Dataset for The influence of sustainable financing on enterprise performance

Title: Dataset for the Thesis on Sustainable Finance and Corporate Performance
Description:
This dataset is used for the research "The influence of sustainable financing on enterprise performance". The study investigates the relationship between sustainable financial practices and corporate profitability, using data from Chinese listed companies. The dataset includes Huazheng ESG ratings as a measure of sustainable financial practices and ROA (Return on Assets) as the primary profitability metric, with ROE (Return on Equity) as a robustness check.

The dataset is structured to facilitate econometric analysis, allowing researchers to replicate or extend the findings.

Research Hypotheses:
H₀ (Null Hypothesis): Sustainable financial practices have no significant impact on corporate profitability.
H₁ (Alternative Hypothesis): Sustainable financial practices have a positive and significant impact on corporate profitability.
H₂: Corporate leverage level acts as a mediating variable in the relationship between sustainable finance and profitability.
Data Collection:
Source: CSMAR, Financial reports of Chinese listed companies, Huazheng ESG ratings, and macroeconomic indicators.
Time Period: [Specify years, e.g., 2014–2023]
Variables Included:
Sustainable Finance Indicators: Huazheng ESG rating
Profitability Metrics: ROA, ROE
Leverage Level: Debt-to-equity ratio, total debt/total assets
Control Variables: Firm size, cash holdings, intangible assets
Findings and Interpretation:
The data suggests that firms with higher ESG ratings tend to have higher ROA, supporting the hypothesis that sustainable financial practices positively affect corporate performance.
The analysis indicates that leverage level partially mediates this relationship, meaning that sustainable finance indirectly influences profitability through capital structure adjustments.
How to Use This Data:
Replication: The dataset allows replication of regression models used in the study.
Further Analysis: Researchers can extend the analysis by incorporating additional financial variables, industry breakdowns, or alternative ESG metrics.
Machine Learning Applications: The dataset can be used to train models predicting financial performance based on ESG scores.
File Format:
DO/Excel format with clearly labeled variables.
Python/Stata/R script for data cleaning and model estimation is included.