Search Results (10,149)

Developing-country construction sectors continue to record disproportionately high occupational accident rates, partly attributable to the slow adoption of digital safety technologies, including Building Information Modeling (BIM) and Internet of Things (IoT) systems. While prior empirical research has established the population-level factors that explain stakeholder adoption intention through survey-based frameworks, the ability to classify individual stakeholder readiness for targeted, pre-deployment intervention remains methodologically unaddressed. This study fills that gap by applying three supervised machine learning classifiers (Random Forest [RF], XGBoost (XGB), and Support Vector Machine (SVM)) to a dataset of 107 construction professionals purposively sampled from large-scale infrastructure projects in Pakistan, including China−Pakistan Economic Corridor (CPEC) packages and the Barakahu Bypass project. Five construct-level features derived from an integrated Technology Acceptance Model and Technology−Organization−Environment (TAM-TOE) survey instrument were used to classify stakeholders into High, Moderate, and Low readiness tiers. XGBoost achieved the best classification performance (accuracy = 93%, macro F1 = 0.93), followed by RF (91%, F1 = 0.91) and SVM (87%, F1 = 0.87). The convergent performance across three structurally different algorithm families indicates that the readiness signal reflects a consistent attitudinal pattern rather than an artifact of any single modeling assumption. Feature importance analysis consistently identified Perceived Benefits (32%) and Technology Awareness (25%) as the dominant predictive features, followed by Organizational Readiness (20%), Perceived Barriers (15%), and Respondent Profile (8%). Attitudinal readiness mapping classified 62% of stakeholders as High readiness, 28% as Moderate, and 10% as Low, providing an exploratory attitudinal segmentation framework to assist construction managers in prioritizing capacity-building investments, subject to longitudinal behavioral validation. The study also finds that awareness of digital technology consistently outpaces Organizational Readiness for implementation, a pattern consistent with findings from analogous developing-country construction contexts. Full article

Against the backdrop of the dual-carbon strategy, China’s civil aviation industry, as a high-energy-consumption and high-carbon-emission sector, faces mounting pressure for low-carbon transformation. As the dominant airlines within China’s civil aviation system, Air China, China Eastern Airlines, and China Southern Airlines play a pivotal role in guiding the industry’s high-quality development. Employing the Global Malmquist–Luenberger (GML) index model, this study constructs a global production frontier incorporating undesirable outputs to systematically measure the dynamic evolution of total factor productivity (TFP) for the three major airlines in the period 2005–2023, and further applies a combined static-dynamic regression framework to identify the firm-level heterogeneous mechanisms through which explanatory factors operate. The results reveal significant heterogeneity in TFP trajectories: China Southern Airlines exhibits the most stable efficiency with the lowest volatility; China Eastern Airlines displays the greatest volatility but the strongest post-crisis rebound; and Air China occupies an intermediate position in both efficiency level and volatility. This differentiation stems from fundamental differences in market positioning, strategic orientation, and resource allocation patterns. Market competitiveness exerts a significantly positive effect on TFP for both Air China and China Eastern Airlines. Technological innovation investment generates short-run negative effects across all three airlines, albeit with divergent magnitudes. Human capital accumulation acts as a positive driver for Air China but produces a negative effect for China Southern Airlines, attributable to a structural mismatch between aggressive talent upgrading and organizational absorptive capacity. Shifting the unit of analysis to the firm level, this study identifies three heterogeneous strategic archetypes—market-led, scale-expansion, and regional-deepening—and constructs a differentiated “one firm, one policy” framework to provide targeted policy guidance for improving airline efficiency and facilitating low-carbon transition under carbon constraints. Full article

Green hydrogen—hydrogen produced from renewable electricity—is central to global decarbonization strategies. However, despite their fragile governance, damaged infrastructure, water scarcity, and limited investment security, conflict-affected developing economies remain largely absent from hydrogen research. This study addresses that gap by developing and validating a multi-evidence strategic framework for green-hydrogen (GH₂) adoption in fragile institutional environments, using Palestine as a challenging test case. Methodologically speaking, the framework integrates four evidence streams—barrier prioritization by 45 Palestinian experts using the Analytic Hierarchy Process (AHP); structural modeling of barrier–adoption–sustainability relationships using partial least squares structural equation modeling (PLS-SEM); strategic-pathway ranking using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); and an original Sustainable Development Goal (SDG) Contribution Index—externally validated by an independent panel of 120 energy experts across 18 Middle East and North Africa (MENA) countries. Three findings stand out. Firstly, expert perception and structural evidence diverge: technical barriers receive the highest expert weight (56.2%) yet show the weakest structural effect on adoption (β = −0.230), whereas social barriers, weighted lowest by experts (4.8%), rank second in predictive power (β = −0.310). Secondly, Small-Scale Community Production is the most robust deployment pathway, ranked first under every weighting scenario tested. Thirdly, government policy quality acts as a governance multiplier, raising the sustainability returns of adoption by 20.2%, with benefits concentrated in SDGs 7, 13, 8, and 9. Practically speaking, the framework yields seven strategic goals and a phased 2026–2040 roadmap for fragile developing economies. Full article

In this work, we present a neural network-based study capable of predicting faults in oil-insulated power transformers through the analysis of dissolved gases. The advantage of this study lies in using data already collected by electric power companies, which gather it to comply with international or regional standards; however, they sometimes act only after the equipment is already in a faulty condition. Therefore, the challenge in this work was data regularization, as collections typically occur at long intervals of 6 to 12 months. Furthermore, samples are often irregular, as data collection depends on factors such as weather and the availability of maintenance teams. As a result of this work, Multilayer Perceptron (MLP), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) were used to predict failures with advanced forecasts ranging from 1 to 6 months, achieving accuracies of 97.5% and 85%, respectively. Thus, these models prove to be important tools for maintenance planning, enabling adequate predictability for organizing equipment shutdowns without the need for high investments in installing tools to capture this information online and adapting substations to send data to control rooms or other analysis centers. Full article

Sea-level rise is accelerating coastal erosion and storm-driven flooding, increasing risks to estuarine ecosystems and coastal communities. Nature-based solutions (NbS), such as those including ecosystem restoration, are widely endorsed for climate change risk mitigation, yet their protective performance under rising sea levels remains poorly quantified across future scenarios. Here we combined scenario-based modelling with spatially explicit exposure mapping to assess how saltmarshes influence shoreline vulnerability under three Intergovernmental Panel on Climate Change (IPCC) Shared Socioeconomic Pathways (SSP) sea-level rise projections for 2050 and 2100. Using the InVEST Coastal Vulnerability Model and the Lima estuary (NW Portugal) as a case study, we showed that existing saltmarshes currently reduce mean shoreline exposure by approximately 5%, but this contribution declines with sea-level rise, falling to 2.6% by 2100 under SSP5-8.5, resulting in an increase in areas subject to High and Very High exposure risk. But under a saltmarsh revegetation scenario, model results indicated that this revegetation significantly increases the protection across all future scenarios, reducing the number of shoreline points in High and Very High exposure classes by up to 58% and lowering the potential coastal population exposure by up to 27% by 2100 under SSP5-8.5. However, the protective effect of saltmarshes diminished under the most extreme sea-level rise trajectories, indicating that saltmarsh revegetation alone may not be enough to fully offset accelerating coastal hazards. Our results demonstrate that saltmarsh restoration can deliver meaningful climate adaptation benefits; however, to safeguard estuarine systems and coastal communities under accelerating climate change in the long term, restoration actions must be integrated into broader adaptation strategies. Full article

Digital infrastructure may improve firm productivity, yet its economic value depends on whether firms can absorb external connectivity and embed it in production, management, and investment decisions. Using the staggered implementation of the “Broadband China” pilot policy as a quasi-natural experiment, this study examines the effect of city-level broadband infrastructure on the revenue-based labor productivity of Chinese A-share listed firms from 2009 to 2023. A multi-period difference-in-differences model shows that the pilot policy is associated with an increase in revenue per employee. The baseline estimate implies an economically meaningful increase of approximately 4.1%, and the result remains robust to alternative productivity measures, sample restrictions, stricter fixed effects, placebo tests, PSM-DID, and IPW-DID. CSDID estimates are positive but not statistically significant at conventional levels and are therefore interpreted as directionally consistent rather than independently confirmatory. Evidence based on total factor productivity, management expense intensity, and investment adjustment is consistent with production efficiency, management coordination, and organizational adjustment channels. Heterogeneity tests show stronger effects among non-state-owned, eastern region, and non-manufacturing firms. The findings suggest that broadband infrastructure generates productivity benefits when firms have the organizational absorptive capacity to convert external digital connectivity into internal operational efficiency. Full article

Accurate assessment of carbon storage dynamics and their driving factors is important for ecological sustainability and land management on the Loess Plateau under China’s dual carbon goals. In this study, the InVEST and PLUS models were integrated to evaluate carbon storage changes from 2000 to 2020 and simulate future carbon storage patterns for 2030 under four development scenarios, including natural development (ND), rapid development (RD), cropland protection (CP), and ecological protection (EP). In addition, the XGBoost-SHAP framework was employed to identify the dominant drivers and nonlinear response relationships controlling spatial variation in carbon storage. During 2000–2020, ecosystem carbon storage across the Loess Plateau generally increased, rising from 5.780 Pg to 5.893 Pg. Spatially, carbon storage displayed a pronounced pattern characterized by higher levels in the southeast and lower levels in the northwest, aligning with forest–grassland restoration belts. Scenario simulations showed that EP produced the largest carbon storage gain, with total carbon storage projected to reach 5.962 Pg in 2030. In contrast, RD reduced carbon storage to 5.858 Pg because of intensive construction land expansion. XGBoost-SHAP results identified net primary productivity (NPP) as the most influential factor controlling spatial variation in carbon storage, accounting for 57.3% of the total explanatory importance, whereas soil erosion (SE) exhibited a strong negative effect on carbon storage. Population density (POPD) also exerted a negative effect, whereas gross domestic product (GDP) showed positive contributions in economically developed counties. These findings enhance understanding of the spatial response characteristics of carbon storage under environmental gradients and human disturbance across the Loess Plateau. They further provide scientific support for differentiated ecological management and regionally adapted carbon mitigation planning. Full article

Fulfilling corporate ESG responsibilities enhances a firm’s sustainable development capabilities but also comes at an economic cost. This study investigates whether firms should invest heavily in ESG or maintain moderate ESG practices to balance cost efficiency and resilience. Using a sample of A-share listed companies in China from 2012 to 2024, we employ OLS regression models to explore the impact of ESG responsibility fulfillment on cost stickiness and the factors that influence this relationship. The study finds that (1) there is an inverted U-shaped relationship between corporate ESG responsibility fulfillment and cost stickiness; (2) the turning point lies between the B and CCC Huazheng ESG rating levels. Below this level, ESG responsibility fulfillment reduces cost stickiness, while above it, excessive ESG fulfillment increases cost stickiness; (3) environmental sensitivity, managerial overconfidence, and state ownership amplify this non-linear effect, making the reduction or increase in cost stickiness more pronounced. This paper deepens the understanding of the drivers of cost stickiness from the perspective of ESG responsibility fulfillment, offering new insights for future research on cost behavior and providing valuable guidance for firms seeking to optimize cost management through ESG strategies. Full article

Against the backdrop of increasingly stringent environmental regulation and increasing uncertainty in supply chain operations, this study examines how environmental penalties affect corporate supply chain resilience. Using Chinese A-share listed firms from 2009 to 2024, this paper constructs a firm-level panel dataset and employs a two-way fixed-effects model to estimate the relationship between environmental penalty intensity and supply chain resilience. Environmental penalty intensity is measured by the annual penalty amount imposed on each firm, while supply chain resilience is captured through an entropy-weighted index reflecting both resistance and recovery capacities. To alleviate endogeneity concerns, this study further uses an instrumental-variable approach based on the interaction between a firm’s one-year lagged penalty amount and city-level thermal inversion days. The results show that environmental penalties reduce corporate supply chain resilience. This negative effect is heterogeneous across firm characteristics and is partially mediated by reduced operational efficiency and crowded-out R&D investment. This conclusion remains robust after replacing the dependent variable, changing the clustering level of standard errors, and excluding observations from the COVID-19 pandemic period. Mechanism tests suggest that environmental penalties weaken supply chain resilience partly by reducing operational efficiency and crowding out R&D investment. Heterogeneity analysis indicates that the negative effect is more pronounced among young firms, non-high-tech firms, and firms located in regions with lower environmental regulation intensity. This study contributes to the literature by distinguishing environmental penalties from broader environmental regulation and by examining their implications for supply chain resilience. The findings also suggest that environmental enforcement should maintain deterrence while improving transparency, predictability, and targeted compliance guidance. Full article

This article provides the theoretical foundation for upcoming primary research on the formation of ‘integral generative AI (GenAI) innovation ecosystems’ in the Chinese higher education context. Based on an adaptation of Gramsci’s idea of the ‘integral state’, which informs the move beyond Western civil society/market-led and Chinese political state-led innovation ecosystem models, key features of an integral innovation GenAI ecosystem are elaborated upon. An expanded framework builds on previously published work on socialised GenAI systems comprising a multi-level approach, with particular emphasis on ‘thickened’ meso-institutional layers (e.g., supportive local investment, institutional governance frameworks and critical practices) mediating between an enhanced macro-strategic direction and upscaled micro-level practices. Theorising the institutional meso-system helps analyse challenges facing non-elite Chinese universities in moving from a ‘low-technological-baseline equilibrium’ (LTBE) constraining GenAI development to demonstrating features of GenAI innovation ecosystem ‘readiness’. The framework also draws on Lury’s ‘problem space’ research methodology, with a particular focus on its ‘within/without’ contextual factors, while also contributing a chrono-dimension to reinforce its conceptual role over time. The article concludes with an outline of a primary research strategy to investigate the challenges of building integral GenAI innovation ecosystems in Chinese higher education institutions more broadly. Full article

Focusing on contrasting climate and land use pathways, this analysis explores the changing trajectories of water conservation function over time. An integrated framework combining the PLUS and InVEST models with Spearman’s correlation analysis and geographically weighted regression (GWR) was applied to examine the spatiotemporal heterogeneity and underlying drivers of water conservation function in the Chengdu–Chongqing Economic Zone during the period 2000–2020. Thus, it further predicted the evolution trend under two scenarios, namely SSP1-1.9 (Sustainable Development Pathway) and SSP2-4.5 (Medium Development Pathway), for the period 2030–2050. The findings reveal that: (1) Between 2000 and 2020, the spatial distribution of water conservation function shifted markedly, with low-value areas contracting and high-value zones expanding, alongside a progressive transition toward a predominantly medium-to-high functional structure. (2) In mountainous and hilly transition zones, precipitation (PRE) and forest cover proportion (FCP) exhibited notably positive effects, whereas evapotranspiration (PET) exerted a negative effect. In contrast, in plain and urbanized areas, built-up land proportion (BUP), population density (POP), and gross domestic product density (GDP) demonstrated pronounced negative effects. (3) Future simulations indicate that under the sustainable development pathway (SSP1-1.9), the combined area of high and extreme functional zones will recover by 2050, whereas under the moderate development pathway (SSP2-4.5), such extreme functional zones will be nearly eliminated. These results underscore the substantial impact of development pathways on regional water security and sustainability. Full article

Precision feeding is an important foundation for improving production efficiency in aquaculture, reducing feed waste, mitigating water pollution, and promoting the intelligent development of aquaculture. Conventional feeding practices remain heavily dependent on operator experience and are typically executed at predetermined times or fixed ration levels. Such approaches frequently result in extensive feeding management, poor adaptability, low feed utilization efficiency, and delayed responses to environmental changes. Advances in machine vision, the Internet of Things, machine learning, deep learning, and automatic control have progressively shifted aquaculture feeding research beyond standalone automatic feeders toward integrated systems encompassing demand perception, intelligent decision-making, precise control, and equipment coordination. This paper reviews the state of the art in precision feeding technologies and equipment in aquaculture. At the technical level, it summarizes advances in feeding demand perception, intelligent feeding decision-making, and precise control and execution. At the equipment level, it reviews the main types, design features, and field application status of precision feeding equipment in intensive aquaculture, pond aquaculture, and offshore aquaculture scenarios. Despite the considerable progress achieved, the practical deployment of precision feeding still faces several limitations. Environmental disturbances, water turbidity, illumination variation, and sensor drift may compromise the reliability of feeding demand perception. Existing decision-making models frequently exhibit limited generalizability across species, growth stages, and aquaculture scenarios. Moreover, insufficient integration of sensing, decision-making, and execution restricts the development of fully closed-loop feeding systems. High initial investment, maintenance costs, and the shortage of skilled personnel further constrain the adoption of precision feeding equipment, particularly in resource-limited regions. On this basis, the main challenges including sensing accuracy, model practicability, closed-loop control, equipment reliability, and standardization, are examined. Future development trends are also discussed, covering multi-source information fusion, synergy between mechanistic models and data-driven methods, system-level closed-loop control, equipment modularization, and industrial application. This review is expected to provide a reference for subsequent research and engineering applications. Full article

To promote the sustainable development of agriculture and reduce resource waste, this paper investigates sustainable financing strategies for a green fresh agri-food supply chain. We employ a purely theoretical Stackelberg game model and numerical simulations based on hypothetical parameters to develop three financing models for a supply chain consisting of one capital-constrained farmer and one retailer, considering consumers’ dual sensitivity to product freshness and greenness. Analytical and numerical results reveal that: (1) with low financing rates, internal financing effectively alleviates under investment in preservation, leading to higher wholesale/retail prices. In a green-sensitive market, the resulting price premium compensates for cost increases, avoiding the “low quality–low price” trap under external financing. (2) The retailer’s total profit decreases as the internal financing rate rises; higher interest income cannot offset demand loss caused by reduced preservation effort. Thus, a low- or zero-interest strategy maximizes the retailer’s operational profit. (3) As consumer sensitivity to freshness and greenness increases, profit growth under internal financing displays convexity. However, under extremely high freshness sensitivity, external financing yields stronger marginal incentives, suggesting that retailers should adjust profit allocation in the high-end market. The findings provide theoretical guidance for financing mode selection and practical insights for promoting green agricultural sustainable development. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article

This study investigates place-based barriers to initial teacher training (ITT) recruitment in rural-coastal regions of England, focusing on Cornwall as a case study. Utilizing semi-structured interviews with nine ITT provider leaders and nine trainee teachers, the research applies the concept of educational isolation to ITT providers in areas that are geographically remote, socioeconomic disadvantaged, and culturally isolated. The analysis is framed by the critical pedagogy of place and social capital theory, moving beyond deficit-based interpretations of rurality to critically examine how place-based inequities are produced through urban-normative policy and resource allocation. Primary data were analyzed using reflexive thematic analysis. Four substantive themes emerged: transport dependency and accessibility constraints that structurally exclude lower-income and disabled trainees; housing displacement driven by the tourist economy, which compounds financial insecurity; an “employment precarity problem” where localized primary school oversaturation coexists with secondary teacher shortages; and cultural and professional isolation that disproportionately impacts ethnically diverse trainees in demographically homogeneous communities. The research further identifies that community resilience, while enabling individuals to navigate structural barriers, can obscure infrastructural inadequacy and diminish impetus for systemic policy reform. This paper contributes to international scholarship on spatial justice and rural teacher education by presenting an integrated conceptual framework with transferable relevance to similar rural-coastal and peripheral contexts globally and by offering policy recommendations for place-weighted ITT funding, infrastructure investment in educationally isolated areas, and the development of collaborative provider models. Full article