Loss Of Revenue Research Articles

Food democracy as food security strategy: The case of a Costa Rican tourism town

Tourism communities such as Monteverde, Costa Rica, the site of this study, have been profoundly impacted by the loss of tourism revenue during COVID-19. Faced with intensified food insecurity caused by the cascading impacts of this pandemic, the community has organized initiatives to stimu­late local, sustainable food production to increase food security during the COVID-19 economic recovery. This paper adopts a food democracy framework to analyze restaurants’ regional food purchases, barriers to local purchasing, and tour­ists’ interest in and ability to identify local food products. Our findings show that nearly all restaurant owners identified benefits of purchasing regional food but reported multiple barriers to buying locally. Tourists reported high interest in eating locally produced food but do not have enough information to identify farm-to-fork options. Local food initiative stakeholder inter­views show that emergent strategies demonstrate a move toward food democracy actions by promot­ing communication and co-learning between res­taurants, food producers, and tourists to rein­force principles of food democracy. Based on our find­ings, we recommend (a) strengthening producer-to-restaurant networks, (b) enhancing communication of local food production benefits and responsi­bilities to restaurants, and (c) promot­ing the locally made certification for restaurants to strengthen localized food networks and direct tourists to transformative food strategies underway in Monteverde.

Comparative Analysis of Costing Methods for Magnetic Resonance Imaging in a Tertiary Care Teaching Hospital: An Observational Study

Abstract Background: Health-care costs, often determined through outdated methods, fail to capture the true costs of care delivery. Traditional costing methods in the health-care sector have proven insufficient, leading to potential revenue losses and increased patient out-of-pocket expenses. Activity-based costing (ABC) and time-driven ABC (TDABC) offer alternative approaches to cost calculation, aiming for a more accurate reflection of resource consumption. Methodology: This descriptive observational study was conducted in the magnetic resonance imaging (MRI) center of a tertiary care teaching hospital, focusing on a single noncontrast MRI scan costing. Direct observations and record reviews, including standard operating procedures, policy letters, financial documents, and miscellaneous records, were employed. Results: Using traditional costing, the unit cost of an MRI scan was calculated at INR 1291.51. ABC yielded a unit cost of INR 3613.77, while TDABC resulted in a unit cost of INR 2764.39. A comparison between the three costing methods revealed potential revenue losses with traditional costing. In contrast, TDABC appeared to strike a balance between financial prudence and reduced patient out-of-pocket expenses. Conclusion: The study emphasizes the significance of understanding true costs in health-care delivery. The results advocate for the adoption of TDABC as a favorable compromise, enabling the hospital to retain revenue without burdening patients with additional costs. Notably, the study highlights the necessity for a granular understanding of true costs for successful transitions from volume to value in health care.

Analisis Yuridis Penerapan Konsep Asas Lex Specialis Sytematis Dalam Tindak Pidana Perpajakan

The sustainability of a country's development heavily relies on its revenue streams, with taxation being a crucial pillar in generating state income. However, corruption within the taxation sector poses a significant threat, leading to substantial losses in state revenue and hindering efforts to enhance societal welfare. This research endeavors to address this challenge by examining the application of criminal law in combating corruption within the taxation sector. Furthermore, the research identifies the role of the Lex Specialis Systematis in delineating taxation crimes from broader financial crimes. While taxation crimes are confined to the realm of tax-related finances, corruption transcends this boundary, impacting broader state finances. This study advocates for a comprehensive legal approach that integrates both tax law and anti-corruption legislation. It underscores the necessity of applying the principles of Lex Systematic Specialist to taxation laws, ensuring that taxpayers are held accountable within the framework of tax regulations. Simultaneously, it emphasizes the applicability of anti-corruption laws, governed by the principles of Lex Specialis, to combat corruption within the taxation sector effectively.In conclusion, this research highlights the imperative of a harmonized legal framework that synergizes tax law and anti-corruption measures. By bridging the gap between these legal domains, the state can enhance its capacity to combat corruption within the taxation sector, safeguard state revenues, and foster sustainable development for the benefit of society at large.

PHILIPPINE MSMES AFTER THE COVID-19 PANDEMIC: A SYSTEMATIC REVIEW OF PANDEMIC IMPACTS AND COPING STRATEGIES

The COVID-19 pandemic negatively impacted the economy's flow in the majority of areas across the world. Reduced economic activity due to travel restrictions and lockdowns imposed to stop the virus's spread caused economic recessions in the pandemic-affected areas. MSMEs are the backbone of the Philippines, providing 99.5% of the nation’s business population and 65.10% of the nation's workforce. In addition to dealing with extraordinary economic and social challenges over the past two years, SME preparation for transitioning to a digital and sustainable economy has been necessary. The researcher will use a Systematic Literature Review to assess the transparency and consistency of the screening procedure and the approaches to coping applied by Philippine MSMEs in the context of the COVID-19 pandemic. The study will make use of three research guide questions. 1) What are the pandemic risks and their impact on Philippine MSMEs? 2) What coping strategies do Philippine MSMEs use to deal with risks and pandemic risk impacts? According to the review, human movement restrictions (Lockdowns), financial constraints/ revenue losses, operating difficulties, economic crisis and recession, and job losses are impacts of the pandemic on MSMEs. Furthermore, the study listed four essential coping strategies: digital and technological adoption/adaptation, government financial support/packages, modification of business models, and implications for financial stability (loans). This study will contribute to ideas in innovation and technological advancement, business improvement, and wise cost distribution.

Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6% for future times of up to 48 h.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

An Adaptive Dual-Mode Task-Oriented Resource Management Strategy for GEO Relay Systems

With the fierce global competition on satellite networks, the building of satellite constellations grows explosively. Sharply increasing on-orbit data will face the challenge of satellite-ground data transmission. GEO satellites become the top choice for satellite data relay due to their stable satellite-ground link. Most existing spectrum resource management for GEO relays is equipment-oriented and benefit priority, which may lead to a waste of spectrum resources. In this paper, we propose a real-time task-oriented resource allocation strategy for GEO relay systems. We model the spectrum allocation problem as a distributed non-cooperative Stackelberg game process. We prove that when both sides of the game pursue the maximization of personal revenue, the system will enter a Nash equilibrium state, whereas spectrum resources are not fully used. Based on the maximization of individual utilities (U-prior) and spectrum utilization (S-prior) methods, we design an adaptive dual-mode pricing mode to maximize the spectrum resources within a certain loss of revenue. The simulation results show that the S-prior and U-prior have better performance than the baseline method and existing optimization methods. Our proposed dual-mode strategy is making more throughputs and has less delay with little loss of utility values than that of individual utility maximization.

Legal Protection for Citizens Affected by The Termination of Access Policy of Unregistered Private Scope Electronic System Operators

Legal Protection for Citizens Affected by The Termination of Access Policy of Unregistered Private Scope Electronic System Operators

Economic Implications of the Nationally Determined Contribution (NDC) of Vietnam: A CGE Analysis with Capital-Energy Substitution Elasticities Estimated Using Firm-level Data

Purpose - The purpose of this paper is to assess the economic implications of Vietnamese Nationally Determined Contribution (NDC) targets towards emission reduction. Design/Methodology/Approach - We devised four scenarios corresponding to economy-wide emission reduction targets and sector targets specified in the most up to date NDC of Vietnam and the NDC technical report. The scenarios were then simulated in a CGE framework to reveal how important economic indicators and welfare change relative to baseline scenarios in 2030. The capital-energy substitution elasticities of 24 Vietnamese sectors were also estimated using a nested CES production function and used for model calibration. Findings - The results indicate that the coal mining sector undergoes a significant decline in output in response to the imposition of emission reduction schemes. Outputs of sectors with high capital-energy substitution elasticities such as non-metallic mineral, metal, and agriculture are more resilient to mild restriction policies compared with sectors with low elasticities. Imposing a moderate economy-wide restriction might lead to slight improvements in GDP and welfare. Under aggressive emission policies, the CGE model predicts major trade shifts in key export sectors, including electronics, electrical equipment, and machinery, and losses in allocative efficiency due to reduced input tax revenue. Research Implications - We recommend excluding the coal mining sector from NDC, providing short-term tax cuts or cost subsidies for exporting industries under aggressive sector-specific emission policies, and imposing a carbon tax on sectors insensitive to emission restrictions to offset the loss in tax revenue.

Tax havens reexamined: The impact of global digital tax reforms on international taxation

This review delves into the evolving landscape of tax havens in the wake of comprehensive digital tax reforms. This study scrutinizes the implications of global efforts to address tax evasion and profit shifting, particularly in the context of the digital economy. With the proliferation of digital services and cross-border transactions, traditional tax frameworks have struggled to capture the economic activities of multinational corporations operating in cyberspace. Consequently, tax havens have emerged as facilitators of aggressive tax planning strategies, exacerbating global tax inequalities and eroding fiscal sovereignty. Against this backdrop, the study analyzes the efficacy and repercussions of recent digital tax reforms spearheaded by international organizations and individual nations. It examines the implementation challenges, compliance burdens, and potential loopholes associated with these reforms. Moreover, the research evaluates the impact of digital tax measures on the behavior of multinational enterprises, exploring their responses in terms of tax planning strategies and business models. Furthermore, the study investigates the broader implications of digital tax reforms on international taxation dynamics, including their influence on tax competition, investment flows, and economic development. Special attention is paid to the implications for developing countries, which often bear the brunt of revenue losses resulting from profit shifting to tax havens. By synthesizing empirical evidence, policy analysis, and theoretical insights, this study offers a comprehensive reevaluation of tax havens in the digital age. It sheds light on the complex interplay between technological advancements, regulatory frameworks, and global economic dynamics shaping contemporary tax landscapes.

FAKE PRODUCT DETECTION USING BLOCKCHAIN TECHNOLOGY

End consumers face threats to their finances, health, and safety when counterfeit or duplicate goods are manufactured and marketed. Through revenue loss, product defamation, downtime, replacement costs, causing brands to spend money battling counterfeits, compromising partner trust, stealing sales, etc., it also has an adverse effect on the economic growth of original manufacturers and businesses. A block chain-based system that uses barcodes and QR codes to authenticate objects is suggested as a solution to this problem, enhancing security with the SHA-256 algorithm. This technology makes use of a camera scanner to confirm the product's unique code that is kept on an impenetrable block chain, guaranteeing the legitimacy of every item thanks to the cryptographic strength of SHA-256. The consumer is notified if a product's code matches the block chain record after it is scanned; if there are any differences, the manufacturer and the customer are notified about the product's counterfeit status. By giving consumers direct verification capabilities and protecting producers' interests with an opaque, safe, and secure verification system against counterfeit goods, this strategy drastically lowers reliance on merchant promises. Keywords: Block chain, smart contracts, QR (Quick Response) code, anti- counterfeit.

Assessment of Nature Reserve Park Management for Sustainable Development: Insights from Idanre Hills in Ondo State Southwest, Nigeria

Despite Nigeria is gifted with a wealth of natural resources and rich cultural heritage, is still backward in terms of economic development. Therefore, ecotourism has therefore been identified as a vital regional approach for harmonizing economic growth and conservation. Thus, the study empirically evaluates the current state of ecotourism in Oke Idanre hills in Idanre Local Government Area of Ondo State, Nigeria. Primary and secondary data were collected and sourced with the aid of questionnaire and scheduled Interviews. The data involved qualitative analysis using descriptive statistics. The results revealed that although ecotourism is still at an early stage, some socioeconomic benefits for the Idanre community exist such as the employment of locals as tourist guides and other investments as a result of this destination. The finding shows the use and access of resources by the locals in the Idanre community as logging and poaching have become the order of the day. Also, Idanre locals have had to cope with reduced access to some parts of the hills especially during the day since the site was listed by UNESCO as a world heritage site. The dilapidated site resort has brought about revenue loss to the community. The site offers rich cultural artefacts and a tour guide is usually attached to educate the tourists. Hence this study is necessary to ascertain the current state of ecotourism in Idanre hills looking from the community perspective, the practices, administration, and management of Idanre hills, also recommendations offered are centered on the study of the findings.

Wind turbine fault detection and identification using a two-tier machine learning framework

A proactive approach is essential to optimize wind turbine maintenance and minimize downtime. By utilizing advanced data analysis techniques on the existing Supervisory Control and Data Acquisition (SCADA) system data, valuable insights can be gained into wind turbine performance without incurring high costs. This allows for early fault detection and predictive maintenance, ensuring that unscheduled or reactive maintenance is minimized and revenue loss is mitigated. In this study, data from a wind turbine SCADA system in the southeast of Ireland were collected, preprocessed, and analyzed using statistical and visualization techniques to uncover hidden patterns related to five fault types within the system. The paper introduces a conditional function designed to test two given scenarios. The first scenario employs a two-tier approach involving fault detection followed by fault identification. Initially, faulty samples are detected in the first tier and then passed to the second tier, which is trained to diagnose the specific fault type for each sample. In contrast, the second scenario involves a simpler solution referred to as naive, which treats fault types and normal cases together in the same dataset and trains a model to distinguish between normal samples and those related to specific fault types. Machine learning models, particularly robust classifiers, were tested in both scenarios. Thirteen classifiers were included, ranging from tree-based to traditional classifiers, neural networks, and ensemble learners. Additionally, an averaging feature importance technique was employed to select the most impactful features on the model decisions as a starting point. A comparison of the results reveals that the proposed two-tier approach is more accurate and less time-consuming, achieving 95% accuracy in separating faulty from normal samples and approximately 91% in diagnosing each fault type. Furthermore, ensemble learners, particularly bagging and stacking, demonstrated superior fault detection and identification performance. The performance of the classifiers was validated using t-SNE and explainable AI techniques, confirming that the impactful features align with the findings and that the proposed two-tier solution outperforms the naive solution. These results strongly indicate that the proposed solution is accurate, independent, and less complex compared to existing solutions.

Design and techno-economic assessment of a standalone photovoltaic-diesel-battery hybrid energy system for electrification of rural areas: A step towards sustainable development

The recent persistent power interruption in Nigeria has significantly disrupted commercial activities, resulting in a magnificent economic loss, supply chain ripples and revenue loss. As a result, harnessing renewable energy sources to generate electricity has become a popular choice for satisfying ever-increasing load demand and reducing apprehensions on global warming and reliance on depleted fossil fuels. The goal of this research is to determine whether powering a remote community with a hybrid energy system (HES) is technologically, financially and environmentally viable. The optimum design of a standalone HES with the diesel generator (DG), photovoltaic (PV) and battery storage system (BSS) is provided in this study to satisfy the electrical power needs of a farm settlement in Kura, Nigeria by considering generation constraints and load demand. This research work presents a genetic algorithm (GA) to minimize wearing cost of the system (WCS), minimize the land needed for the installation of the DG and PV system, minimize the total annual cost of the system (TAC) and maximize the benefit to cost ratio and revenue from electricity consumption. The findings of the research showed that PV/BSS/DG system is a prospective solution to satisfy the load requirements with least TAC of 67374 $/yr, annual maintenance cost (AMC) of 2808.2 $/yr, annual fuel cost (AFC) of 32300 $/yr and annual emission cost (AEC) of 774.2023 $/yr. The outcomes of the study show that a considerable TAC, AMC, AFC and AEC savings of 26766 $/yr (28.43 %), 2808.2 $/yr (53.09 %), 32300 $/yr (25.4 %) and 774.2023 $/yr (60.69 %) are recorded when compared with using DG alone. The control approach applied in this study has reduced the operational capacity of the DG and prevented about 41157 kg/yr, 419.19 kg/yr and 22.52 kg/yr of CO2, NOx and SO2 emissions from being injected into the atmosphere. The simulation outcomes of the research demonstrate that the developed model can significantly reduce cost of electricity in rural communities with the application of HES. Hence, a 45.36 % cost of energy saving has been accomplished through the energy management system introduced in the proposed HES. The study's outcomes can be used as benchmarks to help many countries to enhance access to electricity, raise their living standards and stimulate economic growth. The application of the proposed HES in remote communities can result in greater economic and environmental benefits to a general population of rural dwellers. The findings of the research work are beneficial to designers, independent power providers, investors, researchers and electricity consumers that are looking for a feasible power solution.

EXplainable Artificial Intelligence (XAI) for improving organisational regility.

Since the pandemic started, organisations have been actively seeking ways to improve their organisational agility and resilience (regility) and turn to Artificial Intelligence (AI) to gain a deeper understanding and further enhance their agility and regility. Organisations are turning to AI as a critical enabler to achieve these goals. AI empowers organisations by analysing large data sets quickly and accurately, enabling faster decision-making and building agility and resilience. This strategic use of AI gives businesses a competitive advantage and allows them to adapt to rapidly changing environments. Failure to prioritise agility and responsiveness can result in increased costs, missed opportunities, competition and reputational damage, and ultimately, loss of customers, revenue, profitability, and market share. Prioritising can be achieved by utilising eXplainable Artificial Intelligence (XAI) techniques, illuminating how AI models make decisions and making them transparent, interpretable, and understandable. Based on previous research on using AI to predict organisational agility, this study focuses on integrating XAI techniques, such as Shapley Additive Explanations (SHAP), in organisational agility and resilience. By identifying the importance of different features that affect organisational agility prediction, this study aims to demystify the decision-making processes of the prediction model using XAI. This is essential for the ethical deployment of AI, fostering trust and transparency in these systems. Recognising key features in organisational agility prediction can guide companies in determining which areas to concentrate on in order to improve their agility and resilience.

The socio-economic impacts of COVID-19 on selected mining companies in Limpopo Province, South Africa

This study explores the socio-economic impacts of the pandemic on mining value chains, using a mixed-methods approach. A thematic analysis, based on open-ended survey questions and interviews, was performed to determine the impacts on value chains and employee welfare and determine the coping mechanisms. The survey data, gathered from 408 respondents in Limpopo Province of South Africa, provided quantitative information. Bivariate statistical analysis was used to discern variations in responses between various occupational groups. The results indicate differentiated impacts among occupational groups, including loss of revenue, disruptions of the value chain, and welfare disruptions. Despite these challenges, the study found that some new safety measures helped the mining industry better handle the impacts of COVID-19. In conclusion, the study highlights the importance of taking proactive steps to make the mining industry more resilient. It emphasises the need for support, especially for communities that are vulnerable in times of crisis. The findings suggest that, moving forward, the mining industry should be prepared for future challenges by adopting innovative solutions and ensuring the well-being of its workers.

Optimal species composition for stand establishment in root-rot infected forest areas

Heterobasidion root rot causes severe problems in the coniferous dominated forests of Northern hemisphere by decreasing timber value, reducing tree growth and making trees prone to other disturbance agents such as wind and bark beetles. According to current practices an infected stand is protected by treating fresh stumps at final cutting and generating the stand with tree species other than the host to reduce the current infection levels. However, prevailing guidelines do not provide instructions how to manage the neighbouring stands around the already infected stand. In contrast to earlier studies, we expand the analysis to an entity (a landscape) consisting of both infected and uninfected stands so that stand establishment of uninfected area is taken into account, too. The objective of the study is to optimize stand establishment of the uninfected area so that the revenue losses are simultaneously minimized when the average carbon storage is maximized within the whole landscape (infected + uninfected). Our results demonstrate the complexity of Heterobasidion root rot management: the optimum stand establishment strategy differs depending on the geographic location and interest rate (i.e., yield on capital). Further, the results implicate that for successful fight against Heterobasidion root rot the magnitude of infected area as well as the severity of the infection need to be taken into account.

Anesthesia start time documentation accuracy where peripheral nerve block is the primary anesthetic.

When used as the primary anesthetic, nerve blocks are not billed as separate procedures. In this scenario, the anesthesia start (AStart) time should include the block procedural time. We measured how often AStart time was documented before the nerve block was placed in the preoperative area, and compared cases where a block team performed the nerve block and cases where the intraoperative anesthesia attending supervised the nerve block. We hypothesized that the involvement of a regional anesthesia team would lead to more accurate documentation of AStart. We also estimated the lost revenue due to inaccurate start time documentation. The study population were patients undergoing surgery with a peripheral nerve block as the primary anesthetic. For this analysis, AStart occurring less than 10 min before the in-operating room time was defined as potentially inaccurate. Lost potential revenue was estimated by taking the difference between the documented time of local anesthetic administration and the documented AStart time. A total of 745 cases were analyzed. Overall, 439 cases (58%) cases were identified as having potentially inaccurate start times. There were higher rates of inaccurate AStart documentation by the block team (316/482, 65.5%) compared with blocks supervised by the in-room anesthesia attendings (123/263, 46.7%, p<0.001). Overall, the estimated loss in billable revenue during the study period was a total of $70 265. The performance of primary regional anesthesia procedure by a block team increased the incidence of inaccurate documentation and uncaptured potential revenue. There is need for education about accurate nerve block documentation for anesthesiologists, especially when separate teams are used.

Role of Education and Training to Make Agritourism A Success Story in India

: The paper explores the role of entrepreneurship education and training amongst women entrepreneurs to promote agritourism sector of the Indian economy. This study employ qualitative and quantitative approaches to gather data questionnaires survey and in-depth face to face interviews, based on a semi-structured questionnaire. The research involved a matched sample of 10 registered farmers (from the Punjab Heritage Tourism Promotion Board) and 200 unregistered male and female farmers selected randomly from the Indian State of Punjab. Quantitative analysis suggests that there is a direct and significant impact of EET on the management, efficiency, and use of farming land. The findings also suggested that the fall in the agricultural productivity due to labour shortages; revenue losses are rising as commodity prices are falling. Therefore, it is not a surprise that farmers seek to supplement income through alternative means to enhance their socio-economic stability and viability. The findings suggest that there is high incidence of migration amongst male farming population and female farmers lack EET and reluctance on their part to work with external male population. However, analysis of male owner-managers of micro and small agritourist firms also reported to have lower levels of EET and hence the need for EET male and females; thus, EET and adequate access to finance were two major determinants of agritourist firms’ success in Indian Punjab.

Aggressive tax planning as a threat to national security

The article examines the strategies of aggressive tax planning as a threat to state security. Aggressive tax planning practices are becoming more common. This poses multifaceted challenges to fiscal policy, economic stability, and national security. By conducting a comprehensive analysis of applied strategies, economic impacts, and potential policy measures, this study aims to provide information on the complex mechanism of tax evasion. The article discusses various strategies that companies use in aggressive tax planning. These strategies cover offshore tax havens, transfer pricing manipulation, royalty movement, "fine capitalization" and tax deferral, each of which uses legal nuances to minimize tax liabilities. The study examines the economic consequences of aggressive tax planning, focusing on significant revenue losses for governments around the world. Eroding tax revenues through aggressive tax planning reduces states ' ability to finance basic public services, contributing to budget deficits and economic instability. In addition, it creates material inequality, contributes to social unrest, and jeopardizes the nation's ability to solve national security problems. By analyzing these economic implications, this study highlights the urgency of considering aggressive tax planning as a fiscal and national security issue.