Search Results (11,303)

Consumer trust is critical to the sustainable development of cross-border B2C e-commerce platforms. Accurately evaluating and diagnosing trust weaknesses has become a key concern for both practitioners and researchers. To address the inherent limitations of existing trust evaluation methods, this study proposes an improved AHP-Ridge Regression hybrid model that integrates expert knowledge with actual consumer perception data. First, an improved Analytic Hierarchy Process based on stakeholder-oriented nonlinear programming is employed to optimize the evaluation weights of nine experts, reduce subjective bias, and generate expert prior weights for each dimension and indicator. Second, these prior weights are incorporated as the regularization prior mean of the Ridge Regression model to construct the improved AHP-Ridge Regression model. Based on survey data from 387 valid respondents across five major cross-border platforms (Tmall Global, JD International, Pinduoduo Global, Sam’s Club Global, and CDFG Duty-Free), the model is compared with six baseline models using a 30-times repeated five-fold nested cross-validation. The proposed model achieves the lowest RMSE (0.3179) and highest R² (0.6510) among all compared models, with statistically significant improvements over all baselines (Nadeau–Bengio-corrected p < 0.001, large Cohen’s d effect sizes). However, the improvement over conventional Linear Regression is modest in absolute magnitude (ΔRMSE ≈ 0.0012). The primary value of the proposed model lies not in a dramatic leap in predictive accuracy but in its theoretical grounding, interpretability, and diagnostic capability. Permutation importance analysis reveals that Platform Fluidity, AI Technology Usability, Page Layout & Navigation Clarity, Content Accuracy, and Policy Assurance are the most important predictors of consumer trust. Comprehensive calibration and residual diagnostics (including MAE, normality tests, and heteroscedasticity checks) confirm the model’s predictive reliability. Furthermore, platform-specific diagnostics identify three distinct trust profiles (high-trust benchmark, trust-improvement priority, and mixed-profile platforms), providing managers with actionable insights for resource allocation. This study offers cross-border B2C e-commerce platforms a trust evaluation tool that balances predictive accuracy and interpretability, and provides implications for sustainable platform governance and ESG-oriented management by linking trust diagnostics with platform accountability frameworks. Full article

The digitalization of photovoltaic (PV) systems can support sustainable energy deployment by improving operational efficiency, system visibility, and energy extraction. However, many existing Internet of Things (IoT)-enabled solutions address monitoring and maximum power point tracking (MPPT) separately or depend on proprietary platforms, remote cloud services, and relatively costly hardware, which may restrict their accessibility and replication in small-scale and resource-constrained applications. This study presents the implementation and laboratory-scale experimental evaluation of an edge-IoT architecture that integrates real-time PV monitoring, embedded adaptive MPPT control, local data management, and visualization using low-cost hardware and open-source software. The proposed architecture combines an ESP32 microcontroller with a Raspberry Pi (RPi) local server to enable environmental and electrical sensing, edge-based control, message queuing telemetry transport (MQTT) communication, local data storage, and interactive visualization through the open-source Node-RED, InfluxDB, and Grafana platforms. An adaptive perturb-and-observe (AP&O) algorithm is implemented on the ESP32 to dynamically adjust the duty cycle of a DC–DC boost converter in response to changing operating conditions. The system is experimentally evaluated using a PV test bench equipped with a custom boost converter and sensing modules measuring eleven electrical and environmental parameters. The architecture achieved an average communication latency of 193 ± 23 ms and an average MPPT efficiency of 97.3 ± 0.54%. It also provided a power gain of 0.7 ± 0.5% compared with the conventional fixed-step perturb-and-observe method. By combining local processing, open-source software, low-cost components, and integrated monitoring and control, the proposed system reduces dependence on external cloud infrastructure while supporting responsive and accessible PV energy management. These results demonstrate its potential as a replicable technological framework for improving the operational sustainability and digital management of small-scale PV installations. Full article

Pumped hydro storage is a well-established and reliable form of energy storage, with construction scale expanding steadily in recent years. Underground cavern excavation is an indispensable part of pumped storage construction, while sustained cavern inflow poses potential threats to engineering and regional water security. This study first summarizes the hydrochemical characteristics of cavern inflow from 62 pumped-storage projects in China. Combining field investigations, water pressure tests, hydrochemical analyses, and multi-method inflow forecasting, the study further discusses the cavern inflow features of two typical projects under different climatic environments. The results indicate that across the 62 projects, total dissolved solids (TDS) in inflow water range from 21.0 to 4270.7 mg/L, with pH values of 6.7–8.3, and are dominated by HCO₃-Ca type. Moving from humid toward arid regions, TDS shows a continuous increase, while pH exhibits no significant variation. At the Shanshan site, controlled by evaporation, silicates weathering and evaporite dissolution, cavern inflows are dominated by high-salinity SO₄-Mg type water with pronounced SO₄²⁻ enrichment. Predicted inflows of the underground powerhouse and water conveyance tunnels are 1247.96–5542.97 m³/d and 105.85–211.69 m³/d, respectively. The Ningshanbei site, located in the humid area, is characterized by low-salinity HCO₃-Ca freshwater controlled by carbonate dissolution, with a high conveyance system inflow of 2914.71–3413.91 m³/d. The two sites differ markedly in recharge conditions, inflow characteristics, and water quality, requiring site-specific water management. This study provides engineering references for inflow hazard control, groundwater resource management, and ecological protection in pumped-storage projects across different climatic zones. Full article

This study addresses limited evidence comparing the relative associations of three Green Human Resource Management (GHRM) practices—green recruitment and selection, green training and development, and green employee involvement—with perceived organizational sustainability in Saudi Arabia. These practices were selected because they represent workforce entry, capability development, and employee participation. A quantitative cross-sectional survey was carried out involving 394 employees from both public and private sector organizations in the Central, Western, Eastern, and Northern regions of Saudi Arabia. Data were examined through confirmatory factor analysis, correlation analysis, and multiple regression utilizing heteroscedasticity-robust standard errors. The model accounted for 53.0% of the variation in perceived organizational sustainability. Green recruitment and selection showed the largest positive association (β = 0.412, p < 0.001), followed by green training and development (β = 0.237, p < 0.001) and green employee involvement (β = 0.162, p = 0.006). These associations remained significant in adjusted and sensitivity analyses. The study contributes by comparing the relative associations of the three GHRM practices simultaneously rather than treating GHRM as a single aggregate construct, thereby providing a clearer basis for targeted HR strategies and sustainability initiatives. Full article

The transition towards the circular economy and improved environmental management is a major challenge for manufacturing SMEs under growing pressures for resource efficiency, waste reduction and industrial sustainability. Although circular economy, environmental management, digitalization and artificial intelligence are widely discussed, they are often treated separately and rarely integrated into maturity-assessment frameworks. This article proposes CEEMMI—Circular Economy and Environmental Management Maturity Index—a digital- and AI-oriented diagnostic framework with a multi-criteria structure for manufacturing SMEs. CEEMMI integrates eight dimensions covering circular strategy, eco-design, resource efficiency, life cycle management, circular supply chains, digitalization and AI, organizational capabilities and sustainable performance. In its current version, the model is operationalized as an equal-weighted additive index for preliminary self-assessment, pending future content-validity testing and expert-derived weighting. The framework supports five-level maturity classification, profile-based interpretation, compensability safeguards and illustrative sensitivity analysis, offering a reproducible basis for diagnosis, decision support and future empirical validation. Full article

The global oil-processing industry generates substantial quantities of by-products and secondary streams, including oilseed cakes, pomaces, hulls, and wastewaters, which remain largely underutilized despite being rich sources of high-value bioactive compounds. The development of sustainable strategies for the valorization of these residues is increasingly recognized as a key component of circular bioeconomy and biorefinery frameworks. In this context, deep eutectic solvents (DESs) have attracted considerable attention as a new generation of designer solvents owing to their tunable physicochemical properties, low vapor pressure, ease of synthesis, and potential environmental compatibility. This review critically discusses the current state of knowledge regarding the application of DESs in the processing and valorization of oil industry by-products. Particular emphasis is placed on the relationship between DES composition, physicochemical characteristics, and extraction performance. Recent advances in the recovery of phenolic compounds, proteins, saccharides, and tocopherols from oilseed-derived residues are comprehensively examined, including the integration of DESs with intensified extraction techniques such as microwave-, ultrasound-, and ohmic-assisted extraction. Furthermore, the role of DESs in oil purification processes and the treatment of technological waste stream is evaluated. Emerging evidence indicates that DES-based systems not only enhance extraction efficiency and selectivity but may also improve the stability, bioaccessibility, and purity of the recovered compounds. Finally, the opportunities and challenges associated with the implementation of DES-based technologies within integrated biorefinery schemes are discussed, including solvent recovery, product scalability, sensory acceptability, and regulatory considerations. The available literature demonstrates that DESs constitute a versatile platform for the sustainable valorization of oil-processing residues, supporting the transition from conventional waste management approaches toward resource-efficient and circular production systems. Full article

Restoration of natural sterlet (Acipenser ruthenus) stocks in the transboundary Irtysh River, shared by China, Kazakhstan, and Russia, is of critical importance for biodiversity conservation and represents a strategic priority for sustainable management of transboundary aquatic resources in all three countries. Over the past two decades, sterlet abundance and occurrence in the river have remained low, indicating an unfavorable population status. The Irtysh sterlet population is likely influenced by a combination of habitat degradation, fragmentation of spawning habitats, alterations in the hydrological regime, and increasing anthropogenic pressure. This study summarizes archival and recent data on sterlet catches, biological condition, and habitat characteristics within the Irtysh River basin. Hydrological and hydrochemical parameters, spatial distribution of catches, and the size–age structure of the population were analyzed. The results revealed a localized distribution pattern of sterlet, with the aggregation index reaching I ≥ 1 in some cases, indicating an uneven spatial distribution of individuals. Such aggregation patterns may partially reflect the presence of ecologically important habitats and provide a basis for future conservation planning and targeted monitoring, although this relationship could not be confirmed within the scope of the present study. A decline in the proportion of older age groups and deterioration of key biological indicators suggest weakening natural reproduction. The study initiated the establishment of a replacement broodstock and formed an initial broodstock of 34 sterlet individuals collected from the wild. This provides a foundation for future artificial propagation and the recovery of the endangered Irtysh River sterlet population. Full article

In response to the growing demand for sustainable regeneration and efficient operation of urban spaces, the adoption of digitalised facility management (FM) has emerged as an effective strategy. FM facilitates data-driven planning and optimised resource use, contributing to more sustainable and resilient urban environments. This research combines Building Information Modelling (BIM) with Visual Programming Languages (VPLs) to support the regeneration and optimisation of an urban park. Autodesk Revit is employed to develop an LOD 300 semantic and geometric model, while Dynamo enables the implementation of two parametric scripts: Script A, governing on/off control of monitored systems based on environmental, temporal and contextual conditions, and Script B, providing continuous operational monitoring, fault diagnosis and color-coded visual feedback directly within the BIM environment. The framework is tested at Parco Gioeni, an 8.6-hectare historically and geologically urban park in Italy, across three management systems: irrigation, nebulization and green area monitoring. A total of 28 what-if scenarios are conducted across systems, indicating the framework’s functional consistency and contextual adaptability under the tested conditions. The proposed approach is built on accessible open software tools, making it transferable to public administration contexts, and is designed to be scalable and replicable across different urban green space typologies. Full article

The water–energy–carbon (WEC) nexus is central to sustainability, yet the role of water quality as a potential moderator within this nexus remains empirically underexplored. Using a fixed-effects panel regression model applied to provincial-level data from the Yellow River Basin over the period of 2007–2022, this study provides systematic evidence that water quality significantly moderates the WEC nexus. The empirical findings indicate that (1) water quality negatively affects the WEC nexus significantly; (2) the moderating effect presents spatial heterogeneity, showing greater carbon reduction potential in middle and lower reaches; and (3) the marginal impact of water cycle energy consumption on carbon emissions declines monotonically with improvements in water quality, with a sample-specific estimated critical point identified at a compliance rate of 88.159%—below which the marginal effect remains positive and above which it turns negative. (4) The moderating effect exhibits pronounced spatial heterogeneity, being significantly positive in the middle and lower reaches but insignificant in the upper reaches, and is significantly strengthened after the implementation of China’s strictest water resource management policy in 2013. These results suggest that water quality serves as a strategic lever for synergizing WEC governance. The findings offer theoretical and practical insights for adjusting the tightly coupled WEC nexus in the Yellow River Basin, with implications for region-specific and threshold-oriented policy design. Full article

Artificial intelligence (AI) is transforming healthcare and the built environment, yet its application to healthcare real estate (HRE) remains fragmented and poorly understood. This study systematically reviews AI applications across the HRE asset lifecycle to identify evidence gaps and evaluate their potential to improve decision-making, operational performance, and sustainable healthcare infrastructure. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, the search identified 2881 records, of which 87 studies met the inclusion criteria. Building on the evidence gaps identified through this mapping, this study develops conceptual contributions, including a lifecycle maturity index, the Algorithm-to-Asset-Value Translation Chain, and the AI-HREDF, that serve as theoretically grounded, testable proposals for future empirical investigation. Each study was classified by lifecycle stage, evidence directness, and evidence strength. Only 14 studies (16%) provided direct evidence linking AI to HRE decisions, while most focused on operations and facility management, leaving major gaps in site selection, planning, construction, and investment. This review identifies three evidence translation gaps that prevent AI advances from becoming measurable improvements in asset performance and financial value. To address these challenges, we propose the AI-Integrated Healthcare Real Estate Decision Framework (AI-HREDF), the Algorithm-to-Asset-Value Translation Chain, and a research agenda for future work. The findings provide a foundation for integrating AI into healthcare infrastructure planning, management, and investment while supporting more resilient, resource-efficient, and sustainable healthcare facilities. Full article

Hydroclimatic variability and intensive human regulation have reshaped surface water dynamics in plain river network regions (PRNs), altering both water extent and stability. Unraveling the frequency structure of surface water and its nonlinear associations is critical for sustainable water resource management. Based on Landsat-derived surface water dynamics from 2015 to 2025, this study constructed water occurrence frequency (WOF) indicators for Lixiahe Plain (LP) and employed XGBoost-SHAP to identify key natural and human factors, nonlinear responses, and interaction effects. The results showed that: (1) the annual water surface ratio (WSR) exhibited a phased inverted-U pattern, increasing from 5.22% in 2015 to 6.04% in 2018, remaining high during 2018–2021, and decreasing to 4.57% in 2025; (2) WOF revealed marked stability restructuring, with low-frequency water accounting for 31.0–53.7% of annual water and becoming more dominant after 2023; (3) the XGBoost-SHAP models showed reliable performance, with R² of 0.714–0.813 and RMSE below 0.018. Natural factors contributed more than human factors, and wind speed (WS), normalized difference vegetation index (NDVI) related features, and cropland ratio (CropR) were the influential factors, with approximate response transitions of 2.5–3.0 m s⁻¹, 0.50–0.52, and 65–68%. These findings support refined water resource management and sustainable development in PRNs. Full article

The growing demand for natural aggregates and continued stockpiling of steel slag have increased interest in using steel slag aggregate (SSA) in road engineering. However, delayed hydration of free calcium oxide (f-CaO) and free magnesium oxide (f-MgO), porous and rough surfaces, and the potential release of hazardous elements constrain its long-term application. This review compares aging treatment, surface modification, direct carbonation, microbially induced calcium carbonate precipitation (MICP), and combined treatments from a raw-material heterogeneity and defect-oriented perspective. Their effectiveness is strongly condition-dependent. Aging treatment can control volume expansion, but reaction depth and treatment uniformity remain limited. Surface modification can reduce water absorption and improve interfacial performance but cannot eliminate internal expansive phases. Direct carbonation and MICP can stabilize reactive phases, refine pore structures, and reduce the mobility of some elements, but are limited by mass transfer and equipment requirements, and by mineralization uniformity and ammonium by-product management, respectively. Combined treatments can address multiple defects but increase process complexity, resource consumption, and quality-control requirements. Because material properties and evaluation methods vary among studies, reported performance gains should not be directly used for technology ranking. Instead, technology selection should follow the framework of “raw-material characteristics–dominant defects–preferred technology–engineering boundaries”. From a life-cycle perspective, sustainability depends on balancing resource and environmental benefits against additional treatment burdens. Near-term implementation should integrate raw-material classification, process monitoring, long-term durability and dynamic leaching verification, and the progressive incorporation of key performance and environmental indicators into road-material specifications and engineering acceptance criteria. Full article

Sustainable business models (SBMs) describe how organizations create, deliver, and capture value while simultaneously generating economic, environmental, and social benefits for a broad set of stakeholders. Although the field has expanded rapidly since the late 2000s, the literature remains conceptually dispersed: definitions proliferate, design tools and archetypes coexist without integration, and findings on the link between sustainability practices and firm performance remain inconsistent. To address this fragmentation, this study conducts a PRISMA 2020-guided systematic literature review of foundational and recent contributions published between 2008 and 2025. Of 300 records identified through database searching, 30 studies met the eligibility criteria and were synthesized qualitatively; these are interpreted together with complementary theoretical and methodological literature. Rather than reporting bibliometric indicators, the review synthesizes the field across three dimensions: the evolution of the SBM concept, its dominant design logics and archetypes, and its contemporary thematic landscape. The findings trace a progression from foundational triple-bottom-line conceptualization, through archetype- and tool-based design approaches such as the Triple-Layered Business Model Canvas and pattern taxonomies, toward digitally enabled, circular, and resilience-oriented models. These patterns are interpreted through complementary theoretical lenses—the resource-based and natural-resource-based views, dynamic capabilities, information-processing theory, and stakeholder theory. The review consolidates these insights into an integrative conceptual framework that links theoretical foundations, value-design dimensions, enabling capabilities, and triple-bottom-line outcomes. It further discusses implications for managers and outlines a research agenda emphasizing digital and circular enablers, longitudinal designs, and the under-examined context of emerging markets, with an applied emphasis on Central Asia and Kazakhstan. Full article

Energy planning should focus on actions to save energy and reduce consumption while also implementing renewable energy sources to support sustainable urban development. In addition to global regulations, individual countries also have documents that facilitate energy management at the local level. This is a particularly valuable source of information about resources that influence energy efficiency at the national level. This article analyzes the Low-Emission Economy Plans (LEEPs) developed for selected cities in Poland. Based on selected provisions, fuzzy modeling solutions are proposed to support energy decisions in municipalities. The research thesis assumes that the appropriate selection of criteria for emission reduction, as well as their objectification and hierarchization, when supported by fuzzy logic modeling and multi-criteria analysis, enables local governments to identify key variables and structures and compare decision scenarios relevant to local energy policies. To verify this thesis, an analysis of the LEEP provisions of four city municipalities located in the Kuyavian–Pomeranian Voivodeship was conducted. Based on these criteria, a set was identified, and diagrams were developed to identify variables and concepts that occupy key positions in the modeled pathways leading to emission reductions. Fuzzy logic modeling and multi-criteria analysis were used as decision-support tools in the research process. A comparison of the applied approaches enables the identification and prioritization of variables of greatest importance within the adopted set of criteria. The obtained results allow us to determine how the adopted energy strategies are linked to the implementation of local policy objectives and which relationships play a key role in the modeled decision-making structure. The analysis indicates that the decision-making variants differ in their impact on the paths leading to reduced final energy consumption and greenhouse gas emissions. Variant W1, which is based on investments in renewable energy sources, is strongly associated with the path leading to reduced greenhouse gas emissions, while increased public awareness and acceptance also play a significant role in the model’s structure. The strongest relationships were identified between increased energy efficiency and building energy standards, between building energy standards and reduced final energy consumption, and between reduced final energy consumption and reduced greenhouse gas emissions. The reasoning map thus highlighted the particular importance of the sequence of relationships linking energy efficiency, building energy standards, and reduced final energy consumption. The proposed approach can also provide a basis for further comparisons with solutions used in other countries, thus expanding the possibilities of analyzing low-emission policies at the local and national levels. Full article

Natural resource management under climate change generates interdependent economic, social, and environmental impacts. However, the scientific evidence remains fragmented. This fragmentation limits the design of integrated policies capable of reducing vulnerability and preventing the degradation of natural capital. The objective of this study is to analyze recent scientific literature to assess how natural resource management in the context of climate change simultaneously influences economic stability, social resilience, and environmental sustainability. To this end, a systematic review of literature published in indexed journals on environmental economics, climate change, and natural resource management was conducted, selecting quantitative and mixed-methods studies that examine economic, social, or biophysical impacts associated with environmental degradation, extractive dependence, and adaptation and mitigation strategies. The review integrated research at macroeconomic, microeconomic, and ecological scales, organized using comparative matrices that allowed for the identification of common patterns in indicators of economic loss, emissions, natural capital depreciation, and effects on social welfare. Subsequently, a comparative analysis was conducted to detect relationships between management failures, social vulnerability, and long-term costs, as well as to identify conceptual, methodological, and geographical gaps in the literature. The results show that the degradation of natural resources under climate change produces simultaneous effects on macroeconomic stability, household income, and ecosystem resilience, increasing the costs of inaction when policies are designed sectorally. The evidence synthesized in this review indicates that dependence on extractive activities, limited productive diversification, and institutional weaknesses are frequently associated with greater economic and social vulnerability, particularly in communities dependent on natural resources. The reviewed studies also suggest that adaptation and mitigation strategies that incorporate participatory governance, social capital, and natural capital conservation may contribute to strengthening resilience. However, given the heterogeneity of methodologies, spatial scales, and indicators among the analyzed studies, these findings should be interpreted as evidence of consistent patterns rather than causal relationships. Therefore, integrated approaches that consider economic, social, and environmental dimensions represent a promising direction for sustainable natural resource management under climate change, although further empirical research is required to evaluate their effectiveness across different contexts. Full article