Search Results (1,816)

Sub-Saharan Africa is rich in agricultural biomass, yet its biogas utilization is far below its potential. Most earlier studies failed to identify the nonlinear, multi-factor relationships that shape real national biogas yields and fully clarify this imbalance. This study constructs a 2007–2023 panel dataset for ten sub-Saharan African countries, merging agricultural output, socioeconomic, and infrastructure metrics. Gradient Boosting model and SHapley Additive exPlanations (SHAP) analysis are applied for empirical evaluation. SHAP analysis confirms that charcoal consumption yields the largest contribution to biogas production, with a mean absolute SHAP value of 1.018. The correlation between the two variables is negative under the threshold and becomes positive beyond this critical level. Urbanization has an inverted U-shaped correlation with biogas output, and the marginal contributions of predictors vary substantially across sampled countries. Instead, fragile supply chains, rural labor loss, and fierce competition in clean energy markets curb local biogas production. Forecasts show that regional biogas output will continue to fall until 2030. Targeted national policies matching each country’s core influencing factors are therefore urgently required. Full article

Ecological resilience represents a region’s fundamental capacity to withstand external disturbances and achieve sustainable development. As a typical ecologically fragile region in China and globally, Southwest China warrants particular attention in terms of understanding the spatiotemporal evolution and determinants of ecological resilience. Taking 47 cities in Southwest China as the study area, this study constructs an urban ecological resilience evaluation index system based on the “Pressure–State–Response–Adaptability” framework. By integrating centroid migration analysis, standard deviation ellipse analysis, kernel density estimation, spatial autocorrelation analysis, the Spatial Durbin Model (SDM), and the XGBoost–SHAP model, the spatiotemporal evolution and associated factors of urban ecological resilience from 2005 to 2024 are systematically examined. The results indicate that: (1) During the study period, disparities in urban ecological resilience across Southwest China gradually widened, accompanied by pronounced regional differentiation. (2) Under the economic-distance matrix, ecological resilience exhibits significant spatial dependence, with regional economic development generating spatial spillover effects. (3) The local determinants of ecological resilience are multidimensional, with FDI exhibiting the highest relative importance and nonlinear contributions in the XGBoost–SHAP analysis. (4) The relationships between influencing factors and ecological resilience show substantial regional heterogeneity, requiring differentiated enhancement strategies. These findings enrich the analytical framework for urban ecological resilience research and provide important scientific support for differentiated ecological governance and high-quality sustainable development in ecologically fragile regions. Full article

Shifting climatic conditions challenge simplified, yield-oriented agriculture, particularly in fragile mountain regions. In the Ecuadorian Andes, agriculture functions as a socio-ecological system shaped by biocultural relationships integrating production, agrobiodiversity, and farmer decision-making. This study examines how climate variability interacts with crop phenology and management practices within ancestral associated cropping systems (chakras). The analysis focuses on maize, common bean, faba bean, and potato during the 2024–2025 agricultural cycle in Imantag, Ecuador. A total of 30 native, introduced, and improved varieties were sown in traditional rows (wachos) and monitored within a single 420.8 m² Andean chakra. Using multiple ordinary least squares (OLS) regression (n = 30; R² = 0.440, adj. R² = 0.351, F (4,25) = 4.914, p = 0.005), results show that maize significantly outperformed the faba bean reference group (β = 20.34, p = 0.017), while common bean showed intermediate performance (β = 15.31, p = 0.048). Seed mass at planting was positively associated with relative yield (β = 6.71, p = 0.069), highlighting early-stage management decisions. Elevated maximum temperatures during maturation negatively affected yield (r = −0.42, p = 0.020), while accumulated precipitation had a positive effect (r = +0.45, p = 0.014). Model quality criteria, including VIF diagnostics (max. VIF = 3.37), Shapiro–Wilk residual normality test (W = 0.983, p = 0.896), and cross-validation using Ridge and Lasso regression, confirm the robustness of the statistical findings. These findings demonstrate that chakras function as adaptive socioecological systems that enhance productivity, agrobiodiversity conservation, and resilience under climate variability. Full article

Artificial intelligence (AI) is widely viewed as a technological foundation for organizational resilience. Yet firms may strategically exaggerate their AI-related narratives without corresponding substantive investment. This study examines whether such AI washing is associated with lower organizational resilience. We conceptualize AI washing as a narrative–investment misalignment within organizational systems, in which symbolic AI claims move ahead of substantive AI investment and capability formation. Based on Chinese A-share listed firms during 2010–2024, we develop a firm-level AI washing index by comparing firms’ within-industry ranking in AI disclosure with their within-industry ranking in actual AI investment. AI disclosure is identified from annual reports using a large language model, while actual AI investment is measured through AI-related software and hardware investments. Using double-debiased machine learning, we estimate a significantly negative association between AI washing and organizational resilience. Economically, a one-standard-deviation increase in AI washing is associated with a decline in organizational resilience equivalent to approximately 3.276% of the average annual change in organizational resilience. This estimated pattern remains stable when we employ alternative variable constructions, replace the machine learning algorithms, adjust the cross-fitting folds, use propensity score matching, and further apply a deep instrumental variable strategy. Mechanism tests based on organizational legitimacy provide evidence consistent with legitimacy-related transmission channels, suggesting that AI washing is associated with lower resilience through weakened pragmatic, moral, and cognitive legitimacy under the maintained mediation assumptions. Further analysis reveals an asymmetric pattern: firms whose AI narratives exceed actual investment experience lower resilience, whereas firms whose actual investment exceeds external narratives exhibit higher resilience. The negative estimated association is particularly evident in high-tech industries, enterprises with established bank-firm ties, and enterprises with higher educational heterogeneity in their top management teams. This study advances research on AI disclosure and organizational resilience by showing that symbolic AI narratives can signal system-level fragility when technological claims are misaligned with substantive capability formation. Full article

Residents’ pro-environmental behavior is important for sustainable tourism destination governance, particularly in ecologically fragile and culturally distinctive regions. Drawing on Value–Belief–Norm theory and the Norm Activation Model, this study examines how the New Environmental Paradigm, social norms, and nature bonding influence residents’ pro-environmental behavior through perceived obligation, and whether Religious–Cultural Embeddedness moderates the perceived obligation–behavior relationship. A structured questionnaire survey was conducted among 608 residents in Xizang, China, and the data were analyzed using confirmatory factor analysis and structural equation modeling. The results show that the New Environmental Paradigm, social norms, and nature bonding all have significant positive effects on residents’ pro-environmental behavior, with nature bonding showing the strongest direct effect. Perceived obligation mediates the effects of social norms and nature bonding, but not the effect of the New Environmental Paradigm. Religious–Cultural Embeddedness negatively moderates the relationship between perceived obligation and pro-environmental behavior, indicating that the behavioral effect of individual obligation becomes weaker in highly embedded religious–cultural contexts. These findings suggest that residents’ environmental behavior is shaped by cognitive, social, affective, moral-responsibility, and cultural mechanisms, offering implications for culturally sensitive sustainable destination governance. Full article

The Three-River Source Region (TRSR) is an environmentally fragile area in China and on the Qinghai–Tibet Plateau that is extremely vulnerable to climate change. The time lag between climate change and vegetation responses in high-altitude regions is a critical component of ecosystem–climate coupling that has not yet been fully quantified. The purpose of this project is to look into the influence of extreme climatic change in the TRSR on vegetation development, as well as to give data support for vegetation restoration and ecological security on the Qinghai–Tibet Plateau. This study used normalized difference vegetation index (NDVI) data from the TRSR from 2000 to 2024 to investigate the pixel-level lag effects of 12 ETCCDI (Expert Team on Climate Change Detection and Indices) extreme climatic indices. Pixel-level maximum Pearson correlation analysis was used to determine the ideal lag period and correlation direction within an 0–6-month lag window, and the lag ratios for seven vegetation kinds were quantified in stratified order. The results show that precipitation, and not temperature, is the primary climatic limiting factor for changes in NDVI in the TRSR. Furthermore, there is a clear distinction between the temperature- and the precipitation-driven lag responses: temperature extreme indices have shorter average response times and highly polarized correlation directions (positive correlation proportions range from 1.0% to 95.9%), whereas precipitation extreme indices have longer average lags and are predominantly positively correlated. Vegetation types have a significant impact on lag sensitivity: grassland and desert respond faster, reflecting shallower root depths and limited soil moisture buffering capacity, whereas meadows and shrubland have the longest lags, consistent with the water-holding capacity promoted by deeper root systems and higher soil organic matter. These findings contribute to our understanding of time-structured vegetation–climate coupling and provide a solid scientific foundation for proactive vegetation management in the TRSR to meet future extreme climate events. Full article

Background: Eldecalcitol (ELD) and denosumab are some of the most common therapeutic options for osteoporosis management. ELD effectively increases bone mineral density (BMD) in osteoporotic patients, independent of baseline vitamin D status or calcium intake. However, the efficacy of denosumab combined with either ELD or native vitamin D plus calcium in postmenopausal Chinese women with osteoporosis has not been established. Methods: In this single-center, randomized, open-label, active-controlled clinical trial, postmenopausal women with osteoporosis (defined as a BMD T-score ≤ −2.5 at the lumbar spine [LS], total hip [TH], or femoral neck [FN], or low bone mass with fragility fracture history) were enrolled from Xi’an Honghui Hospital in China and randomized 1:1 to two 12-month treatment regimens: the ELD group received ELD (0.75 μg orally daily) combined with denosumab (60 mg subcutaneously every 6 months), while the control group received the same denosumab regimen plus native vitamin D (800 IU orally daily) and calcium (600 mg orally daily). The primary endpoint was the 12-month percent change in LS BMD from baseline. Secondary endpoints included changes in FN- and TH-BMD from baseline, bone turnover markers, serum parathyroid hormone, quality of life, and the incidence of new fractures. Results: Of the 100 randomized participants, 45 in the ELD group and 46 in the control group were included in the efficacy analysis. After 12 months of treatment, LS-BMD increased significantly in both groups (both p < 0.05), with a greater increase in the ELD group than in the control group (6.75% vs. 4.99%), yielding a statistically significant between-group least-squares mean difference of 1.75% (95% CI, 0.10 to 3.41; p = 0.038). The reduction in serum β-CTX was significantly smaller in the ELD group than in the control group at 3 months (91.00% vs. 93.62%, p = 0.002), with no significant between-group differences thereafter. Seven fractures were reported (one non-vertebral in the ELD group; one vertebral and five non-vertebral in the control group). No significant between-group differences in FN- or TH-BMD, quality of life, or overall adverse event rates were observed. Both regimens were generally well-tolerated, without clinically meaningful calcium-related safety signals. Conclusions: Combination therapy with denosumab and eldecalcitol improved LS-BMD more effectively than denosumab with native vitamin D and calcium in postmenopausal Chinese women with osteoporosis. Clinical trial number: ClinicalTrials.gov identifier NCT05884372, registered on 1 June 2023. Full article

This paper focuses on the development of 3D digital resources and a learning platform for the Engineering Drawing course, exploring construction pathways and implementation methodologies while further investigating the application potential of artificial intelligence techniques in model construction and instructional processes. The 3D digital resources encompass basic geometric elements, complex structures, and engineering entities, supporting interactive operations such as rotation, scaling, and sectioning. These resources effectively overcome the inherent limitations of traditional engineering drawing laboratories, including high costs, fragility, delayed updates, limited coverage, and inadequate adaptability to individual student differences. The integration of AI technologies significantly enhances the efficiency of digital resource development and effectively stimulates student engagement in learning. At the pedagogical practice level, this paper proposes two AI-based conceptual teaching frameworks, namely the Adversarial Learning Model and the Challenge-based Learning Model. The learning platform incorporates knowledge graphs and process-based assessment mechanisms, achieving an organic integration of personalized and contextualized learning. A three-year longitudinal teaching performance analysis reveals a marked improvement in overall student grades, with a notable increase in high-grade proportions and a decrease in failure rates. Questionnaire survey results further confirm that students’ spatial imagination, comprehension, and practical application abilities have been strengthened, with minimal adverse effects. Moreover, in extracurricular activities, students participating in graphic design competitions have achieved outstanding performance. Comprehensive findings indicate that the synergistic application of digital resources, online learning platforms, and advanced AI technologies show a positive correlation with improved teaching effectiveness and provide robust support for the cultivation of innovative engineering talents. Full article

Old neighborhoods in China’s mountainous cities generally face the dual challenge of a decline in social vitality intertwined with the fragility of ecosystem functions; there is an urgent need to develop a socio-ecological performance evaluation tool suited to the mountainous context to guide decisions on sustainable regeneration. Taking nine typical aging communities in Daxigou Subdistrict, Yuzhong District, Chongqing as case studies, this study, based on socio-ecological systems theory, constructed an evaluation system comprising 31 indicators across social, ecological, and spatial environmental subsystems. The entropy-weighted TOPSIS method was employed to quantitatively measure the communities’ comprehensive socio-ecological performance, whilst K-means clustering was used for community classification and the identification of weaknesses. The results indicate that the overall socio-ecological performance of Daxigou Subdistrict is relatively low, with scores across the various subsystems distributed unevenly. Correlation analysis reveals the decisive role of resource-related factors in community performance. Based on the evaluation results, an integrated and operational decision making framework combining diagnosis, classification, and policy implementation was established, providing a reference for the targeted regeneration and sustainable development of aging communities in mountainous cities. Full article

Coastal saline-alkali land represents an important reserve land resource and a highly fragile ecosystem in China. Accurate identification, dynamic monitoring, ecological restoration, and sustainable management of these lands have become major research priorities in the field land-resource management, ecological conservation, and environmental governance. Owing to its broad spatial coverage, high temporal resolution, efficient data acquisition, continuous monitoring capability, and non-destructive observation, remote sensing has become a vital tool for the investigation, monitoring and management of coastal saline-alkali land, providing essential technical support for both scientific research and practical decision-making. This review systematically summarizes recent advances in the application of remote sensing to coastal saline-alkali land in China. First, we examine the development of remote sensing data sources, soil salinity inversion algorithms, and information extraction methods for saline-alkali land identification, with particular emphasis on the contributions of multi-source data fusion, machine learning, and deep learning techniques to improving mapping accuracy and reliability. Second, we review recent applications of remote sensing in ecological restoration, focusing on vegetation recovery monitoring and landscape pattern analysis in restored coastal ecosystems. Third, we summarize the use of remote sensing products in sustainable land management, including regional land-use planning, ecological risk assessment, salinization early warning, and precision management. Finally, we discuss the major challenges that remain, including the limited transferability of salinity inversion models, the insufficient adaptability of remote sensing approaches under complex environmental conditions, and the weak integration of remote sensing technologies with field-scale management practices. Future research should emphasize physics-informed artificial intelligence, multi-source data fusion, and intelligent decision-support systems to enhance the operational application of remote sensing for the sustainable utilization and ecological restoration of coastal saline-alkali land. Full article

This paper explores the relationship between institutional governance quality (IGQ), digital transformation, and corporate social responsibility (CSR) performance in a fragile institutional environment. The study employs an unbalanced panel dataset comprising 473 firm-year observations from firms listed on the Palestine Exchange over the period 2014–2024 and uses fixed-effects regression analysis, robustness tests, and System GMM estimation to ensure the reliability of the findings. The results reveal a significant and robust positive association between institutional governance quality and CSR performance, indicating that higher levels of governance quality are associated with greater corporate engagement in CSR activities. Furthermore, the baseline fixed-effects results show that digital transformation significantly strengthens the positive relationship between institutional governance quality and CSR performance, suggesting that technological progress enhances transparency, information exchange, and institutional monitoring, thereby improving the effectiveness of governance mechanisms in promoting CSR. Robustness tests confirm the stability of the baseline findings, while the dynamic System GMM estimation provides additional evidence after accounting for endogeneity and the persistence of CSR performance. These results indicate that CSR performance exhibits strong persistence over time and that the moderating role of digital transformation becomes more nuanced under a dynamic specification. The study contributes to the literature by providing empirical evidence from a fragile emerging economy that remains underrepresented in governance and CSR research. In addition, the findings offer important policy implications by highlighting the complementary roles of institutional governance quality and digital transformation in promoting CSR and supporting sustainable digital transformation in developing economies. Full article

The Upper Minjiang River Basin is a critical ecological barrier in the upper Yangtze River, where forest ecosystems play a vital role in carbon sequestration, water conservation, and soil retention. Given that different forest types exhibit significant variations in community structure, species composition, and ecological processes, their ecosystem service (ES) supplies and trade-off/synergy relationships are also expected to show distinct heterogeneity. However, systematic research on the trade-offs and synergies of ESs across different forest types remains limited, constraining the development of precision forest management and differentiated management strategies. To deal with this, we used the InVEST model and calculated five key services across the basin: carbon stock (CS), water yield (WY), soil conservation (SC), habitat quality (HQ), and forest stock volume (FSV). We then applied Spearman’s correlation, root mean square deviation (RMSD), and the GeoDetector model to analyze trade-offs and uncover driving mechanisms. Finally, we used spatially constrained K-means clustering to map different management zones. The results indicate that the Upper Minjiang River Basin stored 1.78 × 10⁸ t of carbon, retained 2.98 × 10⁸ t of soil, produced 6.48 × 10⁹ m³ of water yield, maintained a mean habitat quality of 0.78, and supported a forest stock volume of 1.20 × 10⁸ m³. Coniferous forests exhibited the highest CS (181.07 t ha⁻¹) and FSV (176.37 m³ ha⁻¹), whereas shrublands contributed the largest share (52.17%) of regional water yield. At the regional scale, CS and FSV showed the strongest synergy (r = 0.71, p < 0.01), while WY displayed significant trade-offs with most other services. GeoDetector analysis revealed that forest type acts as the primary driver shaping the relationships among services, while elevation and precipitation play supporting roles. Based on the ES bundles identified via spatially constrained K-means clustering, the Upper Minjiang River Basin was divided into four distinct management zones: a carbon sequestration core zone, an ecological balance zone, an ecologically fragile zone, and a multifunctional conservation zone. Therefore, findings from the Upper Minjiang River Basin may provide insights applicable to other mountain forest ecosystems facing similar environmental and management challenges. Full article

The study investigates whether cultivated land abandonment (CLA) reflects structural transformation or an intensifying crisis. CLA is defined as land that has remained uncultivated for a minimum of two consecutive years, with the exclusion of land that is subject to deliberate programs such as the “Grain-for-Green” initiative. Utilizing the China Land Cover Dataset and a moving-window approach, we conducted a comprehensive analysis of spatiotemporal patterns across 2847 Chinese counties from 1992 to 2022. The research employed OLS, Tobit, high-dimensional fixed effects and instrumental variable regressions. The findings of the present study indicate an annual average abandonment rate of 2.3995%, with 12.3649% of cropland abandoned at least once and 9.2028% reclaimed, suggesting a fragile equilibrium. The Huang-Huai-Hai region and Northeast China’s plains emerged as low-abandonment clusters. Cropland fragmentation was found to trigger abandonment, while a higher ecological land ratio significantly exacerbates CLA. Rural labor migration and urbanization drive cumulative abandonment, worsened by the COVID-19 pandemic. Effective governance requires context-specific interventions that address key constraints and integrate land reuse into sustainable rural development frameworks. The research methods and theoretical mechanisms presented offer a reference for balancing food security, rural revitalization, and ecological sustainability worldwide. Full article

Due to the fragility of table grapes, berries are prone to detachment during harvesting, transportation, and postharvest handling. This study aimed to clarify the effects of mechanical loading on the mechanical–physiological coupling process of berry detachment and to establish a predictive model for berry detachment percentage. In this study, the postharvest detachment mechanism of ‘Kyoho’ grapes was investigated by applying different levels of combined tensile–bending loads to the berry abscission zone. The results showed that combined tensile–bending stress accelerated the deterioration of visual quality and caused a marked decline in nutritional and flavor-related components. Furthermore, this stress altered respiration rate and endogenous hormone levels. In addition, combined tensile–bending loading increased the activities of cell wall-degrading enzymes, enhanced cell membrane permeability, and disturbed reactive oxygen species metabolism, with antioxidant enzyme activities in the abscission zone reaching their peaks earlier. Correlation analysis identified key physical and physiological indicators closely associated with berry detachment percentage. Physical–physiological indicators, mechanical loading level, and storage time were used as input variables, whereas berry detachment percentage was used as the output variable. Three predictive models, including multiple linear regression (MLR), back-propagation neural network (BPNN), and genetic algorithm-optimized BP neural network (GA-BP), were developed to predict berry detachment percentage. The results showed that the GA-BP model achieved higher prediction accuracy than the MLR and BPNN models, with R² = 0.9968 and RMSE = 1.5807. This study provides important insights into the mechanical–physiological coupling process underlying postharvest berry detachment in table grapes under mechanical loading. Moreover, the developed model may provide a useful tool for evaluating berry detachment risk during postharvest storage and transportation, thereby helping to improve quality management and extend shelf life. Full article

Environmental damage assessment has become an important institutional component of regional sustainability transitions, especially in resource-dependent northern territories where industrial risks, fragile ecosystems, sparse monitoring networks, and limited institutional capacity intersect. Existing oil-spill valuation approaches provide well-developed legal, economic, and ecosystem-service tools; however, their use as decision-support instruments for compensation governance, prevention investment, monitoring design, and regional resilience policy remains insufficiently operationalized. This article addresses this gap by developing a modular institutional framework for selecting and combining established environmental damage valuation methods under the ecological, legal, data-related, and socio-cultural constraints of the Russian North. The study applies a transparent narrative methodological review and framework-building design based on international and Russian literature, official regulatory sources, natural-resource damage assessment practice, ecosystem-service valuation studies, and publicly available evidence on the 2020 Norilsk diesel fuel spill. The analysis organizes six established valuation approaches into three complementary modules: direct harm, indirect and opportunity-related losses, and non-market ecosystem-service losses. The Norilsk case is used as an illustrative plausibility check rather than a full empirical recalculation. It shows that legally recognized compensation can provide a liability signal, but indirect losses and ecosystem-service components require separate evidence, sensitivity analysis, and double-counting control. The contribution of the article is to adapt established valuation methods into a transparent decision-support protocol that links environmental liability, compensation governance, prevention, monitoring, institutional resilience and regional sustainability transitions in Arctic and sub-Arctic resource regions. Full article