Forests, food, and fuel in the tropics: the uneven social and ecological consequences of the emerging political economy of biofuels
Bibliographic record
Abstract
The global political economy of biofuels emerging since 2007 appears set to intensify inequalities among the countries and rural peoples of the global South. Looking through a global political economy lens, this paper analyses the consequences of proliferating biofuel alliances among multinational corporations, governments, and domestic producers. Since many major biofuel feedstocks - such as sugar, oil palm, and soy - are already entrenched in industrial agricultural and forestry production systems, the authors extrapolate from patterns of production for these crops to bolster their argument that state capacities, the timing of market entry, existing institutions, and historical state-society land tenure relations will particularly affect the potential consequences of further biofuel development. Although the impacts of biofuels vary by region and feedstock, and although some agrarian communities in some countries of the global South are poised to benefit, the analysis suggests that already-vulnerable people and communities will bear a disproportionate share of the costs of biofuel development, particularly for biofuels from crops already embedded in industrial production systems. A core reason, this paper argues, is that the emerging biofuel alliances are reinforcing processes and structures that increase pressures on the ecological integrity of tropical forests and further wrest control of resources from subsistence farmers, indigenous peoples, and people with insecure land rights. Even the development of so-called 'sustainable' biofuels looks set to displace livelihoods and reinforce and extend previous waves of hardship for such marginalised peoples.
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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 | 0.001 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".