This paper highlights the complex relationship between entrepreneurship, sustainable development, and economic growth in 41 European countries, using a reliable K-Means cluster analysis. The research thoroughly evaluates three key factors: the SDG Index for sustainable development, GDP per capita for economic well-being, and the New Business Density Rate for entrepreneurial activity. Our methodology reveals three distinct narratives that embody varying degrees of economic vitality and sustainability. Cluster 1 comprises the financially stable and sustainability-oriented countries of Western and Northern Europe. Cluster 2 showcases the variegated economic and sustainability initiatives in Central and Southern Europe. Cluster 3 envelopes the economic titans with noteworthy business expansion but with the potential for better sustainable practices. The analysis reveals a favourable association between economic prosperity and sustainable development within clusters, although with nonlinear intricacies. The research concludes with a series of strategic imperatives specifically crafted for each cluster, promoting economic variation, increased sustainability, invention, and worldwide collaboration. The resulting findings highlight the crucial need for policy-making that considers the specific context and the potential for combined European resilience and sustainability.

This paper investigates the evolving clustering and historical progression of “Asian regionalisms” concerning their involvement in multilateral treaties deposited in the United Nations system. We employ criteria such as geographic proximity, historical connections, cultural affinities, and economic interdependencies to identify twenty-eight candidate countries from East Asia, Southeast Asia, South Asia, and Central Asia for this empirical testing. Using a social network analysis approach, we model the network of these twenty-eight Asian state actors alongside 600 major treaties from the United Nations system, identifying clusters among Asian states by assessing similarities in their treaty participation behavior. Specifically, we observe dynamic changes in these clusters across three key historical eras: Post-war reconstruction and transformation (1945–1968), Cold War tensions and global transformations (1969–1989), and post-Cold War era and globalization (1990–present). Employing the Louvain cluster detection algorithm, the results reveal the evolution in cluster numbers and changes in membership status throughout the world timeline. The results also identify the current situation of six distinct Asian clusters based on states’ inclinations to engage or abstain from multilateral treaties across six policy domains. These findings provide a foundation for further research on the trajectories of Asian regionalisms amidst evolving global dynamics and offer insights into potential alliances, cooperation, or conflicts within the region.

Rural tourism plays a crucial role in rural development in Indonesia by providing employment opportunities, livelihood, infrastructure, cultural preservation, and environmental preservation. However, it is prone to external shocks such as natural disasters, public health events, and volatility in the national and global economy. This study measures the resilience of rural tourism to external shocks caused by the COVID-19 pandemic in 24 rural tourism destinations in Indonesia covering four years from 2019 to 2022. A synthetic composite index of the Adjusted Mazziotta-Pareto index (AMPI) is used to measure rural tourism resilience followed by clustering analysis to determine the typology of the resilience. The AMPI measure is also compared with the conventional Mazziotta-Pareto index (MPI) method. The resilience index is composed of capacity and performance components related to resilience. The results show that in the first year of COVID-19, most tourism villages in Indonesia were severely affected by the pandemic, yet they were able to recover afterward, as indicated by positive differences in the AMPI index before and after COVID-19. Thus, rural tourism villages in Indonesia have a strong capacity and performance to recover from pandemic shock. Lessons learned from this analysis can be applied to policies related to rural tourism resilience in developing countries.

The research aims to map environmental protection strategies and the related control tools and to identify the links among companies with the largest number of employees and sites in Hungary. The research questions were answered using a questionnaire survey method. The authors used cluster analysis to classify the 205 company strategies into the identified strategy clusters: Leaders, Awakeners, and Laggards. Then, the examined 21 environmental management control tools in the sample were divided into four groups: strategic, administrative, methodological and economic. Economic and strategic methods were the most common in the sample. The authors used cross-tabulation analysis to examine whether there is a statistically proven relationship between belonging to environmental strategy clusters and specific control tools. The analysis showed significant but weak to moderate relationships. According to Cramer’s V and the contingency coefficient, the closest relationship between the tested environmental management control tools and membership in environmental strategy clusters is shown by evaluating investments, assessing the economic viability of environmental strategies, and running an environmental training program for employees. In case of the robust lambda indicator, a significant relationship was found by examining the economics of environmental strategies and identifying environmental success factors and eco-balances. It can be concluded that the companies under examination follow a set of environmental goals, which they have incorporated into their strategic objectives. They use the available environmental management control toolbox to develop their strategies and to monitor their implementation to varying degrees.

The world has changed to a massive degree in the past thousands of years. Most of the time, the amount of carbon dioxide in the atmosphere remains constant. In the late 18th century, according to the sources of CDIAC and NOOA, the level of carbon dioxide began to rise, and then in the 20th century, it went through the roof, reaching levels that had not been seen in nature for millions of years. The increase in carbon in the atmosphere is the major contributing factor to climate change. The key to reversing the damage is restoring the earth’s delicate, balanced carbon cycle. As carbon cycle depicts the way carbon moves around the earth. It consists of sources that emit the carbon component into the atmosphere. The biological side of the carbon cycle is well balanced due to respiration, where carbon dioxide is released into the atmosphere, then plants, bacteria, and algae take carbon dioxide out of the atmosphere during photosynthesis and the process they use to generate chemical energy. On the other hand, oceans are the best sources and sinks; carbon dioxide is endlessly being absorbed into the ocean and released from the oceans almost exactly at the same rate, which is rapidly influencing the carbon cycle. Similarity is a methodology that has many applications in the real world. The current research article is destined to study how statistics of carbon emission metrics are alike and belong to one cluster. In the current study, the research is destined to derive a similarity analysis of several countries’ carbon emission metrics that are alike and often fall in the range of [0, 1]. And deriving the proximity of the carbon emission metrics leading to similarity or dissimilarity. In the current context of data matrices of numerical data, an Euclidian measure of distance between two data elements will yield a degree of similarity. The current research article is destined to study the similarity analysis of carbon emission metrics through fuzzy entropy clustering.

This paper presents a quantitative exploration of the functionality of cost accounting systems and their determinants in social welfare organizations. We conducted a questionnaire survey of managers of social welfare organizations running special nursing homes for the elderly and conducted a cluster analysis based on the data collected. The questionnaire was created based on the scales used in previous studies, with some new scales developed. For data analysis, the statistical analysis environment R was used. The clValid package of R was used to assess the validity of the cluster analysis. Based on the results of the analysis in this paper, it is expected that social welfare organizations that pursue cost leadership strategies and have a strong public interest orientation will benefit greatly by being able to utilize a highly functional cost accounting system. Such organizations will be able to improve their business efficiency by utilizing cost information, and their social contribution activities based on the resulting resources will truly be a contribution to public welfare. The findings from this study are of practical significance because they can be used by business managers of social welfare organizations to review the functionality of their cost accounting systems. We also focus on the degree to which nonprofit organizations focus on social contribution activities (in this paper, we call this public interest orientation). The public interest orientation of an organization is thought to affect the functionality of the cost accounting system in the same way as the organization’s strategy, but there has not been enough quantitative research on this point. By focusing on the public interest orientation of social welfare organizations, this study contributes to deepening our knowledge in this area.