Geothermal energy resources: potential environmental impact and land reclamation
Bibliographic record
Abstract
With increasing costs, finite sources, and adverse environmental impacts of fossil fuels, global attention has focused on developing renewable and clean sources of energy. Although geothermal energy is considered one of the most promising sources of renewable and clean energy, it may not be as benign as widely believed. In this paper, we evaluate the environmental challenges for geothermal resource extraction and describe potential reclamation strategies for disturbed ecosystems. Generally, the environmental impacts of geothermal power generation and direct use are minor and in most cases controllable. Geothermal plants have low emissions of carbon dioxide, hydrogen sulfide, and ammonia, and low land and water usage; these impacts can be minimized through appropriate mitigation measures. Other potential emissions such as mercury, boron, and arsenic may result in local and regional environmental consequences, although their impacts are poorly understood on a global scale. Geothermal plants can alter vegetation and wildlife habitat by reducing species diversity and community composition. There are small risks of subsidence, induced seismicity, and landslides, with potential serious consequences. Integration of timely reclamation during and after plant operation can significantly contribute to reducing long term reclamation costs while enhancing ecosystem recovery. This paper is expected to contribute to understanding environmental impacts associated with geothermal energy production and to determining appropriate mitigation and land reclamation strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".