Search Results (254)

To explore whether the causal chain of “Industry–University–Government Integrated Innovation Ecosystem → Novel Energy Storage Technology Innovation → Dual-Control Energy Targets” can be achieved, this study analyzes panel data from 30 provinces, municipalities, and autonomous regions in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2010 to 2022. By employing a complex mediation effect model combining dynamic Qualitative Comparative Analysis (QCA) and the dynamic panel system Generalized Method of Moments (GMM) model, this study identifies five configuration pathways for driving innovation in novel energy storage technologies within an integrated innovation ecosystem. These include two industry digitalization–university innovation resource-dominant pathways: a government-light and digitally driven “university–industry” resource-sharing and knowledge-conversion synergy, and an industry leadership pathway embedded with university collaborative innovation under a digitalization framework. Two policy-driven hybrid and industry–leadership synergistic pathways are also extracted: a growth pathway for policy-supported hybrid organizations under insufficient industry digitalization and a policy-driven innovation substitution pathway compensating for the absence of university niche roles. Additionally, a multidimensional collaborative development pathway is identified, reflecting comprehensive collaboration. In the dynamic panel system GMM model, all five pathways collectively suppress total energy consumption and energy intensity, while also indirectly driving the achievement of dual-control energy targets through innovation in novel energy storage technologies. Pathways driven by government-light and digitally facilitated collaboration, industry leadership, and comprehensive collaboration show significant direct negative effects on energy consumption and intensity. However, the policy-driven innovation substitution pathway exhibits limited contribution due to the absence of university innovation components, thereby failing to significantly advance regional dual-control energy goals. Full article

Escalating climate volatility, particularly the El Niño/Southern Oscillation (ENSO), poses severe operational and financial risks to corporate sustainability in the energy sector. However, quantitative evidence regarding how macro-level climate shocks transmit to micro-level operational performance remains scarce. Integrating dynamic capability and social network theories, this study analyzes a panel of 103 Chinese listed energy firms (2005–2022) using System GMM, mediation, and moderation models. The results indicate that ENSO intensity significantly impairs performance; specifically, a 1 °C rise in sea surface temperature anomalies decreases firms’ return on assets (ROAs) by 0.142%. We identify supply chain resilience as a critical strategic mechanism for climate adaptation, where response capacity acts as the dominant mediating channel, while recovery capacity functions as an independent compensatory mechanism. Conversely, supply network complexity—across horizontal, vertical, and spatial dimensions—amplifies the negative impact of climate disruptions by hindering resource mobility. Heterogeneity analysis reveals that state-owned enterprises exhibit stronger institutional resilience, and firms in southern regions partially offset impacts through hydropower advantages. This study bridges climate science with operations management, offering strategic guidance for managers to configure resilient, sustainable supply chains capable of withstanding environmental turbulence. Full article

To address challenges in complex group decision-making (GDM)—specifically preference fuzziness, intricate subgroup segmentation, and non-cooperative behavior—this study proposes an adaptive consensus model based on probabilistic linguistic term sets (PLTSs). By integrating fuzzy C-means (FCM) clustering with a Gaussian mixture model (GMM), a fuzzy Gaussian mixture model (FGMM) is constructed to achieve soft segmentation of expert preference distributions. On this basis, an adaptive consensus feedback mechanism is developed, which dynamically integrates interactive and automated adjustment strategies via multi-level consensus thresholds, thereby balancing decision efficiency and quality. To identify and control non-cooperative behaviors, a cooperation index and a three-tier management strategy, which incorporates intra-group negotiation, weight penalties and an exit-delegation mechanism, were introduced. In the case of strategic decision-making of new energy vehicles (NEV), after four rounds of feedback iterations, the group consensus level increased from the initial 0.316 to 0.804, reaching the preset threshold and verifying the effectiveness of the consensus mechanism. Compared with the existing literature methods, the framework in this paper achieves more comprehensive integration and innovation in four aspects: preference expression, clustering mechanism, consensus feedback and behavior management. Full article

The aim of this study is to investigate and demonstrate the role of correction functions and optimisation-based refinement algorithms in enhancing the performance of machine learning models, particularly in predictive anomaly detection tasks applied in industrial environments. The performance of machine learning models is highly dependent on the quality of data preprocessing, model architecture, and post-processing methodology. In many practical applications—particularly in time-series forecasting and anomaly detection—the conventional training pipeline alone is insufficient, because model uncertainty, structural bias and the handling of rare events require specialised post hoc calibration and refinement mechanisms. This study provides a systematic overview of the role of correction functions (e.g., Principal Component Analysis (PCA), Squared Prediction Error (SPE)/Q-statistics, Hotelling’s T², Bayesian calibration) and adaptive improvement algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Simulated Annealing (SA), Gaussian Mixture Model (GMM) and ensemble-based techniques) in enhancing the performance of machine learning pipelines. The models were trained on a real industrial dataset compiled from power network analytics and harmonic-injection-based loading conditions. Model validation and equipment-level testing were performed using a large-scale harmonic measurement dataset collected over a five-year period. The reliability of the approach was confirmed by comparing predicted state transitions with actual fault occurrences, demonstrating its practical applicability and suitability for integration into predictive maintenance frameworks. The analysis demonstrates that correction functions introduce deterministic transformations in the data or error space, whereas improvement algorithms apply adaptive optimisation to fine-tune model parameters or decision boundaries. The combined use of these approaches significantly reduces overfitting, improves predictive accuracy and lowers false alarm rates. This work introduces the concept of an Organically Adaptive Predictive (OAP) ML model. The proposed model presents organic adaptivity, continuously adjusting its predictive behaviour in response to dynamic variations in network loading and harmonic spectrum composition. The introduced terminology characterises the organically emergent nature of the adaptive learning mechanism. Full article

To address the challenge that existing vehicle chassis coordinated control methods struggle to balance the nonlinear couplings and control conflicts among Four-Wheel Steering (4WS), Direct Yaw-moment Control (DYC), and Active Suspension Systems (ASS), this paper proposes a Cooperative Distributed Model Predictive Control (Co-DMPC) strategy. First, the 4WS, DYC, and ASS are modeled as three interacting agents that effectively mitigate inter-subsystem control conflicts through information exchange and coupling compensation. Second, a Gaussian Mixture Model (GMM) is utilized to extract features from vehicle state data to enable the real-time grading of instability risks, which dynamically adjusts the control weights of the 4WS, DYC, and ASS agents. Finally, a distributed iterative optimization algorithm is designed to ensure that all agents converge to a global Pareto-optimal solution through rapid negotiation, achieving a balance between control performance and computational burden. Simulation results demonstrate that compared with No-Control and CMPC, the proposed Co-DMPC strategy significantly enhances the comprehensive performance of the vehicle. In terms of path tracking accuracy, the maximum tracking errors under high- and low-adhesion road conditions are reduced by 32.73% and 17%, respectively. Regarding roll stability, the peak roll angles of the vehicle are 0.27 rad and 0.01 rad under the respective conditions. For lateral stability, the proposed method maintains a more compact sideslip angle-yaw rate phase plane envelope, effectively achieving the coordinated optimization of chassis subsystems. Hardware-in-the-Loop (HIL) experiments further validate the performance and effectiveness of the controller. Full article

The current research examines the impact of climate change disclosure (CCD) on firm value (FV) of Egyptian listed non-financial companies. The current research also investigates the moderating role of board gender diversity (BGD). The study sample incorporates Egyptian non-financial companies indexed in EGX 100 whose reports were available from 2018 to 2023. The final sample comprises 82 companies with 492 observations. Statistical analysis was conducted using a POLS and Fixed Effects Model, GMM, and the 2SLS method to address potential endogeneity and dynamic panel concerns. The results revealed a positive and significant impact of CCD on FV. Furthermore, BGD had a positive and significant moderating impact as BGD enhanced the relationship between CCD and FV. Moreover, the critical mass (CM) analysis of female representation revealed that the number of females on the board significantly moderates the CCD-FV relationship; as CM increases, the effect on the CCD-FV relationship becomes stronger. Although advanced panel techniques and instrumental variable approaches are used to mitigate identification concerns, the results should be interpreted in light of the observational nature of the data and the reliance on disclosure-based proxies. These findings are significant for governments, regulators, investors, and company leaders because the moderating role of BGD demonstrates how board governance affects firm value, particularly in emerging markets. This research adds to the academic discussion by emphasizing the beneficial effects of both BGD and CCD on FV, with a particular focus on developing economies. Full article

Water scarcity poses a critical challenge to sustainable agricultural development, particularly in arid regions such as Saudi Arabia. This study examines whether AI-compatible smart irrigation, digital water monitoring, and integrated water resource management (IWRM) are associated with improvements in agricultural water sustainability. Using a regional–crop panel dataset covering 13 Saudi administrative regions and six major crops over the period 2010–2024, the analysis evaluates their relationships with water-use efficiency, crop water productivity, and crop yield. To address persistence, endogeneity, and unobserved heterogeneity, the study employs a comprehensive multi-method empirical strategy combining dynamic panel models (System GMM), difference-in-differences, and event-study designs. The results provide internally consistent and empirically robust evidence in support of the proposed hypotheses. AI-compatible smart irrigation is positively and significantly associated with improvements in agricultural water efficiency and productivity, with effects that strengthen over time, reflecting gradual technology assimilation and learning processes. These findings capture the performance gains from irrigation modernization that enables data-driven and algorithm-supported decision-making, rather than the direct causal impact of autonomous artificial intelligence deployment. Integrated water resource management independently exhibits a positive association with higher agricultural performance, underscoring the importance of coordinated governance alongside technological adoption. Digital water monitoring shows a positive and statistically significant relationship with all outcome measures and appears to reinforce the effectiveness of both AI-compatible irrigation and integrated water governance. Robustness analyses excluding extreme drought years confirm that these relationships reflect persistent efficiency patterns rather than transitory climatic shocks. Overall, the findings provide context-specific and methodologically rigorous evidence that AI-compatible irrigation, digital monitoring, and integrated water governance operate as complementary components of agricultural water sustainability in a highly water-scarce economy, offering evidence-informed and policy-relevant insights, aligned with Saudi Vision 2030. Full article

In an increasingly volatile global financial environment, strong institutions and sound governance are essential for safeguarding banking stability and mitigating systemic risks in emerging economies. Across the 11 emerging economies examined, weaknesses in institutional quality and inconsistencies in governance frameworks continue to elevate credit risk and undermine financial resilience. This study investigates the effects of institutional quality (IQ) and corporate governance (CGG) on bank stability, drawing on the Financial Stability and Risk Management (FSRM) theory, which highlights robust institutions, effective risk oversight, and sound governance as core determinants of financial system strength. Using dynamic panel data from 2011–2024, the study applies the generalized method of moments (GMM) approach to assess bank performance through non-performing loans (NPLs) and Z-Score as key dependent variables. The model incorporates IQ, CGG, bank-specific characteristics (bank assets, capital adequacy, cost-to-income ratio), and macroeconomic indicators (GDP, inflation, exchange rate, real interest rate) as explanatory factors, addressing endogeneity, unobserved heterogeneity, and persistence in banking outcomes. The results reveal strong persistence in NPLs (lag = 0.965, p < 0.01) and Z-Score (lag = 0.920, p < 0.01), indicating notable path dependence in bank performance. Institutional quality significantly enhances bank stability (Z-Score coefficient = 0.073, p = 0.040), while BA shows a negative but insignificant effect (coefficient = 0.005, p = 0.432), implying that rapid asset growth without prudent risk management may weaken resilience. CGG shows negative but insignificant effects, while macroeconomic factors also appear insignificant, indicating limited short-term impact. Countries with stronger institutions, such as South Korea, display lower NPLs and higher stability, whereas weaker institutional environments like Iran, Pakistan, and Bangladesh face higher credit risk and reduced stability. Overall, the study highlights IQ and prudent balance sheet management as key to stronger bank stability, urging policymakers to reinforce institutional frameworks, tighten regulatory discipline, and ensure controlled asset growth to reduce systemic vulnerabilities. Full article

FinTech adoption is increasingly viewed as a catalyst for sustainable finance, yet empirical evidence on how and under what conditions it enhances environmental, social, and governance (ESG) performance remains mixed, particularly in emerging economies. This study examines the relationship between FinTech adoption and ESG performance in MENA banks, explicitly modeling corruption risk as an internal governance transmission channel. Using a panel of 152 listed banks across 11 MENA countries over the period 2013–2023 and a novel bank-level FinTech Adoption Index constructed through textual analysis of annual reports, we employ fixed-effects and dynamic System GMM estimations to examine both direct and indirect effects. The results show that FinTech adoption is positively associated with ESG performance. More importantly, corruption risk partially mediates this relationship, indicating that FinTech enhances sustainability outcomes not only through improved disclosure and transparency, but also by strengthening internal governance and constraining integrity-related risks. The indirect effect is economically meaningful, underscoring the role of digital governance mechanisms in institutionally constrained settings. Pillar-level analysis reveals stronger effects for the governance and social dimensions, while environmental effects are comparatively weaker. Additional robustness analyses confirm the persistence of these findings across institutional settings and crisis periods. These findings contribute to the FinTech–ESG literature by identifying corruption risk as a key governance mechanism and provide policy-relevant insights for regulators and banks seeking to leverage digital transformation to achieve substantive sustainability outcomes in emerging banking systems. Full article

This paper examines the relationship between corruption and foreign direct investment (FDI) inflows in European Union member states using a dynamic panel framework. Using an unbalanced EU panel from 2002 to 2022 and an Arellano–Bond difference-GMM specification, we model inward FDI inflows per capita as a function of institutional integrity (measured by Transparency International’s Corruption Perceptions Index), market size, development level, and trade integration. The results show a robust positive association between improvements in perceived integrity (higher CPI scores) and increases in inward FDI inflows per capita, conditional on macroeconomic controls and dynamic adjustment. Market size and trade variables have the expected signs, while GDP per capita is the empirically sensitive margin, consistent with the idea that higher development can indicate greater purchasing power but also higher costs and saturation effects in advanced economies. Robustness checks using the inverse hyperbolic sine transformation—suited to heavy tails, zeros, and negative net flows—confirm that the governance association is not an artifact of scaling. The findings highlight the importance of institutional quality and market openness as correlates of FDI attractiveness within the EU. Full article

This study examines the determinants of renewable energy consumption among BRICS countries (Brazil, Russia, India, China, South Africa, Saudi Arabia, Egypt, the United Arab Emirates, Ethiopia, Iran, and Indonesia) between 2000 and 2022. Using static (Fixed and Random Effects) and dynamic (First-Difference GMM) panel data models, the research investigates how economic, institutional, and social factors influence renewable energy transition. The results reveal structural heterogeneity within the bloc. Among the founding members, renewable energy consumption is positively associated with governance quality and trade openness, while GDP per capita exhibits a negative relationship, consistent with the Environmental Kuznets Curve hypothesis. In contrast, the new members show strong energy dependence and limited institutional capacity, with dynamic models confirming high persistence in energy consumption and weak responsiveness to economic and policy changes. Variables such as education and life expectancy were omitted in the dynamic specification due to limited temporal variation, without compromising model consistency. Diagnostic tests (Hansen, Sargan, and AR(2)) confirm the robustness of the estimates. Overall, the findings highlight the importance of strengthening institutional governance, technological innovation, and intra-bloc cooperation to advance energy transition and achieve sustainable development across the BRICS economies. Full article

This study investigates whether Corporate Social Responsibility Disclosure (CSRD) serves as a risk-mitigating or cost-inducing signal for firms’ market value in an emerging market. Utilising a panel dataset of 120 companies listed on the Tehran Stock Exchange (2015–2023) and employing content analysis alongside panel regression and System GMM models, we find that disclosure quality in social, employee, and environmental dimensions is positively associated with market value, while customer-related disclosure is not. The role of family ownership is nuanced: baseline specifications suggest no broad moderating influence, yet robust dynamic modelling reveals that family ownership significantly enhances the positive market valuation of environmental disclosure. The primary contribution is a nuanced, dimension-specific analysis of CSRD’s value relevance, challenging blanket assumptions about family firm behaviour and offering granular, methodologically informed insights for stakeholders in institutionally complex environments. Full article

This study develops an integrated panel econometric framework for modeling investment–output dynamics in circular economy sectors, explicitly addressing dynamic propagation, long-run equilibrium relationships, endogeneity, and nonlinear responses. Building on the Samuelson–Hicks Multiplier–Accelerator model, the analysis combines two complementary approaches. A dynamic panel specification estimated by the Generalized Method of Moments (Arellano–Bond) is employed to capture output inertia, intertemporal transmission of investment shocks, and stability properties of the dynamic system. In parallel, a nonlinear panel ARDL model estimated using the Pooled Mean Group (PMG/NARDL) methodology is used to identify cointegration and to distinguish between the long-run and short-run effects of positive and negative investment variations. The empirical analysis relies on a balanced panel of 28 European economies (EU-27 and the United Kingdom) over the period 2005–2023, using sectoral circular economy data, with gross value added as the output variable and gross private investment as the main regressor. The results indicate the existence of a stable cointegrated relationship between investment and output, characterized by significant asymmetries, with expansionary investment shocks exerting larger and more persistent effects than contractionary shocks. Dynamic GMM estimates further confirm delayed investment effects and a stable autoregressive structure. Overall, the paper contributes to mathematical economic modeling by providing a unified dynamic–equilibrium panel framework and by extending the empirical relevance of Multiplier–Accelerator dynamics to circular economy systems. Full article

China’s cross-border e-commerce (CBEC) has expanded rapidly, yet province-level evidence remains limited on how AI development conditions the contribution of logistics fulfilment efficiency (LEF) to cross-border e-commerce development (CBED), especially across regions with uneven digital maturity. This study tests whether AI capability amplifies the marginal effect of logistics fulfilment efficiency (LEF) for CBED and whether this complementarity varies across eastern, central, and western China. Using a balanced panel of thirty-one provinces over 2017–2023 (N = 217), we combine a Super-SBM DEA logistics fulfilment efficiency measure (LEF), a four-pillar AI Development Index (AIDI), and customs-based CBED indicators. Two-step System GMM models are estimated for the full sample and regional subsamples to account for dynamic persistence and endogeneity concerns. Results indicate that higher LEF is associated with higher CBED and that AIDI strengthens this relationship via the interaction term; the complementarity is the largest in eastern provinces and remains positive but smaller in central and western regions. Overall, the evidence suggests that logistics fulfilment efficiency and AI capability act as complementary enablers of cross-border e-commerce development, supporting provincial competitiveness as CBEC scales. Sustainability implications are therefore discussed via operational-efficiency channels rather than direct environmental outcomes. Full article

Despite its rich cultural heritage, the Yellow River Basin (YRB) faces challenges of ecological fragility and unbalanced development that constrain residents’ welfare improvement. Cultural–tourism integration (CTI)—aimed at creating employment, optimizing industrial structure, and improving public services—is increasingly promoted as a pathway to enhance people’s livelihood and well-being (PLW). Grounded in industrial integration theory and welfare economics, this study examined the impact effects, transmission mechanisms, and spatial spillovers of CTI on PLW. Panel data from 75 prefecture-level cities in the YRB, spanning 2011 to 2023, were utilized, and multi-dimensional indices were constructed for both CTI and PLW. Impact effects, mediating mechanisms, and spatial spillovers were examined through kernel density estimation, a dynamic system generalized-method-of-moments (SYS-GMM) model, mediation analysis, and a spatial Durbin model (SDM). The results showed that CTI and PLW both improved over time and displayed a spatial pattern of “midstream and downstream leading, upstream lagging”. CTI significantly promoted PLW, after controlling for dynamics and endogeneity (SYS-GMM coefficient = 0.130, p < 0.01). Industrial structure upgrading acted as a positive mediator, whereas digital infrastructure exhibited a short-term suppressing (negative mediating) effect, implying a phased mismatch between CTI investment priorities and digital input. Spatial estimates further indicated that CTI generated positive spillovers, improving PLW in neighboring cities, in addition to local gains. These findings suggest that basin-wide coordination and better alignment between CTI projects and digital infrastructure are essential for inclusive and sustainable well-being improvements, supporting regional progress toward the Sustainable Development Goals. Full article