Decarbonization Commitment, Political Connections, and Firm Value: Evidence from China
Bibliographic record
Abstract
On 22 September 2020, China announced an ambitious decarbonization commitment, leading to significant stock market reactions. Using a comprehensive dataset of China’s listed firms and a manually updated political connections index, we employ an event study approach with regression analysis to examine the effects of political connections and industry heterogeneity on firm value following the announcement. Our analysis reveals several key findings: First, there were overall negative market reactions to the announcement. Second, political connections negatively impact firm value by acting as a “grabbing hand” in China’s private sector, as private firms with strong political ties often prioritize political agendas over shareholders’ profit maximization objectives. Third, the adverse effects of political connections are industry-specific, with firms in the environmental protection and decarbonization sectors being more vulnerable to environmental policies. Lastly, we observe a limited moderating effect of the economic development of the firm’s host province. Our results are robust across different estimation techniques, model specifications, and major financial announcements such as quarterly financial statements, M&A, and dividend offering.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".