Search Results (70)

In light of the frequent occurrence of uncertain events, supply chain resilience has emerged as a critical issue for the survival and development of enterprises. This study empirically examines the impact of corporate environmental, social, and governance (ESG) performance on supply chain resilience, utilizing data from A-share listed companies in China from 2015 to 2023. The findings reveal that strong ESG performance positively influences supply chain resilience. The concept of “new quality productive forces” provides a novel perspective for understanding corporate sustainable development. Mechanism tests indicate that new quality productive forces play a significant mediating role between ESG performance and supply chain resilience. Specifically, by enhancing ESG performance, enterprises indirectly promote the growth of new quality productive forces, thereby further strengthening supply chain resilience. The robustness of these results is confirmed through tests involving the replacement of core explanatory variables, expansion of sample size, inclusion of additional control variables, and Hausman Tests. Furthermore, heterogeneity analysis demonstrates that state-owned enterprises exhibit a more pronounced effect of ESG performance on supply chain resilience compared to private enterprises. Full article

In the context of digital intelligence, innovation is vital for manufacturing enterprises to establish sustainable competitive advantages. As the cornerstone of decision-making, the information-processing capability of top management teams plays an essential role in driving organizational success. Using panel data from A-Share manufacturing listed companies between 2015 and 2023, we conducted programming in the R language employing hierarchical clustering and k-means algorithms for faultline grouping calculations. The empirical analysis portion utilized STATA software, where the Hausman test was implemented to determine the use of a fixed-effects model for computation. The results demonstrate that task-related faultlines, driven by factors such as educational background, tenure, career experience, and years of service, have a positive impact on innovation performance. In contrast, relationship-related faultlines influenced by gender and age exhibit a negative effect. Furthermore, long-term investment decision preferences mediate the relationship between faultlines and innovation performance. Performance expectation gaps amplify the positive influence of task-related faultlines and mitigate the negative effects of relationship-related faultlines. In comparison with the majority subgroup, when the chairperson is part of a minority subgroup, the faultline has a more significant impact on innovation performance. This study presents a novel framework for fostering innovation within the manufacturing industry under the digital intelligence context. By combining R programming with empirical analysis, we thoroughly examine how the characteristics of top management teams’ faultlines influence innovation performance through an information processing perspective. Our findings provide actionable insights for optimizing executive structures and aligning decision-making strategies, thereby advancing organizational effectiveness. Full article

A clean environment enhances well-being and drives economic growth. BRICS nations aim to cut emissions while sustaining growth, aligning with global sustainability goals. Their strong economic progress underscores the need to explore the links between communication technology, financial efficiency, education, and renewable energy consumption (RENC). Therefore, to analyze these dynamics, this study examines data spanning from 1990 to 2020 using a rigorous methodological framework. Initially, model selection was guided by AIC and BIC criteria by ensuring optimal model fit. Furthermore, multicollinearity was assessed using the Variance Inflation Factor (VIF), while heteroscedasticity and autocorrelation issues were tested through the Breusch–Pagan Test and the Ljung–Box Test, respectively. Additionally, cross-sectional dependence (CSD) was checked, followed by stationarity analysis using the second-generation CIPS. The Westerlund Cointegration Test was employed to confirm long-run relationships. As a final preliminary test, the study uses the Hausman test for selection of the appropriate model specification. Subsequently, the PMG-ARDL approach was utilized to examine both short- and long-term dynamics. The findings reveal a significant negative relationship between RENC, Gross Domestic Product (GDP), and CO₂ emissions. Conversely, RENC exhibits a strong positive association with education (EDUC), information and communication technology (IACT), the financial markets efficiency index (FMEI), and the financial institutions efficiency index (FIEI). Finally, the robustness of the PMG-ARDL results was validated through advanced techniques, including Fully Modified OLS (FMOLS) and the Generalized Method of Moments (GMM), reinforcing the reliability of the findings. The study offers valuable policy recommendations to support sustainable development in BRICS nations. Full article

The extraction of excessive natural resources, as well as economic and social development, has created several ecological issues. Therefore, this study examines the effects of globalization (GLZN), digitalization (DGTZ), economic development (ECDV), natural resources use (NRRS), and technological innovation (TCIN) on ecological footprints (EFPR) in G10 economies from 2000 to 2021. We examined the cross-sectional dependence, lack of slope homogeneity, stationarity characteristics through the CIPS unit root test, and panel co-integration among the variables through the Westerlund test. We then used Pooled Mean Group Autoregressive Distributed Lag to examine the long-term and short-term associations, validated by the Hausman test. The empirical findings show that DGTZ and TCIN improve environmental quality by lowering EFPR. However, in G10 economies, ECDV, GLZN, and NRRS reduce environmental quality by increasing the impact of EFPR on the environment. Without sustainable practices, the extraction and consumption of natural resources lead to a higher EFPR, which indicates greater environmental strain. Moreover, the results indicate that TCIN and DGTZ are crucial for environmental protection in the G10; therefore, we should promote their use to maintain ecological sustainability in these economies. Full article

The issue of non-performing loans (NPLs) in a bank’s portfolio is important for a bank’s stability and sustainability. Their increased presence indicates a potential worsening of the economy and a lower quality of the bank’s assets. We estimated determinants of non-performing loans in the Eurozone for quarterly data 2015–2020. The results confirmed spatial spillover effects within Eurozone countries, which means that when a shock happens in one country in the Eurozone, it will also affect the other economies of the Eurozone area. Based on the Hausman test, a fixed-effects model was chosen as appropriate and showed that bank-specific and macroeconomic determinants significantly affect NPLs in these economies. In relation to previous studies that dealt with this issue, a co-integration analysis was introduced. A significant impact of return on assets, return on equity, and the loan-to-deposit ratio, as well as the gross domestic product, inflation, and exchange rate on NPLs in the short run and long run, was confirmed using a Pooled Mean Group (PMG) estimator. Bank management should customize credit policy based on both internal and external conditions to improve their performance, focusing on enhancing profitability and maintaining a lower loan-to-deposit ratio to reduce NPLs. The research suggests that a higher gross domestic product (GDP) growth rate is associated with fewer NPLs, while inflation uncertainty and a volatile exchange rate can increase NPLs, highlighting the importance of adjusting strategies to the macroeconomic landscape. Full article

Background: Assessing frailty from middle age onward offers valuable insights into predicting healthcare expenditures throughout the life cycle. Objectives: This paper examines the use of physical frailty as an indicator of healthcare demand across all age groups. The originality of this work lies in extending the analysis of frailty indicators beyond the typical focus on individuals under 50 years old to include those in mid-life and older. Methods: For this study, we used a database where frailty was measured in 2012 in a sample of individuals aged 15 to over 90. These individuals were tracked for their healthcare expenditures from 2012 to 2016. Results: Among the sample of 6928 individuals, frailty in 2012 resulted in a statistically significant increase in costs at the 5% level for the population aged 15 to 65. We applied multilevel linear regression models with year fixed effects, controlling for demographic factors, education level, precarity, social dimensions, lifestyle factors (e.g., vegetable consumption), physical activity, emotional well-being, and medical history. A Hausman test was conducted to validate the model choice. For mortality rate analysis, Cox models were used. Conclusions: Our findings demonstrate that physical frailty provides valuable information for understanding its impact on healthcare expenditure. The effect of frailty on mortality is particularly significant for the elderly population. Moreover, frailty is a predictor of healthcare costs not only in older adults but also across the entire life cycle. Full article

Sustainable watershed development focuses on building resilience to drought through better water resource management, ecosystem protection, and adaptation strategies. In this study, the spatiotemporal dynamics and inter-seasonal characteristics of meteorological drought across Ankara Province, Turkey, were investigated and compared using a conditional fixed effect logistic regression model (CFELogRM) and a random effect logistic regression model (RELogRM). To assess the statistical validity and effectiveness of these models, we conducted significance tests, including the log-likelihood ratio chi-square, and Wald chi-square tests. The obtained p-values associated with both the RELogRM and CFELogRM models for the selected seasons demonstrate their statistical significance. Additionally, we conducted the Hausman test (HT) to compare the efficiency of the RELogRM and CFELogRM models. Remarkably, the results of the HT suggest that RELogRM is the optimal model for modeling fall-to-winter season drought dynamics across the study area. Notably, the significant coefficient derived from RELogRM indicates a statistically significant negative correlation between spring moisture conditions and the probability of summer droughts. Specifically, the odds ratio of 0.2416 reflects a 24.16% reduction in the likelihood of transitioning to a higher drought category, emphasizing the crucial role of antecedent moisture conditions in influencing drought propensity. Full article

This study examines how board gender diversity affects listed non-financial European companies’ financial performance. Data from the Refinitiv Eikon Platform—LSEG and World Bank databases was used to complete the analysis. The total sample included 4257 companies for the period 2011–2023. This study examined board gender diversity and its interaction with liquidity while controlling for board characteristics such as board size, independence, and board meetings. Controlling for firm characteristics (firm size and leverage) and macroeconomic variables like inflation and GDP. This study estimated the connection using panel regression. Due to Hausman test significance, fixed effect estimation was used. The findings demonstrated a notable and favorable influence of board features, such as gender diversity, board independence, and board size, on European nonfinancial companies. Additionally, liquidity positively affects firm performance. Furthermore, the findings indicated that leverage had a significant negative impact on profitability. Finally, both the size and GDP have a significant beneficial impact on profitability. Our findings indicate that an increased representation of women on the board of directors is associated with greater independence among board members and a higher number of board members being hired. This, in turn, has a positive impact on profitability due to the extensive experience shared among board members. Additionally, this leads to improved governance, enabling better control over decisions and a greater focus on the long-term investment strategy of the company. Our results are robust, as are similar results reported by the GMM regression. Full article

A growing number of countries are concerned about the reliability of environmental indicators; as a result, there is a pressing need to find ways to improve ecological welfare on a global scale. This study investigates the dynamic linkages among CO₂ emissions, AI, economic policy uncertainty (EPU), and renewable energy consumption. To analyze these relationships empirically, this study used panel data for East Asian and Pacific countries from 2000 to 2023. This study used fully modified ordinary least squares (FMOLSs), dynamic ordinary least squares (DOLSs), Hausman fixed effects (FEs) and random effects (REs), the generalized method of moments (GMM), and variance decomposition tests. This study’s results show that AI has a positive relationship with CO₂ emissions in terms of the benchmark regression, while it shows minimal impact on CO₂ emissions according to the variance decomposition test. Similarly, economic policy uncertainty shows a strong positive relationship with CO₂ emissions through benchmark regression FEs and REs, GMM, and the variance decomposition test. An increase in EPU will positively affect CO₂ emissions. Renewable energy consumption has a strong negative impact on CO₂ emissions in East Asian and Pacific countries. These findings reveal that a unit increase in renewable energy consumption will decrease CO₂ emissions. Based on the results of this study, it is suggested that policy certainty and an upsurge in renewable energy consumption are essential for environmental upgrading. In contrast, adopting AI has no robust effect on ecological degradation (CO₂ emissions). East Asian and Pacific countries need to focus on the adoption of renewables, as well as the control of economic policy uncertainty. While AI in East Asian and Pacific countries is still in the initial stage of adoption, policy formation is essential to overcome the possible carbon footprint of AI in the short term. Full article

This study analyses macroeconomic trends in Southeast Asian countries and their implications for climate change, focusing on urbanisation, GDP per capita, energy intensity, FDI, inflation, and trade. Using panel data from 1970 to 2020, we investigate climate change drivers across Indonesia, Malaysia, the Philippines, Singapore, and Thailand through panel ARDL with PMG and MG analyses, along with Hausman tests. Our results highlight the need for tailored urbanisation policies for sustainability, as the consistent positive correlation between GDPs per capita and emissions, underscores the challenge of decoupling economic growth from emissions. Urbanisation’s varying impact calls for proactive planning, and mixed FDI results suggest nuanced investment approaches aligned with sustainability. Inflation’s negative impact hints at environmental benefits during price increases, necessitating integrated economic and climate policies. The positive relationship between trade openness and emissions emphasises the need for eco-conscious trade agreements to mitigate emissions from industrial activity. Our study stresses the importance of considering macroeconomic heterogeneity in crafting climate policies. Policymakers must adopt multifaceted approaches that prioritise sustainability across economic growth, energy efficiency, technology adoption, and trade to balance development with environmental preservation. This approach enables Southeast Asian countries to contribute effectively to global climate change mitigation. Full article

This article provides panel data estimations of the tax revenue determinants in VG (Visegrad Group) countries (the Czech Republic, Hungary, Poland, and Slovakia) for the period 1994–2023. The aim of this research was to determine how the macroeconomic determinants affect the tax revenues in the selected countries. Within the static models, the Hausman test showed that the FE (fixed effects) model is appropriate and reflects the significant effects of the gross domestic product, population, inflation, unemployment, import, government revenue, government expenditure, and EU enlargement on the tax revenue. The PMG (Pooled Mean Group) model is an adequate model among the dynamic models and manifests the significant effect of the lagged value of the tax revenue. In the short term, growth of the gross domestic product and population by 1% causes higher changes in the tax revenue of 0.14% and 2.93%. Likewise, growth of the inflation rate by 1% decreases the tax revenue by 0.037%, which is higher than in the long term. Further, the results show that EU enlargement is significant for tax revenue in the short term, as well as in the long term. In the long term, unemployment has a greater significant effect on tax revenue, where 1% growth decreases the tax revenue by 0.15%. In contrast, government revenue is significant for tax revenue only in the long term, where 1% growth increases the tax revenue by 0.77%. Full article

This research investigates the crucial role of transportation infrastructure in influencing economic activity, thus employing advanced econometric methods including Moran’s I index, LM, Hausman, and LR tests to ensure analytical accuracy and select the appropriate spatial model. Our findings reveal that freight volumes across road, waterway, and civil aviation significantly enhance economic activity by bolstering domestic trade, industrial production, and supply chains. Conversely, the impact of passenger turnover is comparatively minor, although it still contributes to labor mobility and urban accessibility. This study highlights the need for strategic investment in transportation infrastructure and efficient public transport systems to foster economic growth and sustainable development. We recommend that policymakers focus on optimizing transportation networks and integrating intelligent transport technologies to boost economic competitiveness and societal well-being. This analysis not only sheds light on the direct economic impacts of transportation but also underscores the broader social implications, thus advocating for a holistic approach to transportation planning and policymaking. Full article

Remittances are a crucial part of economic expansion, especially in Central Asia. Nevertheless, it is not possible to ignore its environmental damage. This paper is a pioneer in investigating the association among CO₂ emissions, remittances, energy consumption and economic development in Central Asian countries (Uzbekistan, Kazakhstan, Kyrgyzstan and Tajikistan) spanning the period of 1995–2022. As a methodology, the FMOLS estimator is applied to check linear impact and long-run association as well. Panel threshold regression model and 2SLS method are applied to examine potential non-linear relations among the studied variables. Hausman–Taylor and Amacurdy estimators are employed to control the endogeneity issue among the variables of interest. The results suggest the existence of a long-run relationship among the studied variables. Precisely, applying the FMOLS method, remittances negatively impact CO₂ emissions in the long run. The relationship between CO₂ emissions and remittances is distorted when the endogeneity issue is considered with the Panel threshold regression model, 2SLS method, and Hausman–Taylor and Amacurdy estimators. This distortion validates the linear impact of remittances on CO₂ emissions in CA. The Dumitrescu–Hurlin causality test shows that all independent variables have a causal effect on the dependent variable, validating the effect of the studied variables. Consequently, decision-makers should facilitate remittances towards more environmentally friendly and sustainable solutions to prevent the detrimental effects of remittance inflows on carbon emissions in Central Asia. Full article

For a panel data linear regression model with both individual and time effects, empirical studies select the two-way random-effects (TWRE) estimator if the Hausman test based on the contrast between the two-way fixed-effects (TWFE) estimator and the TWRE estimator is not rejected. Alternatively, they select the TWFE estimator in cases where this Hausman test rejects the null hypothesis. Not all the regressors may be correlated with these individual and time effects. The one-way Hausman-Taylor model has been generalized to the two-way error component model and allow some but not all regressors to be correlated with these individual and time effects. This paper proposes a pretest estimator for this two-way error component panel data regression model based on two Hausman tests. The first Hausman test is based upon the contrast between the TWFE and the TWRE estimators. The second Hausman test is based on the contrast between the two-way Hausman and Taylor (TWHT) estimator and the TWFE estimator. The Monte Carlo results show that this pretest estimator is always second best in MSE performance compared to the efficient estimator, whether the model is random-effects, fixed-effects or Hausman and Taylor. This paper generalizes the one-way pretest estimator to the two-way error component model. Full article

Rapid advancements in digital technologies have accelerated global change, underscoring the critical role of resilience in addressing the escalating energy, economic, and environmental challenges. This paper investigates the effects and mechanisms of the digital economy on energy, economic, and environmental resilience within the context of these challenges. By utilizing panel data from 66 countries spanning the period from 2000 to 2020, this analysis employs robust panel data models and incorporates tests such as the Hausman and Leamer tests, and exploratory factor analysis. The results reveal a notable positive impact of the digital economy on resilience across various countries and time periods. However, when it comes to carbon emissions, a more intricate pattern emerges, suggesting a negative influence on resilience in environmental, energy, and economic domains. Interestingly, countries with below-average carbon emissions show more positive effects on economic resilience due to the digital economy. On the other hand, the effect of the digital economy on energy resilience is less prominent in below-average carbon-emitting nations, while carbon emissions have a more significant impact within this subgroup. Above-average carbon-emitting countries experience limited effects of the digital economy on environmental resilience, while below-average carbon-emitting countries face challenges with significant carbon emissions impacting their environmental resilience. Full article