With the continued growth of the tourism industry, increasing attention has been devoted in recent years to how tourist cities should formulate appropriate tourism policies. As a well-known cruise destination in Alaska, Juneau faces the critical challenge of balancing the economic growth generated by tourism with the visitor carrying capacity of the local area, which has become a key concern for local policymakers. This paper develops a GDP growth accounting model based on data on tourism consumption, tax revenue, and government investment. By formulating the maximization of tourism-driven GDP growth as the objective function, the model identifies the optimal number of visitors and the corresponding tax policy for the region, thereby providing concrete policy recommendations for local government decision-making. A sensitivity analysis with respect to the visitor-capacity limit further shows that relaxing the maximum visitor capacity increases both the feasible visitor scale and the upper bound of GDP gains, while the optimal tax policy remains relatively stable in the low-capacity region. These findings provide a quantitative basis for sustainable cruise-tourism management in Juneau and other small port destinations with similar structural characteristics.

In the context of global competition, scientific salary management is a crucial factor for enterprises to attract talents, enhance efficiency, and achieve sustainable development. This study takes Company Y as the research object and employs questionnaire surveys and data analysis methods. It reveals that the company's salary system has problems such as poor internal fairness and insufficient performance incentives. Based on this, the study proposes optimization suggestions such as establishing a position value assessment salary structure and establishing a dynamic performance salary linkage mechanism, with the aim of providing references for the improvement of salary systems in Company Y and other enterprises in the same industry.

This report presents a comprehensive systematic review of 15 international and domestic publications, analyzing the transmission mechanism through which macroeconomic factors affect electricity prices across five dimensions: primary energy price transmission, macroeconomic cycle linkages, financial market mechanism design, market structure and pricing rules, and policy regulatory frameworks. Key findings include: natural gas and crude oil prices are Granger-causal sources of industrial electricity price changes, with a transmission cycle of approximately one year; a significant long-term cointegrating relationship exists between electricity consumption and GDP; financial crises exert sustained impacts on electricity demand through FDI channels; financial derivatives such as electricity futures and financial transmission rights (FTR) play crucial roles in price stabilization and hedging; while market structure factors such as the abuse of market power and information asymmetry profoundly influence the formation of electricity prices and transmission efficiency. Based on a systematic analysis of the transmission mechanism, this report proposes policy recommendations for improving China's electricity price management system, aiming to provide theoretical support for deepening electricity market reforms.

In the modern economic system, the profit-motive is seen as an important power that makes market efficiency and innovation. However, the boundedness and potential risk also trigger concern. The essay uses a method of literature review, complemented with economic theory and actual cases, discussing the function of profit-driven motive and their boundedness and analyzing the role of government and charity organizations in the modern economic system. The essay first illustrates the challenge of behavioral economics academia to the homo economicus assumption, and then uses cases such as the financial crisis and negative externality to indicate that a single profit-driven motive will cause systemic risk. Furthermore, the essay appreciates the positive function of the profit-motive in technological innovation, efficiency improvement, and creating shared value. The study also figures that relying on only the market or the government makes it difficult to examine the total development of society. The collaborative system among the market, the government, and the charity organization is more likely to strike a balance among efficiency, fairness, and social value. The article believes that through clarifying the divisions of different entities, a more stable and sustainable economic system can be built.

With rising Research and Development (R&D) costs and increasing innovation pressure in the pharmaceutical industry, mergers and acquisitions (M&A) have become a major way for pharmaceutical companies to achieve strategic expansion. This paper takes Pfizer's 2022 acquisition of Biohaven as a case study to analyze the strategic motivations of M&A and its potential impact on corporate value. The paper employs case study and event study methods to examine market reactions before and after the acquisition, and evaluates the impact by analyzing changes in daily financial indicators before and after the acquisition. The results show that the cumulative abnormal return rate showed an upward trend after the acquisition announcement, indicating a positive market response. However, from a financial perspective, post-acquisition profitability indicators still fluctuated, suggesting uncertainty regarding the long-term development of the company. In conclusion, this acquisition has potential for resource integration and R&D synergy at the strategic level, but its value realization still requires observation of the subsequent cooperation effects.

In the context of increasingly fierce competition in the retail industry, traditional supermarkets face multiple challenges such as declining performance, increasing financial pressure and business model transformation, resulting in its financial risks constantly increasing. Establishing a scientific financial risk early warning system helps enterprises identify potential crises in advance, which is of great practical value for optimizing financial decisions and ensuring business safety. The article analyzes the financial data of Yonghui Superstores over the past five years (2020-2024) based on Altman's z-score for early warning of financial risks. The research shows that the z-score of Yonghui Superstores has generally been on a downward trend and has entered the gray warning zone, indicating the existence of several potential risks requiring attention. On this basis, the article analyzes in more detail the main factors leading to these risks from the perspectives of the external environment and internal business operations,and proposes corresponding risk prevention suggestions.

Financial fraud seriously damages the order of the capital market and the interests of investors. This paper takes Evergrande real estate, a typical case of major financial fraud, as the research object. Based on the dual perspectives of fraud triangle theory and gone theory, it deeply analyzes its motivation, means and governance path of financial fraud. The study found that Evergrande's fraud is the result of the joint action of four-dimensional factors: pressure (need), opportunity, rationalization (greed) and exposure. The fraud means are very covert and systematic, mainly by falsely increasing income and concealing liabilities, falsely increasing income by 564.1 billion yuan and falsely increasing profit by 91.9 billion yuan in two years, the highest in history. On this basis, this paper puts forward targeted governance suggestions from the four dimensions of pressure/need, opportunity, rationalization/greed and exposure, so as to provide reference for preventing financial fraud in real estate enterprises, maintaining the order of the capital market and protecting the legitimate rights and interests of investors.

Against the background of the rapid development of cultural and creative industries, the traditional commodity-dominant logic is gradually finding it difficult to explain the new enterprise value creation model with Intellectual Property (IP) as the core. Based on the theoretical framework of service-dominant logic, this paper takes Pop Mart as the research object, combines its asset-light IP operation practice and financial data from 2020 to 2024, and systematically analyzes the internal relationship between its operation mode and financial performance. The study found that Pop Mart, through IP service operation, asset-light structural configuration and user participation mechanisms, achieves improvements in profitability, asset efficiency and growth elasticity. The service-dominant logic does not stay at the theoretical level, but is transformed into observable financial performance through a specific operation mechanism. This paper constructs a mechanism model of how service-dominant logic affects financial performance, which provides practical enlightenment for business model optimization and financial strategy formulation of cultural and creative enterprises.

Urban renewal has become a critical strategy for sustainable urban development, with accurate commercial value assessment being paramount for project success and viability. In the contemporary digital landscape, User-Generated Content (UGC) has emerged as a rich, real-time data source reflecting public perceptions and experiences. This paper explores the application and future development of UGC data in evaluating the commercial value of urban renewal projects from a business analytics perspective. Using a literature analysis method, this study synthesizes existing research to examine how UGC, when analyzed via business analytics techniques, can provide profound insights into consumer behavior, brand perception, and the overall commercial vitality of renewed urban spaces. The findings indicate that UGC serves as a valuable proxy for measuring commercial performance and public acceptance. However, challenges such as data subjectivity, representational bias, and verification difficulties persist. The paper concludes that future development lies in integrating UGC with authoritative data sources and advancing analytical models to mitigate these limitations, thereby enhancing the reliability and depth of commercial value assessments for more informed urban planning and investment decisions.

Under the dual backdrop of global economic integration and the digital transformation of financial markets, the European capital market, as a crucial global financial hub, has seen an increasing level of attention from investors, financial institutions, and regulatory authorities regarding the stock price fluctuations and risk characteristics of its listed companies. And this research subject SAP, as a listed company on Frankfurt Stock Exchange in Germany, engages in enterprise management software and cloud service businesses, which represents a blue-chip stock in the European technology sector. Hence, there stock price is not only to stand for company's own operating condition, but also reflects the influence of European technology industry, capital market liquidity, and the macro environment. Meanwhile, the stock price sequence of SAP Company exhibits typical characteristics of financial time series: clustered fluctuations, sharp peak and heavy tail, which are suitable for quantitative modeling. Therefore, this is a typical research subject of exploring the value of time series in stock prices. Moreover, with the advancement of technology, quantitative finance has experienced rapid growth. Various models and AI algorithms can be used to simulate events in multiple fields, such as option pricing, stock price simulation, and daily trading. This research has successfully established a precise model for stock returns and volatility, while enabling reproducible simulations of future price trajectories and delivering quantitatively accurate stock price forecasts. This model demonstrates strong applicability for short-term stock price forecasting, and this quantitative prediction approach is built upon rigorous academic rationale and practical feasibility. Free from subjective judgments and empirical assumptions, it ensures fully reproducible and verifiable prediction outcomes.