The objective of this research was to evaluate the unit rates of MSW generation in Cumba in the years 2016 and 2022. The calculations were based on the weights of the MSW disposed in the dump located 5 km from the city of Cumba since 2012. The GPC, physical composition, density, humidity were determined in the years 2016 and 2022, studied according to the methodology and group classification of Peruvian regulations. The results show that 5.45 Tn/day⁻¹ are generated in 2016, 4.37 Tn/day⁻¹ in 2022; according to its physical composition, 82% RO, 14% MICVC and 4% MISVC in 2016; 77% RO, 16% MICVC, 7% MISVC in 2022; density 137.90 kg/m⁻³ in 2016 and 172.69 kg/m⁻³ in 2022; humidity 67.67% in 2016 and 63.43% in 2022. It was also found that in 100.00% there is no solid waste treatment; Everything generated in homes, businesses and streets is evacuated to the final disposal site, which is a dump. In 2022, Cumba acquired 10 hectares to have adequate sanitary infrastructure and begin the closure and recovery of its current dump. This study will contribute to providing accurate data on MSW generation that allows the local government to promote the optimization of collection routes and schedules, resulting in cost savings and reduction of carbon emissions in the Amazon Region. Therefore, it is necessary to raise awareness at all levels of society through various means of communication and education, so that the risks of spreading health risks can be minimized by improving MSW management.

In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.

Climate Compatible Development (CCD), which aims to mitigate greenhouse gas emissions and promote economic growth while adjusting to the effects of climate change, necessitates integrated policy approaches across several sectors. However, little attention has been given to the types of institutional structures collaborating and competing in conceptualizing CCD and understanding its functioning. This paper develops and applies a qualitative analysis to determine the compatibility of national and local policies and multi-stakeholder roles with the aims of the three dimensions of CCD (development, climate adaptation, and climate mitigation) using the mangrove governance case. Results indicate that mangrove governance policies currently support shifts towards CCD, especially by national governments. The existence of Ministry of Development National Planning that play roles in formulating climate change policy and development planning in Indonesia proved beneficial for CCD attainment. However, several regulations showed that political intervention and sectoral interests were present in multilevel governance toward CCD. Institutional challenges in this context were described, particularly in the existence of a hierarchy of statutory powers in Indonesia.

Personal data privacy regulation and mitigation are critical in implementing financial technology (fintech). Problems with fintech users’ data might result from data breaches, improper usage, and trade. Issues with personal data will result in financial losses, crimes, and violations of personal information. This legal research used three approaches: conceptual, comparative, and statute-based. In order to implement the statutory method, all laws and regulations pertaining to the legal concerns of information technology, fintech, personal data security, and protection are reviewed. Due to the nature of the sources of data, this study mainly used literature study and document observation to collect the data. Then, legal interpretation, legal reasoning, and legal argumentation are all included in the qualitative juridical analysis. This article recommends two strategies that Indonesia should take to provide personal data protection, including: 1) establishing the Personal Data Protection Commission (PDPC); and 2) improving the financial literacy of consumers.

This study examines the factors influencing e-government adoption in the Tangerang city government from 2010 to 2022. We gathered statistics from multiple sources to reduce joint source prejudice, resulting in a preliminary illustration of 1670 annotations from 333 regions or cities. These regions included major urban centers such as Jakarta, Surabaya, Bandung, Medan, Makassar, and Denpasar, as well as other significant municipalities across Indonesia. After removing anomalous values, we retained a final illustration of 1656 annotations. Results indicate that higher-quality digital infrastructure significantly boosts e-government adoption, underscoring the necessity for resilient digital platforms. Contrary to expectations, increased budget allocation for digital initiatives negatively correlates with adoption levels, suggesting the need for efficient spending policies. IT training for staff showed mixed results, highlighting the importance of identifying optimal training environments. The study also finds that policy adaptability and organizational complexity moderate the relationships between digital infrastructure, budget, IT training, and e-government adoption. These findings emphasize the importance of a holistic approach integrating technological, organizational, and policy aspects to enhance e-government implementation. The insights provided are valuable for policymakers and practitioners aiming to improve digital governance and service delivery. This study reveals the unexpected negative correlation between budget allocation and e-government adoption and introduces policy adaptability and organizational complexity as critical moderating factors, offering new insights for optimizing digital governance.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.