Payments versus Direct Controls for Environmental Externalities in Agriculture
Bibliographic record
Abstract
Abstract The production of food, fiber, and fuel often results in negative externalities due to impacts on soil, water, air, or habitat. There are two broad ways to incentivize farmers to alter their land use or management practices on that land to benefit the environment: (1) provide payments to farmers who adopt environmentally beneficial actions and (2) introduce direct controls or regulations that require farmers to undertake certain actions, backed up with penalties for noncompliance. Both the provision of payments for environmentally beneficial management practices (BMPs) and a regulatory requirement for use of a BMP alter the incentives faced by farmers, but they do so in different ways, with different implications and consequences for farmers, for the policy, for politics, and consequently for the environment. These two incentive-based mechanisms are recommended where the private incentives conflict with the public interest, and only where the private incentives are not so strong as to outweigh the public benefits. The biggest differences between them probably relate to equity/distributional outcomes and politics rather than efficiency. Governments often seem to prefer to employ beneficiary-pays mechanisms in cases where they seek to alter farmers’ existing practices, and polluter-pays mechanisms when they seek to prevent farmers from changing from their current practices to something worse for the environment. The digital revolution has the potential to help farmers produce more food on less land and with fewer inputs. In addition to reducing input levels and identifying unprofitable management zones to set aside, the technology could also alter the transaction costs of the policy options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 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".