Search Results (7,789)

Cupressus duclouxiana is an ecologically and economically important conifer endemic to southwestern China (e.g., central Yunnan and southern Sichuan), yet its potential distribution under future climate change remains insufficiently understood. In this study, we employed an ensemble species distribution modeling framework implemented in biomod2 to predict the current and future suitable habitats of C. duclouxiana across China. A total of 154 occurrence records and 17 key environmental variables were used to construct ensemble models integrating twelve algorithms. The ensemble model showed high predictive performance (TSS = 0.99, Kappa = 0.98). Temperature-related variables dominated habitat suitability, with the minimum temperature of the coldest month identified as the primary limiting factor, accounting for 44.1%. Under current climatic conditions, suitable habitats are mainly concentrated in southwestern China, particularly in Sichuan, Yunnan, and Xizang (Tibet). Future projections under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, SSP5-8.5) consistently indicate habitat expansion by the late 21st century, accompanied by pronounced northward and northwestward range shifts. The largest expansion is projected under the SSP3-7.0 scenario, highlighting the sensitivity of C. duclouxiana to intermediate warming trajectories. Overall, climate warming is expected to increase habitat availability while reshaping the spatial distribution of C. duclouxiana across China. These findings provide scientific support for climate-adaptive afforestation planning and conservation management, and offer broader insights into the responses of subtropical coniferous species to future climate change. Full article

With socioeconomic development, cities face increasingly complex and diverse disaster risks, making the construction of resilient cities an inevitable choice. However, the driving forces and tactical approaches behind urban resilience development remain unclear for urban safety development, thus posing challenges to cities urgently needing to enhance their resilience. Therefore, this paper investigates this issue, covering the following aspects: (1) Eighteen influencing factors within the complex system of urban resilience were identified and summarized from five perspectives: Economic, Social, Environmental, Infrastructure, and Organizational & Institutional. The attributes of the influencing factors were analyzed using the Decision-Making Experimentation and Evaluation Laboratory (DEMATEL) method, and key factors were identified accordingly. (2) The Total Adversarial Interpretive Structure Model (TAISM) method was applied to construct a multi-perspective adversarial recursive structural model with integrated impact values. This model illustrates the interrelationships among the influencing factors and clarifies their hierarchical structure. (3) A Fuzzy Reachability Matrix (FR) was introduced to handle uncertain relationships between factors in the comprehensive influence matrix, enabling an explicit analysis of the hierarchical structure of the urban resilience complex coupling giant system, clearly showing the impact of factor hierarchical changes on the system structure. (4) Building upon the analysis of factors affecting urban resilience, the specific pathways and mechanisms were articulated, followed by recommended measures formulated from both internal (governmental) and external (community) perspectives. The results can provide theoretical support for resilient city construction and serve as a practical cornerstone. Full article

Daily nighttime light (NTL) observations made by remote sensing satellites can monitor human activity at high temporal resolution, but are often constrained by residual physical disturbances. Even in standard products, such as NASA’s Black Marble VNP46A2, factors related to sensor viewing geometry, lunar illumination, atmospheric conditions, and seasonality can introduce noise into daily radiance retrievals. This study develops a locally adaptive framework to diagnose and correct residual disturbances in daily NTL data. By estimating location-specific regression models, we quantify the residual sensitivity of VNP46A2 radiance to multiple disturbance factors and selectively remove statistically significant components. The results show that the proposed approach effectively removes statistically significant residual disturbances from daily NTL data in the VNP46A2 product. An application for COVID-19 containment periods in China demonstrates the effectiveness of the proposed approach, where corrected daily NTL data exhibit enhanced temporal stability and improved interpretability. Further analysis based on event study approaches demonstrates that corrected daily NTL data enable the identification of short-run policy effects that are difficult to detect with lower-frequency indicators. Overall, this study enhances the suitability of daily NTL data for high-frequency socioeconomic applications and extends existing preprocessing approaches for daily NTL observations. Full article

Traditional villages, protected as cultural heritage in our country, are rich in historical information, cultural landscapes, and traditional domestic architecture. This article explores the spatial distribution of traditional villages and proposes a new paradigm for the sustainable development of traditional dwellings. It addresses the challenges these villages face, such as natural, social, and inherent issues, arising from rapid socioeconomic development and urbanization. This study analyzes the spatial distribution and architectural features of traditional villages and dwellings in Northern Shaanxi based on 179 national and provincial villages. Using ArcGIS 10.1, the geographic concentration index, kernel density analysis, and the analytic hierarchy process, this study applied both macro and micro level perspectives. The research shows that: (1) The traditional villages in northern Shaanxi exhibit a spatial distribution pattern of “overall aggregation, local dispersion, and uneven distribution.” This pattern is influenced by interactions between natural and human factors. (2) Traditional dwellings in these villages are primarily cave dwellings and courtyard buildings, each reflecting unique architectural features in terms of floor plan layout, facade form, structure, materials, and decoration. (3) Traditional village dwellings in northern Shaanxi face practical challenges related to protection, development, and governance. The top three challenges, based on weighted indicators, are issues related to inheritance, an imperfect protection mechanism, and inherent shortcomings of the buildings. Based on these findings, this study proposes three practical suggestions for the sustainable development of traditional village dwellings in Northern Shaanxi. These suggestions aim to enhance the comprehensive and multi-dimensional sustainable development of traditional village dwellings. Full article

This study aimed to develop a credit risk index to classify coffee producers according to socioeconomic, agronomic, and financial performance variables, with the purpose of strengthening financial inclusion. We combined qualitative and quantitative methods to understand credit risk factors among smallholder coffee producers. The study followed a descriptive-analytical approach structured in consecutive methodological phases. The systematic review, conducted following the Kitchenham protocol, identified theoretical factors associated with credit risk, while fieldwork with 300 producers provided the socioeconomic and productive contexts of coffee-growing households. Producer income, cost of living, and farm management expenses were modeled using regression, statistical, and machine learning methods. Subsequently, these variables were integrated to construct a financial risk index, which was normalized using expert scoring. The index was validated using data from 100 additional producers, for whom annual repayment capacity and maximum loan amounts were estimated according to their risk level. The results indicated that incorporating municipal-level economic variables, such as estimated average prices, income, and expenses, enhanced predictive accuracy and improved the rational allocation of loan amounts. The study concludes that credit risk analysis based on variables related to human, productive, and economic capital constitutes an effective strategy for improving access to finance in rural areas. Full article

Dental caries remains a major global public health problem characterized by pronounced social inequalities. This study aimed to identify, critically appraise, and synthesize the most recent evidence on the relationship between socioeconomic indicators and dental caries among adults aged 19–60 years, providing an updated systematic review that builds upon our previous reviews from 2012 and 2018. Reported following the PRISMA 2020 guidelines, we conducted a systematic search of eight electronic databases for observational studies published between March 2017 and April 2024 (PROSPERO: CRD42017074434). Two independent reviewers performed study selection, data extraction, and risk of bias assessment using the Newcastle–Ottawa Scale. Due to substantial methodological heterogeneity across the 22 included studies, a narrative synthesis was undertaken. The findings demonstrated a strong inverse association between socioeconomic position and caries experience. Lower income, lower educational attainment, and unemployment or employment in manual/unskilled occupations were associated with a higher overall caries experience. Advanced analytical approaches in recent studies, including life-course, reinforced that education and income are key contributors of these oral health inequalities, with persistent social disadvantage conferring the greatest risk. In conclusion, dental caries in adults aged 19–60 years is a social condition reflecting the cumulative effects of socioeconomic inequality across the life course. Addressing adult dental caries requires integrated approaches that combine clinical prevention with social and public policies aimed at reducing structural inequalities. Full article

This study investigates the factors influencing the expansion of organic farming in Europe between 2000 and 2022. Driven by consumer demand and EU support through the Common Agricultural Policy, organic farming has grown significantly. The research uses panel data and linear regression to assess the impact of socio-economic, agronomic, and environmental variables, including GDP, HDI, population density, education, broadband access, pesticide use, and biodiversity indicators. Data sources include FAOSTAT, FiBL, Eurostat, and the World Bank. The analysis also incorporates crop-specific organic farming data and environmental metrics such as ammonia emissions. The results show that expansion is shaped simultaneously by environmental pressures and socio-economic conditions: greater pesticide use, larger land availability, higher human development, and agricultural employment support organic adoption, while intensive livestock-related emissions and indicators of urbanization, such as broadband access, tend to constrain it. Full article

Background: Adolescence is a sensitive period for psychological, academic, and social development, and sports participation has been described as a potential protective factor for academic performance and psychological well-being. However, limited research has examined the combined influence of sports involvement, sport type, and family background on adolescents’ academic and psychological outcomes. This study aimed to investigate the associations between organized sport participation, sport type (football vs. judo), psychological well-being, psychosomatic symptoms, academic performance, and family socioeconomic background among adolescent boys. Methods: The sample consisted of 52 boys aged 11–14 years from a rural school, divided into football players (n = 13), judo athletes (n = 13), non-athletes (n = 13), and a contextual subgroup of students with special educational needs (SEN; n = 13), with the latter included for exploratory purposes only. Data included school-record-based academic performance and validated self-report measures of life satisfaction, depressive symptoms, psychosomatic complaints, perceived physical fitness, and socioeconomic background. Results: Athletes demonstrated significantly higher academic achievement than non-athletes in overall grade point average (p < 0.001), mathematics (p < 0.001), Romanian (p < 0.001), English (p = 0.03), and Hungarian (p < 0.001). They also reported higher life satisfaction (p < 0.001) but simultaneously showed slightly elevated depressive symptom scores (p < 0.001), indicating a paradoxical pattern of concurrent psychosocial benefits and psychological strain. Parental education (p < 0.001), parental occupational status (p = 0.01), and fathers’ occupational position (p = 0.02) were significantly higher among athletes’ families. Perceived physical fitness was also rated higher by athletes (p < 0.001). No significant differences were found in body mass index, family structure, or most psychosomatic symptoms. Conclusions: Sport participation was associated with more favorable academic and psychological indicators, yet also with elevated depressive symptoms, highlighting the dual nature of organized sport during adolescence. Future research should apply longitudinal designs, include female participants, and incorporate objective indicators of training load. Full article

Recognizing the evolution of landscape patterns in the Chishui River source region is essential for protecting ecosystems and sustainable growth in the Yangtze River Basin and other similar areas. However, knowledge of landscape pattern evolution within the primary channel zone remains insufficient. To address this gap, the current study used 2000–2020 land-use, geography, and socio-economic data, integrating landscape pattern indices, land-use transfer matrices, dynamic degree, the GeoDetector model, and the PLUS model. Results revealed that forest and cropland remained the prevailing land-use types throughout 2000–2020, comprising over 85% of the landscape. Grassland had the highest dynamic degree (1.58%), and landscape evolution during the study period was characterized by increased fragmentation, enhanced diversity, and stable dominance of major forms of land use. Anthropogenic influence on different landscape types followed the order: construction land > cropland > grassland > forest > water bodies. Land-use change in this region is a complex process governed by the interrelationships among various factors. Scenario-based predictions demonstrate pronounced variability in various land types. These findings provided a more comprehensive understanding of landscape patterns in karst river source regions, provided evidence-based support for regional planning, and offered guidance for ecological management of similar global river sources. Full article

Technological innovation plays a vital role in enhancing the economic growth and sustainability of the forestry sector. However, research on the nature, dynamics, and impact of such innovations, particularly within small and medium-sized enterprises (SMEs), remains limited. The forestry sector is often characterised by low levels of technological advancement and a traditionally conservative attitude toward change. Limited expertise, financial constraints, and ownership structures further influence the potential for innovation. This study examines the development of innovation among SMEs in Lithuania’s forestry sector and its contribution to sustainable forest management. Forestry innovations are understood as new processes, products, or services introduced by forest owners and managers to improve management efficiency and sustainability. The study employed the method of a structured questionnaire survey to evaluate technological, organisational, and financial aspects of innovation adoption among small and medium-sized enterprises in the forestry sector. Drawing on comparative survey data from 2005 and 2024, the study analyses the types of innovations implemented by forestry enterprises, the factors driving or hindering their adoption, and the evolving trends in innovation application. The results reveal a significant shift toward digitalisation and technology-based management practices, suggesting that Lithuanian forestry enterprises are gradually transitioning toward a more innovation-driven model. These developments appear to be influenced by the EU Green Deal policy framework, evolving innovation support mechanisms, and broader socio-economic changes. Nonetheless, technological transformation introduces new challenges, including the need for workforce upskilling and enhanced adaptability to rapidly changing market conditions. Full article

Objective: To evaluate the clinical success outcomes and risk factors associated with dental implants placed with simultaneous bone augmentation in a large-scale, real-world cohort. Methods: A retrospective analysis was conducted on 158,824 implants, including 45,715 Dental Bone Grafts, placed between 2014 and 2022 within a national healthcare network. Multivariate Generalized Estimating Equations were utilized to assess the impact of demographic, anatomical, and procedural variables on implant failure. Results: The augmented cohort demonstrated a high clinical success rate of 97.83% (2.17% failure), statistically comparable to the general implant population. Failures were predominantly early (<1 year), accounting for 70% of losses. Significant independent risk factors included immediate implant placement (3.08% failure vs. 2.07% for delayed), male gender, and maxillary location. Notably, low socioeconomic status (SES) emerged as a significant predictor, with a failure rate of 3.07% compared to 2.06% in high-SES groups. Conclusions: Simultaneous bone augmentation is a predictable modality that does not inherently increase implant failure risk, supporting the stabilization hypothesis. However, failure is modulated by specific variables. The identification of lower SES, male gender, and immediate placement as significant risk indicators highlights the necessity for personalized risk assessment and targeted protocols to optimize outcomes in augmented sites. Full article

Quality of life (QoL) is a multidimensional concept involving physical, psychological, social, and environmental dimensions. Therefore, it reflects not only individual well-being but also the overall well-being and sustainability of societies. Current approaches to QoL have expanded from purely economic or health-based indicators to incorporate a range of multidimensional analyses at urban, regional, and national levels, with more recent emphasis on interlinkages between socio-economic and spatial factors. This research investigates how geoinformation methodologies, including remote sensing, spatial analysis, and machine learning, can be applied to assess QoL across multiple spatial scales. Through a systematic review and comparative evaluation, the study aims to identify which indicators, data sources, and analytical tools are used at each spatial level—from neighborhood and urban scale to regional and national levels. Emphasis was placed on understanding how methodological approaches vary across scales and how spatial resolution, data availability, and urban context influence the design and implementation of QoL assessment frameworks. The main objective was to establish a common analytical framework for evaluating QoL across different spatial scales. The review revealed that combining data, machine learning algorithms, and spatial analysis approaches in a common framework will enhance comparative and predictive capabilities beyond the state of the art, although it will face significant data heterogeneity challenges. Future research aims to develop consistent, multidimensional models supportive of policies fostering sustainability and spatial equity in urban and regional contexts. Full article

The rapid shrinkage of the climate-regulating cryosphere, driven by global warming and anthropogenic activities, underscores the urgency of understanding its impact on regional ecological vulnerability. This study develops a Sensitivity–Resilience–Pressure (SRP) model-based framework comprising 21 natural and socio-economic indicators, employs spatial autocorrelation and center of gravity migration to characterize spatiotemporal patterns in the Qilian Mountains region, and integrates Random Forests (RF) with Shapley Additive Explanations (SHAP) to identify key drivers. Results reveal a downward trend in the Ecological Vulnerability Index (EVI) from 2000 to 2020, with areas of very heavy vulnerability declining from 21.05% to 14.73%, indicating gradual ecological recovery. The study area exhibits moderate vulnerability, with the western region dominated by heavy and very heavy vulnerability, whereas the eastern region is characterized by potential and light vulnerability, indicating a high-west, low-east spatial pattern. A significant positive spatial autocorrelation is observed, revealing that areas with high vulnerability are highly clustered and primarily overlap with regions of high elevation and sparse vegetation. The RF–SHAP analysis demonstrates that natural factors dominate the EVI, with fractional vegetation cover, biological abundance, glacial meltwater volume, annual precipitation, and the landscape diversity index emerging as the main drivers, and the EVI changing sequentially as each indicator approaches its threshold: 0.16, 56.57, 2.23 mm, 400.73 mm, and 0.39. In conclusion, although ecological vulnerability in the Qilian Mountains has declined, future management strategies should leverage these threshold effects to implement precise, indicator-based monitoring and regulation. Full article

Urban expansion and its driving factors are frequently analyzed within administrative regions to inform regional urban planning, yet such analyses often fall short at the natural basin scale (referring to the spatial extent defined by hydrological drainage boundaries) due to the scarcity of statistical data. Geographic and socio-economic spatial data can offer more detailed information across various research scales compared to traditional data (such as administrative statistical data, survey-based data, etc.), providing a potential solution to this limitation. Thus, this study took the Indus Basin as an example to reveal its urban expansion patterns and driving mechanism based on natural–economic–social time-series (2000–2020) spatial data, landscape expansion index, and geographical detector model (GDM). Future urban expansion distribution under different scenarios was also projected using Cellular Automata and Markov model (CA-Markov). The results indicated the following: (1) The Indus River Basin experienced rapid urban expansion during 2000–2020 dominated by edge-expansion, with urban expansion intensity showing a continuous increase. (2) Between 2000 and 2010 as well as 2010 and 2020, the dominant factor influencing urban expansion shifted from altitude to population (Pop), while the strongest interacting factors shifted from fine particulate matter (PM_2.5) and altitude to Gross Domestic Product (GDP) and Pop. (3) Future urban expansion probably occupies substantial mountainous area under the normal scenario, while the expansion region shifts towards the central plains to protect more ecological zones under a sustainable development scenario. Findings in this study would deepen the understanding of urban expansion characteristics of the Indus Basin and benefit its future urban planning. Full article

This study examines the social determinants of the digital divide in pre-service teacher education through the design and validation of the Digital Hospitality Scale (DSBD-HD-FID). The instrument was developed to diagnose social inequalities across six key dimensions: socioeconomic status, geographic location, gender, age, disability status, and interculturality. These dimensions are understood as structural factors shaping access to, use of, and participation in digital environments within teacher education. The research followed a non-experimental, quantitative, and cross-sectional design, including content validation through expert judgment and statistical analysis based on a pilot sample of education students from Chile and Portugal. An exploratory factor analysis was conducted, and internal consistency was assessed using Cronbach’s alpha coefficient. The results confirm strong content and construct validity, as well as high reliability (α = 0.93). Empirical findings indicate that socioeconomic status and geographic location significantly condition access to connectivity and digital literacy, while gender differences emerge mainly in recreational uses and frequency of digital training. Beyond these results, the study highlights the relevance of addressing digital inequalities in teacher education through inclusive and equity-oriented training policies. The findings support the integration of digital hospitality, human rights education, and the Sustainable Development Goals into initial teacher training curricula as measurable and evaluable dimensions, providing an evidence-based framework to inform future teacher education policies aimed at reducing digital divides and promoting social cohesion. Full article