‘Manage and mitigate punitive regulatory measures, enhance the corporate image, influence public policy’: industry efforts to shape understanding of tobacco-attributable deforestation
Bibliographic record
Abstract
BACKGROUND: Deforestation due to tobacco farming began to raise concerns in the mid 1970s. Over the next 40 years, tobacco growing increased significantly and shifted markedly to low- and middle-income countries. The percentage of deforestation caused by tobacco farming reached 4 % globally by the early 2000s, although substantially higher in countries such as China (18 %), Zimbabwe (20 %), Malawi (26 %) and Bangladesh (>30 %). Transnational tobacco companies (TTCs) have argued that tobacco-attributable deforestation is not a serious problem, and that the industry has addressed the issue through corporate social responsibility (CSR) initiatives. METHODS: After reviewing the existing scholarly literature on tobacco and deforestation, we analysed industry sources of public information to understand how the industry framed deforestation, its key causes, and policy responses. To analyse industry strategies between the 1970s and early 2000s to shape understanding of deforestation caused by tobacco farming and curing, the Truth Tobacco Documents Library was systematically searched. The above sources were compiled and triangulated, thematically and chronologically, to derive a narrative of how the industry has framed the problem of, and solutions to, tobacco-attributable deforestation. RESULTS: The industry sought to undermine responses to tobacco-attributable deforestation by emphasising the economic benefits of production in LMICs, blaming alternative causes, and claiming successful forestation efforts. To support these tactics, the industry lobbied at the national and international levels, commissioned research, and colluded through front groups. There was a lack of effective action to address tobacco-attributable deforestation, and indeed an escalation of the problem, during this period. CONCLUSIONS: The findings suggest the need for independent data on the varied environmental impacts of the tobacco industry, awareness of how the industry seeks to work with environmental researchers and groups to further its interests, and increased scrutiny of tobacco industry efforts to influence environmental policy.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| 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".