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.

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.

Properties and Characterization of Two Clays Raw Material from Mountain District (West of Côte d’Ivoire) for Use in Low-Carbon Cements

This work was devoted to the study of the physico-chemical properties of two clay minerals from the Mountain District (West Côte d'Ivoire) referenced ME1 and ME2. These samples were characterized by the experimental techniques, such as X-ray diffraction (XRD), Infrared spectroscopy (IR), Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES), Differential Thermal Analysis and Thermogravimetry (DTA-TG), Brunauer, Emett and Teller method (BET), laser particle size analysis and Scanning Electron Microscope (SEM). The main results of these analyses reveal that the two clay samples mainly contain quartz (52.91% for ME1 and 51.72% for ME2), kaolinite (36.60% for ME1 and 41.6% for ME2) and associated phases, namely goethite and hematite (13.47% for ME1 and 11.00% for ME2). The specific surface values obtained for samples ME1 and ME2 are 34.78 m2/g and 29.18 m2/g respectively. The results obtained show that the samples studied belong to the kaolinite family. After calcination, they could have good pozzolanic activity and therefore be used in the manufacture of low-carbon cements.

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.

Does China's digital economy contribute to low-carbon transition?

The digital economy,as a new emerging economic form,has become an important power for realizing Chinese-style modernization and promoting green development in China.This paper measures the digital economy and low-carbon transition index based on the data of 30 provinces in China from 2013 to 2020 and analyzes the mechanism and path of the digital economy affecting low-carbon transition using the fixed effect panel data model and the threshold effect model.It is found that,(1)The digital economy and low-carbon transition in China are various in different regions,with characteristics of being unbalanced and insufficient.(2)The digital economy significantly promotes low-carbon transition,with the greatest influence in the Central region,followed by the Eastern region and the Western region.Under different dimensions,the development of informatization and digital transactions promote low-carbon transition,but the development of the internet plays an inhibiting role.(3)The higher the degree of urbanization and environmental regulation,the greater the influence of the digital economy on low-carbon transition.

Effect of ZrC Modified Graphite on Structure and Properties of Low-carbon Al_(2)O_(3)-C Refractories

To address the issues of reduced performance and shortened lifespan during the low-carbonizating process of Al_(2)O_(3)-C refractories,nano-crystalline ZrC modified graphite was prepared using Zr powder and flake graphite as raw materials,with NaCl and NaF mixed salt serving as the medium.The flake graphite was gradually replaced by ZrC modified graphite in the preparation of Al_(2)O_(3)-C refractories,and its impact on the material’s structure and properties was investigated.The results indicate that,compared to samples with only flake graphite,the introduction of 1 mass%to 5 mass%nano-crystalline ZrC modified graphite can significantly enhance the mechanical performance of low-carbon Al_(2)O_(3)-C refractories.When 5 mass%ZrC modified graphite is added,the mechanical properties of the samples are optimal,with the cold modulus of rupture and elastic modulus reaching 22.5 MPa and 65.0 GPa,respectively.

Performance Evaluation of Low-Carbon and Clean Transformation of China’s Coal Economy

In China,the oversupply of coal occurred in 2009,and from that year onwards,China’s coal economy began a low-carbon and clean transformation.Evaluating transformation performance is the research goal of this paper.The data collection for this paper includes data on deep processing of Chinese coal products from 2009 to 2020,as well as data on asset structure evolution and financial performance of 34 listed companies in the Chinese coal mining.Entropy value method is used to calculate the entropy value of low-carbon transformation,and the regression analysis is used to study the performance of cleaner transformation,the conclusion is as follows:(1)From 2009 to 2020,in China’s total energy consumption,coal consumption accounted for 71.6%in 2009 and 56.8%in 2020,the goals set by the state have been achieved.(2)The national goal of reducing the proportion of coal consumption and reducing carbon emissions has forced the transformation of deep processing of coal products.The transformation of coal enterprises towards low-carbon and clean production has achieved remarkable results.(3)From 2009 to 2020,the non coal industry income of 34 listed companies in China’s coal mining industry increased by 8.21%annually.At the same time,the asset structure was adjusted,and nearly 80%of the asset structure evolution showed an orderly development trend.(4)The regression analysis results show that the entropy value of coal deep processing products and the entropy value of asset structure adjustment are significantly related to transformation performance.The paper proposes to summarize the successful experience of China’s coal energy economic transformation,lay a foundation for achieving the carbon peak and carbon neutral goals in the future,further increase the intensity of coal deep processing,increase the proportion of clean energy in total energy consumption,and strive to control asset operation towards the goal of increasing the proportion of non coal industry income.

A Mechanism for Digital Finance to Drive Green and Low-Carbon Development

Given the global focus on green and low-carbon development and the increasing prominence of digital finance,it is particularly important to explore how to leverage digital finance to achieve these environmental goals.This study,through mechanism analysis,deeply examines how China’s digital finance promotes green and low-carbon development and elucidates the positive interaction between digital finance and the green industry.The study found that digital finance,through more flexible and efficient financial functions,alters the cost structure of carbon emissions,and reduces the risks and costs of green investments,thereby creating a cooperative green mechanism benefiting all parties,and guiding social groups toward a green and low-carbon transformation.Additionally,the rapid development of digital finance has strengthened the implementation of environmental protection policies,effectively promoted the expansion of the environmental protection industry,and established the green ethos as a mainstream concept in financial development.This study aims to provide reference perspectives and suggestions,assist policymakers in promoting the green and lowcarbon development of digital finance,and offer insights into the integrated development of digital finance and the green environmental protection industry.

Plasma-electrified up-carbonization for low-carbon clean energy

Low-value,renewable,carbon-rich resources,with different biomass feedstocks and their derivatives as typical examples,represent virtually inexhaustive carbon sources and carbon-related energy on Earth.Upon conversion to higher-value forms(referred to as“up-carbonization”here),these abundant feedstocks provide viable opportunities for energy-rich fuels and sustainable platform chemicals production.However,many of the current methods for such up-carbonization still lack sufficient energy,cost,and material efficiency,which affect their economics and carbon-emissions footprint.With external electricity precisely delivered,discharge plasmas enable many stubborn reactions to occur under mild conditions,by creating locally intensified and highly reactive environments.This technology emerges as a novel,versatile technology platform for integrated or stand-alone conversion of carbon-rich resources.The plasma-based processes are compatible for integration with increasingly abundant and cost-effective renewable electricity,making the whole conversion carbon-neutral and further paving the plasma-electrified upcarbonization to be performance-,environment-,and economics-viable.Despite the chief interest in this emerging area,no review article brings together the state-of-the-art results from diverse disciplines and underlies basic mechanisms and chemistry underpinned.As such,this review aims to fill this gap and provide basic guidelines for future research and transformation,by providing an overview of the application of plasma techniques for carbon-rich resource conversion,with particular focus on the perspective of discharge plasmas,the fundamentals of why plasmas are particularly suited for upcarbonization,and featured examples of plasma-enabled resource valorization.With parallels drawn and specificity highlighted,we also discuss the technique shortcomings,current challenges,and research needs for future work.

An Intelligent Control Method for the Low-Carbon Operation of Energy-Intensive Equipment

Based on an analysis of the operational control behavior of operation experts on energy-intensive equipment,this paper proposes an intelligent control method for low-carbon operation by combining mechanism analysis with deep learning,linking control and optimization with prediction,and integrating decision-making with control.This method,which consists of setpoint control,self-optimized tuning,and tracking control,ensures that the energy consumption per tonne is as low as possible,while remaining within the target range.An intelligent control system for low-carbon operation is developed by adopting the end-edge-cloud collaboration technology of the Industrial Internet.The system is successfully applied to a fused magnesium furnace and achieves remarkable results in reducing carbon emissions.

Risk preferences and the low-carbon agricultural technology adoption:Evidence from rice production in China

Climate change which is mainly caused by carbon emissions is a global problem affecting the economic development and well-being of human society.Low-carbon agriculture is of particular significance in slowing down global warming and reaching the goal of“carbon peak and carbon neutrality”.Therefore,taking straw incorporation as an example,this paper aims to investigate the impact of risk preferences on farmers’low-carbon agricultural technology(LCAT)adoption.Based on a two-phase micro-survey data of 1038 rice farmers in Jiangsu,Jiangxi,and Hunan provinces,this paper uses experimental economics methods to measure farmers’risk aversion and loss aversion to obtain the real risk preferences information of the farmers.We also explore the data to examine the actual LCAT adoption behavior of farmers.The results revealed that both risk aversion and loss aversion significantly inhibit farmers’LCAT adoption:more risk-averse or more loss-averse farmers are less likely to adopt LCAT.It is further found that crop insurance,farm scale and governmental regulations can alleviate the negative impact of risk aversion and loss aversion on farmers’LCAT adoption.Therefore,we propose that local governments need to promote low-carbon agricultural development by propagating the benefits of LCAT,extending crop insurance,promoting appropriate scale operations,and strengthening governmental regulations to promote farmers’LCAT adoption.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

Cold plasma-activated Cu-Co catalysts with CN vacancies for enhancing CO_(2)electroreduction to low-carbon alcohol

Electrocatalytic CO_(2)reduction reaction to low-carbon alcohol is a challenging task,especially high selectivity for ethanol,which is mainly limited by the regulation of reaction intermediates and subsequent C–C coupling.A Cu-Co bimetallic catalyst with CN vacancies is successfully developed by H_(2)cold plasma toward a high-efficiency CO_(2)RR into low-carbon alcohol.The Cu-Co PBA-V_(CN)(Prussian blue analogues with CN vacancies)electrocatalyst yields methanol and ethanol as major products with a total low-carbon alcohol FE of 83.8%(methanol:39.2%,ethanol:44.6%)at-0.9 V vs.RHE,excellent durability(100 h)and a small onset potential of-0.21 V.ATR-SEIRAS(attenuated total internal reflection surface enhanced infrared absorption spectroscopy)and DFT(density functional theory)reveal that the steric hindrance of V_(CN)can enhance the CO generation from*COOH,and the C–C coupling can also be increased by CO spillover on uniformly dispersed Cu atoms.This work provides a strategy for the design and preparation of electrocatalysts for CO_(2)RR into low-carbon alcohol products and highlights the impact of catalyst steric hindrance to catalytic performance.

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.