Analytical Framework Research Articles

Automating Medicaid Redetermination and Risk-Sharing Models: A Blueprint for Cost-Effective and Equitable Healthcare Delivery

Purpose: The primary purpose of this paper is to explore how automation in Medicaid redetermination and risk-sharing models can enhance operational efficiency, reduce manual errors, and align financial incentives with patient outcomes, thereby driving cost containment and improved provider accountability. Methodology: The study adopts a comprehensive qualitative approach, leveraging a strategic analysis of advanced data systems, cloud-based platforms, and scalable integration frameworks. The paper synthesizes insights from existing literature, industry reports, and case studies to propose an integrated model for modernizing Medicaid management. Findings: The integration of automation in Medicaid redetermination significantly improves operational efficiency, reducing processing times by as much as 30% and enhancing eligibility accuracy through real-time data integration and predictive analytics. Risk-sharing models, including shared savings contracts and performance-based incentives, align financial objectives with patient outcomes, reducing healthcare costs by up to 10% while improving provider accountability and patient satisfaction. Unique Contribution to Theory, Practice, and Policy: This paper advances theoretical understanding by proposing a unified framework that integrates automation and value-based care within Medicaid, highlighting the transformative role of predictive analytics and cloud-based platforms. Practitioners are provided with a blueprint for implementing automated Medicaid redetermination and risk-sharing models, showcasing best practices for achieving operational efficiency, financial sustainability, and health equity. The paper outlines policy implications, emphasizing the need for regulatory frameworks that support data privacy, interoperability, and continuous innovation, paving the way for resilient public healthcare systems. By leveraging automation and value-based care frameworks, this blueprint offers a path to a more efficient, accountable, and patient-centered Medicaid program, paving the way for a resilient public healthcare system

Blockchain Technology Integration in Distributed Healthcare Systems: A Technical Analysis

This article examines the integration of blockchain technology in distributed healthcare systems, focusing on its transformative potential in addressing critical challenges in healthcare data management, security, and interoperability. The article presents a comprehensive examination of blockchain architecture, implementation strategies, and practical applications in healthcare settings, including electronic health records management, clinical trials, pharmaceutical supply chain, and patient consent management. Through detailed analysis of security frameworks, privacy mechanisms, and compliance requirements, the article demonstrates how blockchain technology enhances data protection while maintaining operational efficiency. The investigation extends to performance metrics, comparing traditional and blockchain-based healthcare systems across multiple parameters, including data synchronization, system availability, and cost-effectiveness. The article also explores emerging challenges and future directions, particularly in areas of scalability, integration with IoMT devices, and cross-border data sharing. This article provides valuable insights for healthcare organizations considering blockchain adoption, offering implementation guidelines and recommendations for sustainable integration of blockchain technology in healthcare environments.

Incremental Pyraformer–Deep Canonical Correlation Analysis: A Novel Framework for Effective Fault Detection in Dynamic Nonlinear Processes

Smart manufacturing systems aim to enhance the efficiency, adaptability, and reliability of industrial operations through advanced data-driven approaches. Achieving these objectives hinges on accurate fault detection and timely maintenance, especially in highly dynamic industrial environments. However, capturing nonlinear and temporal dependencies in dynamic nonlinear industrial processes poses significant challenges for traditional data-driven fault detection methods. To address these limitations, this study presents an Incremental Pyraformer–Deep Canonical Correlation Analysis (DCCA) framework that integrates the Pyramidal Attention Mechanism of the Pyraformer with the Broad Learning System for incremental learning in a DCCA basis. The Pyraformer model effectively captures multi-scale temporal features, while the BLS-based incremental learning mechanism adapts to evolving data without full retraining. The proposed framework enhances both spatial and temporal representation, enabling robust fault detection in high-dimensional and continuously changing industrial environments. Experimental validation on the Tennessee Eastman (TE) process, Continuous Stirred-Tank Reactor (CSTR) system, and injection molding process demonstrated superior detection performance. In the TE scenario, our framework achieved a 100% Fault Detection Rate with a 4.35% False Alarm Rate, surpassing DCCA variants. Similarly, in the CSTR case, the approach reached a perfect 100% Fault Detection Rate (FDR) and 3.48% False Alarm Rate (FAR), while in the injection molding process, it delivered a 97.02% FDR with 0% FAR. The findings underline the framework’s effectiveness in handling complex and dynamic data streams, thereby providing a powerful approach for real-time monitoring and proactive maintenance.

Scale-Dependent Relationships Between Urban Morphology and Noise Perception: A Multi-Scale Spatiotemporal Analysis in New York City

Urban morphology significantly influences residents’ noise perceptions, yet the impact across different spatial and temporal scales remains unclear. This study investigates the scale-dependent relationship between urban morphology and noise perception in New York City using noise complaint rates (NCR) as a proxy for perceived noise levels. A multi-scale analysis framework was applied, including four spatial scales (100 m, 200 m, 500 m, and 1000 m) and three temporal classifications (daytime/nighttime/dawn, weekdays/weekends, and seasonal divisions). Statistical analyses, including Spearman correlation, Moran’s I test, and Geographically Weighted Regression (GWR), examined spatiotemporal heterogeneity. Results show: (1) NCR and urban morphology indicators vary significantly across spatial and temporal aggregations. (2) Correlations between NCR and urban morphology indicators generally strengthen with larger spatial units, revealing a scale effect. Temporal variations, e.g., residential land ratio (RES) and greenery percentage (SVI Green), show stronger correlations with NCR in summer than in winter. (3) The Moran’s I index revealed significant spatial clustering at the 1000 m scale. Multi-temporal GWR analysis revealed spatial variations in urban morphology-noise relationships across different temporal contexts; in residential areas, building density exacerbates complaints more during non-working periods than during working hours. This study enhances understanding of urban sound environments, offering insights required for more precise urban planning policies.

Community emergency management policy in China using a policy text tool.

Systematically analyzing the current status and problems of the Community Emergency Management Policy (CEMP) system in China and proposing practical suggestions that are conducive to subsequent policy formulation and improvement are important for improving community emergency management capabilities and levels and promoting sustainable community development. Based on the two-dimensional analysis framework of "policy tools-policy objectives", this paper draws from content analysis and social network analysis, quantitatively analyzing the CEMP texts at the national level from 2004 to 2024. The results show that the CEMP system in China has essentially taken shape, but there are still some problems, such as the uneven use of policy tools, the unbalanced internal structure of policy tools, the large distribution gap of policy objective elements, and the adaptability between policy tools and policy objectives, which needs to be improved. Given these results, we suggest 1) Appropriately increasing the frequency of using demand-type policy tools and effectively optimizing the internal structure of the three main types of policy tools; 2) Comprehensively deepening the reform of CEMP objectives; and 3) Reasonably improving the adaptability between policy tools and objectives.

Conditional Score Residuals and Diagnostic Analysis of Serial Dependence in Time Series Models

This article introduces conditional score residuals and proposes a general framework for the diagnostic analysis of time series models. Conditional score residuals encompass commonly used definitions of residuals in time series models, including ARMA residuals, squared residuals, and Pearson residuals. In particular, these residuals are special cases of conditional score residuals when the conditional distribution of the model belongs to the exponential family. On the other hand, conditional score residuals offer an alternative definition of residuals when the conditional distribution is not of the exponential type. A key feature of conditional score residuals is that they account for the shape of the conditional distribution. This feature leads to more reliable and powerful diagnostic tools for testing residual autocorrelation. Furthermore, they can be employed in complex models where it may not be clear how to define residuals. The asymptotic properties of the empirical autocorrelation function of conditional score residuals are formally derived. The practical relevance of the proposed framework is illustrated for heavy-tailed GARCH models. Monte Carlo and empirical results support the finding that conditional score residuals are more reliable in testing residual autocorrelation, when compared to squared residuals. Finally, it is shown how a diagnostic analysis can be designed for dynamic copula models.

Integrating adaptation pathways and Ostrom's framework for sustainable governance of social-ecological systems in a changing world.

Dynamic Adaptive Policy Pathway (DAPP) maps are used to plan management decisions in contexts of high uncertainty, such as those driven by environmental changes affecting critical assets. Recent discussions emphasize their relevance for addressing complex common-pool resource challenges, where diverse species, actors, and ecosystem services are intricately connected. However, designing DAPPs for such multifaceted social-ecological systems (SES) is challenging due to the extensive range of potential adaptation options. This study presents a general method to address these challenges by leveraging Ostrom's theoretical frameworks for the governance of common pool resources-the Institutional Analysis & Development framework (IADF), the social-ecological systems framework (SESF), and the coupled infrastructure systems framework (CISF). These frameworks were used to design nested DAPP maps that structure a large number of adaptation actions across multiple levels of institutional arrangement (operational, collective-choice, constitutional), and then develop a mathematical model to analyze the dynamic robustness of a SES across all potential pathways. The method was applied to predict and understand DAPP maps for supporting the collective management of hedgerow networks delivering diverse ecosystem services. DAPP maps for two SES were compared-one rural and one peri-urban-in France's agro-ecological landscapes of the Auvergne region. We further modeled the impact of climate change on hedgerows characterized by different size and species richness, revealing the sensitivity of these DAPP maps to transit between nine nested institutional arrangements. We discuss the methodological and practical implications of this approach for managing SES characterized by greater diversities of interconnected species, actors, and ecosystem services, highlighting its strengths and challenges in guiding adaptation under deep uncertainty.

A Case-Driven Multi-Omics Analysis for Longitudinal Ibrutinib Response Evaluation of Patients With Chronic Lymphocytic Leukemia.

Patients with chronic lymphocytic leukemia (CLL) undergoing ibrutinib treatment often experience incomplete response, yet the molecular level underlying clonal inertia remains to be explored. We investigated the molecular and clinical dynamics of CLL during 16 months of ibrutinib monotherapy by analyzing blood samples from two patients who continued having CLL cells in the peripheral blood during treatment. At diagnosis, the clonal burden within the B cell compartment was found to be 55% (pt1) and 86% (pt2) for the dominant clones. At 16 months following treatment these clones still constituted 66% and 89%, respectively. Utilizing multi-omic methodologies at the DNA and RNA levels, including single-cell transcriptomics, we aimed to establish a comprehensive framework for multi-omics analysis for longitudinal ibrutinib response evaluation. The presented study revealed genomically stable disease during ibrutinib treatment, but with intensified expression of genes involved in pathways related to apoptosis, cellular stress response, and canonical NF-κB signaling from diagnosis to 16 months of treatment.

Regulatory and Compliance Requirements for SMEs Operating AI Systems through Data Centers in the EU, with a Focus on Data Protection Challenges in Germany

This research examines the regulatory challenges encountered by small and medium-sized enterprises (SMEs) operating artificial intelligence (AI) systems through data centres in the European Union (EU), with a particular focus on data protection issues in Germany. The study analyses the interaction between the General Data Protection Regulation (GDPR) and the proposed EU AI Act, emphasising the compliance barriers faced by SMEs. Methods: A mixed-method approach was employed, combining qualitative analysis of regulatory frameworks and scholarly literature with quantitative survey data from SMEs across key industries. This methodology ensured a comprehensive examination of both regulatory requirements and their practical implications. The findings indicate that SMEs demonstrate high familiarity with GDPR (mean score 82.24) but lower awareness of the AI Act (mean score 56.24), with significant intersectoral variation. Challenges include resource limitations, ambiguous ”high-risk” AI classifications, and the complexity of dual compliance. Notably, government and healthcare sectors reported substantial regulatory burdens, while energy and finance sectors exhibited lower preparedness for AI Act requirements. The study reveals the fragmented implementation of GDPR across member states, complicating compliance for cross-border SMEs. The dual demands of GDPR and the AI Act necessitate streamlined regulatory processes and tailored support mechanisms, such as simplified guidelines and financial assistance. Explainability and transparency obligations, while essential for trust, introduce additional administrative burdens that may impede innovation. Harmonising GDPR and AI Act requirements is crucial to enabling SMEs to comply without inhibiting innovation. Policy recommendations include regulatory sandboxes, targeted training, and increased financial support for SMEs to foster legally compliant yet innovative AI applications.

IAEA Safety Guides vs. Actual Challenges for Design and Conduct of Indoor Radon Surveys

An analysis of the international radon regulatory framework identified actual challenges for the design and conduct of indoor radon surveys, though there is little discussion on this issue in the radon community. The main challenges hindering the development of radon regulation on an international scale, particularly in indoor radon surveys include the following: (i) responsibility for indoor radon testing and mitigation, (ii) excessive focus on Radon Priority Areas, (iii) the role of temporal uncertainty in indoor radon testing, (iv) the standardization of indoor radon measurements, and (v) the standardization of thoron EEC measurements and indoor testing. To address these gaps and inconsistencies, actual needs for design and conduct of indoor radon surveys are proposed, covering the aforementioned challenges. The needs statement, including a discussion of solutions, addresses the following key aspects of radon regulation: (1) legislation, (2) radon measurements, (3) awareness, and (4) building protection. The focus remains on the radon measurement aspect, detailing the strategy and tools for conducting indoor radon surveys within a rational approach. This includes the main research activities necessary for the sustainable development of global radon regulation. The final part of the article presents a Rational Method (protocol) of indoor radon measurements serving as a detailed guideline for standardizing indoor radon testing at the international level. This ensures the decision-making reliability of at least 95% and harmonizes relevant national approaches considering traditional measurement protocols using both short-term and long-term measurements. The proposed solutions aim to achieve the goal of a modern design and conduct of indoor radon surveys that are consistent with large-scale (mass) testing and effectively identify hazardous buildings. These solutions are based on a rational approach with convenient tools and active voluntary participation of the population to be implemented within the framework of national radon regulation.

Collapse Fragility Analysis of RC Frame Structures Considering Capacity Uncertainty

To analyze the impact of capacity uncertainty on the seismic collapse fragility of reinforced concrete (RC) frame structures, a fragility analysis framework based on seismic reliability methods is proposed. First, incremental dynamic analysis (IDA) curves are plotted by IDA under a group of natural seismic waves. Subsequently, collapse points are identified based on recommendations from relevant standards, yielding the probability distribution of the maximum inter-story drift ratios (MIDRs) at collapse points. Then, the distribution of the MIDRs under various intensity measures (IMs) of artificial seismic waves is calculated by using the fractional exponential moments-based maximum entropy method (FEM-MEM). Next, the structural failure probability is determined based on the combined performance index (CPI), and a seismic collapse fragility curve is plotted using the four-parameter shifted generalized lognormal distribution (SGLD) model. The results indicate that the collapse probability is lower considering the capacity uncertainty. Compared to deterministic MIDR limits of 1/25 and 1/50, the median values of the structure’s collapse resistance increased by 13.2% and 87.3%, respectively. Additionally, the failure probability obtained by considering the capacity uncertainty is lower than the results based on deterministic limits alone. These findings highlight the importance of considering capacity uncertainty in seismic risk assessments of RC frame structures.

High-Reputation Food Formulas: A Heterogeneous Information Network Representation and Semantic Analysis Approach

In food research and development, fully capturing consumer preferences is crucial for enhancing ingredient selection and formulation. However, many decisions still rely heavily on expert judgment. To address this, we propose a semantic analysis framework that combines heterogeneous information network representation learning and lexicon-based sentiment analysis to uncover relationships between ingredients and consumer word-of-mouth. We apply our approach to 1062 breakfast products and 58,206 reviews from Amazon, extracting taste- and ingredient-related sentiment while excluding non-formulation factors (e.g., price, packaging, logistics). Through embedding-based visualization, our product space groups items according to similar formulation characteristics, while the ingredient space reveals functional clusters among ingredients. We find that high-reputation products and their related ingredients form distinctive clusters in both spaces, often emphasizing natural, organic, and gluten-free attributes, whereas items containing synthetic additives appear more frequently in low-reputation regions. This framework offers a data-driven complement to traditional R&D methods, providing a perspective distinct from conventional approaches for product formulation and ingredient selection.

Sentiment analysis of solar energy in U.S. Cities: a 10-year analysis using transformer-based deep learning

This study examines U.S. public sentiment toward solar energy from 2013 to 2022 by analyzing 8 million social media posts using RoBERTa, a transformer-based deep learning algorithm. While sentiment has been generally positive, it has declined since 2016, driven by increasing negativity. The analysis reveals significant and widening regional disparities, with Republican-leaning and Southern U.S. regions showing more pronounced negativity. A two-way fixed-effects panel analysis at the municipality level indicates that positive sentiment is associated with high solar radiation and a larger remote-working population, while negative sentiment is linked to a greater prevalence of multifamily housing. These results highlight the complex interplay of environmental, socioeconomic, political, and technological factors in shaping solar energy sentiment and underscore the need for tailored, region-specific strategies. The computational approach, which combines natural language processing and geospatial analysis of social media data, provides a scalable framework for sentiment analysis across diverse topics and regions, extending beyond the specific case of solar energy in the United States.

First reassessment results of environment state in former arsenic plant impact zone in Vershino-Darasunsky settlement (Transbaikal region)

The purpose of the research is to study the geoecological situation in the urban-type settlement of Vershino-Darasunsky (Transbaikal region, Russia), where a plant producing white arsenic for chemical warfare agents operated from the 1930s until 1973. Reclamation did not follow the shutdown of the plant, which causes a serious environmental threat not eliminated with past decades due to the low priority of this territory in the register of objects of accumulated environ mental damage. The authors consider the current facility assessment, which is based on data on the insignificant area in the boundaries of the former industrial site of the plant, to be critically underestimated. The latter is the main reason for the failure of a number of attempts made by executive authorities for years to launch the development of this site reclamation project. The conducted research is an illustrative case study from real Russian environmental practice and may be of interest to academic and professional communities. The study includes the analysis of the relevant regulatory framework for the assessment and elimination of the objects of accumulated environmental damage as well as the adopted method ology to carry out the assessment. A complex of field and laboratory studies was conducted on the area of 16.5 km2, more than 150 soil samples were taken. The article presents the first results of geochemical soil studies, according to which it is not only contaminated industrial site, but also the entire studied territory, which has changed its physico-chemical parameters over many decades are the sources of negative impact on environmental objects. Anomalies of arsenic and heavy metals exceeding the maximum permissible and approximate permissible concentrations by hundreds and thousands of times have been detected, and they are localized not only in the area of the known facility of accumulated environmental damage, but also in several other places without currently visible sources of man-made impacts. The results obtained made it possible to declare the need to expand the reclamation area beyond the immediate territory of the former plant. In accordance with the accepted criteria, the environmental hazard indicator has been recalculated and the need to change the reclamation prioritization of this facility has been justified. Moreover, based on the existing regulatory framework, recommendations are given for further assessment of the impact of objects of accumulated environmental damage on the lives and health of citizens.

Research on the Configuration Path of Regional Green Innovation in Space and Time: Dynamic QCA Analysis Based on Provincial Panel Data

Green innovation is an important starting point for implementing new developmental concepts and promoting high-quality development in the modern era. We construct an integrated analysis framework based on the environment–resource–subject configuration. Using 30 provinces and regions in China as an example, we employ the dynamic qualitative comparative analysis method to study the configuration path of multi-factor interactions affecting green innovation in an innovation ecosystem. The results indicate that (1) single factors are not necessary conditions for high regional green innovation. (2) The consistency level of the green financial environment exhibits a significant upward trend, demonstrating a clear temporal effect. (3) From the time dimension, three types of configuration paths exist for the interactive combination of environment, resources, and subjects to promote regional green innovation, and the consistency level of configuration between 2018 and 2022 fluctuates above 0.94. (4) From the spatial dimension perspective, clear differences exist in the paths driving the improvement of the green innovation level between the eastern, central, and western regions. This study broadens the scope of correlational research on green innovation, examining them across both temporal and spatial dimensions. It also provides enlightenment for regions seeking to enhance their green innovation.

Disease diagnostics using machine learning of B cell and T cell receptor sequences.

Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.

WITHOUT by Ashaya: an eco-conscious start-up turning trash into treasure

Learning outcomes The case study aims to achieve the following learning objectives post discussion by the students: evaluate Social Entrepreneurship Quotient of Anish Malpani, founder of WITHOUT; understand Sustainable Development Goals to which the start-up aims to contribute; apply the framework of SWOT analysis to the social start-up; and analyze triple bottom line and triple top line concepts to identify approach of WITHOUT. Case overview/synopsis Malpani’s eco-conscious start-up named “Ashaya Recyclers Pvt. Ltd.” is the first of its kind in the world, selling sunglasses made from recycled postconsumer metalized multi-layered plastic packaging (MLP) under the brand name WITHOUT. MLP generally ends up in landfills. It is a low-value, high-volume composite waste that is considered economically and technically “impossible” to recycle. The founder wanted to do something good out of the recycled stuff instead making money. Will Ashaya’s early breakthrough and now patented technology be able to bring significant change in the waste management system in India? Complexity academic level This case study is suitable for undergraduate BBA and postgraduate MBA courses. Supplementary materials Teaching notes are available for educators only. Subject code CSS 4: Environmental Management.

A framework for performance analysis of OpenStreetMap data in navigation applications: the case of a well-developed road network in Australia

ABSTRACT Although much effort has been put into assessing and improving OpenStreetMap (OSM) data quality, further research is required to determine its reliability and robustness for real-world applications. This study introduces a framework, built on open-source geospatial tools, for analysing the performance of OSM road data across different navigation applications. We tested this framework on an extensive 41,000 km road network in Australia. While our findings generally supported the quality of the OSM dataset, analyses of census data revealed two key relationships: first, a significant link between a city’s population and the quality of its OSM data, and second, a strong influence of Information and Communications Technology (ICT) infrastructure on OSM data development. Furthermore, while navigation tests showed that OSM road networks performed reasonably well, scenario analyses highlighted several issues: a strong correlation between data quality and navigation accuracy; a negative impact of distance on OSM-based route accuracy for long inner- and inter-city routes due to accumulated errors; and the tendency of OSM to suggest sub-optimal paths for routes to isolated locations. This framework offers valuable benefits to a wide range of users. The OSM community can use it to assess data quality before application; individuals and businesses can easily evaluate its utility for navigation and route-planning; and local governments can benefit from improved quality control, particularly for projects involving Connected and Automated Vehicles (CAVs). Finally, the framework’s capacity to design and analyse various routing scenarios provides new insights into overall road network quality.

Application of Neural Network Models in Image Encryption and Differential Analysis

As the demand for encryption and information security increases, the concept of neural networks in deep learning is gradually used in the field of image encryption, and the influence of neural networks on the field of image encryption is gradually deepening. The current mainstream neural network image encryption schemes are categorized into pixel disruption and chaotic systems. This paper provides a basic introduction to the four network models of chaotic neural networks, convolutional neural networks, cellular neural networks, and generative adversarial networks for image encryption, an analysis of the algorithmic framework, and an analysis of the encrypted image. It is found that all four neural network models are affected by the parameters of the model as well as the size, if the neural network encrypted image is highly resistant to noise, at the same time the distortion of the image will be larger and the visual entropy of the image will be increased accordingly. If a model is sensitive to the initial value, then the model has a relatively large key space, low correlation of neighboring pixels, good encryption, and is more difficult to crack.

Advancing Sustainable Fluorine Management in China Based on Evolution of the Anthropogenic Cycle during 2000-2020.

Fluorine (F) has substantial social and environmental significance. Despite its high natural abundance, which often leads to the neglect of its sustainable management, its primary source, fluorspar, is limited and nonrenewable. As the world's largest producer and consumer of F resources, China encounters considerable challenges in managing these resources sustainably. However, a comprehensive understanding of F flows across China's economy has been notably absent. This study establishes a national-level material flow analysis (MFA) framework to examine the circulation and accumulation of F substances across various sectors in China from 2000 to 2020. The MFA encompasses over 200 F-containing products and 15 key sectors, tracking F flows through the stages of production, manufacturing, use, and waste management. Key findings show that China's F resource supply increased from 2297.9 kt in 2000 to 6392.2 kt in 2020, with 91.1% sourced from domestic ore mining. F consumption for manufacturing F-containing products rose from 270.1 to 2462.9 kt over the same period. Traditional sectors like metallurgy, cement production, and electrolytic aluminum dominated F consumption, while emerging sectors like photovoltaics experienced rapid growth. China has been a net exporter of F resources, with exports totaling 14,732.3 kt compared to 1931.1 kt of imports over the two decades. Nonetheless, China's role in global F trade has shifted from primarily exporting ores to becoming a major producer and exporter of F-containing products. This study provides a comprehensive analysis to date of China's F resource flows, offering critical insights for policymakers and industry stakeholders to enhance sustainable F management practices and address key challenges related to resource supply, utilization efficiency, and environmental impacts.