In Indonesia tax reform has undergone multiple revisions in recent years, all within a brief timeframe. Digital tax reform in Indonesia began with significant milestones in recent years to adapt to the digital economy’s challenges. The specific start date for digital tax reform in Indonesia can be traced back to the passing of the Tax Regulations Harmonization Law on 7th October 2021, which officially became Law No 7/2021 on 29th October 2021. This law marked a crucial step in Indonesia’s journey towards modernizing its tax system to address the implications of the digital economy. The provisions of this law have varying effective dates, such as for income tax purposes from the 2022 fiscal year and for VAT purposes from 1st April 2022. These changes under the Tax Regulations Harmonization Law are extensive and wide-reaching, signifying a pivotal moment in Indonesia’s digital tax reform efforts. This shows that the Indonesian government intends to radically overhaul the tax system, yet there are inconsistent approaches to deciding on the long-term course of tax policy. It is critical to investigate the concept of tax legislation in Indonesia in order to provide legal clarity on digital tax reform. Normative juridical research methodology is employed, together with a qualitative research strategy and descriptive-analytical research specifications. The findings suggest that the Indonesian government’s efforts to establish strict policies governing taxes on digital activity are inadequate and uneven. In order to apply to digital platform enterprises, the definition of permanent establishment as outlined in a number of national regulations must incorporate a substantial economic presence criterion. Legislative progress toward the establishment of a framework for digital tax collection is necessary to mitigate the possible income loss of states in this area, which could result from the rapid advancement of information technology. The OECD consensus is still in the process of drafting an international tax reform that will require adjustments from national tax reform. Therefore, it is imperative that the Indonesian government establish a thorough framework for tax regulation that can ensure robustness, economic efficiency, fairness, against motivation compatibility, administrative ease, and avoidance.
Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.
The present paper discusses the case of the Madrid Nuevo Norte Project (MNNP) in order to examine the relation of this mega-project with the city’s sustainable development. For this reason, the study used a qualitative approach using semi-structural interviews with experts (Madrid’s town hall, Madrid State, and the program management office and other external) that relayed strongly with MNNP. The expert panel requirements are split in six expertise areas: sustainability, urban development, urban planning, government or public affairs, project management or Madrid Nuevo Norte (MNN) key stakeholders. The study highlighted the vital importance of MNNP as a flagship sustainable project for the rest of Europe, that meets sustainability criteria for contributing substantially in the improvement of the quality of life of final users and for the community in general. For instance, it contributes to the regeneration of the city’s degraded area, to the interconnection of an isolated part of the city and public transportation connection, improving the external image of Madrid. Despite of it, there are some challenges that should be carefully managed such as applying sustainable solutions from other cities not properly tailored to Madrid, housing pricing accessibility increase due to the lack of terrain in Madrid and the politization of the project as discussion topic between local parties. In this context, local authorities should give particular emphasis in complying with the principles of sustainability for improving the overall performance of MNNP, ensuring social justice and prosperity for the people of Madrid.
This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.
This study analyzes the impact of a high-speed rail line on tax revenues and on the economy of affected regions within the country. The economic impact of infrastructure investment can be induced by changes in tax revenues when the infrastructure is in operation. Accurate regional GDP data are not necessarily available in many Asian countries. However, tax data can be collected. Therefore, this study uses tax revenue dates in order to estimate spillover effects of infrastructure investment. The Kyushu high-speed rail line was constructed in 1991 and was completed in 2003. In 2004, the rail line started operating from Kagoshima to Kumamoto. The entire line was opened in 2011. We estimated its impact in the Kyushu region of Japan by using the differencein- difference method, and compared the tax revenues of regions along the high-speed railway line with other regions that were not affected by the railway line. Our findings show a positive impact on the region’s tax revenue following the connection of the Kyushu rapid train with large cities, such as Osaka and Tokyo. Tax revenue in the region significantly increased during construction in 1991–2003, and dropped after the start of operations in 2004–2010. The rapid train’s impact on the neighboring prefectures of Kyushu is positive. However, in 2004–2013, its impact on tax revenue in places farther from the rapid train was observed to be lower. When the Kyushu railway line was connected to the existing high-speed railway line of Sanyo, the situation changed. The study finds statistically significant and economically growing impact on tax revenue after it was completed and connected to other large cities, such as Osaka and Tokyo. Tax revenues in the regions close to the high-speed train is higher than in adjacent regions. The difference-in-difference coefficient methods reveal that corporate tax revenue was lower than personal income tax revenue during construction. However, the difference in corporate tax revenues rose after connectivity with large cities was completed. Public–private partnership (PPP) has been promoted in many Asian countries. However, PPP-infrastructure in India failed in many cases due to the low rate of return from infrastructure investment. This study shows that an increase of tax revenues is significant in the case of the Kyushu rapid train in Japan. If half of the incremental tax revenues were returned to private investors in infrastructure, the rate of return from infrastructure investment would significantly rise for long period of time. It would attract stable and long-term private investors, such as pension funds and insurance funds into infrastructure investment. The last section of the paper will address how incremental tax revenues created by the spillover effects of infrastructure will improve the performance of private investors in infrastructure investment.
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