Search Results (2,459)

Achieving long-term company financial performance requires strategic initiatives on the part of board management that can stimulate the sustainable development of companies through green strategies and eco-innovation. The research conducted in this study aims to identify the impact of executive management on financial performance based on the achievement of the sustainable development framework within European companies operating in various industries. An advanced empirical analysis was configured on a cross-sectional dataset based on 4.219 European companies, which was collected for the period of the 2022 fiscal year, considering dimensions such as corporate governance, sustainability, and financial performance. The methodological endeavor was founded on several modern econometric techniques, namely Generalized Structural Equation Modeling (GSEM) and Bayesian Network Analysis through Gaussian Graphical Models (GGMs). The main results highlight that companies having well-structured board management and corporate governance policies aligned with the SDGs facilitates the transition to sustainable economic models, enhancing financial performance, innovations, and long-term sustainable growth. Furthermore, policies should be tailored to emphasize the importance of optimal board management size and continuous professional training for human capital involved in sustainable activities, thus enhancing long-term financial performance. Full article

Minimum wage is an important tool for reducing income inequality and supporting social welfare. Consequently, governments around the world have established minimum wage systems. As such, minimum wage policies connect distributive justice with the economy’s capacity to sustain broad-based welfare over time, placing the equity–efficiency trade-off at the center of societal sustainability. However, the micro-level impact of the minimum wage system on firms has always been an important topic for scholars. This study uses panel data from listed Chinese manufacturing firms over a period from 2005 to 2021 to construct an indicator of the minimum wage standards implemented in the firm locations. Employing the multiple linear regression model, this paper empirically examines the effects of minimum wage on labor productivity. The empirical findings demonstrate that minimum wage significantly reduced the sample firms’ labor productivity. Moreover, the negative impact of the minimum wage was primarily concentrated among non-state-owned firms, labor-intensive firms, firms operating in industries characterized by intense product market competition, firms situated in regions with strong legal protections, firms with comparatively low average employee wages, and export-oriented firms. Subsequently, this study delves into the mechanism through which minimum wage negatively affects labor productivity. We find that implementation of minimum wage leads to a reduction in corporate investment, indicating that there is no significant substitution relationship between capital and labor. These adjustment margins provide microfoundations through which statutory wage floors can influence the resilience and inclusiveness of development, indicating that the pace and design of wage increases should balance income protection with the preservation of productive capacity to support sustainable human development—grounded in steady productivity growth, equitable income distribution, and stable firm investment. Our findings contribute to a better understanding of the mechanism through which minimum wage affects labor productivity in theory, while concurrently furnishing policy insights for the optimization of the minimum wage system and maintaining sustainable societal development in practice. Full article

Clade I mpox virus (MPXV) is endemic in the Democratic Republic of the Congo (DRC). Recent studies have described the changing epidemiology of mpox in the country, which has been mainly characterized by the emergence of new MPXV lineages, Clade Ib/sh2023 and Ia/sh2024, associated with sustained human-to-human transmission. Both Clade Ib/sh2023 and Ia/sh2024 are co-circulating in Kinshasa, the capital city of the DRC. Here, we report the first two cases of Clade IIb/sh2017 identified in Kinshasa, DRC, imported from West Africa and locally transmitted. Clinical specimens were collected and tested by PCR. We performed whole genome sequencing using a tiled-amplicon sequencing approach with Clade IIb MPXV-specific primers. The phylogenetic tree shows that Kinshasa Clade IIb MPXV is assigned to Clade IIb/sh2017 within the newly designated lineage G.1, as identified in January 2025 in Sierra-Leone. Full article

Against the backdrop of rising farm household incomes alongside a widening internal income gap in rural China, investigating the impact of peer effects in ecological assistance (PEEA) on farmers’ policy satisfaction is crucial for formulating more targeted support policies and mitigating rural income inequality. Utilizing 2023 survey data from designated assistance counties of the National Forestry and Grassland Administration (NFGA) of China, this study employs methods such as Oprobit and moderation effects to examine the factors and mechanisms through which peer effects in ecological assistance affect farmers’ policy satisfaction. The results indicate that PEEA exert a negative influence on farmers’ policy satisfaction, a finding that remains robust after endogeneity checks using the conditional mixed process (CMP) model and residual analysis. The information transmission mechanism can strengthen the suppressive effect of these peer effects on satisfaction, whereas the social interaction mechanism exhibits a substitution effect with the peer effects. The peer effects are asymmetric, with a more pronounced negative impact on policy satisfaction among farmers over 50 years old and those with lower human capital. Furthermore, the peer effect is most significant for forestry property income, followed by forestry wage income and forestry operating income. Farmer groups with low-to-medium forestry income are more significantly affected by PEEA. Furthermore, among these, the medium forestry income cohort demonstrates the highest sensitivity to the influence of PEEA on policy satisfaction. Therefore, ecological assistance policies should be further optimized, and forestry income should be distributed equitably to enhance the sense of policy benefit and well-being among farmers. Meanwhile, information channels should be improved to guide rational interpersonal expenditure, target groups with strong peer demonstration effects, explore diversified forestry management projects, and broaden income-increasing channels through ecological assistance. Full article

This study examines the determinants of talent retention in the hotel sector of Extremadura, a peripheral European region facing depopulation, labour scarcity and structural limitations that threaten the sustainability of its human capital base. Grounded in the Job Demands–Resources (JD-R) theory, the research analyses how a set of key labour resources, specifically professional training, organisational trust, job satisfaction and sustainability commitment, influence employees’ intention to remain in their organisations. These resources are conceptualised as organisational and motivational mechanisms that enhance employees’ capacity to cope with job demands and reinforce their attachment to the organisation. A quantitative survey was conducted with hotel-sector employees in Extremadura; 255 questionnaires were validated, and the proposed structural model was tested using SEM. The findings show that organisational trust is the strongest predictor of retention, followed by professional training and sustainability commitment, while job satisfaction also exerts a significant, though more moderate, effect. These results indicate that enhancing fairness perceptions, strengthening continuous training pathways and integrating sustainability-oriented values are essential strategies for retaining qualified personnel in territories with limited external opportunities. Rather than measuring human capital sustainability directly, the study shows that talent retention operates as a central empirical mechanism through which the sustainability of human capital can be supported in peripheral tourism economies. It concludes by highlighting the need for managerial practices that support transparent leadership, structured professional development and participatory sustainability initiatives, and encourages future research to incorporate longitudinal designs and direct measures of human capital sustainability. Full article

As the digital economy becomes increasingly integrated with the real economy, agricultural production is experiencing fundamental transformation. Digital–real integration has emerged as strategically important for cultivating agricultural new quality productive forces and safeguarding national food security. This study examines provincial panel data from 13 major grain-producing regions in China between 2012 and 2023. We develop an evaluation index system to assess both digital–real integration and agricultural new quality productive forces. Using the entropy weight method, we quantify the development levels of these two dimensions. Our empirical analysis employs fixed effects models, mediation effect models, and spatial econometric approaches to investigate how digital–real integration influences agricultural new quality productive forces in major grain-producing regions. The research findings indicate the following: (1) Digital–real integration demonstrates a robust positive correlation with agricultural new quality productive forces in major grain-producing regions. (2) Both agricultural industrial structure upgrading and agricultural green total factor productivity serve as significant mediating channels through which digital–real integration enhances agricultural new quality productive forces. (3) The impact exhibits notable heterogeneity across three dimensions: regional characteristics, industrial structure levels, and fiscal decentralization levels. (4) Digital–real integration generates substantial positive spatial spillover effects on agricultural new quality productive forces, facilitating coordinated improvements in neighboring regions. (5) A significant threshold effect exists in how digital–real integration promotes agricultural new quality productive forces. Specifically, the promotional effect intensifies once innovation level and human capital level exceed certain critical thresholds. These findings offer both theoretical insights and practical guidance for advancing high-quality development in agriculture within major grain-producing regions while strengthening the national food security strategy. Full article

The world has fully entered the era of the intangible economy, in which intangible capital serves as the primary driver for achieving sustainable development. This paper employs a nonparametric dynamic distribution approach to analyze the short-term transitional patterns and long-term steady-state trends of intangible capital investment across 30 developed economies, shedding light on pathways for national sustainable development from the perspective of intangible capital. Meanwhile, this paper examines the impact of industrial structure, income structure, and external-demand dependence on intangible capital investment. The results show that (1) intangible capital investment exhibits persistence, and its long-term development shows signs of unconditional convergence; (2) the tertiary industry significantly promotes the development of intangible capital, highlighting the crucial role of industrial structure upgrading in fostering intangible-driven sustainability; (3) the development of intangible capital does not necessarily substitute for human capital or reduce the labor income share; and (4) extremely high reliance on the external market may hinder the growth of intangible capital. Full article

This study investigates workplace stress-management practices and their relationships with employees’ well-being and productivity in accordance with Tayma University College’s goals in Saudi Vision 2030. Although stress-relief programs have been studied in detail in Western cultural environments, efficacy in the context of Saudi higher education institutions has proven to be limited, particularly as employee reactions are shaped by cultural, organizational, and institutional factors. This paper aims to explore the relationships between various other indicators, namely, mindfulness, time management, scheduling autonomy, and coworker support, and stress, job performance, and work–life balance. A convergent mixed-methods design was utilized, based on survey responses from 104 academic and administrative employees and semi-structured interviews with 20 respondents. The presentation of data demonstrated that time management was most consistently and significantly effective using SEM. In conclusion, time management was positively and significantly associated with increased schedule control, coworker support, and job performance, resulting in a more balanced work–life experience. Mindfulness had no significant or meaningful influence on perceived stress levels, while the influence of coworker support was more variable, and job performance experienced greater variation. Qualitative results confirmed this trend, as evidenced by the fact that time-management-oriented activities were incorporated into the daily routine, while mindfulness-related exercises were not well integrated with the cultural norms and work requirements. Within the university context of Saudi Arabia and with reference to the Job Demands–Resources (JDs–Rs) framework and the Transactional Model of Stress and Coping, the study also reveals that situational influences constitute a significant contribution to the development and use of stress-relief resources. Ultimately, the findings highlight the value of culturally relevant stress-management practices to facilitate the well-being, performance, and stability of employees with the backdrop of Saudi Vision 2030. Full article

Network security tools are indispensable in testing and evaluating the security of computer networks. Existing tools, such as Hping3, however, offer a limited set of options and attack-specific configurations, which restrict their use solely to well-known attack patterns. Although highly parameterizable libraries, such as Scapy, provide more options and scripting capabilities, they require extensive manual setup and often a steep learning curve. The development of powerful AI models, capitalizing on the transformer architecture, has enabled cybersecurity researchers to develop or incorporate these models into existing cyber-defense systems and red-team assessments. Prominent models such as NetGPT, TrafficFormer, and TrafficGPT can be effective, but require extensive computational resources for fine-tuning and a complex setup to adapt to proprietary networking environments and protocols. In this work, we propose AgentRed, a lightweight tool for generating network attack traffic with minimal human configuration and setup. Our tool integrates an AI agent and a large language model with fewer than a billion parameters into the network traffic generation process. Our method creates lightweight Low-Rank Adaptation (LoRA) adapters that can learn specific traffic patterns in a particular network environment. Our agent can autonomously train the LoRA adapters, search online documentation for attack patterns and parameters, and select appropriate adapters to generate network traffic specific to the user’s needs. It utilizes the LoRA adapters to create an intermediate traffic representation that can be parsed and executed by tools such as Scapy to generate malicious traffic in a virtualized test environment. We assess the performance of the proposed approach on six popular network attacks, including flooding attacks, Smurf, Ping-of-Death, and normal ICMP ping traffic. Our results validate the ability of the proposed tool to efficiently generate network packets with 97.9% accuracy using the LoRA adapters, compared to 95.4% accuracy using the base pre-trained Qwen3 0.6B model. When the AI agent performs online searches to enrich the LoRA adapters’ context during traffic generation, our method maintains an accuracy of 96.0% across all tested traffic patterns. Full article

This study creates and refines a risk–effectiveness–integrated dynamic simulation framework that brings together risk and effectiveness factors affecting qualified workforce allocation in multi-project contexts, specifically in the construction of industrial production facilities. Based on a case study of three overlapping projects in West Java, Indonesia, this study examines the requirements for an expert workforce across the Engineering, Procurement, and Construction (EPC) phases. Conventional mitigation measures generally assume that a qualified expert workforce is immediately available. However, hiring the right personnel with specific qualifications for a project takes time. To fill this gap, this paper presents a system dynamics-based model that explicitly integrates quantified project risks and execution effectiveness to determine expert workforce requirements at the multi-project level. This aspect is often addressed implicitly in the existing workforce planning approaches. This mixed-methods strategy includes a literature review, variable validation, simulation modeling, and case analysis. The results show that workforce planning based on integrated risk and effectiveness factors significantly improves project delivery by anticipating expert workforce shortages and reducing the need for reactive solutions. Model validation using real project data demonstrates that the simulated expert workforce demand reproduces both the average behavior and variability observed in real-world practice, satisfying quantitative behavioral validation criteria across projects and the EPC phases. The model contributes to sustainability by enhancing long-term workforce resilience, reducing resource waste, and supporting more efficient industrial project delivery. Full article

This study investigates how economic development interacts with sustainability performance in the European Union, focusing on the structural and technological factors that shape progress in the green transition. Using Eurostat data for 27 EU member states over the period 2015–2023, the analysis employs panel econometric models (Pooled Ordinary Least Squares, Fixed Effects, and Random Effects) to explore how circular economy performance, innovation capacity, human capital, and renewable energy use influence environmental and economic outcomes across member states. The results show that R&D intensity and skilled human resources are key drivers of sustainability. Higher levels of circular material use and resource productivity contribute to long-term competitiveness. In contrast, uneven progress in renewable energy deployment points to persistent regional disparities and possible structural constraints that limit convergence. Northern and Western Europe record the strongest advances in innovation and environmental efficiency, whereas Southern and Eastern regions remain affected by industrial legacies and lower absorptive capacity. The findings highlight that, in the short term, renewable energy expansion may involve adjustment costs and potential trade-offs with economic competitiveness in less technologically developed economies. This study provides new comparative evidence on the differentiated pathways of the green transition across the EU. Policy implications suggest the need to reinforce R&D investment, expand circular manufacturing, and support an inclusive technological transition consistent with the European Green Deal and the United Nations 2030 Agenda. Full article

Anti-Poverty Programmes (APPs) are closely linked to rural livelihoods, yet comparative evidence on how participants and non-participants differ in livelihood-capital composition and income-generation patterns remains limited in ecologically fragile pastoral regions. This study draws on a cross-sectional household survey conducted in Northern Tibet in July 2020, covering 696 households—including 225 APP participants and 471 non-participants. Using the Sustainable Livelihoods Framework and the entropy weight method, we construct multidimensional livelihood-capital indices (human, social, natural, physical, and financial capital) and compare the two groups. We further apply Ordinary Least Squares (OLS) regressions to examine factors associated with per capita net income. The results reveal substantial heterogeneity in livelihood capital and income across both groups. APP participants exhibit higher human-capital scores, largely driven by a higher share of skills training, whereas they show disadvantages in physical and financial capital relative to non-participants. Natural capital shows no statistically significant difference between the two groups under the local grassland contracting regime. Significant differences are observed and identified in certain dimensions of social capital. Regression results suggest that income is positively associated with skills training, contracted grassland endowment, and fixed assets, with skills training showing the strongest association. For participants, herd size and labour capacity are not statistically significant correlates of income; for non-participants, larger herds and greater labour capacity are associated with lower income. Taken together, the findings indicate that APP participation is associated with stronger capability-related capital (notably training) alongside persistent constraints in productive assets and financial capacity. Policy implications include improving the relevance and quality of training, strengthening cooperative governance and market linkages, and designing complementary packages that connect skills, inclusive finance, and productive asset accumulation. Given the cross-sectional design and administratively targeted certification of programme participation, the results should be interpreted as context-specific associations rather than strict causal effects. Full article

This study identifies key determinants shaping countries’ Sustainable Development Goals performance and develops classification models for predicting country group membership based on the SDG Index. The research addresses the urgent need to optimize development policies amid limited resources and the approaching 2030 Agenda deadline. Using data from 154 countries (2024), the analysis reveals that key SDG determinants are fundamentally method-dependent: discriminant analysis identified Goals 10, 6, 15, and 5 as most influential for differentiating countries by SDGI level, while Random Forest identified Goals 4, 9, and 2 as the most important predictors. This divergence reflects fundamentally different analytical perspectives—linear contributions to group separation versus complex nonlinear interactions and synergies between goals—with critical policy implications for prioritization strategies. Correlation analysis demonstrates that sustainable development dynamics operate differently across development stages: high-development countries show strongest associations with technological advancement and institutional capacity, while low-development countries exhibit compensation effects where basic infrastructure provision occurs alongside lagging human capital development. The discriminant model achieved 94.08% overall accuracy with perfect classification for extreme SDGI categories, while the Random Forest model provides complementary insights into interactive pathways. The scientific contribution lies in demonstrating that perceived variable importance depends on analytical framework rather than representing objective reality, and in providing validated classification tools for rapid assessment in data-limited contexts. These findings offer actionable guidance for evidence-based resource allocation and policy prioritization in the critical final years of SDG implementation. Full article

Promoting synergistic economic–resource–environmental development in resource-based cities (RBCs) is a fundamental requirement for ensuring national energy security and advancing regional sustainable and coordinated development. This study innovatively proposes the theoretical framework of “green transformation resilience (GTR)” based on evolutionary resilience theory, and then empirically explores the GTR of 114 RBCs in China from the perspective of urban development stages using multiple data models. The findings indicate that the GTR demonstrated an overall upward trend, though it remained at a consistently low level. Regenerative RBCs exhibited the highest GTR levels. GTR exhibits an uneven spatial distribution, primarily caused by super-variation density. The factor detection results indicate that factors such as government intervention, income level, and human capital have strong explanatory power for the spatial variation of GTR. Interaction analysis confirmed the significant nonlinear enhancement or bivariate enhancement of all pairs of factors. This study provides a basis for the differentiated development paths of GTR in China’s RBCs. Moreover, through factor interaction testing, it also offers guidance on policy combinations and prioritization for RBCs in different development stages. Full article

Recent societal transformations have intensified debates on corporate purpose, yet empirical evidence linking these debates to measurable sustainability outcomes remains fragmented. This study advances the literature by conceptualizing corporate purpose as a country-level sustainability orientation and empirically examining how European Union (EU) member states align with distinct corporate purpose models. Using cluster analysis and artificial neural networks applied to data from the Candriam Sovereign Sustainability Report and the Sustainable Development Report, this study identifies three dominant national-level corporate purpose models—traditional, goal-based, and duty-based. The findings reveal a systematic shift toward a sustainability-oriented corporate purpose model, in which social, human, and natural capital play a more decisive role than economic capital alone. Moreover, countries aligned with the duty-based model exhibit significantly higher Sustainable Development Goal (SDG) index scores, providing novel empirical evidence that national sustainability orientations are closely associated with progress toward the SDGs. By linking corporate purpose models to macro-level sustainability outcomes, this research offers a new quantitative framework for understanding how institutional and policy contexts shape contemporary interpretations of corporate purpose and their relevance for sustainable development. Full article