The CSR perspective: Interplay of technological innovation, ethical leadership and government regulations for sustainable financial performance

The financial performance of Chinese public and private sector banks is changing over time. There is no stability in the financial performance of Chinese banks which hurts businesses and the market. The purpose of current research was to determine the influence of corporate social responsibility (CSR) on driving the sustainable financial performance of Chinese banks. From methodological perspective, data was collected from 329 banking sector employees from China to partial least square-structural equation model (PLS-SEM) is employed for data analysis. The research used SPSS 24 and Smart PLS 4 as statistical analysis tools. This research confirmed that achieving sustainability in financial performance for Chinese banks can be achieved with CSR influenced by technological innovation, ethical leadership, and government regulations. This research has statistically confirmed that transformational leadership leading to CSR with technological innovation, ethical leadership, and government regulations can make significant improvements in financial performance. The framework developed by current research is a novel contribution to the literature. The findings of this research improve the literature on the banking sector and advanced performance. Furthermore, this research has highlighted significant ways that can help the banking sector employees to improve their financial performance with sustainability.

Foreign shareholder, overseas sale and corporate profit margin

China is actively promoting the development of a robust trading nation. In this context, utilizing data from China's A-share listed companies spanning from 2003 to 2021, this study investigates the impact of foreign shareholders on enterprises in a scenario where overseas sales reduce the profit margin of Chinese firms. The findings reveal that overseas sales do indeed decrease the profit margin of Chinese enterprises; however, foreign shareholders mitigate this negative effect and various robustness tests support this conclusion. Mechanism analysis confirms that foreign shareholders primarily enhance enterprise productivity through improved production technology spillover effects, thereby alleviating the adverse impact of overseas sales on Chinese firms' profit margins. Heterogeneity analysis demonstrates that both longer holding periods for foreign shareholders and multiple foreign shareholders significantly alleviate the negative influence of overseas sales on Chinese firms' profit margins. Moreover, there is significant heterogeneity in how foreign shareholders alleviate these detrimental consequences based on property rights nature, institutional environment, overseas related party transactions and subsidiaries, as well as industry attributes. These findings have important reference value for China's efforts towards becoming a strong trading nation and can contribute to enhancing trade capacity in other countries.

Satellite mapping reveals extensive industrial activity at sea

The world's population increasingly relies on the ocean for food, energy production and global trade1-3, yet human activities at sea are not well quantified4,5. We combine satellite imagery, vessel GPS data and deep-learning models to map industrial vessel activities and offshore energy infrastructure across the world's coastal waters from 2017 to 2021. We find that 72-76% of the world's industrial fishing vessels are not publicly tracked, with much of that fishing taking place around South Asia, Southeast Asia and Africa. We also find that 21-30% of transport and energy vessel activity is missing from public tracking systems. Globally, fishing decreased by 12 ± 1% at the onset of the COVID-19 pandemic in 2020 and had not recovered to pre-pandemic levels by 2021. By contrast, transport and energy vessel activities were relatively unaffected during the same period. Offshore wind is growing rapidly, with most wind turbines confined to small areas of the ocean but surpassing the number of oil structures in 2021. Our map of ocean industrialization reveals changes in some of the most extensive and economically important human activities at sea.

Financial influence on global risks of zoonotic emerging and re-emerging diseases: an integrative analysis

BACKGROUND

Emerging and re-emerging infectious diseases (EIDs), such as Ebola virus disease and highly pathogenic influenza, are serious threats to human health and wellbeing worldwide. The financial sector has an important, yet often ignored, influence as owners and investors in industries that are associated with anthropogenic land-use changes in ecosystems linked to increased EIDs risks. We aimed to analyse financial influence associated with EIDs risks that are affected by anthropogenic land-use changes. We also aimed to provide empirical assessments of such influence to help guide engagements by governments, private organisations, and non-governmental organisations with the financial sector to advance a planetary health agenda.

METHODS

For this integrative analysis, we identified regions in the world where there was evidence of a connection between EIDs and anthropogenic land-use changes between Nov 9, 1999, and Oct 25, 2021, through a targeted literature review of academic literature and grey literature to identify evidence of drivers of anthropogenic land-use change and their association with commodity production in these regions. We only included publications in English that showed a connection between deforestation and the production of one or more commodities. Publications merely describing spatial or temporal land-use change dynamics (eg, a reduction of forest or an increase of palm-oil plantations) were excluded. As we were assessing financial influence on corporate activities through ownership specifically, we focused our analysis on publicly listed companies. Equity data and data about ownership structure were extracted from Orbis, a company information database. We assessed financial influence by identifying financial entities with the largest equity ownership, descriptively mapping transboundary connections between investors and publicly listed companies.

FINDINGS

227 public and private companies operating in five economic sectors (ie, production of palm oil, pulp and wood products, cocoa, soybeans, and beef) between Dec 15, 2020, and March 8, 2021, were identified. Of these 227, 99 (44%) were publicly listed companies, with 2310 unique shareholders. These publicly listed companies operated in six geographical regions, resulting in nine case-study regions. 54 (55%) companies with complete geographical information were included in the countries network. Four financial entities (ie, Dimensional, Vanguard, BlackRock, and Norway's sovereign wealth fund) each had ownership in 39 companies or more in three of the case-study regions (ie, north America, east Asia, and Europe). Four large US-based asset managers (ie, Vanguard, BlackRock, T Rowe Price, and State Street) were the largest owners of publicly listed companies in terms of total equity size, with ownership amounts for these four entities ranging from US$8 billion to $21 billion. The specific patterns of cross-national ownership depended on the region of interest; for example, financial influence on EIDs risks that was associated with commodity production in southeast and east Asia came from not only global asset managers but also Malaysian, Chinese, Japanese, and Korean financial entities. India, Brazil, the USA, Mexico, and Argentina were the countries towards which investments were most directed.

INTERPRETATION

Although commodity supply chains and financial markets are highly globalised, a small number of investors and countries could be viewed as disproportionally influential in sectors that increase EIDs risks. Such financial influence could be used to develop and implement effective policies to reduce ecological degradation and mitigate EIDs risks and their effects on population health.

FUNDING

Formas and Networks of Financial Rupture-how cascading changes in the climate and ecosystems could impact on the financial sector.

Hybrid deep learning models with multi-classification investor sentiment to forecast the prices of China's leading stocks

The prediction of stock prices has long been a captivating subject in academic research. This study aims to forecast the prices of prominent stocks in five key industries of the Chinese A-share market by leveraging the synergistic power of deep learning techniques and investor sentiment analysis. To achieve this, a sentiment multi-classification dataset is for the first time constructed for China's stock market, based on four types of sentiments in modern psychology. The significant heterogeneity of sentiment changes in the sectors' leading stock markets is trained and mined using the Bi-LSTM-ATT model. The impact of multi-classification investor sentiment on stock price prediction was analyzed using the CNN-Bi-LSTM-ATT model. It finds that integrating sentiment indicators into the prediction of industry leading stock prices can enhance the accuracy of the model. Drawing upon four fundamental sentiment types derived from modern psychology, our dataset provides a comprehensive framework for analyzing investor sentiment and its impact on forecasting the stock prices of China's A-share market.

Research on U-shaped relationship between short-term debt for long-term use and supply chain enterprise default risk: Evidence from Chinese listed firms

This paper empirically investigates the impact mechanism of short-term debt for long-term use and the default risk of supply chain firms with the data of Chinese A-share listed firms from 2007 to 2021. The study shows that there is a significant U-curve relationship between short-term debt for long-term use and supply chain firms' default risk, and too high or too low a level of short-term loans and long-term investments will worsen firms' default risk. In addition, firm performance plays an mediating effect in the process of short-term debt for long-term investment affecting the default risk of supply chain firms. Finally, customer effect and firm heterogeneity play a moderating role in the impact of short-term loans and long-term investments on the default risk of supply chain firms, and the U-shaped relationship will be strengthened under the high-intensity customer effect. This study has important theoretical and practical significance for analyzing the impact of default risk contagion in supply chain enterprises.

Common institutional investors and the quality of management earnings forecasts-Empirical and machine learning evidences

Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model and empirically found that common institutional investors positively impact the precision of earnings forecasts. This article also uses graph neural networks to predict the precision of earnings forecasts. Our findings have shown that common institutional investors form external supervision over restricting management to release a wide width of earnings forecasts, which helps to improve the risk warning function of earnings forecasts and promote the sustainable development of information disclosure from management in the Chinese capital market. One of the marginal contributions of this paper is that it enriches the literature related to the economic consequences of common institutional shareholding. Then, the neural network method used to predict the quality of management forecasts enhances the research method of institutional investors and the behavior of management earnings forecasts. Thirdly, this paper calls for strengthening information sharing and circulation among institutional investors to reduce information asymmetry between investors and management.

Macro tax incentives and corporate sustainable innovation: Evidence from Chinese Enterprises

Innovation has become the driving force behind China's economy's sustainable growth. Due to the efficient transmission of taxation leverage, preferential tax policies are frequently used to stimulate innovation. Therefore, the incentive effect of preferential tax policies on sustainable innovation has gradually become the focus of attention. This paper takes the 2016-2019 China A-share listed high-tech enterprises as a sample, calculates tax incentive intensity with the aid of B-index, and studies the incentive effect of preferential tax policies on the sustainability of corporate innovation. This study shows that: (1) Tax incentive intensity has a positive incentive effect on corporate sustainable innovation. (2) The R&D expenses plus deduction policy and the preferential tax rate policy can significantly enhance corporate sustainable innovation, but there is a substitution effect between them. (3) Based on the heterogeneity of institutional environment and enterprise characteristics, the incentive effect of tax preferential policies is more obvious in enterprises which are non-state-owned and in areas with low government intervention and sound legal system. However, the incentive effect of different types of preferential policies differs in the size of the enterprise. This study will provide reference for the improvement of preferential tax policy system and the optimization of innovation policy environment.

Digital finance and M&As: An empirical study and mechanism analysis

With the rapid growth and wide application of digital technology, enterprises have entered the digital era with both opportunities and challenges existing. Mergers and acquisitions are one of the most efficient ways to integrate resources and achieve profit growth, giving enterprises advantages in competing in the new mode of economic growth. Based on this, this research tries to explore whether the development of digital finance will contribute to the emergence of M&As activities through combining M&As data of the Chinese stock market with the digital finance inclusion index between 2012 and 2020. The results show that the development of digital finance largely influences M&As activities through lower acquirers' financial constraints. We further replace digital finance with three sub-indexes including coverage breadth, usage depth, and digitalization level to explore the impact of different dimensions of digital finance on M&As. Results show that coverage breadth plays a more important role. In addition, heterogeneity tests reveal that the relationship between the development of digital finance and M&As activities varies significantly. The influences of digital finance on private and western and central enterprises are more significant compared with state-owned and eastern enterprises. According to the study, since the development of digital finance can be an efficient way to ease financial constraints and boost M&As activities, the government should promote the development of digital finance while companies strive to make the most use of it.

Evaluation of the coordinated development between Chinese cultural industry and scientific & technological innovation

Based on the coupling and interaction relationship between China's cultural industry (CI) and scientific & technological innovation (STI), this study constructed an index system for their coordinated development. The weight of each indicator was determined by using the entropy value method (EVM), and the coupling coordination degree (CCD) model was used to calculate CCD and coordination degree of China's CI and STI from 2012 to 2020. On this basis, the key factors in the coupling effect were analyzed using grey correlation degree (GCD). The results demonstrate that: (1) there is a high-level coupling relationship between China's CI and technological innovation; (2) the level of coupling coordination between the two is generally on the rise, experiencing a development process from serious maladjustment to high coordination; (3) Industry resources, policy support and the cost of cultural undertakings are the endogenous factors restricting the development of CI, and the environment and output of STI are the key factors restricting the coupling and coordinated development of Chinese CI and STI.

Exploring the effect of industrial agglomeration on income inequality in China

Income inequality is a good indicator reflecting the quality of people's livelihood. There are many studies on the determinants of income inequality. However, few studies have been conducted on the impacts of industrial agglomeration on income inequality and their spatial correlation. The goal of this paper is to investigate the impact of China's industrial agglomeration on income inequality from a spatial perspective. Using data on China's 31 provinces from 2003 to 2020 and the spatial panel Durbin model, our results show that industrial agglomeration and income inequality present an inverted "U-shape" relationship, proving that they are the non-linear change. As the degree of industrial agglomeration increases, income inequality will rise, after it reaches a certain value, income inequality will drop. Therefore, Chinese government and enterprises had better pay attention to the spatial distribution of industrial agglomeration, thereby reducing China's regional income inequality.

Absence of ultimate controller and investment efficiency: Evidence from China

In modern enterprises with a separation of powers, the ultimate controller can effectively influence the implementation of corporate strategy and operational management efficiency, as well as improve corporate governance by monitoring and limiting the management entrenchment effect within enterprises. Based on the information pertaining to ultimate controllers disclosed by enterprises in their annual reports, this study empirically tested whether the absence of the ultimate controller impacts investment efficiency using the data of Chinese A-share listed companies from 2007 to 2020. It was found that the investment efficiency of enterprises without ultimate controllers is relatively lower than those with ultimate controllers. This is reflected in the insufficient investment of enterprises without an ultimate controller. Moreover, the effect is more significant when the financial environment, internal governance environment, and external governance environment of firms are worse. The mechanism analysis demonstrated that the absence of an ultimate controller causes a more severe insider agency problem and a significantly higher degree of financing constraints, which leads to underinvestment and reduces investment efficiency of firms. The economic consequence test also found that the inefficient investment caused by the absence of ultimate controllers would damage the future value of enterprises, but would increase managers' compensation. Overall, this study suggests that ultimate controllers are an important part of a firm's internal governance, especially for monitoring management behavior and resolving agency conflicts.

The impact of bank's engagement in shadow banking activities on bank's sustainability: Evidence from Chinese commercial banks

The economic and environmental consequences of bad banking practices have aroused much attention. In China, banks are at the center of shadow banking activities through which they avoid regulation and support environmentally unfriendly businesses such as fossil fuel companies and other high-pollution enterprises. In this paper, we study the impact of bank's engagement in shadow banking activities on its sustainability by using annual panel data of Chinese commercial banks. The result shows that bank's engagement in shadow banking activities has a negative impact on its sustainability and the negative impact of bank's engagement in shadow banking activities is more pronounced for city commercial banks and unlisted banks which are less regulated and lack corporate social responsibility (CSR). Furthermore, we explore the underlying mechanism of our findings and prove that bank's sustainability is impeded because it transforms high-risk loan into shadow banking activities which are less regulated. Finally, by using difference-in-difference (DiD) approach, we find that bank's sustainability improved after the financial regulation on shadow banking activities. Our research provides empirical evidence that the financial regulation on bad banking practices is beneficial for bank's sustainability.

Equity incentive contract characteristics and company operational performance-An empirical study of Chinese listed companies

Equity incentive, as an institutional arrangement for the coordination of the interests of shareholders and managers, has been widely implemented by public companies in developed capital markets throughout Europe and America. However, does it work and/or when might it be more effective in emerging market economies such as China? We aimed to understand the effects of equity incentive plans implemented by listed companies in China and the potential influence of the general characteristics of contracts on the effectiveness of equity incentive plans. Based on behavioral decision theory, this paper adopts a multivariate linear regression model to analyze the 1695 equity incentive plans implemented in Chinese listed companies between 2010 and 2018 with their two-year lagged performance data. The empirical results show that the operational performance of companies after implementing equity incentive plans shows a trend of polarization. In the 95% confidence interval, the effect of restrictive stock incentive and exercise-constrained variables is not significant, while the validity period has a significant positive correlation and incentive intensity has a significantly negative correlation with the company's operational performance. Furthermore, the negative effects mentioned above become more obvious with a longer plan implementation period. Based on these conclusions, we suggest that companies could adopt equity incentive plans with a relatively longer validity period and more reasonable incentive intensity. Additionally, it would be better for companies to select non-restricted stocks as incentive tools if there is no obvious preference.

Comparison of Industry Payments to Physicians and Advanced Practice Clinicians

This study uses Open Payments data to characterize and compare payments to physicians and advanced practice clinicians.

Profitability determinants of the natural stone industry: Evidence from Spain and Italy

The natural stone sector is an important driver of the Spanish and Italian economies, which underwent internationalization after the financial crisis of 2008 as part of a survival and development strategy. This article aims to study the financial and economic profitability of this sector in the two leading European production countries, as well as its determinants. For this purpose, the economic-financial data of a sample composed of 453 companies (203 Spanish and 250 Italian) from 2015-2019 were analyzed using the multiple linear regression methodology. To address the problems of possible endogeneity and omission of variables in the model, the dependent variable was used as a regressor with one and two lags, and panel data with fixed effects were considered after performing the Hausman test. The results show significant differences between the two countries, with higher profitability in Italy. Company size, company growth (measured as the change in assets), and the variation in the country's GDP all positively affected profitability. At the same time, the level of indebtedness showed a negative relationship. The country's inflation rate and gender diversity in top management were shown to be non-relevant variables. The research conducted indicates that, to increase profitability, Spanish and Italian companies in the natural stone sector should undergo mergers in order to grow in size, increase efficiency in the use of assets, reduce their dependence on external financing, and promote equity capital. In addition, Italian companies should reduce the average period of payment to suppliers to lower deferral costs, and boost exports to become less dependent on the country's domestic economy.

Non-linear effects of heterogeneous environmental regulations on industrial relocation: Do compliance costs work?

Reasonably designing environmental regulations for compliance-driven industrial relocation can avoid new pollution havens. The Cournot duopoly model simulates that the necessary condition for industrial relocation is differentiated market costs. Then, based on the province-industrial data of six Chinese pollution-intensive industries during 2005-2019, this study applies spatial Durbin model to explore the non-linear effects of heterogeneous environmental regulations on industrial relocation. Results shown that command-and-control environmental regulation manifests a U-shaped curve with local industrial relocation, with inverted U-shaped spillover effect radiating a road distance of 650 km, and both internal and external costs play the mediating roles; Market incentive environmental regulation has inverted U-shaped curves with industrial relocation in local and neighboring regions, it creates dual costs and works well in both short and long terms, which is the most potential regulatory tool to avoid pollution relocation accompanying industrial relocation; Voluntary environmental regulation exhibits inverted U-shaped relationships with industrial relocation in direct and spillover effects, and works through increased external cost rather than internal cost. Its spatial spillover radiates the longest 1250 km due to rapid spread of public opinions, but this effect takes more than 3 years to be effective.

Assessment of Medical Industry Compensation to US Physicians by Gender

Importance

It has been well established that female physicians earn less than their male counterparts in all specialties and ranks despite controlling for confounding variables.

Objective

To investigate payments made from highest-grossing medical industry companies to female and male physicians and to assess compensation and engagement disparities based on gender.

Design, Setting, and Participants

This retrospective, population-based cross-sectional study used data from the Open Payments database for the 5 female and 5 male physicians who received the most financial compensation from each of the 15 highest-grossing medical supply companies in the US from January 2013 to January 2019.

Main Outcomes and Measures

The primary outcome was total general payments received by female and male physicians from medical industry over time and across industries. The secondary outcome was trends in industry payment to female and male physicians from 2013 to 2019.

Results

Among the 1050 payments sampled, 1017 (96.9%) of the 5 highest earners were men and 33 (3.1%) were women. Female physicians were paid a mean (SD) of $41 320 ($88 695), and male physicians were paid a mean (SD) of $1 226 377 ($3 377 957) (P < .001). On multivariate analysis, male gender was significantly associated with higher payment after adjusting for rank, h-index, and specialty (mean [SD], $1 025 413 [$162 578]; P < .001). From 2013 to 2019, the payment gap between female and male physicians increased from $54 343 to $166 778 (P < .001).

Conclusions and Relevance

This study found that male physicians received significantly higher payments from the highest-grossing medical industry companies compared with female physicians. This disparity persisted across all medical specialties and academic ranks. The health care industry gender payment gap continued to increase from 2013 to 2019, with a wider compensation gap in 2019.

Environmental administrative penalty, environmental disclosures, and the firm's cash flow: evidence from manufacturing firms in China

Environmental administrative penalty is a strong tool for the government to regulate environmental pollution. The influence of an environmental administrative penalty on a firm's cash flow has become a hot topic in academics and industry. By employing ordinary least square and the bootstrap method, it is established that the penalty significantly reduces the firm's cash flow in the succeeding year through incremental disclosures which play a mediating role. Further, we distinguished environmental information's nature and then measured it from negative, sensitive (hard) information and positive (soft) information aspects. However, it is found that the mediating effect of the two types of disclosure increment did not exist, respectively. These results help us to comprehend the significance of environmental management to the stability of corporate cash flow and have practical illumination to corporate environmental management and environmental pollution control.

The clean energy claims of BP, Chevron, ExxonMobil and Shell: A mismatch between discourse, actions and investments

The energy products of oil and gas majors have contributed significantly to global greenhouse gas emissions (GHG) and planetary warming over the past century. Decarbonizing the global economy by mid-century to avoid dangerous climate change thus cannot occur without a profound transformation of their fossil fuel-based business models. Recently, several majors are increasingly discussing clean energy and climate change, pledging decarbonization strategies, and investing in alternative energies. Some even claim to be transforming into clean energy companies. Given a history of obstructive climate actions and "greenwashing", there is a need to objectively evaluate current and historical decarbonization efforts and investment behavior. This study focuses on two American (Chevron, ExxonMobil) and two European majors (BP, Shell). Using data collected over 2009-2020, we comparatively examine the extent of decarbonization and clean energy transition activity from three perspectives: (1) keyword use in annual reports (discourse); (2) business strategies (pledges and actions); and (3) production, expenditures and earnings for fossil fuels along with investments in clean energy (investments). We found a strong increase in discourse related to "climate", "low-carbon" and "transition", especially by BP and Shell. Similarly, we observed increasing tendencies toward strategies related to decarbonization and clean energy. But these are dominated by pledges rather than concrete actions. Moreover, the financial analysis reveals a continuing business model dependence on fossil fuels along with insignificant and opaque spending on clean energy. We thus conclude that the transition to clean energy business models is not occurring, since the magnitude of investments and actions does not match discourse. Until actions and investment behavior are brought into alignment with discourse, accusations of greenwashing appear well-founded.

Factors predicting companies' crisis in the engineering industry from the point of view of financial analysis

A key factor for business management is the assessment of the financial situation of companies. Nowadays, it is essential to monitor the liquidity crisis, which is closely linked to corporate crises. The aim of the paper is to analyse a selected sector of the economy from the perspective of the corporate crisis and to identify the factors of crisis. More than 2000 engineering companies in Slovakia were analysed during the period from 2015 to 2019 with the aim of analysing financial results, especially in the area of financial forecast for the future. In the analysis, statistical testing of the significance of relationships using the Spearman correlation coefficient, the significance of differences by the power of t-test, regression and clustering were used. A significant part of the paper is the analysis of selected indicators of the company's crisis-Altman's Z score and the IN05 index. The results indicate that engineering companies in Slovakia are achieving good results and their financial situation is improving within the years between 2015-2019. The results can also be used as a starting point for research concerning the impact of COVID-19 in this area. In the context of corporate crisis management, engineering companies behave in the same way but it is necessary to monitor individual factors that can detect a corporate crisis. Possible measures would thus lead to the stabilization of financial results and long-term sustainable positive prospects for companies in the future.

Payments by Industry to Residency Program Directors in the United States: A Cross-Sectional Study

PURPOSE

To assess the proportion, nature, and extent of financial payments from industry to residency program directors in the United States.

METHOD

This cross-sectional study used open-source data from Doximity and the Centers for Medicare and Medicaid (CMS) open payments database. Profiles of 4,686 residency program directors from 28 different specialties were identified using Doximity and matched to records in the CMS database. All payments received per residency program director over the years 2014 to 2018 were extracted, including amount in U.S. dollars, payment year, and nature of payment (research versus general payments). Total payments (research plus general payments) received over the 5 years were added up per residency program director. Only personal payments were included.

RESULTS

Overall, 74% (3,465/4,686) of all residency program directors received 1 or more personal payments, totaling $77,058,139, with a median of $216 (interquartile range, $0-$2,150) and a mean of $16,444 (standard deviation, $183,061) per residency program director over the 5 years. Ninety-five percent of total payment value were general payments, and 5% were personal research payments. About 11% (536/4,686) of residency program directors received more than $10,000, while 3% (133/4,686) received more than $100,000 in the study years. There was a substantial difference in the proportion (P < .001), nature (P < .001), and amount (P < .001) of payments of residency program directors between specialties. Almost all residency program directors of interventional radiology (96% [74/77]), vascular surgery (96% [53/55]), and orthopedic surgery (92% [184/201]) received payments, while only one-third to one-half of those in preventive medicine (29% [18/62]), pediatrics (43% [90/211]), and pathology (51% [73/143]) received payments.

CONCLUSIONS

Industry payments to residency program directors are common, although large variation exists between specialties. The majority of direct payments to residency program directors are for non-research-related activities.

Firing discrimination: Selective labor market responses of firms during the COVID-19 economic crisis

The speed of the economic downturn in the wake of the COVID-19 pandemic has been exceptional, causing mass layoffs-in Germany up to 30% of the workforce in some industries. Economic rationale suggests that the decision on which workers are fired should depend on productivity-related individual factors. However, from hiring situations we know that discrimination-i.e., decisions driven by characteristics unrelated to productivity-is widespread in Western labor markets. Drawing on representative survey data on forced layoffs and short-time work collected in Germany between April and December 2020, this study highlights that discrimination against immigrants is also present in firing situations. The analysis shows that employees with a migration background are significantly more likely to lose their job than native workers when otherwise healthy firms are unexpectedly forced to let go of part of their workforce, while firms make more efforts to substitute firing with short-time working schemes for their native workers. Adjusting for detailed job-related characteristics shows that the findings are unlikely to be driven by systematic differences in productivity between migrants and natives. Moreover, using industry-specific variation in the extent of the economic downturn, I demonstrate that layoff probabilities hardly differ across the less affected industries, but that the gap between migrants and natives increases with the magnitude of the shock. In the hardest-hit industries, job loss probability among migrants is three times higher than among natives. This confirms the hypothesis that firing discrimination puts additional pressure on the immigrant workforce in times of crisis.

The effect of different government subsidies on total-factor productivity: Evidence from private listed manufacturing enterprises in China

Private enterprises play an increasingly important role in China. They can improve the total-factor productivity (TFP) and help transform and upgrade industrial structures. This study uses data for private listed manufacturing companies from 2009 to 2017 to examine the effects of different types of subsidies on TFP. We also analyze the heterogeneity and specific mechanism of subsidy effects. We find that R&D subsidies and production subsidies positively affect private enterprises’ TFP. Moreover, R&D subsidies and production subsidies lagged by one period can also significantly increase private enterprises’ TFP. In terms of industry, R&D subsidies have more obvious effects on technology-intensive industries, while production subsidies have more significant effects on labor-intensive and capital-intensive industries. In terms of scale, R&D subsidies’ effects on the TFP of medium-sized enterprises are the largest, while production subsidies have the greatest effect on small enterprises’ TFP. Government subsidies increase private enterprises’ TFP through two mechanisms: improving technological innovation capability and alleviating financing constraints. Our results suggest that governments should formulate different subsidy policies according to industry and enterprise scale.

Investor sentiment-aware prediction model for P2P lending indicators based on LSTM

In recent years, online lending has created many risks while providing lending convenience to Chinese individuals and small and medium-sized enterprises. The timely assessment and prediction of the status of industry indicators is an important prerequisite for effectively preventing the spread of risks in China’s new financial formats. The role of investor sentiment should not be underestimated. We first use the BERT model to divide investor sentiment in the review information of China’s online lending third-party information website into three categories and analyze the relationship between investor sentiment and quantitative indicators of online lending product transactions. The results show that the percentage of positive comments has a positive relationship to the borrowing interest rate of P2P platforms that investors are willing to participate in for bidding projects. The percentage of negative comments has an inverse relationship to the borrowing period. Second, after introducing investor sentiment into the long short-term memory (LSTM) model, the average RMSE of the three forecast periods for borrowing interest rates is 0.373, and that of the borrowing period is 0.262, which are better than the values of other control models. Corresponding suggestions for the risk prevention of China’s new financial formats are made.

Research on the impact of COVID-19 on Chinese small and medium-sized enterprises: Evidence from Beijing

COVID-19 leads small and medium-sized enterprises (SMEs) to survive very hard. The development difficulties of SMEs lead to weak employment and GDP growth in various countries. In the process of COVID-19's continuous spread, what is the major reason for the difficulties of SMEs? This paper hopes to answer this question by studying SMEs in Beijing. On this basis, this paper uses structural equation model (SEM) to study the relatively fast recovery of SMEs in Beijing, China, to explore the factors affecting SMEs in the pandemic. After detailed desk research and interviews with relevant entrepreneurs, this paper collects 234 valid questionnaires from SMEs in various industries in Beijing with the help of Federation of Industry and Commerce and Chamber of Commerce in Beijing. Then the data is analyzed with the SEM, which shows the relationship between cash flow from financing activities, markets, employees, costs, government policies and the impact of the pandemic. Finally, an impact model of the pandemic on SMEs is established. The result of the model indicates that the direct effect of the pandemic on the market is the most prominent, and government policies can significantly reduce the negative impact of the pandemic on SMEs indirectly. Based on this, this paper puts forward some policy suggestions, such as the targeted issuance of consumption vouchers and the reduction of administrative barriers. This will enable megacities in various countries to improve policy support for SMEs and promote the recovery and development of SMEs.

Spatial distribution evolution and accessibility of A-level scenic spots in Guangdong Province from the perspective of quantitative geography

As a typical representative of tourism resources, the spatial distribution of A-level scenic spots has a profound impact on the layout of tourism industry. Scenic spot accessibility is also important for the development of tourism. However, the relationship of regional accessibility and spatial distribution of A-level scenic spots are understudied. The study used quantitative geography and geographic information system spatial analysis methods and analyzed the evolution of spatial distribution and regional accessibility of A-level scenic spots in Guangdong Province from 2001 to 2020. The results present the following: 1. Agglomeration distribution is the main distribution type of A-level scenic spots in Guangdong Province, and the spatial distribution is unbalanced. 2. From 2001 to 2020, the spatial distribution of A-level scenic spots in 21 prefecture-level cities of Guangdong Province has gradually developed from "wide gap" to "relatively reasonable." 3. Distribution density of A-level scenic spots in Guangdong Province has evolved into the main core area of high density. 4. Center of the gravity of A-level scenic spots in Guangdong Province developed from east to west during 2002-2007 and moved to the east after 2007. 5. Accessibility between A-level scenic spots and tourist source areas in Guangdong Province is good, with an evident aggregation phenomenon. This study reveals the spatial distribution evolution law and regional accessibility of A-level scenic spots, which is conducive to healthy, sustainable, and stable development of tourism in Guangdong Province.

Analyzing enterprise asset structure and profitability using cloud computing and strategic management accounting

The study expects to further exploring the role of asset structure in enterprise profitability, and analyze the relationship between them in detail. Taking the express industry as the research object, from strategic management accounting, the study uses edge computing and related analysis tools and compares the financial and non-financial indicators of existing express enterprises. The study also discusses the differences between asset structure allocation and sustainable profitability, and constructs the corresponding analysis framework. The results reveal that SF's total assets are obviously large and the profit margin increases. While the total assets of other express enterprises are small, and the express revenue drops sharply. Heavy assets can improve the enterprises' profitability to a certain extent. SF has a good asset management ability. With the support of the capital market, SF's net asset growth ability has been greatly improved. The edge computing method used has higher local data processing ability, and the analysis framework has higher performance than the big data processing method. The study can provide some research ideas and practical value for the asset structure analysis and profitability evaluation of express enterprises.

Do economic effects of the anti-COVID-19 lockdowns in different regions interact through supply chains?

To prevent the spread of COVID-19, many cities, states, and countries have 'locked down', restricting economic activities in non-essential sectors. Such lockdowns have substantially shrunk production in most countries. This study examines how the economic effects of lockdowns in different regions interact through supply chains, which are a network of firms for production, by simulating an agent-based model of production using supply-chain data for 1.6 million firms in Japan. We further investigate how the complex network structure affects the interactions between lockdown regions, emphasising the role of upstreamness and loops by decomposing supply-chain flows into potential and circular flow components. We find that a region's upstreamness, intensity of loops, and supplier substitutability in supply chains with other regions largely determine the economic effect of the lockdown in the region. In particular, when a region lifts its lockdown, its economic recovery substantially varies depending on whether it lifts the lockdown alone or together with another region closely linked through supply chains. These results indicate that the economic effect produced by exogenous shocks in a region can affect other regions and therefore this study proposes the need for inter-region policy coordination to reduce economic loss due to lockdowns.

Structural characteristics and proximity comparison of China's urban innovation cooperation network

How to promote and improve the level of urban innovation cooperation is a major issue in China’s current high-quality economic development. Thus, enhancing innovation ability is essential to achieving high-quality economic growth under the "new normal". Based on the data of Chinese invention patents from 1985 to 2017, this paper analyzes the characteristics of China’s urban innovation cooperation network and the different roles of proximity by using social network analysis and exploratory spatial data analysis methods. The analysis results show that: (1) On the whole, the development of China’s urban innovation cooperation network is characterized by stages (initial development stage, rapid development stage, and gradual decline stage); The urban innovation cooperation network has strong connectivity and centripetal concentration but its imbalance needs to be further improved; The degree of urban participation has gradually increased, consolidating the stability of the network structure. (2) The centrality of urban innovation cooperation network has obvious characteristics of administrative center orientation, coastal areas orientation, and ‘strong east and weak west’; Beijing is the center and bridge of the network, and the network flattening characteristics are obvious; A hierarchical ‘core-edge’ structure is gradually formed for the urban innovation cooperation network, and the pyramid structure with Beijing standing at the top is being consolidated. (3) The geographical proximity presents a significant global spatial positive correlation, while the network proximity and pure network proximity have a more significant global spatial negative correlation; The local spatial autocorrelation of China’s urban innovation cooperation system based on network proximity is more obvious and identifiable than that based on the geographical proximity, which better reflects the new development model of "relationship economy".

The Shortest Duration Constrained Hidden Markov Model: Data denoise and forecast optimization on the country-product matrix for the Fitness-Complexity Algorithm

The Economic Fitness Index describes industrial completeness and comprehensively reflects product diversification with competitiveness and product complexity in production globalization. The Fitness-Complexity Algorithm offers a scientific approach to predicting GDP and obtains fruitful results. As a recursion algorithm, the non-linear iteration processes give novel insights into product complexity and country fitness without noise data. However, the Country-Product Matrix and Revealed Comparative Advantage data have abnormal noises which contradict the relative stability of product diversity and the transformation of global production. The data noise entering the iteration algorithm, combined with positively related Fitness and Complexity, will be amplified in each recursion step. We introduce the Shortest Duration Constrained Hidden Markov Model (SDC-HMM) to denoise the Country-Product Matrix for the first time. After the country-product matrix test, the country case test, the noise estimation test and the panel regression test of national economic fitness indicators to predict GDP growth, we show that the SDC-HMM could reduce abnormal noise by about 25% and identify change points. This article provides intra-sample predictions that theoretically confirm that the SDC-HMM can improve the effectiveness of economic fitness indicators in interpreting economic growth.

RD internationalization, domestic technology alliance, and innovation in emerging market

Although R&D internationalization plays an important role in enterprises’ globalization, few studies explore the mechanism of R&D internationalization and emerging market companies’ innovation, or the relationship between R&D internationalization, domestic technology alliances and absorptive capacity. How does the R&D internationalization of emerging market enterprises affect the innovation of those enterprises? Under fierce market competition, do absorption capacity and domestic technology alliances have a significant impact on enterprise innovation? From the perspective of the knowledge-based view, this paper studies 185 enterprises undergoing R&D internationalization in China from 2012 to 2017, using high-dimensional Poisson fixed effects model, we use instrumental (HDFE IV) estimation to explain the impact of R&D internationalization on the innovation of the parent company and the mechanism behind it. The study finds that R&D internationalization positively promotes the parent company’s innovation, and domestic technology alliances and absorptive capacity play a partial mediator role in R&D internationalization. In the face of fierce market competition, domestic technical alliances play a significant role in promoting enterprise innovation, while absorptive capacity plays a negative role in promoting enterprise innovation with the moderating effect of market competition.

Analyzing the regional economic changes in a high-tech industrial development zone using machine learning algorithms

The aims are to improve the efficiency in analyzing the regional economic changes in China's high-tech industrial development zones (IDZs), ensure the industrial structural integrity, and comprehensively understand the roles of capital, technology, and talents in regional economic structural changes. According to previous works, the economic efficiency and impact mechanism of China's high-tech IDZ are analyzed profoundly. The machine learning (ML)-based Data Envelopment Analysis (DEA) and Malmquist index measurement algorithms are adopted to analyze the dynamic and static characteristics of high-tech IDZ's economic data from 2009 to 2019. Furthermore, a high-tech IDZ economic efficiency influencing factor model is built. Based on the detailed data of a high-tech IDZ, the regional economic changes are analyzed from the following dimensions: economic environment, economic structure, number of talents, capital investment, and high-tech IDZ's regional scale, which verifies the effectiveness of the proposed model further. Results demonstrate that the comprehensive economic efficiency of all national high-tech IDZs in China is relatively high. However, there are huge differences among different regions. The economic efficiency of the eastern region is significantly lower than the national average. The economic structure, number of talents, capital investment, and economic efficiency of the high-tech IDZs show a significant positive correlation. The economic changes in high-tech IDZs can be improved through the secondary industry, employee value, and funding input. The ML technology applied can make data processing more efficient, providing proper suggestions for developing China's high-tech industrial parks.

Emission impacts of China's solid waste import ban and COVID-19 in the copper supply chain

Climate change will increase the frequency and severity of supply chain disruptions and large-scale economic crises, also prompting environmentally protective local policies. Here we use econometric time series analysis, inventory-driven price formation, dynamic material flow analysis, and life cycle assessment to model each copper supply chain actor's response to China's solid waste import ban and the COVID-19 pandemic. We demonstrate that the economic changes associated with China's solid waste import ban increase primary refining within China, offsetting the environmental benefits of decreased copper scrap refining and generating a cumulative increase in CO2-equivalent emissions of up to 13 Mt by 2040. Increasing China's refined copper imports reverses this trend, decreasing CO2e emissions in China (up to 180 Mt by 2040) and globally (up to 20 Mt). We test sensitivity to supply chain disruptions using GDP, mining, and refining shocks associated with the COVID-19 pandemic, showing the results translate onto disruption effects.

Financial disclosure quality and sustainability disclosure quality. A case in China

This paper empirically examines whether there is an association between financial reporting disclosure quality and sustainability disclosure quality of the top 100 socially reputed Chinese listed firms. The paper computed financial disclosure quality by empirically combining earning qualities of accrual, persistence, predictability, and smoothness. Using content analysis and survey questionnaire research methods, it calculated sustainability quality by combining disclosure quantity (through quantitative weightings), disclosure type (through qualitative weightings), and disclosure item importance (through qualitative weightings) of economic, social, and environmental disclosures made in annual and sustainability reports, ascertained using the Global Reporting Initiative sustainability framework. The study finds that sustainability disclosure in the current period is sufficiently associated with financial disclosure quality of the current period and future period. Consistent with stakeholder theory, firms with a social reputation are perceived as trustworthy by stakeholders and shareholders. The findings lead to a cultural stakeholder theory where underlying values of societal culture create a condition supporting mutual stakeholder relationships between firm and various stakeholders. Demonstrating trustworthiness through disclosures can help boost consumer confidence and foreign trade relations for Chinese firms. The Chinese government can design innovative schemes to reward and promote trustworthiness in firms, such as regulating base-point reductions in interest rates on borrowing or raising funds.

Sustainability assessment and improvement strategies research for typical arid and resource-developing regions

Located in the Eurasian continent’s hinterland, Xinjiang is a typical arid and resource-developing region in China’s northwest. Problems such as excessive resource consumption, environmental pollution, and ecological imbalance are becoming severe, which have become the bottleneck that further restricts Xinjiang’s sustainable development. Due to its outstanding quantitative advantages, ecological efficiency has become a significant indicator and analytical tool for measuring the green economy and sustainable development. In this study, we analyzed ecological efficiency variation for Xinjiang’s 14 prefectures between 2001 and 2015 using a super-efficient data envelopment model (DEA), Malmquist Index, and Tobit model. These analyses indicated that: (1) The overall ecological efficiency level of Xinjiang is low, and development among regions is unbalanced, out of sync, lacks sustainability. (2) From 2001 to 2015, Xinjiang’s ecological efficiency showed a W-shaped rising trend and finally increased by 5.7%. It is due to the substantial improvement in environmental efficiency. (3) By analyzing the environmental efficiency and resource efficiency, 14 prefectures in Xinjiang consist of four development modes: low energy consumption and low emission, high energy consumption and low emission, low energy consumption and high emission, and high energy consumption and high emission. (4) Water resources are restricting factors of arid regions. In most prefectures, there exist excessive water resource investment, excessive COD, and NH₃-N emissions. (5) By analyzing the Malmquist index, it shows that the technical progress index(TC) restricted ecological efficiency. In contrast, the technical efficiency index (EC) promoted ecological efficiency.(6)The ecological efficiency was positively correlated with the utilization of foreign capital, urbanization rate, and average education degree but negatively correlated with the marketization degree. The study has guidance and reference function for the sustainable development of Xinjiang—a vital corridor of the Silk Road Economic Belt, and also provides a reference to the research work of other arid resource-based regions.

Evolution of diversity and dominance of companies in online activity

Ever since the web began, the number of websites has been growing exponentially. These websites cover an ever-increasing range of online services that fill a variety of social and economic functions across a growing range of industries. Yet the networked nature of the web, combined with the economics of preferential attachment, increasing returns and global trade, suggest that over the long run a small number of competitive giants are likely to dominate each functional market segment, such as search, retail and social media. Here we perform a large scale longitudinal study to quantify the distribution of attention given in the online environment to competing organisations. In two large online social media datasets, containing more than 10 billion posts and spanning more than a decade, we tally the volume of external links posted towards the organisations' main domain name as a proxy for the online attention they receive. We also use the Common Crawl dataset-which contains the linkage patterns between more than a billion different websites-to study the patterns of link concentration over the past three years across the entire web. Lastly, we showcase the linking between economic, financial and market data by exploring the relationships between online attention on social media and the growth in enterprise value in the electric carmaker Tesla. Our analysis shows that despite the fact that we observe consistent growth in all the macro indicators-the total amount of online attention, in the number of organisations with an online presence, and in the functions they perform-we also observe that a smaller number of organisations account for an ever-increasing proportion of total user attention, usually with one large player dominating each function. These results highlight how evolution of the online economy involves innovation, diversity, and then competitive dominance.

Influence of the enterprise's intelligent performance evaluation model using neural network and genetic algorithm on the performance compensation of the merger and acquisition parties in the commitment period

The purpose is to study the performance compensation of the bid purchased during the mergers and acquisitions (M&A) process. An intelligent model of enterprise performance appraisal is built to analyze the performances of the acquired enterprises. First, the evaluation indicators of enterprise performance are selected from both financial and non-financial aspects. An enterprise performance appraisal model is established based on the neural networks and optimized by the factor analysis method and Genetic Algorithm (GA). The principal factors affecting enterprise performance are analyzed. Then the M&A parties' performances during the M&A commitment period under the earnings compensation mechanism are analyzed quantitatively. Corresponding hypotheses and evaluation indicators are established. Mean test results and regression analyses demonstrate that the hypotheses proposed are valid under particular circumstances. Introducing the earnings compensation mechanism during the M&A process can improve the enterprise performance effectively so that the earnings forecasted in the commitment period are significantly higher than the historical profitability. Hence, the earnings compensation mechanism plays a positive role in guiding enterprise performance. Comparison with models proposed in previous research reveals that the output error ratio of the designed corporate performance evaluation model is 1.16%, which can effectively evaluate corporate performance. The above results provide a reference for studying the impact of the earnings compensation mechanism on enterprise performance during the M&A process.

Probiotics in Cosmetic and Personal Care Products: Trends and Challenges

Probiotics, defined as "live microorganisms that, when administered in adequate amounts, confer a health benefit on the host," are becoming increasingly popular and marketable. However, too many of the products currently labelled as probiotics fail to comply with the defining characteristics. In recent years, the cosmetic industry has increased the number of products classified as probiotics. While there are several potential applications for probiotics in personal care products, specifically for oral, skin, and intimate care, proper regulation of the labelling and marketing standards is still required to guarantee that consumers are indeed purchasing a probiotic product. This review explores the current market, regulatory aspects, and potential applications of probiotics in the personal care industry.

Latent Dirichlet allocation model for world trade analysis

International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. This new possibility opens a research gap, as new, data-driven, ways of understanding international trade, can help our understanding of the underlying phenomena. The present paper shows the application of the Latent Dirichlet allocation model, a well known technique in the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries’ exports of goods from 1962 to 2016. The results show that this technique can encode the main specialisation patterns of international trade. On the country-level analysis, the findings show the changes in the specialisation patterns of countries over time. As traditional international trade analysis demands expert knowledge on a multiplicity of indicators, the possibility of encoding multiple known phenomena under a unique indicator is a powerful complement for traditional tools, as it allows top-down data-driven studies.

Follow the money: Investigating gender disparity in industry payments among senior academics and leaders in plastic surgery

Introduction

Differences in academic qualifications are cited as the reason behind the documented gender gap in industry sponsorship to academic plastic surgeons. Gendered imbalances in academic metrics narrow among senior academic plastic surgeons. However, it is unknown whether this gender parity translates to industry payments.

Methods

We conducted a cross-sectional analysis of industry payments disbursed to plastic surgeons in 2018. Inclusion criteria encompassed (i) faculty with the rank of professor or a departmental leadership position. Exclusion criteria included faculty (i) who belonged to a speciality besides plastic surgery; (ii) whose gender could not be determined; or (iii) whose name could not be located on the Open Payment Database. Faculty and title were identified using departmental listings of ACGME plastic surgery residency programs. We extracted industry payment data through the Open Payment Database. We also collected details on H-index and time in practice. Statistical analysis included odds ratios (OR) and Pearson’s correlation coefficient (R).

Results

We identified 316 senior academic plastic surgeons. The cohort was predominately male (88%) and 91% held a leadership role. Among departmental leaders, women were more likely to be an assistant professor (OR 3.9, p = 0.0003) and heads of subdivision (OR 2.1, p = 0.0382) than men. Industry payments were distributed equally to male and female senior plastic surgeons except for speakerships where women received smaller amounts compared to their male counterparts (median payments of $3,675 vs $7,134 for women and men respectively, p<0.0001). Career length and H-index were positively associated with dollar value of total industry payments (R = 0.17, p = 0.0291, and R = 0.14, p = 0.0405, respectively).

Conclusion

Disparity in industry funding narrows at senior levels in academic plastic surgery. At higher academic levels, industry sponsorship may preferentially fund individuals based on academic productivity and career length. Increased transparency in selection criteria for speakerships is warranted.

Impact of Environmental Regulation on Scientific and Technological Competitiveness of Resource-Based Cities in China-Based on Panel Data of 33 Resource-Based Cities

After the economy enters a “new normal” era in China, resource-based cities are under pressure in terms of transformation, upgrading and sustainable development. This paper uses the panel data of 33 resource-based cities from 2008 to 2018 to empirically analyze the impact of environmental regulation and innovation compensation on scientific and technological competitiveness. The results show that there is a positive U-shaped relation between environmental regulation and scientific and technological competitiveness. This means that when environmental regulations exceed a certain level, continuing to increase regulations will significantly enhance technological competitiveness, but most samples are still on the left side of the turning point. At the same time, the labor productivity and fiscal capacity of non-agricultural industries in the region may have a strong regulatory effect. In a region with higher labor productivity in non-agricultural industries or stronger local fiscal capacity, environmental regulation is more likely to reflect the attribute of “innovation compensation” and advance scientific and technological competitiveness. At this stage, we should optimize the trans-regional compensation mechanism for resource-rich regions, increase investment in pollution management and ecological protection and impose stricter admission standards on industrial projects. Besides, skilled laborers should be cultivated and innovation and entrepreneurship be supported to realize the green and sustainable development of resource-based cities in the new era.

The sustainability and the survivability of Kyoto's traditional craft industry revealed from supplier-customer network

Due to the changes in consumer demand and generational transformations, Kyoto’s traditional craft industry has suffered substantial revenue losses in recent years. This research aimed to characterize Kyoto’s traditional craft industry by analyzing the supplier-customer network involving individual firms within the Kyoto region. In the process, we clarify the community structure, key firms, network topological characteristics, bow-tie structure, robustness, the vulnerability of the supplier-customer network as crucial factors for sustainable growth. The community and bow-tie structure analysis became clear that the traditional craft industry continues to occupy an important position in Kyoto’s industrial network. Furthermore, we clarify the relationship between modern and traditional craft industries’ network characteristics and their relative profitability and productivity. It became evident that the traditional craft industry has a different network structure from the modern consumer games and electric machinery industries. The modern industries have the strongly coupled component, and the attendant firms there create high value-added and play a significant role in driving the entire industry, while more traditional craft industries, such as the Nishijin silk fabrics and Kyoto doll industries, do not have this strongly coupled component. Moreover, the traditional crafts industry does not have a central firm or a dense network for integrating information, which is presumed to be a factor in the decline of the traditional craft industry.

Trends in Industry Payments to Physicians in the United States From 2014 to 2018

This study uses Medicare Open Payments data to characterize trends in the prevalence and value of physicians’ interactions with industry overall and by specialty between January 2014 and December 2018 after implementation of the federal Open Payments transparency program in 2013.

Spatial variation in socio-ecological vulnerability to Covid-19 in the contiguous United States

The health and economic impacts of the Covid-19 pandemic vary across space because social, economic, health and ecological factors are also spatially variable. Social vulnerability indices are attempts to create a relative ranking of vulnerability to a natural or anthropogenic hazard across space and have been widely used to quantify community vulnerability to natural disasters. Here, we develop a hierarchical socio-ecological vulnerability index that compares counties in the contiguous United States based on 18 variables grouped into four dimensions (ecological, social, health, and economic) in order to capture a range of factors that might contribute to community vulnerability to Covid-19. Variables were chosen based on a review of the emerging literature about the factors associated with poor health outcomes from Covid-19, information about the economic sectors most at risk from the pandemic and pandemic response, and existing social vulnerability indices. We find that socio-ecological vulnerability to Covid-19 and its related economic effects varies across the contiguous U.S., with especially high vulnerability in the Southeast U.S. and especially low vulnerability in the Upper Midwest, Great Plains, and Mountain West.

The interdependency structure in the Mexican stock exchange: A network approach

Our goal in this paper is to study and characterize the interdependency structure of the Mexican Stock Exchange (mainly stocks from Bolsa Mexicana de Valores) for the period 2000-2019 which provide a one shot big-picture panorama. To this end, we estimate correlation/concentration matrices from different models and then compute centralities and modularity from network theory.

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side

In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side.