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.

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.

Innovation management and economic sustainability have become one of the business challenges to consolidate. given the above, the objective of the study is to determine the relationship between innovation and economic sustainability in small and medium-sized enterprises (SMEs) in Latin America. through an empirical study, 2660 SMEs were examined, 1729 small and 931 medium-sized, located in 13 Latin American countries. the data obtained by applying a survey were processed using a non-linear canonical correlation analysis (NLCCA). The findings identify functional and operational risks in SMEs that weaken innovative potential, in addition to technical-operational barriers—lack of knowledge and low investment that limit economic sustainability, whose importance transcends towards transformations of business models and effectiveness of resources that promote business sustainability. contributions are suggested for the management of public policies aimed at strengthening innovation and economic sustainability to project the emerging economies of Latin America.

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.

Universities continue to provide solutions to private and public sectors of the economy by providing a skilled economy, increasing employment potentials, and improving employee performance. This study offered a theoretical model on the contributing factors to graduate employability among student entrepreneurs in Malaysian Higher Education and the mediating mechanism of perceived support and usefulness in social entrepreneurship to solve the graduate unemployment problem. We attained data using purposive and face-to-face sampling methods with acceptable data from 296 undergraduates and analyzed with the SEM software from respondents of various cultural backgrounds. Findings suggest a positive significant relationship between motivations, skills in social entrepreneurship, knowledge, and social elements on graduate employability. Similarly, perceived support explained skills, knowledge and social elements’ relationship to graduate employability except for perceived usefulness. The outcome further discovered the perceived support role for graduates of social entrepreneurship in fostering job crafting and future employability with various implications and recommendations. The results require the application of other research approaches to provide concrete implementations and social and economic solutions. Insightful results and proposals helpful to policymakers like higher education curricula developers and implementers, scholars, government and private universities of this study can help curb graduate unemployment through social entrepreneurship.