With the rapid development of science and technology after the two world wars, humanity has gained the ability to explore space and view our planet from another dimension. Upon returning our focus to Earth, people are surprised to find that the realization of sustainable development is imminent. Industrialization has led to environmental pollution and extreme weather due to global warming are seriously disrupting the entire Earth's environment, resulting in considerable loss of human life and property. This paper examines the urgent need for sustainable development in the context of accelerating environmental degradation and resource depletion. Through a comprehensive literature review, it addresses key challenges cased by industrialization, climate change, and resource management, with a particular focus on international cooperation and policy frameworks. This study highlights critical issues such as the unequal global distribution of environmental responsibilities and the impact of political and economic instability on sustainability efforts. The paper concludes that addressing these challenges requires global collaboration, technological innovation, and adaptive policy measures that balance economic growth with ecological sustainability. These insights contribute valuable perspectives for policymakers and scholars alike.

Since its establishment in 2003, Tesla has been a pioneer in the global electric vehicle industry and entered the Chinese market in 2014, quickly becoming a leader. However, its success has been accompanied by controversies and scandals. In 2020, a sudden price reduction sparked dissatisfaction among car owners, leading to legal actions. Additionally, incidents of vehicle self-ignition, charging failures, and braking issues in China have raised widespread consumer concerns. Tesla executives often deny responsibility, blaming external factors or improper operation, which further damages the brand image. This report uses qualitative analysis to explore the impact of Tesla's public relations crises in China on consumer loyalty. Research shows that product quality, service level, and technological capability are key factors. Despite Tesla's achievements, its improper handling of crises has weakened its brand. To restore trust, Tesla must focus on consumer needs and expectations, improving service quality.

With the progress of the times, the business cooperative marketing model has started from the form of B2B, B2C, C2C, and O2O have emerged one by one. This is a result of the dramatic changes in the development of industrial marketing due to the interference of factors such as digitalization and globalization. In the current economy, social media marketing has multiple advantages, the only disadvantage being that it requires companies to bring together the strengths of various industries. Only in this way can companies integrate more actively into the flexible mechanisms of global markets dominated by emerging economies and innovative products. After the COVID-19 of 2020, the primitive laws of the market economy inherited from geopolitical and economic policies in the first two decades of the new millennium face dramatic changes. This is because the transformation of the global economic environment and the dramatic change in the macroeconomic structure require completely new rules for the game. More than ever, industrial companies need to redirect the work of all company functions. In the future market, engineers will not work alone to achieve product innovation but will work with customers to design and realize new product generations. The question of how to make social media, the current dominant form of marketing, combined with the advantages of traditional advertising, has become a key reference for industrial companies when pooling audience preferences. The search for consumer psychology takes on a further significance.

Preferred shares, as a special and important financing tool, combine the characteristics of both bonds and stocks. Issuing preferred shares can prevent equity dilution, increase corporate capital, and improve the capital structure and governance mechanisms of enterprises. This study uses the event study method to measure the market impact of preferred shares issuance, analyzing all listed companies in China that issued preferred shares for the first time by the end of September 2023. The results show that the stock prices of related companies generally increased after issuing preferred shares, indicating a positive impact on common stock prices. Overall, issuing preferred shares produces beneficial effects for enterprises.

Digital transformation offers numerous advantages to enterprises, including enhanced operational efficiency, optimized financial risk management, and improved data-driven decision-making. However, it also introduces new challenges such as cybersecurity threats and increased dependence on technology. This paper explores how enterprises can develop comprehensive risk management strategies, strengthen cybersecurity defenses, manage the risks associated with technological dependency, protect data privacy, and ensure compliance. By fully leveraging the opportunities presented by digital transformation, companies can achieve long-term stability and growth. This paper reviews the definition and background of digital transformation, examines its impact on operational risks, and proposes corresponding mitigation strategies, aiming to provide some reference for enterprises undergoing digital transformation.

In corporate strategy, ESG behaviors have emerged as a critical component for driving sustainable growth and enhancing overall enterprise performance. Functioning as a "green engine," ESG integrates principles of environmental stewardship, social responsibility, and strong governance, collectively contributing to a company's resilience and success. By integrating ESG considerations into operational models, companies can not only mitigate risks but also capitalize on new opportunities that arise from a rapidly changing global landscape. This paper examines the multifaceted impact of ESG behaviors on enterprise performance, providing a comprehensive understanding of how these practices can serve as a catalyst for growth within the corporate sector using qualitative research method. Findings reveal that ESG contributes to the long-term sustainable development of businesses by affecting green innovation, refining the investment portfolio, enhancing financial performance, bolstering customer relationships, and other comprehensive factors. The results suggest that companies that adopt ESG principles are better positioned to navigate the complexities of the global market and are likely to experience enhanced brand reputation and stakeholder engagement.

This study analyses the impact and challenges of International Financial Reporting Standards (IFRS) in global financial reporting against the backdrop of rapid economic development in the context of globalisation. The research focuses on the role of IFRS in standardising global financial reporting and enhancing transparency, as well as its contribution to promoting comparability and consistency among multinational enterprises and capital markets. Through literature analysis and case studies, this paper explores the application of IFRS in multinational enterprises and its profound impact on global capital markets. The main research objects include enterprises and capital markets that have implemented IFRS. The findings show that IFRS positively influences corporate international competitiveness and promotes global capital market integration, though it faces challenges such as cultural and legal differences in global adoption. The conclusion highlights the promising prospects for IFRS adoption with the ongoing trends of globalization and technological advancement.

JD’s Financial Analysis considers JD’s stock and financial performance from 2019.1.1 to 2023.12.31, including aspects on profitability, debt and equity evaluation, risk and return management, etc. Methods use to conduct the evaluation include profitability ratios, liquidity ratios, leverage ratios, and relative evaluation models, such as P/S ratios and the CAPM model. JD’s income and profit growth for the five years are steadily increasing, though compared to relative sectors, its growth is mediocre. It not being able to experience any dramatic increase could be due to market competition of similar companies in the e-commerce sector, such as BABA and PDD, which would divide the market share. In terms of stock growth, JD’s stock is following in a downward trend, investments made on JD stocks may result in loss. For JD’s income or stock price to experience any new surge, it would need to be innovative for capturing the market’s attention.

To reduce the information asymmetry between investors and lenders and enable P2P lending platforms to develop more accurate lending standards, this study uses Lending Club data to build a credit scoring model based on machine learning (ML) and artificial neural network (ANN) algorithms. The data is first cleaned and preprocessed through feature engineering techniques, and then trained using XGBoost to evaluate feature importance. To address the class imbalance problem, undersampling is used to divide the dataset into training and test sets. The study also uses grid search and cross-validation methods to determine the best hyperparameters for four algorithms (logistic regression (LR), random forest (RF), lightweight gradient boosting machine (LightGBM), and XGBoost). Finally, this paper compares and analyzes the performance of different models and points out the practicality of different models. The results of this process provide insights into the most important features for predicting creditworthiness and identify the most effective model for improving lending decisions on P2P platforms.

Customer churn poses a significant challenge in the telecommunications industry, leading to substantial financial losses. Retaining customers is widely recognized as more cost-effective than acquiring new ones. However, existing churn prediction models often lack accuracy due to poor data quality, including issues like missing values and inconsistencies. This study explores the impact of data-cleaning techniques on improving the predictive accuracy of churn models, specifically focusing on telecommunications. Using the IBM Telco Customer Churn dataset, it applied methods such as imputation, one-hot encoding, and outlier removal to preprocess the data, followed by testing logistic regression, decision trees, and gradient boosting models. Our analysis revealed that data cleaning notably enhanced model performance, with accuracy improvements ranging from 78.4% to 89.4%, alongside increases in precision, recall, and AUC scores. These results underscore the importance of high-quality data in churn prediction, suggesting that telecommunications companies can benefit from implementing robust data-cleaning processes. The findings contribute to the development of reliable churn prediction models that support effective customer retention strategies.