Search Results (83)

Climate change risk has significant impacts on corporate financial activities. Using firm-level data from A-share listed companies in China from 2010 to 2022, we examine how climate risk affects corporate debt financing capacity. We find that climate change risk significantly weakens firms’ ability to raise debt, leading to lower leverage and higher financing costs. These results remain robust across various checks for endogeneity and alternative specifications. We also show that reducing corporate carbon emission intensity can mitigate the negative impact of climate risk on debt financing, suggesting that supply-side credit policies are more effective than demand-side capital structure choices. Furthermore, we identify three channels through which climate risk impairs debt capacity: reduced competitiveness, increased default risk, and diminished resilience. Our heterogeneity analysis reveals that these adverse effects are more pronounced for non-state-owned firms, firms with weaker internal controls, and companies in highly financialized regions, and during periods of heightened environmental uncertainty. We also apply textual analysis and machine learning to the measurement of climate change risks, partially mitigating the geographic biases and single-dimensional shortcomings inherent in macro-level indicators, thus enriching the quantitative research on climate change risks. These findings provide valuable insights for policymakers and financial institutions in promoting corporate green transition, guiding capital allocation, and supporting sustainable development. Full article

As an emerging e-commerce model, live streaming e-commerce integrates instant interaction, content marketing, and online sales to bring consumers a new shopping experience. However, there are many risks in the process of live e-commerce transactions. Identifying key risk factors and implementing targeted control measures are crucial for promoting the sustainable and healthy development of live streaming e-commerce. This paper firstly constructs a business model of live streaming e-commerce transactions according to the transaction scenario and summarizes 24 risk factors from the three dimensions of live streaming e-commerce platforms, merchants, and anchors based on relevant national standards and other relevant literature. Secondly, the Delphi method is employed to modify and optimize the initial risk factors. On this basis, the social network model of risk factors is constructed to determine the influence relationship among risk factors. By calculating the degree centrality, factor types are segmented, and key risk factors as well as influence paths are identified. Finally, corresponding countermeasures and suggestions are proposed. The results indicate that Credit Evaluation System Perfection, Service Evaluation System Perfection, Qualification Audit Mechanism Perfection, Dispute Complaint Handling Channels Perfection, Risk Identification Mechanism Perfection, Platform Qualification, Merchant Qualification, and Merchant Credit are the critical risk factors affecting live streaming e-commerce transactions. Full article

Family structure has long been regarded as an important determinant of household saving, yet the empirical evidence for developing economies remains limited. Using the 2018–2022 panels of the China Family Panel Studies (CFPS), a nationwide survey that follows 16,519 households across three waves, the present study investigates how family size, the elderly share, and the child share jointly shape saving behavior. A household fixed effects framework is employed to control for time-invariant heterogeneity, followed by a sequential endogeneity strategy: external-shock instruments are tested and rejected, lagged two-stage least squares implement internal instruments, and a dynamic System-GMM model is estimated to capture saving persistence. Robustness checks include province-by-year fixed effects, inverse probability weighting for attrition, balanced-panel replication, alternative variable definitions, lag structures, and sample filters. Family size raises the saving rate by 4.6 percentage points in the preferred dynamic specification (p < 0.01). The elderly ratio remains insignificant throughout, whereas the child ratio exerts a negative but model-sensitive association. A three-path mediation analysis indicates that approximately 26 percent of the total family size effect operates through scale economy savings on quasi-fixed expenses, 19 percent is offset by resource dilution pressure, and less than 1 percent flows through a precautionary saving channel linked to income volatility. These findings extend the resource dilution literature by quantifying the relative strength of competing mechanisms in a middle-income context and showing that cost-sharing economies dominate child-related dilution for most households. Policy discussion highlights the importance of public childcare subsidies and targeted credit access for rural parents, whose saving capacity is the most constrained by additional children. The study also demonstrates that fixed effects estimates of family structure can be upward-biased unless dynamic saving behavior and internal instruments are considered. Full article

Amid the global drive to enhance public sector performance in the digital economy era, improving government spending efficiency has become a critical governance objective. This study investigates the impact of digital inclusive finance on government spending efficiency from a digital finance systems perspective using county-level panel data in China’s Yangtze River Delta for the period 2014–2022 and constructing the fixed-effects model and instrumental variable method to estimate the effect of digital inclusive finance and explore its underlying mechanisms. Heterogeneity across regions with varying economic development levels is analyzed, and fiscal pressure is examined as a potential mediating factor. The results indicate that (1) digital inclusive finance significantly enhances government spending efficiency, primarily through broad service coverage and deep usage of digital financial services such as mobile payments, digital credit, and insurance; (2) the positive effect is more pronounced in counties with lower government spending efficiency and economic development; and (3) fiscal pressure acts as a key transmission channel, with broader digital inclusive finance coverage helping to alleviate fiscal stress and improve government spending efficiency. These findings offer empirical insights into the role of digital finance in promoting effective and adaptive public financial governance. Full article

With the continuous integration of globalization and financial markets, the linkage of global financial risks has increased significantly. This study examines the risk spillover effects and transmission dynamics among the financial markets in G20 countries, which together represent over 80% of global GDP. With increasing globalization and the interconnectedness of financial markets, understanding risk transmission mechanisms has become critical for effective risk management. Previous research has primarily focused on price volatility to measure financial risks, often overlooking other critical dimensions such as liquidity, credit, and operational risks. This paper addresses this gap by utilizing the vector autoregressive (VAR) model to explore the spillover effects and the temporal and spatial characteristics of risk transmission. Specifically, we employ global and local Moran indices to analyze spatial dependencies across markets. Our findings reveal that the risk linkages among the G20 financial markets exhibit significant time-varying characteristics, with spatial risk distribution showing weaker dispersion. By constructing a comprehensive financial risk index system and applying a network-based spillover analysis, this study enhances the measurement of financial market risk and uncovers the complex transmission pathways between sub-markets and countries. These results not only deepen our understanding of global financial market dynamics but also provide valuable insights for the design of effective cross-border financial regulatory policies. The study’s contributions lie in enriching the empirical literature on multi-dimensional financial risks, advancing policy formulation by identifying key risk transmission channels, and supporting international risk management strategies through the detection and mitigation of potential contagion effects. Full article

The Law on Credit Institutions 2010, amended and supplemented, was applied on 15 January 2018, causing many changes in senior personnel in Vietnamese banking. The period (2018–2014) had many changes. This was also a period of many business difficulties. Four commercial banks had to carry out mandatory transfers at the request of the State Bank to ensure the development of the Vietnamese banking system in 2024. Profitable investment channels of commercial banks sometimes generate income and, at other times, suffer losses. Managers often analyze and make investment decisions by observing developments recorded on graphs and estimating the future fluctuation trends of each profitable investment channel. However, no research has been conducted on how the simultaneous implementation of all information from investment channels affects the final profit results of commercial banks. This study investigates all banking activities, from trading to investing, to consider which investment channel has a stable impact on bank profits over a long period. The S-GMM estimation method is used, due to the consideration of endogenous variables in quarterly panel data of 27 Vietnamese commercial banks from the first quarter of 2018 to the third quarter of 2024. This study provides statistical evidence indicating that all investment channels of commercial banks contribute to increased profits, except for short-term securities trading channels and capital contributions to subsidiaries. This study also reveals that economic growth and systemic risk affect commercial bank profits. Several solutions are proposed for commercial banks to develop future profitable investment channels. Full article

In recent years, supply chain risks and stability have become a focal point of public attention. However, there is no consensus on how exogenous shocks affect the sustainability of supply chain relationships, nor a clear mechanism of influence. This study uses data from all A-share listed companies in China from Q2 2018 to Q4 2021, constructing a “supplier–quarter–customer” relationship dataset, with the COVID-19 pandemic serving as an exogenous shock. The results show that after experiencing exogenous shocks, the sustainability of supply chain relationships actually strengthens. This suggests that companies may take measures to enhance supply chain stability and maintain existing relationships to ensure sustainability. Channel analysis reveal that trade credit serves as a channel for the impact of exogenous shocks on the sustainability of supply chain relationships, with companies adjusting trade credit supply to downstream customers to maintain and strengthen stability. Additionally, the impact of exogenous shocks on the sustainability of supply chain relationships varies with market concentration, product input heterogeneity, and firms’ ownership type. Therefore, companies should enhance supply chain relationship management, utilize trade credit as a risk buffer, and optimize the supply chain structure to reduce risk transmission and maintain sustainability. Full article

This study examines the strategic decisions and profit dynamics of suppliers marketing their products through both offline and online channels, alongside online e-commerce platforms providing their own consumer credit services. We develop a model that incorporates consumer disposable income, channel preferences, and credit utility. Four supply chain scenarios are analyzed: wholesale and agency models with either private or open credit strategies. Using Stackelberg game theory, we explore suppliers’ sales model choices and the conditions under which platforms extend credit to offline channels. Our results show that increasing credit utility generally leads to higher equilibrium prices, while higher service fees compel suppliers to adjust their prices, favoring lower-cost channels. Notably, suppliers are more likely to adopt the wholesale model to secure platform credit for offline sales, especially when credit service fees or credit utility are high. Furthermore, platform credit strategies are strongly influenced by suppliers’ sales model choices: In wholesale models, platforms are more inclined to extend credit to offline channels under specific conditions of high disposable income (DPI) and credit utility, whereas in agency models, open credit strategies are only adopted when both DPI proportions and credit utility are low. This research provides new insights into how platforms can tailor credit offerings based on supplier strategies, offering a theoretical foundation for consumer credit policies in multi-channel sales environments and valuable guidance for managers in determining optimal channel strategies and credit service offerings. Full article

The high-quality development of specialized, refined, distinctive, and innovative enterprises (SRDIEs) is essential for advancing an innovation-driven strategy. This paper investigates the impact of financial technology (Fintech) on sustainable innovation within SRDIEs that face financing challenges, analyzing it from supply-side, demand-side, and environmental perspectives. We utilize fuzzy-set Qualitative Comparative Analysis (fSQCA) and Necessary Condition Analysis (NCA) to explore the configurational paths and complex causal effects of Fintech in facilitating the innovation of SRDIEs amid financing challenges. By employing a combination of NCA and fsQCA, this study identifies several effective pathways through which Fintech enhances the innovation efficiency of SRDIEs. We develop an integrative model to enhance innovation inputs, outputs, and sustainability. The key findings include the following: (1) Fintech significantly enhances innovation output, supported by business efficiency and digital intelligence; (2) two distinct pathways for achieving high-innovation inputs are identified, driven by Fintech intensity and effective credit allocation, with specialization and financial mismatches serving as auxiliary factors; (3) the core conditions of Fintech intensity and the financing environment, along with competitive banking, promote innovation motivation and sustainability in highly specialized enterprises. The conclusions of this study provide both theoretical and practical insights for SRDIEs to tackle innovation challenges characterized by an “inability to innovate”, a “lack of willingness to innovate”, and “ineffectiveness in innovation”, enabling their transition from merely being “able to innovate” and “daring to innovate” to becoming “proficient in sustainable innovation”. These findings offer differentiated sustainable innovation solutions for enterprises through three avenues: capacity building on the demand side, channel optimization on the supply side, and ecological cultivation on the environmental side. Full article

As an important pillar of the national economy, the green transformation of SMEs is the key to promoting sustainable economic development. However, SMEs generally face issues such as information opacity and high operational risks, which make it difficult for them to obtain traditional financing support, thereby hindering green development. Green Supply Chain Finance has opened up new financing channels for SMEs, but the accuracy of credit risk evaluation remains a bottleneck that limits its widespread application. This paper constructs a credit risk evaluation index system that integrates multiple sources of information, covering factors such as the situations of SMEs themselves, stakeholder feedback, and expert ratings. It compares and analyzes the performance of the genetic algorithm-optimized random forest model (GA-RF), the BP neural network, the support vector machine, and the logistic regression model in credit risk evaluation. The empirical results indicate that the GA-RF model is significantly better than the other models in terms of accuracy, precision, and F1 score, and has the highest AUC value, making it more effective in identifying credit risk. In addition, the GA-RF model reveals that the asset–liability ratio, the time of establishment, the growth rate of operating revenue, the time of collection of accounts receivable, the return on net assets, and daily shipments are the key indicators affecting the credit risk assessment. Full article

The Nielsen Report points out that credit card fraud caused business losses of USD 28.65 billion globally in 2019, with the US accounting for more than one-third of the high share, and that insufficient identification of credit card fraud has brought about a serious loss of financial institutions’ ability to do business. In small sample data environments, traditional fraud detection methods based on prototype network models struggle with the loss of time-series features and the challenge of identifying the uncorrected sample distribution in the metric space. In this paper, we propose a credit card fraud detection method called the Time-Series Attention-Boundary-Enhanced Prototype Network (TABEP), which strengthens the temporal feature dependency between channels by incorporating a time-series attention module to achieve channel temporal fusion feature acquisition. Additionally, nearest-neighbor boundary loss is introduced after the computation of the prototype-like network model to adjust the overall distribution of features in the metric space and to clarify the representation boundaries of the prototype-like model. Experimental results show that the TABEP model achieves higher accuracy in credit card fraud detection compared to five existing baseline prototype network methods, better fits the overall data distribution, and significantly improves fraud detection performance. This study highlights the effectiveness of open innovation methods in addressing complex financial security problems, which is of great significance for promoting technological advancement in the field of credit card security. Full article

Robert Lowell’s Life Studies won the National Book Award for Poetry in 1960 and is credited with initiating the confessional poetry movement, which included followers and students of Lowell such as Anne Sexton and Sylvia Plath. In Life Studies, Lowell channeled his 1950s experiences with bipolar disorder and mental health hospitalizations into poems such as “Man and Wife”, “Waking in the Blue”, and “Home After Three Months Away”. Lowell’s hard-won Life Studies triumph, though most recently analyzed through socioeconomic and “divine madness” lenses, can also be understood through Carl Jung’s individuation concept which posits that self-realization can be attained through the reconciliation of one’s own conscious and unconscious mental processes. This article argues that Lowell’s Life Studies poems, when examined through Jungian individuation, enabled Lowell to achieve self-realization, and paved the way for mentally ill individuals to learn how to achieve psychological wholeness through art. Full article

Improving industrial energy efficiency (IEE) is crucial for reducing CO₂ emissions. Green finance (GF) provides an essential economic instrument for investment in IEE improvement. However, previous studies have not reached a consensus on whether GF can promote energy efficiency. In addition, more research is needed in the industrial sector. Therefore, this study focused on the industrial level to investigate GF’s impact on IEE and its heterogeneity using a two-way fixed effects model. The moderating effect, threshold effect, and spatial lag models were used to test the various effects of GF on IEE. In addition, the spatial clustering characteristics of IEE were analyzed. The results indicate the following: GF can significantly promote IEE, positively improves IEE in the central and eastern areas, and has a negative impact in the western area; the marketization level (ML) is an important channel through which GF can further improve IEE; GF’s impact on IEE exhibits a single threshold effect of the level of economic development (EDL) and green credit (GCL); GF promotes local IEE improvement but prevents neighboring IEE improvement; and IEE shows four types of clusters, but only in about one-third of the provinces. Based on these results, several recommendations are provided. Full article

This study delves into the influence of banks’ governance and ownership structures on green lending. To examine this, we utilized the two-step system GMM and PCSE methods on the panel data of Vietnamese commercial banks spanning from 2010 to 2023. The findings suggest that board characteristics, precisely board size, board independence, and gender diversity, play a significant role in encouraging banks to provide green credit. The study highlights the importance of ownership structure in green lending. Banks with a high percentage of government ownership tend to fund more green projects, while foreign counterparts are reluctant to fund green finance. A mechanism test is also conducted to point out that banks’ disclosure of their green loan commitments is an influential channel whereby corporate governance and ownership structure impact green loans. Additionally, this research finds that the issuance of the Green Loan Principles in 2018 can facilitate banks’ governance of sustainable lending. Full article

Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm’s individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment. Full article