Emerging countries are approaching economic prosperity. However, the development process has enhanced their ecological footprints, thus promoting low-carbon competitiveness among E7 countries’ industries. Therefore, it is essential to identify the factors that affect a country’s ecological footprint (EF) in order to safeguard the environment. This study explored the effect of financial development, human capital, and institutional quality on the EF of emerging countries. Furthermore, we explored the effect of financial development on the EF of emerging countries through the human capital channel. In addition, we investigated the role of institutional quality in the financial development–EF nexus. Using panel data from 1990 to 2018, we employed the cross-sectional autoregressive distributed lag (CS-ARDL) technique to conduct a short-term and long-term empirical analysis. The empirical outcomes revealed that financial development degrades ecological quality by raising the EF. The findings further demonstrated that human capital and institutional quality reduce the EF. Moreover, financial development fosters environmental sustainability through the channel of human capital. Additionally, institutional quality reduces the negative ecological impacts of financial development. The causality analysis suggested that any policy related to financial development, human capital, and institutional quality will affect the EF. However, the inverse conclusion was not sustained. Based on these findings, emerging economies should increase their environmental sustainability by promoting human capital and effectively using financial resources.

This study presents a novel hybrid framework combining feature selection, oversampling, and machine learning (ML) to improve the prediction performance of vehicle insurance. The framework addresses the class imbalance problem in binary classification tasks by employing principal component analysis for feature selection, the synthetic minority oversampling technique for oversampling, and the random forest ML classifier for prediction. The results demonstrate that the proposed hybrid framework outperforms the conventional approach and achieves better accuracy. The purpose of this study is to provide insurance managers and practitioners with novel insights into how to improve prediction accuracy and decrease financial risks for the insurance industry.

The research aims to assess the relationship between fiscal decentralization and climate change mitigation to warrant the direction for the carbon-capturing systems of China. The study estimated the results of China and applied unit root test, cointegration analysis, CS-ARDL test, and robustness analysis. The survey results highlighted a significant relationship between the fiscal decentralization index and climate change mitigation. More specifically, the budgetary decentralization index’s economic, governmental, and institutional factors play a substantial role in climate change mitigation in the short run. While governmental factors are found insignificant asein the long run, economic, institutional, and cultural factors revealed a significant connection in the Chinese setting. The results of the study are robust in both long-run and short-run perspectives. The study also presented prudential guidelines for using fiscal decentralization as an environmental tool for climate change mitigation and launching an effective system for carbon capture management from the Chinese perspective.

This study investigates the impact of Industry 4.0 technologies, including IoT, big data, and smart factories, on logistics sector performance, along with the moderating role of sustainable Human Resource Management (HRM) practices. Data from 552 respondents in Pakistan’s logistics industry were analyzed using structural equation modeling. The results demonstrate that Industry 4.0 implementations positively influence logistics service sector performance, while sustainable HRM practices significantly mediate between Industry 4.0 and performance. Specifically, integrating technologies from Industry 4.0 facilitates operational optimization, cost reduction, and enhanced efficiency in goods delivery. This study underscores the importance of strategic alignment between technological advancements and sustainable HRM practices in fostering logistics service resilience and competitiveness. By leveraging Industry 4.0 innovations and prioritizing sustainable HRM practices, logistics companies can effectively navigate the evolving market landscape and achieve long-term success. Moreover, this study offers a novel roadmap for logistics companies, showing how the synergy between sustainable HRM practices and Industry 4.0 can pave the way for resilience and success in an ever-evolving market landscape.

Purpose: The novel coronavirus (Covid-19) has pushed the hospitality industry towards an unprecedented crisis. Hotels around the world were under tremendous pressure to survive through this pandemic by adopting safety guidelines and communication with customers. This study examines the policies about safety guidelines of Indian hotels on Covid-19 with the help of their web pages, as reflected on the biggest aggregator website of India. Design: The study investigates the web pages of hotels in Agra, Delhi, and Jaipur, as shown in the biggest aggregator website of India, make my trip application and website using content analysis. Methodology: The authors consider coded statements and words, which the Government of India prescribed to the hotels for ensuring the protection against Covid-19. Implications: The study has the potential to understand the different policies adopted by the Indian hotels on Covid-19. The information obtained could help the customers to choose the right hotels for them during the pandemic. It could also provide suggestions to hotels interested in initiating best practices on dealing with Covid-19.

Business leadership in tourism startups is becoming a decisive condition for transforming sustainable business practices into competitive advantage and stronger financial performance, because responsible tourism and resource efficiency shape long-term venture survival. Prior research has mainly examined sustainability and performance in established hospitality firms, whereas the statistical mechanism through which sustainable practices become financially valuable in early-stage tourism ventures through competitive advantage remains insufficiently quantified. This study aims to test the direct effect of sustainable business practices on financial performance and to estimate the mediating role of competitive advantage in tourism startups. The calculations are based on questionnaire data collected from March 2024 to January 2025 from 441 founders, co-founders, managers and employees representing 127 recognised tourism startups in Uttarakhand, India, operating in hospitality, wellness, experiential travel, food services and related tourism activities. The empirical design used a five-point Likert scale, reliability analysis, outer-loading assessment, composite reliability, average variance extracted, Fornell–Larcker criterion, Heterotrait–Monotrait Ratio analysis, bootstrapping, mediation testing, Stone–Geisser predictive relevance analysis and partial least squares structural equation modelling in SmartPLS 3.0. The measurement model showed strong reliability and validity: Cronbach’s alpha ranged from 0.893 to 0.934, composite reliability from 0.921 to 0.943, average variance extracted from 0.582 to 0.701 and all Heterotrait–Monotrait Ratio values remained below 0.85. The structural model demonstrated that sustainable business practices significantly affect financial performance, with a standardised coefficient of 0.190, t-statistic of 2.867 and explanatory power of 0.597. A stronger relationship was identified between sustainable business practices and competitive advantage, with a standardised coefficient of 0.869, t-statistic of 57.608, effect size of 3.071 and explanatory power of 0.754, showing that sustainability is transformed mainly through market positioning and differentiation. Competitive advantage had a significant effect on financial performance, with a standardised coefficient of 0.602 and t-statistic of 8.732, while the indirect effect through competitive advantage reached 0.523 and the variance accounted for value of 73.3 per cent confirmed partial mediation. Predictive relevance strengthened the model, with Stone–Geisser values of 0.750 for competitive advantage and 0.502 for financial performance, both indicating large predictive effects. The findings show that business leadership in tourism startups creates financial value not merely by adopting sustainable practices, but by converting them into measurable competitive capabilities, offering a statistically grounded basis for entrepreneurial decision-making and startup policy.