Global market integration increases likelihood that a future African Green Revolution could increase crop land use and CO <sub>2</sub> emissions
Bibliographic record
Abstract
There has been a resurgence of interest in the impacts of agricultural productivity on land use and the environment. At the center of this debate is the assertion that agricultural innovation is land sparing. However, numerous case studies and global empirical studies have found little evidence of higher yields being accompanied by reduced area. We find that these studies overlook two crucial factors: estimation of a true counterfactual scenario and a tendency to adopt a regional, rather than a global, perspective. This paper introduces a general framework for analyzing the impacts of regional and global innovation on long run crop output, prices, land rents, land use, and associated CO2 emissions. In so doing, it facilitates a reconciliation of the apparently conflicting views of the impacts of agricultural productivity growth on global land use and environmental quality. Our historical analysis demonstrates that the Green Revolution in Asia, Latin America, and the Middle East was unambiguously land and emissions sparing, compared with a counterfactual world without these innovations. In contrast, we find that the environmental impacts of a prospective African Green Revolution are potentially ambiguous. We trace these divergent outcomes to relative differences between the innovating region and the rest of the world in yields, emissions efficiencies, cropland supply response, and intensification potential. Globalization of agriculture raises the potential for adverse environmental consequences. However, if sustained for several decades, an African Green Revolution will eventually become land sparing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 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".