Given the multifaceted nature of crime trends shaped by a range of social, economic, and demographic variables, grasping the fundamental drivers behind crime patterns is pivotal for crafting effective crime deterrence methodologies. This investigation adopted a systematic literature review technique to distill thirty key factors from a corpus of one hundred scholarly articles. Utilizing the Principal Component Analysis (PCA) for diminishing dimensionality facilitated a nuanced understanding of the determinants deemed essential in influencing crime trends. The findings highlight the necessity of tackling issues such as inequality, educational deficits, poverty, unemployment, insufficient parental guidance, and peer influence in the realm of crime prevention efforts. Such knowledge empowers policymakers and law enforcement bodies to optimize resource allocation and roll out interventions grounded in empirical evidence, thereby fostering a safer and more secure societal environment.

The role of technology in stimulating economic growth needs to be reexamined considering current heightened economic conditions of Asian developing Economies. This study conducts a comparative analysis of technology proxied by R&D expenditures alongside macroeconomic variables crucial for economic growth. Monthly time-series data from 1990 to 2019 were analyzed using a vector error correction model (VECM), revealing a significant impact of technology on the economic growth of India, Pakistan, and the Philippines. However, in the cases of Indonesia, Malaysia, Thailand, and Bangladesh, macroeconomic indicators were found more crucial to their economic growth. Results of Granger causality underlined the relationship of R&D expenditures and macroeconomic variables with GDP growth rates. Sensitivity analyses endorsed robustness of the results which highlighted the significance and originality of this study in economic growth aligned with sustainable development goals (SDGs) for developing countries.

The livelihood of ethnic minority households in Vietnam is mainly in the fields of agriculture and forestry. The percentage of ethnic minorities who have jobs in industry, construction, and services is still limited. Moreover, due to harsh climate conditions, limited resources, poor market access, low education level, lack of investment capital for production, and inadequate policies, job opportunities in the off-farm and non-farm activities are very limited among ethnic minority areas. This paper assessed the contribution of livelihood diversification activities to poverty reduction of ethnic minority households in Son La Province of Vietnam. The analysis was based on the data using three stages sampling procedure of 240 ethnic minority households in Son La Province. The finding showed that the livelihood diversification activities had positively significant contribution to poverty reduction of ethnic minority households in Son La Province. In addition, the factors positively affecting the livelihood choices of ethnic minority households in Son La Province of Vietnam are education level, labor size, access to credit, membership of associations, support policies, vocational training, and district. Thus, improving ethnic minority householder’s knowledge through formal educational and training, expanding availability of accessible infrastructure, and enhancing participation of social/political associations were recommended as possible policy interventions to diversify livelihood activities so as to mitigate the level of poverty in the study area.

The article provides evidence on the effect of local public governance on the impact of public investment on local and regional economic growth, using spatial and regional logic. The research uses the spatial Durbin model and produces a panel data set that was conducted on 63 provinces of Vietnam from 2006 to 2022. Based on the interaction between public governance and public investment, the main findings indicate that their interaction plays an important role in adjusting the effects of public investment and public governance on economic growth not only in the locality but also spillover to neighboring localities in both the short and long terms. It suggests that local public governance not only hampers the impact of local public investment on local economic growth but also has spillover effects on the growth of neighboring provinces or regions in Vietnam. Additionally, the results of detailed analysis of PCI component indicators show that many aspects of local public governance are hindering local economic growth but contributing to promoting neighboring localities economic growth. Or, it has no effect the locality but promote or hinder the regional economic growth. The findings in this study implies that authorities of localities need to be cautious when using resources to improve the various aspects of public governance when designing strategies to enhance the quality of local public governance. It also suggests that this spillover effect is a crucial factor in advocating for more redistributive fiscal policies and regional governance policies aimed at reducing economic disparities caused by territorial boundaries. Therefore, authorities should prioritize regional cooperation strategies in their decisions regarding public governance and public capital allocation.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

This study analyzes the studies on project finance (PF) and renewable energy (RE) arena, employing a comprehensive scientometric analysis to illuminate the current research landscape, identify prominent scholars, and uncover emerging trends. Encompassing several analyses, we have charted the evolution of this domain from 1993 to March 2024 and showed the way for further research. We analyzed 80 studies selected from several databases by means scientometric tools. Despite decent citation rates, research in this relatively young field is surprisingly scarce. While geographically diverse, research leadership stems from the UK, USA, Australia, and Germany. Interestingly, a significant portion of the studies originates from broad energy and sustainability areas, highlighting a potential knowledge gap in finance and economics areas. Additionally, the prevalence of case studies points to a strong connection between theory and practice. The research also revealed prominent topics like the interplay between PF and RE, various renewable resources, infrastructure development, financial considerations, risk management, among others. While many themes exist, areas like technological advancements, diverse cost approaches, valuation methodologies, and policy considerations remain underexplored. Other results unveiled an unexpected finding: limited evidence of large-scale collaborations, with individual or small-group research efforts currently dominating the field. However, existing collaborative networks promise future advancements through the emergence of more formalized research groups, which can perform future research endeavors with a wide spectrum of unexplored topics.