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.

Environmental regulation is globally recognized for its crucial role in mitigating environmental pollution and is vital for achieving the Paris Agreement and the United Nations Sustainable Development Goals. There is a current gap in the comprehensive overview of the significance of environmental regulation research, necessitating high-level insights. This paper aims to bridge this gap through an exhaustive bibliometric review of existing environmental regulation research. Employing bibliometric analysis, this study delineates publication trends, identifies leading journals, countries, institutions, and scholars. Utilizing VOSviewer software, we conducted a frequency and centrality analysis of keywords and visualized keyword co-occurrences. This research highlights current hotspots and central themes in the field, including “innovation”, “performance”, “economic growth”, and “pollution”. Further analysis of research trends underscores existing knowledge gaps and potential future research directions. Emerging topics for future investigation in environmental regulation include “financial constraints”, “green finance”, “green credit”, “ESG”, “circular economy”, “labor market”, “political uncertainty”, “digital transformation”, “exports” and “mediating effects”. Additionally, “quasi-natural experiments” and “machine learning” have emerged as cutting-edge research methodologies in this domain. The focus of research is shifting from analyzing the impact of environmental regulation on “innovation” to “green innovation” and from “emissions” to “carbon emissions”. This study provides a comprehensive and structured understanding, thereby guiding subsequent research in this field.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

The government’s increased cigarette tariff aims to lower smoking rates and avoid adverse impacts. This study’s goal was to offer process innovation for lowering Asian’ smoking behavior. The participants were chosen by stratified random selection from a total of 738 people residing in Pathum Thani Province, Thailand. The instrument was a questionnaire. A software programmer was used to examine descriptive and inferential statistics using EFA and one-way ANOVA techniques. A strategic framework guideline using a SWOT analysis and TOWS matrix to encourage smoking reduction was proposed. The findings revealed two components: smoking behavior change and continues smoking that were based on SWOT analysis and TOWs matrix. There were nine strategies for the excise department to consider for the adjustment of the next policy in terms of reducing the number of smokers. The practical and policy suggestions could help reduce the negative impact of the cigarette industry on public health and increase government revenue while addressing weaknesses and threats in the industry.