Hasil untuk "Finance"

Enrico G. Berkes, U. Panizza, J. Arcand

This paper examines whether there is a threshold above which financial development no longer has a positive effect on economic growth. We use different empirical approaches to show that there can indeed be "too much" finance. In particular, our results suggest that finance starts having a negative effect on output growth when credit to the private sector reaches 100% of GDP. We show that our results are consistent with the "vanishing effect" of financial development and that they are not driven by output volatility, banking crises, low institutional quality, or by differences in bank regulation and supervision.

Stijn Claessens, Stijn Claessens, Stijn Claessens et al.

P. Embrechts, Hanspeter Schmidli

Umberto Cherubini, E. Luciano, Walter Vecchiato

Jith Jayaratne, Philip E. Strahan

Cesare Fracassi

J. Banga

ABSTRACT This paper examines the potential of green bonds in mobilizing adaptation and mitigation finance for developing countries. Building upon a theoretical approach, it identifies the key drivers of the green bond market over the last few years and the barriers that impede its appropriation by developing countries. The results suggest that the rise of green bonds is a fact in developed and emerging countries, backed by an increasing climate-awareness from investors. However, in developing countries, the market remains incipient, and its full potential seems to be underappreciated. The lack of appropriate institutional arrangements for green bond management, the issue of minimum size, and high transactions costs associated with green bond issuance, are the key barriers to the development of green bonds in developing countries. In order to cope with these challenges, this paper suggests an efficient use of multilateral and national development banks as intermediary institutions for local green bond management. Furthermore, local governments are required to provide local green bond issuers with guarantees aimed at covering the transaction costs associated with green bond issuance.

Kaile Zhou, Yiwen Li

Abstract Climate change has become one of the major challenges that humans have faced in recent years. As an economic measure to cope with climate change, carbon finance has drawn considerable attention in recent years, since it can promote low-cost emission reductions. As the largest developing country with rich carbon emission resources, China plays an important role in establishing a sound carbon trading market. This study presents a systematic review of the research and development progress and challenges of China's carbon finance and carbon market. Based on the brief introduction of some related concepts and background, this study mainly discusses the role of China's financial institutions in carbon finance, and the development of China's carbon market. Particularly, it focuses on the recent practices and the carbon trading pilots established in China. In addition, the major challenges for China to establish a national unified carbon market were pointed out, in terms of awareness, relevant professionals, sound legal systems and carbon financial product innovation. Finally, we put forward the policy implications for the future development of carbon finance and carbon market, which provides important decision support for establishing a unified global carbon market.

J. Sachs, W. Woo, Naoyuki Yoshino et al.

Friedemann Polzin, M. Sanders

Abstract In this paper, we use standard scenarios focussing on renewable energy, energy efficiency and grid investments. We take stock of the literature and quantitative data on available sources of financing for clean energy to qualitatively match supply and demand of specific sources of finance in the European context. Our analysis shows that under the current investment mandates and lending criteria the required funds for a successful energy transition are available. In fact, the current landscape of financing sources can provide between two and six times what is necessary. However, institutional investors and lenders such as pension funds and banks in particular are reluctant to invest in the renewable energy or grid infrastructure because of expected (policy) discontinuities. In addition, more venture capital and household investment are needed to finance low-risk small-ticket projects in the early stages of innovative clean energy technologies, to complement the abundantly available funds for large-scale investments. Based on our analysis, we develop a matrix indicating the role and availability of different sources of finance and new intermediation channels in the energy transition and how these should be deployed.

M. Hasan, Yajuan Lu, Shajib Khan

While much progress has been made in promoting inclusive finance through the ownership of a basic personal account, billions of people in developed and emerging markets are still underrepresented in financial services. Also, they are unable to contribute to the provision of better access to financial services. The purpose of this study was defined as to explore the contribution of digital financial services (DFSs) in promoting inclusive finance in China. This study presents a theoretical discussion on how DFSs play an important role in promoting China’s inclusive finance. This study uses the systematic review method of qualitative sampling to achieve the goal of this study. Different forces play different roles behind the promotion of inclusive finance. However, DFSs are considered to be one of the most influential forces in the development of inclusive finance in the present world. Many examples of how DFS can improve inclusive finance are discussed in the literature. In addition, different contributions to DFS usage are presented here to achieve the objectives of this study. The contents of the study contributed to a better understanding of the practical impact and implication of DFS tools in transforming the financial sector. In this study, first, a structured review method is followed; second, most important discussion on the contribution of DFS in promoting inclusive finance is presented and third, the relation between the topic and related research is identified.

D. Zetzsche, D. Arner, Ross P. Buckley

DeFi (‘decentralized finance’) has joined FinTech (‘financial technology’), RegTech (‘regulatory technology’), cryptocurrencies, and digital assets as one of the most discussed emerging technological evolutions in global finance. Yet little is really understood about its meaning, legal implications, and policy consequences. In this article we introduce DeFi, put DeFi in the context of the traditional financial economy, connect DeFi to open banking, and end with some policy considerations. We suggest that decentralization has the potential to undermine traditional forms of accountability and erode the effectiveness of traditional financial regulation and enforcement. At the same time, we find that where parts of the financial services value chain are decentralized, there will be a reconcentration in a different (but possibly less regulated, less visible, and less transparent) part of the value chain. DeFi regulation could, and should, focus on this reconcentrated portion of the value chain to ensure effective oversight and risk control. Rather than eliminating the need for regulation, in fact DeFi requires regulation in order to achieve its core objective of decentralization. Furthermore, DeFi potentially offers an opportunity for the development of an entirely new way to design regulation: the idea of ‘embedded regulation’. Regulatory approaches could be built into the design of DeFi, thus potentially decentralizing both finance and its regulation, in the ultimate expression of RegTech.

Kai Chang, Xiaochang Cheng, Yiran Wang et al.

ABSTRACT This article explores the interactive effects of digital finance and environmental, social responsibility, and corporate governance (ESG) performance on corporate financing efficiency using data envelopment analysis (DEA) and panel data analysis. Our empirical results demonstrate that higher ESG performance and digital finance enhance corporate financing efficiency at the 1% significance level, and digital finance alleviates the positive marginal effect of ESG performance on corporate financing efficiency.

Nina Yan, Yang Liu, Xun Xu et al.

Abstract Small and medium-sized enterprises (SMEs) often face obstacles in reaching consumers and obtaining sufficient capital for their production and operations processes. Owning channel advantages and rich transaction data regarding suppliers’ sales, inventory, and credits, e-commerce platforms (henceforth, e-retailers) can offer online distribution channels and online financing service for SMEs to facilitate their distribution and alleviate their capital constraints. This study analyzes the pricing competition in a dual-channel supply chain consisting of one capital-constrained supplier and one e-retailer providing finance. The supplier can sell her products either through the e-retailer using the online channel or through her direct offline channel. The e-retailer offers finance to the supplier if she is capital-constrained. We examine the equilibrium price and the associated optimal quantity and profits in dual channels when supplier may face capital constraint and compete with e-retailer horizontally or vertically. We find that e-retailer finance is a value-added service for e-retailer and that the increased profits generated from financing offerings can offset the lowered revenue in the online distribution channel. E-retailer finance can increase market share, which also benefits the supplier. Participating in the vertical competition through announcing pricing decisions earlier than does the supplier can help the e-retailer seize the first-mover advantage. Further, we present the value of e-retailer finance and examine the impact of various financing, operational, and consumer-related factors on pricing and channel structure. We also provide guidelines for e-retailers and financing-constrained suppliers to utilize e-retailer finance to optimize their dual-channel structure and to make optimal pricing decisions.

Harry Williamson, Dermot Coates, Kevin Daly et al.

Abstract The adoption of AI technology by industry could significantly disrupt our current understanding of “typical” economic activity. As AI comes to pervade more sectors and occupations over time, it is likely that this technology will give rise to challenges and risks but also opportunities and benefits. There is, however, a significant degree of uncertainty regarding how future waves of technological change will impact the economy, including the labour market. Recent research has found that 40% of employment globally is exposed to AI and that this rises to 60% of employment in advanced economies. We analyse exposure and complementarity in tandem in order to better understand the potential impact across occupation types in Ireland. We find that Ireland is relatively more exposed to AI than is the case for other advanced economies. We also find find that female workers in Ireland are more likely to work in highly exposed roles compared to males, that younger Irish workers are more exposed to AI than are older workers, and that both exposure complementarity to AI increase in line with educational attainment. Finally, we contend that the extent to which AI augments, or replaces, human labour in the medium to long-run will depend on a variety of economic, social and policy factors, including levels of AI regulation. JEL classification: J21, J24, O31.

Ammad Aslam, Octavian Postolache, Sancho Oliveira et al.

Sharding is an emerging blockchain technology that is used extensively in several fields such as finance, reputation systems, the IoT, and others because of its ability to secure and increase the number of transactions every second. In sharding-based technology, the blockchain is divided into several sub-chains, also known as shards, that enhance the network throughput. This paper aims to examine the impact of integrating sharding-based blockchain network technology in securing IoT sensors, which is further used for environmental monitoring. In this paper, the idea of integrating sharding-based blockchain technology is proposed, along with its advantages and disadvantages, by conducting a systematic literature review of studies based on sharding-based blockchain technology in recent years. Based on the research findings, sharding-based technology is beneficial in securing IoT systems by improving security, access, and transaction rates. The findings also suggest several issues, such as cross-shard transactions, synchronization issues, and the concentration of stakes. With an increased focus on showcasing the important trade-offs, this paper also offers several recommendations for further research on the implementation of blockchain network technology for securing IoT sensors with applications in environment monitoring. These valuable insights are further effective in facilitating informed decisions while integrating sharding-based technology in developing more secure and efficient decentralized networks for internet data centers (IDCs), and monitoring the environment by picking out key points of the data.

Dongri He, Ming Yang, Rong Jiang et al.

Abstract In recent years, the financial industry has become a disaster area for information leakage, which has serious implications for user privacy security. In the absence of risk identification and assessment, the risk will be difficult to prevent, and once the risk occurs it will directly cause serious losses. Therefore, this study plans to construct a comprehensive assessment framework combining fuzzy analytic hierarchy process (FAHP) and Dempster-Shafer (D-S) theory, aiming at assessing the weights and risk levels of the privacy security risks of financial services. (Privacy security risks refer to integrated factors in management, security, or other aspects that may lead to user privacy leakage, and they are considered an integrated concept.) The case study illustrates that the model and method proposed in this paper are effective and feasible. Finally, a comparison with the current mainstream privacy security assessment methods demonstrates that the method proposed in this paper is more capable of objectively and quantitatively reflecting the real privacy risks, providing users with more perspectives of the assessment results, and helping users to reasonably manage their personal privacy information, so as to effectively prevent and control the privacy risks.

Ramón Sanguino, Nilgün Çağlarırmak Uslu, Pınar Karahan-Dursun et al.

Education–employment mismatch represents a persistent structural issue across Europe, especially among young people. In line with the digital transformation, green transformation and population aging, new jobs are emerging every day, and some of the older jobs are disappearing. However, existing skills of job seekers may not fit these new jobs. This article presents results from the EMLT + AI project, which aimed to explore how artificial intelligence (AI) tools could contribute to reducing such mismatches and supporting inclusive labor market integration. Based on a sample of 1039 participants across European countries, we analyzed the alignment between individuals’ educational background and their current employment, as well as their willingness to reskill. Using binary logistic regression models, the study identifies key factors influencing mismatch and reskilling motivation, including educational level, type of occupation, the presence of meaningful career guidance, and AI-based job search practices. The results indicate that individuals who hold a master’s degree and work in positions requiring at least bachelor’s level degrees are more likely to be matched with jobs that align with their field of study. However, access to mentoring remains limited. The paper concludes by proposing an AI-supported training model integrating career recommendation systems, flexible learning modules, and structured mentoring. These findings provide empirical evidence on how emerging technologies can foster more responsive and adaptive education-to-employment transitions, contributing to policy innovation and the development of inclusive digital labor ecosystems in Europe.

Yan Zhu, Shengnan Li, Jianbo Niu et al.

Against the backdrop of a worldwide consensus on green development and rising expectations for corporate accountability, greenwashing—an implicit, strategic form of adaptation—undermines institutional trust in environmental governance and hampers the efficient allocation of organizational resources. Using the pilot policies for climate-adaptive urban development as a quasi-natural experiment and drawing on upper-echelons theory and endowment theory, this study systematically examines how incentive-based regulation curbs corporate greenwashing and through which channels it operates. The evidence shows that the pilot policies significantly reduce greenwashing by (i) elevating executives’ environmental awareness through a suite of incentives and (ii) improving corporate access to green financing, thereby dampening their motivation to misrepresent environmental performance. The mitigating effect is more pronounced in firms whose top teams have limited green experience, in regions with low public environmental concern, in non-state-owned enterprises, and in highly competitive industries. Additional analysis reveals that the policies enhance firm value precisely by suppressing greenwashing. By focusing on these pilot policies, the paper underscores the positive spillovers of incentive-oriented environmental regulation and offers guidance for governments seeking to steer enterprises and build a resilient climate-governance framework.

Antoine Bigeard, Langston Nashold, Rayan Krishnan et al.

Artificial Intelligence (AI) technology has emerged as a transformative force in financial analysis and the finance industry, though significant questions remain about the full capabilities of Large Language Model (LLM) agents in this domain. We present the Finance Agent Benchmark, featuring challenging and diverse real-world finance research problems that require LLMs to perform complex analysis using recent SEC filings. We construct the benchmark using a taxonomy of nine financial task categories, developed in consultation with experts from banks, hedge funds, and private equity firms. The dataset includes 537 expert-authored questions covering tasks from information retrieval to complex financial modeling, each validated through a rigorous review process to ensure accuracy and relevance. Moreover, we implement an agentic harness that equips LLMs with tools sufficient to produce accurate responses, including Google Search and EDGAR database access. Overall, the Finance Agent Benchmark provides a comprehensive testbed for measuring the progress of LLM-driven finance agents. Our evaluation reveals significant limitations in current AI capabilities - even the best-performing model (OpenAI o3) achieved only 46.8% accuracy at an average cost of $3.79 per query. This underscores the need for further advancements before reliable deployment in high-stakes finance settings.