Data and Code

data from 2009 to 2021, this study chose research samples from listed firms in the Shanghai and Shenzhen A-share markets. Not all sample companies were present for the full 13-year period, as the sample period encompassed 13 years of unbalanced panel data. The first sample data were handled in accordance with current research methods as follows: In order to prevent the atypical circumstances of these firms’ continuous losses from materially influencing the empirical results, the first step involved deleting samples that contained missing data; the second step involved excluding ST, *ST, and PT companies; and the third step involved excluding listed companies in the banking industry. A 1% bilateral winsorizing procedure was applied to all continuous variables in order to remove the influence of extreme values on the empirical findings. A total of 30192 samples from 2009 to 2021 were acquired using the aforementioned criteria. The Wind (Wind) and Guotai An (CSMAR) databases provided the listed companies’ financial information.The Python software used in this research is used to gather the carbon emission statistics of listed firms and associated computation data from the social responsibility, sustainable development, and environmental reports that are issued annually by listed corporations.The CSMAR and Wind databases were used to gather information on Chinese A-share listed firms and their top five suppliers between 2000 and 2022 for the extended research section. After manual sorting, the following processing is carried out: (1) Since the upstream enterprises (U) may correspond to multiple downstream customers (D1, D2, D3) in the current year, and the downstream enterprises (D) may also correspond to multiple upstream customers (U1, U2, U3) in the current year, this paper constructs a “bottom-up” supply chain data set of downstream enterprises-upstream enterprises-year, and a “top-down” supply chain data set of upstream enterprises-downstream enterprises-year. Through the processing of the above steps, 1861 observation values of the “bottom-up” supply chain data set are finally obtained, and 1162 observation values of the “top-down” supply chain data set are obtained.