The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

Floods have always been an unavoidable natural disaster globally. Due to that, many efforts have been taken in order to alleviate the effect, especially in protecting the victims from losing their lives as well as their belongings. This study focuses on ensuring a smooth allocation process for flood victims to the relief centres considering the nature of their location, near the river, inland, and coastal. The finding indicated that a few implications have been highlighted for disaster management, such as changes in flood victim allocation patterns, classification of prone areas based on three areas, identification of most disaster areas, and others. Thus, to enhance the efficiency of allocation and to avoid any bad incidents happening during the flood occurrence, the allocation of flood victims is proposed to be started at a more critical area like the river area and followed by other areas. The finding also indicated that the proposed allocation procedure yielded a slightly lower average travel distance than the existing practice. These findings could also provide valuable information for disaster management in implementing a more efficient allocation procedure during a disaster.

Presently, any development initiatives without considering sustainability can barely be imagined. There has been a paradigm shift in the focus of the development partners from the mere development to sustainable development. However, the role of development partners in bringing sustainability in livelihood assets of the rural community has long been questioned. Hence, this study aims to explore the sustainability in the form of changes in livelihood assets of a local community in Bangladesh. This study considers the changes in livelihood assets of the community over the three-time frames - before, during, and after a project implemented by a national NGO called ‘UST’ and subsequently identifies the community’s capacity to sustain the project outcomes after the completion of the project. ‘Sustainable Livelihood Framework (SLF)’ developed by Department for International Development (DFID) was utilized in this study to analyse the vulnerability and livelihood issues of the community members. Data has been collected through focus group discussions, household survey and key informants’ interviews from three distinct villages of ‘Khutamara’ union in the ‘Nilphamari’ district of Bangladesh. The finding of the study states that all the livelihood assets such as the social capital, human capital, natural capital, financial capital, physical capital have positively changed due to the interference of the development partners. This study further finds that even after the completion of project tenure, such positive trends continue to exist among the community members indicating sustainable development. Moreover, political capital- a new type of livelihood has also emerged because of the project implementation which was not quite evident before the inception of the project. In addition, this study explored the unique phenomenon of the Shabolombee Gram, where the transformation altering farmers’, livelihoods does not come from the government or the private sector but originates from a Non-Governmental Organization (NGO). Therefore, the government and its development partners may adopt and incorporate the Modified Sustainable Livelihood Framework (MSLF) to ensure the sustainable development.

The COVID-19 pandemic has fundamentally transformed the global education landscape, compelling institutions to adopt e-learning as an essential tool to sustain academic activities. This research examines the critical impact of e-learning on arts and science college students in Coimbatore, with an emphasis on its influence on their readiness for campus recruitment. Using a survey of 300 students, this study investigates their perceptions of online education, highlighting both its advantages, such as flexibility and accessibility, and its challenges, including engagement barriers and technical limitations. Data was collected through structured questionnaires and analyzed using statistical methods to draw meaningful insights. The research also explores the efficacy of online assessments in recruitment processes and assesses students’ awareness of available e-learning platforms and courses. The urgency of this study lies in addressing the pressing need to optimize digital education models as institutions globally transition toward blended learning post-pandemic. The findings underline the dual potential and limitations of e-learning, concluding with actionable recommendations to enhance its effectiveness, particularly in preparing students for competitive employment opportunities.

In Central and Eastern European countries, the labour shortage is becoming increasingly pronounced, posing a challenge for the economy. Labour shortages limit the potential national income as many positions remain unfilled, which could lead to a slowdown in economic growth. To address this issue, various solutions need to be explored. This research aims to analyze solutions for alleviating labour shortages, with particular emphasis on measures that encourage workforce participation. One strategy is introducing training and retraining programs that help workers develop skills and adapt to labour market demands. Another option is to promote part-time employment, which may be especially attractive to groups unable or unwilling to work full-time. Enhancing population mobility could also be crucial in addressing labour shortages, particularly in bridging regional disparities. Integrating certain inactive groups, such as retirees, homemakers, students, people with disabilities, and those with low education levels experiencing generational poverty, into the labour market could also yield significant benefits. The study employs quantitative analysis methods and includes a survey that examines citizens’ perspectives on the effectiveness of measures aimed at increasing labour market participation and their economic impact on the Slovak economy. The survey data were collected in 2023 in the region of Rožňava and its surrounding areas.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.