Accounting for the Environmental “Bottom Line” along the U.S.-Mexico Border
Bibliographic record
Abstract
This article explores the prospects for establishing an environmental accounting system along the U.S.-Mexico border. After reviewing the rationale for environmental accounting and developing an accounting framework, three studies from the San Diego-Tijuana region are presented. In the first study, we estimate the total proportion of government expenditures made to defend the environment against human-induced changes in San Diego. This study reveals that defensive expenditures absorb 1.23 percent of total economic output and more than 21 percent of local government expenditures. The second study focuses on a smaller area along the border where the environmentally sensitive Tijuana Estuary on the U.S. side connects to the heavily populated Canon de los Laureles on the Mexican side. Expenditures made in Mexico aim to protect against threats to human health and safety, while those in the U.S. target the preservation of recreational resources and ecosystem health. Our third study estimates the value of agriculture land losses in San Diego between 1990 and 1995. Using an average price estimate for agricultural output and discount rates ranging from 0 to 5 percent, we find that the present value of the losses ranges from 0.18 percent to 1.8 percent of the total economy. Many of the areas lost to urban development are close to the existing urban area, suggesting the possibility of secondary impacts, such as increased air-pollution emissions resulting from longer transportation distance to market.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".