This study analyzes the highly disruptive transportation business in Indonesia. The purpose of observation is to completely synthesize disruptive transportation that causes bad externalities in society. Data sources come from primary data of interviews and secondary data of related literature. The research method uses critical qualitative with a combination of in-depth interviews with several stakeholders. Key findings suggest that trust, consistency, capital ownership and proximity of new entrants to incumbents are important in disruptive innovation processes, empirical implications that transportation in Indonesia has undergone a definite economic shift. The results showed that although the government has publicly expressed its full support for any individual who will develop a business in the digital economy model, it is not effective enough to be consistent in the transportation business. Policy recommendations include adaptive training incentive programs for incumbent groups and accelerated funding assistance for new entrant groups, in addition to strengthening active collaboration between the government and the private sector is urgently needed.

With the advancement of the green economy, the labor market is experiencing the emergence of new employment forms, positions, and competencies. This arises from the special relationship between the green job market and the transforming energy sector. On the other hand, the energy sector’s influence on the green labor market and the creation of green jobs is particularly significant. It is because, the energy sector is one of the fundamental foundations of any country’s economy and impacts its other sectors. Key components of this influence include green employment and green self-employment. The purpose of this study is to identify elements of the green labor market within the context of the green economy and the energy sector. The methodology employs a hybrid literature review, combining a systematic literature review facilitated by the use of VOSviewer software. Exploring the Scopus database enabled the identification of keywords directly related to the green economy and the energy sector. Within these identified keywords, elements of the green labor market were searched. The main result is the empirical identification of the crucial term ‘green skills,’ which links elements of the green labor market, as presented in bibliometric maps. The research results indicate a gap in the form of insufficient discussion on green self-employment within the energy sector. Aspects of green jobs and elements of the green labor market are prominently featured in current research. However, there is a notable gap in the literature regarding green self-employment, presenting promising avenues for further research.

This research explores the role of digital economy in driving agricultural development in the BIMSTEC region, which includes Thailand, Myanmar, Sri Lanka, Nepal, India, Bangladesh and Bhutan (with Bhutan excluded due to data limitations) with a particular focus on mobile technologies, computing capacity and internet connectivity which were the most readily available data points for BIMSTEC. Using a combination of document analysis, and panel data analysis with the data covering 10 years (2012–2021), the study examines the interplay of key digital technologies with agricultural growth while controlling for factors including water usage, fertilizer consumption, and land temperature and agricultural land area. The analysis incorporates additional variables such as infrastructure development, credit to agriculture, investment in agricultural research, and education level. The findings reveal a strong positive correlation between mobile technology, Internet and computing capacity in BIMSTEC. This study underscores that digital tools are pivotal in enhancing agricultural productivity, yet their impact is significantly combined with investment in infrastructure and education. This study suggests that digital solutions, when strategically integrated with broader socio-economic factors can effectively challenges in developing countries, particularly in rural and underserved regions. This research contributes to the growing body of literature on digital economy in agriculture, highlighting how digital technologies can foster agricultural productivity in developing countries.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

The covid-19 pandemic has adversely affected the sustainability of micro and small enterprises (MSEs), with a particularly pronounced impact in Central Java. Entrepreneurs who struggle to adapt to reduced consumer purchasing power and the increasing reliance on digital technology are at heightened risk of business closure. Despite these challenges, inclusivity remains a crucial element for MSEs in fostering local economic development. Accordingly, this study seeks to examine the role of inclusivity in the sustainability of MSEs that are based on digital technology. Data were collected through the use of questionnaires and focus group discussions. Respondents were digital-based MSEs entrepreneurs from five selected regions, with Central Java having the largest number of digital media users. Key informants included experts from Diponegoro University, the International Council of Small Business (ICSB), the Department of Cooperatives and Micro, Small and Medium Enterprises at the provincial and district levels, and non-governmental organizations. The collected data was analyzed using the Rapid Appraisal for Micro and Small Enterprises (Rap-MSE’s) method. To assess the sustainability status, the study utilized several dimensions, including economic, environmental, social, institutional, technological, and inclusivity factors. Both multidimensional and individual analyses indicated that the sustainability status was relatively robust. MSEs that integrated digital technology into their operations were able to withstand the challenges posed by covid-19 and adapt to the new normal. In conclusion, the inclusivity dimension in the adoption of digital technology has gained increased importance in driving local economic development.

Although the problems created by exceeding Earth’s carrying capacity are real, a too-small population also creates problems. The convergence of a nation’s population into small areas (i.e., cities) via processes such as urbanization can accelerate the evolution of a more advanced economy by promoting new divisions of labor and the evolution of new industries. The degree to which population density contributes to this evolution remains unclear. To provide insights into whether an optimal “threshold” population exists, we quantified the relationships between population density and economic development using threshold regression model based on the panel data for 295 Chinese cities from 2007 to 2019. We found that when the population density of the whole city (urban and rural areas combined) exceeded 866 km⁻², the impact of industrial upgrading on the economy decreased; however, when the population density exceeded 15,131 km⁻² in the urban part of the cities, the impact of industrial upgrading increased. Moreover, it appears that different regions in China may have different population density thresholds. Our results provide important insights into urban economic evolution, while also supporting the development of more effective population policies.