The Circular Economy is one of the most prominent cross-disciplinary and cross-sectoral concepts to emerge in recent decades. It has permeated academia, policymaking, business, NGOs, and the general public, leading to numerous applications of the concept, some of which only partially overlap. In this article, we review recent debates and research trends in the Circular Economy, outlining the ten most common groups of its conceptualizations using the PRISMA (Preferred Items for Systematic Reviews and Meta-Analysis) method. We then propose a post disciplinary and transnational research program on the Circular Economy that would not only combine hard and soft sciences in unprecedented ways but also have important practical applications, such as developing tools to embed the Circular Economy in natural, technical, economic, and socio-cultural settings.

In response to the increasing global emphasis on sustainability and the specific challenges faced by small and medium-sized enterprises (SMEs) in China, this study explores the integration of green reverse logistics within these enterprises to enhance their sustainability and competitiveness. The aim of this study is to understand the relationship between reverse logistics, green logistics, and sustainable development. Data analysis was conducted utilizing a combination of descriptive statistics and correlation analysis. A survey of 311 participants examined SMEs’ performance in reverse logistics practices and their initiatives in green logistics and sustainable development. The empirical findings reveal significant progress in reverse logistics practices among SMEs, reducing environmental impact and improving resource efficiency. Moreover, a notable positive correlation was identified between reverse logistics promotion and advancements in green logistics and sustainable development. SMEs’ investment in reverse logistics is closely linked to their efforts in environmentally conscious and sustainable supply chain management. These insights benefit SMEs and supply chain practitioners and offer a valuable reference for future research and practical applications in this field.

This study intends to explore the idea of a vocational village strategy to foster sustainable rural development. Vocational villages, offering targeted skills training and economic opportunities, present a compelling soft approach to rural development, addressing the need for sustainable livelihoods and community empowerment. Drawing upon the collaborative governance (the penta-helix model); underpinning the social capital perspective; and highlighting the economic, institutional, cultural, environmental, technological, and institutional dimensions of sustainable development, a vocational village strategy is expected to level up village capacities and facilitate modernization. The research was narratively developed through a qualitative methodology using primary and secondary data sources. Primary empirical data was employed to analyze vocational village practices in Panggungharjo Village, Yogyakarta, Indonesia as a representative example. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) framework provided secondary data to present comparative literature on vocational village development. The findings determined a four-staged vocational village model includes initiation, training, business development, and independence. The success of this model is contingent upon political, bureaucratic, and sociocultural factors (social capital), as well as the effective collaboration of government, academia, industry, and community (penta-helix). This research contributes to the urgency of vocational village practices and models as a viable strategy for achieving equitable and sustainable rural development.

In the context of establishing businesses in a new region, neglecting environmental orientation may lead to the omission of crucial motives for entrepreneurs’ migration and the subsequent course of their businesses. This present study aims to investigate the effect of green space quality (GSQ), green campaign (GC), and green attitude (GA) on green entrepreneurship pioneering intention (GEPI). Further, national pride (NP) was added as a moderator. This study utilized a cross-sectional approach using a survey method targeting small and medium-sized enterprise (SME) owners who will be relocated to the new capital city. Partial least square structural equation modeling was employed in the data analysis. The results revealed that GSQ, GC, and GA positively influence GEPI. Also, NP moderates the positive influences of GC and GA on GEPI. Entrepreneurs were motivated to pioneer green entrepreneurship in the new region due to environmental factors. Furthermore, their nationalism reinforces the connection between environmental motivations and the aspirations to undertake such pioneering endeavors. The findings present valuable insights for governments to formulate policies that encourage entrepreneurs to migrate internally and establish new economic nodes. Further, the results demonstrate how nationalism encourages green business pioneering endeavors in an untapped market.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

Sketching on stimulus-organism-response theory, this study aims to investigate the mediating effect of environmental passion on the relationship of the environmentally specific servant leadership with employees’ green behavior. Using purposive sampling approach, the authors adopted one month time-lagged approach to collected data from 232 academic employees in higher education institutions of China. Response rate in this study is 46.40%. The partial least-structural equation modeling (PLS-SEM) analysis was conducted in the smartpls 4.0 software to test the proposed hypotheses. The current empirical findings confirm that environmentally specific servant leadership significantly positively influence employee’s environmental passion and environmental passion significantly positively affects the employee’s workplace green behaviors. This current finding offered support in favor of mediating impact of environmental passion on the “environmentally specific servant leadership-employees workplace green behaviors” relationship. To the best of authors, this study is among pioneers’ studies to investigate the integrated relationship of environmentally specific servant leadership, environmental passion and green behavior in higher education institutions context of China. Limitations and implication have been elaborated at the end.