Reckless behavior by business leaders can be a systemic risk for individual firms and the economies in which the firms exist. We propose that a synthesis of behavioral economics, in particular prospect theory, and the study of psychopathy may help researchers better understand why some business leaders engage in high-risk criminal activity. We propose that psychopathy is associated with an abnormal response to negative consequences. Where traditional Prospect Theory proposes that people are loss avoidant, we propose that people high on the trait of psychopathy may be more motivated by gains and will be risk seeking in high reward/high risk situations for which most people will avoid risk. We propose two empirical study designs that may be used to test the framework in the future.

"Climate Economics and Finance" has risen to prominence as a crucial field, encompassing three primary dimensions: Climate Economics, Climate Finance, and the Intersection of Climate Change and Financial Markets. The adverse externalities arising from climate change, exemplifying a classic instance of "market failure" within market economic frameworks, have garnered global attention. Subsequently, we delve into the literature across three dimensions: the interplay between climate change and macroeconomics, the nexus of climate change and financial markets, and economic or financial resilience. In conclusion, we outline various promising avenues for prospective research in the realm of climate economics and finance.

In China, the land arrangement behavior of over 160 million rural-urban migrant workers is closely related to the optimal allocation of rural land resources and sustainable development of urban and rural areas. Although previous studies show that social capital affects migrant workers’ land arrangement behavior, few empirical studies reveal the relationship between them, and the corresponding interventions remain unclear. Using survey data collected in Henan Province, China, and a multinomial logit model, this study empirically analyzes the mechanism behind the impact of social capital on migrant workers’ land arrangement behavior from the perspective of social capital. Results illustrate that social capital has a significant impact on the land arrangement behavior of migrant workers. The behavior is significantly correlated with the scale of migrant workers’ urban social networks, the degree of urban social trust, and urban belonging. More social capital in urban areas indicates a higher tendency for migrant workers to abandon their land contracting rights and become permanent urban residents. This study reveals the mechanism of social capital affecting migrant workers’ land arrangement behavior and provides a reference for decision-making with respect to guiding migrant workers’ land management behavior for other countries facing similar social problems.

The study explores online consumers’ impulse buying intentions and behaviors on live streaming platforms. Unlike traditional shopping modes, the development of real-time video streaming provides online consumers with a distinct approach to interacting with live streamers and browsing online products in real-time, potentially causing their impulse buying intentions. To understand online consumers’ impulse buying intentions and behaviors, the paper establishes the research model based on the theory of planned behavior (TPB) model and analyses influencing factors from attitude, subject norm and perceived control aspects. Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the research results show that, three factors positively affect online consumers’ impulse buying intentions and lead to their final behaviors. Meanwhile, control variables, including gender, age, and income level, demonstrate insignificant effects across the model. Unlike existing literature, the current study displays the distinct features of live streaming platforms and discovers online consumers’ impulse buying intention based on the TPB model. The results are helpful for related scholars and departments to pay more attention to the live shopping environment and understand online consumers’ impulse buying issues.

Rental unaffordability is defined as spending more than 30% of a household’s gross income on rent. Post-pandemic inflation and interest rate increases have intensified rental unaffordability. This research examines rental affordability in the U.S., the U.K., and Canada. It also explores the effect of renters’ “affordability knowledge” – defined as the expertise tenants have and use to make economical rental housing choices – on rent expenditure and affordability positioning and compares personal finances, economic perspectives, and demographics based on renters in affordable and unaffordable situations. The results show that nearly two-thirds of the renters studied are in unaffordable rental situations. Interestingly, affordability knowledge was found to reduce rent spent and increase affordability situations. Significant demographic differences were found between those in affordable and unaffordable rental situations, including rent spending, food spending, transportation spending, savings, perceived homeownership likelihood, and age. The research offers important insight into current rental affordability economics, recommendations for policymakers, and opportunities for real estate organizations.

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.

In the behavioral domain, this study discloses the pattern between microblogging-opinionated information and yields on the investment. This phenomenon is particularly related to the political instability in the Pakistan’s economy through the multivariate techniques. Pre-political crisis, the pessimistic sentiments were priced in yields on the investment. In environments of political instability, the intensity of decline in yields was more responsive against an incline in the negative opinions. Meanwhile, the intensity of increase in returns was less responsive against an incline in the positive opinions. The findings were further supported by the Bayesian approach. Before the political instability takes place in the Pakistan’s economy, the occurrence of yields was noted in response to the bearish market period. Post-political instability, there was a higher posterior probability for occurrence of returns against the bearish market period. Conversely, a higher posterior likelihood was noted for occurrence of investment’s yields in response to the bullish market period, but this relevance was not completely probable. From the impulse response analysis, the response of returns was reported against the standard deviation shocks in the microblogging sentiment indicators. The analysis may have potential implications in terms of disclosing the investors’ behavior from a political perspective.

There must be a better match between the supply of financial news graduates in colleges and the media's demand for financial news talents. Based on 300 questionnaires and in-depth interviews from three universities, namely Shanghai University of Finance and Economics, Nanjing University of Finance and Economics, and Jiangxi University of Finance and Economics, this paper uses the interview method to excavate the professional quality improvement path for majoring in financial news students. Results reveal that undergraduates majoring in financial news have problems such as low professional identity, insufficient recognition of the discipline system, poor sense of professional identity, common matching between job selection and majors, and worrying about the status quo of professional literacy. Therefore, the improvement of the professional quality of students majoring in financial news and the reform of the training program needs to drive the professional quality of students in multiple dimensions, change their professional cognitive problems, strengthen their professional identity, and achieve an effective match between supply and demand of financial media professionals.

Since the establishment of neoclassical economics in the nineteenth century, there has been a debate in the economics profession over the role played by mathematics. Mathematics can add precision to discussion of real-world empirical problems in economics, but care needs to be taken when formalizing a problem to ensure that errors of translation are not made. Formalism allows one to be sure that a chain of reasoning is correct but applying conclusions back to an empirical science problem is fraught if an error of translation has been made. We illustrate such a difficulty in the context of Arrow’s impossibility theorem, specifically the mistranslation of the non-dictatorship condition. The notion of dictatorship entails causality, but causality does not correspond to the usage of the implication sign in mathematics or logic. We use the rules of logic to illustrate that the way that dictatorship is rendered mathematically in the impossibility theorem makes the existence of a dictator (or dictators) not only reasonable but likely.

Vector Error Correction Models (VECM) have become a standard tool in empirical economics for analyzing nonstationary time series data because they integrate two key concepts in economics: equilibrium and dynamic adjustment in a single model. The current standard VECM procedure is limited to time series data with the same degree of integration, i.e., all I(1) variables. However, empirical studies often involve time series data with different de‐grees of integration, necessitating the simultaneous handling of I(1) and I(0) time series. This paper extends the standard VECM to accommodate mixed I(1) and I(0) variables. The conditions for the mixed VECM are derived, and consequently, we present a test and estimation for the mixed VECM.

This study aims to investigate the factors influencing consumers’ cognitive attitude towards the brands of products during their consumption willingness, behaviors and choices. Taking pork brands as an example, this study conducts a questionnaire by using a random sampling method in 165 farmers’ meat markets and 54 supermarkets in Tianhe District, Yuexiu District, Liwan District, the representative districts in Guangzhou city in China, and obtains 1,050 valid observations of the pork consumers. The empirical results via a logit regression show that the factors significantly enhance consumers’ brand cognitive attitude include younger age; lower perception of product safety; less cognition of green food, frailer awareness of fresh, cold, and frozen meats, as well as weaker ability to identify a specific product. Also, stronger consumers’ brand attitude results from larger amount of money spent in a single purchase, stronger cognition of nuisance-free food, and higher identification of different types of pork meats.

Liquidity and its associated issues are one of dominant strands in the market microstructure. In this study, microblogging-based behavioral perspective on economic unrest is linked to the market liquidity. The concept of liquidity is examined in terms of price dispersion relative to the quantity traded. The analysis contains the quantification of multiple linear regression, Gaussian distribution technique, and vector error correction methodology. In the economic stability period, the investor’s mood, either in positive manner or pessimistic context, had an influential role on the price impact volume-based liquidity. Meantime, the probability was higher for occurrence of price impact volume-based liquidity in response to the sentiment indicators. In the economic unrest environments, the positive bias investor’s mood was not vigorous enough to influence the dispersion of asset’s prices and trading quantity. Most importantly, the negative bias investor’s emotion was linked to increase the dispersion of asset’s prices relative to the quantity traded. Investors with a lower amount of trading quantity had declined the liquidity in the market. Additionally, there was a higher probability for occurrence of illiquidity in the pessimistic market periods. However, changes in past sentiment series were not associated with changes in pervious liquidity series, either in short run or long run. The findings may be potentially applicable to manage the behavioral perspective of liquidity risk.

This paper provides an overview of the existing literature on the use of artificial intelligence (AI) in various fields, including economics, finance, mining, manufacturing, and innovation. The paper identifies the drivers and effects of AI deployment in the context of innovation and highlights the challenges and opportunities that arise from the use of AI. The studies reviewed in this paper cover various topics related to forecasting, including the impact of AI on professional skills, hybrid forecasting techniques for predicting commodity prices, and novel deep reinforcement learning algorithms for crude oil price forecasting. The paper’s contribution lies in its systematic and comprehensive approach to reviewing the literature, which allows for a better understanding of the impact of AI on various fields and the identification of strategies to address the challenges that arise from its deployment.

A high inflation period of seven years (1978-1985) in Israel, which turned into a hyperinflation, puzzled Israeli economists, who tried to understand its causes and mechanisms. As a result, they provided fourteen different explanations. Although all of the explanations were based on the same data, the researchers’ conclusions were either different or contradictory. This situation provides a virtual laboratory for the study of the ways and methods of economic thought; it is a classical Ceteris patribus situation. This laboratory further raises other subjects and questions. Are there principal differences between the economists' explanations? Is it possible to classify these differences? How do economic explanations for inflation differ from non-economic ones? In this article, I classified thirteen economic studies and one non-economic study into five clusters. This was the methodological tool used to inquire into the economic thought in this virtual laboratory.

Giving full play to the interaction of multi-dimensional environmental regulation is an important method to accelerate the green transformation of manufacturing enterprises which is a significant way to achieve carbon peaking and carbon neutrality goals. Based on the theory of evolution game, a green transformation strategy evolution game model participated by the government, consumers, and manufacturing enterprises has been constructed. Stability research is conducted based on the behavioral mode of the three parties participating subjects and the paper uses MATLAB for numerical simulation to analyze the impact of environmental regulations of different dimensions on the green transformation of enterprises. The research shows that: (1) When the government adopts a single environmental regulation of carbon emission right transaction or green consumption subsidy, it will not affect the choice of green transformation strategies of the enterprise. (2) When the government adopts a single environmental regulation of environmental protection tax, it only promotes enterprises to choose green transformation strategies under limited conditions. (3) Under the multi-dimensional environmental regulations of the government, by affecting the cost and income of enterprise green technology innovation, consumers’ green consumption behavior, and the manufacturing enterprises choose green transformation as a stable strategy. Therefore, the government should give full play to the role of multi-dimensional environmental regulations in promoting consumers' green consumption and enterprises’ green technology innovation to accelerate the green transformation of manufacturing enterprises.

Carbon emissions trading is essential for reducing carbon emissions, and its role in regional carbon unlocking needs further clarification. This study uses the difference-in-differences (DID) model and synthetic control model (SCM) to evaluate the carbon unlocking effect of China’s six pilot carbon trading provinces. This study found that (1) carbon lock-in effects in China are mainly influenced by technology lock-in and fixed input lock-in; (2) each province’s overall carbon lock-in level presents a decreasing trend yearly, and the regional distribution presents characteristics of “low in the east and high in the west”; (3) carbon emissions trading pilot policies effectively promote the carbon unlocking effect in pilot regions overall, with Guangdong having the most significant unlocking effect. Conversely, Beijing, Hubei, Chongqing, and Shanghai also had different degrees of carbon unlocking. Finally, (4) an assessment of impact mechanisms indicates that technology and institutions have a significant mediating role in effectively promoting carbon unlocking under the carbon trading policy. Conversely, social behavior has an inverse effect, and fixed assets are not affected by the policy. This study demonstrates the carbon unlocking effect of carbon emissions trading and provides a quantitative reference for implementing carbon emissions trading policies and determining carbon unlocking paths.

Stock indexes are used as a barometer of economic health, and fluctuations in these markets can have a substantial influence on the economy. For example, the Covid-19 pandemic caused severe economic disruption, as reflected in stock market indexes. Similarly, Russia's invasion of Ukraine has geopolitical implications that might undermine global trade and economic stability, impacting stock market indexes. Considering these developments, the purpose of this article is to examine the co-movements of the stock markets of Austria (ATX), Poland (WIG), the Czech Republic (PX Prague), Hungary (BUX), Croatia (CROBEX), Serbia (BELEX 15), Romania (BET), and Slovenia (SBI TOP) from February 16, 2018, to February 15, 2023. To achieve the research objectives, the aim is to answer the following research question: i) Have the events of 2020 and 2022 accentuated the co-movements between the stock markets in Central Europe? The results show the presence of 21 shocks between markets (out of a potential 56) during the Tranquil subperiod, with the WIG stock index having a greater predictive influence on the behavior of its peers (4 shocks out of 7 possible). During the Stress subperiod, 45 shocks were confirmed (out of 56 possible). The markets that triggered the most market shocks (7 out of 7 possibilities) were BET, BUX, CROBEX, and SBI TOP. The research question was validated based on the conclusion supplied, as all markets increased their movements, showing a considerable effect of the 2020 and 2022 events on these markets.

The purpose of the study is to determine the relationships between the factors, attitude towards food consumption and green purchase intention in organic food markets of Pakistan. A Total of 301 sample data was analyzed by SPSS and PLS-SEM. These statistical techniques were used to test the hypotheses. The study was supported the theories include theory of Reasoned Action, Planned Behavior, and expectancy-value model of attitude theory. It also enhances the intention of purchase to buy organic food. The study found that environment, consciousness of health, and safeties of food have significantly direct and indirect influence on Intention of purchase while, taste of food, and exposure of media only have significantly indirect effect on Intention of purchase. The study recommended that retailers should advertise organic food via social media or channels as media persuasively increase the chances to grab the consumer attention to buy the product.

Despite the advantages of nanoscale drug delivery systems, traditional nanoparticles often encounter challenges such as detection and elimination by the immune system. To circumvent these limitations, scientists have created biomimetic nanoparticles that extend circulation time, decrease clearance rates, and optimize drug delivery. The integration of cell membranes onto nanoparticle surfaces yields Cell Membrane-coated Nanoparticles (CMNPs) that exhibit behavior akin to actual cells while offering superior structural robustness and stability. A variety of cell membranes, including those of red blood cells, white blood cells, and cancer cells, lend unique properties and targeting capabilities to CMNPs. This review outlines the diagnostic and therapeutic roles of CMNP-based drug delivery systems in oncology and contemplates their possible clinical impact.

The dawn of the Artificial Intelligence (AI) era presents a plethora of new possibilities for analyzing regional economic development. The present article provides an in-depth exploration of the methods employed in this field, highlighting the immense opportunities that AI offers while also addressing potential challenges. The role of AI is crucial in complex data handling, enabling efficient analyses of intricate regional economic patterns. This capacity is paramount in shaping economic policies and strategies that are reflective of each region's unique needs and potential. The article firstly explores various AI methods used in economic analysis, including but not limited to machine learning, deep learning, and natural language processing. It delves into the application of these methods in discerning development trends, predicting economic shifts, and identifying strategic economic drivers unique to various regions. Subsequently, the potential of AI to transform regional economic analysis is discussed, encompassing its capability to process large and complex datasets, its power to predict future trends based on past and present data, and its ability to aid in strategic decision-making. However, this new era of AI-driven economic analysis is not without challenges. The latter part of this article thus confronts the issues related to data privacy, ethical use of AI, and the necessity of interdisciplinary skills in AI and economics. This exploration contributes to a broader understanding of how AI is transforming the landscape of regional economic development analysis, illuminating both its present use and future implications. By understanding these dynamics, we can better harness the potential of AI to advance economic prosperity in various regions around the globe.