Search Results (572)

Urban Waterlogging Susceptibility Assessment (UWSA) is vital for resilient urban planning and disaster preparedness. Conventional methods depend heavily on Historical Waterlogging Records (HWR), which are limited by their reliance on extreme rainfall events and prone to human omissions, resulting in spatial bias and incomplete coverage. While hydrodynamic models can simulate waterlogging scenarios, their large-scale application is restricted by the lack of accessible underground drainage data. Recently released flood control plans and risk maps provide valuable physics-informed priors (PI-Priors) that can supplement HWR for susceptibility modeling. This study introduces a dual-source integration framework that fuses HWR with PI-Priors to improve UWSA performance. PI-Priors rasters were vectorized to delineate two-dimensional waterlogging zones, and based on the Three-Way Decision (TWD) theory, a Multi-dimensional Connection Cloud Model (MCCM) with CRITIC-TOPSIS was employed to build an index system incorporating membership degree, credibility, and impact scores. High-quality samples were extracted and combined with HWR to create an enhanced dataset. A Maximum Entropy (MaxEnt) model was then applied with 20 variables spanning natural conditions, social capital, infrastructure, and built environment. The results demonstrate that this framework increases sample adequacy, reduces spatial bias, and substantially improves the accuracy and generalizability of UWSA under extreme rainfall. Full article

This study explores the convergence in greenhouse gas emissions (GHGs) and its determinants across 38 OECD countries during the period 1996–2022, employing the novel approach which combined club convergence method with supervised machine learning algorithm Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) method. The findings reveal the presence of three distinct convergence clubs shaped by structural economic and institutional characteristics. Club 1 exhibits low energy efficiency, high fossil fuel dependence, and weak governance structures; Club 2 features strong institutional quality, advanced human capital, and effective environmental taxation; and Club 3 displays heterogeneous energy profiles but converges through socio-economic foundations. While traditional growth-related drivers such as technological innovation, foreign direct investments, and GDP growth play a limited role in explaining emission convergence, energy structures, institutional and policy-related factors emerge as key determinants. These findings highlight the limitations of one-size-fits-all climate policy frameworks and call for a more nuanced, club-specific approach to emission mitigation strategies. By combining convergence theory with interpretable machine learning, this study contributes a novel empirical framework to assess the differentiated effectiveness of environmental policies across heterogeneous country groups, offering actionable insights for international climate governance and targeted policy design. Full article

The emigration of highly skilled individuals has become a critical concern for many countries amid increasing global labor mobility. This study employs the Improved Fuzzy Step-Wise Weight Assessment Ratio Analysis (IF-SWARA) method within a fuzzy multi-criteria decision-making (FMCDM) framework to identify and prioritize the key drivers of skilled migration. Drawing on opinions from sixteen Turkish emigrants currently residing abroad, the study captures firsthand perspectives on the structural factors influencing their migration decisions. The results indicate that the most influential factors are workplace conditions, living standards, and academic standards. These findings underscore the multifaceted nature of brain drain and highlight the necessity for comprehensive policy approaches that address both push and pull dynamics. By systematically ranking these determinants, the study contributes to the growing body of evidence-based research on international human capital flows. Full article

Based on panel data from 173 Chinese listed companies in the new energy vehicle industry from 2016 to 2023, this study constructs a two-way fixed effects model to examine the impact of data elements on corporate innovation capability. Systematically address issues and validate results through multiple measurement approaches, including robustness checks, instrumental variables methods, moderation effect analysis, and heterogeneity tests. The results indicate: (1) Data elements significantly enhance the innovation capability of new energy vehicle enterprises, and a conclusion that remains robust after a series of endogeneity and robustness checks. (2) Moderating effect tests reveal that human resources strengthen the relationship between data elements and corporate innovation capability. (3) Heterogeneity analysis shows that, in terms of capital sources, data elements have a more substantial promoting effect on the innovation capability of domestic enterprises compared to foreign-funded ones; regionally, the innovation-driven effect of data elements is more pronounced in eastern and central China than in the western region. This study not only offers practical guidance for new energy vehicle enterprises to allocate data elements and human resources effectively, but also provides an empirical basis for policymakers to formulate market-oriented data policies, thereby offering a new perspective for enhancing the innovation capabilities of new energy vehicle enterprises. Full article

Social workers are required to have the capacity to effectively support welfare recipients to restore their labor participation for social inclusion. However, a systematic method for process evaluation of this capacity has not yet been established. In this study, we developed the Index of Social Work Process in Promoting Social Participation of Welfare Recipients (SWP-PSP) to address this gap. Item domains and pools were prepared by referring to existing social work guidelines and human capital management theories, and content and face validity were confirmed by an expert panel review. The initial 75 items were revised to 44. We conducted a cross-sectional survey with 139 social workers working in public livelihood support at various municipal authorities in Japan. Item response theory analysis was performed for item selection, followed by the criterion-related validity test for convergent validation using Utrecht Work Engagement (UWE) scale scores as a reference. The selected 20 items with four domains were moderately correlated with UWE scores (Pearson’s correlation coefficient r = 0.35). Certified social workers demonstrated a stronger correlation with UWE (r = 0.44) than social workers without certification (r = 0.26). Cronbach’s alpha coefficients in each domain were over 0.77. These results indicate the reliability and validity of the SWP-PSP. This measure may be helpful for the evaluation of social workers’ capacity to promote social inclusion of welfare recipients. Full article

Context: As a pivotal productive service industry, industrial design is increasingly recognized for its enabling role in manufacturing innovation through industrial agglomeration. Objectives: This study examines the factors influencing regional manufacturing innovation performance driven by industrial design agglomeration, identifying differential effects of agglomeration elements on innovation input versus output. Methods: Utilizing panel data from 10 prefecture-level cities in Zhejiang Province, China (2015–2022), we constructed an industrial design agglomeration index system spanning four dimensions—industrial agglomeration, industrial scale, human resources, and development environment—alongside manufacturing innovation performance metrics covering both input and output. A two-way fixed-effects model was employed for empirical analysis, with robustness checks conducted to ensure methodological rigor. Results: The findings demonstrate a statistically significant positive correlation between industrial design agglomeration and manufacturing innovation performance. Notably, industrial scale and developmental environment emerged as the most influential factors for innovation output enhancement, while human capital exhibited limited supportive capacity. The agglomeration effect was particularly pronounced in technologically advanced regions. Conclusions: Industrial design agglomeration can underpin the sustainable advancement of manufacturing innovation capabilities. We propose context-specific strategies to refine service platforms, accelerate design-manufacturing integration and digital transformation, and foster sustainable high-quality development in manufacturing. Full article

Background: Infectious diseases pose a serious epidemiological and economic challenge for all healthcare systems. However, there is a lack of comprehensive analyses assessing the cost of absenteeism attributable to all infectious diseases. Our objective was to evaluate the burden of absenteeism-related costs due to infectious diseases in comparison with other major public health challenges. Methods: We applied the human capital approach to estimate the indirect costs of absenteeism caused by infectious diseases in Poland between 2018 and 2023. In particular, we assessed the relative contribution of different groups of infectious diseases to the overall economic burden. Data were obtained from the Social Insurance Institution (ZUS). Results: The total cost of absenteeism due to infectious diseases in Poland during the six-year period was EUR 5.3 billion. Over 78% of these costs were attributed to pneumonia and other acute lower respiratory tract infections (ICD-10: J12–J22): EUR 1.89 billion, COVID-19 (ICD-10: U07–U09): EUR 1.82 billion, and influenza (ICD-10: J09–J11): EUR 444.5 million. Infectious diseases imposed a greater economic burden in terms of absenteeism than each of the three conditions used as comparators: malignant neoplasms, depression, and ischemic heart disease. Conclusions: Our six-year analysis of sickness absence in Poland indicates that infectious diseases—particularly during the COVID-19 pandemic—are major drivers of productivity loss. When compared with other leading public health challenges, their economic burden is substantial. These findings underscore the importance of investing in preventive measures, particularly vaccination programs. Full article

Digital transformation has been accelerating the development of the global tax landscape, giving multinational companies the potential to generate revenue from certain jurisdictions without any physical presence in the relevant countries. This condition has triggered global initiatives aiming to prevent cross-jurisdictional tax evasion through the Global Minimum Tax (‘GMT’) consensus. This study will discuss how tax incentive policies in Indonesia can face the challenges brought by GMT while guaranteeing a good business climate for foreign investors. A normative research method alongside a descriptive and comparative approach will be used to analyze regulations and tax policies on investment in Japan and Vietnam, highlighting learning opportunities for Indonesia. The results of our research show that Japan and Vietnam still use tax incentives as a means to attract foreign investors, but only as additional factors. In contrast, the a quo condition in Indonesia shows an attachment to tax incentives as the main stimulus of investment, despite Indonesia’s natural resources, human resources, and existing markets having the potential to become the main capital drawing interest from foreign investors. Furthermore, the adoption of GMT in Indonesia is currently at the ministerial regulation level and is still considered insufficient, since it is not in line with the hierarchy of law, both in terms of legal norms and the principle of legality in taxation. Thus, Indonesia needs to immediately shift its focus to alternative incentives and ensure the integration of GMT into the national law through the reformation of policies and rules and regulations concerning taxation and investment. Full article

This paper presents ALMODES, a discrete-time modeling approach for social systems that uses matrix algebra and directed graphs. The method bridges the gap between static network analysis and continuous System Dynamics, offering a transparent framework that reduces data requirements. The method enables clear causal mapping, rapid simulation, straightforward sensitivity analysis, and natural hybridization with agent-based or discrete-event models. Two case studies illustrate its utility for sustainable-development strategy: in an urban public-health setting, modernization and sanitation policies drive sustained declines in disease despite growth, whereas reversing the population-to-modernization link triggers a morbidity rebound that can be prevented by strengthening the modernization-to-sanitation pathway; in a high-tech services Balanced Scorecard model, a baseline backlog spike depresses customer satisfaction, aggressive hiring shortens the spike but erodes income, and coordinated boosts to training and incentives (about twelve percent productivity gain) remove the backlog early, stabilize customers, and improve income, highlighting human-capital policy as a robust lever. ALMODES thus supports pragmatic policy design under limited, expert-elicited parameters. Future research will address uncertainty quantification, time-varying structures and shocks, automated calibration and empirical validation at scale, optimization and control design, richer integration with hybrid simulation, participatory interfaces for stakeholders, and standardized benchmarks across domains. Full article

Moving beyond Gross Domestic Product (GDP) as the sole measure of economic performance is increasingly critical for addressing the complex challenges of sustainable development. The Inclusive Wealth Index (IWI) offers a more comprehensive framework for assessing long-term sustainability by accounting for changes in produced, human, and natural capital. This paper contributes to this debate by examining the comparative dynamics of these three forms of capital in Greece in relation to European Union averages. Specifically, we employ a repeated-measures design and the mixed ANOVA method to analyse their interactions over time (1990–2020) and across regional contexts. The novelty is to cover the research gap on how the different capitals interact, with Greece serving as a critical case given its environmental vulnerabilities, economic challenges, and position within the European sustainability agenda. The empirical results demonstrate a consistent hierarchy (human > produced > natural), significant growth over time, and pronounced regional disparities, with Western and Northern Europe outperforming Eastern and Southern Europe in overall capital stocks. Moreover, human, produced, and natural capital differed significantly (${\eta }_p^2=0.967)$, with the EU-27 dominated by human and produced capital, while Greece lagged substantially (${\eta }_p^2=0.71$). A robust interaction effect indicated structural divergence (${\eta }_p^2=0.811$). The pairwise comparisons confirmed these results with very large effect sizes (Cohen’s d = 2.3–11.2 in the 95% CI). These findings underscore the importance of moving beyond GDP and highlight the policy relevance of inclusive wealth accounting for ensuring resilience and intergenerational equity. Full article

This study investigates how Environmental, Social, and Governance (ESG) performance is shaped across 31 countries in the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) from 1990 to 2020. To explore these relationships, we employed the Continuously Updated Generalized Method of Moments (CUE-GMM) and the Limited Information Maximum Likelihood (LIML), with additional robustness checks using Instrumental Variables Two-Stage Least Squares (IV-2SLS), Panel-Corrected Standard Errors (PCSE), and Driscoll-Kraay regressions. The results highlight democratic governance as a consistent driver of ESG advancement. Military expenditure can also support sustainability by reinforcing institutional stability, particularly in developing and upper-middle-income countries. Economic factors such as foreign direct investment, industrialization, and human capital show context-dependent effects, whereas globalization and natural resource rents generally enhance ESG performance, and inflation tends to constrain it. Overall, the findings underscore the importance of tailored, context-specific sustainability policies, showing that effective ESG progress depends on the interaction between institutions, economic structures, and global integration. Full article

This study analyzes the factors that affect the technical efficiency (TE) of firms in the supporting industry in the context of Vietnam’s digitalized economy. Stochastic frontier analysis (SFA), Fixed Effect Models, and System-GMM methods are applied to reach the findings that the quality of human resources, capital intensity, and firm size have positive effects on TE. Furthermore, exogenous environmental factors, such as the domestic demand of an industry impacting all upstream businesses, which use inputs that are products of that industry (BSpill-ratio), and the FDI backward effect (BFSpill), also exhibit positive effects. These confirm that the linkage between domestic supporting industry suppliers and FDI assembly enterprises plays an important role in improving TE. Vietnam’s digital transformation since 2020 has also created some interesting changes in the correlation coefficient. Location, sectors, competitiveness, and investment environment are also considered, and the results suggest that they are all determinants to be considered in management policies at both the firm level and the government level. Our contribution in this study is new policies aligned with many major changes in the world economic context, such as the tough tariff policy implemented by recent presidential administrations and a series of reforms of the Vietnamese Government, as well as strong digital transformation in Vietnam. The key findings of this research are important as they confirm which factors are really determinants for the Vietnamese government to implement investment policies for this industry effectively. Full article

The increasing number of catastrophic events has relentlessly disrupted production and distribution processes across the globe. To address this issue, the current study developed a research model that combines factors such as human capital, relational capital, structural capital, HR practices, risk management capability, and artificial intelligence to investigate logistic firm resilience capability. The research design was based on quantitative methods. Data were collected from logistic managers. A total of 213 questionnaires were retrieved for the research survey. Statistical findings revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained $R^2$ 86.5% of the variance in logistic firm resilience capability. Nevertheless, the relationship between risk management and resilience capabilities was found to be insignificant. On the other hand, logistic service quality and firm resilience capability explained $R^2$ 79.5% of the variance in logistic firm performance. Practically, this study suggests that adequate logistic service quality, appropriate intellectual capital, good HR practices, and the deployment of artificial intelligence in logistic operations could boost firm resilience capability, resulting in better performance during catastrophic events. The present study is original in that it investigated logistic firms’ resilience capability with intellectual capital, HR practices, and artificial intelligence. Another unique aspect of this study is that it established the moderating impact of logistic service quality on the relationship between logistic firm resilience capability and firm performance. Full article

Saudi Arabia is actively boosting women’s labor force participation, exceeding 2022 targets to enhance human capital. The purpose of this study is to examine the nexus between women’s empowerment and economic growth using secondary data from 1997 to 2022. Empowerment was proxied by Saudi women employed in government, with Gross Domestic Product (GDP) and female B.Sc. holders used as explanatory variables. The result of the Johansen test depicts a long-run equilibrium relationship between these variables. The Vector Error Correction Model (VECM) revealed a significant negative short-term impact of GDP on women’s empowerment at the 5% level, while female graduates had a positive short-term effect. The model results indicated rapid adjustment, correcting about 71% of disequilibrium per period towards long-run equilibrium. Importantly, a significant positive long-run relationship exists between women’s empowerment and economic growth. Diagnostic tests confirmed the VECM’s reliability, reflected by normally distributed residuals, with no significant autocorrelation, and overall model stability. The study findings contribute valuable insights for policymakers and stakeholders striving to achieve gender equity and sustainable economic development. Moreover, qualitative methods could be employed in future research to enhance the comprehensive understanding of the cultural and social barriers to maximize the long-run virtuous cycle of empowerment and economic growth. Full article

Conservation of the natural balance to hinder global warming is a contemporary task for policymakers. To this end, several policy tools have been proposed. Technological innovation, which increases productivity and aids in the development of eco-friendly technologies, and human capital, which fosters environmental awareness and provides knowledge of technology use, are among the policy options. Therefore, the primary aim of this study is to test whether human capital accumulation and technological innovation improve environmental sustainability in emerging countries by utilizing the recently proposed novel Cross-sectionally Augmented Autoregressive Distributed Lag method in ten of N-11 countries from 1996 to 2019. The empirical results suggest that economic development and human capital negatively impact environmental sustainability, proxied by the load capacity factor. In contrast, renewable energy has a positive impact on sustainable development. Lastly, empirical estimations using several technological innovation indicators uncover that technological innovation may have no systematic impact on the load capacity factor. Therefore, technological advances and human capital may not create the desired impact on environmental quality. Full article