Renewable energy has attracted researcher attention in recent years, and the number of studies conducted on the topic has increased. The importance of renewable energy has increased because certain energy resources are exhaustible and they damage the environment in various ways. Fossil fuel-based energy is the main culprit for environmental damage and lately renewable energy is the main focus as a safe alternative to fossil fuels. However, replacement of fossil fuels by renewables may have a negative impact on human development, even if it has a positive impact on the environment. With this rationale, this study investigates the relationship between renewable energy and human development in 28 OECD (Organization for Economic Cooperation and Development) countries from 1990 to 2017 by using the Westerlund and Edgerton panel cointegration test with structural breaks and the Dumitrescu and Hurlin causality test. The results of the panel data analysis revealed that renewable energy affected human development positively. In addition, the causality test determined the presence of a bidirectional causality relationship between renewable energy and human development. This study is unique in the sense that it is the only study in the literature examining the relationship between human development index and renewable energy for the countries in question. While similar analyses were conducted in the past for different regions or for just one type of renewable energy, no such study has been conducted in this scale with this method. Another differentiating feature of the study is that it demonstrates the bidirectional nature of the study not just the unidirectional causality. Policymakers are advised to invest in renewable energy projects and also create frameworks which provide incentives to the private sector for renewable energy production.

The globalized world has experienced significant environmental degradation together with raising global production and population. In this context, the employment of renewable energy use has become crucial for a sustainable environment and development. In the research, the mutual causality among renewable energy, trade and financial globalization, real GDP per capita, and CO₂ emissions in EU transition economies experiencing the integration with global economy was explored through bootstrap panel Granger causality test for the period of 1995–2015. The causality analysis revealed a unilateral causality from trade globalization to renewable energy in Estonia, Latvia, and Slovenia, and from renewable energy to trade globalization in Croatia and Lithuania. However, no significant causality between financial globalization and renewable energy was revealed. On the other side, a unilateral causality from CO₂ emissions to renewable energy in Lithuania and Slovenia, and from renewable energy to CO₂ emissions in Czechia, Hungary, and Latvia and a reciprocal causality between renewable energy to CO₂ emissions in Romania and Slovakia and a unilateral causality from real GDP per capita to renewable energy in Czechia, Romania, and Slovenia was discovered in the causality analysis.

Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda.

This study aims to compare the accuracy performances of different machine learning algorithms (Logistic Regression, Decision Tree, Support Vector Machines (SVMs), Random Forest, Artificial Neural Network, and XGBoost) using World Happiness Index data. The study is based on the 2024 World Happiness Report data and employs indicators such as Ladder Score, GDP Per Capita, Social Support, Healthy Life Expectancy, Freedom to Determine Life Choices, Generosity, and Perception of Corruption. Initially, the K-Means clustering algorithm is applied to group countries into four main clusters representing distinct happiness levels based on their socioeconomic profiles. Subsequently, classification algorithms are used to predict the cluster membership and the accuracy scores obtained serve as an indirect measure of the clustering quality. As a result of the analysis, Logistic Regression, Decision Tree, SVM, and Neural Network achieve high accuracy rates of 86.2%, whereas XGBoost exhibits the lowest performance at 79.3%. Furthermore, the practical implications of these findings are significant, as they provide policymakers with actionable insights to develop targeted strategies for enhancing national happiness and improving socioeconomic well-being. In conclusion, this study offers valuable information for more effective classification and analysis of World Happiness Index data by comparing the performance of various machine learning algorithms.

This study examined the interplay between economic growth and environmental pollution in G20 countries, employing panel data analysis techniques. The primary aim was to assess the validity of the Environmental Kuznets Curve (EKC) hypothesis using contemporary econometric methods, while considering the pertinent literature. Preliminary findings of the study affirm the EKC hypothesis, which posits that environmental pollution initially escalates with economic growth but diminishes after surpassing a specific income threshold. Nevertheless, upon integrating per capita energy consumption into the analysis, the descending trajectory postulated in the inverted U‐shaped hypothesis emerged as statistically non‐significant. This undermines the EKC hypothesis's assertion that industrialized nations with elevated per capita incomes predominantly contribute to reduced environmental pollution. Consequently, in the context of developed nations, this research bolsters the prevailing consensus in scholarly discourse that economic growth augments resource consumption, thereby adversely impacting environmental quality.