This study delves into the complex relationship between China's international trade and economic growth, specifically focusing on emission intensity and its environmental ramifications within the context of energy efficiency. The primary objective is to provide a comprehensive understanding of how China's international trade dynamics influence economic growth, emphasizing emission intensity and employing a model that considers the nonlinear impact of trade on output. Through this model, structural breakpoints are identified, revealing distinct outcomes for China amidst positive and negative shocks from 1972 to 2021. The study uncovers the dual nature of the energy sector, acting both as a source of environmental degradation and a potential avenue for positive change through increased efficiency. Complex interplays among economic activities, inflation, and investment on carbon intensity are revealed. Additionally, the varying effects of export volume on carbon emissions intensity across lag thresholds underscore the significance of global trade dynamics for environmental sustainability. The study concludes by emphasizing the importance of balancing economic prosperity with environmental responsibility, advocating for carefully balanced policies that foster sustainable growth. Recommendations include considering the dual nature of the energy sector and implementing policies promoting increased efficiency. Policymakers are urged to adopt growth models prioritizing environmental responsibility, balancing economic activities, inflation, and investment to mitigate carbon intensity while acknowledging the diverse effects of global trade dynamics on environmental sustainability.

Adenosine alterations to RNA, which are largely determined by RNA modification writers (RMWs), are critical for cancer growth and progression. These RMWs can catalyze different types of adenosine modifications, such as N6-methyladenosine (m6A), N1-methyladenosine (m1A), alternative polyadenylation (APA), and adenosine-to-inosine (A-to-I) RNA editing. These modifications have profound effects on gene expression and function, such as immune response, cell development. Despite this, the clinical effects of RMW interactive genes on these cancers remain largely unclear. A comprehensive analysis of the clinical impact of these epigenetic regulators in pan-cancer requires further comprehensive exploration. Here, we systematically profiled the molecular and clinical characteristics of 26 RMWs across 33 cancer types using multi-omics datasets and validated the expression level of some RMWs in various cancer lines. Our findings indicated that a majority of RMWs exhibited high expression in diverse cancer types, and this expression was found to be significantly associated with poor patient outcomes. In the genetic alterations, the amplification and mutation of RMWs were the dominant alteration events. Consequently, the RNA Modification Writer Score (RMW score) was established as a means to assess the risk of RMWs in pan-cancer. We found that 27 of 33 cancers had significantly higher scores compared with normal tissues, and it was significantly correlated with prognosis. We also evaluated their impact on the tumor microenvironment and the response to immunotherapy and targeted therapy. These findings verified the important role of RMWs in different aspects of cancer biology, and provided biomarkers and personalized therapeutic targets for cancer.

The high price of energy due to green energy policy will cause adjustments across the US economy predicted in the present general equilibrium model that includes energy Btu input with capital and labor to produce manufactures and services. This same model in trade theory examines the effects of a tariff on an imported factor of production such as a natural resource or capital.  Corresponding error correction estimates of the reduced form equations of the model in annual 1970-2018 data prove robust and suggest model modifications. A parametric approach to noncompetitive pricing based on unit cost diminishing with output brings the model much closer to the estimates. Manufactures are revealed to have a higher degree of noncompetitive pricing than services. Assuming constant elasticity of substitution production, a weak degree is revealed to provide the best fit to the error correction estimates. The high price of green energy will cause an inelastic decrease in energy input resulting in increased energy revenue. Outputs of both sectors fall, more in manufactures due to its energy intensity. The capital return and wage also fall given the weak substitution of capital and labor relative to the price of energy. The wage rises slightly in the model but falls considerably in the error correction estimate. The only clear winner is energy input with increased revenue. The government owns a large share of hydrocarbon reserves and will benefit from a higher price of energy.

The use of All-Terrain Vehicles (ATVs) has gained considerable attention within the recreation industry. The incorporation of ATVs into tourism and recreational activities has demonstrated substantial economic potential for local economies. However, there is a lack of comprehensive knowledge regarding the economic impact of ATV recreation. This paper conducts an extensive review of existing literature from 2004 to 2022, evaluating the economic impact of ATVs on local economies in the United States. The review aims to identify commonalities and disparities among existing economic impact studies and assess whether these studies have included costs associated with ATV-induced environmental, societal, and public health damages in their economic assessments. To this end, there is a scarcity of scientific peer reviewed journal articles that exclusively focus on the economic impact of ATVs. As such, we reviewed a total of seven publications comprising reports. The results of our analysis shed light on similarities and differences in aspects of resident and non-resident ATV spending, type of ATV expenditures, and the approaches used to evaluate economic impact assessment. The findings indicate that existing studies have assessed the economic impact of ATVs solely based on gross expenditures, lacking comprehensive inclusion of the costs associated with ATV damages to the environment, society, and human health. This study advocates for the need for a more comprehensive consideration of both benefits and costs in the economic impact of ATVs to assist ATV business managers, policymakers, and researchers in making informed decisions and planning future programs.

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.

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.

Credit markets around the world are undergoing digital transformation which has led to the rise in FinTech and BigTech lending. FinTech and BigTech lending is the provision of credit by FinTech and BigTech providers who have more capital, cutting-edge IT systems, worldwide recognition, greater online presence and are able to handle more big data on computers and mobile phones than traditional banks. FinTech and BigTech lending is growing in importance, but the determinants of FinTech and BigTech lending have received little attention in the literature. This study investigates the determinants of FinTech and BigTech lending. The study focused on the effect of financial inclusion and financial development on FinTech and BigTech lending. Using data for 18 countries from 2013 to 2019 and employing the difference-GMM and 2SLS regression methods, the findings reveal that financial inclusion and financial development are significant determinants of FinTech and BigTech lending. Financial development is a positive determinant of FinTech and BigTech lending while financial inclusion has a significant effect on FinTech and BigTech lending. Also, FinTech and BigTech lending lead to greater banking sector stability and also poses the risk of rising nonperforming loans. There is also a significant positive correlation between financial development and FinTech and BigTech lending. These findings add to the emerging literature on the role of FinTech and BigTech in financial intermediation. This research is significant because it provides insights into the role of financial inclusion and financial development in the digital transformation of credit markets.

Financial literacy seems to be more important and focused than ever. Several recent studies have found that Portugal is at bottom of the table compared to the Eurozone countries regarding financial literacy. As it’s a key factor in the development of a country, it appears to be crucial to understand what people know about financial matters when they complete compulsory schooling. With a sample of 296 Portuguese students who enrolled in the polytechnic higher education system for the first time, we studied if they understand simple concepts related to financial issues (such as loans), as well as whether they are aware of the risk of not recovering the money invested that is embedded in some of the most talked-about and common financial products that are available in the market. This last objective seems to be relevant since a lot of fraudulent schemes involving teenagers, young adults, and financial markets have emerged in recent years. From the results, some disturbing conclusions can be drawn. For instance, more than two-thirds of the sample do not know what Euribor is (similar result regarding Spread). About a third of the simple do not understand inflation’s consequences. 16,9% do not know what term deposits are, and derivative products and saving certificates are seen as very alike regarding the risk they represent. Students who chose a business-related course as their first choice when applying showed a higher level of financial literacy, although it is still low compared to what one would expect.

In light of the events of 2020 and 2022, this study aims to examine the co-movements between the capital markets of the Netherlands (AEX), France (CAC 40), Germany (DAX 30), the United Kingdom (FTSE 100), Italy (FTSE MIB), Spain (IBEX 35), Russia (IMOEX), and spot prices of crude oil (WTI), silver (XAG), gold (XAU), and platinum (XPT) from January 1, 2018 to December 31, 2022. The purpose of this analysis is to answer the following research question: (i) Did the events of 2020 and 2022 increase the shocks between stock markets and WTI, XAG, XAU, and XPT prices? The findings indicate that time series do not follow a normal distribution and are stationary. In response to the question of investigation, we found that during the Tranquil period, it was possible to verify the existence of 28 causal relationships (out of 110 possibilities). During the stress period, there was a very significant increase in the number of causal relationships between the market pairs under analysis (62 causal relationships out of 110 possibilities), including a relative increase in the influence of commodities on capital markets and capital markets on commodities. These findings show that during the events of 2020 and 2022, capital markets and commodities significantly accentuated their co-movements among themselves, indicating that alternative markets such as WTI, XAG, XAU, and XPT do not provide safe-haven properties. These results have implications for portfolio diversification during times of global economic uncertainty.

Global distributions of net factor income from abroad (NFI) during 1990-2019 have witnessed that (1) the United States is the top one country accounting for 40% of surpluses of the global total, while a surge in China’s deficit with its GDP increase; (2) GDP growth in emerging economies has a price scissors with NFI deficits; (3) asymmetric NFI has covered up the severity of rich countries’ global arbitrages especially from emerging economies; (4) China’s economic power is exaggerated by the PPP-based GDP implemented by the World Bank. It concludes that (1) developing countries have paid for huge hidden cost for their emergence; (2) the statement of the United States suffering losses absolutely does not hold; (3) GDP is not a universal tool for measuring what matters. It suggests that (1) emerging economies countries should beware of the potential misleading of GDP on economic measurement and economic power comparison ; (2) GDP should be critiqued from the applicability perspective of economies’ types; (3) it is urgent to clarify some misjudgment and misleading concepts in the economic affairs surrounding the global value chain patterns; (4) the construction of national governance capacity in emerging economies should focus on “social infrastructure”, of which one of the important parts is an effective economic statistics system; (5) emerging economies should carry out the strategic layout of international economic statistics talents to enhance their soft powers.

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.

The answer to how to stop economic growth must be political rather than economic. Economic growth made by men has an impetus which can only be stopped by a deliberate choice. It takes national and international decisions to stop it. In order to organize a moratorium on economic growth (which eventually must lead to a stop on growth) I suggest to create blocks of nations in the world. The European Union can function as a model for such a block. The E.U. is on its way of becoming a supra-national government which was created to ensure a level playing field for its members. Now it is meant to create economic growth for its members, in the future its administration should be used to stop it. Blocks should agree on the targets to reach a durable economy. Globally or between certain more advanced blocks a date should be set to stop economic growth after that date. Each block should implement the goals to reach a durable economy and prevent products reaching its market that violate the goals of durability. Blocks in developing countries cannot be expected to stop economic growth. They need that growth for two reasons. Eventually they must reach the level of existence in the more advanced block and secondly they must have the time to improve the standard for durability of their products. They need the aid of the developed countries to reach a higher but also more durable level of production, but this aid comes with a quid pro quo, In the meantime they must do their utmost to stop wars in their regions and conduct a responsible population-policy to reduce the growth of their population and let it reduce on the long term.

The global agricultural supply chain grapples with inherent challenges, including opacity, inefficiency, and ethical concerns, necessitating innovative solutions, particularly within Indian Agriculture Supply Chain Management. Despite these challenges, a notable research gap emerges due to the limited exploration of adoption factors specific to the Indian agricultural context. To bridge this gap, a comprehensive investigation is initiated to delve into the nuanced dynamics of blockchain technology adoption in the Indian agricultural supply chain. The primary aim of this study is to provide insights into these adoption dynamics, employing a three-stage methodology. Starting with an extensive literature review to identify key adoption factors, the study then distributed a well-crafted questionnaire to 200 respondents, yielding 150 complete and analyzable responses. The chosen methodologies, including a one-sample t-test and exploratory factor analysis, allow for a quantitative assessment of the significance of various factors. The study's findings reveal crucial dimensions influencing blockchain adoption, such as perceived benefits, ease of use, trust, efficiency, ethical considerations, data security, integrity, regulatory compliance, and collaboration and stakeholder engagement, providing a comprehensive understanding of the adoption landscape. In conclusion, this research significantly contributes to comprehending the nuanced adoption dynamics of blockchain technology in the Indian agricultural supply chain, supported by a substantial sample size and robust methodological choices. The implications extend to stakeholders navigating the evolving agricultural supply chain, offering valuable insights for future research, policy considerations, and practical strategies, with a central focus on fostering transparency, efficiency, and ethical standards to address global agricultural supply chain challenges.

Background: Bladder cancer (BLCA), particularly invasive BLCA, has become a medical burden worldwide as it is associated with recurrence and easy metastasis. There are specific differences in the expression of various miRNAs in tumor and normal tissues. Hence, miRNAs can be used as biomarkers for tumor diagnosis and prognostic evaluation. The current study aimed to predict the downstream target genes of BLCA-related miRNAs and explore their association with immune infiltration. Method: Data on BLCA-related mRNA and miRNA expression levels were downloaded from The Cancer Genome Atlas. Correlation analysis and Cox regression analysis were performed to validate the miRNA risk model. The infiltration of various immune cells should be compared to determine the distinct differences between the immunological microenvironment of the two risk groups. Results: A predictive framework of BLCA was established using the expression levels of two miRNAs. Cox regression analysis showed that the low-risk group had a better prognosis. Then, the target genes of miRNA were predicted, and the target genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Moreover, variations in immune cells and functions between the high- and low-risk groups were assessed. Conclusion: The prognostic features composed of two associated miRNAs (MIR-25, MIR-548AN) may help predict the overall survival of BLCA.

Gold has been traditionally well recognized as a safe heaven for financial markets. Lately, Bitcoin has been gradually considered as a popular alternative. Since the outbreak of COVID-19 in early 2020, it has become even more necessary and critical to examine the diversification capability of them to hedge financial risks associated with an unexpected crisis comparable to the pandemic. This paper hence employs the wavelet analysis, complemented by the multivariate DCC-GARCH approach, to measure the coherence of the gold and Bitcoin prices with six representative stock market indices, three for developed economies and three for emerging economies, all of which are heavily affected by the pandemic. To have a more balanced and comprehensive analysis, two-year data are used, spanning from 12th April 2019 to 15th April 2021, which covers approximately one year before and one year after the announcement of the COVID-19 pandemic. The results suggest that the returns of both gold and Bitcoin are generally not strongly correlated with the market returns of all six indices, particularly for short-term investment horizons. That is, investors in all six indices can benefit through gold, as well as Bitcoin, in terms of hedging. Meanwhile, compared with Bitcoin, gold shows to be less correlated with the indices, particularly for long-term investment horizons. The findings hence suggest that gold and Bitcoin offer diversification benefits to investors in the market indices during a crisis such as the COVID-19 pandemic, especially for short-term investment horizons. The study also reminds policymakers thinking beyond the pandemic about the future of the earth, including air pollution and health, for sustainable development of the whole world.

Despite the status of the United States as one of the world’s economic superpowers, the country is plagued by child poverty. The issue of child poverty is most prominent in rural, majority-minority communities, where years of limited opportunity, often created by systematic oppression, have created a vicious cycle of economic despair. There are a number of policies that have been explored which could limit the effects of child poverty, most notably the Expanded Child Tax Credit (ECTC), which was introduced as part of the American Rescue Plan Act (ARPA) of 2021. This paper uses a combination of qualitative and quantitative methods to thoroughly examine this policy (ECTC) and its potential to both cut child poverty in the short term and increase educational attainment and economic mobility in the long term for recipients. The ECTC program ended in 2022 and only lasted in a short period. By examining the impacts of a similar and long-standing but less accessible and generous program, the Earned Income Tax Credit (EITC), on educational attainment in specific communities we can theorize and determine the significance the Expanded CTC would have. We use EITC program as a proxy of ECTC and reach inference of the long-term impact of ECTC program on children’s education attainment. The result of this research indicates a number of benefits of the Expanded Child Tax Credit, including its potential to cut child poverty and increase educational attainment, its impact on future economic prosperity in rural, majority-minority communities, and the policy’s ability to decrease economic disparities between non-metro and metropolitan areas.

Spence’s signaling model (Spence, 1973) suggests that education can signal workers’ unobserved ability to employers thereby mitigating discrimination. There have been several studies concerning education’s impact on labor market discrimination against minority or disadvantaged groups. Our approach in this inquiry is unique in that we utilize the data of PhD recipients, a group of people with the highest education attainment, to test Spence’s theory. Another novelty of this paper is that in addition to examining possible discrimination against women and foreign-born, as has been done in previous studies, we further explore possible discrimination against the physically challenged individuals. Our baseline results show conflicting results that Ph.D. education can reduce discrimination against disability and foreign-born but not against gender. Further analysis by the Blinder-Oaxaca Decomposition shows that the wage gaps of gender and disability come more from the unobserved part than the explained part, while the foreign-born wage gap come more from the observable human capital differences. Since prejudice is an unobserved factor and we know that the disadvantaged groups are likely to suffer from prejudice (Oaxaca, 1973; Blinder, 1973; Montes-Rojas et al., 2017; Deshpande and Khanna, 2018), we conjecture that prejudice might be attributable to the unexplained part of the wage gaps. Furthermore, prejudice might be deeply rooted in one’s mind, thus difficult to remove even with the influence of education. Hence, our results reveal that it would be hard for Ph.D. education to eradicate the discrimination against gender and disability, but not against foreign-born.

Less-Lethal Weapons, including electric Tasers, velocity-reduction bullet-capture projectiles, rubber bullets, beanbag rounds, and other less-lethal projectiles used in police departments, federal and local law enforcement agencies, and military policing operations, as well as for private use, are topics of significant debate in recent years. These weapons can reduce collateral damage, reputational risk, and legal exposure while preventing wrongful and unnecessary deaths caused by conventional bullet wounds. Yet, opponents have highlighted concerns regarding the safety and effectiveness of these weapons, as well as their potential to cause injury or even death. While the primary duty of law enforcement officials is to preserve order and execute the law while protecting individual rights, they ultimately achieve these goals by exercising their coercive authority. Society expects police officers to rely on their professional training and judgment when using physical force to defend citizens and themselves or to apprehend criminal suspects. Because police officers must occasionally participate in physical acts of coercion, it is unavoidable that some of these interactions may result in bodily injuries to individuals, including the officers themselves. These injuries have real costs, such as medical treatment, lost wages, municipal liability, court claims and settlements, riots, and legal or even criminal exposure, as well as less-tangible collateral costs, such as the erosion of police legitimacy and public trust, which are more difficult to quantify but arguably more influential in shaping long-term public perceptions than personal injuries alone. Less lethal technology eliminates some of the concerns raised by lethal weaponry. This study reviews the advantages and disadvantages of these less-lethal weapon technologies versus traditional firearms for use by the police force, federal agencies, and military police while also performing advanced analytics to model the valuation and return on investment for a law enforcement organization implementing such technologies.

Occupying a preeminent position as the globe’s most extensive economic corridor, the Yangtze River Economic Belt (YREB) serves as a crucial aquifer for 800 million individuals and harbors almost 50% of the country's paramount water-intensive contaminant producers. The ecological efficiency of these producers within the belt has increasingly come under scholarly scrutiny. Leveraging data from 2004-2012 encompassing 725 such manufacturers and employing the DEA-Malmquist index methodology, this research delineates the eco-efficiency trajectories of these water-centric pollutant producers, shedding light on their spatial-temporal dynamics across the expansive Yangtze Economic Belt. The results are as follows: (1) there’s a discernible enhancement in the eco-efficiency (ML) of these manufacturers situated within the YREB, predominantly propelled by technological change (TC) and shifts in technical efficiency change (TEC); (2) From a spatial perspective, notable disparities emerge in both technological change (TC) and technical efficiency change (TEC) among the belt’s upper, middle, and lower tiers. Intriguingly, the hierarchy for TC and TEC descends as follows: Lower > Upper > Middle; (3) Examining the spatial evolution nuances, 2004 witnessed eco-efficiency distributions ranking from Upper > Middle > Lower. Fast forward to 2012, a marked reconfiguration appears with a distribution pattern of Middle > Lower > Upper.

This study delves into the intricate interconnections among the financial sector, macroeconomic dynamics, and the advancement of sustainable energy production in South Asia. Utilizing data from 1987 to 2022 in Bangladesh, India, Pakistan, and Sri Lanka, the research employs the Structural Vector Autoregressive (SVAR) method to analyze financial market responses. The findings reveal nuanced insights into the complex relationship between economic and environmental factors, fostering a comprehensive understanding of the region's intricate nexus between financial markets and sustainable energy initiatives. In the Economic Growth model, the negative coefficient for CO2 emissions underscores environmental concerns, indicating an inverse relationship with economic growth. Conversely, positive coefficients for non-fossil energy production, Gross Fixed Capital Formation, stock exchange value, and domestic credit emphasize their positive impact on economic growth. A positive GDP coefficient in the CO2 emission model signifies heightened carbon emissions with economic growth, while a negative coefficient for non-fossil energy production suggests potential environmental benefits. The nearly unitary positive coefficient for Gross Fixed Capital Formation highlights its significant role in driving carbon emissions. The Non-Fossil Energy Production model reveals an inverse relationship between GDP and clean energy, suggesting a potential reliance on fossil fuels during economic growth. Positive coefficients for CO2 emissions, Gross Fixed Capital Formation, stock exchange value, and domestic credit highlight factors influencing non-fossil energy production. Long-run stable coefficients affirm variable significance, while a negative error correction term suggests convergence in developing countries. These models offer nuanced insights into the intricate relationships among economic, environmental, and energy variables, guiding sustainable development policies in South Asia.