The impact of big data tax administration on corporate ESG—A quasi-natural experiment based on Golden Tax Project Ⅲ

Environmental,social and governance(ESG)practices are pivotal to global sustainability yet face challenges.Based on the implementation of Golden Tax Project Ⅲ,we find that big data tax administration decreases corporate ESG performance.Mechanism tests indicate that Golden Tax Project Ⅲ can reduce tax avoidance,cash flow and green innovation,thereby inhibiting ESG through the“taxation effect.”Conversely,the project can reduce agency costs and improve information transparency,thus promoting ESG performance through the“governance effect.”Overall,however,the project inhibits corporate ESG performance.According to further analysis,the negative effect on ESG performance mainly impacts the environmental responsibility(E)element.This paper provides insights relevant to advancing China’s“dual carbon”policy and formulating a“Chinese approach”to global sustainable development.

Does big data tax administration expand bank credit loans?

The application of big data technology to global tax management is becoming increasingly widespread.China has been implementing increasingly mature technologies for tax governance using big data systems in recent years.By collecting data through web scraping on the earliest implementation times of big data tax administration in various provinces of China,we explore the relationship between big data tax administration and corporate bank credit in emerging markets.Our results show that big data tax administration enhances firms’ability to obtain bank loans.Mechanism tests indicate that big data tax administration improves the quality of corporate information disclosure,facilitating access to bank credit loans.We find that big data tax administration improves the corporate financing environment,enhancing the efficiency of resource allocation in the credit market.

Survival and comorbidities in lung cancer patients:Evidence from administrative claims data in Germany

Lung cancer is the most common cancer type worldwide and has the highest and second highest mortality rate for men and women respectively in Germany.Yet,the role of comorbid illnesses in lung cancer patient prognosis is still debated.We analyzed administrative claims data from one of the largest statutory health insurance(SHI)funds in Germany,covering close to 9 million people(11%of the national population);observation period was from 2005 to 2019.Lung cancer patients and their concomitant diseases were identified by ICD-10-GM codes.Comorbidities were classified according to the Charlson Comorbidity Index(CCI).Incidence,comorbidity prevalence and survival are estimated considering sex,age at diagnosis,and place of residence.Kaplan Meier curves with 95%confidence intervals were built in relation to common comorbidities.We identified 70,698 lung cancer incident cases in the sample.Incidence and survival figures are comparable to official statistics in Germany.Most prevalent comorbidities are chronic obstructive pulmonary disease(COPD)(36.7%),followed by peripheral vascular disease(PVD)(18.7%),diabetes without chronic complications(17.4%),congestive heart failure(CHF)(16.5%)and renal disease(14.7%).Relative to overall survival,lung cancer patients with CHF,cerebrovascular disease(CEVD)and renal disease are associated with largest drops in survival probabilities(9%or higher),while those with PVD and diabetes without chronic complications with moderate drops(7%or lower).The study showed a negative association between survival and most common comorbidities among lung cancer patients,based on a large sample for Germany.Further research needs to explore the individual effect of comorbidities disentangled from that of other patient characteristics such as cancer stage and histology.

Development and Application of Statistical Business Register Guidelines in African Countries

Within the framework of its Statistical Capacity Building Program the African Development Bank （AfDB） is supporting development and improvement of statistical business registers （SBRs） in African countries. As a first step, the AfDB prepared a document entitled Guidelines .for Building Statistical Business Registers in Africa, which describes SBR design, construction, introduction, use and maintenance. To support dissemination, interpretation and effective use of the Guidelines, the AfDB is now sponsoring a programme of review and recommendations for enhancements to SBRs in selected African national statistical offices. The paper outlines the content of the Guidelines and experiences in their application. The views expressed are those of the authors and ,do nnt nec＇e^arilv reflect an official nosition of the AfDB.

Evaluation of CLDAS and GPM Precipitation Products over the Tibetan Plateau in Summer 2005–2021 Based on Hourly Rain Gauge Observations

Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).The Global Precipitation Measurement(GPM) data are widely recognized as the most reliable satellite precipitation product for the TP.The China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS) precipitation fusion dataset(CLDAS-Prcp),hereafter referred to as CLDAS,is a high-resolution,self-developed precipitation product in China with regional characteristics.Focusing on the TP,this study provides a long-term evaluation of CLDAS and GPM from various aspects,including characteristics on different timescales,diurnal variation,and elevation impacts,based on hourly rain gauge data in summer from 2005 to 2021.The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP.They both perform well for precipitation amount and frequency on multiple timescales.CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP.However,GPM tends to overestimate both precipitation amount and frequency.The difference between them mainly lies in the trace precipitation.CLDAS and GPM effectively capture rainfall events,but their performance decreases significantly as intensity increases.They both show better accuracy in diurnal variation of precipitation amount than frequency,and their performance tends to be superior during nighttime compared to the daytime.Nevertheless,there are some differences of the two against rain gauge observations in diurnal variation,especially in the phase of the diurnal variation.The performance of CLDAS and GPM varies at different elevations.They both have the best performance over 3000–3500 m.The elevation dependence of CLDAS is relatively minor,while GPM shows a stronger elevation dependence in terms of precipitation amount.GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations.CLDAS and GPM exhibit unique strengths and weaknesses;hence,the choice should be made according to the specific situation of application.

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.