Green accounting is a new discipline that has emerged in order to deal with environmental governance, ensure that the economic benefits of enterprises are harmonized with environmental development, and implement the implementation of sustainable development strategies. In today's deteriorating ecological environment, the emergence of green accounting is particularly important. This paper describes the concept of green accounting and the necessity of green accounting for the current global ecology, discusses the possible problems of implementing green accounting according to China's national conditions and the development history of green accounting in countries around the world, and then gives the corresponding practicable solutions to the existing problems. Although the implementation of green accounting has not been widely popularized at this stage due to the imperfections of laws and regulations and the lack of consciousness of companies and individuals, it is expected to be vigorously implemented in the future as the economy develops and people's awareness of environmental protection increases.

Within the framework of the digital economy age, artificial intelligence is sweeping across the world today. Artificial intelligence technology has made remarkable progress, and various industries have been affected, resulting in significant and even profound changes. The employment market has also been impacted as a result. This article uses case and problem analysis methods to explore artificial intelligence's influence on the labor market and societal structures in the digital age. AI's effects on the employment sector are mostly focused on in three aspects: the dangers of automation at work, the effects of AI on employment that are balanced, and how AI affects employment structures. It also has short-term and long-term effects on income inequality and requires the transformation of worker skills to high-tech digitization. Based on this, this article also puts forward suggestions for the follow-up development of enterprises, government, society, and education and puts forward thoughts.

The main axis of the US-China game deepens from Trump's trade war to Biden's tech wars. At the same time, the chip industry has gradually become the main battlefield for the United States to suppress and curb China's scientific and technological fields. Whereas chips are an essential part of modern military equipment, communications facilities, nuclear power plants, transport systems and other critical infrastructure. Early mastery of the core chip technology can be early not to be constrained by others, to avoid the risk of being "necked".This paper will analyse the reasons why China makes the US feel that its national security is threatened, the impact of the US-China trade friction on China's science and technology sector, and find out the measures that China can take to deal with the US sanctions related to the science and technology sector, as well as the right way for China to get along with the US.

The objective of this study is to predict the Chevron’s Corporation stock market performance by conducting a comparative analysis of contemporary and conventional machine learning approaches, with a particular focus on the CNN-LSTM and ARIMA models. Given the unpredictable characteristics of the crude oil industry, forecasting stock prices with precision has emerged as a pivotal dilemma for both investors and analysts. This research utilizes ARIMA, which is representative of conventional time series forecasting methods, and CNN-LSTM, which embodies the latest advancements in deep learning techniques, to address the intricacies associated with predicting stock prices in the energy sector. Through a comprehensive data preparation process and the application of sophisticated modeling techniques, this study aims to rigorously assess the predictive capabilities of both models in forecasting Chevron's stock prices. Traditional statistical analysis often relies on the ARIMA model as a benchmark, while the CNN-LSTM model seeks to identify the complex, non-linear patterns prevalent in financial market time series data. This research conducts a comparative evaluation of the two models, focusing on their accuracy, strengths, and limitations. The findings carry important implications for the realm of financial forecasting, shedding light on how modern deep learning techniques stack up against traditional approaches in predicting stock market movements. Beyond contributing to scholarly debates on financial prediction, this study also provides actionable insights for financial analysts.

As an integral component of the financial market, stock prices have attracted the attention of many investors. Due to the frequent fluctuations and sensitivity to market dynamics, predicting stock prices is challenging. The volatility of stock prices and potential significant differences across different periods add to the difficulty of forecasting and reduce its accuracy. The Random Forest model and the LSTM model, as representative models in decision trees and deep learning algorithms respectively, demonstrate high accuracy and adaptability in predicting stock prices. The paper will separately utilize the Random Forest model and the LSTM model to fit the S&P 500 price data from 2013 to 2018 (represented by Apple's stock prices) as training and testing sets, and then compare the fitting results of the two models. The conclusion is as follows: In the absence of white noise in the data, the Random Forest model demonstrates smaller biases in predicting data compared to the LSTM model, and it can also respond more swiftly to price fluctuations.

In this study, cutting-edge methods of applying deep learning techniques to stock market predictions were explored, specifically focusing on the stock data of Tesla Inc. Long Short-Term Memory networks (LSTMs), an advanced form of Recurrent Neural Networks (RNNs) capable of effectively addressing the issues of vanishing and exploding gradients that traditional RNNs face, were employed. This enhances the model's learning capability and predictive accuracy for time series data. The innovation of this research lies in the integration of the LSTM model with the Random Forest algorithm, forming a hybrid model aimed at leveraging the complementary strengths of both models to improve the accuracy of stock price predictions. Through empirical analysis of Tesla's stock data, it was found that the hybrid model outperformed the individual LSTM model. This result not only proved the effectiveness of LSTMs in handling complex time series prediction problems but also demonstrated the potential of enhancing predictive performance by integrating different types of models. The findings offer a new perspective for financial market analysis and prediction, especially in the use of deep learning technologies for stock price forecasting. They provide valuable references for future research and practice in this field. Further investigations could explore the applicability of this hybrid approach to other financial instruments and markets.

To provide an objective analysis, this study examines three quantitative trading strategies: Momentum, Moving Average Crossover, and Machine Learning individually but in a common methodological setting. In order to achieve higher returns at lower levels of risk due to the advent of algorithmic trading, such strategies must be explored. The two strategies that we analyze include the Momentum strategy that capitalizes on the persistence in price trends and the Moving Average Crossover strategy that relies on average price movements as trading signals. In addition, in this study, Machine Learning methods are applied which implement predictive algorithms to predict the price movements in the future based on their historical patterns. In order to assess the performance of each strategy, this investigation relies on one data set and uses a series of financial metrics to see how well each strategy performs with the objective of identifying both strengths and weaknesses that these strategies exhibit within different market situations.

The science of examining how corporate authority is distributed is known as corporate governance, when used broadly. Viewed narrowly, it is a branch of science that sits at the level of company ownership, investigates the process of appointing professional managers, and performs regulatory activities regarding the discharge of professional managers' obligations. The "management right level" of enterprise management is based on science and involves the enterprise owner and management right authorization, or management right in the case of authorization, in order to accomplish company goals and utilize all available methods of operation behavior. On the other hand, corporate governance is built at the "ownership level" of the business based on science and deals with professional managers' scientific approval and oversight. This article investigates the role of corporate governance in the financial crisis and why stock prices did not anticipate bad corporate governance, setting the scene for the global financial crisis of 2008. On the basis of existing research, analytical studies were conducted and summarized into conclusions. As shown in this paper, inappropriate corporate governance ultimately leads to an increased risk of economic crisis. Therefore, it is important to adopt the necessary tools to improve corporate governance. Management should formulate appropriate corporate strategies and ensure that they are effectively implemented, ensure that internal controls are effective and develop a good corporate culture, etc. The government should also improve the relevant regulations and ensure their implementation.

Since the creation of Bitcoin in 2008, these digital currencies have not only attracted widespread attention from the public and economists, but have also triggered a rethinking of the nature of money, the store of value, and the modes of exchange. This paper explores the transformative impact of Bitcoin and digital currencies on global finance, emphasizing their emergence as a challenge to the traditional concept of money and a paradigm shift. Furthermore, the paper delves into the birth of Bitcoin, its decentralized nature and its pioneering role in the field of digital currencies, discusses the historical background, technological underpinnings, and monetary functions of digital currencies, and highlights the potential and challenges of their integration into the financial system. It aims to examine the characteristics and functions of bitcoin and digital currencies in the contemporary financial landscape, focusing on how they can challenge traditional monetary policy as an emerging financial asset, as well as their potential impact and integration challenges in the global economic system.

With the popularity of blind boxes, they have changed from the original toy products to today’s trend goods, and even more and more products appear in the market in the form of blind boxes. Therefore, this paper would like to analyze the reasons for the popularity and success of blind boxes, as well as the psychology of people who like to buy blind boxes. In this paper, the marketing strategies and consumer psychology of the blind box are analyzed. The first is about the history of the blind box and its development. The second is to analyze the marketing strategies used by blind box companies, including the ordinary style and secret style of blind box, and the strategies of blind box companies to launch products through cooperation with various intellectual properties. At the same time, this paper believes that the marketing strategies of blind box companies are formulated to grasp the psychology of consumers to a certain extent, so this paper also analyzes the psychology of consumers who purchase blind boxes. This paper finds that the ordinary and secret style marketing strategy adopted by blind box enterprises can stimulate the purchasing behavior of consumers, and the strategy of cooperating with popular IP or independently developing new Intellectual property (IP) can promote innovation and the development of the blind box industry. With the expansion of the blind box market, it promotes economic growth to a certain extent.

The Internet has revolutionized the operational framework of enterprises, notably in the retail sector, where online sales have emerged as a pivotal trend. And the beverage industry, as a key segment of the consumer goods industry, has also experienced profound impacts due to this paradigm shift. Thanks to the Internet, it is easier for enterprises to convey marketing information and establish contacts with customers. In addition, the Internet has introduced innovative avenues for companies to increase sales, presenting opportunities, such as access to new marketing channels and enhanced brand visibility through word-of-mouth marketing. Nevertheless, these benefits are accompanied by new challenges, including competitive pressure and difficulties in online traffic acquisition. Therefore, this paper aims to analyze the specific impact of online sales on the beverage industry. To achieve this goal, this study conducts a thorough analysis of existing literature and data, as well as the numerical changes in the value of the Chinese beverage industry since the establishment of the online sales model, to explore the impact of online sales on the Chinese beverage industry and enterprises. The conclusion drawn from this article is that the impact of online sales models on the Chinese beverage industry is multifaceted, including market size, consumer behavior, and brand competition.

At a time when the digital economy is developing rapidly, household disposable assets are also gradually increasing, and reasonable and efficient allocation of assets for investment is the goal pursued by every family. Based on data from the 2019 China Household Finance Survey (CHFS), this paper examines the impact of the development of the digital economy on Chinese households' financial market participation and financial investment through the methods of literature analysis, comparison and synthesis. The study finds that the development of the digital economy has given Chinese households more investment options. Meanwhile, with the addition of digital literacy, the direction of engaging in a career, financial knowledge and investment experience, urban and geographical differences, and the level of digital economy development will have a huge impact on household financial investment. In addition, reasonable investment in digital economy products, strengthening risk awareness and accumulating investment experience are all conducive to Chinese families' profitability in financial investment.

Against the backdrop of the rise of digital payments, more and more people are choosing to use virtual currencies for their transactions. Thus, in this paper, the reasons for the global rise of digital currencies, the characteristics of digital payments, and the possible global impact are presented, and the possible risks and challenges of digital payments are analyzed, as well as how to protect customers' privacy and data and how to improve payment methods based on users' experiences. The focus of this paper is on discussing the impact that digital payments will have on the financial industry, comparing and analyzing the advantages and disadvantages, and exploring the indirect or direct impact it will have on financial services and money. Furthermore, it analyzes the habits of consumers using digital payments and whether digital payments will quietly change their habits.

As a globally recognized fashion label, ZARA is famous for its quick fashion designs and efficient supply chain management. However, to stay competitive in an age of rapid technological advancement, ZARA must continuously innovate and leverage technology to enhance its operations. This paper primarily explores the following aspects: Firstly, how artificial intelligence is utilized by ZARA to achieve swift responses and adaptable methods in the design and production processes; Secondly, how artificial intelligence is employed by ZARA to optimize supply chain management, enhance production efficiency, and mitigate inventory risks; Lastly, it will analyze the operational model of ZARA as a source of inspiration for fast fashion enterprises and its relevance to other industries. After conducting thorough analysis and research on the integration of science and technology in ZARA's operations, this study has arrived at the following conclusions: ZARA effectively utilizes artificial intelligence to swiftly respond to market demands and make flexible adjustments, thereby enhancing its ability to meet customer needs. AI also enhances the efficiency and reliability of ZARA's supply chain management, leading to improved production efficiency and reduced inventory risk. By leveraging AI, ZARA can offer personalized shopping experiences to customers, ultimately boosting satisfaction levels and fostering loyalty. This paper delves into how technology drives operational innovation and value creation at ZARA, offering valuable insights for other fashion brands while serving as an empirical case for research in the realm of science, technology, and operations management.

With the introduction of the concept of low-carbon economy, all countries in the world strive to achieve the coordinated development of economy, energy conservation and emission reduction. China has also announced its plan to the world to achieve carbon emission reduction. However, most places in China have not yet reached the stage of coexistence of low-carbon and economic development .and the emergence of information and communication technology has brought hope for the realization of this "win-win" goal. This paper synthesizes the three major international carbon emission reduction measurement standards and quantifies the potential of direct ICT-enabled social carbon reduction in combination with the research on the products of China Telecom Operator A, and forecasts the carbon reduction of Operator A in 2025 and 2030 in combination with its future business development. This study provides a reference for Chinese telecom operators to better realize the market expansion of low-carbon products.

Interviews plays a crucial role in the hiring process, influencing decisions that can significantly organizations and individuals. This literature review comprehensively examines the role of interviews in the hiring process, focusing on three dimensions: structure, response bias, and interviewee experience. It begins by defining each interview structure and discussing the shortcomings of unstructured interviews in terms of reliability and validity, the flexibility of semi-structured interviews, and the high validity of structured interviews. In addition, this paper addresses the issues of response bias and interviewee experience that frequently arise in interviews. Potential solutions to these issues are explored, leveraging the distinctive features of the three structures. And the finding suggests that structured interviews are more effective in mitigating bias, while semi-structured interviews prioritize the interviewee’s experience. The strong replicability of structured interviews makes them suitable for extensive study and future improvement. Therefore, this paper proposes that enhancing interviewee experience in structured interviews should be a key area of future research.

This study explores the burgeoning field of qualified art derivatives as an innovative investment channel, leveraging the unique attributes of art for inflation resistance, low correlation to traditional financial markets, and cultural significance. Through the analysis of art market trends, the research identifies individuals, family offices, pension funds, foundations, and investment funds as potential purchasers, with artists, galleries, museums, and financial institutions as key sellers. Utilizing data from art market transactions, auction records, and expert appraisals, the study examines the design, operation, and market acceptance of art derivatives. It highlights the potential of art derivatives to offer diversification, hedge against inflation, and contribute to the art market's growth, while also pointing out challenges such as low liquidity, valuation complexities, regulatory uncertainties, and limited traditional investor acceptance. The findings underscore the significant prospects for wealth growth and market development through art derivatives, balanced with a need for careful consideration of their inherent risks and challenges.

The financial crisis of 2008 precipitated unprecedented challenges for the global economy, necessitating innovative responses from central banks. This study compares the quantitative easing (QE) policies adopted by the Federal Reserve of the United States and the Bank of England in the United Kingdom in response to the 2008 financial crisis. Utilizing a comparative analytical framework, the research delves into the strategies, execution, and economic impacts of these policies. The findings reveal that while both countries aimed to mitigate the crisis's negative effects, their QE implementations differed due to distinct financial market structures and targeted objectives. In the U.S., QE successfully reduced unemployment rates and stabilized the housing market, whereas in the UK, it primarily prevented deflation and had a more modest impact on employment. The study concludes that while QE was instrumental in stabilizing the economies of both countries, it also highlights the complexities and controversies surrounding such policies, including their potential impact on wealth inequality and financial market integrity. This comparative analysis provides valuable insights into the effectiveness and nuances of QE policies in different economic contexts.

Blind box, an unlabeled box containing different styles of random uncertain goods, is a newly spring-up marketing strategy that utilizes consumers’ intention of pursuing uncertainty and surprise to stimulate the consumption temptation. The blind box economy has obtained rapid development in recent years, arousing a consumption boom of this kind of uncertain goods. Despite the large amount of sales revenue received in the blind-box market, this fresh and developing economic form still has some drawbacks and problems awaiting to be solved. Although current studies on blind box economy are scarce in number, researchers are still making efforts to extend the analysis scope. This paper sorts and reviews previous studies of the blind box economy. Firstly, the background information about the blind-box economy trend is introduced. Secondly, the paper clarifies the reasons for the blind box market emergence based on behavioral economics. Finally, the potential problems and relevant suggestions for the blind box market are covered. In the conclusion part, the limitations and possible improvements of current research are discussed. This paper finds out that consumers’ irrational purchasing behavior is one of the most significant reasons for the blind-box economy prevalence and potential problems.

This paper conducts a comprehensive time series analysis of new and used car sales in the United States, focusing on intrinsic patterns captured by Seasonal AutoRegressive Integrated Moving Average (SARIMA) models. SARIMA models are applied to forecast sales over the next two years, and select the model based on standards such as Akaike Information Criterion with correction (AICc), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Notably, the ARIMA (4, 0, 3) (3, 1, 1) [12] model emerges as the optimal fit for new car sales, displaying superior time series fitting and lower errors. For used car sales, the ARIMA (2, 0, 3) (2, 1, 3) [12] model, although not the best-fitting, exhibits the lowest prediction errors. Consequently, these models are chosen for forecasting. The results suggest a continued upward trajectory in new and used car sales in the United States over the next two years, capturing the inherent cyclic and seasonal patterns inherent in the data.