Does managerial myopia affect firms’ green merger and acquisition? Evidence from Chinese firms in high-polluting industries
Bibliographic record
Abstract
Utilizing a dataset comprising 1,536 merger and acquisition transactions involving Chinese A-share listed firms operating in high-polluting industries during the period of 2001 to 2020, this paper aims to investigate managerial myopic impact on green merger and acquisition. The primary findings indicate that managerial myopia significantly impedes the occurrence of green merger and acquisition activities within firms operating in high-polluting industries. This adverse effect is consistently observed across various models. Mechanism tests reveal that myopic managers exert an adverse influence on firms’ green merger and acquisition by reducing the level of analyst attention and the environmental, social, and governance ratings among these companies. Furthermore, the results of the moderating tests demonstrate that stronger internal supervision, a higher firm value, and a male chief executive officer can substantially alleviate managerial myopic negative impact on green merger and acquisition. Additionally, heterogeneity checks propose that managerial myopic detrimental impact on firms’ green merger and acquisition is particularly pronounced in companies characterized by weak governance structures, lower tax burdens, and those operating in the decline stage. The insights from this research carry significant policy implications for industries with high levels of pollution, particularly in emerging economies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".