The urgency of urban health in Indonesia is very worrying because most of Indonesia’s population now lives in urban areas with minimal supporting infrastructure. That prompted this study to analyze the government’s response to the healthy city development plan in the new capital city. This study uses a qualitative approach that focuses on thematic analysis. It helps check official government documents related to healthy city development plans. The relevant documents that were found were in the form of regulations. This regulation is Law of the Republic of Indonesia Number 3 of 2022 concerning the National Capital (Ibu Kota Negara, IKN). This official document was coded by maximizing the analysis tool, namely NVivo 12 Plus. This study succeeded in mapping several bare references in the healthy city development plan for the new capital city by the Indonesian government. Some of these primary references include the healthy city model (World Health Organization, WHO), the healthy city strategy (Cardiff), and (Vancouver). All of these primary references aim to improve the quality of life of residents in cities through city development that focuses on health. However, there are several challenges that the Indonesian government may face in the future, including problems with air pollution, environmentally friendly transportation, and the provision of green public spaces, health facilities, universal health services, and other infrastructure. This all requires adequate capacity and budget plans, including ensuring transparency in budget management. This study also encourages collaboration between the government, the private sector, and civil society to support the development of healthy cities that run well and sustainably.

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.

In the face of growing urban problems such as overcrowding and pollution, we urgently need innovative ideas to build smarter and greener cities. Current urban development strategies often fail to address these challenges, revealing a significant research gap in integrating advanced technologies. This study addresses these gaps by integrating green technologies and artificial intelligence (AI), studying its impact on achieving smart and sustainable habitats and identifying barriers to effective use of these technologies, considering local variations in infrastructural, cultural, and economic contexts. By analyzing how AI and green technologies can be combined, this study aims to provide a vision that can be used to improve urban development planning. The results emphasize the significance of environmental responsibility and technological innovation in the development of sustainable urban environments and provide practical recommendations for improving the overall quality of life in cities through planning and urban planning.

The main objective of the study was to assess the impact of fiscal management on macroeconomic stability in emerging countries between 2012 and 2022. The study drew on macroeconomic theory, which postulates the importance of responsible fiscal policies for economic stability. Information was taken from ten emerging Latin American countries, and the analysis was carried out through a quantitative approach, using an econometric model. A significant relationship was found between fiscal management and macroeconomic stability, evidencing that effective fiscal policies are crucial for macroeconomic stability in emerging countries. The findings emphasize that balanced fiscal management, which avoids falling into cycles of debt and deficit, is essential for long-term stability. Practices that promote fiscal stability, such as greater efficiency in public spending and effective tax collection, can contribute significantly to economic stability and sustained growth. The results also suggest that fiscal policies should take into account human development conditions and annual particularities in order to formulate effective fiscal policies. It highlights those countries with best fiscal practices, reflected in low debt-to-GDP levels and high fiscal stability, are more likely to achieve macroeconomic stability and sustainable economic growth.