There Is No Planet B: Aligning Stakeholder Interests to Preserve the Amazon Rainforest
Bibliographic record
Abstract
How do firms address complex collective action problems effectively? Institutional and stakeholder research suggests that firms may avoid the tragedy of the commons by aligning the interests of critical proximate stakeholders in ways that governments cannot accomplish. This phenomenological paper investigates this possibility by analyzing Amazon rainforest preservation by Natura, a Brazilian cosmetics company. The results indicate that Natura internalized environmental externalities by linking ecologically conscious consumers with rural Amazonian communities. A differences-in-differences analysis compares forest preservation and fire activity in the municipalities that Natura entered with those in which it did not enter. Natura’s impact is identified through an instrumental variable analysis using missing satellite images, which Natura relied upon to decide which municipalities to enter. Quantitative results tie Natura’s entry into municipalities with forest preservation. Analysis of three mechanisms associates Natura’s involvement with stakeholder decisions to cultivate diverse forest-generated crops instead of clearing the land for conventional agriculture. This study contributes to the management literature by suggesting how firms can address important global challenges, such as rainforest preservation, by investing in stakeholder capability development and by creating institutional arrangements in line with those envisioned elsewhere. This paper was accepted by George Serafeim, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the Clarkson Centre for Business Ethics [CAD 7,500.00] and Canada’s Social Sciences and Humanities Research Council. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4884 .
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".