The economic consequences of an international water market in the Paso del Norte region
Bibliographic record
Abstract
This research estimates the economic benefit of a water market within the Rio Grande Project (Project). Currently, water is released from Elephant Butte Dam for the purpose of agricultural production. Cities including Ciudad Juárez obtain municipal water from groundwater sources. Ciudad Juárez and El Paso are significantly depleting the underlying Hueco Bolsom and will need to find more expensive external sources of water if the Project water remains unavailable. This research constructed a large optimization model of water users in the Rio Grande Project. For agriculture, multiple water irrigation activities including deficit and trickle irrigation were defined. For municipalities, desalination, groundwater recharge and import of distant groundwater activities were also defined. The model incorporates concepts of consumptive and priority water rights. The net income from water use in agriculture (net income remaining after payment to other factors) and willingness to pay based on urban demand formed the objective function. Environmental benefits of water allocated to riparian restoration are considered in an additional scenario. The model simulates allocation and net benefits of water use that would occur with exchange of consumptive use water rights assuming trade is free of transaction costs. An international water market would have substantial benefits in aggregate to water users in the Project. In particular, the City of El Paso and Ciudad Juárez would have present value benefits of $490 million dollars. The water market would not kill off agriculture. By 2043, there would still be very active production in the Mesilla Valley. Net income to farmers in both Elephant Butte Irrigation District and El Paso Water Conservation District #1 would increase between 200% and 250% respectively from current levels when water sales are factored into net income. Under full trade, restoration of the riparian Bosque cottonwoods along the river banks (4,000 acres) is economically viable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".