Green Finance Promotes High-Quality Development of Regional Economy

With the rapid development of the social economy,the role of green finance in promoting the high-quality development of regional economies is increasing day by day.The advancement of green finance not only aids in fostering the green transformation and upgrading of regional economies but also helps mitigate the risks of environmental damage stemming from traditional economic activities.In this new era,it is imperative to embrace the concept of green finance development and innovate green finance practices to further drive high-quality regional economic development.This paper will analyze the significance of green finance in regional economic development,assess the current state of green finance development,and propose optimization strategies for green finance to facilitate high-quality economic development.

Research on the Impact of Digital Finance on Energy Efficiency in China

Digital finance and green technology innovation(GTI)serve as powerful engines for promoting energy efficiency(EE)and economic development.This paper explores the mechanism by which digital finance impacts EE based on panel data from 30 provinces in China spanning from 2011 to 2019.The results demonstrate that digital finance can significantly enhance EE,with a particularly pronounced effect in the eastern region.Through mechanistic analysis,it is evident that GTI serves as the transmission pathway through which digital finance influences EE,accounting for 45.3%of the effect.The policy implication of this study suggests that China should expedite the digitization of financial markets to further harness the development of digital finance,particularly in pursuit of its technological innovation and green,lowcarbon environmental protection effects.

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.

我国企业财务数字化转型研究热点与发展趋势——基于CiteSpace的可视化分析

财务数字化转型是企业数字化转型的重要突破口,能够帮助企业提升财务运营效率,进而实现企业的整体战略目标。本文以CNKI期刊数据库中2017—2023年发表的企业财务数字化转型相关文献为研究对象,运用可视化知识图谱软件CiteSpace绘制作者、机构和关键词图谱并进行具体分析,总结我国企业财务数字化转型的研究概况,探析未来的研究主题与方向。研究表明:(1)我国关于企业财务数字化转型的研究起步于2017年;2020年起在国家政策的助力下进入快速发展阶段,随后该领域的发文量稳步上升,整体研究成果呈快速增长趋势。(2)高产作者合作频次低且合著网络松散,高产研究机构之间的合作也不够紧密。(3)当前我国企业财务数字化转型研究聚焦于转型路径和策略、企业财务共享服务优化和企业高质量发展研究等方面。未来领域内的研究方向或将侧重于运用数字化手段进行企业合规风控体系建设,以期有效防控企业合规风险,促进企业健康发展。

乡村振兴背景下数字普惠金融发展的脉络与趋势——基于CiteSpace的文献计量分析

随着新一轮科技革命,数字普惠金融成为农业现代化和乡村振兴的重要抓手。运用Cite Space软件对2018—2023年乡村振兴背景下数字普惠金融发展的脉络与趋势进行系统梳理,结果发现:从年发文量来看,文献数量总体呈现两阶段变化趋势,在2020年乡村振兴的支持力度不断增大后,数字普惠金融的文献数量呈现快速增长态势;从发文机构和作者分布来看,学者间没有形成核心群体,同时各机构之间合作交流较少,联系密度较小;从关键词演化图谱来看,研究热点主要集中于乡村振兴、共同富裕、数字经济、城乡收入差距等方面的效应分析和影响机制的研究。未来研究热点将会逐渐转向中微观层面,强化数字普惠金融使用深度、建立健全农业信息共享平台和数字普惠金融信用评价机制等方面。

基于CiteSpace知识图谱的新财经人才培养模式研究

为了促进新财经人才培养模式创新发展,基于宏观视角,借助CiteSpace软件分析近10年新财经人才培养领域的发文特点,梳理出新财经人才培养模式研究的现状、热点与演化脉络,在此基础上提出我国新财经人才培养的具体路径:加强校企合作、校校合作,重点发挥核心机构辐射带动作用;开展“新财经+地方特色”教学培养模式;全方位立体化建立纵深式产教融合培养模式;深化学科融合力度,推动新财经人才综合素质培养。

基于ITSA模型的浙江省世界银行贷款千岛湖项目成效分析

针对国家级战略规划《千岛湖及新安江上游流域水资源与生态环境保护综合规划》的出台,浙江省于2018年11月启动了世界银行贷款浙江千岛湖及新安江流域水资源与生态环境保护项目。为评估该项目对千岛湖水资源的阶段性保护成效,本文采用中断时间序列分析ITSA模型进行定量分析,研究千岛湖项目与千岛湖水基金协同执行2年之后的治理成效。研究结论显示,千岛湖部分水质指标的改善已进入瓶颈期,要获得水质的进一步提升,仍需继续加大科技投入,除提升上游来水水质外,还要从提升水环境治理的科技水平、治理广度与深度等方面着手,才能获得更大突破。

Journal Introduction:China Finance Review International

Background The China Finance Review International is a flagship academic journal broadly covering the Chinese and international financial markets.The journal is founded by Antai College of Economics and Management at Shanghai Jiao Tong University,one of the top universities in Asia.The China Finance Review International aims to publish quality empirical and theoretical works on important financial and economic issues in the profession.We encourage ground-breaking research related to new and niche areas in finance,such as Fintech and cryptos,ESG,climate finance,and socially responsible investments.We welcome critiques of existing literature and comparative analysis between emerging markets and developed economies.

A Review on the Security of the Ethereum-Based DeFi Ecosystem

Decentralized finance(DeFi)is a general term for a series of financial products and services.It is based on blockchain technology and has attracted people’s attention because of its open,transparent,and intermediary free.Among them,the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention.However,the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years.Herein,we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues.To that end,we investigate the Ethereum-based DeFi security issues:1)inherited from the real-world financial system,which can be solved by macro-control;2)induced by the problems of blockchain architecture,which require a better blockchain platform;3)caused by DeFi invented applications,which should be focused on during the project development.Based on that,we further discuss the current solutions and potential directions ofDeFi security.According to our research,we could provide a comprehensive vision to the research community for the improvement of Ethereum-basedDeFi ecosystem security.

Impact of carbon disclosure on debt financing costs

Creditors,such as banks,often use disclosed environmental information to assess a company’s environmental risk and ensure the safety of debt funds.Consequently,carbon disclosures have become an important consideration for creditors when making investments.This study explores the relationship between carbon disclosure and debt financing costs using data on listed companies from 2008 to 2019.The results show that carbon disclosure can reduce the debt financing costs of enterprises,and that this influence is more significant for private companies than for state-owned enterprises.Instrumental variables and Propensity Score Matching(PSM)were used to evaluate the robustness of negative relationships.Furthermore,carbon disclosure has a more significant impact on debt costs with less environmental supervision pressure,weak residents’environmental awareness,and weak product market competition.These findings provide guidance for companies’carbon information disclosure and support the establishment of official carbon disclosure standards.

Charting the course:India's health expenditure projections for 2035

Objectives Understanding past trends and forecasting future changes in health spending is vital for planning and reducing reliance on out-of-pocket(OOP)expenses.The current study analyzed health expenditure patterns in India and forecasted future trends and patterns until 2035.Methods Data on health expenditure in India from 2000 to 2019 was collected from the Organisation for Economic Co-operation and Development(OECD)iLibrary and National Health Accounts 2019 databases.Gross domestic product(GDP)data from the World Bank was also utilized.Descriptive statistics analyzed the composition and pattern,while the exponential smoothing model forecasted future health expenditures.Results The findings revealed that expenditure made by OOP is the primary health financing source,followed by government and pre-paid private spending.The percentage of GDP allocated to total health expenditure remains stable,while the per capita health expenditure fluctuates.Variations in expenditure among states are observed,with Karnataka relying heavily on pre-paid private coverage.Future projections suggest a decline in per capita and total health expenditure as a share of GDP,with a slight increase in the government’s share.Pre-paid private expenditure per capita and OOP health expenditure as a share of the total is projected to remain relatively constant but still high in absolute terms.Conclusion The study highlights variations in health spending in India,characterized by high OOP spending,limited public coverage,and a need for investments,and reforms to improve healthcare access and equity.

The Impact of Digital Finance on High-Quality Development of the Chengdu-Chongqing Economic Circle

This paper developed a comprehensive evaluation system that was able to quantify the levels of high-quality development across the cities within the Chengdu-Chongqing economic circle,and investigate the impact that digital finance had on the cities’high-quality development and the underlying mechanisms through which it achieved this.This comprehensive evaluation system was constructed using statistical data from these cities for the period 2014 to 2020 while also taking China’s high-quality development philosophy into account.The key findings revealed that:(a)Digital finance was able to significantly promote high-quality development in the Chengdu-Chongqing economic circle;(b)Digital finance had a significant positive effect in promoting innovative,coordinated,green,open,and shared development;(c)Digital finance was able to stimulate the high-quality development in the Chengdu-Chongqing economic circle by boosting entrepreneurial dynamism;(d)Digital finance had a significant impact on the high-quality development of the axis areas,while its impact was less discernible in non-axis areas.The insights from this research offer a deeper understanding of the factors that drive high-quality development,the role digital finance plays,and the mechanisms through which digital finance is able to propel high-quality development at the city cluster scale.

State-Owned Capital Participation in Private Enterprises:A Perspective of Debt Financing

This study takes debt financing as the entry point and explores the impact of state-owned capital participation in private enterprises from the perspectives of“unarticulated rules”and“articulated rules”.The study finds that state-owned capital participation significantly reduces the debt financing costs of private enterprises and expands the scale of their debt financing.This conclusion remains valid after a series of endogeneity and robustness tests.Further analysis of the mechanism reveals that state-owned capital participation improves the debt financing of private enterprises through multiple channels:Enhancing their social reputation,mitigating the“statistical bias”they face,optimizing their information quality,and reducing the“shareholder-creditor”agency problems.This paper conceptualizes these benefits as the“complementary advantages of heterogeneous shareholders”.This not only constructs a theoretical framework for“reverse mixed-ownership reform”but also better narrates the Chinese story of“mixed-ownership reform”by adopting a more universally applicable theory of equity structure.Additionally,the paper supplements existing research on the macro-and meso-level relationship between the government and the market by exploring the government’s positive role at the micro-level.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Automated Actuarial Data Analytics-Based Inflation Adjusted Frequency Severity Loss Reserving Model

In this paper, the Automated Actuarial Loss Reserving Model is developed and extended using machine learning. The traditional actuarial reserving techniques are no longer compatible with the increase in technological advancement currently at hand. As a result, the development of the alternative Artificial Intelligence Based Automated Actuarial Loss Reserving Methodology which captures diverse risk profiles for various policyholders through augmenting the Micro Finance services, Auto Insurance Services and Both Services lines of business on the same platform through the computation of the Comprehensive Automated Actuarial Loss Reserves (CAALR) has been implemented in this paper. The introduction of the four further types of actuarial loss reserves to those existing in the actuarial literature seems to significantly reduce lapse rates, reduce the reinsurance costs as well as expenses and outgo. As a matter of consequence, this helps to bring together a combination of new and existing policyholders in the insurance company. The frequency severity models have been extended in this paper using ten machine learning algorithms which ultimately leads to the derivation of the proposed machine learning-based actuarial loss reserving model which remarkably performed well when compared to the traditional chain ladder actuarial reserving method using simulated data.

基于QR-MS(2)-EGARCH(1,1)-st模型的互联网金融指数风险度量

基于2012—2021年互联网金融指数的日收盘价数据,采用二区制MS-GARCH(1,1)类模型刻画互联网金融指数收益率的波动过程,通过分析选出较优的模型MS(2)-EGARCH(1,1)-st,结果显示,互联网金融指数收益率存在两种划分明显的波动状态:平缓波动状态比剧烈波动状态的持续性更强,且剧烈波动存在非对称效应。将MS-EGARCH模型与分位数回归(QR)模型的组合模型进行互联网金融指数收益率的风险测度,并通过Kupiec回测检验方法计算拟合成功率,结果表明,QR-MS(2)-EGARCH(1,1)-st求解得到的风险价值(VaR)具有较高拟合成功率。

Access to Financial Services——An Analysis from the Human Rights Perspective

There is a broad connection between finance and human rights,with finance having both positive and negative impacts on human rights.Everyone has a need for access to financial services.Documents in both the international human rights and international finance fields address the relationship between financial services and human rights.Among financial services,microcredit and inclusive finance have the closest connection to human rights and potentially the greatest impact on human rights.Access to financial services promotes economic,social,and cultural rights as well as the rights of specific groups.The conditions for access to financial services to promote human rights require the state to assume obligations to recognize,respect,protect,and fulfill the need for individuals to access financial services,and to ensure the availability,accessibility,acceptability,and adaptability of basic financial services.Access to financial services has played a significant role in China’s comprehensive victory in the battle of poverty alleviation,providing valuable experience for the international community in poverty eradication,achieving sustainable development goals,and protecting and promoting human rights.

基于SWOT分析的高校财务信息化建设研究

大数据时代的到来和新政府会计制度的实施,推动了高校财务建设的信息化和数字化发展。高校财务是高校资金分配、使用、核算、监督、考核的基本平台,是高校其他管理工作得以顺利进行的基础保障,高效便捷的财务信息化系统建设会优化高校财务核算和管理工作。采用SWOT模型通过对高校财务信息化建设内部的优势与劣势,外部的机会与威胁进行全面的分析,以期为高校财务信息化系统建设提供建设策略和路径,进一步提升高校财务各项业务的科学化管理水平。

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

The Macroeconomic Impact of Internet Finance

This paper investigates the macroeconomic impacts of Internet finance,highlighting its advantages and challenges.Internet finance,a fusion of Internet technology with traditional financial practices,introduces innovative models for global asset management,capital financing,payments,investments,and intermediary services.While it enhances the accessibility and efficiency of financial services,it also introduces new risks,such as higher credit default rates.This study explores how Internet finance contributes to financial inclusivity and macroeconomic growth yet poses potential threats to traditional financial stability.The dual aspects of Internet finance are analyzed:its application in existing processes and its capacity to generate novel business models.Furthermore,the paper proposes strategic responses to mitigate the negative impacts of Internet finance,mainly focusing on risk management and regulatory improvements to safeguard economic stability.