A decent income is an important part of overcoming economic disparities in agricultural development, especially in developing countries where most of the population are small farmers. As a developing country, Indonesia has also established a decent standard of living by setting a minimum wage as a reference for a decent income at the national and regional levels. However, this benchmark is not relevant to be applied uniformly at all levels of workers. This research determines the national coffee development area as the study center. We developed the Anker living wage methodology as a simple concept for determining living income for certain worker communities, especially for small farmers in rural areas who dominate the type of work in Indonesia. a socio-spatial approach is used to visualize the distribution of the dynamics of a decent life in various conditions of farming households. We found that 96.6% of coffee farming households in the national coffee development area had an inadequate living income, and only 3.4% were at an adequate level. We conclude that the current state of agricultural land management does not guarantee a decent income, even though efforts have been made to maximize agricultural crop productivity. The spatial description also shows that this condition is evenly distributed throughout residential areas. It is hoped that this approach can become an essential reference in implementing agricultural development programs that focus on welfare and equitable development as benchmarks for sustainable development goals in the future.

This paper models 54,559 Chinese news items about education industry and scientific industry by machine learning during the COVID-19 epidemic to build China’s increased scientific research policy (ISRP) index. The result of interrupted time series analysis indicates that, the ISRP has an emphatic positive causality on the education industry advancement and promotes the development of the education industry. The ISRP also has a remarkable positive causality on the development of the scientific industry. Moreover, the result of causal network indicates that, a virtuous circle within the ISRP, the education industry and the scientific industry has been formed, which has promoted the sustainable development of the education chain.

The complex interactions of industrial Policy, structural transformation, economic growth, and competitive strategy within regional industries are examined in this research. Using a dynamic capabilities framework, the study examines the mediating roles of organizational innovation and adaptability in the link between competitiveness and macroeconomic variables. A two-way fixed effects model is used in this study to examine the influence of structural transformation (ST) on Industrial Policy (IP). Using regional data covering the years 2010 to 2022, the research undertaken in this paper explores the dynamics of the Indonesian economy by empirically assessing the consequences of structural change on industrial Policy. In order to establish a comprehensive model that clarifies the mechanisms through which industrial policies and structural shifts impact the development of dynamic capabilities, ultimately influencing competitiveness strategies, this research draws on a large amount of empirical data and integrates insights from seminal works. Our research adds to our knowledge of strategic management in regional industries by providing detailed information on how economic development and policy interventions influence businesses’ ability to adapt and gain a competitive edge. In addition to advancing scholarly discourse, this study offers business executives and politicians valuable insights for managing the intricacies of global economic processes.

The purpose of this study is to analyze issues related to the use of green technology and to provide a theoretical basis for how the application of green technology in agriculture can reduce inequality. Additionally, the study aims to explore policy alternatives based on the analysis of inequality reduction issues through farmer surveys. For this purpose, this study used survey data to analyze farmers’ perceptions, acceptance status, willingness to accept green technology, and perceptions of inequality. The quantitative analysis was performed to analyze the relationship between the acceptance of green technology and perceptions of inequality. The results confirmed that access to information, perception of climate change, and awareness of the need to reduce greenhouse gas emissions are major factors. In particular, the higher the satisfaction with policies regarding the introduction of green technology, the lower the perception of inequality. Specifically, the acceptance of green technology showed a significant positive correlation with access to information, perception of climate change, and awareness of the need to reduce greenhouse gas emissions, while perceptions of inequality showed a significant negative correlation with policy satisfaction. In conclusion, green technology in agriculture is vital for reducing climate change damage and inequality. However, targeted policy support for small-scale farmers is essential for successful adoption. This study provides policy implications related to the application of green technology in the agricultural sector, which can promote sustainable agricultural development.

This paper investigates the transformative role of Artificial Intelligence (AI) in enhancing infrastructure governance and economic outcomes. Through a bibliometric analysis spanning more than two decades of research from 2000 to 2024, the study examines global trends in AI applications within infrastructure projects. The analysis reveals significant research themes across diverse sectors, including urban development, healthcare, and environmental management, highlighting the broad relevance of AI technologies. In urban development, the integration of AI and Internet of Things (IoT) technologies is advancing smart city initiatives by improving infrastructure systems through enhanced data-driven decision-making. In healthcare, AI is revolutionizing patient care, improving diagnostic accuracy, and optimizing treatment strategies. Environmental management is benefiting from AI’s potential to monitor and conserve natural resources, contributing to sustainability and crisis management efforts. The study also explores the synergy between AI and blockchain technology, emphasizing its role in ensuring data security, transparency, and efficiency in various applications. The findings underscore the importance of a multidisciplinary approach in AI research and implementation, advocating for ethical considerations and strong governance frameworks to harness AI’s full potential responsibly.