Search Results (1,291)

In the last 10 years, the global green bond market has reached an estimated value of USD 6.8 trillion. However, credibility concerns persist due to greenwashing risks and issues regarding the reporting system. The current measurement, reporting, and verification systems (MRV) have high uncertainty levels of 10–30%, and so they contribute to information asymmetries and fuel investor skepticism when allocating capital to green bond instruments. The scope of this study is to develop an integrated management approach that links air quality and greenhouse gas monitoring with financial incentives throughout the lifecycle of green bonds. The central contribution is a four-phase lifecycle model covering issuance, allocation, monitoring, and impact reporting, which systematically identifies where greenwashing risks and verification gaps arise across the investment cycle. Methodologically, the study combines qualitative content analysis, a novel Disclosure Quality Score (DQS) instrument, based on the Regulation (EU) 2023/2631, four documentary case studies, and an advanced verification framework. The content analysis shows that regulatory and market-performance studies dominate the literature, while integrated lifecycle verification frameworks remain less explored. The DQS uses eight indicators, applied to a matched sample of green bonds, in accordance with the European Green Bond Standard (EuGB) and the ICMA Green Bond Principles (GBP). The results demonstrate that bonds issued under the EuGB present higher disclosure quality (mean DQS = 15.4/16) compared to GBP-aligned bonds (mean DQS = 11.4/16). Case studies show strong issuance-stage disclosure, but weak post-issuance verification. The framework enables lifecycle-wide accountability by reducing information asymmetry. The proposed lifecycle framework and DQS instrument offer a replicable model for improving disclosure quality and ESG performance standards, with direct implications for sustainable investment screening and ESG fund selection. Overall, the findings show that improving green bond credibility requires moving beyond issuance-focused disclosure toward lifecycle-wide verification. Full article

This paper examines whether quantile-based connectedness measures contain useful information for portfolio risk management. Using U.S. sector equity data, we estimate connectedness measures within a quantile vector autoregression framework and construct portfolios based on the cross-sectional distribution of net connectedness. In particular, sectors identified as extreme shock transmitters receive lower portfolio weights. Our results reveal substantial asymmetries across quantiles. Portfolios constructed using lower-tail connectedness measures exhibit smaller maximum drawdowns and lower expected shortfall relative to both equal-weight benchmarks and portfolios based on upper-tail connectedness. By contrast, median connectedness measures tend to provide more stable overall portfolio performance and lower turnover. The findings also suggest that the informational content of connectedness depends critically on the quantile considered. Lower-tail connectedness becomes particularly informative during crisis periods, suggesting that downside spillovers play an important role in portfolio resilience and systemic risk transmission. Overall, the results demonstrate that quantile connectedness measures provide economically meaningful information for downside risk management and offer a simple and transparent framework for incorporating systemic risk into portfolio construction. Full article

This paper analyzes a model of the mortgage market, allowing for scenarios with and without government-sponsored mortgage securitization. Conventional wisdom says that securitization, by fostering diversification and creating a “safe” asset in the form of a mortgage-backed security (MBS), will reduce risk and enhance liquidity, thereby abating financial crises. Our contribution is to examine this claim by imbedding the mortgage market with a sequential strategic game played between the securitizer and banks. In this setting, adverse selection arises from the securitizer’s first-mover advantage rather than from informational asymmetries. In the model, the securitizer chooses the MBS contract terms, including the guaranteed rate and the criterion that qualifies a mortgage for securitization. Banks respond by selecting which qualifying mortgages to exchange for the MBS. Our analysis yields a central result: within this framework, government-sponsored securitization is, somewhat counterintuitively, more likely to exacerbate the severity and frequency of financial crises. This outcome arises in particular when mortgage demand is sufficiently low that originators optimally choose not to retain any higher-risk mortgages on their balance sheets. Full article

Limited evidence exists regarding longitudinal changes in neuromuscular function throughout a competitive season and following time-loss injuries in professional football. The aim of this study was to explore the longitudinal alterations related to time-loss injuries on surface electromyography (SEMG) and vertical jump performance in professional football players. Fourteen U-23 male professional football players were assessed at two time points during the 2019–2020 competitive season. SEMG activity of the rectus femoris, vastus medialis, vastus lateralis, biceps femoris, and semitendinosus was evaluated during the bulgarian squat (BS) and single knee straight bridge (SKSB) exercises. Vertical jump performance was assessed using squat jump (SJ) and countermovement jump (CMJ) tests performed on force platforms. Participants were classified as players experiencing time-loss injuries (IPs; n = 6) or players who remained continuously available (NIPs; n = 8) according to the occurrence of a time-loss injury (>8 days). Repeated-measures ANOVA was used to examine the effects of time point, group, limb, and exercise phase. Significant time point × group interactions were observed for CMJ inter-limb asymmetry variables, with IPs demonstrating reduced asymmetry values over time. During the SKSB exercise, biceps femoris activation was significantly lower in IPs (η²p = 0.38) compared with NIPs. No consistent alterations related to time-loss injuries were identified during the BS exercise. No consistent differences related to time-loss injuries were observed in conventional vertical jump performance measures. These observations suggest that SEMG assessment during posterior-chain exercises may provide complementary information regarding neuromuscular function in professional football players experiencing time-loss injuries beyond that obtained from conventional vertical jump assessments. Nevertheless, these observations should be interpreted with caution given the small sample size, the heterogeneous injury profile, and the exploratory nature of the present investigation. Full article

This study constructs a Stackelberg game model for a supply chain comprising a capital-constrained manufacturer and a retailer with private demand information to analyze the interplay between the manufacturer’s carbon abatement financing strategy (bank credit vs. trade credit) and the retailer’s information sharing decision. The results show that the impact of information sharing is contingent on market sentiment. Sharing is beneficial, promoting abatement investment and supply chain performance under optimistic market forecasts, but it can suppress investment and increase emissions under pessimistic ones. Furthermore, the manufacturer’s financing choice is driven by the investment cost and carbon price; trade credit becomes more attractive for emission reduction as investment costs decrease and carbon prices rise. Counter-intuitively, higher forecast accuracy does not always incentivize information sharing, with its effects on disclosure and environmental performance exhibiting distinct threshold characteristics. Full article

Promoting the coordinated development of digitalization and greening has become an important pathway for firms to achieve high-quality growth. Using panel data for A-share listed firms in China’s Yangtze River Basin from 2010 to 2022, this study examines the effect of supply chain innovation policy on firms’ digital–green development. We measure the synergy between digitalization and greening using a composite system synergy approach and identify the policy effect through a quasi-natural experiment based on the supply chain innovation policy, combined with a synthetic difference-in-differences model. The results show that the policy significantly improves the coordinated development of firm digitalization and greening, and the findings remain robust across a series of tests. Mechanism analysis indicates that this effect operates through three channels: easing financing constraints, increasing supply chain diversification, and promoting industrial chain modernization. Moderating effect tests further show that supply chain efficiency, supply chain resilience, and entrepreneurship strengthen the policy’s positive effect on digital–green development. Heterogeneity analysis suggests that the policy effect varies systematically with firm size, market competitiveness, and information asymmetry. This study provides micro-level evidence on how supply chain innovation policy can promote firms’ digital–green transformation and offers useful implications for policies aimed at improving firm competitiveness and supporting sustainable development. Full article

Government data governance has become an important institutional mechanism for reducing information frictions, improving data-resource allocation, and supporting firm innovation in the digital economy. However, whether government data governance can promote firms’ technological catch-up remains insufficiently understood. Based on the quasi-natural experiment of the establishment of Big Data Administrations, this study constructs a multi-period difference-in-differences model to examine the impact of government data governance on firms’ technological catch-up. Using panel data from Chinese A-share listed firms from 2011 to 2021, the DID estimates indicate that the establishment of Big Data Administrations significantly improves firms’ technological catch-up. This estimated effect remains robust across placebo tests, specifications controlling for differential trends associated with pre-treatment city characteristics, and double/debiased machine learning estimation. Mechanism analyses provide evidence consistent with three channels: technology stimulation, digital-ecosystem optimization, and competition strengthening. Heterogeneity analyses further show that the effect is evident among non-state-owned enterprises, firms with higher information asymmetry, and larger firms. Additional spatial analyses suggest that neighboring cities’ data governance capacity does not generate stable positive spillovers; instead, it may be associated with negative spatial externalities, potentially reflecting siphoning or competitive crowding-out pressures. These findings highlight government data governance as an institutional driver of firm technological progress and provide policy implications for improving digital governance capacity, optimizing digital ecosystems, and promoting high-quality development. Full article

The open sharing of data as a factor of production is an important institutional mechanism for promoting sustainable innovation in the digital economy. Using Chinese A-share listed firms as the research sample and exploiting the staggered rollout of government open data (GOD) platforms across prefecture-level cities as a quasi-natural experiment, this paper constructs a staggered difference-in-differences (DID) model to examine the effect of GOD on green collaborative innovation (GCI) and its underlying mechanisms. The results show that GOD significantly promotes GCI, indicating that open government data can help firms strengthen collaboration in green innovation and contribute to more sustainable development. Mechanism analysis shows that GOD promotes GCI through four channels: increasing government subsidies, reducing information asymmetry, raising public environmental awareness, and advancing corporate digital transformation. Heterogeneity analysis reveals that the innovation-promoting effect of GOD is more pronounced in large cities, non-resource-based cities, and southern cities, and is more salient among state-owned enterprises, capital-intensive firms, and mature firms. This paper provides empirical evidence on the microeconomic effects of market-oriented data allocation and highlights the role of GOD in supporting GCI, corporate sustainable transformation, and the sustainable development of the digital economy. Full article

Global retail e-commerce sales have surged, yet product fit uncertainty remains a significant challenge, leading to rising product return rates. To address consumer concerns about return shipping costs, major Chinese online retail platforms have introduced return shipping insurance (RSI). Retailers can choose between Retailer-RSI (RRSI), which is provided by the retailer, and Customer-RSI (CRSI), which is purchased by consumers. Despite these options, information asymmetry causes insurers to assess return rates with bias—referred to as managerial confidence bias. Consequently, platforms are increasingly partnering with insurers to enhance their RSI offerings. This study develops a game-theoretical model to examine the dynamics between a platform and an insurer, as well as the impact of managerial confidence bias on RSI strategies. Our analysis reveals that the platform–insurer relationship is crucial in determining the optimal RSI strategy. Under an independent insurer, RSI is viable only if the insurer underestimates product return rates (i.e., exhibits overconfidence bias); RRSI is preferred if the bias is sufficiently strong, whereas CRSI is chosen otherwise. In contrast, under a dependent insurer, CRSI is favored by the retailer only when its return handling costs are substantially high; otherwise, RRSI is preferred. Furthermore, RSI consistently increases consumer surplus by reducing return hassle costs while only mildly raising the product price. However, the independent insurer’s bias leads to its own profit loss, resulting in a “loss–win–win–win” scenario across stakeholders. In contrast, the dependent insurer, supported by platform subsidies, can yield a “win–win–win–win” outcome that aligns stakeholder interests and enhances long-term platform benefits. Full article

Conventional ultrasound-based assessment of aortic stiffness relies on two-point distension metrics using maximum and minimum vessel diameters within a cardiac cycle, which may not fully reflect time-resolved aortic wall dynamics. This retrospective pilot study investigated the feasibility and clinical relevance of a time-series-based stiffness parameter, termed pulsatile stiffness-β, derived from automated segmentation of archived aortic M-mode ultrasound images. Seventy-nine cases with available aortic M-mode images were analyzed. Automated image processing was used to segment the anterior and posterior aortic walls and reconstruct diameter waveforms. Conventional stiffness-β, pulsatile stiffness-β, and wall asymmetry-related parameters were calculated and compared with demographic, tonometry-derived, hemodynamic, coronary burden, cardiovascular risk, and echocardiographic variables. Conventional and pulsatile stiffness-β were strongly correlated and showed directionally consistent associations with established vascular functional parameters, including systolic blood pressure, pulse pressure, augmentation pressure, age, and cardiovascular risk burden. Pulsatile stiffness-β demonstrated association patterns broadly comparable to conventional stiffness-β, suggesting its role as a waveform-informed extension rather than a superior alternative. Wall asymmetry-related parameters were associated with the Syntax score. Automated analysis of archived aortic M-mode images may provide feasible time-resolved vascular biomarkers for stiffness and wall motion assessment. Full article

This study examines how two forms of financial literacy—objective financial literacy (OFL; demonstrated knowledge of interest rates, inflation, and diversification) and perceived financial literacy (PFL; self-assessed confidence in financial matters)—relate to financial wellbeing through distinct capability pathways, and whether self-regulation conditions these links. We use three nationally representative cross-sections from Portugal (2015, 2020, 2023; N = 3648), a European setting marked by declining objective literacy and constrained market participation. Guided by capability theory, we propose a dual-lane model in which OFL operates through behavioural capability (BC; enacted saving, investing, and planning behaviours) to shape objective financial wellbeing (OFW; resilience, assets, and saving), while PFL operates through perceived capability (PC; financial self-efficacy and perceived control) to shape subjective financial wellbeing (SFW; perceived security, satisfaction, and freedom from financial stress). We also test whether non-impulsive, future-oriented behaviour (NIB) strengthens the associations along the objective lane. Structural equation models provide partial support for the dual-lane model, revealing three asymmetries with implications for European policy: (1) the link between behavioural capability and objective financial wellbeing weakens in 2023, suggesting that macroeconomic conditions can undercut even prudent financial behaviour; (2) perceived financial literacy directly predicts subjective financial wellbeing, but perceived capability does not mediate this association, indicating that financial confidence shapes wellbeing independently of self-efficacy; and (3) non-impulsive, future-oriented behaviour amplifies the association between objective literacy and objective wellbeing in 2015 and 2023 but not in 2020, showing that the benefits of self-regulation are context-dependent. The findings inform financial education and policy across Europe by distinguishing intervention levers for objective versus subjective outcomes and identifying conditions under which behavioural interventions are most effective. Full article

The rapid growth of the green bond market has intensified the need for transparent and reliable monitoring systems, particularly in circular-economy environments characterized by complex, multi-stakeholder, and dynamic interactions. However, existing monitoring approaches still rely heavily on static, issuer-driven disclosures, which sustain information asymmetry and increase the risk of greenwashing. This study systematically reviews the role of digital technologies in enhancing green bond monitoring within circular economy systems. A systematic literature review (SLR) was conducted using the Scopus database, covering publications from 2022 to 2026 and yielding 56 eligible studies. A bibliometric analysis using VOSviewer identified major research trends, thematic clusters, and collaboration patterns within the field. The findings reveal four dominant technological pillars—blockchain, artificial intelligence (AI), Internet of Things (IoT), and digital twin—that support data verification, automated analytics, real-time environmental monitoring, and system-wide integration. Although these technologies show significant potential, the literature remains fragmented and lacks comprehensive monitoring architectures that integrate technological, governance, and regulatory dimensions. This study contributes to the literature by synthesizing these technologies through a business informatics perspective and highlighting digital twin architectures as a promising foundation for integrated green bond monitoring. The findings provide practical insights for regulators, issuers, and investors seeking interoperable, transparent, and trustworthy monitoring ecosystems that strengthen accountability and credibility in sustainable finance. Full article

Coastal typhoon deformation plays a critical role in determining typhoon tracks, intensity changes, precipitation and related flooding, storm surges, and typhoon waves, and thus is highly associated with coastal disaster patterns. This study proposes a three-level framework for typhoon wind field modeling through the integration of geometric characterization with physical-informed reconstruction. At its core, an elliptical fitting method is developed based on second-order moments to quantify the structural asymmetries. This geometric fitting method is incorporated into the reconstruction method of Holland–Miyazaki, creating a physically consistent model capable of simulating typhoon deformation processes during landfall. Validation through high-resolution Weather Research and Forecasting (WRF) simulations of Typhoon Chan-hom (2015) demonstrates the framework’s effectiveness, capturing elliptical eyewall deformation with aspect ratios exceeding 1.5, primarily driven by coastal topography and surface friction interactions. The method is further validated through Typhoon Mitag (2019), with mean wind component errors below 1 m/s, the average correlation coefficients surpassing 0.9, and wind direction mean absolute errors largely below 10°. This research provides a practical framework for quantifying and characterizing the wind field deformation during typhoon landfall in coastal regions, thereby supporting ther operational forecasting and disaster reduction in vulnerable coastal regions. Full article

Background and Objectives: Developmental dysplasia of the hip (DDH) is an orthopedic condition in the pediatric population, affecting between 0.1% and 3% of infants. Chile has one of the highest incidences in South America, reaching 1 per 500 live births. Given the potential of adverse consequences of DDH on infant health, preliminary studies are needed to determine its prevalence in the population and assess its association with relevant risk factors. Materials and Methods: The study is single-center, conducted in a Chilean population. The sample size calculation determined the use of 100 pelvic radiographs, considering a 95% confidence level and a proportion of 0.5. The infants were between 90 and 150 days old. Information was collected on possible DDH-related risk factors. For the analysis, normality tests, Chi-square tests, independent samples t-tests, Mann–Whitney U tests, and multivariate analyses were applied. Results: The prevalence of DDH was determined to be 12%, affecting the left hip to a greater extent. Female infants had a higher frequency of DDH. A statistically significant association was found between the prevalence of DDH and the presence of asymmetry in the abduction of the hip joint (p = 0.023), acetabular roof obliquity (p = 0.003), left hip involvement (p = 0.002), and height at two months of age (p = 0.016). Conclusions: The prevalence of DDH in infants was higher than that reported in the literature. However, with regard to sex, the data coincide with those previously reported by other authors. Full article

This study investigates the determinants and predictability of stock return volatility by integrating firm-specific financial characteristics with advanced econometric and volatility modeling techniques. Using an unbalanced panel dataset comprising 1596 firms and 19,752 firm-year observations from MENA stock markets over the period 2010–2024, the analysis employs fixed-effects panel regression models, conditional volatility models, and machine learning-based forecasting approaches. Following extensive diagnostic testing, including tests for heteroskedasticity, serial correlation, cross-sectional dependence, and model specification, a two-way fixed-effects model with Driscoll–Kraay standard errors is adopted as the preferred estimation framework. The results indicate that liquidity ratio, cash ratio, sales growth, firm age, lagged volatility, and lagged returns are significant determinants of stock return volatility, whereas leverage, tangibility, board independence, firm size, Tobin’s Q, and profitability do not exhibit statistically significant effects after controlling for firm-specific and time-specific heterogeneity. The volatility analysis reveals substantial persistence in stock return volatility, with the EGARCH-t specification providing the best fit among the competing GARCH-family models according to the Akaike Information Criterion. The estimated asymmetry parameters indicate that volatility responds differently to positive and negative shocks, supporting the presence of asymmetric volatility dynamics and the suitability of asymmetric volatility models. The forecasting analysis shows that advanced machine learning and deep learning models achieve competitive predictive performance; however, differences in predictive accuracy across models are generally modest. Full article