Search Results (316)

As the fundamental physical carrier for human production and socio-economic endeavors, enhancing urban land green use efficiency (ULGUE) is crucial for realizing sustainable development. To effectively enhance urban land green use efficiency, this study systematically examines the intrinsic relationship between industrial policies and ULGUE based on panel data from 286 Chinese cities (2010–2022), employing an integrated methodology that combines the Difference-in-Differences (DID) model, Super-Efficiency Slacks-Based Measure Data Envelopment Analysis model, and ArcGIS spatial analysis techniques. The findings clearly demonstrate that the establishment of the “Made in China 2025” pilot policy significantly improves urban land green use efficiency in pilot cities, a conclusion that endures following a succession of stringent evaluations. Moreover, studying its mechanisms suggests that the pilot policy primarily enhances urban land green use efficiency by promoting industrial upgrading, accelerating technological innovation, and strengthening environmental regulations. Heterogeneity analysis further indicates that the policy effects are more significant in urban areas characterized by high manufacturing agglomeration, non-provincial capital/non-municipal status, high industrial intelligence levels, and less sophisticated industrial structure. This research not only provides valuable policy insights for China to enhance urban land green use efficiency and promote high-quality regional sustainable development but also offers meaningful references for global efforts toward advancing urban sustainability. Full article

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

Amid rising climate concerns, understanding how renewable energy adoption, human capital, fossil fuel efficiency, and globalization collectively shape CO₂ emissions is crucial for unlocking pathways to a cleaner, resilient, and globally connected low-carbon future. Using China as a case study, this research investigates the drivers of CO₂ emissions, focusing on fossil fuel efficiency, renewable energy adoption, and globalization, utilizing quarterly data from 1984Q1 to 2023Q4. To ensure robust and nuanced insights, the study integrates advanced machine learning techniques alongside Quantile-on-Quantile Kernel Regularized Least Squares (QQ-KRLS) and a Modified Quantile Regression as robustness checks, capturing complex distributional dynamics often overlooked in conventional analyses. To the authors’ knowledge, this is the first empirical study examining such relationships for the case of China. The results reveal that globalization, fossil fuel efficiency, renewable energy, human capital, and financial risk all contribute to increasing CO₂ emissions. The study proposes precise policies based on the findings obtained. Full article

With the digital economy continuing to integrate deeply into the agricultural sector, agricultural digital transformation has emerged as a pivotal driver of rural revitalization and the development of a robust agricultural economy. Although existing studies have affirmed the positive role of agricultural digital transformation in promoting rural development and enhancing agricultural efficiency, its spatiotemporal evolution patterns, regional disparities, and underlying driving factors have not yet been systematically and thoroughly investigated. This study seeks to fill that gap. Based on provincial panel data from China spanning 2011 to 2023, this study employs the Theil index, kernel density estimation, Moran’s index, and quantile regression to systematically assess the spatiotemporal dynamics and driving factors of agricultural digital transformation at both national and regional levels. The results reveal a steady overall improvement in agricultural digital transformation, yet regional development imbalances remain prominent, with a shift from inter-regional disparities to intra-regional disparities over time. The four major regions exhibit a stratified evolutionary trajectory marked by internal differentiation: the eastern region retains its lead, while central and western regions show potential for catch-up, and the northeastern region faces a “balance trap.” Economic development foundation, human capital quality, and policy environment support are identified as the core driving forces of transformation, while other factors demonstrate pronounced regional and phase-specific variability. This study not only deepens theoretical understanding of the uneven development and driving logic of agricultural digital transformation but also provides empirical evidence to support policy optimization and promote more balanced and sustainable development in the agricultural sector. Full article

Grounded in coupling theory, this study investigates the interplay among three key elements of economic growth, namely the digital economy, carbon emissions efficiency, and high-quality economic development. Drawing on data from 30 Chinese provinces from 2000 to 2023, we employ exploratory spatiotemporal data analysis and the GeoDetector model to examine the spatial–temporal evolution and underlying driving forces of coupling coordination. This research enriches the theoretical framework of multi-system synergistic development in a green transition context and offers empirical insights and policy recommendations for fostering regional coordination and sustainable development. The results reveal that (1) both the digital economy and high-quality economic development show a steady upward trend, while carbon emissions efficiency has a “U-shaped” curve pattern; (2) at the national level, the degree of coupling coordination has evolved over time from “mild disorder” to “on the verge of disorder” to “barely coordinated,” while at the regional level, this pattern of coupling coordination shifts over time from “Eastern–Northeastern–Central–Western” to “Eastern–Central–Northeastern–Western”; (3) although spatial polarization in coupling coordination has improved, disparities fluctuate in a “decline–rise” pattern, with interregional differences being the main source of that variation; (4) the degree of coupling coordination has a positive spatial correlation, but with a declining trend with fluctuations; and (5) improvements in the level of economic development, human capital, industrial structure, green technological innovation, and market development capacity all contribute positively to coupling coordination. Among them, green technological innovation and market development capacity are the most influential drivers, and the interactions among all driving factors further enhance their collective impact. Full article

Over the past decade, multifactor models have shown enhanced capability compared to single-factor models in explaining asset return variability. Given the common assertion that higher risk tends to yield higher returns, this study empirically examines the augmented human capital six-factor model’s performance on thirty-two portfolios of non-financial firms sorted by size, value, profitability, investment, and labor income growth in the Indian market over the period July 2010 to June 2023. Moreover, the current study extends the Fama and French five-factor model by incorporating a human capital proxy by labor income growth as an additional factor thereby proposing an augmented six-factor asset pricing model (HC6FM). The Fama and MacBeth two-step estimation methodology is employed for the empirical analysis. The results reveal that small-cap portfolios yield significantly higher returns than large-cap portfolios. Moreover, all six factors significantly explain the time-series variation in excess portfolio returns. Our findings reveal that the Indian stock market experienced heightened volatility during the COVID-19 pandemic, leading to a decline in the six-factor model’s efficiency in explaining returns. Furthermore, Gibbons, Ross, and Shanken (GRS) test results reveal mispricing of portfolio returns during COVID-19, with a stronger rejection of portfolio efficiency across models. However, the HC6FM consistently shows lower pricing errors and better performance, specifically during and after the pandemic era. Overall, the results offer important insights for policymakers, investors, and portfolio managers in optimizing portfolio selection, particularly during periods of heightened market uncertainty. Full article

In the world of globalization and increasing business competition, innovation has become a significant component of the sustainability of an organization. One of the important components affecting an organization’s ability to innovate is human resource management (HRM). This study analyzes how HRM practices relate to banking sector sustainability, testing theoretical pathways through organizational innovation and human capital as potential mediators. SPSS v25 was used to analyze data collected from 207 banking sector employees. The results demonstrate that the human capital of an organization can be increased by the practices of human resource management, which stimulates organizational innovation in the same fashion. This study also shows that human capital is a partial mediator of the relationship between human resource management practices and organizational innovation, highlighting its importance for converting human resource management activities into innovative results. Considering these results, banks are advised to implement complete human resource management strategies that combine operational efficiency with workforce capacity development to create a dynamic banking environment allowing for continued innovation. The proposed mediation model based on empirical data contributes to the literature and provides insights for banking institutions, which can use human capital to drive innovation in difficult situations. Full article

This paper examines the relationship between investment in water education and economic performance, focusing on the context of widening countries (EU Member States and Associated Countries with lower research and innovation performance). Through time-series data and panel regression analysis, the study investigates whether increased spending on education correlates with Gross Domestic Product (GDP) growth. While the initial static model indicates a positive but statistically insignificant association, a dynamic model with lagged GDP significantly improves explanatory power, suggesting that educational investments may influence growth with a temporal delay. Complementing the macroeconomic data, the paper analyses how targeted investments in educational innovation, especially in digital technologies such as virtual reality (VR) applications, enhance teaching quality and student engagement. Examples from partner universities involved in the WATERLINE project (Horizon Europe, 101071306) show how custom-built VR modules, aligned with existing hydraulic labs, contribute to advanced water-related skills. The paper also presents a cost-benefit analysis of VR applications in water education, highlighting their economic efficiency compared to traditional laboratory equipment. Additionally, it explores how micro-level innovations in education can generate macroeconomic benefits through widespread adoption and systemic impact. Ultimately, the research highlights the long-term value of education and innovation in strengthening both economic and human capital across diverse regions. Full article

The relationship between firm size and the financial performance of advertising and marketing companies remains understudied in the academic literature, including in the regional context. Using a panel data methodology, this study analyzes the impact of three proxies for firm size (total assets, number of employees, and sales) on the financial performance (return on assets and profit margin) of the 500 most profitable advertising and marketing companies from 16 Western European countries over the period 2019–2023. Weighted least squares regression analysis revealed statistically significant negative effects of all three proxies for firm size on financial performance, with the strongest negative effects on total assets on return on assets and sales on profit margin, which is similar to return on sales. Empirical data confirm the inverse relationship between total assets and their profitability; this indicates the advantages of resource-optimized business models with high management flexibility and effective use of intellectual capital compared to material-intensive structures. The inverse relationship between the number of employees and financial performance is due to higher operating personnel costs and the difficulty of effectively managing human resources as the number of employees increases. Increased sales negatively affect profit margins, demonstrating a decrease in the efficiency of converting revenue into profits as operations expand. These findings are important for developing effective financial management strategies and making investment decisions in the industry under study. The research contributes to SDGs 8, 9, and 12 by demonstrating how resource-optimized structures with higher management flexibility and effective use of intellectual capital can outperform material-intensive structures in the advertising and marketing industry. Full article

Indonesia’s high logistics costs—approximately 14.6% of its GDP—pose a significant challenge to national economic competitiveness. Key contributing factors include complex geography, fragmented multimodal transport systems and inefficient container terminal operations, particularly concerning the handling of empty containers. This study investigates operational optimization in a container terminal using Agile and Lean principles, without additional investment or infrastructure expansion. It compares throughput before and after optimization, focusing on equipment productivity and reduction in idle time, especially related to equipment and human resources. Field implementation began in 2015, followed by simulation-based validation using system dynamics modeling. The terminal demonstrated a sustained increase in capacity beginning in 2016, eventually exceeding its original design capacity while maintaining acceptable berth and Yard Occupancy Ratios (BOR and YOR). Agile practices improved empty container handling, while Lean methods enhanced berthing process efficiency. The findings confirm that significant reductions in port operational costs, shipping operational costs, voyage turnover time, and logistics costs can be achieved through strategic operational reforms and better resource utilization, rather than through capital-intensive expansion. The study provides a replicable model for improving terminal efficiency in ports facing similar constraints. Full article

Low-carbon transformation and common prosperity are critical pillars of China’s economic growth. To explore the mechanism relating the two, this paper analyzes how carbon efficiency influences the urban–rural income gap, including its transmission mechanism and heterogeneity, and uses panel data from 240 Chinese prefectural cities (2006–2019). The results reveal an “inverted U-shaped” relationship between the low-carbon transition and urban–rural income gap. Specifically, as the carbon emission efficiency improves, the impact of the low-carbon transition on the urban–rural income gap changes from positive to negative. This finding remains robust under robustness tests. The heterogeneity test indicates that the “inverted U-shaped” relationship exhibits regional heterogeneity, resource endowment heterogeneity, economic development stage heterogeneity, and urban–rural income gap level heterogeneity. Furthermore, urban low-carbon transition influences the urban–rural income gap through industrial structure, employment structure, and human capital. This paper discusses the combination of low-carbon transformation and common prosperity, and takes into account both ecological sustainability and social sustainability. The findings of this paper offer policy proposals for advancing the achievement of dual-carbon goals and common prosperity, and provide references for developing countries. Full article

This paper extends the resource-based view (RBV) of social entrepreneurship by introducing artificial intelligence (AI) as a dynamic, integrative capability that enhances the acquisition and optimization of four foundational forms of capital: human, social, political, and financial. While social ventures have long faced constraints in scaling impact due to resource limitations and institutional barriers, AI technologies—such as predictive analytics, machine learning, and natural language processing—offer new pathways for improving operational efficiency, stakeholder engagement, advocacy strategies, and financial sustainability. Through the development of a conceptual model and a series of theoretical propositions, this study positions AI as a transformative force that not only strengthens individual resource domains but also enables synergistic feedback loops across them. In doing so, the paper contributes to emerging debates on technology adoption in hybrid organizations, scalability in resource-constrained contexts, and the evolution of strategic management theory in the digital age. Practical implications are outlined for social entrepreneurs, policymakers, and funders seeking to responsibly integrate AI into social impact ecosystems, and future research directions are proposed to empirically test the framework across sectors and global settings. Full article

As the primary force driving the sustainable development of the rural economy, the improvement of the total factor productivity (TFP) of agricultural enterprises (AEs) is of great strategic significance. This study innovatively zeroes in on AEs, leveraging micro-level data from agricultural listed companies in China’s A-share market spanning from 2007 to 2023. It aims to investigate the impact of supply chain finance (SCF) on the TFP of these enterprises and elucidate the underlying mechanisms. Uniquely, this study incorporates enterprise digital transformation and innovation capability as moderating variables into the mechanism analysis framework. Furthermore, it examines the heterogeneous effects across different characteristics of AEs. The findings reveal that SCF significantly boosts the TFP of AEs. Specifically, a one-standard-deviation increase in the level of SCF is associated with a 0.2658% increase in TFP relative to the mean. This conclusion holds robustly across various tests. Moreover, the interaction terms of SCF with both enterprise digital transformation and innovation capability are significantly positive. This indicates that greater digital transformation and stronger innovation capability amplify the positive effect of SCF on TFP. The heterogeneous analysis further indicates that for AEs with highly optimized human capital, higher financing constraints, and more efficient credit resource allocation, the positive impact of SCF on TFP is particularly pronounced. Full article

The effects of initial institutions and change in institutions on the growth of ex-socialist countries is unsettled in the literature. This is due to difficulties in modeling the effects of institutions and their change. The objective of this paper is to contribute to this area. Ex-socialist countries faced heterogenous initial conditions at transition. Those that joined the EU experienced institutional integration as well as institutional improvement. Using publicly available data from about ten years before and after EU accession and two-way fixed effects differences-in-differences estimation, this paper finds that these countries experienced growth boosts post-EU accession. Achieving institutional integration cum improvement by accepting and implementing EU’s regulations and norms in all details permitted this boost. The initial conditions mattered—the effect was greater in “new” ex-socialist countries (which had the additional burden of creating new administrative structures and economic relationships) than in the “old.” Using the neo-classical growth model, the paper then examines whether this boost in growth was due to a higher contribution of inputs or due to an increase in the efficiency with which inputs were used. It indicates that it was due to increased contribution of human capital rather than an increase in the amount of human capital or other economic or political confounders. These countries’ skilled labor, already high in skills at transition by OECD standards, needed the right institutions to unlock its potential. Full article

This study investigates the role of public health expenditure on human capital development in South Africa to promote economic development. Despite extensive public health investments and economic reforms, persistent socioeconomic challenges such as poverty, unemployment, and inequality impede sustainable economic growth. This study uses an autoregressive distributed lag model, a vector error correction model (VECM), quantile regression, and Granger causality analysis to assess the relationship between fiscal health policies and human development. The findings confirm that government health spending significantly enhances human development in the short and long run, while unemployment and population growth exert adverse effects. VECM variance decomposition results indicate that the influence of public health expenditure remains persistent, though diminishing over time, with growing contributions from unemployment. Quantile regression shows the heterogeneous impact of health spending across different levels of economic development, emphasising its greater effectiveness at higher development stages. Causality analysis reveals a unidirectional relationship from public health expenditure to human development; this shows the need for sustained healthcare investment. The study calls for policies combining health spending with economic strategies to boost productivity, reduce inequality, and promote inclusive growth. Strengthening institutional efficiency and ensuring macroeconomic stability are crucial for maximising long-term human capital to promote sustainable development. Full article