From green revolution to green technology: the unintended consequences of Brazil’s ethanol program
Bibliographic record
Abstract
Large-scale rollouts of new technologies, even so-called green technologies, pose the risk of adverse unintended consequences. Brazil’s national program to produce ethanol-powered cars beginning in the mid-1970s is often considered as a successful case of an energy transition from fossil fuels to renewable resources. The development of ethanol biofuel in Brazil was the result of a complex sequence of policy innovations in three different sectors: large-scale agriculture; the sugar and alcohol industry; and the automobile industry. Brazil’s green revolution paved the way for its transition to green technology in the form of ethanol biofuel. What were the unintended consequences of Brazil’s ethanol biofuel program? What lessons does this case teach us for addressing the potentially adverse outcomes of technological rollouts at scale of climate change mitigation technologies? Based on the process tracing method, we analyze policy shifts in the development of ethanol biofuel in Brazil and find, that instead of reducing carbon emissions, the rapid growth in sales of flex fuel personal vehicles led to a surge in gasoline consumption in the country that contradicts policy expectations. Greater analytical attention to unintended consequences is warranted to minimize or avoid the potentially negative outcomes of seemingly positive actions to address climate change.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".