Green M&A Deals and Bidders’ Value Creation: The Role of Sustainability in Post-Acquisition Performance
Bibliographic record
Abstract
A transition to natural and renewable resources is deemed necessary to preserve the environment and satisfy future energy needs globally. In this context, green economy can be considered a viable alternative paradigm that preserves growth expectations while protecting the earth’s ecosystems.The objective of this study is to investigate whether “green” acquisitions represent a suitable way to support the green economy’s growth, given that public subsidies alone do not suffice. To this end, we analyse bidders’ post-acquisition performance (return on assets), based on data from the most recent deals, and try to decode whether bidders that “green” themselves find the potential to improve their financial performance and simultaneously enhance their corporate image.Results confirm that bidders opting for “green” deals can obtain better financial outcomes compared to firms that perform deals in other sectors. This implies that firms may favor such transactions both to foster their external growth and obtain better operating and financial results, while attributing a green identity to their corporate image and protecting the environment. These findings bestow and elevate confidence in the potential of relevant research, raising focus on unexplored Mergers and Acquisitions (M&A) aspects of growing interest among investors worldwide.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".