The paradigm of sustainable energy is gaining ground at the historical juncture of the present worldwide push for sustainable development. This is being driven by the latest technological advancements and by a maturing process of public policy evolution toward support for a sustainable energy transition. This paper analyzes, with the support of bibliometric analysis, the specialized literature in order to capture the main themes of interest, as well as their evolution, thus offering a panoramic view of research trends and the significance of implementing correct environmental measures and policies. Covering the period from 1991 to 2024, our exploration filters 2990 articles from the Web of Science database using a query that intersects “sustainable energy”, “renewable energy”, and “sustainable development”, with a nuanced consideration of the political landscape that shapes these domains. Using the advanced capabilities of the R program, the methodology employed facilitates a nuanced workflow of extraction and analysis, allowing for a detailed examination of the proliferation of research over the decades. This paper provides significant results, demonstrating the increasing impact of research through international collaborations, the importance of high-impact journals on sustainability policies, and the growing focus on “renewable energy” and “CO₂ emissions”. The analysis of the relevance of term groups in environmental policy development and the correlation between economic growth and CO₂ emissions confirms emerging trends. Furthermore, critical directions for future research and the necessity for formulating and implementing coherent sustainability policies are highlighted.

The rise of the experience economy, driven by disruptive technologies delivering innovative experiences, has transformed the interactions between tech-enabled shoppers and the phygital retail complex system. An important knowledge gap is addressed in our study by evaluating shoppers’ perceptions of disruptive technologies and the adaptive challenges that retailers face in securing consistency within a highly complex e-commerce landscape shaped by transformative interactions. A quantitative analysis was carried out using structural equation modeling (SEM) and survey data from an international supermarket chain integrating physical and digital retail spaces. We propose a novel framework to explore how retailers can harness data-driven insights and disruptive technologies to optimize the phygital shopping experience and adapt to the shift from multichannel and omnichannel strategies to optichanneling, as well as respond to societal shifts, including the role of digital natives and the expanding influence of the metaverse. This framework integrates key principles such as emergence, feedback, and criticality. The research reveals key findings about transformative shopper experiences across phygital retail touchpoints that influence shoppers’ perceptions and behaviors. Based on these identified key insights, as shoppers increasingly expect seamless interactions, the framework includes practical recommendations for retailers relating to several key areas, including leveraging the metaverse for refined shopper engagement.

This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML’s ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments.