Entering the third decade of the 21st century, global supply chains reveal considerable systemic vulnerabilities due to the cumulative effects of pandemic disruptions, geopolitical conflicts, and recurrent extreme weather phenomena. Conventional supply chain models focused on cost efficiency and lean operations encounter ongoing difficulties. This study establishes a comprehensive framework for evaluating global supply chain hazards from a multidimensional risk viewpoint, incorporating political, economic, and natural concerns. It methodically examines the initiating mechanisms, transmission routes, and interconnected impacts of each risk category. The study provides a streamlined risk assessment methodology that equips organizations with actionable analytical tools, including an indicator system and a multidimensional risk matrix, to ascertain risk exposure levels and comprehend risk structures. This study formulates a supply-chain resilience strategy system tailored for complex and uncertain situations, which is based on four essential dimensions: structural robustness, process adaptability, information-driven capability, and collaborative governance. The research demonstrates that by recognizing risks from various dimensions and integrating conceptual quantitative assessment tools, enterprises can more efficiently pinpoint critical vulnerabilities, improve supply chain resilience, and bolster their shock resistance and enduring competitive advantage in high-uncertainty contexts.

Currently, the article discusses the reasons why the clinical implementation of Class III artificial intelligence (AI) medical devices in China is limited, regardless of the accelerating technology and robust policy endorsement via programs such as the Healthy China 2030. Beyond technology and risk-based studies, the paper utilizes Kingdon’s Multiple Streams Framework (MSF) to determine how the lack of alignment across these three streams of the problem, policy, and political core has resulted in institutional wrangles that have remained. Using regulatory documents, secondary data, and policy evidence, the analysis can pinpoint three linked causes of friction: lengthy and mismatched regulatory approval processes, hospital system mismatch of capacity and responsibility, and the unresolved reimbursement/trust gap at the patient level. Such obstacles are reflections of institutional stalemateness, and not outdated technology, where current regulatory and payment strategies are ill-equipped to respond flexibly and data-driven AI applications. To resolve this misalignment, the paper compares two policy courses, including a market-led adaptive model and a model of rule-based governance, and suggests a step-by-step reform agenda involving the use of regulatory sandboxes, nationally standardized reimbursement, centralized data and reporting, etc. This strategy aims at compensating the innovation incentive, equity, and system persistence by balancing short-term experimentation with long-term institutional coordination. The research paper can add to the literature as it generalizes on the use of MSF and its use in technology governance and provides practical policy recommendations to enhance regulatory consistency and promote the seamless introduction of AI-driven medical devices in China.

With the widespread application of algorithmic trading and artificial intelligence in financial markets, high-frequency trading (HFT) has become an integral component of modern securities markets. Although HFT is characterized by high speed and automation, the trading strategies and algorithmic designs underlying these systems remain significantly influenced by human behavioral biases. Drawing on the behavioral finance framework, this paper provides a systematic review of recent domestic and international empirical studies on behavioral biases in high-frequency trading. The analysis focuses on the manifestations and transmission mechanisms of overconfidence, herding behavior, and the disposition effect in high-frequency traders’ decision-making processes. The literature review indicates that these biases may amplify short-term market volatility, weaken price discovery efficiency, and intensify systemic risk during periods of extreme market conditions. Finally, this paper discusses directions for future research, including the incorporation of behavioral characteristics into algorithm design and the improvement of market regulation and risk management from a behavioral perspective.

Against the macro backdrop of sustainable development transformation and dual carbon goals, ESG performance has gradually evolved into a core dimension for assessing a company's potential for high-quality growth. This paper examines the constraining effect of corporate ESG ratings on actual earnings management behavior and its underlying logic by constructing an empirical model using Chinese A-share listed companies from 2011 to 2020 as the research sample. Findings reveal that outstanding ESG performance generates significant governance spillover effects, effectively curbing management's tendency to embellish financial statements through abnormal business activities. At the sub-dimensional level, environmental (E) and social (S) performance demonstrate robust governance efficacy, while the constraining effect of governance (G) has yet to fully manifest at this stage. Mechanism analysis further confirms that information transparency fully mediates the relationship between ESG performance and earnings management. Notably, this governance efficacy exhibits pronounced dynamic lag characteristics, typically achieving substantive suppression of earnings manipulation through enhanced disclosure quality only after two cycles of improved ESG performance. This study not only enriches the literature on the economic consequences of ESG but also provides crucial empirical insights for regulators to refine disclosure standards and guide investors in identifying micro-level earnings quality.

Amid the escalating market competition, continuous technological innovation, and evolving corporate production models, cost accounting—recognized as the cornerstone of enterprise cost control - has undergone a transformation from traditional accounting methods to modern management approaches. This paper systematically dissects the accounting logic of conventional cost accounting and its limitations in corporate cost control, analyzes the core advantages and application scenarios of modern cost accounting methods, with a specific focus on Activity-Based Costing and Target Costing. Furthermore, the paper explores the driving forces and core values behind cost accounting development, and proposes future trends for addressing challenges in the new economic environment. The research provides theoretical references for enterprises to enhance cost control capabilities and strengthen market competitiveness through optimizing cost accounting systems.

As a core financial asset valuation method, fair value measurement’s "fairness" hinges on active market quotes or reliable valuation techniques. Rooted in the decision-usefulness principle of financial reporting, it has become increasingly pivotal since the 2008 global financial crisis—when its procyclicality amid market turmoil sparked intense debates about its stability. Yet, factors such as market conditions, flawed models, and artificial manipulation often lead to valuation bias, distorting accounting information and increasing financial risks. Drawing on IFRS 13 and CAS 22, this paper examines three main biases: market quote - dependent (irrationality in extreme markets), valuation technique - dependent (subjective parameter judgments), and artificial manipulation (e.g., discount rate adjustments). It further proposes a correction mechanism covering optimized market disclosure, improved valuation technologies (dynamic factors, AI aid), and strengthened supervision. The research aims to boost valuation reliability and provide relevant references.

In recent years, with young people becoming the main consumers, consumption patterns in cities have begun to change, shifting from merely purchasing functional goods to a greater pursuit of emotional satisfaction and identity expression. Against this backdrop, Pop Mart's collectible toys and blind boxes have rapidly gained popularity. This paper focuses on this phenomenon, aiming to explore why Pop Mart has stood out in the emerging consumer culture and its economic impact. The purpose of this study is to explain the formation conditions of the collectible toy economy and understand the choice logic of young consumers. The article employs methods such as literature review, case observation, and simple data comparison, relying mainly on industry public data and brand development materials. The research finds that the popularity of Pop Mart is not accidental; it relies on the excitement brought by blind boxes, the spread on social media platforms, and the emotional value of its serialized images to attract consumers. Meanwhile, the company's stable production system and marketing strategies have further promoted market expansion. Overall, Pop Mart reflects the dual demands of emotionality and sociality in contemporary youth consumption.

With the rapid development of live-streaming e-commerce, its immediacy, interactivity, and entertainment value have made it a core growth engine of my country's digital economy. However, behind this industry boom, fraudulent pricing, a new, covert, and systematic form of price fraud, is rampant, seriously infringing on consumers' right to know and right to fair trade, and disrupting healthy market competition. Against this backdrop, clarifying the legal responsibility boundaries of e-commerce platforms as key hubs is of urgent theoretical and practical significance for regulating market behavior and protecting consumer rights. This paper, based on the principle of good faith and related legal liability theories in civil and commercial law, and in conjunction with provisions of the E-commerce Law and the Consumer Rights Protection Law, systematically analyzes the legal responsibility of platforms in live-streaming fraudulent pricing. Using normative analysis and case studies, this paper finds that the determination of platform responsibility should follow the principle of "consistency between rights and responsibilities." If a platform knows or should know about fraudulent pricing but fails to take necessary measures, it must bear corresponding joint liability; its pre-screening and in-process monitoring obligations are key to determining responsibility. Simultaneously, combining judicial practice and relevant cases, this paper explores ways to improve the determination of platform responsibility, thereby promoting the construction of a fairer, more transparent, and trustworthy digital consumption ecosystem.

This study examines the relationship between China's export performance and exchange rate dynamics against the backdrop of global value chain reorganization and intensified external shocks. The study employed monthly data (2016-2024) and time series regression methods, and found that the explanatory power of the real effective exchange rate (REER) is limited. Time series regression confirmed that the REER coefficient was not statistically significant, which contradicted the simple "devaluation promotes exports" model. The research explains this pattern through recent evidence: (i) the role of processing trade and imported intermediate goods in shaping China's export results, where the exchange rates of supply chain economies may have as significant an impact as the RMB exchange rate; (ii) The exchange rate effect varies under different product complexities and qualities, which may weaken the average exchange rate flexibility, especially for higher-end export product baskets. (iii) The partial and asymmetric exchange rate transmission effect is influenced by the restrictions at the enterprise level and the amplification of market frictions. These findings are consistent with the reconfiguration of the global production network and the adjustment of response strategies after the pandemic, and also include "risk reduction" as well as the adjustment of trade patterns and incentive mechanisms due to geopolitical changes. Overall, these findings suggest that China's export resilience is increasingly driven by structural factors embedded in the global value chain rather than short-term exchange rate fluctuations. This indicates that models that merely focus on price competitiveness may not fully reflect the dynamic changes in modern, upgrade-oriented manufacturing exports.

Nowadays, sustainable development has become a hot topic in the securities market. Indicators reflecting corporate sustainability, such as ESG ratings, exert an increasingly significant influence on corporate financing activities. This paper explores how ESG ratings and the securities market drive the development of green sustainable technologies in enterprises. The ESG-rating mechanism will be outlined, then linking ESG scores to financing costs and equity premiums, testing their effect on development expenditure on green sustainable innovation, and finally identifying the limits and improvements. The methodology proceeds in three concise steps. First, systematic searches were conducted on relevant websites to collect peer-reviewed studies and policy reports related to ESG, green finance and innovation in carbon-intensive industries. Second, these findings were merged with panel data on listed firms’ ESG scores, R&D expenditure, green patents and financing costs data. Third, quantitative findings were integrated with comparative case analyses to demonstrate how ESG-motivated financing is transformed into tangible green technology initiatives. Enterprises will actively develop green and low-carbon technologies to achieve a higher ESG rating, thereby attracting more investment and increasing their popularity in the securities market. Consequently, such financing will also provide financial support for the subsequent development of green sustainable technologies, encouraging enterprises to increase their investment in R&D.