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.

This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

With the increasing climate change crisis, the ongoing global energy security challenges, and the prerequisites for the development of sustainable and affordable energy for all, the need for renewable energy resources has been highlighted as a global aim of mankind. However, the worldwide deployment of renewable energy calls for large-scale financial and technological contributions which many States cannot afford. This exacerbates the need for the promotion of foreign investments in this sector, and protecting them against various threats. International Investment Agreements (IIAs) offer several substantive protections that equally serve foreign investments in this sector. Fair and Equitable Treatment (FET) clauses are among these. This is a flexible standard of treatment whose boundaries are not clearly defined so far. Investment tribunals have diverse views of this standard. Against this background, this article asks: What are the prominent international renewable energy investment threats, and how can FET clauses better contribute to alleviating these concerns? Employing a qualitative method, it analyses the legal aspects and properties of FET and concludes that the growing security and regulatory threats have formed a sort of modern legitimate expectations on the part of renewable energy investors who expect host states to protect them against such threats. Hence, IIAs and tribunals need to uphold a definite and broadly applicable FET approach to bring more consistency and predictability to arbitral awards. This would help deter many unfavourable practices against investments in this sector.