This study employs the Standard Error Estimation technique to investigate the connections between the digitalization of economy, population, trade openness, financial development, and sustainable development across 127 countries from 1990 to 2019. The findings revealed associations between financial development, population growth, trade openness, economic growth, Digitalization development, foreign direct investment (FDI), and sustainable development. Financial development negatively impacts sustainable development, suggesting that countries with advanced financial systems may struggle to maintain sustainability. Trade openness exhibits a negative association with sustainable development, implying that countries with open trade policies may face challenges in maintaining sustainability, possibly due to heightened competition or resource exploitation. These findings highlight the multifaceted relationship between economic factors and sustainable development, underscoring the importance of comprehensive policies and governance mechanisms in fostering sustainability amidst global economic dynamics.

Growing urbanization in sub-Saharan Africa, with its attendant degradation of natural vegetation, is a real scourge. It takes the form of urban sprawl, with its corollary of native vegetation degradation. The aim of this study is to assess the impact of urban sprawl in Brazzaville and the related degradation of the vegetation covering on the urban site. The methodological approach was based on the collection of documentary and field data, as well as the analysis of Landsat satellite images from 2002, 2012 and 2022. The results show a regressive evolution of natural plant formations in favor of urbanization. The area of vegetation cover fell from 17,523 ha in 2002 to 8355.5 ha in 2022, representing a regression rate of 52.32% in 20 years. At the same time, the urban area has grown from 12,164 ha in 2002 to 29,892 ha in 2022, an increase of 145.74%. This deterioration in vegetation cover is reflected in water erosion, resulting in silting-up and flooding of homes and sanitation facilities.

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.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This study examines the development and influence of the international anti-corruption regime, utilizing Critical Discourse Analysis (CDA) to dissect the discursive practices that shape perceptions of corruption and the strategies employed to combat it. Our analysis reveals how Western institutional entrepreneurs play a pivotal role in defining corruption predominantly as bribery and governance failures, underpinned by a neoliberal ideology that prescribes societal norms and identifies corrupt practices. By exploring the mechanisms through which this ideology is propagated, the research enriches institutional entrepreneurship theory and highlights the neoliberal foundations of current anti-corruption efforts. This study not only enhances our understanding of the institutional frameworks that govern anti-corruption discourse but also demonstrates how discourse legitimizes certain ideologies while marginalizing others. The findings offer practical tools for altering power dynamics, promoting equitable participation, and addressing the imbalanced North-South power relations. By challenging established perspectives, this research contributes to transformative discourse and action, offering new pathways for understanding and combating corruption. These insights have significant theoretical and practical implications for improving the effectiveness of corruption prevention and counteraction strategies globally.

Sustainability has become a generalized concern for society, specifically businesses, governments, and academia. In the specific case of universities, sustainability has been approached from different perspectives, some viewing it from environmental practices, management initiatives, operational criteria, green buildings, and even education for sustainable development. This research focuses on sustainability as a managerial practice and investigates how it affects the performance of five private universities in Medellin, Colombia. For this purpose, a literature review using a mixed sequential approach, including bibliometric and content analysis, was initially conducted. In the s second phase, more than 5000 responses from students, professors, and employees of the five mentioned private universities were collected. A previously validated instrument for both sustainability and performance was applied in the quantitative phase, and a novel dimensionality of the constructs was proposed by conducting an exploratory factor analysis using the SPSS software. Results were then processed through a structural equation analysis with the Smart PLS software. The impact of sustainability on university performance is verified, making some managerial recommendations.