Significant variations in electric vehicle (EV) deployment exist across China. To inform policy for more balanced EV deployment, this paper performs a nationwide analysis to explore influential factors associated with EV deployment. This paper uses a comprehensive province-level dataset covering 2017-2023 on EV stock, charging infrastructure, province-level demographics and economics, and policies, and the author uses a comprehensive multi-model framework (linear, regularized, and machine learning models) that the author validates using cross-validation. Charging infrastructure density, education level, and urbanization rate are the most consistent predictors of EV deployment; Random Forest achieves the best performance in prediction, and policies and economics show limited explanatory variation. Therefore, to achieve a more balanced deployment of EVs, policymakers should focus on investments in charging infrastructure and stations, increasing public understanding of sustainable technologies, and incorporating EV deployment into urban planning, as infrastructure and socio-demographic fundamentals are more explanatory than pure economic or policy measures.

This article aims to systematically organize and present a series of important financial and valuation data about the three technology giants of Intel, Apple and Salesforce from 2020 to 2024. The researcher uses a descriptive methodology, and the three categories of core metrics are used, which include leverage levels, profitability, and market valuation. The paper presents readers with consumer electronics, enterprise services and semiconductor sectors, the five-year data of these companies respectively in longitudinal presentation and cross-sectional comparison which can give readers an intuitive panorama of these financial sectors. It attempts to enable the preliminary appreciation of the basic structures and dissimilarity between corporate finance and valuation. When ordering the financial indicators and the market valuation rates in these various segments of technologies, the research will not only disclose the financial trend of every enterprise but will also indicate those sector dynamics that contribute to the profitability and shareholder attitude. In the end, this comparative study is an organized basis to understand the influence of strategic positioning, industry maturity, and innovation orientation on the financial results and market valuation of the successful technology companies.

Public rental housing (PRH) constitutes a core component of China’s social security system, with its allocation mechanism frequently grappling with the balance between fairness and operational efficiency. This study examines Hangzhou’s PRH system, employing a quantitative analytical framework based on unilateral allocation models and sequential dictatorship theory in market design research to systematically evaluate the mechanism’s performance. It concludes by assessing the mechanism’s compatibility with Pareto efficiency and individual rationality, proposing optimization strategies. Findings indicate that Hangzhou’s current allocation mechanism essentially constitutes a “sequential dictatorship system based on priority”, which ensures fundamental fairness. However, there remains room for improvement in resource matching efficiency and the inclusiveness of non-local residents’ welfare coverage. The proposed optimization strategies—including the introduction of an improved Top Trading Cycles(TTC) algorithm and refined tie-breaking rules—provide theoretical support and practical pathways for refining Hangzhou’s public rental housing allocation mechanism. These approaches also offer valuable references for optimizing allocation systems in other cities.

Chinese automakers face fierce competition domestically and are now expanding into global markets making foreign market development one of their most critical strategies. This paper selects BYD and Seres to compare financial performance differences from their respective strategies. Data of the two companies between 2022 and 2024 comes from publicly available financial information on Sina Finance. Case comparison and financial ratio analysis show BYD’s overseas expansion strategy has successfully helped it gain a foothold in international markets while Seres’ current approach needs further refinement to better adapt to overseas markets. This study provides empirical references and financial decision-making basis to help Chinese automakers improve global expansion strategies and mitigate micro-financial risks. In addition, this study also warns that enterprises need to pay close attention to micro-financial risks such as exchange rate fluctuations and compliance costs in the expansion process to achieve stable internationalization development. Through comparative analysis, it provides an important basis for Chinese automobile companies with different endowments to formulate differentiated international strategies.

Profit quality constitutes one of the important elements in enterprise valuation. Issues concerning the sources of revenue stability, the genuineness of accounting profit, and the effectiveness of cash generation flows are related to it. For firms in the luxury sector, including LVMH, defining Profit and Earnings Quality is especially relevant. This paper takes LVMH as an example to study the connection of profit quality with enterprise valuation by using plenty of methods, including financial reporting, various financial ratios, the cash flow to earnings ratio, accruals ratio, and return on assets, which measure profit quality, to analyze their impact on multiples and discounted cash flow analysis. The results indicate that the most important factors are accrual quality and cash conversion, which link directly to the valuation. The findings, in particular, have implications for investors and managers in the luxury sector. For investors, a better appreciation of the driving forces behind the quality of profits may provide better assessments of reality and perceived risk in investment in luxury companies. For managers, this study could contribute to improving a firm's market position by focusing on enhancing profit quality through better management of its cash flows and accruals.

Since going public in 2010, Tesla has become one of the world's most watched stocks on the stock market. By 2025, its market capitalization had grown to over $13 trillion, turning it into a phenomenon-level company. Tesla has attracted all kinds of economic groups worldwide through technological leadership, product innovation, its own vertical integration model and corporate culture. In this paper, "mental accounting" from behavioral finance is used as the theory of the analysis, and Tesla investors are divided into institutional investors, high net worth individuals, and ordinary investors. By looking at past changes in stock prices, the paper tries to figure out the mental accounting reasons behind investment choices among different groups of people. The research shows that different investor groups display different behaviors because of differences in the characteristics of the capital, risk tolerance, and mental accounting categories. Then it can find the different investor groups with this study and hope that every person won't be tricked into making bad choices through taking shortcuts in the head and have good investment strategy.

This paper takes the abnormal volatility of China's stock market in 2015 as a case study, analyzing how overconfidence drives investors to trade frequently and affects market efficiency from the perspective of behavioral finance. Research points out the limitations of the traditional "rational people" hypothesis, and constructs with a psychological deviation analysis framework with a core of overconfidence. Through the analysis of high-frequency data such as financing balance and turnover rate, it is shown that overconfidence during the bull market phase boosts the influx of leveraged funds and the deviation of asset prices from fundamentals. However, after the market reversal, the psychological deviation reverses, triggering panic selling and a liquidity crisis, which damages the market's pricing function. Finally, systematic suggestions are put forward from three aspects: investor education, market mechanism, and policy supervision, the entire text revolves around overconfidence as the core psychological factor, providing theoretical references and practical paths for enhancing market stability.

Confronted with the serious infringement of AI face-swapping template infringement, such infringements have the characteristics of high generation efficiency, extensive dissemination, covert infringers, cumulative damages, and aggravated damages due to rapid dissemination of content. The current legal rules are established on the basis of the "passive dissemination" model of traditional online infringement scenarios, and it is difficult to adapt to the inherent attributes of generative AI platforms— "active content generation" and "pre-positioning of infringement risks". Therefore, the liability, enforcement, and relief of the platform are hindered, which not only disrupts the order of cyberspace, but also affects the protection of rights for rights holders. This paper reviews the relevant domestic and foreign studies and finds out the following core problems: unclear liability standard, operational procedure, and relief mechanism. Specifically, the determination of liability lacks quantified standard of fault assessment and fails to differentiate liability according to the "depth of participation" of the platform; enforcement lacks proactive screening standard and effective counter-notice path; relief lacks method of calculating exclusive compensation and error correction mechanism in the "three-strikes" system. This paper proposes the following optimization measures based on the relevant regulations. That is, establishing quantified four-dimensional fault presumption system (including dimension of copyright compliance of face-swapping materials, etc.), perfecting the "notice–takedown–counter notice" process, and designing multidimensional compensation formula and three-tier appeal mechanism based on multidimensional compensation formula. The above-mentioned proposals provide reference for relevant liability determination and dispute resolution in practice, which will promote the standardized development of generative AI industry.

Under the context of rapid and explosive growth in generative artificial intelligence technology, its large-scale use of copyrighted works has significantly impacted the current copyright system. Among them, the fair use doctrine is trapped in the dilemma of difficult rule application and ambiguous boundaries of judgment, lagging behind the novel emerging application scenarios driven by artificial intelligence innovation. Thus, it shows an urgent need for further improvement. As a global pioneer in artificial intelligence research and development, the United States has initially established a judicial logic centered on the determination of transformative use and the assessment of market substitution effect for such disputes,which is rooted in the case law system. Based on clarifying the specific infringement risks in the data input and output phase, this study, by sorting out the dynamic evolution of rules through typical cases in the United States and considering the current situation in China for reference and exploration, it put forwards targeted countermeasures such as revising hierarchical provisions, setting information disclosure obligations, and strengthening regulatory coordination mechanisms. Eventually, it aims to offer a practical reference for the rule optimization and application improvement of the fair use doctrine in the field of artificial intelligence applications, which resonates with China’s legal framework.