The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus’s curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI’s accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development.

Recently nostalgia has received attention from academic researchers for increasing destination attractiveness and tourist footfalls. Despite increasing interests in food tourism, little research exists regarding gastro-nostalgia to motivate tourists for visiting gastro-festivals. The current study identifies six potential dimensions of gastro-nostalgia through extant literature review viz., cuisine, gastro-experience, gastro-festive ambiance, socialization, personal identity, and cultural identity. Furthermore, it developed a robust instrument for measuring gastro-nostalgia as a reflective-reflective second-order construct using exploratory factor analysis and confirmatory factor analysis. The study collected perceptual responses from tourists and individuals having positive memories associated with two prime gastro-festivals in India: (a) Durga puja (221 responses) and (b) Onam (257 responses). The instrument is empirically validated through factorial validity, nomological validity, and measurement invariance. The study showed that gastro-nostalgia could positively influence tourists’ willingness to recommend intentions and revisit intentions. The study offers several implications for destination managers and event managers.

Research background: The realization of United Nations Sustainable Development Goals (SDGs) 8 hinges on the sustained growth of both the economy and the global employment outlook. In order to ensure progress in fostering inclusive and equitable growth and employment opportunities, it is essential to undertake a comprehensive analysis of the SDG findings across all relevant subtargets. Purpose of the article: This study provides an in-depth analysis of the research surrounding SDG 8 (Decent Work and Economic Growth), emphasizing its pivotal role in sustainable development. Methods: Utilizing the PRISMA framework and BERTopic method, it explores the intricate interconnections between SDG 8 and other goals, such as reducing inequalities (SDG 10), clean energy (SDG 7), climate action (SDG 13), poverty alleviation (SDG 1), and innovation and infrastructure (SDG 9). Findings & value added: The findings emphasize the importance of subtarget 8.1 (sustained per capita growth) and subtarget 8.5 (full employment and decent work for all). Additionally, the study underscores the need for policies supporting resource efficiency and sustainable consumption (subtarget 8.4), while green innovation and the digital economy are strongly linked to subtarget 8.2 (enhanced productivity through technological upgrading). Ensuring that economic growth is inclusive and equitable, as outlined in subtarget 8.3, is crucial for reducing inequalities. Policymakers must balance economic growth with environmental sustainability, promoting decent work conditions (subtarget 8.8) and adopting green technologies. These findings offer valuable insights for advancing SDG 8, ensuring that economic progress benefits all segments of society while safeguarding natural resources and fostering long-term prosperity.

This study examines the dimensions of wine-experienscape that influence customer satisfaction. Using the framework of experienscape, the dimensions for wine tourism was explored. The data was collected from TripAdvisor for Indian wineries. Results indicate that the Sensory dimension and Natural dimension of wine-experienscape increase the likelihood of customer satisfaction. The results are seen to vary with reviewer heterogeneity. The results have managerial and theoretical implications.

User-generated content is a major source of information particularly in tourism industry where consumers seek unbiased and unregulated information. While making their hotel booking decisions, consumers refer to the previous guests’ experiences expressed in the hotel reviews across social media. Studies in the literature have focused on enhancing the understanding of what makes customers satisfied or dissatisfied. They have analyzed the text reviews and the patterns in the overall rating and ranking of the hotels given by hotel guests. While most emerging destinations have been studied in the literature, studies related to India, a fast-growing leisure destination are scant. The present study tries to understand the hotel attributes that contribute towards customer satisfaction or dissatisfaction using online reviews for all hotels of Andaman & Nicobar Islands in India. Among the eight attributes identified from the most frequently used words in the text reviews, the study finds that while the location has a significant probability of increasing the ratings of both high- and low-rated reviews, rooms seem to have the most significant impact on lowering the probability of high scores irrespective of positive or negative sentiment review. The study also finds that guests of luxury hotels rate the hotels significantly higher than the guests of midrange and budget hotels.