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Abstract With the wide application of big data technology and the continuous innovation of the financial industry, the development of Internet finance has become irreversible. The new financial model has improved the efficiency of financial services and formed new ways of payment and transaction. The traditional credit information system has been unable to meet the needs of the development of the financial industry. The research methods adopted in this paper mainly include literature research, case analysis and comparative analysis. The development process, current situation and characteristics of China's personal credit system, as well as the development process and current situation of China's personal credit system under Internet finance are analyzed. It also points out the main problems faced by China's personal credit reporting system under the Internet finance. Then, aiming at the specific problems faced by China's personal credit reporting system under Internet finance, this paper makes case studies of representative market-oriented credit reporting institutions such as Beijing, Tianjin and Hebei, and uses convolutional neural network to analyze the specific practices of these individual credit reporting institutions and achieves results. At the same time, it compares how different credit agencies make up for the shortcomings of personal credit reporting system. Finally, it draws the conclusion of perfecting the personal credit system of our country under the Internet finance, points out the new problems that may be brought by the superposition of various solutions, and puts forward the countermeasures and suggestions of perfecting the personal credit system of our country under the Internet finance. [ABSTRACT FROM AUTHOR]

This study proposes a series of statistical and regression approaches to investigate city-cluster transport connectivity patterns. Three major city clusters in China are selected for this study, namely, the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) economic zones. Our analyses examine three aspects of city-cluster connectivity patterns: (i) connectivity distribution among cities within the city cluster; (ii) dependence of the non-center city on the center city; and (iii) the impact of improved intra-city-cluster rail connectivity on air-connectivity distribution. First, it is found that the BTH has the most concentrated connectivity among the three city clusters, with Beijing clearly dominating the other cities in every kind of connectivity. The connectivity distributions are more balanced in the YRD and PRD. Second, the calculated "survival connectivity" and regression analyses suggest that non-center cities heavily rely on the center city as the hub to develop their network connectivity. Such a "hub-and-spoke system" helps a non-center city improve total connectivity but reduces its direct connectivity to other cities outside the city cluster. Finally, our regression analysis shows that improved intra-city-cluster rail connectivity further deteriorates the air-connectivity disparity within the city cluster, which occurs because the center city benefits more from the upgraded air-HSR (high speed rail) intermodal connectivity because it can attract air passengers from neighboring non-center cities. Relevant policy implications and suggestions are also discussed in the paper. We believe the proposed statistical and regression approaches can be easily applied to examine city-cluster connectivity in other contexts and countries. [ABSTRACT FROM AUTHOR]

From 1990 to 2017, the number of vehicles in China increased sharply from 5 million to 310 million, and vehicle emissions became one of the major sources of air pollution. Among the main sources of PM 2.5 in Beijing, the contributions from vehicle sources increased from 5.6% (2000) to 31.1% (2012) to 45% (2017). In 2010, the CO, hydrocarbon (HC), NO x , and particulate matter (PM) emissions of yellow-label vehicles (YLVs) accounted for 46.1%, 47.9%, 63.1% and 86.1% of the total emissions from on-road vehicles. In recent years, China has been implementing a YLV scrappage subsidy policy to control air pollution, and this policy has cost several hundred billion yuan nationwide. However, China has not evaluated the costs and benefits of the policy. Many people question whether the YLV scrappage subsidy policy is worthwhile, but all the stakeholders involved complicate the process of accurately evaluating the true value of the policy. In this study, we evaluated and compared the costs and benefits of the policy in the Beijing-Tianjin-Hebei (BTH) region based on 1.362 million YLVs from 2008 to 2015. Because of the implementation of the policy, the PM 2.5 , PM 10 , NO x , CO and HC emissions in the BTH region decreased by 16.46, 18.12, 174.44, 669.11, and 91.64 kt, respectively, in 2008–2015. The results show that the policy played an important role in reducing pollutant emissions and improving environmental quality in the BTH region. The costs, benefits, and net benefits of the policy were 13.69, 34.03, and 20.34 billion yuan , respectively. The cost-benefit ratio in the BTH region was 1:2.49; at the city scale, Beijing ranked first (1:7.27), and the cost-benefit ratios of Tianjin and Hebei Province were 1:2.06 and 1:1.14, respectively. Of the 13 cities in the BTH region, Beijing had the highest net benefits, amounting to 16.43 billion yuan , while Handan City has the lowest net benefits, amounting to −0.58 billion yuan. The results may provide scientific support for the promotion of older vehicle and diesel vehicle scrappage subsidy policies, the development of cost-benefit analysis (CBA), and effective environmental decision-making in China. • Based on net number of scrappage YLVs, net benefits of the policy are calculated. • This paper estimates costs and benefits of the policy based on 1.27 million YLVs. • The costs, benefits, and net benefits are 13.69, 34.03, and 20.34 billion yuan. • The results help China promote vehicle scrappage subsidy policy on a large scale. • The results promote the development of the cost-benefit analysis in China. [ABSTRACT FROM AUTHOR]