The European Union (EU) stands as one of the pioneering areas worldwide in the planning of the Circular Economy (CE), despite the fact that practical advances in this area are currently ahead of the related research. Significant gaps remain to be filled, particularly with regard to the measurement of circularity. This work is based on the need for global tools to measure the circular transition which, in turn, can be useful in evaluating the implementation of the action plans under development by the EU. A composite index of economic circularity (CECI) has been built for the EU and its Member States with which to approximate the progress after completion of the first Action Plan for the Circular Economy (APCE): 2014–2020. This index is based on recycling and downcycling, which is the main strategy developed by APCE. In contrast to previous indicators, CECI globally and rigorously summarises the transition towards the circular economy throughout the period of validity of the plan, by using a simple and easy-to-interpret calculation methodology known as Principal Component Analysis. Likewise, the results of the CECI make it possible to establish a ranking of the EU Member States based on their CE performances for each year under consideration. The results of the CECI show that the EU as a whole has advanced in economic circularity by 17.9%, which is almost 3% on average per year. The situation is very uneven across the various Member States and, although differences have narrowed during the APCE period, in recent years this rapprochement has deaccelerated. The countries that continue to lead the circular transition of the EU are the Netherlands, Belgium, and Germany, while trailing behind are Romania, Malta, and Cyprus. In light of the results of the CECI and with the aim of achieving greater convergence in the transition to economic circularity in the EU, it would be advisable to intensify actions in those Member States whose starting situation is comparatively worse.

Purpose - This study analyses the relationships between perceived architectural authenticity, localised identity, place attachment and support for sustainable tourism at a World Heritage Site. Methodology/Design/Approach - A quantitative methodology is developed through questionnaires applied to residents in the Colonial Zone of Santo Domingo, in the Dominican Republic. 509 valid responses were obtained and the analysis used structural equation models (PLS-SEM). Findings - The results show that perceived architectural authenticity significantly influences the residents’ place attachment and localised identity. These, in turn, impact their support for the creation of sustainable tourism initiatives at heritage sites. Specifically, architectural authenticity enhances the residents’ connection to their community and cultural identity, fostering a supportive attitude toward the sustainable tourism practices that preserve cultural heritage. Originality of the research - The findings suggest that architectural authenticity is relevant when it comes to strengthening the community ties and cultural identity among the residents of World Heritage Sites. A model is presented where architectural authenticity is analyzed and variables are integrated that analyze how it influences support for sustainable tourism. This model, unlike similar models, offers updated data after the pandemic

Traditional tourism analytics have primarily relied on isolated sentiment analysis and image processing techniques, often failing to capture the subtle interaction between textual expressions and visual aesthetics inherent in tourist experiences. This study addresses these limitations by proposing a novel multi-modal framework that transforms textual reviews into AI-generated images using standardized prompts, thereby converting affective signals into explicit visual features. Leveraging state-of-the-art models—such as Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) for fine-grained emotion recognition and Contrastive Language–Image Pre‑training (CLIP) for semantic extraction of visual attributes—our approach maps complex sentiments onto interpretable visual characteristics, integrating explainable features to uncover the underlying structure in tourist perceptions. This approach enhances classification performance and provides a transparent mechanism for understanding how distinct emotional states correspond to specific visual cues. Experimental evaluations on a dataset encompassing four diverse tourist destinations—Berlin, Dublin, Cairo, and Málaga—demonstrate high classification accuracy and robust correlations between text-derived emotions and image-based features, close to more powerful embedding methods. Significant correlations were observed between emotions and visual features, e.g., brightness and contentment, as well as between entropy and shame, indicating that our method efficiently captures the affective resonance between visual and textual modalities. Our findings underscore the transformative potential of converting textual sentiment into visual representations to facilitate more accurate, interpretable, and actionable analytics in the tourism sector. This framework suggests promising avenues for dynamic destination characterization, informed marketing strategies, and enhanced urban planning initiatives, laying the foundation for future advancements in multi-modal tourism analytics.

This work delves into the increasing relevance of Large Language Models (LLMs) in the realm of sustainable policy-making, proposing an innovative hetero-intelligence framework that blends human and artificial intelligence (AI) for tackling modern sustainability challenges. The research methodology includes a hetero-intelligence performance test, which juxtaposes human intelligence with AI in the formulation and implementation of sustainable policies. After testing this hetero-intelligence methodology, seven steps are rigorously described so that it can be replicated in any sustainability planning related context. The results underscore the capabilities and limitations of LLMs, underscoring the critical role of human intelligence in enhancing the efficacy of hetero-intelligence systems. This work fulfils the need of a rigorous methodological framework based on empirical steps that can provide unbiased outcomes to be integrated into sustainable planning and decision-making processes. • Assesses LLMs’ limitations and capabilities regarding sustainable planning issues • A replicable methodology is proposed based on the combination of both human and artificial intelligence • It proposes and systematises the integration of a hetero-intelligent approach into the formulation of sustainability policies to be more efficient and effective

Little research has been conducted specifically on emotional intelligence (EI) and perceived quality of service in the hospitality industry. The main goal of this research consists of providing empirical evidence related to EI regarding its importance in quality of hospitality service (QHS). To do so, a hypothetical and conceptual model was tested by Partial Least Square-Structural Equation Modeling (PLS-SEM) techniques based on a sample of 408 employees in the hospitality sector in the region of Murcia (Spain). The findings of this work reveal the incidence that EI has on the QHS of human capital, despite a scenario that is characterized by a high rate of talent shortage worldwide and an increasing digitalization that could reduce human interaction. Also, implications are given so that EI could be more deeply analyzed for EI strategy-building within organizational behavior and human resource management areas from a practical approach, increasing engagement and preventing the so-called quality service sabotage.