The development of green finance, especially the development of carbon finance, can greatly stimulate the green innovation power of enterprises. Green innovation is the key driving force for enterprises to cope with environmental challenges and achieve high-quality development. Under the background of "dual carbon" goal, how to dedicate full attention to guiding role of carbon finance in the development of green innovation of new energy enterprises has become an important topic. New energy enterprises are selected by this paper from 2000 to 2021 to analyze A robustness test is conducted to examine the influence in carbon finance on the green innovation of new energy enterprises. It is found that carbon finance helps to increase the level of green innovation of new energy enterprises, and has the least promotion effect on green invention patents. The mechanism test results show that carbon finance is a suitable environment for green innovation in new energy enterprises by reducing The financial limitations of enterprises. Carbon finance is conducive to improving the businesses' R&D expenditures, so encouraging green innovation in new energy businesses. Lastly, we offer specific policy recommendations and useful takeaways from the research to serve as a helpful resource for advancing green innovation in new energy company ventures.

This article first introduces the relevant concepts of quantitative investment and the development history of multi-factor theory as well as the advantages of machine learning in the field of quantitative investment. After that, this article mainly establishes the quantitative stock selection model. The establishment of quantitative stock selection models mainly includes two aspects: the screening of quantitative factors and the construction of Long Short-Term Memory (LSTM) models. The screening of quantitative factors includes data preprocessing and testing the validity of single factors through the Information Coefficient (IC) method. In the establishment of the stock selection model, this article first introduces the advantages of LSTM in time series prediction, then introduces the structure of the LSTM model in detail, and then introduces the use of selected quantitative factors and the LSTM model to build the stock selection model of this article. Finally, the stock selection model constructed in this article is backtested to verify the effectiveness of the model.

The application of Blockchain to enhance the industry's social economic resilience is widely of concern in the science, technology, and pharmaceutical industries. The research discusses the resilience of the Active Pharmaceutical Ingredient (API) in the face of global health challenges and how blockchain can improve supply chain transparency and efficiency through its unique distributed ledger (DLT) technology. The report uses literature review methods to analyze the application of blockchain technology to real-time data sharing in the API supply chain and its impact on improving API supply chain adaptability, optimizing resource allocation, and enhancing risk management capabilities. As can be seen from the result, through the real-time data sharing by the distributed ledger (DLT), the API supply chain can more effectively respond to the fluctuation of the environment and market to enhance its social and economic resilience. This research demonstrates the potential of blockchain technology in optimizing pharmaceutical supply chain management to safeguard human health and well-being.

Since facing widespread skepticism after the 2008 economic crisis, nowadays structured products have gradually become the preferred investment choice for individual consumers, driven by the development of technology and economy. This study analyzes the price of the two most recent structured products offered by Lowes, which closely resemble the FTSE 100 index. It focuses on analyzing the differences in predicted returns resulting from modest structural changes and explores the best solutions for different time periods. The variability is determined by combining historical variability with the GARCH model, based on the principle of Risk Neutral Pricing Theory. The final result is acquired by the application of the Monte Carlo simulation method. Based on the trial results, product B has a greater yield rate compared to product A, although being more conditional. This compensates for the shortage and resulting in a much higher yield rate for product B. Furthermore, based on the findings on the projected output of various investment goods over different time periods, it can be observed that as the length increases, the anticipated return also increases. Ultimately, the sensitivity analysis reveals that the primary source of product risk stems from the impact of risk-free yields on this particular product.

The introduction of the Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect programs represents a pivotal juncture in the evolution of China’s capital markets, signaling a significant move towards increased openness and integration. These initiatives have sparked considerable academic interest, underscoring their potential to transform the financial landscape. This research employs these programs as a quasi-natural experiment to investigate their profound impact on financial stability. Our analysis focuses on a comprehensive dataset encompassing A-share firms listed on both the Shanghai and Shenzhen Stock Exchanges over a thirteen-year period, from 2008 to 2021. Employing a robust difference-in-differences analytical approach, this study seeks to discern the effects of capital market liberalization on the incidence of stock price crashes. The empirical findings reveal a compelling association: greater openness in capital markets correlates significantly with a reduced risk of stock price crashes. This correlation is largely attributed to enhancements in corporate accounting transparency facilitated by these initiatives. Thus, these findings underscore the dual role of enhanced capital market openness in not only fostering stability in stock prices but also in mitigating market risks, safeguarding investor wealth, reducing systemic financial vulnerabilities, and fostering sustainable development across capital markets. This study contributes meaningfully to the existing literature on stock price crash risk by providing nuanced insights and actionable recommendations for policymakers and market participants alike, aiming to further bolster capital market openness and resilience.

With the continuous development of society, the importance of urban economy has become particularly important. At the same time, sports events have also become an important way to enhance the competitiveness of cities and realize the government's improvement of urban economy. this article takes Hangzhou as a representative to conduct corresponding research and analysis. It is found that the Hangzhou urban government has issued relevant policies for sports events to drive the city's economy in order to better organize events, in order to promote the linkage between event preparation and urban development. This article is based on the perspectives of public management, economics, sports and other disciplines, and comprehensively uses research methods such as literature review and case analysis to study the empowerment of urban economic development by the Hangzhou Asian Games e-sports project. This paper mainly explores three aspects: first, how international events promote urban economy, second, how events promote urban infrastructure construction, and finally, how electronic competitions promote urban economic development. Through the research results, it has been found that as the influence and scale of the e-sports industry gradually increase, the proportion and linkage effect of the industry continue to increase. The development of the e-sports industry-enabled city is also full of uncertainties. On the other hand, from the perspective of driving urban economic development through the e-sports industry, it is proposed to strengthen the development foundation of the e-sports industry, improve the development system of the e-sports industry, and expand the implementation path of the e-sports industry development direction.

This paper examines the creation of investment portfolios through convex optimization, multifactor models, and the multi-armed bandit (MAB) algorithms, focusing on the KL-UCB strategy to optimize decisions in uncertain settings. It explores the impact of systematic risk factors using the Fama-French three-factor model, estimating the influence of market, size, and value premiums via linear regression. The use of Monte Carlo simulation is detailed for generating potential asset allocations and calculating their expected returns, volatility, and Sharpe ratios. The optimize minimize function from the SciPy library is employed to construct an efficient frontier and determine optimal asset allocation, aiming to maximize returns or minimize volatility across various risk levels. The findings suggest that the strategy of dynamic weight adjustments combined with the KL-UCB algorithm enhances portfolio returns, particularly during market volatility. The research also reveals a portfolio inclination towards large-cap growth stocks due to the negative impacts of size and value premiums. It concludes that dynamic weight adjustment strategies offer significant potential in optimizing portfolio performance in complex market conditions, though leveraging increases risk and should be carefully managed according to investor risk tolerance.

Using sample data from the China General Social Survey (CGSS2021), this study employs econometric methods based on the basic cost-benefit economics perspective. It investigates the relationship between educational investment and labor income in China in recent years, utilizing the Mincer earnings model as well as an extended model incorporating additional control variables. An Ologit model is constructed to analyze the impact of gender, ethnicity, family economic status, and regional registration on educational attainment among the sample population. Furthermore, the study compares educational returns across different genders, regions, workplaces, and job positions. Finally, based on empirical analysis, suggestions are proposed to enhance individual income levels and optimize the allocation of social educational resources from both individual and environmental perspectives.

The stock market plays a pivotal role in the national economy, while the application of artificial intelligence (AI) in stock price prediction has gained traction. This paper evalu-ates the performance of five advanced deep learning (DL) models: Long Short-Term Memory (LSTM), Self-attention, Convolutional Neural Network-LSTM with attention (CNN-LSTM-attention), Gated Recurrent Unit-LSTM with attention (GRU-LSTM-attention), and CNN-Bidirectional LSTM-GRU with attention (CNN-BiLSTM-GRU-attention), utilizing a decade of data on Amazon’s closing prices. Our results show that the CNN-BiLSTM-GRU-attention model exhibits superior performance, achieving a root mean square error (RMSE) of 1.054589 and a coefficient of determination (R2) of 0.970123, indicative of its proficiency in handling intricate financial data. This paper’s significance lies in its validation of the effectiveness of attention-based ensemble models in stock market prediction, as well as the introduction of the innovative application of the CNN-BiLSTM-GRU-attention model in financial forecast-ing, which holds potential for wide-ranging applicability.

Green consumption represents the fundamental driving force behind the development of an ecologically sustainable society. Consequently, the analysis of green consumption behavior patterns has attracted significant attention from scholars. Nevertheless, there is a paucity of research addressing the impact of reference group type on consumers' intentions to consume in an environmentally responsible way. Building on previous reports, this study presents the concept of individual perceived efficacy and examines distinctions among the impacts of three reference groups on consumers' intentions to consume green products: member, secondary, and aspirational groups. Findings from our experimental analysis demonstrate that the reference group type has a notable effect on consumers' green consumption intentions. Additionally, the green consumption behaviors of different reference groups positively influence consumers' green product consumption intentions by affecting individual perceived efficacy. Moreover, the size of the reference group exerts a moderating influence on the effect of reference group type on green consumption intentions. Specifically, a larger group size exerts a greater positive effect on green consumption intentions. This study advances the theoretical research framework of green consumption and offers insights for manufacturers to refine their marketing strategies for green products. Furthermore, it provides recommendations for environmental organizations and governmental bodies to enhance the promotion of green consumption.