Search Results (447)

Pakistan’s construction sector exhibits a disproportionate safety burden, accounting for 19.7% of workplace accidents despite comprising only 9.5% of the industrial workforce. While prior studies have diagnosed safety culture deficiencies through qualitative assessments, behavioral interventions, and risk identification approaches, no research has provided quantitatively prioritized intervention recommendations integrating regulatory mechanisms across project lifecycle phases with role-specific training programs. Grounded in Hudson’s safety culture maturity model and Reason’s organizational accident theory, this study employed a quantitative survey methodology to prioritize regulatory and training interventions for safety culture improvement. A 43-item questionnaire was administered to 105 construction industry professionals across client organizations (21%), contractor firms (30%), and consulting companies (49%) using stratified purposive sampling. Data were analyzed using Relative Importance Index calculations, supplemented by Kruskal–Wallis tests and correlation analyses, with instrument reliability being confirmed through Cronbach’s alpha (α = 0.859). Key findings reveal that 75.2% of respondents reported absence of safety culture implementation at project sites. Stakeholder effectiveness analysis identified sponsoring agencies (RII = 0.869) and regulatory authorities (RII = 0.819) as primary safety culture drivers, with clients demonstrating the lowest influence (RII = 0.568). Training prioritization established personal protective equipment utilization (RII = 0.886) and basic induction protocols (RII = 0.850) as critical worker competencies, while managerial requirements emphasized site-specific briefings (RII = 0.821) and incident protection training (RII = 0.808). Regulatory intervention priorities included safety credit points for contractor licensing (RII = 0.737) and departmental safety manual integration into contracts (RII = 0.726). These findings provide quantitative prioritization to guide resource allocation for safety culture interventions, although the descriptive methodology constrains causal inference regarding intervention effectiveness. Full article

Rural revitalization constitutes a vital strategic initiative in advancing China’s socialist modernization. At the 2023 Central Economic Work Conference, the objective of building China into a financial powerhouse was formally articulated, thereby establishing higher benchmarks for financial support of rural revitalization. A critical question arising from this agenda is how to simultaneously advance agricultural technological innovation while effectively implementing green development principles. Accordingly, it is essential to investigate the role of the integrated development of sci-tech finance and green finance in promoting rural revitalization. Against this backdrop, this study employs provincial-level panel data from China spanning the period from 2011 to 2021. A two-way fixed effects model is adopted to examine the impact of the integrated development of sci-tech finance and green finance on rural revitalization. The analysis identifies three primary transmission mechanisms: financial supply, green agricultural development, and linkages between smallholder farmers and modern agriculture. Furthermore, the study explores heterogeneity across different financial environments from two dimensions: the level of digital inclusive finance development and the intensity of financial regulation. The empirical results indicate that (1) the integrated development of sci-tech finance and green finance significantly promotes rural revitalization, exhibiting a nonlinear effect whereby its catalytic impact intensifies markedly once the coupling coordination between the two surpasses a critical threshold; (2) such integration alleviates rural financing constraints, enhances agricultural green total factor productivity, and facilitates rural revitalization through the establishment of green agricultural cooperatives; and (3) the enhanced impact of this holistic progress is particularly noticeable in areas with advanced digital financial inclusion and robust financial oversight. In light of these results, this research puts forth three policy suggestions. First, institutional and policy preparations for integrating green finance and sci-tech finance should be accelerated through coordinated government policies, financial product innovation, and financial market reforms. Second, the channels through which sci-tech finance and green finance support rural revitalization should be strengthened by expanding agricultural credit, improving the coverage of rural financial institutions, and fostering specialized green agricultural cooperatives. Third, the financial ecosystem should be optimized by prioritizing investment in digital infrastructure and reinforcing financial supervision throughout the development of digital inclusive finance, particularly in rural regions. Full article

Credit cards play a central role in household financial behavior by combining payment and short-term financing functions shaped by socioeconomic, cognitive, and attitudinal factors. This study examines the determinants of credit card use and repayment behavior in Ecuador, focusing on purchasing power, financial literacy, and institutional trust. A quantitative, cross-sectional, and explanatory design was applied to a probabilistic sample of 550 credit card users from Quito and Santo Domingo. Multivariate analyses and Partial Least Squares Structural Equation Modeling (PLS-SEM)—including formative and hierarchical constructs—were used to validate the proposed behavioral framework. The results show that higher income is associated with more responsible repayment, while financial literacy and trust mediate this relationship through cognitive and attitudinal mechanisms. Moderate R² values and small-to-moderate f² effect sizes align with patterns observed in other Latin American credit markets. Behavioral differences also emerge across age, gender, and household composition, underscoring the heterogeneity of financial capability in the region. The findings demonstrate that responsible credit card indebtedness depends not only on economic capacity but also on financial knowledge and institutional trust, offering practical implications for financial inclusion policies and targeted education programs in emerging economies. Full article

Credit risk refers to the possibility that a borrower fails to meet contractual repayment obligations, posing potential losses to lenders. This study aims to enhance post-loan default prediction in credit risk management by constructing a time-series modeling framework based on repayment behavior data, enabling the capture of repayment risks that emerge after loan issuance. To achieve this objective, a Residual Enhanced Encoder Bidirectional Long Short-Term Memory (ResE-BiLSTM) model is proposed, in which the attention mechanism is responsible for discovering long-range correlations, while the residual connections ensure the preservation of distant information. This design mitigates the tendency of conventional recurrent architectures to overemphasize recent inputs while underrepresenting distant temporal information in long-term dependency modeling. Using the real-world large-scale Freddie Mac Single-Family Loan-Level Dataset, the model is evaluated on 44 independent cohorts and compared with five baseline models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) across multiple evaluation metrics. The experimental results demonstrate that ResE-BiLSTM achieves superior performance on key indicators such as F1 and AUC, with average values of 0.92 and 0.97, respectively, and demonstrates robust performance across different feature window lengths and resampling settings. Ablation experiments and SHapley Additive exPlanations (SHAP)-based interpretability analyses further reveal that the model captures non-monotonic temporal importance patterns across key financial features. This study advances time-series–based anomaly detection for credit risk prediction by integrating global and local temporal learning. The findings offer practical value for financial institutions and risk management practitioners, while also providing methodological insights and a transferable modeling paradigm for future research on credit risk assessment. Full article

Leveraging the exogenous shock of Chinese commercial banks’ adoption of the United Nations Principles for Responsible Banking (UNPRB) as a quasi-natural experiment, this study employs a Time-Varying Difference-in-Differences (TV-DID) approach to investigate how formally committed responsible credit, augmented by Fintech, enhances ESG performance in private enterprises. The findings reveal that banks adopting UNPRB significantly improve the post-loan ESG performance of their private enterprise borrowers compared to non-adopting banks, with Fintech serving as a positive moderator. Mechanism analysis indicates that, under the empowerment of financial technology, commercial banks that extend loans to enterprises are influenced by the signing of the United Nations Principles for Responsible Banking (UNPRB). Banks promote the sustainable development of enterprises through pre-loan “screening effects” and post-loan green “governance effects”. Heterogeneity analysis indicates stronger ESG improvement effects for enterprises in environmentally sensitive industries, those with high capital intensity, and those holding long-term loans. Extended research further identifies a significant enhancement in ESG alignment between banks and enterprises following UNPRB adoption. By examining responsible credit investment, this study not only broadens the scholarly discourse on sustainable finance and Fintech but also offers empirical insights from a representative emerging market context. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a growing global health challenge, driven in part by how antibiotics are accessed, distributed, and used within complex value chains. In peri-urban India, these supply chains involve a range of formal and informal actors and practices, making them a critical yet underexamined focus for antimicrobial stewardship efforts. While much research has focused on the manufacturing and regulatory end, less is known about how antibiotics reach consumers in rural and peri-urban settings. This study aimed to map the human antibiotic value chain in West Bengal, India, and to analyse how formal and informal governance structures influence antibiotic use and stewardship. Methods: This qualitative study was conducted in two Gram Panchayats in South 24 Parganas district, West Bengal, India. Semi-structured interviews were carried out with 31 key informants, including informal providers, medical representatives, wholesalers, pharmacists, and regulators. Interviews explored the structure of the antibiotic value chain, actor relationships, and regulatory mechanisms. Data were analysed thematically using a value chain governance framework and NVivo 12 for coding. Results: The antibiotic value chain in rural West Bengal is highly fragmented and governed by overlapping formal and informal rules. Multiple actors—many holding dual or unofficial roles—operate across four to five tiers of distribution. Informal providers play a central role in both prescription and dispensing, often without legal licences but with strong community trust. Informal norms, credit systems, and market incentives shape prescribing behaviour, while formal regulatory enforcement is inconsistent or absent. Conclusions: Efforts to promote antibiotic stewardship must move beyond binary formal–informal distinctions and target governance structures across the entire value chain. Greater attention should be paid to actors higher up the chain, including wholesalers and pharmaceutical marketing networks, to improve stewardship and access simultaneously. This study highlights how fragmented governance structures, overlapping actor roles, and uneven regulation within antibiotic value chains create critical gaps that must be addressed to design effective antimicrobial stewardship strategies. Full article

This study estimates the prevalence of food insecurity and coping mechanisms among low-income rural households in Sri Lanka, collecting primary data from 400 households in the Ayagama Divisional Secretariat in Rathnapura District. The results uncover that around 38.1% of the households faced food scarcity within a year prior to the survey date, with 77.9% being uncertain about maintaining a nutritious diet in the next 30 days. Notably, household dietary diversity scores reveal that they are either moderately (62%) or severely (22.3%) lacking essential nutrients, irrespective of the gender of the household head. The leading cause is the unaffordability of protein-rich foods and certain fruits. Coping strategies are primarily short-term and consumption-based, such as purchasing food on credit and reducing meal sizes, which propagate future food insecurity. The findings underscore the need for government interventions that combine short-term safety nets with long-term agricultural productivity improvements, alongside nutrition-sensitive practices and market stabilisation to enhance food availability and affordability. Consequently, targeted social protection programmes for vulnerable groups, combined with livelihood support and climate-resilient agriculture, could reduce reliance on harmful coping mechanisms. Lastly, this study proposes integrating food security goals within broader development frameworks and community initiatives as pivotal for long-term stability and resilience. Full article

Rising residential land prices push up housing prices and worsen credit misallocation. These patterns emerge amid cyclical real estate fluctuations and heavy land-based public finance. Such pressures undermine macroeconomic stability and sustainable land-use. The land price circuit breaker is widely applied with a price cap and state dependence, yet its trigger mechanism and interaction with inflation targeting remain underexplored. This study addresses three core questions. First, how does the circuit breaker’s discrete trigger and rule-switching logic differ from traditional static price ceilings? Second, can the mechanism, via the collateral channel, restrain excessive land price hikes, improve credit allocation, and, thereby, stabilize land price dynamics and long-run macroeconomic performance? Third, how does the circuit breaker interact with inflation targeting, and through which endogenous channels does a strict target dampen housing prices and raise activation probability? This study develops a multi-sector DSGE model with an embedded land price circuit breaker. The price cap is modeled as an occasionally binding constraint. A dynamic price band and trigger indicator capture the policy’s switch between slack and binding states. The framework incorporates interactions among local governments, the central bank, developers, and households. It also links firms and the secondary housing market. Under different inflation-targeting rules, this study uses impulse responses, an event study, and welfare analysis to assess trigger conditions and macroeconomic effects. The findings are threefold. First, a strict inflation target increases the probability of a circuit breaker being triggered. It channels housing-demand shocks toward land prices and creates a “nominal anchor–relative price constraint” linkage. Second, once activated, the circuit breaker narrows the gap between land price and house-price growth. It weakens the procyclicality of collateral values. It also restrains credit expansion by impatient households. These effects redirect credit toward firms, improve corporate financing, reduce the decline in investment, and accelerate output recovery. Third, the circuit breaker limits new land supply and shifts demand toward the secondary housing market. This generates a supply-side effect that releases existing stock and stabilizes prices, thereby weakening the amplification mechanism of housing cycles. This study identifies the endogenous trigger logic and cross-market transmission of the land price circuit breaker under a strict inflation target. It shows that the mechanism is not merely a price-management tool in the land market but a systemic policy variable that links the real estate, finance, and fiscal sectors. By dampening real estate procyclicality, improving credit allocation, and stabilizing macroeconomic fluctuations, the mechanism offers new insights for sustainable land-use policy and macroeconomic stabilization. Full article

Farmers’ low-carbon transition has become a critical issue for achieving sustainable agricultural development. Fundamentally, this transition is driven by multi-actor collaboration and is subject to stochastic disturbances. However, the collaborative governance mechanisms that facilitate farmers’ low-carbon transformation remain insufficiently understood, particularly under the influence of random factors. To address this gap, we construct a four-party game model involving farmers, government, enterprises, and financial institutions by employing a stochastic evolutionary game approach that incorporates random disturbance factors to capture real-world uncertainty. Numerical simulations are conducted to examine how different policy tools and external environments shape the system’s evolutionary path. The results show the following: (1) In the early transition stage, external uncertainties cause notable fluctuations in strategy evolution, during which the government, farmers, and enterprises gradually form a collaborative mechanism, while financial institutions remain reluctant to participate due to risk and policy uncertainty. (2) Government subsidies, profit returns, and risk-sharing mechanisms exhibit a substitutive relationship, and an appropriate mix of these tools can effectively enhance the willingness of farmers and enterprises to adopt low-carbon practices. (3) Excessive government incentives may crowd out the role of green credit from financial institutions. (4) The profit-sharing ratio among farmers exerts the strongest motivational effect in the early stage, while higher levels of risk-sharing and reputation benefits are more effective in stabilizing the system structure and enhancing transition resilience. This study reveals the dynamic mechanisms of multi-actor interaction in agricultural low-carbon transition and provides theoretical and policy insights for differentiated government strategies and collaborative emission reduction. Full article

Agricultural insurance, as a stabilizer, is crucial for the promotion of agricultural modernization. Therefore, exploring the impact mechanism of agricultural insurance on agricultural modernization and seeking ways to promote it has important practical significance. This study uses China’s provincial panel data from 2008 to 2023 to empirically analyze the direct effect of agricultural insurance on agricultural modernization. The mediation effect, spatial Durbin, and threshold models are used to further explore the internal mechanism of agricultural insurance on agricultural modernization. Results reveal that (1) agricultural insurance plays a significant role in promoting agricultural modernization, with its robustness verified across various models and endogeneity tests. (2) Agricultural insurance can promote agricultural modernization effectively by expanding the scale of agricultural operations, increasing agricultural capital input, enhancing agricultural technology input, and promoting green agricultural production. (3) Agricultural insurance has a positive spatial spillover effect on the development of agricultural modernization in neighboring provinces. Furthermore, there is a threshold effect of agricultural insurance in promoting agricultural modernization, showing stronger effects in rural areas where the human capital level exceeds the single threshold or where the economic development level falls between the single and triple thresholds. (4) Heterogeneity analysis reveals that agricultural insurance exerts stronger promotional effects on agricultural modernization in non-grain-producing regions, in eastern and central areas, and during the initial stages of insurance development. The study proposes recommendations such as the differentiated promotion of agricultural insurance, enhancing the directionality of agricultural insurance policies, and improving the linkage mechanism between agricultural insurance and credit. Full article

Remediating complex-contaminated soils demands the synergistic optimization of efficiency, cost-effectiveness, and carbon emission reduction. Currently, ultra-high-temperature thermal desorption technology is mature in terms of principle and laboratory-scale performance; however, ongoing efforts are focusing on achieving stable, efficient, controllable, and cost-optimized operation in large-scale engineering applications. To address this gap, this study aimed to (1) verify the energy efficiency and economic benefits of removing over 98% of target pollutants at a 7.5 × 10⁴ m³ contaminated site and (2) elucidate the mechanisms underlying parallel scale–technology dual-factor cost reduction and energy–carbon–cost optimization, thereby accumulating case experience and data support for large-scale engineering deployment. To achieve these objectives, a “thermal stability–chemical oxidizability” classification criterion was developed to guide a parallel remediation strategy, integrating ex situ ultra-high-temperature thermal desorption (1000 °C) with persulfate-based chemical oxidation. This strategy was implemented at a 7.5 × 10⁴ m³ large-scale site, delivering robust performance: the total petroleum hydrocarbon (TPH) and pentachlorophenol (PCP) removal efficiencies exceeded 99%, with a median removal rate of 98% for polycyclic aromatic hydrocarbons (PAHs). It also provided a critical operational example of a large-scale engineering application, demonstrating a daily treatment capacity of 987 m³, a unit remediation cost of 800 CNY·m⁻³, and energy consumption of 820 kWh·m⁻³, outperforming established benchmarks reported in the literature. A net reduction of 2.9 kilotonnes of CO₂ equivalent (kt CO₂e) in greenhouse gas emissions was achieved, which could be further enhanced with an additional 8.8 kt CO₂e by integrating a hybrid renewable energy system (70% photovoltaic–molten salt thermal storage + 30% green power). In summary, this study establishes a “high-temperature–parallel oxidation–low-carbon energy” framework for the rapid remediation of large-scale multi-contaminant sites, proposes a feasible pathway toward developing a soil carbon credit mechanism, and fills a critical gap between laboratory-scale success and large-scale engineering applications of ultra-high-temperature remediation technologies. Full article

Integrating Environmental, Social, and Governance (ESG) metrics into supply chain finance is critical for promoting sustainable development. However, the dynamic mechanisms through which real-time ESG performance influences credit allocation and, consequently, shapes credit risk and environmental risk exposures for financial institutions, remain poorly understood, especially when compared to traditional static and retrospective ESG evaluations. To address this, we developed an agent-based model that simulates interactions among green enterprises, a financial institution, and a regulator, featuring a dynamic credit algorithm that adjusts credit lines based on real-time ESG scores. Our simulations demonstrate that ESG-driven credit policies significantly boost green technology adoption among SMEs, raising adoption rates from 20% to over 85% under strong incentives, which in turn drives a substantial reduction of the supply chain’s carbon footprint by more than 50%. Notably, this environmental benefit is achieved without a commensurate surge in credit risk, as the non-performing loan ratio only experienced a moderate increase. Additionally, sensitivity analysis reveals a non-linear relationship between the ESG weighting in credit decisions and environmental outcomes, identifying a critical threshold for policy effectiveness. Our findings offer risk managers and policymakers evidence-backed strategies for designing dynamic incentives that effectively promote supply chain decarbonization while managing associated financial risks. Full article

Digital technologies have rapidly penetrated rural China, transforming household economic structures and reshaping agricultural factor markets. Drawing on 3930 household-level observations from six provinces in the Yellow River Basin (2020–2023), this research aims to examine how digital technology adoption influences the optimal allocation of household labor, land, and capital. To address self-selection and endogeneity, we employed an Endogenous Switching Probit (ESP) model and conducted counterfactual analysis, supplemented by propensity score matching (PSM), instrumental variable probit (IV-Probit), replacement of the core explanatory variable, and exclusion of special samples as four robustness checks. The Average Treatment Effects on the Treated show that digital technology adopters would have reduced the probabilities of non-farm employment, farmland transfer-out, and productive loan access by 24.5, 19.3, and 16.7 percentage points, if they had not adopted digital technology. Similarly, digital technology non-adopters would have improved 27.4, 22.2, and 18.9 percentage points if they adopted digital tools. These impacts are stronger over time, in the upper reaches of the Yellow River Basin, among households with larger landholdings, and among younger farmers. Mechanism analysis further indicates that digital technologies expand information access, strengthen social networks, and ease credit constraints, thereby jointly promoting more efficient labor, land, and capital allocation. The policy implications of these findings are as follows: the importance of improving rural digital infrastructure, tailoring regional policies, and enhancing farmers’ digital skills to narrow the digital divide and support inclusive rural revitalization. Full article

This study aimed to evaluate the performance of the main Arabica and Conilon coffee-producing regions in Brazil in the 2018–2019 and 2020–2021 harvest years, through the triangulation of the principal component analysis (PCA) and data envelopment analysis (DEA) techniques. To this end, the study followed a qualitative–quantitative approach, with descriptive character and inductive logic. The timeframe for this was 12 months to complete all methodological stages. Regarding efficiencies, six inefficient producers were identified for 2018–2019 and nine for 2020–2021. The results showed, in the 2018–2019 biennium, that production effectiveness is related to reductions in labor hiring and the creation of mechanisms to increase income on inefficient properties. On the other hand, in the 2020–2021 biennium, the intensive use of organic fertilizer and government credit were the most impactful aspects on the efficiency of properties. The contributions of this study were related to the identification of inefficient producers and, above all, the variables that most impact the performance of these sampling units so that they can reestablish their efficiencies. This study allowed for the generation of sustainable indicators to measure producers’ performance. For the agenda of future studies, it is suggested to replicate this study for other cultures and to expand the sample set. Full article

As a vital component of urban transportation systems, subways play a crucial role in the development of a city. However, opportunistic behaviors by subway construction contractors frequently occur, adversely affecting project objectives. This study employs literature review methods to identify six key dimensions that influence the governance of opportunistic behaviors by subway project contractors, thereby constructing a theoretical model of governance factors. Based on this theoretical framework, hypothesis testing and questionnaire design were conducted. Structural Equation Modeling (SEM) path analysis identified construction process management as the direct cause influencing the governance of opportunistic behavior by subway project contractors, exerting a direct effect on such governance. Meanwhile, industry standardization, external oversight mechanisms, project governance quality, contractor credit evaluation, and internal organizational controls within the construction firm were identified as indirect governance factors. A system dynamics model was employed for dynamic simulation analysis of the governance system, revealing the dynamic evolution of opportunistic behavior governance levels under various influencing factors. Scenario simulations identified the pathway, industry standardization → internal controls within the construction organization → project governance quality → construction process management → opportunistic behavior governance, as yielding the lowest frequency of opportunistic behavior occurrence and optimal governance levels. The findings provide a governance basis for addressing the frequent occurrence of opportunistic behavior in subway construction projects. Full article