Natural Protected Areas (NPAs) are critical for biodiversity conservation and ecological balance. These areas are not only refuges for wildlife but also pivotal in promoting sustainable tourism. Geoparks, a unique subset of NPAs, emphasize geological heritage, offering distinctive educational and recreational opportunities. This article explores the significance of Geoparks in Portugal for geotourism and assesses the accessible digital communication strategies of Portuguese Geoparks, emphasizing the analysis of pedagogical concerns. The study highlights the importance of online engagement in enhancing visitor experiences and promoting sustainable tourism practices.

In recent decades, the redevelopment of waterfronts in global cities has become a focal point for large-scale real estate investments, often driven by neoliberal policies. These projects, characterized by the increasing involvement of state agencies, aim to transform obsolete industrial areas into lucrative spaces for tourism, commerce, and luxury living. This article scrutinizes the intricate dynamics of state-led waterfront re-development, through the lens of Istanbul’s Galataport project. It analyzes the multifaceted dimensions of the transformation process, shedding light on the historical backdrop, socio-political underpinnings, and economic imperatives that have shaped the development of Galataport from 2002 to 2022. Through a comprehensive analysis of primary sources, including governmental reports, policy documents, and scholarly literature, the article accentuates the pivotal role of the state and state actors in orchestrating the transformation of Istanbul’s urban landscape. Furthermore, it examines the implications of the Galataport project on urban governance and socio-cultural and spatial dynamics. It concludes that the central government pursued a speculative entrepreneurial approach in the Galataport project, clearing various legal obstacles while neglecting public interest. This case study takes the first step towards a comprehensive critical re-evaluation of the recent urban development/governance model to contribute to a nuanced understanding of contemporary urban/waterfront development paradigms in Türkiye and similar geographies.

Purpose: Today’s challenges underscore the importance of energy across all segments of life. This scientific paper investigates the multifaceted relationship between energy efficiency, energy import reliance, population heating access, renewable energy integration, electricity production capacities, internet utilization, structural EU funds, and education/training within the framework of economic development. Methodology: Using data from selected European countries and employing self-organizing neural networks (SOM) and linear regression, this research explores how these interconnected factors influence the journey toward a sustainable and prosperous economic future. Results: The analysis revealed a strong connection between energy efficiency and numerous socioeconomic factors of modern times, with most of these connections being non-linear in nature. Conclusion: As countries work toward sustainable development goals, prioritizing energy efficiency can contribute to improved quality of life, economic growth, and environmental sustainability.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study aims to scrutinize specific long-term sustainability industrial indicators in Thailand as a representative of an emerging economy. The study uses a Bloomberg database comprising all Thai listed companies on the Stock Exchange of Thailand from 2013 to 2023. The research employs a two-step Generalized Method of Moments (GMM) statistics to assess the enduring impact on industrial sustainability. These results provide consistent, significant and positive relationships between asset turnover and sales with all industrial sustainability. The results additionally reveal that some other factors may moderate industrial sustainability but reveal the GDP growth rate and institutional shareholders are less likely to be corporate sustainability to all indicators. The results provide insight into valuable guidance to management teams, financial statements’ users, investors and other stakeholders on designing effective operations and investment strategies to improve sustainability.

This study analyzes the importance of strengthening the design of Indonesia’s maritime axis policy. This research uses a qualitative approach to systematically explain the dynamics and importance of strengthening world maritime policy, where the Nvivo 12 Plus tool is used to analyze data and answer the research questions posed. This research shows that Indonesia still has complex bureaucratic and institutional problems and aspects of political identity and leadership attitudes that require systematic and comprehensive improvement. Then, the draft for strengthening the maritime axis policy in Indonesia includes three policy recommendations: reformulating the focus of the maritime axis policy, comprehensive and coherent governance, and an integrated administrative framework, as well as improving the political identity and attitudes of leaders in public policy. Substantially, the relative failure of the Global Maritime Axis (GMA) policy, known as Joko Widodo’s concept of regulating the Indonesian government based on geographical location, was caused by the dominance of political factors and domestic bureaucratic problems. Apart from that, the lack of priority narratives in the maritime and development sectors means that the Indonesian government’s priorities are more oriented towards GMA infrastructure aspects and at the expense of other fundamental elements. This study encourages the Indonesian government to accelerate a more substantive GMA. However, this research needs to be expanded because the analysis results were only carried out through secondary data and focused on two important aspects of GMA. Therefore, further research is needed that explains the prospects for GMA policy in Indonesia in more detail.