This study empirically examines the influence of corporate financialization on trade credit financing, focusing on non-financial companies listed in China's A-share market from 2010 to 2022. The results indicate that as corporate financialization increases, companies will obtain less trade credit financing. Further incorporating moderating factors like market position and financing constraints, we find that a higher market position effectively mitigates the negative impact of corporate financialization on trade credit financing, whereas higher financing constraints exacerbate the adverse effects of corporate financialization on trade credit financing. Additional analysis shows that corporate financialization negatively affects trade credit financing by crowding out main business performance and increasing operational risks. Moreover, corporate financialization more strongly inhibits trade credit financing in state-owned companies.

The Hospital Price Transparency Final Rule aims at reducing the cost of healthcare in the United States by making information about the prices of medical services more readily available to market participants. The economic rationale is that higher price transparency should reduce the level and the dispersion of prices by disincentivizing hospitals from charging higher prices than possible. Using data on 3789 prices for three diagnostic imaging procedures and on the compliance with the rule for 86 acute care hospitals in the Greater Los Angeles area, we test the hypothesis that price levels and price dispersion are lower among hospitals that are more compliant. We do not reject this hypothesis, although we do not observe strictly decreasing relationships in all analyses. Prices are lower at hospitals with high or medium compliance than at hospitals with low compliance, but there is not a clear difference between hospitals with high and hospitals with medium compliance. Prices are less dispersed among hospitals with high compliance than among hospitals with low compliance, but the evidence for hospitals with medium compliance is less clear. We conclude that high compliance with the Hospital Price Transparency Final Rule implies lower prices and less price dispersion among hospitals.

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.

With the advancement of science and technology and the advent of the digital economy, the digital economy has become a new driving force for economic development, and the digital economy industry at home and abroad is facing new opportunities for survival and development. However, due to the special development model and profit mode of the digital economy industry, the traditional performance evaluation method is no longer applicable, so it is particularly important to improve the previous performance evaluation method. This paper first adjusts the customer value assessment (CLV) model according to the characteristics of the digital economy industry, and then analyzes the traditional EVA based on the adjusted user value assessment (ULV) model of the digital economy industry. The performance evaluation system is improved, and the representative enterprises of Alibaba, JD and Pinduoduo in the digital economy industry are selected for performance evaluation and analysis, and the user value performance evaluation model of the digital economy industry is integrated, innovated and standardized, in order to provide reference and reference for the follow-up research and practice of performance evaluation in the digital economy industry.

Renewable energy has become a crucial topic in recent years as the world is moving towards environment friendly and sustainable energy sources. The aim of this research paper is to analyze the effectiveness and efficiency of renewable energy resources such as geothermal, hydro, solar and wind energy in comparison to typical and traditional energy resources such as oil and coal. This paper focuses on the current state of renewable energy adoption, the benefits and drawbacks of all types of renewable energy resources, and the challenges faced in implementing renewable energy on a large scale. Through a comprehensive literature review, this paper discusses the methodologies and results of various studies conducted to understand the potential of renewable energy sources by secondary means. This research paper also presents an original analysis of renewable energy data to identify the most efficient and cost-effective renewable energy source. The results of this study indicate that renewable energy sources can be an effective and efficient replacement for traditional energy sources, but they face significant challenges in adoption and implementation. The study concludes with recommendations for future research and policy changes that can support the growth of renewable energy sources.

Green credit policy (GCP) relies on financial means to promote environmental governance. Whether it can achieve the goals of economic development and environmental protection, especially in the context of different institutional supplies, remains to be scientifically tested. Based on the implementation of China’s Green Credit Guidelines in 2012, this study uses panel data of Chinese companies from 2009 to 2019 to explore the influence of GCP on green technology innovation and the role of institutional supply in it. The results show that GCP is instrumental in promoting green innovation in heavily polluting enterprises, and the promotion effect is heterogeneous based on green patent types, firms’ ownership, and regional financial development levels. Further analysis finds that the supply of environmental protection systems by local governments can strengthen the green innovation effect of GCP. However, the institutional supply of innovation has not yet released a promotional effect. This paper finds that green credit can be used as an environmental governance tool and provides inspiration for local governments to issue environmental protection policies scientifically.

At present, global warming poses the greatest threat to the planet's ecosystem. China is the greatest CO2 emitter, expected to account for roughly 33% of overall emissions in 2021, and this has caused authorities to express significant concern in the most latest Paris accord, when they decided to cut global emissions to a particular level. In comparison, 55% of China's power in 2021 appears from coal. As a result, investigating the ecological elements that affect China's CO2 output is essential. This analysis utilizes the autoregressive distributed lag (ARDL) technique to investigate the relationships concerning fossil fuels, renewables, and economic growth from 1990 to 2021. According to the data, using fossil fuels to generate energy and increase GDP significantly increases CO2 emissions. Yet, the use of renewable energy reduces CO2 emissions both in the long run and in the short term. Overall, the outcomes imply that using renewable energy sources is key to reaching sustainable environmental goals and deters the usage of fossil fuels in the energy mix. This study suggests that China take into account empirical findings and undertake long-term initiatives to reduce carbon emissions in favor of sustainable environmental outcomes.

To make an assessment of ratemaking proposals, this article proposes that regulators should consider applying a logical decision-making process, such as multi-criteria decision analysis (MCDA), that selects or modifies those rate mechanisms that come closest to achieving the public interest, as defined by regulators. MCDA seems like an fitting tool to improve regulatory decisions by making more explicit the relationship between different rate mechanisms and the public interest. This article provides a simplified version of MCDA to demonstrate how regulators can apply this tool to evaluate new rate mechanisms in terms of the public interest. As far as the author knows, the MCDA methodology applied to utility ratemaking has not appeared in the literature. While regulators may not want to or lack the resources to execute all the steps presented in this article, MCDA can provide direction to regulators in evaluating different rate mechanisms and ultimately reaching decisions that are more rational and aligned with the public interest. After all, evaluating rate mechanisms is one of the major functions of utility regulators. Doing it wrongly can have a consequential effect on society’s welfare.

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.

This paper attempts to introduce an overlapping generations structure into Paul Krugman's "The world's smallest macroeconomic model" (Krugman (1999)) to examine the implications of fiscal policy, particularly fiscal deficits, in a framework suitable for policy analysis. In that paper, Krugman argued that under the price rigidity assumption, a shortage in the money supply leads to underemployment and recession, so increasing the money supply would eliminate underemployment and restore full employment. But how can the money supply be increased? I show that in order to restore full employment out of a recession, a fiscal deficit is needed to increase the money supply. I also show that in a growing economy, fiscal deficits are necessary to maintain full employment at constant prices or inflation. Fiscal deficits are not only effective in pulling the economy out of recession, they are even necessary for growth to continue without recession or inflation. The fiscal deficit in this paper represents the difference between government spending and government revenues. If this difference is positive, we say that the government is in deficit. Krugman's original model is a one-period static model. I intend to extend this model to a dynamic overlapping generations model.

Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.

In the past, investing in housing has served as an engine of growth for many economies as it is widely recognized that poor housing conditions can have significant negative impacts on human health, education, and economic opportunities. To assess the housing-related quality of life, indicators such as housing quality, housing environment, and cost burdens can be applied. However, recent studies indicate that materials used for construction are critical. Permanent materials, as opposed to temporary materials, typically offer a range of benefits in terms of durability, low maintenance, improved energy efficiency, increased property value, and better safety. The ultimate aim is to identify the key drivers of housing conditions in Cambodia, with a particular focus on materials and water quality, and the legal status of housing. To find the empirical relationship between economic, socio-economic, and demographic variables on the one hand, and variables measuring housing and living conditions in Cambodia on the other, the analysis employs Ordinary Least Squares and Methods-of-Moments regression modeling. Results indicate that high employment rates and entrepreneurship increase home ownership, and improve the quality of drinking water available. Furthermore, in addition to employment and entrepreneurship, the higher-performing construction materials can also be empirically explained by a bigger labor force and variables capturing the wider macroeconomic environment.

Forest resources, as one of the most important natural resources, have high ecological value. The calculation of their ecological value is an important part of improving the accounting of natural resources assets. Based on the analysis of the basic situation of China's forest resources, this paper starts from the economic theory of forest resources, discusses the principle of forest resources in national economic operation, analyzes the output, value, price and other issues of forest resources, and probes into the necessity of studying forest resources. Then, based on the purpose of compiling the balance sheet of natural resources, the forest resources accounting theory was studied from the aspects of subject and object, scope, table design and so on. On the basis of relevant theoretical research, the main ecological functions of different types of forest resources are discussed. In practical calculation, the parameters of some accounting methods are adjusted to adapt to the local situation. Some methods of ecological value calculation such as soil conservation and water conservation are put forward, and some ideas for further improvement are put forward. Finally, taking Qiandao Lake as an example, the ecological value of its forest resources is calculated. Through calculation, the annual soil conservation value of Qiandao Lake forest resources was obtained. Combined with the research conclusion and practice progress, the future research prospect is put forward.

The integration of digital technology and traditional finance has further achieved the goal of low-cost and sustainable financial services and contributed to high-quality economic development. Based on the data from 31 provinces in China from 2011 to 2021, this paper empirically analyzes the impact of digital finance on the urban-rural income gap. The results show that digital finance can significantly narrow the income gap between urban and rural residents. The quantile test shows that the greater the urban-rural income gap, the stronger the role of digital finance in narrowing the urban-rural income gap. The threshold effect test shows that the narrowing effect of digital finance on the urban-rural income gap will first increase and then weaken with the continuous improvement of the development level. Heterogeneity analysis shows that the secondary indicators of digital finance can significantly narrow the urban-rural income gap, and the effect of depth of usage is the most obvious. Moreover, digital finance has a significantly stronger effect on narrowing the urban-rural income gap in the eastern region than in other regions. Therefore, we should vigorously promote the development of digital finance and give full play to its effectiveness in narrowing the urban-rural income gap.

This paper examines impacts of China’s financial liberalization policy on income inequality, through three channels: financial scale, financial structure and financial efficiency, an empirical analysis based on panel data of 30 provinces in China from 1996 to 2013 is conducted. The results confirm the Kuznets effect between financial scale, financial structure and income inequality. As the size of the financial sector expands, the financial structure is tilted toward direct financing, and the income gap among residents will experience a "reverse U-shaped" trend that rises first and then falls. Most of China has not yet passed the turning point, and is still in the upper bound of the "inverted U-shaped" curve. Financial liberalization policies will continue to exacerbate income inequality. The impact of financial efficiency on income distribution is quite different in different regions of China. After dividing the whole China into three regions according to the degree of economic development, it is found that the financial efficiency of the eastern and western regions has a Kuznets effect on the income gap, but compared with the eastern region, more provinces and cities in the western region have entered the stage of reducing the income inequality, and the financial development has a great impact on the western region. The effects of financial development on central China are weak, and income inequality increases with financial efficiency.

Cancer, a global health menace, continues to pose significant challenges in terms of incidence and mortality, necessitating innovative therapeutic strategies. Despite existing treatments, the limitations persist, prompting a quest for novel approaches. The emergence of immunotherapy marked a transformative era in solid tumor treatments, yet its efficacy is constrained by adverse effects. Concurrently, the integration of advanced technologies into cancer treatment explores the vast potential residing at the molecular level through gene analysis and manipulation. This review articulates the role of state-of-the-art genome editing technology, notably clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), in overcoming the constraints of immunotherapy for cancers. Unveiling the intricacies of CRISPR-Cas9-mediated genome editing, the review introduces the formidable CRISPR toolbox. A spotlight is cast on the transformative impact of CRISPR-induced double-strand breaks (DSBs) on cancer immunotherapy, encompassing knockout and knock-in strategies. The utilization of CRISPR/Cas9 technology in pre-clinical cancer research has demonstrated notable success; however, its transition to the clinical setting remains in the nascent stages of development. This review aims to elucidate the fundamental aspects of CRISPR technology and offer a comprehensive survey of its existing applications while outlining its prospective role in the realm of cancer therapies. Through an exploration of CRISPR's mechanisms, current applications, and anticipated future potentials, this review provides valuable insights into the evolving landscape of CRISPR-based cancer treatment strategies.

The enactment of policies that bolster new research and development (R&D) institutions stands as a pivotal strategy to catalyze urban innovation and development. Adopting a strategic lens of innovation chain management and employing the Differences-in-Differences (DID) method to scrutinize panel data from 43 Chinese cities spanning 2008 to 2019, this study probes the efficacy and underlying mechanisms of policies designed to support nascent R&D institutions in facilitating urban innovation. Empirical findings reveal that policies in support of new R&D institutions have markedly enhanced the three integral stages of the urban innovation chain: research and development, transfer, and application. Furthermore, the innovation ambiance within cities and the innovative activities of enterprises emerge as significant mediators between support policies and the output of urban innovation across these stages. A regional heterogeneity analysis unveils that the impact of support policies on the output of urban innovation diverges across regions, with a notably more pronounced effect observed in the eastern region compared to central and western regions. An objective appraisal of the policy's impact on urban innovation not only aids in delving into the profound implementation effects of policy instruments but also furnishes policy-makers with decision-making references for optimizing the utilization of support policies for new R&D institutions to advance the edification of the national innovation system.

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.

The global financial crisis has affected all countries during the period 2007-2014. Tourist companies, especially in Italy, play a significant role in the economy of a country. The performance of tourism companies, and in particular hotels and travel agencies and tour operators, will be analysed in this paper, relative to the period of the crisis. The aim of this paper is to analyze profitability dynamics of the aforementioned tourist companies, to check whether they have suffered the effects of the global crisis. To this end, data from AIDA database relating to Italian companies in the sector were used. The profitability of these companies has been analyzed using the main profitability ratios, ROI and ROS ratios. An analysis of the trends and ANOVA of these ratios was carried out for the period 2007-2015. In this way, it has been verified whether the global crisis has affected profitability dynamics of hotels and travel agencies and tour operators in Italy. There is a parallelism in the trends of the main operators' indices in the three Italian macro-regions. The best performance is in the South, in a less developed economic context. The crisis has reduced the income indices in the first three years, even if the figure remains positive. Since 2012 there is a clear recovery, especially for hotels; intermediaries, on the other hand, suffer from competition from websites. However, the indices confirm the close relationship between travel agencies, tour operators and hotels. ROI mainly rewards first, while ROS intermediaries.

The diplomatic relations between Azerbaijan and Pakistan have undergone significant development since the late 1990s. Despite some studies focusing on diplomatic and political relations, the economic dimension of this partnership has not been systematically covered. In this paper, a robust least squares (RLS) approach is used to model bilateral trade between the two countries and identify statistically significant determinants, including economic, institutional, and cultural factors, after analyzing the main economic and subsectoral trade dynamics of both countries. The results reveal that Azerbaijan's imports from Pakistan are significantly associated with the country's higher income and market size, while Pakistan's elevated exchange rate and trade costs hinder bilateral trade. In addition, Pakistan's rising manufacturing output increases exports to Azerbaijan but decreases imports from Azerbaijan. Among institutional determinants, government effectiveness, the trade freedom index (TFI), and political stability play significant roles in bilateral trade. Lastly, it was found that cultural proximity increases Pakistani exports to Azerbaijan but not imports from Azerbaijan. The analysis also indicates that domestic savings stimulate Pakistani exports. These findings underscore the complex nature of bilateral trade between Azerbaijan and Pakistan and highlight the need for institutional arrangements to harmonize trade flows and boost welfare gains from trade. The results may be of great importance to intergovernmental working groups, domestic policymakers, and businesses.