This study explores the cause and effect of endogenous risk aversion in land pricing, where state intervention through taxation remains a general practice. Using a consumption-based asset pricing model incorporating taxation, it is shown that high taxation, due to the indexation effect, supporting land prices and reducing individuals' risk expectations, could lead to an endogenous decrease in risk aversion, which could result in market dysfunction because risk aversion plays a key role in the market mechanism. China, with its wholly state-owned land and the general use of land sales to cover financial deficits, is a typical case for empirical tests. The tests confirm that there, the rise in land prices was driven by the increase in reserve prices set by local governments, a strong means of taxation, and not by the market, indicating the endogenous decrease in risk aversion.

We offer an extensive analysis of the significance of information within the realm of Gaussian quadratic economies. We build upon the seminal papers of Morris and Shin (2002, 2007) and consider a signal game of incomplete information. Particularly, we question the suitability of partial transparency portrayed by fragmented information in addition to the private signal in terms of welfare effects. We can summarize our findings in two main points. First, fragmented information, in conjunction with a private signal, can reduce the reliance on public signals. Second, a conflicting effect arises between increasing full disclosure and increasing the precision of fragmented (semi-public) information when examining different complex scenarios, involving for example endogenous private information or imperfect correlated signals. For a critical threshold, an optimal communication strategy designed by fragmented information should be implemented whenever that kind of information is acquired at a high precision.

Cryptocurrencies have gained popularity over the past five to six years. Most recently, events like the FTX bankruptcy fueled the interest in regulation. Moreover, it is possible that the FTX event disrupting the cryptocurrency market was a factor in Silicon Valley Bank's failure. While several countries consider regulation, from soft regulation, like Japan, to more rigid standards, like the total ban in China, we study the effect of other news or events on cryptocurrency prices. This paper looks at historical closing prices for Bitcoin, the largest of the cryptocurrencies, and how prices react to various events. Then we focus on modeling the time series considering an 'event,' China's ban on cryptocurrency exchanges, using intervention analysis. We find that intervention analysis provides a reliable approach to quantifying the impact regulation may have on cryptocurrency pricing.

This paper presents an operational framework for assessing the trajectories of production, energy, emissions, and capital accumulation to ensure the implementation of Nationally Determined Contributions (NDCs). The framework combines widely used methodologies (STIRPAT, system dynamics, and optimization) to simulate the pathways of variables until a target year. The CO-STIRPAT dynamic system allows us to identify the spillover pathways from carbon policy to economic growth based on output optimization principles; to conduct a more systematic analysis of the interconnections between the main drivers that determine carbon emissions; to develop a cost-effective climate policy mix that is a backbone for the right combination of carbon pricing, energy efficiency, and carbon intensity; and to assess NDC targets with respect to ambition gaps, implementation gaps, and feasibility.

This paper evaluates the fit-for-purpose of the monetary policy measures implemented by the Bank of Ghana in response to the COVID-19 pandemic. We examine the effectiveness of the BoG’s policy interventions in the context of vector autoregressions augmented with macroeconomic and financial indicators. We demonstrate that the BoG’s monetary policy measures have had nominal, real, and financial effects. The monetary interventions have been successful, as evidenced by the gradual reduction in the sovereign spread, improved financial stability, and increased real economic activity. Our findings suggest that balance sheet actions are less effective and should be moderated in the conduct of monetary policy in jurisdictions without zero lower bound constraints. However, the analysis indicates that in times of crisis, central banks should deploy both standard and non-standard tools to stabilize dysfunctional financial markets and avoid a deflationary spiral. The historical and variance decomposition of the data reveal that monetary policy shocks have historically made the largest contributions to the targeted macro-financial aggregates during the pandemic episodes.

Banks are at the core of economic functioning in various countries and are the cause of their development in various fields. In a changing environment, they must deal with certain risks and maintain financial stability as the ultimate objective. The primary objective of this study is to examine the correlation between credit risk and bank stability within the Tunisian banking sector, with a specific focus on understanding the moderating impact of the COVID-19 pandemic. To achieve this, we employ several econometric techniques, including panel unit root and cointegration tests, panel Vector Error Correction Model (VECM), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). Our empirical analysis relies on a panel dataset that encompasses a comprehensive sample of 8 Tunisian banks over 2000-2020. The results of our analysis unveil a significant negative relationship between credit risk and bank stability, indicating that higher levels of credit risk exert a detrimental effect on the overall stability of Tunisian banks. Furthermore, our study highlights that this adverse impact is further exacerbated during the COVID-19 pandemic, suggesting that the pandemic acts as a moderator. The findings of this study hold substantial implications for policymakers, regulators, and bank managers in Tunisia. They emphasize the critical importance of implementing robust risk management practices to mitigate credit risk and bolster bank stability. Additionally, the research underscores the need to consider the unique challenges introduced by external shocks, such as the COVID-19 pandemic, when assessing the overall stability of the banking system.

This study investigates the impact of financial inclusion on environmental sustainability in 178 emerging and developing countries from 1996 to 2022. Employing a composite index derived through Principal Component Analysis (PCA) as a measure of financial inclusion and covering four aspects (access, depth, efficiency, stability), our analysis reveals negative outcomes. The findings indicate that enhancing financial inclusion is associated with a notable increase in CO2 per capita emissions as well as in Total Greenhouse Gas emissions. We demonstrate that controlling corruption improves environmental quality, yet this measure alone is insufficient to fully mitigate the impact of financial inclusion, as indicated by our moderation analysis. The same analysis, however, shows that fostering globalization through trade openness is an efficient tool to alleviate the positive effect of financial inclusion on the quality of the environment. The study employs various policies targeting the control of development levels, energy consumption, natural resource utilization, industry, and urban population dynamics to contextualize the influence of financial inclusion on environmental sustainability. Through econometric methods and a comprehensive examination of the specified time frame, our results provide insights into the complex interplay between financial inclusion and environmental outcomes in diverse socio-economic contexts. The research contributes to the discourse on sustainable development by highlighting the potential of certain factors as a catalyst for environmental improvement. Understanding these dynamics is crucial for policymakers, as it underlines the trade-off between integrating inclusive financial strategies and achieving environmentally sustainable development trajectories in emerging and developing nations. Moreover, shedding light on the underlying mechanisms, such as trade-offs, fills a significant gap in the literature.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics.  In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

Applying the network analysis’ tools and a large dataset over 2000-2017, this paper provides insights to the patterns and structure of global and regional migration and remittances as well as the connection between these two phenomena inside a complex network. We reveal that even though the structure of migration and remittance networks is slightly altering, the major changes have only occurred in the intensive margin of the networks. In addition, there is a deepening intra-regional trend regarding the structure of migration communities. By contrast, we observe an instability in the structure of migration and remittance communities formed around the U.S.

In this paper, a portfolio analysis is carried out using the Sharpe ratio to identify the optimal market portfolio. The measure of investment performance with a Sharpe ratio is compared to results obtained with bootstrapped resamples of the Sharpe ratio. The results indicate that the choice of the market portfolio is highly affected by the uncertainty regarding the estimation of the expected returns and the variance-covariance matrix between the returns, that is, the estimation risk associated with these parameters.

This paper presents an analytical framework to assess the probability of achieving nationally determined contributions (NDC). The prediction model based on the Kaya identity is used to simulate the pathway of carbon emission until the target year. Applying the modified STIRPAT framework (named CO-STIRPAT) to data observed in South Korea shows that the probability that the predicted pathway with existing climate technology will stay above the NDC target pathway is significantly high. The result suggests that it is necessary to design a climate policy to improve energy intensity and carbon intensity by accelerating the advance in climate technology.

It's critical for environmental governance to understand the effectiveness of policy interventions as well as their working mechanisms. This paper focuses on the vehicle license plate-based management, a sort of policy intervention understudied in environmental governance literature. Specifically, we study the automobile purchase restriction (APR), a major method of license management to address urban air pollution and traffic congestion recently launched by the authorities of China. However, limited studies have quantified the environmental impact of such purchase restriction. Based on the panel data of PM2.5 concentration in 330 cities in China during 1998-2016, this study aims to explore the causal impact of Beijing automobile purchase restriction on air quality, using the regression control method and high-dimensional data regression of machine learning. The average PM2.5 concentration did not decrease but increased because of the purchase restriction. Over time, the deterioration effect on urban air quality will not alleviate. Besides, the restriction cannot enhance urban air quality by decreasing the increase of PM2.5. Despite excluding the impact of driving restriction, the purchase restriction markedly aggravate urban air quality. This paper has some policy implications for policymakers on the impact of urban vehicle license management with caution. Hence, it is essential to promote urban economy, production, and lifestyle to attain the win-win goal of air pollution control and urban development.

Innovation has received a great attention in the creative industries literature. We propose in this study a bibliometric method to examine the literature on innovation in creative industries (ICI). A file of 656 manuscripts published on ICI between 1998 and 2022 was retrieved from the Web of Science Core Collection for analysis. The results highlight the evolution of study volume, authors, affiliated institutions and countries, author networks, keyword co-occurrences, and keyword networks. The study also includes a thematic map that highlights four types of research: driving themes (e.g., digital technology, cultural innovation, performing arts, product innovation, innovation management); core and cross-cutting themes (e.g., creativity, digitization, technology, copyright); emerging themes (e.g., gender, artificial intelligence, sustainability); and specialized and peripheral themes (e.g., gender, blockchain, digital music). We finally conclude by proposing future perspectives and a research agenda in this area.

According to economic theory and common sense, the issuance of consumption coupons on one hand stimulated the increasing of residents' consumption, but on the other pushed up the price level. In this paper, using the theory of mechanism design, the game model was built to analyze the relationship between the effect of consumer coupons during the epidemic and the number of merchants participated. The results show that the smaller the merchants’ number, the weaker the coupons’ effect, vice versa. Then, using KNN regression method, combined with the monthly year-on-year data of CPI from 1998 to 2020, this paper analyzes the changes of CPI forecast value and actual value in the first 10 months of 2020, finding that the actual value of CPI is higher than the predicted one during the first two months of 2020, but lower from March 2020 on, which further verifies the conclusion of game analysis.

In the context of the continuous deepening reform of the foreign investment negative list system and the increasing pressure on financial risk prevention and resolution, it is of great significance to study the impact of the foreign investment negative list system on banking risks. This study is conducted on the basis of systematically sorting out the mechanism of the foreign negative list system affecting the risks of domestic banks. Based on the panel data of 27 provinces (cities, districts) in China from 2006 to 2016, and comprehensively adopting the synthetic control method, the difference-in-difference (DID) model, mediating effect model, and other methods, this article systematically examines the impact of the foreign negative list system on Shanghai banking risks. The study found that the implementation of the foreign investment negative list system has significantly reduced the credit risk of the Shanghai banking industry, and this reduction effect has gradually strengthened over time. Additionally, the implementation of the foreign investment negative list system will also significantly reduce the liquidity risk of the Shanghai banking industry; However, the effect of this positive influence is short-lived and subsequently weakens. The analysis of the mechanism of action indicates that the foreign investment negative list system primarily reduces the risk of Shanghai's banking industry by stimulating the inflow of foreign capital and reducing government intervention. However, the effect of improving the level of the rule of law to mitigate commercial bank risk is not apparent. Regarding other influencing factors, the growth of local government financial resources and the increase in new loans have significantly reduced banking risks, while the rise in residents' leverage has substantially increased the level of banking risks.

How does democracy relate to the initial economic policy responses to Covid-19? Using a cross country analysis, we find that countries with a higher degree of democracy have stronger economic policy responses than their peers. However, when we separate monetary and financial policies from fiscal policy, democracy is not associated with the latter when we control for the income level of a country. Finally, for countries with lower levels of labor participation, high levels of income inequality are associated with weaker policy responses.

We analyze the annual microdata of China's 100 commercial banks from 2007 to 2018 using the mediating effect analysis method, analyze the negative impact of economic policy uncertainty on the credit scale of commercial banks, and then verify the intermediary effects of the operational risks and short-term capital liquidity of commercial banks in the impact of economic policy uncertainty on the scale of commercial bank credit, separately. Moreover, we find that non-state-owned commercial banks, large-scale commercial banks, low-leverage commercial banks, and commercial banks with low profitability are more susceptible to economic policy uncertainty affecting commercial bank credit growth through short-term capital liquidity, and vice versa.

Consideration of optimal commodity storage with different discount rates. Finding that, even with a lower discount rate than private storage, optimal government-financed storage may not narrow price fluctuations compared with optimal privately financed storage because a government has to choose a probability of buffer stock failure greater than zero to economize on storage costs that could conceivably become very large. There is no presumption that government financed storage would be larger, with narrower intervention bands and more stable prices, than with privately-financed storage. The welfare enhancing effect of government financed storage compared to private storage is therefore indeterminate.

This paper explores how the medical expenditure risk affects the households’ portfolio choice across health status theoretically in a life cycle model and empirically using machine learning methods. Medical expenditure risk, as a background risk, has the potential to influence households’ financial decisions. A higher medical expenditure risk leads to a larger fluctuation and more uncertainty in households’ consumption and therefore utility. As a result, risk-free assets become more attractive. Our machine learning analysis provides evidence that aligns with the predictions of the theoretical life cycle model. Specifically, households with better health hold a larger proportion of stocks in their portfolios. Furthermore, when facing increased medical expenditure risk, households in good health demonstrate a greater willingness to invest in safe assets.