We utilize Chinese A-share firms listed on the Shanghai and Shenzhen stock markets as our initial sample. Because the Credit Demonstration City Construction (CDCC) program was implemented in 2015 and 2016, we use a sample period of 2012-2019, which is three years before and after the implementations. We exclude firms in the financial industry, as their accounting standards differ from common industries. To avoid any effect of relocation, we exclude firms whose registered address changed during the study period. Observations with missing values of key variables are also excluded. To ensure that observed changes are attributed to the CDCC program rather than the sample composition, we require each firm included in the sample to have at least one observation both pre- and post-CDCC program implementation. These selection criteria yield a final sample of 14,219 observations, consisting of 2,169 unique firms. All data needed are obtained from the China Stock Market and Accounting Research (CSMAR) Database.

Our sample consists of annual data from firms listed on the A-share markets of the Shanghai and Shenzhen Stock Exchanges in China, covering the period from 2003 to 2022. We gather the necessary data on listed firm from two databases: Chinese Innovation Research Database (CIRD) for firms’ innovation, China Stock Market & Accounting Research Database (CSMAR) for common ownership. CIRD not only includes patent data filed or granted to different entities, distinguishing between three types of patents—invention, utility model, and design—but also provides key information such as the nature of applications (independent or joint), classification numbers, and patent statistics. CSMAR database is positioned as a research-oriented precision database, referring to the standards of authoritative databases such as CRSP and COMPUSTAT, with the aim of researching and quantifying investment analysis. We match the innovation data to the financial data for each firm, and we exclude financial listed companies, exclude ST and * ST listed companies and delete samples with missing data. To avoid extreme value interference, we winsorize all continuous variables at the 1% level. With these filters, our final sample of 48,956 firm-year observations for 4957 firms.

Our sample data are obtained from the China Stock Market Accounting Research Database (CSMAR) for 1999-2016 for all the listed firms. The CSMAR database has been widely used in prior research on China’s listed firms (Chen et al. 2011[4]; Xu et al. 2014[40]). The initial sample had 34,721 firm-year observations. The process of sample selection is shown in Table 1. As in prior studies, we exclude 819 firm-year observations of financial firms because of their unique accounting standards and capital structure. To ensure that the initial debt policy variable can be measured, we also delete the 2,538 firm-year observations for which listing date is missing. To obtain the value of the debt policy in the year of the IPO, we further exclude the 13,674 firm-years observations of 813 firms listed before 1999 that lack of relevant data. In addition, 121 firm-year observations with leverage ratio greater than 1 are also excluded. Moreover, to minimise the effects of outliers, we also winsorise each continuous variable at the 1% and 99% levels. Finally, our sample consists of 2240 unique firms and 17 569 firm-year observations over a period of 18 years from 1999 to 2016.

The data on FTZ establishment is obtained from the Chinese Government Official Website (www.gov.cn). Patent data is sourced from the China National Intellectual Property Administration, while other financial data comes from the China Stock Market and Accounting Research (CSMAR) and China National Economic Research Data Service (CNRDS) databases.

China's economic growth and expanding business market have created opportunities for market research firms. The Market Research industry has developed rapidly over the past five years. Several specialized local research enterprises have entered the market, fueled partly by increased foreign capital in the industry. Industry revenue is expected to grow at a CAGR of 5.5% over the five years through 2023 to total $8.3 billion. This trend includes an anticipated increase of 7.1% in the current year. Although industry profit is high at 15.4% of industry revenue, it has fallen from 17.0% in 2013 due to rising labor costs and increasing competition.China's economy is anticipated to grow and become more globalized over the next five years, driving demand for industry services. The ongoing structural reform of domestic companies will further increase demand for market research services. Industry revenue will grow at a CAGR of 6.5% over the five years through 2028 to total $11.4 billion. The degree of specialization in the industry will likely increase, with customers from the automobile, pharmaceutical, information technology, telecommunication, consumer electronic product, financial, and government sectors accounting for the most significant market shares.Although industry operators will remain highly concentrated in Beijing, Shanghai and Guangzhou over the next several years, some midsized cities such as Chengdu, Xi'an, and Shenyang are projected to become regional centers and gain some market share. The industry will continue contending with issues such as collecting accurate data, gaining access to sales channels and finding appropriate domestic and international business partners over the next five years.

This article presents a comprehensive dataset from annual reports, the China Stock Market and Accounting Research Database, and the Wind database, focusing on digital transformation and strategic risk taking.

Variable construction.

This paper selects China's concept Sci-Tech innovation dual-class share listed companies spanning from 2013 to 2022, amounting to 94 in total, including OTC and delisted companies. The subsequent processing steps are undertaken based on the study's requirements. Initially, financial companies characterized by unique financial attributes are excluded. Subsequently, samples with complete data are included. Lastly, ST companies experiencing financial distress are removed. Ultimately, 81 valid corporate samples are acquired, comprising 336 data points. The data originates from the China Stock Market & Accounting Research Database (CSMAR) and is gathered manually.

This study uses panel data on Chinese A-share listed companies in Shanghai and Shenzhen covering 2014 to 2020 selected through the following screening: first, we exclude listed companies in the finance and insurance sectors; second, we exclude listed companies in ST and *ST (Special Treatment); finally, we exclude samples that lack important data. This approach generates 8,658 valid research sample observations. The data are obtained from several official websites, such as those for CSMAR (China Stock Market & Accounting Research Database), CNRDS (Chinese Research Data Services), and the Shanghai and Shenzhen stock exchanges.In this study, the descriptive and relevance of the final data was tested using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed. The final data were tested for descriptiveness and correlation using Stata software, and baseline regression, threshold regression, and robustness and heterogeneity tests were performed.

We get the data used in our research from China Stock Market & Accounting Research (CSMAR) database and Wind Database. In CSMAR database, we can get the information related to share pledge, controlling shareholders, SOE status and financial condition for independent variables and control variables. The market and individual stock closing prices to calculate the tail risk are obtained from the wind database.With a sample consisted of 21,921 firm-year observations from 2007 to 2018, we find that both the existence and the percentage of shares pledged by controlling shareholder are significantly negatively related to firms' tail risk in the share pledged years, and the results are robust to a variety of tests on PSM-DID, instrument variable, subsample selections and replace of independent variables. Furthermore, we find the controlling shareholders with pledged shares have the intention to whitewash the accounting information, which lead to the fall of the systematic tail risk.

A-share listed companies of the Shanghai and Shenzhen Stock Exchanges from 2008 to 2023 were selected to comprise the research sample. Considering the particularity of the industry characteristics, the financial industry, special treatment companies, and missing data of variables were eliminated. Moreover, the final research sample contained 14,048 firm-year observations. All continuous variables were winsorized at the 1 and 99% levels. Financial and tax risk data were obtained from the China Stock Market and Accounting Research and Wind databases. The data of enterprise digital transformation were obtained using Python technology through text analysis.

its population is characterized as Brazilian, Chinese, and Indian companies that presented financial information to external users through securities markets’ regulatory agencies in Brazil, China, and India and that implemented CDM projects during the 2005–2012 period, ranking in the “registered” status on the UNFCCC website.

Quantitative data were obtaining to test the statistical hypothesis proposed in the study from information referring to the companies and CDM projects that made up the sample as follows: (i) the financial information referring to the equity (E) of companies that have their shares listed in the capital markets of Brazil, China, and India; and (ii) the emission reduction estimates of CDM projects, available from the UNFCCC website.

The data collection, referring to the financial information of the companies that have made themselves available via regulatory bodies in the securities markets of the countries under study, was carried out through Thomson Reuters Eikon’s Electronic and Financial Database on July 30, 2013. Thus, when the data collection was carried out, financial information was obtained and converted into euros, referring to the equity (E) of 380 Brazilian companies, 2,584 Chinese companies, and 4,219 Indian companies, for the period under review.

The collection of data concerning CDM projects with the status “registered” on the UNFCCC site, on the other hand, was carried out using the Bloomberg Economic and Financial Database on July 29, 2013, at which time a total of 289 projects registered by the Brazilian DNA, 3,651 projects registered by the Chinese DNA, and 1,296 projects registered by the Indian DNA were available for analysis for the 2005–2012 period. On November 18, 2004, just one project was registered by the Brazilian DNA, entitled “Brazil NovaGerar Landfill Gas to Energy Project” (UNFCCC, 2014). This project was eliminated from the research because of its set limits defined between 2005 and 2012, the first stage of the Kyoto Protocol.

However, it was necessary to carry out new searches directly on the UNFCCC site for supplementary information that was crucial to implementing the research, given the fact that it did not include full descriptions concerning the names of the receiving agencies in each country (host party), in the Bloomberg Economic and Financial database, on the date mentioned above, information that was characterized as the only link between the CDM project database (Bloomberg) and the financial information database (Thomson Reuters Eikon). These searches were carried during the October 2013–May 2014 period.

Subsequently, on September 1, 2014, new searches were carried out on the UNFCCC website to update the information referring to CDM projects registered by the agency during the 2005–2012 period.

Thus, this research was carried out based on CDM projects located in the “registered” status section of the UNFCCC site over the 2005–2012 period, the records of which were finalized by the body prior to September 1, 2014, containing 299 projects registered by the DNA of Brazil, 3,682 projects registered by the DNA of China, and 1,371 projects registered by the DNA of India, adding up to 5,353 projects, that is, 74.69% of the total implemented projects in all the developing countries that ratified the Kyoto Protocol.

To allow the measurement to be applied to the fair value of estimates of project emission reduction approved by the companies that make up the research sample, we obtained the interest rate EURIBOR – Euro Interbank Offered Rate (average annual rates) from the Bloomberg Financial and Economic Database on July 29, 2013 to adjust the future flows of economic benefits of CER estimates to the present value.

Average financial ratio comparison between industry and CSR disclosures firms.

We use the data of China's A-share listed companies from 2009 to 2022, ESG disclosure data from Bloomberg, ESG performance data from Sino-Securities Index Information Service Ltd.,business environment data from the China Sub-Provincial Business Environment Index Report, media attention data from the Chinese Research Data Service (CNRDS) Platform, and the rest of the data from the China Stock Market and Accounting Research (CSMAR) Database.

Our initial sample consists of all Chinese manufacturing firms listed on the Shanghai and Shenzhen stock exchanges between 2012 and 2021.We collected firm-level financial data from the China Stock Market and Accounting Research (CSMAR) database, a key source for information on Chinese public-listed firms. Data on green patents and patent pledges were sourced from the China National Intellectual Property Administration (CNIPA), the official government authority on intellectual property in China. Additionally, media data were obtained from the Wise News database, which covers a wide range of media sources in mainland China. After removing observations with missing data, our final sample contained 3,011 unique firms and 20,582 firm-year observations.

The initial sample of this study covers the A-share companies listed on the Shanghai and Shenzhen stock exchanges during the period 2008-2021. We then screened and processed the initial sample data, including (a) Screening for companies with both RepRisk's ESG rating and Bloomberg's ESG rating. Specifically, the selection is based on samples with the same ISIN code and companies' English names in the Bloomberg and RepRisk lndex (RRI) databases. The ISIN code is a securities coding standard developed by the International Organization for Standardization (ISO) and is a unique code used to identify securities in each country or region around the world. We exclude samples that do not provide ISIN codes or have inconsistent English names. (b) We exclude observations with missing values for the main variables. (c) We exclude the ST, *ST and PT trading status samples during the observation period. Our final sample contains 1456 firm-year observations.The ESG disclosure score data and ESG performance score data required for the ESG-washing construction are respectively obtained from the Bloomberg database and the RepRisk Index (RRI) database of the Wharton Research Centre for Data Studies (WRDS). Positive media coverage data is sourced from the China Research Data Services Platform (CNRDS), while the instrumental variable (IV_population) is obtained from the EPS database and Juhe Data (https://www.gotohui.com/). Unless otherwise stated, all other data in this study are from the China Stock Market and Accounting Research (CSMAR) database.Data on executive company changes were collected manually by the authors back-to-back and independently. Then we compared and reconciled the data collected by each, and where there were discrepancies, we again collected and calibrated the data to maximize their reliability. We first obtained executive biographies from the CSMAR database, and the missing values were retrieved from Sina Finance ( https://finance.sina.com.cn/). Due to the unstructured nature of the resume data, we manually processed more than 30,000 resumes of executives to get the data of executives' company changes, based on which we calculated the per capita number of job hops of all executives in each company. The number of part-time jobs held by executives also reflects their pursuit of career changes and development, so in the robustness test the per capita mean of the number of part-time jobs held by executives is used as a proxy variable for careerist orientation. These data can be obtained directly from the CSMAR database.

Based on the above, this study selects A-share-listed manufacturing firms from 2014 to 2023 as the research sample. Data on big data applications is extracted from firm annual reports published on the official websites of the Shenzhen Stock Exchange and the Shanghai Stock Exchange. To ensure the validity and robustness of the constructed indicators, the measurement of disruptive innovation draws on patent data from the China National Intellectual Property Administration (CNIPA), covering the period from 2000 to 2023. The specific measurement methodology is detailed in Section 3.2. Additional firm-level data are primarily obtained from the China Stock Market & Accounting Research (CSMAR) database and Wind Information Co., Ltd. (Wind). The data were processed as follows: (1) Firms designated as Special Treatment (ST, Firms that have exhibited financial distress for two consecutive years), particularly Special Treatment (*ST, Firms that have reported consecutive losses for three years or face the risk of trading suspension), or Particular Transfer (PT) were excluded; (2) Financial institutions were removed; (3) Firms with substantial missing values for key variables were excluded. (4) To mitigate the influence of extreme values on the empirical results, selected variables—such as market-oriented disruptive innovation, technology-oriented disruptive innovation, managerial myopia, and government intervention—were winsorized at the 1st and 99th percentiles. After applying the above criteria, a total of 21,203 valid firm-year observations were retained for analysis.

The rolling stock market was estimated to be worth just under **** billion U.S. dollars in 2023, with Asia-Oceania accounting for the largest share. Rolling stock market in Asia-Pacific Among the largest and fastest-growing countries within the Asia-Pacific region for rolling stock are India and China, with Indian Railways and CRRC ranked as key market players for this region. Reasons contributing to expansion in countries such as India include a growing population and a considerable number of migrant workers who rely on rail as a means of commuting. Back in 2020, there were approximately *** million migrant workers in China. Driving innovation in the sector One of the world’s leading rolling stock manufacturers, CRRC, reported investments of over ** billion Chinese yuan in its research and development activity in the 2022 financial year. As the company continues to develop and manufacture innovative technologies, such as electric and hybrid rail systems, CRRC has started to address concerns about sustainability and emissions while ensuring that it maintains a technological advantage over its competitors in the global market.

Due to the lack of a complete statistical data system based on the logistics industry in China, the growth of transportation, warehousing, and postal services has been the backbone of the logistics industry, accounting for a high proportion of its value growth. Given the availability of data, statistical data from the transportation, warehousing, and postal industries are used in this document instead of logistics companies for analysis. This study selected the annual data of Chinese A-share listed companies from 2017 to 2022 as the sample. All data comes from the CSMAR (China Stock Market and Accounting Research Database) database. The data screening is as follows: excluding companies with missing data; Excluding ST (special treatment R, companies that have suffered losses for two consecutive years) and * ST (companies that have suffered losses for three consecutive years); Excluding financial listed companies.

This paper selected the listed companies in Shanghai and Shenzhen A-shares in China from 2010 to 2022 as the initial sample, which was processed as following(1) Excluding companies from the financial and insurance industries; (2) Excluding ST companies from the sample; (3) Excluding companies with missing values. As a result, this study obtained 20404 annual observations from 3716 companies. The sample data were obtained from CSMAR database (China Stock Market & Accounting Research Database) and the Wind database. To eliminate outliers, all continuous variables are trimmed at 1% and 99%. This article will use Stata16 software to conduct relevant data analysis.