Ali Nawaz

Purpose: The role of Industry 4.0 technologies in developing sustainable tourism has been gaining importance progressively. This study aims to investigate the potential role of Industry 4.0 technologies (immersive technologies, artificial intelligence, cloud computing, robotics, big data analytics, etc.) in developing and promoting sustainable tourism. Design/methodology/approach: The database was extracted from Scopus, and 1,037 articles were selected and analyzed in this bibliometric study. RStudio was used to conduct the descriptive analysis to assess the top-performing countries, authors, journals and institutions in this area of research. The keywords co-occurrence analysis was applied using VOSviewer software to identify the most influential topics. Findings: The results of this study reveal that the role of Industry 4.0 technologies has emerged as the key player in achieving sustainability targets in the tourism industry. The results revealed that China is the key contributor to this field of research. The major themes that emerged are: digital destination competitiveness and technology-enabled tourism; digital transformation and smart destination management; integration of the latest technology with transportation systems; technology-supported environmental conservation and ecological tourism; and machine learning–enabled heritage preservation and tourism experience. Originality/value: There is a lack of prior bibliometric study investigating how Industry 4.0 technologies contribute to developing sustainable tourism. This study contributes to the field’s understanding in a number of ways. First, this study offers a thorough bibliometric mapping of Industry 4.0 technologies in sustainable tourism, highlighting important trends and thematic groupings that have never been thoroughly examined before. Second, it provides methodological rigor and repeatable findings by combining quantitative bibliometric analysis (Biblioshiny) with network visualization (VOSviewer). To direct future academic research and real-world applications, this study concludes by identifying gaps within each cluster and suggesting an organized research plan.

Investors today rarely look at a company's balance sheet alone. Environmental, social, and governance (ESG) criteria now sit at the heart of how firms are judged, part of a wider move toward sustainable finance. Companies across sectors have responded by building these principles into their operations, treating ESG not merely as a regulatory obligation but as a way to draw capital and strengthen reputation. In South Korea, however, the policy landscape is unusually turbulent. Frequent shifts leave firms facing an unpredictable environment, which raises a pressing question: how does economic policy uncertainty (EPU) shape the performance of the country's ESG leaders? We address this using wavelet-based quantile-on-quantile regression, which lets us trace distributional links across multiple time horizons. The results reveal a relationship that is neither linear nor symmetric. Over short horizons, moderate uncertainty drags on performance, especially in bearish markets. Medium-term effects depend on the quantile, where low uncertainty accompanies stronger returns and high uncertainty does the opposite. Across longer horizons, persistent uncertainty correlates steadily with weaker outcomes for ESG leaders. Together, these patterns suggest that policy instability undermines the nation's progress toward global sustainability targets. Policymakers should therefore work to dampen uncertainty and build a stable setting in which ESG-oriented firms can thrive.

This study addresses the critical problem of how Australian economic policy uncertainty (AEPU) affects the performance of the traditional energy sector, a topic of increasing importance given the sector's volatility and Australia's evolving policy landscape. Previous research has established that economic policy uncertainty generally depresses energy sector performance globally; however, there is a notable gap regarding non‐linear and frequency‐dependent effects specific to Australia's traditional energy market. To fill this gap, the study employs a novel methodological approach combining quantile‐on‐quantile regression with wavelet analysis. The empirical results reveal that, in the short run, uncertainty in Australian economic policies exerts weak and inconsistent effects on the performance of the traditional energy sector. In contrast, medium‐and long‐run results clearly demonstrate that prolonged uncertainty regarding Australian economic policies leads to a downturn in the performance of the traditional energy sector in the country. Building on these insights, we offer actionable recommendations: clearer, stable long‐term energy policies can buffer the sector from AEPU's drag; fossil‐fuel firms should strengthen liquidity and adopt phased investment plans under economic policy uncertainty.