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This study investigates the characteristics of the raindrop size distribution (DSD) for five typhoons that made landfall or passed by Zhejiang on the eastern coast of China, from 2019 to 2022. Additionally, it examines the raindrop shape–slope (µ-Λ) relationship, as well as the local Z-R relationship for these typhoons. The DSD datasets were collected by the DSG1 disdrometer located in Ningbo, Zhejiang Province. Based on rainfall rate (R), the DSD can be categorized into convective and stratiform rainfall types. Some rainfall parameters can also be derived from the DSDs to further analyze the specific characteristics of rainfall. The histograms of the generalized intercept parameter (log10Nw) exhibit negative skewness in both convective and stratiform rainfall, whereas the histograms of the mass-weighted mean diameter (Dm) of raindrops display positive skewness. During typhoon periods on the eastern coast of China, the DSD characteristic was composed of a lower number concentration of small and midsize raindrops (3.42 for log10Nw, 1.43 mm for Dm in the whole dataset) as compared to Jiangsu in eastern China, Tokyo, in Japan, Miryang, in South Korea, and Thiruvananthapuram in south India, respectively. At the same time, the scatter plots of Dm and log10Nw indicate that the convective rain during typhoon periods exhibits characteristics that are intermediate between "maritime-like" and "continental-like" clusters. Additionally, the raindrop spectra of convective rainfall and midsize raindrops in stratiform rainfall are well-represented by a three-parameter gamma distribution. The µ-Λ relation in this region is similar to Taiwan and Fujian, located along the southeastern coast of China. The Z-R relationship for eastern coastal China during typhoons based on filtered disdrometer data is Z = 175.04R1.53. These results could offer deeper insights into the microphysical characteristics of different rainfall types along the eastern coast of China and potentially improve the accuracy of precipitation estimates from weather radar observations. [ABSTRACT FROM AUTHOR]

Doxorubicin (DOX) is the most clinically important antibiotic in cancer treatment, but its severe cardiotoxicity and other side effects limit its clinical use. Therefore, monitoring DOX concentrations during therapy is essential to improve efficacy and reduce adverse effects. Here, we fabricated a sensitive electrochemical aptasensor for DOX detection. The sensor used gold wire as the working electrode and was modified with reduced graphene oxide (rGO)/gold nanoparticles (AuNPs) to improve the sensitivity. An aptamer was used as the recognition element for the DOX. The 5′ end of the aptamer was modified with a thiol group, and thus immobilized to the AuNPs, and the 3′ end was modified with methylene blue, which acts as the electron mediator. The combination between the aptamer and DOX would produce a binding-induced conformation, which changes the electron transfer rate, yielding a current change that correlates with the concentration of DOX. The aptasensor exhibited good linearity in the DOX concentration range of 0.3 μM to 6 μM, with a detection limit of 0.1 μM. In addition, the aptasensor was used for DOX detection in real samples and results, and showed good recovery. The proposed electrochemical aptasensor will provide a sensitive, fast, simple, and reliable new platform for detecting DOX. [ABSTRACT FROM AUTHOR]

This study systematically assessed the performance of the Integrated Multi‐satellitE Retrievals (IMERG) for Global Precipitation Measurement V06, including the near‐real‐time "Late Run" (IMERG‐L) and the post‐real‐time "Final Run" (IMERG‐F), over Zhejiang Province (ZJP), China. The evaluation was conducted at daily and hourly timescales for a full year and for each season, based on dense rain gauge observations and continuous and categorical validation statistics. For the full year and for each season, IMERG‐F outperformed IMERG‐L in representing the spatial pattern of multiyear mean precipitation. For regional mean of ZJP, IMERG‐F and IMERG‐L overestimated the daily/hourly precipitation for the full year by 6.51 and 4.98%, respectively. Among seasons, the regional mean relative biases for IMERG‐F were between 5.65 and 8.63%; however, for IMERG‐L, they exhibited notable variations with a maximum of 11.09% in fall and a minimum of 0 in spring. Bias composition suggested that the regional mean overestimations were largely due to false bias for the full year and for each season, except in winter, wherein it was due to hit bias. Spatially, the biases for the full year and for each season commonly arose from false and hit biases at daily timescale, and from false and miss biases at hourly timescale. Based on the remaining continuous metrics (i.e., root‐mean‐square‐error [RMSE], correlation coefficient [CC], and Kling–Gupta Efficiency [KGE]) and all categorical metrics, the IMERG daily/hourly performance was acceptable on regional and grid scales throughout the year and in all seasons. From a region‐average perspective, IMERG‐F outperformed IMERG‐L according to CC, RMSE, and KGE, but both products showed the same performance overall based on all categorical metrics; most grids also share these characteristics. This study provides a valuable reference for IMERG developers to improve product accuracy from the perspective of the final postprocessing step and for potential users in ZJP. [ABSTRACT FROM AUTHOR]

Using manually collected data in Chinese-listed firms from year 2008 to 2018, this paper studies that how does teacher-turned businessperson curb or improve accrual-based earning management and real activities manipulation. Results show that top management team (TMTs) with academic experience manage earning less through accruals and manipulating real operating activities. Further studies show that when firms with no-big4 auditors or less analysts tracking, TMTs with (out) foreign experience or political connection, the inhibition of academic experience on the earning management is more pronounced. [ABSTRACT FROM AUTHOR]

Based on the manually collected carbon information disclosure data for the Chinese listed companies from 2009 to 2015, this paper examines the influencing factors of carbon information disclosure from the stakeholders' perspective. The results show that among external stakeholders, pressures from government and customers can improve the level of firm carbon information disclosure, but the effects of creditor, supplier and competitor on firm carbon information disclosure level are not significant. Among internal stakeholders, the major shareholders, institutional investors, and employees can improve the level of firm carbon information disclosure, but the effects of foreign investors (QFII) on carbon information disclosure are not significant. Among third-party stakeholders, environmental protection organizations and audit institutions can improve the level of firm carbon information disclosure. We hope that findings of this paper will provide guidance to the Chinese firms to develop carbon disclosure policies and encourage researchers to conduct future research on this subject. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak seriously affected all economies, especially the operations of listed companies, around the world. This article studies the impact of COVID-19 on firm-level cash holdings using the difference-in-differences method. It finds that COVID-19 has a significant positive impact on cash holdings in serious-impact industries. Goodwill and goodwill impairment can weaken this positive impact, which may be related to higher business risks in these firms. Therefore, managers should raise firms' cash holding level during the pandemic to protect firms against contingencies. Managers should also be aware of financing constraints due to risks. [ABSTRACT FROM AUTHOR]

Using the financial data of listed Chinese companies, we study the impact of COVID-19 on corporate performance. We show that COVID-19 has a negative impact on firm performance. The negative impact of COVID-19 on firm performance is more pronounced when a firm's investment scale or sales revenue is smaller. We show, in an additional analysis, that the negative impact of COVID-19 on firm performance is more pronounced in serious-impact areas and industries. These findings are among the first empirical evidence of the association between pandemic and firm performance. [ABSTRACT FROM AUTHOR]

Regional hybrid energy system (RHES) is an effective way to accommodate clean energy and reduce its uncertainty. It is also an important development direction for energy market reform, playing an important role in the field of energy supply and demand. Based on electricity price, thermal price, and the relationship between load supply and demand, the paper proposes an optimal scheduling model for regional energy systems to promote clean energy consumption. Firstly, the typical structure and system modules of the integrated regional energy system are introduced in detail, and the two subsystem operating models of electricity and heating are given. Secondly, a multi‐objective function that combines the economics and stability of the system is constructed. The model maximizes the economic benefits and minimizes the net load fluctuations on the basis of maximizing the consumption of clean energy. The multi‐objective singularity is realized by fuzzy membership function. The decision‐making risk caused by the uncertainty of clean energy output is analyzed by introducing robust optimization theory. Finally, the effectiveness of the proposed model is verified based on a region in northwestern China. The results show that the proposed multi‐objective model can combine different optimization demands together, maximize the economic characteristics of clean energy while properly controlling risks, and provide reasonable support for decision makers to develop optimal operation plans. [ABSTRACT FROM AUTHOR]

Byline: Huayu Shen, Lingjie Zheng More and more consumers are becoming aware of environmental problems. As a result governments have initiated programs for sustainable development to improve the well being of society. The hotel sector is an important industry for implementing such programs. The aim of this case study was to review the development of 'green hotels' in the People's Republic of China. Basic principles for the operation of such hotels are discussed including obstacles and problems. Lastly, some suggestions and measures are provided which may be useful for hotel managers and governments.

Byline: Huayu Shen, Jia Wei, Lingjie Zheng This study discusses the major factors for travel agency websites quality from the viewpoint of users' perception and develops a conceptual framework. After discussions with users and managers, literature review, and data collection, we draw a conclusion concerning the five key factors affecting travel agency websites quality through empirical analysis. They are, in order of their importance, information quality, infrastructure support, users' trust, online service and website appearance. The findings also show that there is a great gap between users' expectations upon travel agency websites quality and their actual perceptions of it. Meanwhile, users with different personal attributes differ in their expectations and perceptions of the quality in some aspects. Finally, we come up with some recommendations for action in response to the five key factors so that designers and managers of travel agency websites may find of value for reference.

Abstract: Based on a sample of listed companies in Shanghai and Shenzhen, China from 2007 to 2015, this paper discusses the relationship between customer concentration and corporate innovation. The results show that this relationship is nonlinear and U‐shaped. In addition, this paper finds that when the bargaining power of large customers is weak and the level of supply chain integration is high, the U‐shaped relationship becomes more significant. Additional analyses show that the U‐shaped relationship between customer concentration and corporate innovation is more significant for non‐state‐owned enterprises and those with lower levels of cash holding. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is a crucial parameter for hydrological modeling, land-atmospheric interaction investigations and agricultural irrigation management. This study investigated changes in ET0 and attributed those changes to climate variations in a coastal area (Zhejiang province) of China by a numerical experiment method. The results indicated that annual ET0 increased significantly (p < 0.05) at a rate of 1.58 mm·year-1 from 1973 to 2013, which was mainly caused by an obvious increase in ET0 in spring. Air temperature and water vapor pressure deficits increased significantly (p < 0.05) at rates of 0.04 °C·year-1 and 0.005 kPa·year-1, respectively, at an annual time scale during the study period, while wind speed and solar radiation decreased significantly (p < 0.05) at rates of -0.01 m/s·year-1 and -3.94 MJ·m-2·day-1·year-1, respectively. The contributions of changes in air temperature, wind speed, water vapor pressure deficits and solar radiation to increases in ET0 were 0.39, -0.56, 2.62 and -0.61 mm·year-1, respectively. The decrease in wind speed and solar radiation negatively affected the increase in ET0, which was offset by the positive effects of the air temperature and water vapor pressure deficits increase, where the water vapor pressure deficits was the dominant factor in increasing ET0 in the coastal area. Moreover, the impact of topography on ET0 was further discussed. ET0 changes at plain stations were approximately 5.4 times those at hill stations, which may be due to the impact of a large water body and the augment of surface roughness from intense human activities in the well-developed plain area. The results are helpful for investigating spatial and temporal changes in the evaporative demand for well-developed regions under energy-limited conditions. [ABSTRACT FROM AUTHOR]

Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms. [ABSTRACT FROM AUTHOR]

Based on a panel ARDL (AutoRegressive Distributed Lag) model, this paper investigates the environmental and economic consequences of energy tax levied in the four Nordic countries and the G7 countries from 1994 to 2016. Based on the double dividend theory of environmental tax, this paper investigates five variables: energy tax, energy consumption per unit of GDP, income tax, profit tax and capital gains tax, carbon dioxide intensity and fossil fuel burning ratio. A panel ARDL model is established to empirically test the relationship between energy tax and other variables. Experiments show that both the four Nordic countries and the G7 countries have found the existence of green dividends in the long run: the green dividends of the four Nordic countries are reflected in the reduction of carbon dioxide emissions, while those of the G7 countries are reflected in the reduction of fossil fuel use. In terms of blue dividends, the implementation of energy tax in the four Nordic countries can not only reduce distorted taxes in the short term, but also promote economic growth and adjust tax structure in the long term. For the G7 countries, blue dividends are not reflected in the long term. The model used in this paper is a panel ARDL model, which is more suitable for the study of multiple countries, multiple variables and long-term cycles. This model has been seldomly used in previous studies. The application of the panel ARDL model in this paper is not only more scientific and applicable, but also more innovative, which makes up for the shortcomings of previous studies. The research object of this paper selects the energy tax, which is an important part of the environmental tax system, and strives to provide a reference for the implementation of environmental taxation priorities and effects through empirical research. This paper may also serve as a reference for other countries to establish and improve environmental tax. As the first environmental tax law in China, the Environmental Protection Tax Law of the People's Republic of China was formally implemented on 1 January 2018. This paper chooses G7 countries and Nordic countries as the research objects. As these are important economies in the world, their environmental tax implementation is more perfected and has strong representativeness. This study can provide some experience for the continuous improvement of China's environmental tax law. [ABSTRACT FROM AUTHOR]

As one of the largest energy consumers and the greatest emitter of CO2 in the world, China now confronts the dual challenge of reducing energy use while continuing to foster economic growth. To overcome this issue, there is a need of comprehensive economic, financial, and energy policy reforms to promote sustainable development. The objective of this paper is to examine the effects of economic growth, financial development and energy consumption on carbon dioxide emission (CO2) in China from 1982 to 2017. The study applies Johansen cointegration test and vector error correction model (VECM) to investigate the long-term equilibrium and short-term causality relationship among the four variables. The causality is also checked by using the innovative accounting approach (IAA). The empirical results show the long-term cointegration relationship between them. Evidence shows that a unidirectional Granger causality running from energy consumption to financial development. Financial development and energy consumption have a statistically significant positive impact on CO2 emissions. In the long run, economic growth can curb CO2 emissions. Hence, financial innovation should be encouraged in the country to meet the demand of sustainable development. Nevertheless, optimizing energy structure and increasing the efficiency of energy utilization can never be left out from the process of development. We add light to policy makers with the construction of carbon trading to effectively address greenhouse effects in China. [ABSTRACT FROM AUTHOR]

Under the background that environmental tax has increasingly become the main means of environmental governance in various countries, it is particularly important to study the effect of environmental tax on reducing pollutants and then put forward suggestions for building a scientific and rational environmental tax system. The novelty of this paper is the investigation of the pollutant emission reduction effects of environmental taxes in Organization for Economic Cooperation and Development (OECD) countries and Chinese provinces at the same time, and further comparison of the pollutant emission reduction effects of environmental taxes in OECD and China under different environmental tax collection scales, industrial added value levels, and economic development conditions based on Auto-Regressive Distributed Lag Modelling Approach (ARDL). The data are derived from environmental taxes and pollutants of OECD countries from 1994 to 2016 and Chinese provinces from 2004 to 2016. The results show that from the overall regression results, environmental taxes really help to reduce pollutant emissions, both in OECD countries and China. From the grouping regression results, the OECD countries and Chinese inland provinces with small-scale or medium-level of environmental tax revenue and higher level of economic growth all show better emission reduction effects, while OECD countries with low industrial added value and Chinese inland provinces with high industrial added value have more significant effects on pollutant emission reduction via environmental taxes. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product. [ABSTRACT FROM AUTHOR]

OECD countries are the largest energy consuming economies in the world, improving energy efficiency and reducing pollution emissions is one of the important goals of the environmental tax policies of OECD countries. Based on the total factor energy efficiency index, this paper establishes an epsilon based measure-data envelopment analysis (EBM-DEA) model to measure the energy efficiency levels of 32 OECD countries during 1995–2016 when undesired outputs are included and not included. The effect of environmental factors on energy efficiency evaluation is compared by efficiency analysis and projection value analysis. On this basis, a Panel Logit model was established to empirically examine the impact of energy taxes on energy efficiency in 32 OECD countries. This paper finds that undesired output has a large impact on the energy efficiency level of OECD countries. Measuring energy efficiency levels without considering undesired outputs tends to lead to overestimation of the energy efficiency level of environmentally friendly countries and underestimate the energy efficiency level of countries that value environmental protection. The collection of energy tax has an important impact on energy consumption efficiency. Without considering the unexpected output, the energy tax has a significant impact on improving the efficiency of coal energy consumption. When considering the unexpected output, the energy tax has a significant impact on improving the efficiency of oil energy consumption. Regardless of the expected output or not, the energy tax has a positive effect on improving the efficiency of natural gas energy consumption. The experimental results also show that the energy structure and energy price have a negative impact on energy efficiency, while the progress of environmental protection technology and industrial structure have a positive impact on energy efficiency. Energy taxes have a "double dividend". This paper argues that when evaluating a country's energy efficiency, it should consider the undesired output factors of environmental constraints; governments should pay attention to the role of energy taxes in improving energy efficiency, improve the energy tax system, optimize industrial structure upgrades, stabilize energy prices and support the development of environmental technologies and improve energy efficiency. [ABSTRACT FROM AUTHOR]

This paper uses manually collected data of carbon information disclosure for listed companies, from 2009 to 2015 in China, to measure corporate carbon information disclosure, and it explores the impact of external pressure and internal governance on carbon information disclosure through text analysis and a hierarchy analysis process. The results show that, firstly, the greater the external pressure is, the higher the level of carbon information disclosure will be; that is, when listed companies are state-owned enterprises or in heavy pollution industries, the level of carbon information disclosure is higher. Secondly, the higher the level of corporate governance is, the higher the level of carbon information disclosure will be; that is, when the board of directors is larger, the proportion of independent directors is higher, and the chairman and general manager positions are differentiated, the level of carbon information disclosure is higher. Furthermore, when listed companies are state-owned and in heavy pollution industries, the level of carbon information disclosure is higher; when the chairman and general manager are in the same position (lower governance level), the positive impact of government pressure on carbon disclosure is less significant, the positive impact of external pressure on carbon disclosure is less significant, and the positive interactive impact of government pressure and external pressure on carbon disclosure is less significant. The conclusions of this paper are still robust after Heckman two-stage regression, propensity score matching (PSM) analysis, sub-sample regression, and double clustering analysis. [ABSTRACT FROM AUTHOR]