The integration of Artificial Intelligence (AI) into China’s New Energy Vehicle (NEV) industry presents both unprecedented opportunities and significant challenges. Through analyzing the present situation of the NEV market in China, this paper identifies key challenges posed by AI, and proposes strategic countermeasures. Even though the NEV industry is growing rapidly, there are still a number of significant obstacles that NEVs must overcome. Technologically, algorithms, computing power and sensor fusion are critical. Regulatory challenges include preventing data leakage and protecting privacy. Economically, the need for substantial investments in infrastructure poses significant hurdles. Socially, changing market operation and reskilling the workforce are essential. The challenges faced by New Energy Vehicles (NEVs) will drive significant technological innovation and cooperation, particularly in algorithm optimization, hardware upgrade and sensor fusion technology. Regulatory influences will push for network security and enhancing safety and compliance. Economic impacts include the need to improve charging network, build an intelligent transport system and improve information and communication infrastructure. Socially, addressing market operation and workforce reskilling will be crucial. Overall, these challenges will shape market dynamics, competitive landscapes, and global supply chains, ultimately driving the NEV sector towards more sustainable and efficient solutions, and by these measures, China can fortify its NEV industry against AI-related challenges and achieve sustainable growth.

The integration of big data and machine learning has emerged as a pivotal force in revolutionizing corporate financial management and governance amidst an increasingly competitive global landscape. Traditional financial practices, reliant on human expertise and static data analysis, are becoming insufficient in handling the vast volume, velocity, and variety of data available today. This article examines how big data and machine learning enhance financial forecasting, risk management, governance transparency, and compliance processes. By conducting an industry situation analysis, the article highlights the advantages these technologies present while addressing the technical and ethical challenges enterprises face in their implementation. The findings underscore the transformative potential of leveraging data-driven insights for informed decision-making, fostering transparency, and optimizing governance structures. However, challenges such as data privacy, algorithmic bias, and interpretability remain significant barriers. The study calls for further research into sector-specific applications of these technologies to facilitate tailored strategies that effectively harness their capabilities.

Game theory is known to give a framework, though it may not be entirely precise, that can examine behaviors in mergers and acquisitions (M&A). Competing companies, they engage in different bidding battles, negotiations and interact strategically. This paper mainly sees how game theory is applied to M&A. Various models, like Nash equilibrium, subgame perfect equilibrium, signal games, among others, help predict and explain companies' behavior when they take part in mergers or acquisitions. Certain mathematical models are looked into, though not exhaustively, and general examples in reality like the bidding between Disney and Comcast for 21st Century Fox, and another instance of a takeover attempt, where Kraft Heinz tried for Unilever, are shown to show how game theory can matter in corporate strategies.

The situation caused by COVID-19 is a typical case where a major health event has a profound impact on the economy. Additionally, this is also the period with the most significant economic fluctuations since the economic crisis of 2008. This paper researches the impact of the COVID-19 on the United States of America economic conditions and evaluates the effectiveness of the Federal Reserve's monetary policies during the early outbreak. By selecting key timeframes, the paper analyzes fluctuations in treasury yields, stock markets, inflation, exchange rates, and unemployment obtained from Federal Reserve Economic Data. This study also assesses the Federal Reserve System's performance by examining the characteristics of policies themselves and economic response. Using the Taylor Rule, this research further explores the limitations of traditional monetary tools during unprecedented economic disruptions. The findings suggest that while the Fed's actions provided some stabilization, their overall effectiveness in reversing the economic downturn was limited, particularly in mitigating unemployment and wealth inequality.

In recent years, the construction of smart cities has become a critical focus worldwide, as cities leverage digital solutions to address complex governance and societal challenges. This article discusses the achievements, challenges and how to optimize the construction path in the construction of smart cities in the construction of smart cities, and aims to provide reference and reference for other cities to build smart cities. The level of intelligence of urban governance in China is constantly improving, and it is gradually changing from traditional urban governance models to modern science and technology. In the process of the construction of smart cities, it exists to varying degrees as a pioneering city for the development of China's smart cities. Hangzhou has made significant progress in digital infrastructure, smart transportation, public service intelligence, and environmental management. However, with the rapid advancement of the construction of smart cities, Hangzhou still faces challenges in terms of data security and privacy protection, governance collaboration, digital gap and sustainable development.

In the 1990s, the Spanish economy grew rapidly, but due to the internal dependence on real estate, the external impact of the economic crisis resulted in the emergence of the real estate bubble. Through this event, this paper uses the income capitalization model to analyze the impact of monetary policy, especially interest rates, on house prices and also delves into the reasons for the formation of the real estate bubble, including factors, such as labor market reform, immigration policy, bank lending policy and interest rate changes. The focus is to use the income capitalization model and related data to show the relationship between interest rate and room through the graph, which confirms the impact of monetary policy on the real estate bubble in Spain. In addition, the paper analyzes the impact of the bursting of the real estate bubble on the Spanish financial system, the job market, the inflation rate, the fiscal deficit and the public debt, as well as the resulting social problems. This paper provides a perspective for studying the link between monetary policy and real estate bubbles in Spain, as well as a reference for the state to develop relevant strategies.

As the complexity and volatility of the stock market continue to increase, investors are placing greater emphasis on the need for accurate stock price predictions. Traditional statistical models often face limitations in time series forecasting, particularly when it comes to capturing the intricate and dynamic changes in stock prices. This paper explores the application of the LSTM model for stock price prediction by developing a forecasting model based on LSTM. First, relevant stock data is collected through a web crawler, and then an LSTM model is trained using this data to predict the price of a specific stock. To assess the performance of the model, evaluation metrics such as mean squared error (MSE), mean absolute error (MAE), and the coefficient of determination (R²) are employed. The results demonstrate that the LSTM neural network model effectively predicts nonlinear stock trends.

This article explores Lipton's marketing strategies, focusing on the innovative Lipton brisk product, which caters to the evolving preferences of health-conscious consumers. By integrating traditional marketing methods, such as Primetime television advertising, with digital strategies like social media and viral campaigns, Lipton effectively engages a young audience seeking trendy beverage options. The analysis highlights the importance of brand positioning, particularly in appealing to millennials, and how strategic advertising initiatives enhance product awareness and consumer loyalty. Data reveals that Lipton brisk has maintained competitive sales in the ready-to-drink tea market, showcasing the effectiveness of its blended marketing approach. The article underscores the need for a dual strategy that leverages the strengths of both traditional and digital media to maximize brand impact and consumer engagement, ultimately driving sales growth in a crowded beverage landscape.

Since the 20th century, China's national economy has continued to improve, the people's quality of life has taken a leap forward, coffee has become a popular consumption, and the coffee market has shown unprecedented potential. In the face of such a large and dynamic market, many local coffee brands have emerged in an attempt to seize the market share. However, Luckin Coffee, with its unique business management model and innovative marketing strategies, has successfully stood out in the fiercely competitive local coffee market and gradually established its leadership position. This paper adopts a case study approach to analyse in depth the business strategies and marketing strategies of Luckin Coffee in recent years. By comparatively examining the differences between Luckin and other brands, we are able to summarise the key factors of its success. These factors include, but are not limited to: efficient management system, innovative business model, strong marketing promotion, and keen insight into market trends. In addition, this paper discusses the challenges faced by Luckin Coffee, such as how to cope with increasing market competition, how to maintain customer loyalty, and how to adapt to changing consumer demands. In response to these challenges, this paper proposes some specific solutions, aiming to help Luckin Coffee further improve its business strategies and promote its long-term development.

Hunger marketing is a very unique marketing means, that cleverly uses the consumer's psychology and effectively promotes the sale of goods. This kind of marketing method in the blind box industry has a remarkable effect. In the Go-Stop framework theory, buying decisions are driven by two types of brain signals - the GO signal and the STOP signal. A GO signal is a thought, feeling, or unconscious response that energizes the consumer to approach and buy the product. While the STOP signal will stop customers to buy. The purpose of this study is to analyze the specific impact of POPMART's hunger marketing strategy on consumer psychology, and to provide solutions for the STOP signals generated by consumers in their purchase decisions. By means of a literature review, case analysis and go-stop framework, this paper analyzes the GO and STOP signals of consumers in the process of purchasing POPMART to explore the effectiveness of their marketing strategies. Through the analysis and research, we can find that the hunger marketing implemented by POPMART has both positive and negative effects on consumer decision-making. Hunger marketing strategy uses consumers' "gambling", "comparison", "conformity" and the pursuit of well-known IP psychology to stimulate consumers to buy blind boxes and enhance consumers' GO signal in the purchase process. But hunger marketing strategy is also accompanied by some negative effects, excessive shortage of inventory will lead to customer loss. Competition for similar products, high second-hand prices, and consumer dissatisfaction with hunger marketing will curb consumers' desire to buy. This strengthens their stop signal during the purchase process.