The Impact of Environment, Social, and Governance (ESG) Performance on the Change of Z-score before and after the COVID-19 – the Case of Chinese A-Share Manufacturing Industry Companies
Bibliographic record
Abstract
Since the outbreak of the COVID-19, many companies around the world have fallen into financial difficulties to varying degrees due to various factors. But there are also a lot of companies that have taken on more corporate social responsibilities than usual during the epidemic. The enterprises have to pay a huge capital to undertake social responsibility, according to the connotation of sustainable operation of corporate social responsibility, the ultimate goal of implementing corporate social responsibility should be to achieve a win-win situation between enterprises and stakeholders. Therefore, this study uses the A-share manufacturing industry of Chinese listed companies from 2019, 2020, and the third quarter of 2021 as the research samples, and empirically probes the impact of ESG performance on changes in the Z-score of companies before and after the COVID-19. The results of the study found that during the epidemic, the better the ESG performance, and the more unfavorable the overall financial situation of the company, this is due to the huge expenditure for unusual business. However, if the company that ESG performance kept the same or even improved compared with last year would significantly improve the overall financial situation of the company. The improvement was even more pronounced for companies at the high level of financial physique, that is, the continued effort on corporate social responsibility worked. In addition, after the recovery of the epidemic, the performance of ESG has no significant impact on the overall financial situation of the company, but companies with better financial situation recovery can significantly improve the Z-score of the company if the ESG performance can be on par with the previous period or even improve. According to the empirical research results, this study also puts forward corresponding suggestions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".