Search Results (529)

Background/Objectives: The potential of vaccines to trigger autoimmune diseases (ADs) has been extensively investigated and remains a routine focus of vaccine-safety research. This post-marketing, real-world study compared the risk of ADs between females who received bivalent human papillomavirus (HPV) vaccine (Cecolin) and unvaccinated females. Methods: Eligible females aged 9–45 years registered in the Xiamen Health and Medical Big Data Center from September 2020 to December 2023 (post-Cecolin period) were enrolled. Cecolin recipients constituted the exposed cohort, and a matched unexposed cohort was generated in a 1:4 ratio based on age and calendar year. Case validation was conducted to determine optimal identification algorithms. Incidence rates (IRs) were calculated and incidence rate ratios (IRRs) with 95% confidence intervals (CIs) were derived from zero-inflated Poisson regression. Results: Overall incidence of ADs was 70.63 per 100,000 person-years (95% CI: 67.59–73.77) in the post-Cecolin period. Compared with matched unexposed cohorts, vaccinated females showed a significantly lower AD risk in both contemporaneously matched (IRR = 0.23, 95% CI: 0.08–0.65; p = 0.006) and historically matched (IRR = 0.21, 95% CI: 0.07–0.66; p = 0.008) analyses. Conclusions: Consistent with prior evidence, this study provides real-world evidence further confirming that Cecolin vaccination does not increase the risk of ADs. It adds the first large-scale post-marketing safety evidence for this Chinese domestic bivalent HPV vaccine, filling a critical evidence gap. These results may inform vaccination policy and guide post-licensure safety monitoring in China and other countries that have introduced this vaccine. Full article

Whether the marketization of data as a production factor can be redirected toward environmental ends is a central question for the governance of the digital economy. This study investigates whether and how the pilot policy for big data trading platforms improves corporate green development performance (CGDP). Using A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2024, this paper treats the pilot policy for big data trading platforms as a quasi-natural experiment and applies a staggered difference-in-differences (DID) design to estimate its effect on CGDP, together with the transmission channels and boundary conditions that govern it. Because the rollout is staggered, we complement the two-way fixed-effects benchmark with the heterogeneity-robust estimators of Callaway and Sant’Anna, Sun and Abraham, and the Goodman–Bacon decomposition, and cluster standard errors at the city level. The policy raises CGDP by 0.076, about 6.1% of the sample mean. The estimate remains robust to an event-study/parallel-trend test, placebo tests, propensity score matching (PSM), the Oster selection-on-unobservables bound, alternative and broader green outcome measures—including a significant reduction in chemical oxygen-demand emissions—controls for concurrent digital and innovation policies, exclusion of the 2020 pandemic year, and industry fixed effects. Mechanism evidence shows that the effect operates through stronger green dual innovation, upgraded human capital, and heightened scrutiny from media outlets and securities analysts. The impact is stronger for firms whose executives exhibit greater green awareness and whose internal control is of higher quality, and in more competitive industries and regions with stricter environmental regulation. By showing that a market for data can be redirected toward environmental ends, this study links data-factor marketization to corporate green transition and provides policy evidence for aligning digital economy reform with sustainable development. Full article

With the rapid advancement of artificial intelligence technologies in the era of big data, time series forecasting has become indispensable in critical fields such as environmental monitoring and financial market analysis. However, the existing forecasting models often encounter performance limitations when extracting high-dimensional features and generally cannot dynamically focus on critical information within long-term sequences. To address these challenges, this study proposes a multi-head attention fusion model (MAFM) designed to enhance the predictive accuracy and modelling capability for high-dimensional and nonlinear data across diverse application scenarios. Experiments were conducted on two heterogeneous datasets from the environmental and financial domains. After the key hyperparameters of the MAFM were optimized through an orthogonal experimental design, the model achieved coefficients of determination exceeding 0.90 on both datasets. Furthermore, the results of four comparative experiments demonstrate that the MAFM consistently outperforms traditional machine learning models, including support vector regression and extreme gradient boosting, as well as state-of-the-art deep learning models such as long short-term memory, temporal convolutional networks, and transformers. Compared with the best-performing baseline model on each sub-dataset, the MAFM reduced the mean squared error by 44.4%, 8.3%, 29.4%, and 65.5%, respectively, highlighting its superior predictive performance and strong generalization capability. In summary, the proposed MAFM provides an efficient, robust, and interpretable solution for time series forecasting tasks across multiple domains. Its outstanding performance demonstrates significant potential for practical applications in environmental monitoring, financial forecasting, and other real-world scenarios. Full article

The systemic shift in the energy consumption structure from high-carbon fossil fuels to low-carbon clean energy constitutes a critical pathway toward global climate governance and carbon neutrality. However, this sustainable transition is consistently impeded by deep-seated institutional frictions and structural barriers, such as governance fragmentation and carbon lock-in effects embedded in traditional industrial organization. Whether digital government development can overcome these barriers by nurturing resilient business ecosystems and thereby promote a systemic low-carbon energy transition remains an urgent question within sustainable development research. To address this issue, this study integrates digital government development, regional e-commerce ecosystem competitiveness, and the low-carbon transition of the energy consumption structure into a unified analytical and sustainable governance framework. Using panel data from 30 Chinese provinces from 2012 to 2022, we exploit the institutional reform of provincial big data administrations as a quasi-natural experiment to identify the impacts of digital government. Regional e-commerce ecosystem competitiveness is comprehensively evaluated across four sustainable dimensions: ecological innovation capacity, market connectivity, ecological global integration, and inclusive infrastructure. Methodologically, we employ a spatial difference-in-differences model to capture geographic interdependencies alongside a double machine learning framework to handle high-dimensional confounding and nonlinear disturbances. The empirical findings reveal that both digital government development and regional e-commerce ecosystem competitiveness significantly drive the low-carbon transition of the energy consumption structure. The institutional effect of digital government exhibits strong regional embeddedness with localized impacts, whereas e-commerce ecosystem competitiveness generates positive spatial spillovers that accelerate energy optimization in neighboring regions. Crucially, regional e-commerce ecosystem competitiveness serves as a significant partial mediator, constructing a reliable transmission channel from institutional design to market-based decarbonization. Further pathway analysis indicates that market connectivity and inclusive infrastructure function as the primary transmission channels, effectively mitigating transportation energy intensity and bridging the digital-green divide, while the mediating contribution of ecological innovation capacity is relatively constrained due to cross-organizational coordination thresholds. This study clarifies the interactive mechanism between public digital governance and market ecosystem competitiveness in advancing environmental sustainability, thereby offering fresh theoretical insights and actionable policy implications for emerging market economies striving for economic growth and decarbonization. Full article

We introduce the MERI (Market Evolutionary Resilience Index), a cognitive big data framework operationalising jamming transition physics into a daily operational regime detector (low-latency inference, 8 ms per observation). Opaque models cannot be deployed in regulated environments because every automated alert must decompose into auditable feature contributions. The MERI addresses this by treating the market as a complex adaptive system whose metabolic state constitutes the primary observable. Three cognitive layers fuse heterogeneous streaming data: an EGARCH-GED econometric baseline, a Random Forest classifier on a 15-dimensional physics-derived feature space, and a TreeSHAP Gini attribution audit ensuring full prediction-level transparency. Fisher Information Gain epistemic gating restricts automated intervention to predictions exceeding 2.5 nats certainty. Evaluated on South African financial markets (2015–2025, $N=2870$ trading days, Eskom load-shedding as exogenous forcing), the MERI achieves 97.3% accuracy (AUC = 0.9973, recall = 1.000), statistically equivalent to Temporal Fusion Transformers (Model Confidence Set, 90% confidence) while delivering 85.7% high-certainty predictions versus 23.4% for deep learning. A Granger-validated 48-h early warning lead ($F=62.003$, $p<0.001$), $7.78\times$ recovery hysteresis (Cohen’s $d=2.13$), and infrastructure dominance of 78.0% (Gini) confirm that the framework is operationally feasible for daily monitoring in the South African JSE–Eskom setting. Cross-domain portability is proposed as a theoretical extension pending empirical validation. Full article

The article critically examines the emergence of technofeudalism within the European Union’s legal framework, drawing on the theoretical contributions of Yanis Varoufakis, Alfred C. Yen, and Katrina Geddes. We argue that the EU’s historically market-oriented regulatory architecture contributed to conditions that facilitated the rise of dominant technology companies exercising quasi-governance functions over digital environments, extracting value from users while evading meaningful democratic accountability. Our analysis distinguishes between two categories of enabling legislation: structural rules, which govern corporate status, taxation, and market consolidation; and action-oriented rules, which regulate platform behavior, algorithmic governance, consumer relations, and data protection. We demonstrate how fragmented national tax regimes, ineffective merger control, under-regulated algorithms, asymmetric consumer protections, unclear liability frameworks for online content, exploitable private international law mechanisms, and inadequately enforced data protection standards collectively reinforce Big Tech’s dominance. While recent regulatory interventions such as the Digital Services Act and Digital Markets Act represent important steps, they remain embedded in a market-oriented paradigm that insufficiently addresses the broader social, cultural, and democratic implications of platform power. The article concludes by calling for a more coherent, democratically grounded approach to digital regulation—one that moves beyond fragmented, reactive policymaking toward a comprehensive framework capable of strengthening democratic accountability and public oversight within the digital sphere. Full article

This paper examines the moderating effect of corporate digital transformation in the relationship between audit quality and aggressive tax avoidance in the sample of industrial companies listed on the ASE and operating during the 2020–2025 period. They were based on data of a balanced panel of 30 industrial companies listed on the ASE 180 observations. The primary estimator used was the feasible generalized least squares (FGLS) method that was employed after it was established that first-order autocorrelation, groupwise heteroskedasticity, and partial cross-sectional dependence existed. System-GMM estimator was used to confirm the robustness of the results, and to deal with the endogeneity that may arise due to reverse causality between auditor selection and result. There are three key findings of the study. First, there is a strong and consistent negative relationship between affiliation with one of the Big Four audit firms and aggressive tax avoidance in all the models studied, confirming that reputation-based audit quality is an effective institutional deterrent a finding of particular importance given that 73.3% of the Jordanian industrial firms in the sample rely on local auditors and therefore lack similar governance controls. Second, aggressive tax avoidance is positively related to higher audit fees, which are indicative of a more complex client base and an economic dependence on clients by the auditor, rather than a signal of greater monitoring rigour and, therefore, as a challenge to the fee-as-quality assumption common to the developed-market framework. Third, although digital transformation demonstrates a direct positive correlation with aggressive tax avoidance—indicating that firms can use digital capabilities to enhance tax planning and not compliance in the pre-JoFotara regulatory environment—its moderating effect on Big Four affiliation is not statistically significant. It is important to note that the relationship between the intensity of audit fees and digital transformation is positively significant, which is in line with the economic dependence argument. The implications of the findings are important to the Jordan Securities Commission, the tax authorities as well as regulatory bodies who are looking to enhance corporate tax compliance in a dynamic digital regulatory environment, and raise important questions of the portability of audit quality assumptions across institutional settings. Full article

Contemporary ESG (Environmental, Social, and Governance) regulation creates costs and risks for businesses, which are associated with the stringency of requirements. This article demonstrates that the key source of these problems is the fragmentation of legal regulation, the inconsistency of reporting standards, and the methodological heterogeneity of ESG indices. Based on a comparative legal analysis of eight jurisdictions (the US, EU, China, India, Brazil, Russia, South Africa, and Kazakhstan), three models of ESG regulation are identified: prescriptive, market-oriented, and state-centralized. It is shown that extraterritorial pressure (CBAM, CSDDD) and internal regulatory conflicts (e.g., in the US) are associated with increased compliance costs, especially for emerging economies. An empirical analysis revealed significant divergence in the assessments and dynamics of ESG ratings from various agencies. The results obtained are consistent with the findings of other researchers who document discrepancies in ESG assessments reaching approximately 50–60%. This makes global indices of limited applicability for regulatory purposes. In response to the identified issues, a country-specific ESG index integrated into a closed-loop feedback management system was proposed. A two-stage methodology was developed: calculating a company index (taking into account regulatory burden, extraterritorial pressure, and adaptability) and aggregating it into a country index based on macrostatistics, with the ability to transition to big data aggregation. The results can be used by national regulators to improve the comparability of ESG data and differentiate government support measures. Full article

Real-time retail credit scoring is a data-intensive cognitive computing task. Each decision must fuse heterogeneous signals, execute a non-linear model, return a calibrated probability of default (PD), and emit a regulator-compliant local explanation within milliseconds. We address the most demanding segment of unsecured lending in Kazakhstan—Salary-Project-Independent (SPI) borrowers, whose principal income stream is not observable by the lender—and frame scoring as a constrained optimisation problem where we maximise discrimination subject to interpretability, latency, and calibration constraints. We propose a tenure-stratified hybrid framework that couples (i) an online weight-of-evidence logistic regression (WOE-LR) scorecard with (ii) an offline self-attention stacked ensemble (LightGBM, CatBoost, and a tabular self-attention network) whose calibrated PD is quantile-binned, WOE-encoded, and re-injected into the online scorecard as a single auditable predictor. On 551,962 production contracts that originated in 2022–2024, the repeat-client hybrid attains an area under the receiver operating characteristic curve (AUROC) of 0.826, a Gini coefficient of 0.65, and a Kolmogorov–Smirnov (KS) statistic of 0.495, preserving roughly half of the offline ensemble’s lift over the linear baseline (AUROC 0.79→0.897) while retaining a fully auditable twelve-coefficient scorecard in production. The new-client scorecard attains an AUROC of 0.741. Non-parametric isotonic recalibration reduces the expected calibration error from 0.27 to below 0.01 and raises the Hosmer–Lemeshow p-value above 0.99 without altering discrimination. The framework complies with the model risk standards of the Agency of the Republic of Kazakhstan for Regulation and Development of the Financial Market and is delivered as a Spark/MLOps reference architecture, illustrating how big data engineering, attention-based representation learning, and post hoc explanations can be co-designed for a high-stakes, high-throughput, regulated AI application. Full article

Big data has generated massive amounts of multi-source data and complex data correlations, making the efficient extraction of value from these correlations a major challenge in the field. The ongoing development of complex network theory has led to the widespread application of methods that mine system correlations based on network topology. However, if correlation analysis relies solely on low-order topological features, it will overlook higher-order connectivity information at the mesoscale. First, by leveraging complex network construction algorithms, we establish a multi-layer finite-transit visual graph network and develop metrics for measuring correlation and guidance relationships by integrating high-order network information. Second, simulation experiments were conducted on the CML system, the Lorenz system, and the Rössler system, respectively. By comparing the results with traditional low-order metrics and incorporating noise interference tests, the superiority, effectiveness, and robustness of the proposed metrics in identifying correlations were validated. Finally, empirical research was conducted using data from the China Carbon Emission Trade Exchange, the EU Emissions Trading System, the Brent crude oil market, and the Chinese INE crude oil market. This analysis examined the inter-linkage characteristics between the carbon market and the crude oil market, revealing the patterns of dynamic information spillover between the two markets. Full article

Agritourism is increasingly intersecting with digital technologies to foster rural resilience, economic growth, and sustainable development. This study conducts a comprehensive systematic bibliometric review to map the intellectual structure, thematic evolution, and collaborative networks characterizing the digitalization of agritourism from 2010 to 2025. Guided by the PRISMA framework, data from the Scopus database were analyzed using scientific mapping techniques, including keyword co-occurrence, thematic evolution tracking, and spatial collaboration analysis. The findings reveal a paradigm shift categorized into three evolutionary phases: an incubation period of basic web adoption (2011–2017), a disruptive phase catalyzed by the COVID-19 pandemic (2018–2022), and an exponential maturation phase driven by Industry 4.0 technologies such as Artificial Intelligence (AI), Big Data, and Virtual Reality (2023–2025). Four primary thematic clusters emerged: digital marketing and connectivity, smart tourism and advanced analytics, immersive technologies for heritage preservation, and macro-level sustainability policies. Geopolitically, research is driven by two distinct networks: an Asian-centric hub led by China focusing on state-sponsored smart villages, and a Western hub anchored by the USA and Italy emphasizing entrepreneurial diversification. The study concludes that digitalization has transitioned from a reactive survival mechanism to a proactive strategic necessity. It highlights the critical need to bridge the digital divide through human capital investment and provides a future research agenda focusing on the ethical application of AI, the circular economy, and the preservation of rural authenticity in emerging ’phygital’ environments. Full article

This study aims to map and compare the demand for green skills across four selected African countries by analyzing online job vacancies. Accordingly, the study addresses four research questions: (RQ1) how green skills demand has evolved over time; (RQ2) which sectors exhibit the highest demand for green skills; (RQ3) which occupations are most associated with green skills; and (RQ4) which green competencies are most frequently requested by employers. A Big Data and Labour Market Intelligence approach is employed based on secondary data provided by the online job vacancies (OJV). The results reveal a general upward trend in green skills demand, although with significant cross-country variation. Sectorally, sustainable energy dominates across all countries, followed by more context-specific areas such as agriculture, tourism, and production. At the occupational level, environmental engineers and other technical professions are most strongly associated with green skills. The thematic analysis highlights renewable energy, energy efficiency, and environmental sustainability as the most prominent skill domains, alongside emerging competencies in sustainable mobility, circular economy, and green digital skills. The study contributes to the literature by providing empirical evidence from an underexplored African context and demonstrating the value of online job vacancy data for monitoring labour market transformations. Full article

This study investigates the role of big data analytics (BDA) on the relationship between environmental, social, and governance (ESG) scoring and Saudi listed firm value over the four-year period from 2021 to 2024. We employed structural equation modeling (SEM) as a path analysis and a 5000-replication bootstrap method to evaluate the mediation effect of BDA and to enhance inferential robustness. The findings indicate a partial mediation effect of BDA on the relationship between ESG scoring and Tobin’s Q, indicating that ESG contributes to firm value partly through BDA capabilities. The findings underscore the role of leverage ratio and firm size as predictors of BDA effect on firm value. The study is grounded in stakeholder, signaling, and resource-based theories, arguing that ESG performance builds stakeholder trust, ESG scores signal firm value, and BDA capabilities act as strategic assets that enhance market valuation. The findings emphasize the importance of Saudi investors supporting the integration of BDA within ESG practices to maximize firm value, consistent with stakeholder theory. This study contributes to the literature in two ways. First, it demonstrates the mediating role of BDA in the ESG–firm value nexus, reinforcing the market relevance of ESG signals in line with signaling and resource-based perspectives. Second, it extends empirical evidence on the strategic role of BDA in ESG contexts within emerging markets. For policymakers, the results suggest that ESG initiatives require complementary investments in BDA capabilities to achieve their value potential. Full article

Control charts are used in statistical process control (SPC) to keep track of processes and identify changes in the way that they work. The control limits around the center line are uniform, which means they react the same way to changes going in either direction. In contrast, linear charts use imbalance to make it easier to identify individual data points. Therefore, using imbalance in the creation of control charts helps keep track of and maintain consistency with data that has a big impact on results when it goes beyond predetermined limits. In this study, we look at both one-sided and two-sided control charts by getting an explicit closed-form formula for the double-modified EWMA control chart’s average run length (ARL). The study is mostly about developing better ways to spot things using autoregressive fractionally integrated models and external variables (ARX and ARFIX) in the presence of exponential white noise. The ARL is used to test how well the proposed chart works in both modeling systems. The NIE method is used to prove that the explicit closed-form ARL formula works. The closed-form ARL expression is shown to be valid under the given ARX and ARFIX model assumptions, exponential white noise errors, stationarity conditions, and fixed one-sided or two-sided control limits. The results show that %RPC has a value below 10⁻⁶, and the computation times for the ARX and ARFIX models remain below 1.6 s and 3 s, respectively, after that point. To show how much better it is, the suggestion is compared to Type-EWMA control charts, such as classical and modified EWMA charts, in terms of run-length efficiency using ARL and SDRL, as well as overall efficiency by the relative index and with mean and standard deviation. The simulation study checks how well the proposed chart works in both symmetric two-sided and asymmetric one-sided frameworks. For the crude oil application, the one-sided upper control chart is used to detect abrupt upward price shifts, which may indicate precautionary demand shocks, market uncertainty, and risk spillovers to financial markets. According to the findings, the suggested chart is able to identify shifts at a faster rate than both traditional EWMA charts and modified EWMA charts, which demonstrates that it is beneficial in a real setting. Full article

As global markets navigate the dual transition of digitalization and sustainability, the risk of “digital greenwashing” has emerged as a critical corporate governance challenge. Utilizing a comprehensive dataset of Chinese A-share listed firms from 2018 to 2024—an ideal laboratory characterized by rapid regulatory shifts and unique state-market dynamics that provide highly generalizable insights for other emerging economies—this study empirically investigates whether corporate digital transformation acts as a genuine driver for Environmental, Social, and Governance (ESG) enhancement or merely serves as a symbolic disclosure tool. Fortified by rigorous identification strategies, including Propensity Score Matching and Lewbel heteroskedasticity-based instrumental variable estimations, the results confirm that digitalization serves as an incremental yet statistically significant driver for corporate sustainability. Crucially, mechanism analyses reveal a “full moderation” effect: the positive impact of digitalization on ESG performance is completely activated only in the presence of premium external assurance (e.g., Big 4 audits). Without high-quality IT auditing to act as a credibility enforcer and verify the substance of digital signals, technological adoption alone fails to yield significant ESG improvements. Furthermore, a nuanced structural asymmetry is identified: foundational data infrastructures (Cloud Computing and Big Data) directly enhance quantifiable Environmental and Governance metrics, whereas premium audits are strictly required to activate the “soft,” qualitative Social dimension. Finally, the synergy exhibits distinct boundary conditions. It is heavily concentrated within high-pollution industries where digital transition acts as a regulatory survival imperative rather than mere market expansion, and its reliance on external assurance is fundamentally driven by the market-signaling needs of non-State-Owned Enterprises (non-SOEs) rather than the policy-distorted mandates of SOEs. These findings offer critical theoretical extensions and policy implications for standardizing digital-audit infrastructures globally. Full article