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.

The contradiction between the ability of forestry that provides high-quality and abundant forestry products and good ecological services, and the demand for high-quality and diversified forestry products and service in order to meet the people’s rapid growing, has become the main contradiction faced by forestry development in new era. Since the area of forest resources in China is restricted by the expansion space, expanding the effective supply of forestry must mainly depends on the improvement of the quality and structure of forestry resources. Therefore, the focus of promoting forestry development is to comprehensively improve the level of forest management in the new era. Based on the analysis of the causes for the low level of forest management, it is proposed that forestry development in the new era should focus on the positively stimulating and strengthening the human capital development, etc., which come from the current following aspects: innovating forest management theory and model, clarifying the relationship between government and market.

Chinese municipalities have developed a large stock of capital assets during a period of rapid growth and urbanization, but have yet to modernize asset management practices. Cities face challenges such as premature decline of fixed assets and spiking liabilities related to operating and maintaining assets. This paper evaluates the asset management practices in three selected small cities and towns in China, using a benchmarking assessment tool followed by an in-depth field assessment. The paper finds that overall performance is below half the international benchmark for good practice in all three cities. Management practices are considerably more advanced for land than for buildings and infrastructure. Key deficiencies in data availability and reporting, governance, capacity, and financial management indicate increased risks for local government finance and the delivery of public services. For small cities and towns where public revenues are often uncertain and limited, urban public services will be at risk of deterioration unless good asset management practices are put in place. The paper recommends strategic actions for upper and lower levels of government, to advance local asset management practices and facilitate the reform agenda.

This study aims to identify and the implementation of ASN Management policies on career development aspects based on the merit system in the West Java Provincial Government and 6 Regency/City Governments in West Java Province. The failure of the institutionalization of the meritocratic system in ASN career development is partly triggered by the symptoms of the appointment or selection of officials in the central and regional levels not based on their professionalism or competence except for subjective considerations, political ties, close relationships and even bribery. This study uses a qualitative method with a descriptive approach. The operationalization concept in this study uses Merilee S. Grindle's Policy Implementation theory which consists of dimensions of policy content and its implementation context. The factors that cause the implementation of the policy to be less than optimal include: 1. Uneven understanding of meritocracy; 2. Slowness/unpreparedness in synchronizing central and regional rules/policies; 3. The information integration system between the center and regions has not yet been implemented; 4. Limited supporting infrastructure; 5. Limited permits for related officials; 6. Transparency; 7. Collaboration across units/agencies; 8. External intervention; 9. Use of information systems/technology. To optimize these factors, an Accelerator of Governmental Unit's Success (AGUS) model was created, which is a development of the Grindle policy implementation model with the novelty of adding things that influence implementation, including top leader's commitment and wisdom, effectiveness of talent placement, on-point human development, technology savvy, cross-unit/agency collaboration, and monitoring and evaluation processes.

Purpose—In the business sector, reliable and timely data are crucial for business management to formulate a company’s strategy and enhance supply chain efficiency. The main goal of this study is to examine how strong brand strength affects shareholder value with a new Supplier Relationship Management System (SRMS) and to find the specific system qualities that are linked to SRMS adoption. This leads to higher brand strength and stronger shareholder value. Design/Methodology/Approach—This study employed a cross-sectional design with an explanatory survey as a deductive technique to form hypotheses. The primary method of data collection used a drop-off questionnaire that was self-administered to the UAE-based healthcare suppliers. Of the 787 questionnaires sent to the healthcare suppliers, 602 were usable, yielding a response rate of 76.5%. To analyze the data gathered, the study used Partial Least Squares Structural Equation modelling (PLS-SEM) and artificial neural network (ANN) techniques. Findings—The study’s data proved that SRMS adoption and brand strength positively affected and improved healthcare suppliers’ shareholder value. Additionally, it demonstrates that user satisfaction is the most significant predictor of SRMS adoption, while the results show that the mediating role of brand strength is the most significant predictor of shareholder value. The results demonstrated that internally derived constructs were better explained by the ANN technique than by the PLS-SEM approach. Originality/Value—This study demonstrates its practical value by offering decision-makers in the healthcare supplier industry a reference on what to avoid and what elements to take into account when creating plans and implementing strategies and policies.

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.