This paper proposes an incentive model to involve communities and industries in effectively managing coastal waste in Makassar, Indonesia. The model seeks to incentivize stakeholders to invest in waste management solutions and enable public stakeholders to monitor and evaluate the progress of waste management activities. The model actively encourages participation from all stakeholders and builds upon existing efforts to promote environmental accountability. The proposed model includes several key components. It focused on public and private partnerships that should be fostered to coordinate stakeholder approaches and provide capital investment. It also focused on a financial reward scheme that should be adopted to incentivize businesses and individuals that invest in waste management initiatives. Performance bonus awards and tax incentives are proposed as possible incentive schemes. Lastly, a regulatory framework should be developed to ensure environmental standards are met and regulated. The framework should include regular reporting and auditing requirements and the implementation of penalties for those who fail to comply. The proposed incentive model seeks to engage stakeholders in effectively managing coastal waste in Makassar, Indonesia, through public and private incentive schemes.

This study aims to explore the implications of imported electrical equipment in Indonesia, analysing both short-term and long-term impacts using a quantitative approach. The research focuses on understanding how various economic factors, such as domestic production, international pricing, national income, and exchange rates, influence the country’s import dynamics in the electrical equipment sector. Employing an Error Correction Model (ECM) for regression analysis, the study utilises time-series data from 2007 to 2021 to delve into the complex interplay of these variables. The methodology involves a comprehensive analysis using the Augmented Dickey-Fuller and Phillips-Perron tests to assess the stationarity of the data. This approach ensures the robustness of the ECM, which is employed to analyse the short-term and long-term effects of the identified variables on electrical equipment imports in Indonesia. The results reveal significant relationships between these economic factors and import levels. In the short term, imports are shown to be sensitive to changes in domestic economic conditions and international market prices, while in the long term, the country’s economic growth, reflected through GDP, emerges as a significant determinant. The findings suggest that Indonesia’s electrical equipment import policies must adapt highly to domestic and international economic changes. In the short term, a responsive approach is required to manage the immediate impacts of market fluctuations. The study highlights the importance of aligning import strategies with broader economic growth and environmental sustainability goals for long-term sustainability. Policymakers are advised to focus on enhancing domestic production capabilities, reducing import dependency, and ensuring that environmental considerations are integral to import policies. This study contributes to understanding import dynamics in a developing country context, offering valuable insights for policymakers and industry stakeholders in shaping strategies for economic growth and sustainability in the electrical equipment sector. The findings underscore the need for a balanced, data-driven approach to managing imports, aligning short-term responses with long-term strategic objectives for Indonesia’s ongoing development and industrial advancement.

Indonesia, an emerging archipelagic nation, possesses abundant natural resources spanning marine, land (including forests and water sources), and diverse biological riches. The agricultural sector emerges as a pivotal driver of growth across the country, exhibiting extensive distribution. Consequently, there is an urgent imperative for comprehensive research to bolster and optimize the performance of this sector. This study aims to meticulously analyze and scrutinize macroeconomic variables aimed at enhancing Indonesia’s agricultural sector. Through the utilization of a dynamic panel model, the study zeroes in on crucial variables: economic growth in the agricultural sector, farmer terms of exchange, human development index, population density, inflation, average daily wages, and lagged economic growth data from each province in Indonesia. The best model for dynamic panel testing, employing both First Difference Generalized Method of Moments (FD-GMM) and Generalized Method of Moments System (SYS-GMM) approaches, is identified as the SYS-GMM model. This model exhibits unbiased and consistent estimation, as evidenced by the Arellano-Bond (AB) test and Sargan test results. The analysis conducted using this selected model reveals notable findings. Lagging agricultural sector performance, human capital measured by the Human Development Index (HDI), and farmers’ exchange rates are found to significantly and positively influence the economic growth of the agricultural sector. Conversely, inflation exerts a significant and negative impact on sectoral growth. However, wage levels and population density do not demonstrate a significant partial effect on the economic growth of the agricultural sector.

Infectious diseases often occur, especially as diseases such as COVID-19 have claimed many lives in the years between 2019–2021. That’s why it’s called COVID-19, considering that this infectious disease outbreak started in 2019, and its consequences and effects are devastating. Like other countries’ governments, the Indonesian government always announces the latest data on this infectious disease, such as death rates and recoveries. Infectious diseases are transmitted directly through disease carriers to humans through infections such as fungi, bacteria, viruses and parasites. In this research, we offer a contagious illness monitoring application to help the public and government know the zone’s status so that people are more alert when travelling between regions. This application was created based on Web Application Programming Interface (API) data and configured on the Google Map API to determine a person’s or user’s coordinates in a particular zone. We made it using the prototype method to help users understand this application well. This research is part of the Automatic Identification System (AIS) research, where the use of mobile technology is an example of implementation options that can be made to implement this system.

 Public-private partnerships (PPPs) are vital for infrastructure development in developing countries, integrating private efficiency with public oversight. However, PPP models often face risks, particularly in Indonesia’s water sector, due to its unique geographical and regulatory challenges. This study aims to identify and evaluate risk factors specific to drinking water PPP projects in Indonesia. Using a quantitative approach, structured questionnaires were distributed to experts in the sector, and the data was analyzed using a fuzzy evaluation method. Risks were categorized into location, design and construction, financial, operational, revenue, and political. The study emphasizes that effective risk management, including identification, analysis, and mitigation, is essential for project success. It highlights the importance of stakeholder involvement and flexible risk management strategies. Comprehensive and proactive risk management is key to the success of drinking water infrastructure projects. The research suggests that an integrated and collaborative approach among stakeholders can enhance risk management effectiveness. These findings provide valuable insights for policymakers, project managers, investors, and other stakeholders, underscoring the necessity for adaptable regulatory frameworks and robust policy guidelines to improve the sustainability and efficacy of future water-related PPPs.