Smart Grids (SG) represent a key element in the energy transition, facilitating the integration of renewable and conventional energy sources through the use of advanced digital technologies. This study analyzes the main research trends related to SG, energy efficiency, and the role of Artificial Intelligence (AI) and the Internet of Things (IoT) in smart energy management. Following the PRISMA protocol, 179 relevant academic articles indexed in the Scopus database were selected and analyzed using VOSviewer software, version 1.6.20, to identify the main thematic clusters. The results reveal a converging research focus on energy flow optimization, renewable energy integration, and the adoption of digital technologies—including cybersecurity solutions—to ensure grid efficiency, security, and resilience. The study confirms that digitalization acts as a key enabler for building a more sustainable and reliable energy system, aligned with the objectives of the European Union and the United Nations 2030 Agenda. The contribution of this work lies in its integrated approach to the analysis of digital technologies, linking the themes of efficiency, resilience, and infrastructure security, in order to provide valuable insights for future research and sustainable energy policy development.

Sustainable waste management is a critical challenge for ecological transitions. Emerging technologies such as Artificial Intelligence (AI) and Digital Twins (DT) offer new opportunities to optimize collection, treatment, and valorization processes, thereby promoting circular economy models. This study adopts an integrated approach to analyze the state of the art and key research trajectories related to the application of these technologies in waste management. Through a bibliometric analysis based on the Scopus database and mapping with VOSviewer (version 1.6.20), three main thematic clusters were identified: (i) predictive and environmental methods, (ii) sustainability and optimization, and (iii) monitoring and environmental impacts. A qualitative analysis of the 20 most-cited articles further revealed six major research areas, including waste forecasting, recycled materials, process digitalization, and intelligent environmental monitoring. The findings indicate a growing convergence among digitalization, automation, and sustainability. The adopted approach enables the mapping of major research directions and emerging interconnections among AI, the circular economy, and predictive management, providing an up-to-date and systemic perspective on the field.

This paper presents the evolution and role of energy communities in the context of the transition to renewable energy sources. Such communities are presented as an innovative and resilient response to the challenges posed by climate change. Their importance emerges from their ability to decentralize energy production, increasing local security and reducing dependence on non-renewable energy sources and external suppliers. However, despite the obvious benefits, this contribution highlights several challenges, such as the need for significant upfront investment and the presence of regulatory barriers that may hinder the integration of these solutions into existing energy systems. A detailed bibliometric analysis is used, focusing on specific research areas and using tools to map the most relevant keywords and emerging trends. The results of the bibliometric analysis highlight how energy communities are emerging as key players in the sustainability landscape, capable of addressing and overcoming contemporary environmental challenges. The research reaffirms the transformative potential of energy communities in promoting a more sustainable and resilient future.

The construction sector faces growing challenges in integrating sustainability, risk management, and regulatory compliance, in line with initiatives such as the European Green Deal, the Corporate Sustainability Reporting Directive, and international building standards. However, the systematic adoption of ESG metrics in decision-making remains limited due to fragmented data, the lack of predictive tools, and reliance on static reporting. This study proposes and illustrates a digital framework, based on simulated data, that combines Artificial Intelligence, Process Mining, and Robotic Process Automation to enhance ESG risk assessment in sustainable construction management. The model, formalized through Business Process Model and Notation, integrates Machine Learning for risk weighting and classification, and leverages Web Scraping and Business Intelligence for dynamic data acquisition. A simulated case study involving 100 synthetic construction projects is used to demonstrate the internal logic and quantitative feasibility of the framework, showing how automated data integration and predictive modeling can improve the consistency of ESG risk identification and classification. While the results are illustrative rather than empirical, they confirm the analytical coherence and reproducibility of the proposed workflow. From a scientific perspective, it contributes an integrated methodology that bridges predictive analytics and process management for ESG evaluation. From a practical standpoint, it offers a structured and reproducible workflow to anticipate, classify, and mitigate ESG risks, supporting the construction sector’s transition toward data-driven and sustainability-first management practices.

The paper examines the evolution and crucial role of energy communities in the context of the transition to renewable energy sources. Such communities are presented as an innovative and resilient response to the challenges posed by climate change. Their importance emerges from their ability to decentralise energy production, increasing local security and reducing dependence on non-renewable energy sources and external suppliers. However, despite the obvious benefits, the contribution highlights several challenges, such as the need for significant upfront investment and the presence of regulatory barriers that may hinder the integration of these solutions into existing energy systems. A detailed bibliometric analysis is used, focusing on specific research areas and using tools to map the most relevant keywords and emerging trends. The results of the bibliometric analysis highlight how energy communities are emerging as key players in the sustainability landscape, capable of addressing and overcoming contemporary environmental challenges. The research reaffirms the transformative potential of energy communities in promoting a more sustainable and resilient future.

The growing environmental and economic impacts of agri-food waste have intensified interest in digital and circular strategies for more sustainable resource management. This study investigates how digital technologies are being applied to enhance the circular management of agri-food waste. A systematic literature review was conducted following PRISMA guidelines, using Scopus as the primary database. The bibliometric analysis included 373 publications from 2015 to 2025 and was complemented by a thematic review of the 20 most cited articles. Results revealed six major research clusters, ranging from predictive waste analytics and smart traceability systems to circular bioeconomy applications such as anaerobic digestion and pyrolysis. In addition, the study examined structural indicators such as food waste per capita and hunger indices to contextualize geographic disparities in research output. The findings underscore the dual role of digital technologies as both operational tools and mechanisms for reducing systemic inequalities. Overall, the integration of intelligent systems and circular models offers promising pathways to support the Sustainable Development Goals and foster a more inclusive and resilient agri-food sector.