With the rapid development of China's airline industry, more and more funds have been needed for new construction. Capital shortage will be an important reason restricting the expansion of the company. To alleviate the problem of lack of funds, this paper investigates all factors inside and outside the company that influence financing decisions, studies the case of Spring Airlines Co., Ltd., explores ways to optimize its capital structure and monetary funding plan in the aviation industry, and proposes some relevant suggestions. This paper finds that these three specific issues--a single source of funding, a high debt-to-asset ratio, and a low proportion of internal financing--will affect all the sources of funds for the company, increase its leverage-to-asset ratio, and reduce the amount of new domestic funds available for construction. Some specific suggestions have been put forward to address the above problems: restructurise liabilities, increase the channels for attracting overseas capital, and strengthen the internal capital system. With the continuous increase in demand for aviation around the world, Chinese airlines have been raising aircraft purchase plans and have faced higher financial pressure. As a budget airline that focuses primarily on commercial air navigation and passenger transport, the financial situation and capital strategy of Spring Airlines are very representative of the sector.

China's designer toy industry has shifted from a niche subculture to a visible sector of cultural consumption. Unlike traditional toys, designer toys combine artistic design, emotional experience, symbolic identity and collectible scarcity. This paper examines how the industry creates value and how this value can be sustained, using Pop Mart and the Labubu-related IP boom as the main case. It adopts a qualitative case-study approach based on academic literature, public company information, regulatory documents and secondary industry materials. The analysis focuses on three questions: how consumer demand transforms manufactured objects into cultural and emotional goods; how Pop Mart converts IP assets into commercial growth through blind boxes, channels, communities and globalization; and what risks may threaten the long-term development of this model. The paper finds that value creation in designer toys depends less on manufacturing itself than on an IP ecosystem that links artists, products, retail spaces, online platforms, resale markets and fan communities. However, blind-box uncertainty, speculative resale, dependence on hit IPs, product homogenization and cross-cultural expansion risks can weaken sustainability. Future growth should shift from hit-driven sales to transparent operations, diversified IP, responsible consumer protection, and sustainable innovation.

With the development of Social Commerce, consumers increasingly rely on User-Generated Content, platform interaction data and engagement metrics for product evaluation and purchase-related decision-making. In the beauty-related Social E-Commerce context, likes, comments, shares and favorites/collects not only represent content popularity, but also may reflect different levels of consumer interest. This article mainly studies whether favorites/collects are closer to purchase-oriented behavioral signals than likes. This article uses Alibaba Cloud Tianchi Social E-Commerce User Purchase Behavior Dataset as the main public dataset and focuses on beauty and personal care category. Research methods include descriptive statistics, correlation analysis, verification of derived behavioral indicators, stepwise Ordinary Least Squares regression, standardized coefficient comparison, Wald test and robustness checks. The results show that favorites/collects present a more stable positive association with derived purchase-oriented behavioral index, while likes weaken or turn into a negative coefficient after controlling favorites/collects. However, the binary purchase label check did not show significant results. Therefore, the conclusion of this article should be interpreted as exploratory association evidence, not causal evidence of actual purchase conversion.

With the rapid evolution and wide adoption of digital technologies, including artificial intelligence and big data, streaming platforms like Netflix have officially moved beyond traditional digital media business models and entered a new phase of intelligent, targeted media development. The key focus of both industry practice and academic research today is how to leverage artificial intelligence to drive industrial upgrading and efficiently transform user value, content value and commercial value. This paper utilizes literature review and case study methods, focusing on Netflix—a global leader in the streaming industry—as the central case study. It provides a systematic exploration of the business model innovation pathways and core value creation logic for streaming platforms in the era of intelligent media. Additionally, by examining the application characteristics of AI technology, the study identifies various potential risks and industry challenges arising during technology implementation, such as data security, algorithmic bias, content homogenization, and proposes targeted, scientifically sound and feasible optimization strategies. This study systematically clarifies the evolutionary characteristics and developmental patterns of AI-driven business models in the streaming industry, addresses gaps in existing research, and provides effective theoretical references and practical insights to support the intelligent, standardized, and high-quality healthy development of the streaming industry.

Amid Belt and Road energy connectivity and reshaped global energy security, Middle Eastern oil producers are vital for China's stable hydrocarbon supply and overseas energy industrial layout. Boasting rich reserves, stable opening policies and a prime location, the UAE is China's core Gulf strategic partner to diversify crude import risks. Using literature and case analysis, this paper employs official statistics from China Customs, the UAE Energy Ministry and ADNOC, plus EPC and long-term LNG projects of CNPC, Sinopec and Zhenhua Oil. It reviews 40 years of bilateral oil-gas cooperation and analyzes three mainstream cooperation models: crude trade, oilfield EPC and long-term LNG contracts. Their full-chain cooperation covers exploration, engineering, trade and green energy yet faces multiple hurdles: differentiated foreign ownership rules, Islamic commercial laws and local employment policies; volatile oil and LNG prices; lack of Arabic-petroleum interdisciplinary talents and cultural conflicts; and Red Sea and Gulf geopolitical risks. This paper proposes solutions on talent training, integrated fossil-low carbon industries (green hydrogen, CCUS) and comprehensive risk & compliance mechanisms, supporting Chinese firms' sustainable local operation, consolidating the Sino-UAE BRI energy partnership and providing references for China-Gulf energy cooperation.

Accurate credit risk assessment underpins modern financial risk management. Meanwhile, advanced machine learning algorithms have largely replaced traditional linear models in default prediction. However, real-world consumer credit data is scattered across complex relational tables and exhibits extreme class imbalance. As a result, conventional coarse-grained aggregations often fail to capture these nuances, leading to the loss of crucial micro-level default signals. Accordingly, this paper conducts a comparative analysis of the machine learning workflows that performed the best in the Home Credit default risk challenge. In particular, through qualitative deconstruction of pipeline architectures, three representative high-level parallel processing pipelines are analyzed and compared: one based on domain knowledge, one based on heterogeneous stacking framework, and one based on microscopic target aggregation scheme. Furthermore, the key differences among them in terms of feature engineering, handling of imbalanced data, and integration architecture are compared and evaluated. The results indicate that different architectures can effectively extract sparse default signals via their inherent mechanisms, yielding significant gains on imbalanced credit data. It further demonstrate the high effectiveness of feature dimension reconstruction and target dimension reduction in handling extremely imbalanced credit data, providing certain references for industrial credit risk modeling.

The inauguration of island-wide independent customs operations in the Hainan Free Trade Port at the end of 2025 has substantially reconfigured the dynamics of cross-border capital movements. This study provides a quantitative analysis of capital flow efficiency in Hainan during the post-customs closure era, aiming to identify the principal driving factors and simulate the consequences of policy adjustments. Employing a Geometric Brownian Motion (GBM) framework parameterised with quarterly data spanning 2018 to 2026, the research integrates a policy-augmented drift function to capture the influence of regulatory variables. Model evaluation metrics—R² of 0.85, MSE of 0.02, and MAPE of 8.3%—confirm satisfactory predictive performance. The empirical results indicate that Electronic Fence account transaction volume, tax incentive intensity, and the negative list length are the most influential drivers, each exhibiting pronounced nonlinear effects. Policy simulations reveal that moderate tax incentives, a 25% reduction in the negative list, and increased RMB settlement share individually enhance efficiency by 11.2%, 6.8%, and 5.4%, respectively, while a combined scenario yields a 10.7% improvement. The findings further uncover interaction effects, wherein financial infrastructure and tax incentives reinforce one another, whereas capital account restrictions partially offset efficiency gains. Based on these insights, the study proposes optimising tax structures, liberalising capital account restrictions, strengthening RMB settlement infrastructure, and enhancing risk monitoring mechanisms. The research offers data-driven guidance for free trade port policymaking and extends the application of stochastic modelling to regional financial evaluation.

Driven by global carbon neutrality policies and the investor-centric investment philosophy, ESG (Environment, Society, Governance) assessment has become a core component of modern enterprises' comprehensive risk management. This paper proposes two core research questions: The stabilizing and mitigating effect of ESG on enterprises' financial risks, as well as the obstacles that hinder the effective operation of their risk management, is worthy of exploration. This paper adopts qualitative literature research and case analysis methods, combining recent empirical data and the Tesla ESG controversy case, to explore the risk impacts of ESG across multiple dimensions. This study provides practical references for investors to conduct rational ESG valuation, helps enterprises optimize their ESG layout, and proposes feasible suggestions for establishing unified global ESG assessment rules to enhance the applicability of ESG in the long-term risk prevention and control of enterprises.

This paper examines the failure of Russia's 1990s "shock therapy" reforms, which were grounded in neoclassical growth theory but produced catastrophic outcomes: a 40% GDP collapse, hyperinflation exceeding 1,000%, and persistent resource dependency. We argue that these failures stemmed not from implementation errors but from applying a framework beyond its boundaries. The Solow model assumes well-established market institutions and treats growth as driven by factor accumulation; yet Russia entered transition with weak property rights, no competitive traditions, and a lack of an independent legal system, which enabled oligarchic capture of private assets. To address this explanatory gap, we draw on Economic Complexity Theory, which emphasizes tacit knowledge networks and industrial ecosystem density as determinants of prosperity. Russia's manufacturing capabilities collapsed during structural transformation: supply chains disintegrated, engineering cultures eroded, and skilled labor emigrated or shifted to non-productive sectors. This capability destruction created structural inflation, driven by import dependence and supply shortages—that contractionary monetary policy could not resolve. The paper concludes that neoclassical growth theory should be complemented with frameworks accounting for institutional development and productive capability accumulation. The policy implication is that monetary policy cannot substitute industrial policy or institutional investment, and market liberalization alone is insufficient when the productive foundations for growth remain underdeveloped.

The boom of social media platforms, the popularization of mobile e-commerce and the shift in residents' daily consumption lifestyles have jointly driven comprehensive digital transformation within China's fresh tea beverage industry. Fierce competition fills this market, and digital marketing now acts as a decisive factor affecting brand recognition, consumer participation and long-term customer loyalty. This paper conducts a comparative marketing analysis of two well-known domestic tea brands, Chagee and Heytea, covering their brand building paths, social media promotion plans, influencer cooperation schemes and user-generated content operation modes. The research draws a clear strategic contrast: Chagee builds market competitiveness through cultural storytelling and high-end positioning, while Heytea centers its operation on hot topic marketing and accessible mass market positioning. By dissecting Chagee's complete consumer decision-making journey, this paper compares its competitive strengths and existing deficiencies against Heytea, and finally points out that balancing cultural brand shaping and broad public participation is the core condition for brands to achieve sustainable long-term development.