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.标题：可持续金融与企业绩效学位论文数据集 描述： 本数据集服务于研究主题《可持续融资对企业绩效的影响》。本研究以中国上市公司数据为基础，探究可持续金融实践与企业盈利能力之间的关联。本数据集包含用于衡量可持续金融实践的华证ESG评级（Huazheng ESG ratings），以及作为核心盈利能力指标的资产收益率（ROA, Return on Assets），并以净资产收益率（ROE, Return on Equity）作为稳健性检验指标。本数据集的结构便于计量经济学分析，支持研究人员复刻或拓展本研究的核心结论。 研究假设： H₀（原假设）：可持续金融实践对企业盈利能力无显著影响。 H₁（备择假设）：可持续金融实践对企业盈利能力具有显著正向影响。 H₂：企业杠杆水平在可持续金融与盈利能力的关联中发挥中介变量作用。 数据采集： 数据来源：国泰安数据库（CSMAR）、中国上市公司财务报告、华证ESG评级及宏观经济指标。 时间范围：[可指定具体年份，例如2014–2023] 包含变量： 可持续金融指标：华证ESG评级 盈利能力指标：资产收益率（ROA）、净资产收益率（ROE） 杠杆水平：产权比率、总负债/总资产 控制变量：企业规模、现金持有量、无形资产 研究发现与解读： 数据显示，ESG评级较高的企业往往具有更高的资产收益率，这验证了可持续金融实践对企业绩效具有正向影响的研究假设。分析结果表明，杠杆水平在该关联中发挥部分中介作用，即可持续金融可通过调整企业资本结构，间接影响其盈利能力。 数据使用方法： 复刻验证：本数据集可用于复刻本研究中使用的各类回归模型。 拓展研究：研究人员可通过纳入更多金融变量、行业分类维度或替代ESG指标，对本研究进行拓展分析。 机器学习应用：本数据集可用于训练基于ESG评分预测企业财务绩效的模型。 文件格式： 包含变量明确定义的DO格式/Excel格式文件。附带用于数据清洗与模型估计的Python、Stata及R脚本文件。