Effects of formal credit on pastoral household expense: Evidence from the Qinghai-Xizang Plateau of China

Formal credit is critical in agricultural production,allowing more expenditure and productive input,thereby improving farmers'welfare.In pastoral China,formal financial institutions are gradually increasing.However,a limited understanding remains of how formal credit affects herders'household expenses.Based on a survey of 544 herders from the Qinghai-Xizang Plateau of China,this study adopted the propensity score matching approach to identify the effect of formal credit on herders'total household expenses,daily expenses,and productive expenses.The results found that average age,grassland mortgage,and other variables significantly affected herders'participation in formal credit.Formal credit could significantly improve household expenses,especially productive expenses.A heterogeneity analysis showed that formal credit had a greater impact on the household total expense for those at higher levels of wealth;however,it significantly affected the productive expense of herders at lower wealth levels.Moreover,the mediating effect indicated that formal credit could affect herders'household income,thus influencing their household expenses.Finally,this study suggests that policies should improve herders'accessibility to formal credit.

Pricing Catastrophe Options with Credit Risk in a Regime-Switching Model

In this paper,we consider the price of catastrophe options with credit risk in a regime-switching model.We assume that the macroeconomic states are described by a continuous-time Markov chain with a finite state space.By using the measure change technique,we derive the price expressions of catastrophe put options.Moreover,we conduct some numerical analysis to demonstrate how the parameters of the model affect the price of the catastrophe put option.

New findings of Changxingaspis(Xiushuiaspidae,Galeaspida)from the Silurian of Tarim Basin and Zhejiang Province,China

New discovery of the early Silurian fossil fish Changxingaspis(Xiushuiaspidae,Galeaspida),Changxingaspis nianzhongi sp.nov.and C.gui,are described from the Tataertag Formation in Tarim Basin and the Kangshan Formation in Zhejiang Province,respectively.C.nianzhongi mainly differs from C.gui in the shape of the median dorsal opening that is transverse elliptic with a width/length ratio of about 3.0,the long lateral transverse canals extending to the lateral margin of the headshield,and the second lateral transverse canal with dichotomous branchings.Discovery of C.nianzhongi from the Tataertag Formation and C.gui from the Kangshan Formation provide direct evidence on the specific level for the correlation between these two formations,which further supports the Silurian fish-bearing red beds in northwest Zhejiang belonging to the Silurian Lower Red Beds(LRBs)rather than the Upper Red Beds(URBs).Additionally,as the first record of the Changxingaspis in Tarim Basin,it extends the paleogeographical distribution of this genus from the South China Block to the Tarim Block,providing new evidence to support faunal exchanges between these two blocks and the hypothesis of a united Tarim-South China Block during the early Silurian.

Evolutionary analysis of green credit and automobile enterprises under the mechanism of dynamic reward and punishment based on government regulation

To explore the green development of automobile enterprises and promote the achievement of the“dual carbon”target,based on the bounded rationality assumptions,this study constructed a tripartite evolutionary game model of gov-ernment,commercial banks,and automobile enterprises;introduced a dynamic reward and punishment mechanism;and analyzed the development process of the three parties’strategic behavior under the static and dynamic reward and punish-ment mechanism.Vensim PLE was used for numerical simulation analysis.Our results indicate that the system could not reach a stable state under the static reward and punishment mechanism.A dynamic reward and punishment mechanism can effectively improve the system stability and better fit real situations.Under the dynamic reward and punishment mechan-ism,an increase in the initial probabilities of the three parties can promote the system stability,and the government can im-plement effective supervision by adjusting the upper limit of the reward and punishment intensity.Finally,the implementa-tion of green credit by commercial banks plays a significant role in promoting the green development of automobile enter-prises.

Research on Early Warning of Banking Crises from the Perspective of Credit Structures

The relationship between credit expansion and banking crises is complex and cannot be fully explained by total credit alone.A systematic analysis of the relationship between the amount and structure of total credit and banking crises is important for an objective prediction of the influence of potential financial risks.This paper,drawing on data from 15 selected countries,delves into the power of credit indicators in the early warning of banking crises from the perspectives of industrial structure,sector structure,and term structure of credit.Various machine learning methods were used,including Logistic Regression,Random Forest,Decision Tree,Support Vector Machine(SVM),Bagging,and Boosting models.The empirical findings indicate that credit expansion plays a crucial role in triggering banking crises.However,total credit is better suited for the early warning of short-term banking crises,whereas credit structure is more useful for the early warning of long-term banking crises.Moreover,in an early warning system,identifying key early warning indicators is more meaningful than merely increasing the number of indicators.Machine learning can somewhat enhance the early warning power,but it may not always be robust.Therefore,more attention should be paid to potential systemic banking crises resulting from an imbalance in credit structure while regulating the total credit threshold.

A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

Construction of BDS Spatiotemporal Information Agricultural Product Digital Credit System

Spatiotemporal information,positioning and navigation services have become important elements of new type infrastructure.The rapid development of global digital trade provides a large-scale application scenario for the use of Beidou Navigation Satellite System(BDS)spatiotemporal information to support the certification of origin of agricultural products.The BDS spatiotemporal information agricultural product digital credit system uses such modules as BDS,spatiotemporal information collection,spatiotemporal coding,and spatiotemporal blockchain.It incorporates multi-level joint supervision mechanisms such as government,associations,and users.Starting from the initial production link of agricultural products,it realizes the correspondence and locking of online and offline products,effectively improves the integrity and credibility of information in the production process,finished product quality and circulation process of products,effectively manages the green production and anti-channel conflicts of producers,and provides credible information for consumers,thus realizing the digital credit certification of products.The successful practice of characteristic agricultural products in Yunnan Province has verified the application ability of the BDS spatiotemporal information agricultural product digital credit system.This system has played a significant role in promoting the online and offline locking,credible information,effective supervision and high quality and high price of characteristic agricultural products from the field.The BDS provides services for global digital trade and contributes to the further enhancement of the global application scale of GNSS.

Exploring the Spatiotemporal Changes and Driving Forces of Ecosystem Services of Zhejiang Coasts,China,Under Sustainable Development Goals

Ecosystem services(ESs)refer to the continuous provisioning of ecosystem goods and services that benefit human beings.Over recent decades,rapid urbanization has exerted significant pressure on coastal ecosystems,resulting in biodiversity and habitat loss,environmental pollution,and the depletion of natural resources.In response to these environmental challenges,the Sustainable Development Goals(SDGs)were proposed.Given the pressing need to address these issues,understanding the changes in ESs under the SDGs is crucial for formulating specific ecological strategies.In this study,we first analyzed land use and cover change in the Zhejiang coasts of China during 2000–2020.Then,we investigated the spatiotemporal configuration of ESs by integrating carbon storage(CS),soil retention(SR),habitat quality(HQ)and water yield(WY)using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model.The driving mechanisms of ESs,which varied by space and time,were also explored using the Geo-detector method.The results revealed that,over the past two decades:1)the Zhejiang coasts have experienced a significant increase of 2783.72 km^(2) in built-up land areas and a continuous decrease in farmland areas due to rapid urbanization;2)owing to higher precipitation,extensive vegetation cover,and reduced anthropogenic disturbances,forests emerge as a crucial land use type for maintaining ecosystem services such as HQ,CS,WY,and SR;3)ESs have generally declined across the entire Zhejiang coasts,with a significant decrease observed in the northern areas and an increase in the southern areas spatially;4)the expansion of built-up land areas emerged as the primary factor affecting ecosystem services,while the vegetation factor has been increasingly significant and is expected to become predominant in the near future.Our study provides insights of understanding of ecosystem service theory and emphasizing the importance of preserving biodiversity for long-term sustainable development,and valuable scientific references to support the ecological management decision-making for local governments.

The Spatial-Temporal Characteristics of Meteorological Disasters in the Southwest Region of Zhejiang Province during 1953-2022

Meteorological disasters are some of the most serious and costly natural disasters, which have larger effects on economic and social activity. Liuchun Lake is an ecotourism area in the southwest region of Zhejiang province, where also has experienced meteorological disasters including rainstorm and cold wave. Understanding the temporal-spatial characteristics of meteorological disasters is important for the local tourism and economic development. Based on the daily temperature and precipitation from 18 meteorological stations in the southwest of Zhejiang province during 1953-2022 and some statistical approaches, the temporal and spatial characteristics of meteorological disasters (Freezing, Rainstorm, Cold wave) are analyzed. The results indicate that 1) Rainstorm occurred frequently around the Liuchun lake, the frequency was about 8 times/a, it can also reach about 3 times/a in the other region. Freezing and cold wave (including strong cold wave and extremely cold wave) had the same spatial distribution as rainstorm, however, except for Liuchun lake, they occurred less than one time in the other regions;2) The trend of rainstorm had larger spatial difference, it increased in all the study area, but it increased more significantly around the study area than around Liuchun lake. Freezing was on the downtrend in the whole region, with 93.3% of the stations passed the 95% significant level. Cold wave also showed a declined trend, but it was insignificantly at most of the stations, only 33% of the stations passed the 90% significant level. Compared with cold wave, strong cold wave and extremely strong cold wave had weaker decline in all the regions. In general, from 1953 to 2022 rainstorm showed an increasing trend, it was the main meteorological disaster in the study area, cold wave displayed a decreasing trend, but it still occurred about 2 - 3 times/a in most regions.

Analysis of the Impact of Technical Trade Barriers on the Export of Toys-Based on Analysis of Zhejiang,China

Technical barriers to trade,as a non-tariff barrier,have an increasing impact on the international market.As a major province in toy exports,Zhejiang Province’s toy export trade is therefore greatly hindered.This article aims to explore the impact of technical trade barriers on toy exports in Zhejiang Province and analyze countermeasures.Through literature review and some empirical analysis,it is found that technical trade barriers have greatly promoted the development of Zhejiang’s toy industry;however,they have also subjected it to impacts and pressures such as cost and technology.In response to the impact of technical trade barriers on toy exports,this article proposes corresponding countermeasures to avoid such barriers:firstly,establish a quality management system that complies with export product regulations to ensure that the products produced can meet international requirements and standards;secondly,reduce dependence on overseas markets;then enhance one’s own technology,and strengthen product innovation,in order to enhance the international competitiveness of the product and oneself;in addition,we should also explore diversified markets and expand the sales channels and coverage of our products.

Other Cities’Distribution Institutions for Publishing Books in Modern Chinese Libraries:Taking Huating Bookstore,the Shanghai General Distribution Office of Modern Zhejiang Library,as an Example

Many modern Chinese libraries have established distribution agencies in other cities to distribute books published or sold on consignment.Huating Bookstore,which was once the Shanghai General Distribution Office of Zhejiang Library in modern times,is one of them.On the basis of a brief description of modern Zhejiang Library and its book publishing,as well as the overview of Huating Bookstore,the paper introduces and analyzes the ZPL Publishing Book Catalogue sent by Huating Bookstore,and the ZPL publishing and selling consignment books issued by Huating Bookstore.It points out that Huating Bookstore is a bridge between the ZPL located in Hangzhou and various retail ZPL publishing bookstores in Shanghai.Their production and sales relationship is a mutually beneficial one.

Research on the Internal Talent Training Path and Performance Evaluation Mechanism of New R&D Institutions in Zhejiang Province

New research and development(R&D)institutions are an important part of the national innovation system,playing an important role in promoting the transformation of scientific and technological achievements.In recent years,new R&D institutions have gradually become the driving force of innovation-driven development in China.Taking new R&D institutions in Zhejiang Province as the research object,this paper studies the internal talent training path and performance evaluation mechanism of new R&D institutions in Zhejiang Province by using the literature research method,comparison method,case verification method,and other methods.The investigation results show that there are problems such as lack of material and spiritual support and neglect of the absorption of local talents in the internal talent training,and there are problems such as unclear standards,insufficient data,and opaque processes in the performance evaluation mechanism,which greatly affect the establishment and improvement of the performance evaluation mechanism.Given the above problems,this paper puts forward a forward-looking,oriented,flexible,and compatible talent training path and performance evaluation mechanism,hoping to optimize the effective internal talent training path of new R&D institutions,improve the evaluation performance,and promote healthy development of new R&D institutions in Zhejiang Province.

MSEs Credit Risk Assessment Model Based on Federated Learning and Feature Selection

Federated learning has been used extensively in business inno-vation scenarios in various industries.This research adopts the federated learning approach for the first time to address the issue of bank-enterprise information asymmetry in the credit assessment scenario.First,this research designs a credit risk assessment model based on federated learning and feature selection for micro and small enterprises(MSEs)using multi-dimensional enterprise data and multi-perspective enterprise information.The proposed model includes four main processes:namely encrypted entity alignment,hybrid feature selection,secure multi-party computation,and global model updating.Secondly,a two-step feature selection algorithm based on wrapper and filter is designed to construct the optimal feature set in multi-source heterogeneous data,which can provide excellent accuracy and interpretability.In addition,a local update screening strategy is proposed to select trustworthy model parameters for aggregation each time to ensure the quality of the global model.The results of the study show that the model error rate is reduced by 6.22%and the recall rate is improved by 11.03%compared to the algorithms commonly used in credit risk research,significantly improving the ability to identify defaulters.Finally,the business operations of commercial banks are used to confirm the potential of the proposed model for real-world implementation.

A Credit Card Fraud Model Prediction Method Based on Penalty Factor Optimization AWTadaboost

With the popularity of online payment, how to perform creditcard fraud detection more accurately has also become a hot issue. And withthe emergence of the adaptive boosting algorithm (Adaboost), credit cardfraud detection has started to use this method in large numbers, but thetraditional Adaboost is prone to overfitting in the presence of noisy samples.Therefore, in order to alleviate this phenomenon, this paper proposes a newidea: using the number of consecutive sample misclassifications to determinethe noisy samples, while constructing a penalty factor to reconstruct thesample weight assignment. Firstly, the theoretical analysis shows that thetraditional Adaboost method is overfitting in a noisy training set, which leadsto the degradation of classification accuracy. To this end, the penalty factorconstructed by the number of consecutive misclassifications of samples isused to reconstruct the sample weight assignment to prevent the classifierfrom over-focusing on noisy samples, and its reasonableness is demonstrated.Then, by comparing the penalty strength of the three different penalty factorsproposed in this paper, a more reasonable penalty factor is selected.Meanwhile, in order to make the constructed model more in line with theactual requirements on training time consumption, the Adaboost algorithmwith adaptive weight trimming (AWTAdaboost) is used in this paper, so thepenalty factor-based AWTAdaboost (PF_AWTAdaboost) is finally obtained.Finally, PF_AWTAdaboost is experimentally validated against other traditionalmachine learning algorithms on credit card fraud datasets and otherdatasets. The results show that the PF_AWTAdaboost method has betterperformance, including detection accuracy, model recall and robustness, thanother methods on the credit card fraud dataset. And the PF_AWTAdaboostmethod also shows excellent generalization performance on other datasets.From the experimental results, it is shown that the PF_AWTAdaboost algorithmhas better classification performance.

A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.

Prioritizing real estate enterprises based on credit risk assessment:an integrated multi‑criteria group decision support framework

Credit risk assessment involves conducting a fair review and evaluation of an assessed subject’s solvency and creditworthiness.In the context of real estate enterprises,credit risk assessment provides a basis for banks and other financial institutions to choose suitable investment objects.Additionally,it encourages real estate enterprises to abide by market norms and provide reliable information for the standardized management of the real estate industry.However,Chinese real estate companies are hesitant to disclose their actual operating data due to privacy concerns,making subjective evalu-ation approaches inevitable,occupying important roles in accomplishing Chinese real estate enterprise credit risk assessment tasks.To improve the normative and reliability of credit risk assessment for Chinese real estate enterprises,this study proposes an integrated multi-criteria group decision-making approach.First,a credit risk assessment index for Chinese real estate enterprises is established.Then,the proposed framework combines proportional hesitant fuzzy linguistic term sets and preference ranking organization method for enrichment evaluation II methods.This approach is suitable for processing large amounts of data with high uncertainty,which is often the case in credit risk assessment tasks of Chinese real estate enterprises involving massive subjec-tive evaluation information.Finally,the proposed model is validated through a case study accompanied by sensitivity and comparative analyses to verify its rationality and feasibility.This study contributes to the research on credit assessment for Chinese real estate enterprises and provides a revised paradigm for real estate enterprise credit risk assessment.

An author credit allocation method with improved distinguishability and robustness

Purpose:The purpose of this study is to propose an improved credit allocation method that makes the leading author of the paper more distinguishable and makes the deification more robust under malicious manipulations.Design/methodology/approach:We utilize a modified Sigmoid function to handle the fat-tail distributed citation counts.We also remove the target paper in calculating the contribution of co-citations.Following previous studies,we use 30 Nobel Prize-winning papers and their citation networks based on the American Physical Society(APS)and the Microsoft Academic Graph(MAG)dataset to test the accuracy of our proposed method(NCCAS).In addition,we use 654,148 articles published in the field of computer science from 2000 to 2009 in the MAG dataset to validate the distinguishability and robustness of NCCAS.Finding:Compared with the state-of-the-art methods,NCCAS gives the most accurate prediction of Nobel laureates.Furthermore,the leading author of the paper identified by NCCAS is more distinguishable compared with other co-authors.The results by NCCAS are also more robust to malicious manipulation.Finally,we perform ablation studies to show the contribution of different components in our methods.Research limitations:Due to limited ground truth on the true leading author of a work,the accuracy of NCCAS and other related methods can only be tested in Nobel Physics Prize-winning papers.Practical implications:NCCAS is successfully applied to a large number of publications,demonstrating its potential in analyzing the relationship between the contribution and the recognition of authors with different by-line orders.Originality/value:Compared with existing methods,NCCAS not only identifies the leading author of a paper more accurately,but also makes the deification more distinguishable and more robust,providing a new tool for related studies.

RankXGB-Based Enterprise Credit Scoring by Electricity Consumption in Edge Computing Environment

With the rapid development of the internet of things(IoT),electricity consumption data can be captured and recorded in the IoT cloud center.This provides a credible data source for enterprise credit scoring,which is one of the most vital elements during the financial decision-making process.Accordingly,this paper proposes to use deep learning to train an enterprise credit scoring model by inputting the electricity consumption data.Instead of predicting the credit rating,our method can generate an absolute credit score by a novel deep ranking model–ranking extreme gradient boosting net(rankXGB).To boost the performance,the rankXGB model combines several weak ranking models into a strong model.Due to the high computational cost and the vast amounts of data,we design an edge computing framework to reduce the latency of enterprise credit evaluation.Specially,we design a two-stage deep learning task architecture,including a cloud-based weak credit ranking and an edge-based credit score calculation.In the first stage,we send the electricity consumption data of the evaluated enterprise to the computing cloud server,where multiple weak-ranking networks are executed in parallel to produce multiple weak-ranking results.In the second stage,the edge device fuses multiple ranking results generated in the cloud server to produce a more reliable ranking result,which is used to calculate an absolute credit score by score normalization.The experiments demonstrate that our method can achieve accurate enterprise credit evaluation quickly.

Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.

Study on the Changes of Extreme Temperature in the Southwest of Zhejiang Province during the Period of 1953-2022

Under the background of global warming, extreme temperature occurs frequently around the globe, which has a significant and direct impact on social and economic system. Liuchun Lake is an important ecotourism scenic region in Longyou in the southwest of Zhejiang province, it is very important for the local economic development. Based on the daily mean temperature, maximum and minimum temperature from 15 stations, the 13 extreme temperature indices as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated, and the characteristics of extreme temperature in the southwest of Zhejiang province were analyzed. The results showed that: 1) The Warmest day (TXx) and Warmest night (TNx) increased at most of the stations, while the coldest day (TXn) and the coldest night (TNn) basically significantly increased at all the stations;2) The number of frost days (FD0) showed decreased trend, and all the stations passed the 99% significant level, the number of ice days (ID0) also was on downward trend, but it is not significant at all most of the stations, however, both the number of summer days (SU25) and tropical nights (TR20) were on upward trend, and all the stations passed the significant level (p < 0.1);3) Both the number of cold days (TX10P) and cold nights (TN10P) showed a declined trend, while the number of warm days (TX90P) and warm night (TN90P) had an upward trend, especially TN90P had significant increase at all the stations. This implies that the cold events declined and warm events increased in the southwest regions of Zhejiang from 1953 to 2022.