Economic Management and Big Data Application

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As an important grain base in China, the Northeast plays an important role in agricultural production. This paper hopes to analyze the industrial transformation in the northeast, select a series of indicators by AHP, and consider the impact of local agricultural capital investment on the provincial level of agricultural industry transformation through the super-efficiency SBM-DEA model. The results show that although the technical efficiency of agricultural transformation in Northeast China is high, there is a problem of insufficient scale efficiency. The relatively small scale of investment has a greater negative impact on the development of agricultural transformation in the region.

With the rapid development of information technology, the enterprise human resource management system based on cloud computing provides a new solution for global enterprises. The main work of this paper includes the following: This paper points out that the traditional human resource system based on an enterprise’s main body has scattered data storage and limited sharing ability. However, under the current situation of the highly developed Internet, all industries can improve the efficiency of business processing based on the network platform. Using configurable data management technology to solve the problem that the basic information storage format of multi-enterprise employees is not uniform. The system can provide relevant functions of human resource management for enterprise users through the way of service, and the enterprise pays for the purchased services, so as to better solve the problems of high operation and maintenance costs, insufficient data processing capacity, and so on. Using XML-based configuration file management technology, according to the attendance regulations of different enterprises and departments, the attendance policy is set up to realize the flexible management of attendance. According to the correlation model between attendance and salary, the information integration technology of cloud computing platforms is adopted to realize the automation of salary processing. Based on the Map/Reduce parallel processing model, the statistical processing of massive data is realized. The enterprise human resource management system based on cloud computing can provide a complete human resource management solution for the majority of enterprises. Enterprises can only purchase services, saving a lot of development and maintenance costs, but also can customize the functions to better meet users’ needs.

The phenomenon of information cascade widely exists in the financial market, mainly manifested in the herd behavior of investors. With the development of online social networks, stock forums provide conditions for cascading effects. Taking the stock bar forum as the research scenario, this paper used a Python Web crawler to collect forum data and empirically tested the cascading effect of the stock bar Forum. Research shows that, as proxy variables, stock popularity ranking and opinion leader posts are related to investors’ herding behavior.

Taking the county economic development of coal-based cities in Anhui Province as the research object, this paper selects three first-level indicators of thriving businesses, pleasant living environments, prosperity, and 26 second-level indicators to establish an evaluation indicator system of rural revitalization. The entropy weight method is used to determine the weight of each evaluation indicator, and the TOPSIS analysis method is employed to evaluate the county economic development of coal-based cities. The results show that thriving businesses are an important indicator to measure the status of rural revitalization, followed by pleasant living environments, and the weight of prosperity is relatively low. Specifically, the development degree of agriculture, forestry, animal husbandry, fishery, service industries, labor productivity, and reservation rate of cultivated land are important influencing factors of thriving businesses. Forest coverage rate is an important influencing factor of pleasant living environments. The income ratio of urban and rural residents and the per capita disposable income of rural residents are important influencing factors of prosperity. The comprehensive evaluation of rural revitalization in nine counties found that the scores for pleasant living environments and prosperity vary to the greatest extent, while the comprehensive score and the scores for thriving businesses indicator exhibit minimal differences.

In order to promote the construction and development of rural energy Internet, this paper designs corresponding business models for the three stages of rural energy Internet investment - construction - operation, and establishes cost and value measurement models. Based on the actual data in Shandong, five different scenarios are set up to measure the cost and value, and the best means to improve the economic efficiency of rural industries are identified through sensitivity analysis. The results of the example show that considering multiple benefits can bring a better economy to the rural energy industry, and increasing the percentage of direct energy supply is the best way to improve the economy of the rural energy industry.

Based on the panel data of 16 prefecture-level cities in Anhui Province from 2010 to 2020, this paper uses computer technology to construct an evaluation index system for the digital economy and new urbanization, and selects a coupling coordination model for calculation and quantitative analysis. The results show that during the sample period, the coupling and coordination level of the digital economy and new urbanization in 16 prefecture-level cities in Anhui Province showed an upward trend, and the overall spatial distribution characteristics of “high in the south, secondary in the middle, and low in the north” were presented. The coupling and coordination degree of provincial capital cities and areas south of the Huaihe River is higher than that between Jianghuai and north of the Huaihe River. In 2020, cities in Anhui Province will basically enter the advanced coordination level in an all-around way.

The agricultural product incentive policy for quality and safety aims to promote the overall continuous improvement of the agricultural products market’s quality and safety level. But its market effect in the case of information asymmetry determines its actual role. Based on the lemon market theory, this study designed a two-stage single-group post-test experiment. The first stage simulated the consumer’s agricultural product selection behavior in the case of information asymmetry, and the second stage verified the market effect of the agricultural product incentive policy for quality and safety. The results show that under the condition of information asymmetry, the agricultural product incentive policy for quality and safety is indeed conducive to improving the consumers’ perceived quality. Only when the improved perceived quality exceeds the overall average quality of the market, are consumers willing to pay and buy. This study verifies the market effect of agricultural product incentive policy for quality and safety, and reveals its boundary conditions when it works, which has great guiding significance for practice.

It is not only the national strategic instruction but also the development trend for Lingang to make full use of its own advantages to drive the coordinated development of industries in the Yangtze River Delta. This paper first analyzes the internal and external advantage endowments of Lingang based on the map. Then it selects several elements, using SPSS gray correlation degree analysis, and draws line charts, tables, and radar charts. The specific policy of industrial collaboration between the Lingang Special Area and other regions (cities in Zhejiang Province, Jiangsu Province, and Zhejiang Pilot Free Trade Zone, etc.) is obtained.

The tea industry is the characteristic and key industry of rural revitalization in Sichuan. The study on the rationality of its production space layout is conducive to the improvement of the regional tea industry development level and competitiveness. With the help of the Shift-share Method, this paper estimated the competitiveness of tea in Sichuan Province from 2015 to 2019. Meanwhile, ArcGIS software was used to draw the regional layout map of tea production. Studies have shown that: The scale of tea in Sichuan Province keeps expanding and is greatly affected by industrial competitiveness. The spatial layout is unreasonable and the tea production level varies greatly among regions, showing the pattern of lacking comprehensive advantage producing areas, fewer structural and competitive advantages producing areas, and too many comprehensive disadvantage producing areas. To improve the competitiveness of the Sichuan tea industry, this paper puts forward relevant measures to optimize the regional layout.

We estimate the carbon emissions of Hubei Province from 1997 to 2019 based on the Hubei energy balance sheets and national and Hubei statistical yearbook data by the IPCC Emission Factor Method. We construct the carbon peak prediction model for Hubei province based on the STIRPAT extended model to predict the peak carbon emissions and peak time in Hubei province from 2020 to 2050 under different scenarios. Results show that the overall carbon emissions in Hubei Province have been increasing, and the average increase was about 3.8%, but the carbon intensity is decreasing year by year, and the average decline was about 7.9%. The peak time of the low-speed development scenario is 2022, and its peak value is 343.6865 million tons. The peak time of the medium-speed development scenario is 2026, and its peak value is 361.9586 million tons; The peak time of the high-speed development scenario is 2030, and its peak value is 374.5220 million tons. The population size and economic development affect carbon emissions in Hubei Province. The construction of a scientific and technological support system and the adjustment of the energy structure and industrial structure can promote Hubei Province to achieve the carbon peak as soon as possible.

Digital platform is an important infrastructure on which the development of the platform economy depends. The identity evolution of the platform has gone through three processes: the “perfect store”, the platform business model, and the platform ecosystem. This evolution process shows that technology logic and capital logic are constantly infiltrating into the underlying logic of the platform economy, which is fundamentally not conducive to the healthy development of the platform economy. Therefore, the healthy development of the platform economy must start from the perspective of data governance and algorithm governance. Data governance requires a clear definition of the rights and responsibilities of data participants in the value production process. Algorithm governance requires reconfirmation of the inherent principles and values of algorithm design.

The logistics mode selection has always been one of the main problems that plagued the cross-border e-commerce industry. Among the existing modes, the overseas warehouse is recognized as an ideal cross-border e-commerce logistics mode, but there are few studies on whether they are suitable for establishing overseas warehouses for specific countries and regions. This paper studies the countries along the “Belt and Road” for this issue. Firstly, the potential export scale of China’s cross-border e-commerce to countries is analyzed by constructing the evaluation index system and the CRITIC method. Then the logistics performance index is used to measure the logistics capability level of each country. And then, the K-means clustering method is used to conduct a cluster analysis on the “Belt and Road” countries from the above two aspects. Finally, the cross-border e-commerce logistics mode tactics are proposed; that is, based on regional division, we can select the first and second-class countries to set up overseas warehouses and radiate other countries in the region through international express and other logistics modes.

This paper used the Theory of Planned Behavior ((TPB) and Norm Activation Model (NAM) to construct a theoretical framework of low-carbon tourism behavior intention influencing factors. The results showed that attitude is the strongest influencing factor, tourists’ participation in low-carbon tourism attitude is affected by their emotions and ecological value related to humanistic harmony, and the new ecological paradigm and time-saving also have a significant impact on behavioral intention, but the effect of money-saving factors is not significant. The comprehensive model developed in this study can predict the intention of tourists’ low-carbon tourism behavior and help relevant departments put forward countermeasures and suggestions combined with the research conclusions.

The telecommunication industry has encountered severe hits since the eruption of the trade Conflict between the US and China, which exerts an influence on stock price fluctuations. This paper builds a VAR model and an ARMA-GARCH model to analyze the impact of the exchange rate and tariffs on the communication industry index. This article demonstrates that the depreciation of offshore RMB can boost the development in yields during the short term under the trade disputes between the USA and China. However, imposing tariffs has little effect on daily volatility, and the rate of return depends on which effect dominates the communication industry. Therefore, it is necessary for policymakers to respond calmly to the tariff problems and introduce a series of preferential policies to boost the development of relevant enterprises. In addition, it is recommended that investors could strengthen their cooperation with foreign countries to occupy the high-end market of the communication industry chain.

This paper mainly studies the impact of carbon emission trading policies on carbon dioxide emission intensity. Panel data from 30 provinces (municipalities) from 2005 to 2017 are selected, and the research methods of propensity matching (PSM) and difference method (DID) are used. It is found that the implementation of the carbon emission trading rights policy has a significant impact on carbon dioxide emissions. Compared with the pilot areas, the carbon emission trading rights policy can significantly reduce the carbon dioxide emission intensity in the pilot areas. Given this, on the one hand, it can be shown that certain emission reduction policies have a positive impact on reducing carbon dioxide emissions. On the other hand, it has certain practical significance for the unification of carbon dioxide emission rights in China and realizing China’s dual carbon goals.

As the concept of sustainable development progress, enterprises need to balance economic and social benefits and achieve high-quality economic development as micro-entities. Moreover, ESG is closely related to high-quality corporate development as an essential tool to achieve high-quality economic development. This paper empirically examines the relationship between ESG information disclosure and the high-quality development of enterprises based on the entropy-weighted TOPSIS method, using a sample of Chinese A-share listed companies from 2009 to 2020. It is found that ESG disclosure can promote high-quality corporate development by facilitating corporate innovation. Heterogeneity tests show that ESG disclosure’s effect on enterprises’ high-quality development is more significant in non-state enterprises and regions with a high degree of marketization.

Recently, the impact of share pledges on information disclosure of listed companies has been a hot topic. To explore the disclosure of the amount and tone of the R & D text information in the annual reports during the pledge period of the controlling shareholders’ shares, this thesis use python programming to crawl the R & D text data for the 2014-2021 annual reports of A-share listed companies. The study found that during the pledge of the controlling shareholders’ shares, the company will reduce the amount of R & D text information in the annual report and is more likely to disclose positive R & D text and cover up negative R & D information. The research conclusion of this paper has certain policy reference significance for regulatory authorities.

The purpose of this paper is to study the actual economic development of cities in Heilongjiang Province since the occurrence of COVID-19. The Stata software is adopted to carry out descriptive statistics, correlation analysis, regression analysis, and factor analysis on the data of the existing statistical yearbook of Heilongjiang Province in 2021, and then various empirical analysis results are obtained. Through correlation analysis, it can be seen that the regional GDP of each city in Heilongjiang Province has the highest correlation with public financial revenue and total loans. Through regression analysis, it can be seen that the regional GDP has the highest correlation with the gross output value of agriculture, forestry, animal husbandry, fishery, and public finance revenue. It can be seen from the principal component factor analysis that the comprehensive strength of Harbin is the strongest among all cities in the province. Finally, according to the results of various empirical analyses, I give my suggestions. First, give play to the radiation and driving role of Harbin’s modern metropolitan area. Second, build the backbone of the rise of the eastern city cluster industry. Finally, consolidate the achievements of urban green economy development in ecological areas.

Talents are an important and valuable resource for regional economic development. As of the end of 2021, the total population of Liaoning Province has reached 42.295 million, ranking 14th in China. However, according to the provincial panel data from 2008 to 2018, the dual problems of population loss and the outflow of talent are plaguing the economic development of Liaoning Province. According to the Gray Relation Analysis (GRA), one can construct the talent structure and regional economic index system of Liaoning Province, find out the talent elements that stimulate the regional economic growth of Liaoning Province through the empirical analysis of coupling correlation degree and put forward four suggestions to balance the talent structure and help the balanced development of the regional economy of Liaoning Province.

The 14th Five-Year Plan proposes to promote the healthy development of the platform economy and shared economy. In the system of shared manufacturing, manufacturing resources and capabilities can be intelligently sensed and connected to the IoT / shared manufacturing service platform system in real-time, and then managed by the IoT service platform in a unified manner. After virtualization and servitization, these manufacturing capabilities and resources are transformed into shared manufacturing services. With the support of government policies, the shared manufacturing industry is flourishing with IoT technology and information platform. To promote the high-quality transformation and upgrading of the manufacturing industry, we use evolutionary game theory and software simulation to analyze the impact of government subsidies on the shared manufacturing industry and find that appropriate government subsidy can stimulate the transformation of enterprises and promote the development of shared manufacturing models.

This paper constructs the cultural tourism industry development index. Based on the principal component analysis, by collecting the relevant data on China’s inter-provincial cultural tourism industry from 2015 to 2019, one can measure the development level of the cultural tourism industry. Based on the conclusion, one can use the fixed effect model to analyze the influencing factors of the development of the cultural and tourism industry. According to the results, one can put forward the relevant countermeasures.

Rural tourism plays an important role in rural revitalization. Revealing the driving force of rural tourism development is the key to realizing the sustainable development of rural tourism and the hot spot of tourism and rural revitalization research. Using principal component analysis (PCA) and grey relational analysis (GRA), this paper takes 33 national key rural tourism villages in Qinghai as the research object to explore the driving forces and influencing factors of the sustainable development of rural tourism in Qinghai Tibet Plateau. The results show that: (1) The driving forces for the sustainable development of rural tourism in Qinghai Province mainly include rural service quality, ecological environment, and economy. (2) Rural service quality is driven by water, electricity, heating, and communication facilities, the ecological environment is driven by the improvement of village appearance and environment, and economic driving is driven by the per capita disposable income of towns and villages. (3) In the future, we must continue to strengthen rural infrastructure building, enhance the appearance and environment of villages, and raise the per capita disposable income level of rural cities. We will focus on developing villagers’ skills, improving lodging and catering operation standards, increasing the number of tourist reception points and urbanization rate, and raising villagers’ awareness of environmental and ecological protection, water conservation, grassland conservation, and other issues. We hope that this will promote the sustainable development of rural tourism in Qinghai Province.

Global crude oil prices had risen sharply since February 2022, when news broke that Russian soldiers had invaded Ukraine. Many other industries have been affected, including green energy vehicles and transportation. This paper uses XPENG Automobile and the air transportation industry as examples, selecting stock data from June of last year and modeling and analyzing the data using the ARMA-GARCH method. Key findings from this research show that the rise in crude oil prices will inevitably increase the cost of using cars for consumers; hence, the rise in oil prices will indeed increase the sales of Xpeng Motors. Automobile manufacturing involves various sub-sectors of the manufacturing industry, and rising oil prices will increase production costs in these industries. When the price of oil rises, the cost of other items that are derived from oil, such as raw materials, plastics, and every kind of molded good, also increase. The rise in the cost of energy leads to an increase in the cost of manufacturing, and an increase in the cost of oil leads to a rise in the cost of transportation. Consumers will now have less money available for spending resulting in a decline in the economy of the sector. Putting more money into oil and gas companies would not be advisable due to the unstable supply.

“Blind box +X” is a popular marketing method in recent years. It is of theoretical and practical significance to explore the influencing factors of consumers’ purchase intention in this situation. Based on the stimulus-organic-response (S-O-R) model, this paper constructs the influencing factors model of blind box consumers’ purchase intention. Blind box characteristics of the unknown attribute, social attribute, and aesthetic attribute were taken as antecedent variables, with perceived value and consumer trust serving as mediating variables and brand cognition serving as moderating variables. Questionnaire survey and data analysis were used to conduct empirical research to verify the effectiveness of relevant influencing factors, in order to provide a reference for the sustainable development of more “blind box +X” industry.

Investor confidence can influence the decision-making behavior of investors themselves and others to a certain extent. Based on the data on investor confidence and commercial credit financing of A-share listed enterprises in China from 2010 to 2021, this paper uses a multiple linear regression method to establish a model and observes the multiple linear regression relationship with the help of stata16 metrology software. The results show that there is a significant positive correlation between investor confidence and the commercial credit financing ability of enterprises. This relationship only exists in non-state-owned enterprises, and the lower the concern of enterprises in research reports, the stronger the promotion effect of investor confidence on commercial credit financing.

The establishment and improvement of an innovation risk compensation mechanism for small and medium-sized enterprises to effectively promote the development of innovation and entrepreneurship have become the focus of China’s “Mass Entrepreneurship, Mass Innovation” strategy implementation. This paper aims to improve communication between the government, insurance companies, small and medium-sized businesses, financial institutions, and intermediaries to finance subsidies, insurance, and loans, among other things. It uses the supply chain to realize the coordinated compensation for the innovation activities of small and medium-sized enterprises. At the same time, combined with the advantages of big data, a small and medium-sized enterprise innovation risk compensation system is constructed to compensate the small and medium-sized enterprises’ scientific and technological innovation development and risk losses.

Common prosperity is the most essential feature of socialism. Based on the regression analysis of 822 villages in 21 cities and states in Sichuan Province, this paper explores the mechanism of common prosperity in different types of villages governed by people. The results show that village cadres of different types of able men have a significant driving effect on the increase of household income. Moreover, the village cadres of economically able people have a stronger driving effect on the increase of peasant household income than the village cadres of intellectually able people. The village cadres of economically capable persons drive the increase of peasant household income mainly through the development of characteristic industries. The village cadres of intellectually capable persons drive the increase of peasant household income mainly through the establishment and introduction of various industrial management organizations. The follow-up implementation of the rural revitalization strategy should focus on providing a stage for village cadres of different types of talented people to display their talents.

This paper presents a comparative study of the linear and nonlinear relationship between energy consumption and economic growth in China and explores the dynamic dependence between the two. The study has some reference value for the formulation of China’s energy policy. Specifically, the study of the nonlinear relationship between energy consumption and economic growth helps to understand the relationship between the two better. A study of the nonlinear relationship between energy consumption and economic growth will help to grasp the interrelationship between the two in a more rational manner, providing the basic premise for the formulation of energy development strategies that will enable China to achieve healthy economic development and sustainable social development. The study of the nonlinear relationship between energy consumption and economic growth helps to grasp the interrelationship between the two more rationally and provides the basic premise for the formulation of energy development strategies, thus enabling China to achieve healthy economic growth and sustainable social development.

The development of red tourism is an important way to realize the strategy of the rural revitalization along the Long March of the Red Army. Based on the perspective of the harmonious development of red tourism and rural revitalization, this paper constructs the evaluation index system of red tourism and rural revitalization by using projection tracing method and coupling coordination model. The comprehensive evaluation index and coupling coordination level of red tourism and rural revitalization in 15 provinces along the Long March of the Red Army are calculated. The dynamic evolution trend and cluster types of the coupling coordination relationship were studied by Kernel density estimation and cluster analysis, to explore the coupling and coordination mechanism of red tourism and rural revitalization, and to provide theoretical basis and policy reference for promoting the implementation of rural revitalization strategy and high-quality development of red tourism along the Red Army Long March.

As the integration development of the Yangtze River Delta area is defined as a national strategy, both the “integration” and “high level” have become the common development goals of the three provinces and one municipality within the area. Therefore, how to promote high-quality agricultural integration development with the internal resource flow within the Yangtze River Delta so as to solve the problems of large differences and imbalances in the agricultural development in this area is of great importance. Based on the policy orientation for agricultural development, the fiscal expenditure related to agriculture, the total agricultural machinery power, and the cultivated land area are selected as the three independent indicators to analyze the impact they would make on the total agricultural output. By using a multiple linear regression model, we have found that both fiscal expenditures related to agriculture and the cultivated land area will make a significantly positive impact on the total output, while the machinery power will help to improve the output value to some extent.

To explore the relationship between social capital, psychological capital, and the entrepreneurial performance of returning farmers, this research from the perspective of social capital and psychological capital, using 400 sample data collected in Sichuan Province to verify through structural equation modeling, the empirical research has three findings. Firstly, the social capital and psychological capital of returning entrepreneurial farmers affect the identification of entrepreneurial opportunities positively. Among them, social capital plays a more significant role. Secondly, entrepreneurial opportunity identification affects the entrepreneurial performance of returning farmers positively. Finally, entrepreneurial opportunity identification plays a mediating role between social capital, psychological capital, and the entrepreneurial performance of returning farmers.

Based on the data of CLDS2018 (2018 China Labor Dynamics Survey), using the binary logistic regression model method, the rural revitalization evaluation index system is constructed from five dimensions of “industry prosperity, ecological livability, rural civilization, effective governance, and affluent life.” It aims to explore the impact of rural revitalization strategy on the new generation of migrant workers returning home to start a business. The research shows that: industrial prosperity, rural civilization, effective governance, and prosperous life have varying degrees of influence on the new generation of migrant workers returning to their hometowns to start a business, and the impact on ecological livability is not significant. In this regard, with the help of digital empowerment, Internet information dissemination, and other technologies, policy recommendations for improving the new generation of migrant workers to return to their hometowns to start businesses are put forward.

This study will take the EVA-BSC evaluation model as the research object, combine Web technology and data analysis and processing technology, choose Spring, SpringMVC and MyBatis as the system development framework under J2EE specification, and use Java language to complete the construction of enterprise performance evaluation system. The system fully considers the actual needs of enterprises, takes BSC as the main body of performance evaluation and EVA as an important evaluation index, improves the performance evaluation mode that only evaluates a single financial index, and takes Web application as the presentation form. Through the convenient and efficient operation, it can realize the effective mining of enterprise value drivers, quickly complete the establishment of enterprise performance evaluation system, and put forward a comprehensive application solution for modern enterprises to scientifically evaluate their strategic positioning.

The influence mechanism, path of independent innovation, and open innovation of Liaoning’s equipment manufacturing industry on its international competitiveness is not only an important issue to be studied by the theoretical circles but also a realistic issue that the government, relevant departments, and enterprises need to pay attention to. This paper first analyzes the current situation of Liaoning’s equipment manufacturing industry from the aspects of industrial production capacity, current market situation, and industrial scale. Then, it evaluates the international competitiveness of Liaoning’s equipment manufacturing industry by using the RCA index and TC index. It constructs the evaluation index system of international competitiveness of the equipment manufacturing industry and uses SPSS software to do the factor analysis of the international competitiveness indicators of Liaoning’s equipment manufacturing industry and gets the result of Liaoning’s equipment manufacturing industry’s international competitiveness. Lastly, it analyzes the factors influencing the international competitiveness of Liaoning’s equipment manufacturing industry and puts forward countermeasures to improve the international competitiveness of Liaoning’s equipment manufacturing industry from multiple perspectives.

Under the policy “Building National Strength in Transportation”, the integration of provincial ports guided by resource integration and complementary advantages is becoming a new development trend in China’s ports. Combined with the current situation of port integration, this paper compares the competitiveness of 11 major ports in northern countries by constructing an entropy weight TOPSIS evaluation model to make an objective evaluation of the Liaoning Port Group horizontally. Not only that, combined with the development status of China’s shipping industry, this paper focuses on analyzing the changes in the competitiveness of Liaoning Port Group before and after the integration and provides references and suggestions for Liaoning Port Group in its future operations.

With the advent of the era of big data, the modernization of government governance requires big data. Big data is becoming an effective tool for the modernization of government governance in various countries. Using the methods of bibliometric analysis, content analysis, and visual analysis, the literature related to big data and government governance selected from the Web of Science was studied. The results show that scholars have focused on the reform of government governance in the era of big data, the practice of big data in specific areas of government governance, and the opportunities and challenges that big data brings to government governance. Although some basic theoretical results have been achieved in this field, some aspects still need further research. In the future, it is necessary to combine social reality and pay attention to the application research of big data in specific governance fields.

Using the green patent data of A-share listed companies in China’s heavy pollution industry from 2011 to 2020, this paper empirically tests the effect of tax reduction on the green innovation level of enterprises. The findings are as follows: First, tax reduction can significantly improve the level of green innovation. Second, R&D investment is one of the influencing mechanisms of tax reduction to improve the level of green innovation. Third, the incentive effect of tax reduction on the green innovation level of enterprises is more significant in non-state-owned enterprises, and the green innovation level increases by about 3.013% for every 1% reduction in tax burden. Fourthly, after the robustness test, the conclusion of the paper still stands. Finally, this paper puts forward some suggestions on how tax reduction can improve enterprise green innovation.

From the perspective of the relationship between China’s new energy development, energy transition, and geopolitics, this paper analyzes the important role of energy strategic layout in the country’s rise. Powered by the blossoming of China’s new energy, the increasing degree of intensification, and the government’s promotion of new energy, the whole industry has put forward higher requirements for the efficiency and intelligence of production equipment. It has drawn the concerns of many investors over time. For growth companies, stakeholders are more interested in whether the company’s share price is in line with the fundamentals. Thus, this article applies the Schwartz-Moon model in determining the proximity of the company value to the market value at the end of 2020. It was found that the estimated firm value was 0.5% below the market value at the time, suggesting that the Schwartz-Moon model is a good fit for the market’s perception of the case firm’s fundamentals. The traditional DCF valuation method was used as a comparative study tool which was outshone by the Schwartz-Moon model with more accurate valuation results. The results provide insights into the applicability of the Schwartz-Moon valuation model for booming new energy-related companies while demonstrating the limitations of traditional valuation methods, providing enlightenment for valuing new energy firms using data analysis for further research.

The price of bitcoin has changed significantly in recent years. At the same time, there are abundant media attention and news coverage of Bitcoin. This research applied Python to crawl Bitcoin daily exchange data and Google search volume index (SVI) from Coinmarketcap.com and Trends.Google.com and to conduct data cleaning to make the collected search volume data unified and comparable in a time series. This paper focuses on the association between SVI and changes in Bitcoin price. Specifically, this study looks into correlations between SVI, Bitcoin returns, and price volatilities by conducting a vector autoregression model. The results show that SVI has an evidently positive influence on both the return and volatility of Bitcoin, which means SVI could be used as an effective predictor in forecasting Bitcoin earnings. This paper enriches research on the impact of the Bitcoin search volume index on prices, which indicates that SVI is a meaningful evaluation index to invest in Bitcoin.

Promoting scientific and technological innovation and high-quality development of small and medium-sized enterprises is an important part of implementing the national innovation-driven development strategy and manufacturing power strategy. At present, with the rapid development of “digital finance” some “digital finance” tools can effectively alleviate the difficulties of “financing difficulty” and “expensive financing” of SMEs’ technological innovation and provide effective support for SMEs’ technological innovation. Digital transformation is an effective way for our country’s small and medium-sized enterprises to achieve high-quality scientific and technological innovation development. Based on the theoretical framework of TOE, this paper adopts the necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) methods to reflect the so-called technical, organizational, and environmental aspects. The specific action mechanism of various antecedent conditions, including data algorithms, is based on the digital transformation of enterprises. It provides a useful reference for understanding the role of digital finance between the government and enterprises. It also provides new ideas and empirical evidence for designing and improving relevant science and technology innovation policies, which have important theoretical and practical significance.

With the development of digital technology, digital platforms have become important channels for audiences to watch movies. When studying the performance of movies on digital platforms, scholars often only focus on one of the temporal factors or non-temporal factors, but the performance of movies is affected by both factors. Based on relevant data from 128 movies collected by big data technology, this paper uses the OLS and mediating effect models to analyze the influence of the release time interval and other non-time factors on movie performance. The key findings are as follows: (1) box office plays a decisive role in the performance of movies on digital platforms, and quite a few other non-time factors have an impact on the performance of movies through the box office as a mediating variable; (2) the release time interval of the DVD&Blu-ray channel has no effect on the performance of movies in this channel; (3) there is a U-shaped relationship between the release time interval of digital platforms and the performance of digital platforms; (4) movie ratings have an additional impact on the performance of movies in network platform. This paper explains the interaction mechanism between movie release channels in a more realistic way, which has a certain reference value for relevant practitioners and scholars.

In this paper, by using the R language, the published Google stock data obtained from the New York Stock Exchange are used to test the performance of the ARIMA model, KNN model and artificial neural network model for stock price prediction. Experimental results show that the prediction accuracy of the neural network model is higher than that of the other two models. This finding will give us some guidance when we choose the stock price and forecast model.

In 2022, the outbreak of the Russia-Ukraine war, the disruption of the global supply chain, and many other factors have had a huge impact on the global economy. To control the high inflation rate and curb economic overheating, on March 16, the Fed raised interest rates for the first time since 2018 to curb inflation and stabilize prices. The Fed’s interest rate hikes have a significant impact in many areas, including the stock market. To find the relationship between them, the paper uses computer simulation modeling. This paper first introduces the impact caused by the Fed’s interest rate hikes on different markets and analyzes the spillover effect. Then this paper selects data from the USD exchange rate and Apple stock price before and after the Fed’s interest rate hikes and uses the ARMA-GARCH model to model and analyses the data to study how the Apple company has been affected by the Fed’s interest rate hikes and predict the future development of the Apple stock price. Finally, this paper analyzes the factors that will affect the stock market. The analyzing process is mainly based on the Stata application.

This paper uses computer technology to analyze the data of the Yangtze River Delta from 2003 to 2019 and studies the impact of borrowing scale and borrowing function on total factor productivity. The results show that borrowed size and borrowed functions can accelerate the promotion of total factor productivity: technical efficiency and technological progress. The former can be improved by borrowed size and borrowed functions, and the latter can be further advanced by upgrading the industrial structure. Therefore, it is necessary to strengthen the accessibility between cities, improve technical efficiency and technological progress, and promote the matching of the industrial structure with urban scale and function, thereby realizing the integrated development of the Yangtze River Delta.

On July 27, the Federal Reserve announced that it would raise the benchmark interest rate by 75 basis points to the range of 2.25%-2.50%, which is 75 basis points for two consecutive interest rate hikes. The interest rate returned to a high level in 2019, which is near the peak of interest rates. However, the Fed’s rate hike is not over, and the market expects another wave of rate hikes in September. The impact of interest rate hikes has been significant in many areas, including the stock markets in China. This paper is based on Stata to analyze data, selecting the stock indexes in China (Shanghai Composite Index, Shenzhen Component Index) and the U.S. (Nasdaq Index, S&P 500 Index) and intercepting their yields after June 2021. The VAR model and ARMA-GARCH model are used to analyze the data, studying how the Chinese and U.S. stock indexes have been affected by the U.S. monetary policy, and making suggestions for the future development of the Chinese stock market based on data analysis.

By collecting financial big data from the Wind database, this paper takes the panel data of listed companies on the SME Board and GEM Board of Shenzhen Market from 2011 to 2020 as samples, uses the multiple linear regression method to construct a model for empirical analysis, and observes the relationship between multiple linear regression to explore the impact of R&D expenditure on future earnings. The results show that: (1) R&D expenditure positively correlates with future earnings. R&D expenditure has a positive impact on future income level. (2) The correlation between R&D expenditure and future earnings has decreased significantly over time. The impact has weakened with time, which means that the R&D income of listed companies in Shenzhen has shown a downward trend in recent years.

In the era of Industrial Revolution 4.0, digital transformation has become an important strategic choice for the development of logistics. In order to comprehensively evaluate the digital transformation capability of logistics, this paper analyzes the influencing factors and constructs an evaluation index system from five aspects. Then, the index weight is calculated by the structure entropy weight method, the evaluation score is obtained by the Delphi method, and the evaluation results are divided into five categories. Finally, an example is given to illustrate how the proposed evaluation method can effectively support digital transformation logistics decisions.

For a long time, there has been a certain relationship between equity balance and performance. We selected various data of listed companies in China’s game industry from 2017 to 2021, analyzed various data using computer software, conducted a multiple regression model using SPSS software, discussed and tested the impact of R&D investment on the performance of game companies, and discussed its moderating effect on the relationship between these two variables from the perspective of equity balance. The research shows that R&D investment has a significant positive correlation with the current performance of enterprises. Further analysis of the lag effect of R&D investment shows that R&D investment has a significant positive lag effect on enterprise performance, and there is a negative adjustment between equity balance and investment affecting enterprise performance.

This paper examines the relationship between institutional cross-holdings and the risk of stock price volatility of listed companies using data from 2007-2020 for Chinese A-share listed companies, using a panel OLS method of regression. It is found that institutional cross-holdings significantly inhibit the risk of stock price volatility, and the conclusion is still robust after considering endogenous problems. Cross-holding institutions are of great significance in giving full play to the role of “stabilizer” in the capital market and preventing and resolving major financial risks.

To achieve China’s goal of “carbon peak and carbon neutrality”, the energy transition and development are of vital importance and urgent, and there are still many major issues that need to be studied urgently. In this context, based on the existing literature, this article combs and reviews the latest developments in the application of structural equation modeling methods in the energy industry from the dimensions of energy safety production, energy consumption behavior, energy enterprise economy, and new energy development, and believes that Has broad application prospects in the future energy transition.

The holiday effect is one form of the calendar effect, which means the abnormal return or abnormal volatility of the stock market before and after the holiday. The holiday effect can be explained by the behavioral finance perspective. Investors could adopt an active investment strategy to take advantage of the holiday effect and achieve excess returns. This paper examines the holiday effect on the Chinese stock markets of Shanghai and Shenzhen between January 2008 and August 2022. GARCH-Mand EGARCH-M models are used to observe the existence of holiday effects in the mainland Chinese market before and after the Chinese New Year and National Day.

The results suggest that a significantly positive Chinese New Year effect exists for 2 days before, 3 days before, and 2 days after the Chinese New Year in the Shanghai market. There is no or very few holiday effect in the Shenzhen market. The positive National Day holiday effect exists 1 day before, 1 day after, and 2 days after the holiday. Besides, the results show that 1 day after the holiday is more volatile than other days around the holiday in Shanghai and Shenzhen stock markets. In addition, the study found evidence of the mainland China market reacting asymmetrically to positive and negative news.

This paper selects 11 indicators such as expected output and unexpected output of prefecture-level cities in China, with a total of 100280 data as samples. Based on the SBM model and GML index, the GTFP of prefecture-level cities in China is measured. MaxDEA 7 Ultra and Stata software were used to study the effect and internal mechanism of manufacturing market power on urban GTFP based on the double-fixed effect panel regression model. The results of the study found that: (1) After 2015, China’s urban GTFP showed a continuous growth trend. Taking 2015 as the cut-off point, the growth source of urban GTFP has gradually changed from technical efficiency to a technological progress rate. (2) The greater the contribution of the manufacturing sector to the local economy and the performance assessment of officials, the more likely it is to be exempted from strict environmental regulations, which will further inhibit the improvement of urban GTFP.

In recent years, compared with real assets, the research on data assets in Internet service enterprises is still in a very primitive stage, and there are still many challenges in the evaluation of data assets. As an emerging industry, China’s Internet service industry is still in the early stage of development, which is not mature and stable enough. It faces great challenges in asset evaluation, and there is not a fair and suitable data asset evaluation system, which makes it difficult for Internet service companies to measure and estimate the value of data assets. This paper is going to enrich the related theories of the use of data information processing models and data asset value measurement for Internet service enterprises. It discussed present data asset measurement methods and chose the multi-period excess return method for data asset value measurement and valuation. The research improved the multi-period excess return method and proved the importance of data asset valuation for Internet Service Enterprises, which can improve the development of data asset value measurement and valuation systems for Internet service enterprises.

The development of information technologies and the emergence of e-commerce platforms have driven a new wave of research on consumer behavior, but the current literature is full of information, cross-disciplinary and complex, and the research lineage is not yet clear. This study explores the main hotspots, origins, and evolution of current research based on 291 core Chinese and English journals with the bibliometric visualization tool CiteSpace, which explains the connotation of consumption behavior from the SOR perspective. The study finds that consumer behavior research based on the SOR theory paradigm includes two categories of topics, consumer perception and brand building, and three evolutionary stages: the beginning, the early stage, and the prosperous stage. It composes a framework of consumer-brand interaction value co-creation. By building a theoretical library of consumer behavior based on the SOR perspective, this study clarifies the interaction pattern of consumer behavior, provides theoretical insights for the review research based on quantitative tools of literature, and provides a reference for enterprises to grasp the value proposition and improve marketing and service level.

Digital transformation is the trend. Since the 14th Five-Year Plan period, the wave of digitalization has swept through various industries. Central enterprises should also follow the trend of the times. To support the index management of overseas asset operations and improve the digitalization of enterprises, this paper designs the process of constructing the overseas asset operation index model. The process of constructing the index model includes four steps: index screening, normalization, weight calculation, and index calculation. The index model can summarize the experience, guide current project management, and predict future development trends.

Convertible bonds have become an important investment and financing tool that can not be ignored nowadays. Given the aging trend in China and the prospect of the healthcare industry, this paper makes a pioneering study on the pricing efficiency of convertible bonds in China’s healthcare industry. Based on the Least Square Monte Carlo method, this paper calculates the daily model prices of 27 convertible bonds in the first six months of their respective equity conversion periods using the parameter data at the daily level and analyzes the overall deviation between the model prices and the actual prices of these convertible bonds in this pricing period using the relevant statistics, thus approximately reflecting the pricing efficiency of the convertible bonds in the whole health care industry. The empirical results show that the price of convertible bonds in the whole healthcare industry may be overvalued in the first six months of the conversion period. There is a positive correlation between the rating of convertible bonds and pricing efficiency. During the pricing period, the overall pricing efficiency of each rated convertible bond in the first half period is higher than that in the second half period. Although the pricing efficiency in the second half period is low, there is no further downward trend.

Based on the CSMAR database, multiple linear regression models were constructed, which selected a sample of A-share listed agricultural enterprises data from 2011 to 2019, and the data were analyzed and processed by Stata software. The relationship between board independence, market competition, and the growth of agricultural enterprises was examined, and the interactive effects of market competition and board independence on that were discussed. The results showed that both market competition and board independence had a positive effect on the growth of agricultural enterprises, and the effect was an alternative. The study may provide a beneficial reference to the growth and governance of agricultural enterprises.

To analyze the price discovery function of the rapeseed oil futures market is not only helps to improve the price formation mechanism of the oil futures market, but also constructive to the real economy, and promotes the development of the rapeseed oil industry. In this paper, we take the daily price data of the main contract of rapeseed oil futures of Zhengzhou Commodity Exchange from January 2019 to December 2021 and daily spot price as samples. The empirical analysis was undertaken by Stata with the VAR model, Granger model, VECM model, impulse response, and variance decomposition. It was found that the spot price of rapeseed oil played a dominant role in price discovery, while the futures market does not have the capacity for price discovery. According to the conclusion, this paper shows four suggestions to promote the price discovery function in rapeseed oil futures.

To improve the efficiency of enterprise financial management and assist enterprise managers to make business decisions, this paper studies the enterprise financial management and decision-making system based on big data technology. By establishing a Hadoop cluster and spark cluster, the system integrates the internal information data of the enterprise, obtains the updated external data in time, and completes the decision analysis of each enterprise data through MapReduce and the k-means algorithm. The application development of the system adopts SSM architecture and java language for development and design. Experiments show that this system can successfully enhance the dimensions of enterprise decision-making, successfully mitigate decision-making risks, successfully enhance the supervision and control of decision-making, effectively increase the influence of financial management on enterprise decision-making, and successfully advance the modernization, digitization, and intelligence of enterprise financial management.

In recent years, with the rapid development and application of the Internet, big data, and information science in recent years, there has been an increase in the number of types of public information about the financial market. And stock market investors are gradually obtaining relevant information through network platforms and mobile terminals to assist their investment decisions. GEM-listed companies are primarily high-tech enterprises with relatively prominent main businesses, unique technology, and significant product market potential, and they are attracting increasing investor interest. Compared with the large enterprises on the main board, the high-tech enterprises in the GEM have serious problems of “high stock price, high price-earnings ratio, and high fundraising”, but the “high growth” is not prominent enough. Based on behavioral finance theory, combined with professional knowledge and analytical frameworks such as econometrics, statistics, and computer science, and using big data, this paper conducts an empirical analysis and research on the factors affecting the value of GEM companies. The results show that the profitability, solvency, operating ability, growth ability, enterprise scale, and executive characteristics of enterprises all have a significant impact on the value of listed companies. As a result, listed companies should be based on their positioning, make full use of modern information means such as Internet big data, and continuously improve the enterprise’s profitability, solvency, operating ability, and growth ability, as well as enhance the qualifications, ability, and personal charm of leaders, to increase enterprise value. At the same time, the GEM companies should take advantage of the development opportunities of the “Belt and Road” to solve the problem of unreasonable regional distribution, so that the GEM companies will develop faster and better.

In the general debate of the seventy-fifth session of the United Nations General Assembly, China proposed to “strive to achieve a carbon peak by 2030 and strive to achieve carbon neutrality by 2060”.¹ The ambitious vision of China to achieve the “double carbon” goal is deeply rooted in people’s hearts, and the establishment of a carbon market has become an unstoppable trend. The emergence of carbon market trading puts forward new requirements for carbon accounting. Carbon accounting is the core auxiliary means for the rational and effective operation of the carbon market, which has irreplaceable significance. Based on the background of realizing the “double carbon” goal, this paper analyzes the problems existing in the current carbon accounting system through empirical analysis and discusses the development ideas and directions of carbon accounting and computer technology integration in the future. The results show that the development of carbon accounting and carbon trading markets will promote the realization of the “double carbon” goal. Finally, some suggestions are put forward for China to realize the “double carbon” target.

This paper aims at issues of Goldman Sachs’ stock forecasting in a short time by using the time series analysis based on four models: AR, MA, ARMA, and ARMA-GARCH models and chooses the optimal model. In this paper, after selecting the sample data and preprocessing data, the regression evaluation index is used to analyze the preliminary models. After that, use the Sequence Stationarity Test and ADF Test to test the series’ stationarity, and analyze the solution of the ARMA model to conclude the formula. The regression evaluation parameters are then compared to the initial models. Later, by selecting from Gaussian distribution, student t distribution, and biased student t distribution, the solution of the AGMA-GARCH model is analyzed. By constructing ARMA and GARCH models, the short-term forecast stock price results are valid and feasible. It concludes that the Arma-GARCH model greatly improves the accuracy of stock forecasting.

As the main force of the network, college students are closely connected with big data, and various privacy leakage problems are acute and frequent events. The concept of privacy in the context of big data is elaborated, the privacy violation events and characteristics of college students in the context of big data are analyzed, the privacy security factor evaluation system is constructed, and the weight of various factors is analyzed by analytic hierarchy process. In the privacy security problem of college students, the government’s management is the most important factor affecting the privacy security problem, followed by the responsibility consciousness of information industry, and finally, the security literacy and technical means of college students. According to the importance of different, give different suggestions, targeted to solve problems. Provide theoretical support for privacy security protection.

In the era of big data, the perceived ability of urban residents to network risk also determines how urban residents use big data and network correctly. The ability and influencing factors of urban residents’ risk perception of the network society mainly come from network literacy, institutional asylum, trust maintenance and social media. Based on this background, this paper focuses on the impact of urban residents’ network literacy, system asylum, trust maintenance, social media and other factors on the maturity of network big data services and network risk perception through hierarchical regression method. Finally, the hypothesis is tested, and the evaluation model of urban residents’ networks risk perception is obtained through significance analysis.

To control domestic inflation, the Federal Reserve raised interest rates several times in 2022, which caused the appreciation of the US dollar and the depreciation of the RMB in a short period. As two important financial indicators, the relationship between the exchange rate and stock price is of great theoretical and practical significance for China to formulate corresponding macroeconomic regulations and prevent the birth of financial risks. This paper utilizes the VAR model, impulse response function, ARMA-GARCH model, and other statistical methods to select two representative data sets of the RMB/USD exchange rate and the Huawei concept index as research objects. The purpose is to test whether there is a correlation between them and the degree of influence and to focus on the long-term stability of the price fluctuation relationship between the foreign exchange market and the Huawei index. The impulse response function is used to dynamically describe the short-term impact of foreign currency exchange rate and Huawei concept index fluctuation. According to the empirical results of this paper, some suggestions are provided for policymakers. This paper also establishes the ARMA-GARCH model for Huawei concept index and analyzes and predicts its volatility. The prediction results can provide some reference for investors to judge the short-term trend of Huawei concept index and make investment decisions.

In this paper, we use the Vector Autoregression (VAR) approach to examine the robustness of the Phillips Curve in the United States economy from January 2020 to June 2022. The data is characterized by VAR(4) with a cointegrating rank of 2, using the unemployment rate and sticky inflation. The Impulse Response Function (IRF) is used to examine the relationship between the unemployment rate and inflation. The Granger-causal Test results suggest that historical unemployment data is useful for improving inflation projections. With a longer prediction horizon, unemployment shocks have a greater impact on the forecast error variance of inflation. Based on the impulse response function, the Phillips Curve is not alive in the US economy during the COVID-19 pandemic, but it can still be a significant factor legitimate for the government to set economic policy.

This paper tests the Fama-French model using a new approach to estimate the standard error and verify the significance of different factors. Traditional standard error estimation neglects the correlation between stock return observations. As a result, the standard error will usually be underestimated, and some factors will show ostensible significance due to smaller standard error estimation and larger t-stat. This paper assumes a two-way clustering structure, assumes that stock return is correlated in industry and stock itself in two dimensions, and concludes with more decisive factors. Then this paper utilizes influential factors in stock return prediction and selection with the help of bootstrap simulation, and the result is slightly better than the standard OLS regression.

It has been two and a half years since the outbreak of COVID-19, and it is now nearly a year since it entered the post-COVID era globally. During this year, most countries have relaxed, or even lifted, their COVID prevention policies. And will those differences change investors’ attitudes? To explore this topic, this paper uses a time series model to examine whether stock investor behavior has changed under the regular epidemic control compared to the initial era by analyzing Boeing’s stock price since the start of COVID combined with the data of daily new cases globally. Previous studies have shown a downward trend in stock prices following a pandemic outbreak at the beginning of the epidemic, and a few months later, their stock price started to surge again. The conclusion of this paper is that the returns on Boeing stock prices in the post-COVID era are generally repeating history, with investors not gaining much experience from previous investments. However, investor panic has been less severe than at the beginning of the outbreak.

In recent years, the Free Trade Zones(FTZs) have developed rapidly in China, contributing greatly to establishing an open economy. The success of FTZs is related closely to the financial innovation policies. This paper takes the financial leasing industry as an example and evaluates the financial innovation policies in FTZs through data mining. The empirical results show that the financial innovation policies are effectively implemented and attract investors with low funding ability. Meanwhile, investors in capital markets recognize the policies with positive stock price reactions. However, the policies have some limitations, which haven’t attracted enterprises with good profits and high potential.

This paper uses the questionnaire data as samples and the SPSS multivariate linear regression method is used to establish an ANOVA model of business model innovation (BMI for short), enterprise performance and environmental uncertainty, and obtain the multivariate linear regression relationship of the three. The results show that BMI and its two dimensions can well promote the improvement of enterprise performance. Market uncertainty and competitive uncertainty positively regulate the relationship between BMI and enterprise performance, while technological uncertainty has a positive effect on BMI. There is no moderating effect on the relationship with enterprise performance.

In recent years, the technology export control policy with the Entity List as the core tool has become an important tool for the U.S. to exert maximum pressure on China, while the high-tech industry represented by the ICT industry has become an important front for the U.S. to implement strategic containment against China. This paper innovatively measures “export control intensity” by combing the updated history of the Entity List of the United States, and establishes panel data based on the monthly trade data of representative goods in the ICT industry of China and the United States from 2017 to 2021. A multiple linear regression model for the empirical analysis is established with the help of the Stata16.0 software tool to explore the influence of the import and export trade of high-tech industries between China and the United States under the background of the implementation of technology export control against China. The results show that the intensity of the U.S. high-tech export control to China has been increasing continuously in recent years, which not only affects the import and export trade of China’s high-tech industry to a certain extent, but also has a negative impact on the import and export trade of the U.S. high-tech industry.

We construct a hybrid hub-and-spoke logistics transportation network model that allows for direct transportation. In order to minimize the network running cost, the model is solved by a hybrid two-stage random search and genetic algorithm, and the program is written in Python. After collecting relevant data from CR Express for empirical study, the results showed that the system cost is the best. The five cities of Tianjin, Zhengzhou, Chongqing, Xi’an, and Suzhou were selected as hub nodes. The total cost of the hybrid hub-and-spoke logistics transportation network model with direct access is about 20.19 percent lower than the model without direct access, indicating that establishing direct access in the network can reduce transportation costs and improve logistics efficiency. Finally, we study the factorial experiment of the number p of central hubs and the discount coefficient of trunk transportation α. Results show that when the discount coefficient is low, the total network cost can be reduced by increasing the number of hubs. When the coefficient of scale effect is fixed, with the increase in the number of hubs in the network, the total cost shows a U-shaped trend of decreasing first and then increasing.

In order to solve the trust problem in China’s online shared mobility, blockchain technology is used to build a trust mechanism to guide and encourage users’ honest transactions through smart contracts. Then, the agent model is built by AnyLogic software to simulate the behavior of users participating in the transaction. The results show that the willingness of users to perform the contract can be effectively improved under the incentive of the trust mechanism. Finally, some suggestions are put forward to ensure that the trust mechanism can be effectively applied.

In order to reasonably and effectively distribute a large amount of distribution network construction funds (total investment) invested in the whole planning area to each region, a study is proposed to estimate the scale of the distribution network and make decisions on the total investment allocation based on the basic development needs of distribution network, economic benefits and social benefits of investment. This essay introduces the concepts and calculation methods of economic investment, upper and lower limits of investment, and flexible investment in the distribution network. Considering the basic development needs of the distribution network, economic and social benefits of investment and other basic principles, four practical distribution models and methods involving flexible total investment are put forward. They are respectively distributed according to rigid demand ratio, equal rate of return ratio, electricity demand ratio, and weighted comprehensive distribution. The flexible investment allocated to each region will not produce direct income, but it is helpful to improve its distribution network and technical and economic indicators. This paper presents an example of computation and analysis. The example shows the effectiveness and practicability of the proposed distribution network investment distribution model strategy, which can provide some reference for the investment decision of power supply companies. The effectiveness and practicability of the distribution network investment distribution model strategy can provide a certain reference for the investment decision of a power supply company.

It is time-consuming and error-prone to manually determine whether there is a brain tumour in an image. However, traditional automatic classification algorithms have certain limitations, which makes the automation of brain tumour classification still a challenging problem. In this article, a new method for the automatic classification of brain tumours is proposed, combining neural network models with transfer learning methods to improve or solve the problem of slow iteration and long time-consuming model generation, improve accuracy, and reduce parameters. In short, the convolutional neural network model (CNN) is combined with the method of transfer learning to achieve automatic image classification on the Brain Tumour Detection 2020 dataset provided by Model Whale. More specifically, during the experiment, Tensorflow was selected as the deep learning framework. First, the transfer learning method was used, and imagenet weights were used. Then, the Comparing model performance by changing the choice of the backbone network of the CNN. Select the accuracy rate as the evaluation index, compare the performance of the model, use binary cross-entropy as the loss function, and the optimizer uses adam. In this paper, three backbone networks, VGG, MobileNet and ResNet, are compared. Experimental results indicate that the automatic classification of brain tumours with the combination of the CNN model and transfer learning method has better performance and the VGG model has the best performance.

From the perspective of diffusion of technological innovation, a model of factors affecting the structural optimization of cultural industries was constructed using national data from 2009-2019. The results of the stepwise regression method show that patent application, patent grant, and regional GDP per capita significantly affect the structural optimization of cultural industries. Therefore, it is essential to enhance the independent innovation capability of cultural enterprises and promote the efficiency of technology achievement transformation while creating a favorable innovation environment to enhance the structural optimization of cultural industries.

In order to quantitatively analyze the impact of international maritime regulations on ship detention, a grey entropy Euclid correlation degree algorithm based on grey Euclid correlation degree analysis and entropy weight method is proposed. It reveals the relevance of eight international maritime regulations followed by Port State Control to ship detention through data mining, and the order of relevance is LSA > LL > FSS > SOLAS > MLC > MARPOL > ISM > CC. At the same time, according to the correlation degree, it is determined that the important factors affecting ship detention are LSA, LL, FSS, SOLAS and MLC, the comparatively important factors are ISM and MARPOL, and the unimportant factor is CC. It can provide a decision-making basis for Port State Control and reference opinions for shipping companies to reduce ship detention rates.

The risk analysis of key nodes is the foundation and important link to ensure the safety of the sea lanes. To analyze the risk and provide a basis for safety management, this paper proposes a risk assessment model based on a multi-state fuzzy Bayesian network. Firstly, a fault tree model is built for risk events, revealing the causal relationships between the risk events and the influential factors. Secondly, the multiple fault state of nodes is described by fuzzy numbers. The multi-state fuzzy conditional probability table is applied to describe the uncertain logical relationship between nodes. An expert investigation method based on a confidence index is proposed to establish a multi-state, conditional probability interval table that describes the logical relationship between variables. Finally, the model parameters are determined by obtaining the exact values of the conditional probability interval table using the α-weighted valuation method for defuzzification. The risk probability distribution based on prior knowledge and known evidence is calculated. The key risk factors are then identified. Taking the risk analysis for key nodes of sea lanes as an example, the application results demonstrated the feasibility and efficiency of the proposed method.

Stocks have become a common way to manage money and invest. However, it is too risky for initial users to invest directly. In the era of artificial intelligence, computer technology can be combined with many fields. This paper summarizes and designs a stock investment simulation system which combines the front and back end. The front end uses the VUE framework, and the back end builds a database. The back end uses Python to write the server to help novices quickly get familiar with the form of stock investment methods. This system also adds a risk assessment method to evaluate the risk coefficient of the stock, so that beginners can better adapt to real stock investment.

The main purpose of this paper is to analyze the research status of artificial intelligence cross-border e-commerce in China from 2017 to 2022. This paper studied the journals on China Knowledge Network (CNKI) about cross-border e-commerce and intelligent cross-border e-commerce and used the visualization tools city space and Excel to cluster 1024 important and representative literature journals from 2017 to 2022 to understand the co-occurrence of intelligent cross-border e-commerce research keywords and the main researchers in China. Research shows that China’s smart cross-border e-commerce research mainly focuses on digitization, blockchain, digital economy, etc. There is still a large space for development, aiming to provide help for the future research of China’s smart cross-border e-commerce.

After the Centers for Disease Control and Prevention identified the first coronavirus case in the United States on 21st January 2020, the epidemic has rapidly expanded to a global scale, and economic markets in the United States and even the world have been severely affected. The impact has been significant in many areas, including the movie industry. This paper selects the film industry index calculated by East Money Information Co, intercepts the stock data after the pandemic outbreak, and uses the ARMA-GARCH model to model and analyze the data to study how the Chinese movie industry has been affected by the wild spread of the pandemic. Then, it predicts the future development of the Chinese movie industry and makes suggestions for future development of the Chinese movie industry. It is found that the Chinese film industry profitability is affected by the epidemic domestically, and suggests that the Chinese government should actively respond to the increase of the domestic daily new confirmed coronavirus cases and be more proactive in improving the movie industry by opening more movie theaters and reducing policy restrictions on opening cinemas.

This paper selects the panel data of listed companies in China’s coal mining industry from 2015-2021 as the research sample and constructs a theoretical relationship model to empirically analyze the impact of corporate green innovation on financial performance based on the perspective of corporate policy sensitivity. The study shows that: the green innovation of coal enterprises has a significant contribution to financial performance and corporate policy sensitivity; corporate policy sensitivity has a positive relationship with financial performance and a significant moderating effect between green innovation and financial performance. Based on the above findings, relevant suggestions such as increasing the intensity of R&D investment, enhancing policy sensitivity, and increasing environmental protection investment are proposed.

Given the characteristics of technology intensity, long cycle, and multiple supporting facilities in the performance of equipment procurement contracts, this paper uses the WBS method to sort out the main tasks of each link in the performance of contracts and constructs the performance risk index system of equipment procurement contracts. Aiming at the interaction of risks during the procurement contract performance, the Dematel-Aism method is used to build the identification model of key risk factors, and the risk confrontation hierarchy chart is obtained, which visually shows the influence path and interaction of different risks factors in the performance process. Finally, the paper puts forward relevant suggestions on risk control.

Based on the combination of big data technology to the audit of networked financial institutions, this paper takes Hadoop and spark big data processing ecosystem framework as the foundation, combining HDFS distributed storage, map-reduce computing, and spark streaming data interface and other components, and completes the package and release of the system in the SSH development environment in Java language environment. The system is presented in the form of the Web, which is convenient for auditors to complete all kinds of inquiries, identification of financial fraud, and risk assessment of financial fraud through a simple and convenient operation. It provides comprehensive application solutions for the problems of complexity, concealment, difficulty, and risk in the audit of financial fraud in the data age. It not only improves auditors’ ability to deal with financial fraud, but also helps to standardize the behavior of enterprise information disclosure, and further makes a beneficial attempt for the digitalization and informatization construction of financial audits.

Frequent in recent years, all kinds of emergencies, to dissolve the major security risks, timely response to the treatment of all kinds of disasters and accidents, and protect people’s lives and property security and maintain social stability, many experts and scholars in different cases of the incident made a practical scheme of dynamic mining of decision and analysis in recent years, domestic research dynamic emergency logistics and explore its research direction, Based on China National Knowledge Infrastructure (CNKI) database, this paper selected papers on dynamic emergency logistics as the research object from 2006 to 2022. With the help of CiteSpace visualization software, a visual analysis was carried out from four aspects: an annual number of publications, author distribution map, keyword clustering, and research hotspots. Results show that the dynamic emergency logistics research in China can be summarized as an emergency plan, emergency management, dynamic allocation, dynamic decision-making, such as clustering direction, dynamic research of emergency logistics has experienced the start stage, development stage, and development stage, the research focus in this field has focused on the dynamic allocation of emergency supplies, facility location, modeling and vehicle routing optimization problem.

In competitive sports, players are always at high risk for injuries. Sports injuries in rugby sports are directly related to the team’s game performance, especially when the player has an old sports injury or psychological stress. By considering the athlete as a dynamic system and quantifying the features associated with sports injuries, machine learning can be used to predict and assess the associated risks. In this paper, a simplified GRU is proposed to construct the mapping relationship between sports injury features and rugby game results. The comparison experiments with other machine learning models show that this model has better robustness in the prediction tasks of sports injuries and competition results of teenage rugby players.

The growth of smart cities has been driven by digital economy technology, and each city’s economic development has been significantly impacted by the trade and distribution sector. Based on the externality theory, this study develops the MAR externality innovation index, Jacobs externality innovation index, and Porter externality innovation index, and then uses panel data from smart cities from 2013 to 2020 to examine the effects of the three types of indices on the economic standing of cities. First, it is found that Jacobs’ externality innovation in the distribution and commerce industries is better adapted to our current cities. Second, smart cities may innovate just as well as conventional ones in the distribution and business sectors. Third, compared to conventional cities, smart cities’ economic growth is far more influenced by expenditures in research and technology. This study indicates that to support the integration and growth of the distribution of commerce industries, we should build industrial clusters, stimulate the development of industrial clusters and integration, and strengthen the scientific and technological support of cities.

Blockchain technology has many advantages, such as openness, transparency, non-denaturation, distributed accounting, smart contract, and so on. With its unique advantages and characteristics, it is deeply concerned and favored by financial staff from all walks of life, and plays a very important role in improving the accuracy and effectiveness of financial work. This paper analyzes the important role and basic characteristics of blockchain technology in the present financial work and discusses its application in the financial work to bring some reference for researchers in the financial field.

Under the new development pattern, innovation-driven is the fundamental driving force for promoting the development of commercial circulation, but the level of innovation and the development level of commercial circulation of different regions are different. Therefore, it is worth exploring whether there are regional differences in the influence of the level of scientific and technological innovation on the development of the commercial circulation industry. Based on the provincial panel data, this paper studies the impact of scientific and technological innovation on commercial circulation and regional differences. Studies have shown that, on the whole, scientific and technological innovation has a significant role in promoting the development of the commercial circulation industry. In terms of regions, scientific and technological innovation in the western, central, and eastern regions also has significant positive impacts on the development of the commercial circulation industry, but its impact on the commercial circulation industry in the northeast region is not significant. Finally, it puts forward relevant suggestions such as promoting the integration of production, education, and research, strengthening government support, and promoting international scientific and technological cooperation.

This paper uses the three-stage Malmquist index and spatial Durbin model to analyze the spatial spillover effect and the enhancing aggregation of high-tech industries’ effects affecting regional green innovation efficiency and its characteristic features using 30 Chinese provinces and cities’ panel data from 2010 to 2019. It is discovered that: Overall, each province and city’s high-tech sectors’ green innovation efficiency index is rising gradually, but the overall innovation efficiency is not high. The potential for improvement is large. According to the pattern’s distribution, there is a diminishing trend in both the east and the west. Agglomerations of high-tech industries have a geographical impact on the effectiveness of local green innovation. In particular, high-tech industry clusters can encourage the improvement of the region’s green innovation efficiency and also spur improvements in the bordering regions’ efficient green innovation. It is essential to strengthen the regional innovation system, support the growth of the regional high-tech industry, and ensure the efficient distribution of innovation components. The development environment of the high-tech industry must be further optimized.

As an indispensable means of green development, green finance promotes the harmonization of green and innovation. In this paper, information technology and big data are used to search for relevant information about the establishment of pilot policies in green finance reform and innovation pilot zones and to select data of original SMEs in 2014∼2019 from CSMAR and other databases. With the help of Stata software in computer technology, this paper uses the model to empirically assess the impact of the pilot policy on the innovation of technological enterprises. The research demonstrates that the pilot zones’ establishment has extensively promoted innovation in technological enterprises. Secondly, the influence mechanism test shows that the pilot zones have increased their endogenous financial capabilities through the taxation mechanism, promoting technological innovation. However, enterprise innovation ability cannot be enhanced by government subsidies.

Under the background of the big data era and the low-carbon goal of “carbon peaking and carbon neutralization,” accelerating the promotion of regional green innovation capability is the main theme of achieving low-carbon economic development. This paper firstly constructs the index system of environmental regulation system, market system, innovation network, and regional green innovation capability. Based on panel data from 30 Chinese provincial units from 2013 to 2019, a structural equation model was used to investigate the driving mechanism of regional green innovation capability, and an innovation network was introduced as an intermediary variable. The results show that : (1) although the direct effect of environmental control institutional environment on regional green innovation capacity is not significant, the institutional environment for environmental control has an indirect negative effect on regional green innovation capacity through innovation network mediating effect as a full mediating effect; (2) the market institutional environment has a positive direct influence on regional green innovation capability, while it has an indirect positive influence on regional green innovation capability via the intermediary effect of the innovation network, which is a partial intermediary effect; (3) the innovation network has a positive direct influence on the regional green innovation capability.

According to Maslow’s hierarchy of need theory, happiness is a higher-level demand that people have spare no effort to pursue until they meet basic needs. As a subjective feeling, happiness is affected by many factors. For example, personal characteristic factors include gender, age, marital status, health level, economic income, nationality, and others. However, the impact of objective factors on happiness has gradually attracted scholars’ and the governments’ attention, including urban economic development, social infrastructure, and economic infrastructure, which will have a corresponding impact on residents’ happiness. This study applies spatial visualization technology to investigate the importance of the urban environment on residents’ happiness. The spatial visualization map of residents’ environmental satisfaction shows that people in the east and north are happier than the rest of the regions. Moreover, in terms of education, medical care, transportation, and lighting in residential areas, cities in the eastern region have higher satisfaction. Overall, nine environmental factors are considered in the econometric model, and the results show that urban traffic satisfaction has the greatest impact on residents’ happiness. This study aims to systematically investigate the mechanism of residents’ happiness from the perspective of the urban environment, which is different from previous studies, and points out the importance of the urban environment to citizens.

This paper analyzes the integrity problems in the tourism industry and proposes the design of an integrity tourism APP system based on blockchain. The scheme takes advantage of the fact that the data of the blockchain is difficult to forge and tamper with, and solves the integrity problem in the tourism industry.

Using a high-tech industrial park in a city as an example, this paper discusses the possibility and path to “near-zero carbon emissions” in industrial parks with new industries and clean energy of the same type. It calculates and analyzes the park’s carbon emissions, predicts and discusses the relevant indicators of future carbon emissions using the scenario analysis method, and makes planning recommendations based on this result. The results show that the growth rate of total carbon emissions in the city’s industrial parks has slowed down, and the carbon intensity has shown a downward trend. There is great potential for emission reduction in the field of power consumption, and it is expected to achieve “near zero carbon emissions” in the future. At the same, high-tech industrial parks should actively promote clean power, clean energy use, the low-carbon transformation of high-energy-consuming industries, and green industry development, as well as further tap the potential of carbon sinks and actively participate in the carbon market. They should take the lead in carrying out absolute emission reduction, gradually form a low-carbon development model that can be popularized and replicated, and build a “near-zero carbon emission” infrastructure.

High liquidity is one of the most significant objectives in the banking industry. To ensure stable liquidity, client prediction is a common method adopted by bank managers. Although there has been some literature discussing the methods to make client predictions, it lacks quantitative comparison between algorithms. This study will focus on the prediction of bank clients and compare the effectiveness of different algorithms in machine learning (neural network, decision tree, logistic regression). It is designed to compare the five metrics (Type I sample f1-score, Type II sample f1-score, accuracy, Area Under Curve, Kolmogorov-Smirnov) to distinguish the feasibility of different algorithms. The higher index represents the better algorithm. As the results, the neural network has the highest AUC (0.85) and highest Type I sample f1-score (0.50), while the logistic regression has the highest accuracy (0.90), KS (0.64), and Type II sample f1-score (0.50). (0.94). According to reality, the neural network is suggested to be the optimal algorithm that needs to be adopted by bank managers for client prediction.

Since the global financial crisis of 2008, it has been common sense that it is significant to identify and evaluate the risk to survive in the banking industry. The goal is to achieve a more accurate and efficient method of managing bank credit risk. Weighted k-nearest neighbor (k-NN) with information entropy is proposed, and real data is utilized to conduct comparison experiments with other different models and algorithms. As the result shows, weighted k-NN with information entropy is the best tool in contrast with other models. The method of weight k-NN with entropy could be applied in reality.

With the rapid development of the digital economy, traditional financial and business processing methods can no longer meet the development needs of enterprises. Financial sharing and business and finance integration play an important role in promoting corporate financial transformation. However, at present, there is no complete and reasonable evaluation system for the effect of business and finance integration. The practical effect of business and finance integration under G Group’s financial sharing mode is evaluated and studied. It uses the analytic hierarchy process and the fuzzy comprehensive evaluation method to construct an evaluation system for the effect of business and finance integration. The research shows that the effect of G Group’s business and finance integration is relatively good, but it is relatively lacking in business and finance system integration, customer satisfaction, and talent training, which needs to be further optimized.

Empirical research is one of the important ways to objectively present the current situation of the research object. This paper conducts a comparative analysis of the current situation of college Civics class teachers’ online political discourse and objectively presents the current situation of college Civics class teachers’ online political discourse. It analyzes its construction effectiveness and problems and provides a theoretical and practical basis for the study of the improvement path of college Civics class teachers’ online political discourse.

With the economic growth rate slowing down, the increase in production cost of enterprises and the intensification of competition at home and abroad have begun to erode the profits of enterprises, making it increasingly difficult for enterprises to repay their debts. Continuing to implement the new preferential tax policies for the market will help enterprises to run smoothly and further stimulate the vitality of the market. This paper selects listed companies in the China A-share market from 2009 to 2017 as the research object and empirically analyzes the correlation between tax incentives, financing constraints, and corporate debt default. The study finds that tax incentives effectively ease the financing constraints of enterprises; At the same time, tax incentives can curb corporate debt default; Through further verification, financing constraints play a part in mediating the effect of the relationship between tax incentives and corporate debt default.

The acceleration of the digital world calls upon banks and other financial institutions to have a clear understanding, predict and protect themselves against risks brought by uncertainties. Artificial Intelligence is one of the technologies used by banks to forecast activities, which can have a clear insight into trends, and analyze opportunities in real-time so that their businesses are protected and customers are served effectively. This paper takes Kazakhstan as the research object, uses the survey to collect data, and analyzes the positive role of AI in risk control of commercial banks, as well as the existing risks and obstacles.

In this study, we construct a green innovation network by using the gravity model and propose a panel model to analyze the mechanism by which the green innovation network structure of the urban agglomeration affects eco-efficiency and take the Beijing-Tianjin-Hebei urban agglomeration as an example. Results show that the urban agglomeration of Beijing, Tianjin, and Hebei is characterized by radial development, with “Beijing-Tianjin” serving as the core. The central position of city nodes in the innovation network of the Beijing-Tianjin-Hebei urban agglomeration significantly impacts its eco-efficiency, whereas other characteristics do not.

With the rapid development and fierce competition, new researches that based on scientific papers emerge in an endless stream. As the scientific research projects issuers, how to publish the great demand scientific projects become a difficult problem. This paper, firstly, drawn knowledge graph using co-occurrence matrix of the key words that based on the key words of papers in China National Knowledge Infrastructure (CNKI), taking Applied Economy as an example. Secondly, it used k-NN algorithm to get the best recommendation effect, which find using the split=0.67 between training set and test set getting the best recommendation effect. This finding can help the scientific research projects issuers, to find the implicit relationship features between papers published in last year, and then output an ordered list of keywords as its recommendation.

This study investigates the impact of China’s stock market openness on corporate firm innovation. China launched Shenzhen-Hongkong and Shanghai-Hongkong Stock Connect programs to allow international investors to trade with domestic stocks. The empirical regression results demonstrate that the firms traded with foreign investors highly benefited from quality patents. In addition, the corporate firms profited from the influence of foreign investors on the firm’s innovation based on gaining financial capital, reducing information asymmetry, and efficient stock market price. Thus, our study provides robustness analysis to clarify the positive relationship between stock market openness on firm innovation.

The Urban Agglomeration of Beibu Gulf was established in 2017 with the strategic positioning of facing ASEAN, serving the “Southwest, Central-south, and South of China”, and being livable and business-friendly. Green innovation is the key to promoting high-quality urban development. It is of great significance to analyze and compare the spatial characteristics of green innovation efficiency and its development differences in the Urban Agglomeration of Beibu Gulf for promoting higher quality development in the Beibu Gulf region. This paper measures the dynamic changes of green innovation efficiency and in the Urban Agglomeration of Beibu Gulf based on the super-efficient SBM-GML model, and uses a variety of convergence methods to examine their convergence characteristics. The research results show that (1) green innovation efficiency displays remarkable regional differences, and the overall trend is fluctuating and declining; (2) green innovation efficiency has prominent convergence characteristics in the Urban Agglomeration of Beibu Gulf, which has passed the tests of σ convergence and β convergence.