Macroeconomic and Distributional Impacts of Jatropha-Based Biodiesel in Mali
Bibliographic record
Abstract
Mali, a landlocked West African nation at the southern edge of the Sahara Desert, has introduced a program to produce biodiesel using jatropha curcas, a non-edible shrub widely available throughout the country by farmers for generations as a living fence for their gardens. The aim of the program is to partially substitute diesel, which is entirely supplied through imports, with domestic biodiesel produced from a feedstock that does not have any commercial value otherwise and thus has zero opportunity cost. This paper uses a computable general equilibrium model to investigate economy-wide and distributional impacts of large-scale jatropha production on different types of lands, and conversion of jatropha oil to biodiesel for domestic consumption. It assesses impacts on agricultural and other commodity markets, resource and factor markets, and international trade. The results are fed into a detailed household survey-based micro-simulation model to assess impacts on poverty and income distribution. The study finds that the expansion of jatropha farming would be beneficial in terms of both macroeconomic and distributional impacts as long as idle lands, which have been neither used for agriculture nor protected as forests, are utilized. However, if jatropha plantation is carried out on existing agriculture lands, the economy-wide impacts would be negative although it would still help reduce rural poverty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".