scenario projections for 2030 show clear divergence in provincial carbon budgets, with emissions of $\mathrm{12,326.59}\times {10}^4$ t, $\mathrm{11,745.26}\times {10}^4$ t, and $\mathrm{13,243.42}\times {10}^4$ t under natural development, ecological protection, and economic development, respectively, and corresponding sequestration of $-87.10\times {10}^4$ t, $-100.29\times {10}^4$ t, and $-85.61\times {10}^4$ t. Rather than treating carbon accounting and land-use simulation in isolation, this workflow bridges the analytical gap between physical land-use transitions and socioeconomic spatial emission spillovers, translating structural interactions into actionable spatial planning strategies. Relying on widely available data, the framework demonstrates strong methodological transferability for comparable subnational systems, provided that local parameters and sink coefficients are properly recalibrated. Full article

Based on the high relevance of climate change issues and the extent of their investigation at global and regional levels, this study aims to quantify current changes in average annual air temperature, relative humidity, and precipitation within the Chu River watershed, and to identify patterns of their spatiotemporal variability under conditions of intensifying global warming. The study is based on long-term observational data from 18 meteorological stations collected during the period 1940–2024 under diverse physical and geographical conditions across the basin. The analysis of climatic dynamics within the Chu River basin catchment during the period 1940–2024, conventionally divided into four physiographic-altitudinal zones (high-mountain, mid-mountain, low-mountain, and plain regions), and conducted using a linear trend assessment approach with the application of statistical criteria, revealed the presence of multidirectional trends in climatic variables. It was established that the spatial and temporal variability of the trend coefficients for mean annual air temperature ranged from −2.50 to 2.45, for mean annual relative humidity from 0.93 to 1.03, and for annual atmospheric precipitation from 0.59 to 1.42. The identified positive trend in mean annual air temperature, occurring simultaneously with negative trends in relative humidity and atmospheric precipitation, is characterized not only by a stochastic component but also by a pronounced deterministic component manifested in the form of persistent positive and negative trends. These observed patterns are associated with the complex nature of climatic responses occurring in the northern part of the Tien Shan mountain system and along the eastern periphery of the Turan Lowland. Full article

Background: Geographic peripherality in Israel is linked to poorer health outcomes and may disproportionately affect patients requiring chronic therapies such as hemodialysis (HD). Though malnutrition and inflammation are strong predictors of morbidity and mortality in HD patients, regional differences in nutritional status and dietary adherence are unclear. Objectives: To examine the association between peripherality and malnutrition risk, dietary intake, and adherence to nutrition guidelines among Jewish Israeli adults on HD. Methods: In this multi-center, cross-sectional study, 154 adult Jewish HD patients were recruited from the northern periphery (n = 66) and central regions of Israel (n = 88). Demographic, clinical, laboratory, and anthropometric data were obtained from medical records. Nutrient intake was assessed using the multi-pass 24 h dietary recall method. Malnutrition risk was classified using BMI and serum albumin; the C-reactive protein-to-albumin ratio (CAR) was also calculated. Adherence to International Society of Renal Nutrition and Metabolism (ISRNM) dietary guidelines was evaluated. Between-group comparisons and multivariable regression analyses were conducted. Results: Overall, participant characteristics were similar between groups; however, coronary heart disease prevalence and dialysis vintage were higher in the periphery. Participants from the periphery had lower serum albumin, blood urea nitrogen, hemoglobin, and blood pressure, but higher LDL cholesterol. Sodium intake was significantly higher and adherence to ISRNM sodium guidelines markedly lower in the periphery. In multivariable analysis, peripherality reduced the odds of meeting sodium recommendations by 92.8%. Adherence to energy and protein guidelines was low in both groups. Nearly half of participants had some level of elevated malnutrition risk using the categorized variable, and an overall difference in the categories of malnutrition risk was detected, driven by the increase in the moderate risk category in the periphery. The composite malnutrition risk variable (any increase in risk vs. no increase in risk) did not differ by peripherality. Peripherality was independently associated with higher percent ideal body weight (%IBW), but not with CAR. Conclusions: Peripherality among Jewish Israeli HD patients is associated with differences in nutrition biomarkers, cardiovascular burden, and dietary adherence, especially sodium intake. Interventions considering peripherality should be explored. Full article

Municipal solid waste landfills may remain sources of environmental concern long after closure because heavy metals can persist in soils and affect ecosystem recovery. This study presents an integrated assessment of a closed municipal solid waste landfill in Kokshetau, Northern Kazakhstan, by combining field-based soil geochemical analysis with remote sensing monitoring of vegetation dynamics. A radial-gradient sampling design was used to characterize spatial patterns of contamination and to distinguish zones with different levels of anthropogenic impact. The results showed a clear concentration of heavy metals, particularly Zn and Pb, in the central part of the landfill, where integrated pollution and ecological risk indices indicated the highest levels of technogenic pressure. Time-series analysis of Landsat-derived vegetation indices for 2017–2025 revealed poorer vegetation condition in the most contaminated areas, with NDVI and EVI values increasing toward the landfill periphery. The observed negative association between vegetation indices and ecological risk suggests that remote sensing indicators can provide useful information on the ecological condition of closed landfill sites, although they should be interpreted together with field measurements. The novelty of this study lies in the combined use of geochemical contamination indices and long-term vegetation-index monitoring to assess post-closure landfill conditions in an arid continental region of Central Asia, where such integrated studies remain limited. The findings highlight the persistence of environmental risks after landfill closure and support the use of vegetation indices as non-invasive tools for monitoring rehabilitation and prioritizing further field investigations. Full article

Despite Mozambique’s substantial natural gas reserves, most households rely on solid biomass for cooking, with serious consequences for public health, livelihoods, and the environment. The domestic use of these resources could improve energy efficiency, security, and sustainable development. This mixed-methods study uses household interviews, descriptive statistics, multinomial, and conditional logit models, analyzing data from a random survey of 434 households in energy-rich peripheries of northern Inhambane and Maputo City to ascertain the determinants of household cooking energy choice. Results reveal that rising income increases the odds of choosing electricity, LPG, and biomass over natural gas. In energy-rich peripheries, the odds of selecting biomass over natural gas are reduced by 96.2% compared to non-energy-rich regions. Educational and urban habitation are positively correlated with the adoption of electricity and liquefied petroleum gas (LPG). Price serves as a significant negative predictor of fuel selection (OR ≈ 0.000001), whereby each unit increase in price per GJ substantially diminishes the likelihood of opting for alternatives over domestic gas. Monthly fuel expenditure positively predicts electricity, LPG, and biomass adoption (OR = 1.0042), with effects accumulating meaningfully across realistic spending ranges. Households that experienced energy system incidents were more than twice as likely to switch away from natural gas (OR = 2.072), reflecting the critical role of infrastructure reliability in fuel choice. Given natural gas’s potential as a clean cooking transition fuel, the government should prioritize investment in gas infrastructure, expand domestic supply, and promote public awareness of the health and environmental benefits of clean cooking energy. Full article

As a core and priority region of the Beijing-Tianjin Sandstorm Source Control Project, the mountainous area of northern Hebei plays a critical role in restraining desertification and ensuring the ecological security and sustainable development of the capital region. To reveal the ecosystem service flow mechanism of windbreak and sand fixation and support regional ecological sustainability, this study first used the Revised Wind Erosion Equation (RWEQ) to evaluate the spatial distribution of windbreak and sand-fixation services in northern Hebei mountainous area. Then, the HYSPLIT model was applied to simulate the spatial flow paths, identify the radiation scope, and quantify the radiation intensity of these ecosystem services. The results reflect the modeled patterns under given assumptions rather than fully verified actual ecosystem service supply. In 2024, the total amount of windbreak and sand-fixation service in the study area reached 10.3553 × 10⁶ tons. Sand-dust weather mainly occurred in spring and autumn, accounting for 39.23% and 33.46% of the total, respectively. The spatial flow paths were dominated by the northwest pathway (43.01%) and west pathway (38.17%). The total radiation scope of windbreak and sand-fixation services was 396.16 × 10⁴ km², among which 342.96 × 10⁴ km² was within the study area, accounting for 86.57%. The average service density was 21.06 kg/hm². The service density along flow paths decreased with increasing transport distance, while the radiation scope expanded with the increase in trajectory frequency. Spatially, the sand-fixation material density showed a circular decreasing trend from the center to the periphery of the study area. This study clarifies the flow characteristics and radiation benefits of windbreak and sand-fixation ecosystem services, which can provide a scientific basis for regional ecological protection, ecosystem service management, and the promotion of regional ecological sustainability. Full article

Despite extensive global progress monitoring under the 2030 Agenda, existing Sustainable Development Goal (SDG) assessment frameworks remain structurally blind to within-country distributional disparities. This study addresses this gap by developing a methodologically transparent composite SDG index for multi-level governance assessment, applying it to 218 Nomenclature of Territorial Units for Statistics (NUTS 2) regions across the European Union over the period 2015–2022 (1744 region-year observations). In this context, the term “region-year observations” refers strictly to the balanced panel data structure, which is calculated by observing 218 distinct sub-national regions continuously over an 8-year period (218 regions × 8 years The index aggregates four dimensions—social, economic, educational, and institutional—using min-max normalization. The analysis yields three main results: (1) Spatial econometric analysis reveals strong, persistent positive spatial autocorrelation, with high-performing clusters concentrated in Northern and Western Europe and lagging clusters in Eastern and Southern peripheries. (2) A spatial error model identifies institutional governance quality as a consistent statistical predictor of sub-national SDG performance. The significance of the spatial error parameter (λ = 0.497) suggests that unobservable institutional and geographical common shocks systematically link neighboring regions. (3) Cluster analysis further distinguishes four regional archetypes: Disadvantaged, Leaders, Educated, and Transitional. These findings underscore the need for spatially aware SDG monitoring infrastructure and investment in institutional capacity as prerequisites for equitable governance, as integrating spatial dependencies is crucial to prevent national averages from masking severe regional developmental traps. Full article

Based on the characteristics of cities as organic entities, this paper constructs a five-dimensional evaluation framework encompassing economy, industry, society, population, and space. A three-stage process of “fuzzy comprehensive evaluation—bi-level K-means clustering—state stability correction” is adopted to identify the development stages and spatiotemporal evolution of 286 Chinese cities from 2008 to 2023. The study finds that China’s urban development has shifted from “universal growth” to “divergent evolution,” exhibiting multiple characteristics such as the decline in the initial-stage cities and differentiation in the growth stage. Significant regional spatial differentiation is observed, with notable development gaps among the eastern, central, western, and northeastern regions, as well as between the northern and southern regions. Furthermore, most urban agglomerations exhibit a “mature center–lagging periphery” structure. Full article