Journal of Economic Surveys最新文献

Over the last decades, macroeconomists have renewed their efforts to reduce the gap between monetary macroeconomics and real-world central banking. This paper reviews how macroeconomics has since 2016 approached the possible introduction of retail central bank digital currencies (CBDC). A review of the literature reveals that macroeconomic models of CBDC often rely on CBDC design features and narratives which are no longer in line with the one of central banks actually working on CBDC. In particular, the literature often (i) does not take into account the nature of central banks’ CBDC issuance plans as a “conservative” reaction to profound technological and preferential shifts in the use of money as a means of payments, (ii) does not start from design features communicated by central banks, such as no-remuneration, quantity limits, access restrictions, and automated sweeping functionality linking CBDC wallets with commercial bank accounts; (iii) does not explain well enough the difference between CBDC and banknotes within their macroeconomic models, apart from remuneration (which central banks actually do not foresee); and (iv) assume that CBDC will lead to a significant increase in the total holdings of central bank money in the economy, although (i) and (ii) make this unlikely.

The green bond market and related scholarly research have grown exponentially recently. However, the literature remains scattered. This study addresses the gap by analyzing 744 Scopus-indexed journal articles using a mix of bibliometric and content analyses to unveil trends, current dynamics, and knowledge structures. The bibliometric analysis shows dynamic transformation and differential development among clusters. It indicates the following three prominent research themes: (a) reducing carbon emissions, (b) premium and financing, and (c) connectedness and portfolio risk effects. From policymakers' perspective, the content analysis reveals positive linkages and channels between green bonds and the environment. Pertinent to issuers and investors, estimates of green bond premiums vary by time, context, and methodology. Future scholarly research should focus on causality, regional variations, and the role of policy design in the impact of green finance. Researchers must further investigate the motives of green bond investors and issuers.

Leading to enormous effects on the global financial markets, oil shocks have received full attention from academic literature. The objective of this survey is to organize and consolidate existing theoretical and empirical research on oil shocks and financial markets, with the goal of uncovering potential areas for future investigation. The paper begins with a brief overview of oil shocks against a historical background, and makes a summarized survey of the existing literature regarding oil shocks. Then this paper discusses the underlying sources of the oil shocks and then turns to make a review of the connections between oil shocks and the economy, the stock market, and the commodity market. The paper concludes with recommendations for subsequent research endeavors, and hopes to facilitate further research in the many strands of the oil shocks-related literature.

Integrating solar energy into power grids is essential for advancing a low-carbon economy, but accurate forecasting remains challenging due to solar output variability. This study comprehensively reviews solar energy forecasting models, focusing on how Artificial Intelligence (AI) and Machine Learning (ML) enhance forecast accuracy. It examines the current landscape of solar forecasting, identifies limitations in existing models, and underscores the need for more adaptable approaches. The primary goals are to analyze the evolution of AI/ML-based models, assess their strengths and weaknesses, and propose a structured methodology for selecting and implementing AI/ML models tailored to solar power forecasting. Through comparative analysis, the study evaluates individual and hybrid models across different forecasting scenarios, identifying under-explored research areas. The findings indicate significant improvements in prediction accuracy through AI/ML advancements, aiding grid management and supporting the low-carbon transition. Ensemble methods, deep learning techniques, and hybrid models show great promise in enhancing reliability. Combining diverse forecasting approaches with advanced AI/ML techniques results in more reliable solar forecasts. The study suggests that improving model accuracy through these integrated methods offers substantial opportunities for further research, contributing to global sustainability efforts, particularly UN SDGs 7 and 13, and promoting economic growth with minimal environmental impact.

Smart cities leverage blockchain technology to increase the quality of urban life by effectively tackling the many challenges that arise from the rapid process of urbanization. Recognizing the increasing significance of the subject matter, it becomes essential to conduct a comprehensive assessment of the current state of research in this domain. To do so, 79 articles from top-tier journals were selected from the Scopus database and studied using a systematic literature review approach to identify the performance of contributing research constituents and underlying themes that map the topic. The findings of the performance analysis indicated that authors associated with American, Chinese, and Indian institutions were the primary contributors in terms of number, whileauthors affiliated with French and American institutions were the primary contributors in terms of impact. Furthermore, the findings of the science mapping analysis revealed the identification of five knowledge clusters: (1) Blockchain-based urban mobility and transportation; (2) blockchain for secure and transparent urban governance; (3) decentralized and efficient resource management; (4) internet of things integration and data security; and (5) digital identity and access control. Based on a content analysis approach, future research avenues were determined for each of the aforementioned knowledge clusters.

The aim of this study is to provide a systematic and bibliometric overview of neuroeconomics and neurofinance research. The study analyzes 507 articles published in peer-reviewed journals in the Web of Science (WoS) database spanning from 1994 to February 2024. The research is conducted in two phases; initially, a performance analysis, followed by a science mapping analysis. Through performance analysis, we pinpoint significant authors, notable journals, leading countries, and influential articles within these research fields. With the science mapping, we utilize co-citation, bibliographic coupling, and co-word analyses to uncover the thematic and intellectual framework of research in neuroeconomics and neurofinance. Overall, we reveal prominent research themes, including reward, decision-making under risk, value, game theory, and intertemporal choices. Classical decision-making theory divides the decision-making process into three interconnected stages: identifying options, evaluating them, and making a choice. The research themes we revealed through science mapping analysis constitute different aspects of the evaluation stage. Accordingly, our results suggest that neuroeconomics and neurofinance research has mainly concentrated on the evaluation stage (and, to a lesser extent, the choice stage with value theme), while the process of identifying options has been relatively understudied, despite its vital significance in less structured, real-world situations. Alongside revealing research themes, we ultimately spotlight research directions within neuroeconomics and neurofinance.

We conduct a meta-literature review of safe haven and hedge assets covering 617 papers published in 170 sources from 1996 to 2022 based on the Scopus database. This review includes a qualitative analysis of the bibliometric content and a quantitative analysis of the citations to identify the primary research streams and offer future research directions. The analysis identifies four research streams in the hedge and safe haven literature: (1) Gold as a hedge and safe haven asset; (2) various models estimating the hedge and safe haven ability of potential assets such as precious metals, crude oil, and cryptocurrencies; (3) Bitcoin as a safe haven asset; and (4) the role of various safe haven assets, particularly Gold and Bitcoin, during the COVID-19 crisis. The meta-review also classifies the most influential authors focusing on hedging and safe haven research through co-authorship and collaborative network analysis. Finally, future research directions are formulated with a wide set of potential research questions and areas. The outcomes of this meta-review study are useful for researchers, financial analysts, and investors searching for the best safe haven assets during unfavorable market conditions.