This review paper delves into the intricate landscape of the digital economy, focusing on the multifaceted interplay between innovation, competition, and consumer dynamics. It investigates the transformative impact of digital technologies on market structures and consumer behaviors, spanning areas such as e-commerce, online publishing, taxation, and big data challenges. By analyzing network effects, market concentration, and the influence of key players like Google and Amazon, this study draws on insights from previous research. Furthermore, it examines evolving regulations with an emphasis on consumer protection, competition law, and privacy concerns. Through a comprehensive exploration of the digital ecosystem, this paper offers a nuanced understanding of how businesses, consumers, and policymakers navigate the complexities of the digital marketplace.

Creating products and services that satisfy individual and community needs is impossible without raw materials. This study takes a novel approach by integrating the economic dynamics and raw material consumption indicators of the European Union (EU). The study uses different econometric methods to analyze the relationship between GDP (gross domestic product) and the EU’s raw material consumption (RMC) from 2014–2023. Among the results, the panel data analysis model shows that the resource productivity of the EU improved during the period under review, whereas the material intensity decreased significantly. These trends significantly contributed to the relative decoupling of material consumption from GDP in the last decade. The results of the K-means cluster analysis highlight the regional economic differences within the EU. According to the results of the correlation analysis, EU member countries differ significantly in the efficiency of raw material use. Nevertheless, five member countries are robustly vulnerable to large-scale raw material use. The divergence calculation results show that while some countries use raw materials extremely efficiently to produce GDP, others achieve low efficiency. This unique approach and the resulting findings provide a new perspective on the complex relationship between economic growth and raw material use in the EU.

This study investigates the relationship between hydrological processes, watershed management, and road infrastructure resilience, focusing on the impact of flooding on roads intersecting with streams in River Nile State, Sudan. Situated between 16.5° N to 18.5° N latitude and 33° E to 34° E longitude, this region faces significant flooding challenges that threaten its ecological and economic stability. Using precise Digital Elevation Models (DEMs) and advanced hydrological modeling, the research aims to identify optimal flood mitigation solutions, such as overpass bridges. The study quantifies the total road length in the area at 3572.279 km, with stream orders distributed as follows: First Order at 2276.79 km (50.7%), Second Order at 521.48 km (11.6%), Third Order at 331.26 km (7.4%), and Fourth Order at 1359.92 km (30.3%). Approximately 27% (12 out of 45) of the identified road flooding points were situated within third- and fourth-order streams, mainly along the Atbara-Shendi Road and near Al-Abidiya and Merowe. Blockages varied in distance, with the longest at 256 m in Al-Abidiya, and included additional measurements of 88, 49, 112, 106, 66, 500, and 142 m. Some locations experienced partial flood damage despite having water culverts at 7 of these points, indicating possible design flaws or insufficient hydrological analysis during construction. The findings suggest that enhanced scrutiny, potentially using high-resolution DEMs, is essential for better vulnerability assessment and management. The study proposes tailored solutions to protect infrastructure, promoting sustainability and environmental stewardship.

Oil spills (OS) in waters can have major consequences for the ecosystem and adjacent natural resources. Therefore, recognizing the OS spread pattern is crucial for supporting decision-making in disaster management. On 31 March 2018, an OS occurred in Balikpapan Bay, Indonesia, due to a ship’s anchor rupturing a seafloor crude oil petroleum pipe. The purpose of this study is to investigate the propagation of crude OS using coupled three-dimensional (3D) model from DHI MIKE software and remote sensing data from Sentinel-1 SAR (Synthetic Aperture Radar). MIKE3 FM predicts and simulates the 3D sea circulation, while MIKE OS models the path of oil’s fate concentration. The OS model could identify the temporal and spatial distribution of OS concentration in subsurface layers. To validate the model, in situ observations were made of oil stranded on the shore. On 1 April 2018, at 21:50 UTC, Sentinel-1 SAR detected an OS on the sea surface covering 203.40 km². The OS model measures 137.52 km². Both methods resulted in a synergistic OS exposure of 314.23 km². Wind dominantly influenced the OS propagation on the sea surface, as detected by the SAR image, while tidal currents primarily affected the oil movement within the subsurface simulated by the OS model. Thus, the two approaches underscored the importance of synergizing the DHI MIKE model with remote sensing data to comprehensively understand OS distribution in semi-enclosed waters like Balikpapan Bay detected by SAR.

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.