Potential Impacts of Energy Development on Shrublands in Western North America
Bibliographic record
Abstract
Impending rapid development of the abundant energy resources found in western North America may have dramatic consequences for its terrestrial ecosystems. We used lease and license data to provide an approximate estimate of direct and indirect potential impacts from renewable and non-renewable energy development on each of five major terrestrial ecosystems and completed more detailed analyses for shrubland ecosystems. We found that energy development could impact up to 21 percent (96 million ha) of the five major ecosystems in western North America. The highest overall predicted impacts as a percent of the ecosystem type are to boreal forest (23-32 percent), shrublands (6-24 percent), and grasslands (9-21 percent). In absolute terms, the largest potential impacts are to shrublands (9.9 to 41.1 million ha). Oil, gas, wind, solar, and geothermal development each have their greatest potential impacts on shrublands. The impacts to shrublands occur in all ecological regions across western North America, but potential impacts are greatest in the North American Deserts (up to 27 percent or 25.8 million ha), Great Plains (up to 24 percent or 8.9 million ha), and Northern Forests (up to 47 percent or 4.3 million ha). Conventional oil and gas development accounts for the largest proportion of the potential impact in all three of these regions. Some states or provinces may experience particularly large impacts to shrublands, including Alberta and Wyoming, where potential for oil and gas development is especially high, and New Mexico, where solar development could potentially affect large areas of shrubland. Understanding the scale of anticipated impacts to these ecosystems through this type of coarse-scale analysis may help to catalyze policy makers to engage in proactive planning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".