A comprehensive review of geothermal energy evolution and development
Bibliographic record
Abstract
Global energy demand is increasing, driven by population rise, technological development, and a desire for a better lifestyle. However, because environmental issues such as fossil-fuel-sourced greenhouse gas (GHG) emissions are emerging as constraints on the nature of energy sources, using renewable and sustainable energy sources is the appropriate and applicable response. Geothermal energy is one form of renewable and sustainable energy, which has certain advantages such as consistency, a vast amount of untapped potential, availability, and a wide range of possible applications that make it an interesting and viable solution for helping meet the world’s energy needs while reducing GHG emissions (especially CO2). We provide a comprehensive review on the evolution of geothermal energy production from its obscure beginnings to the present time by reporting production data from individual countries and collective data of worldwide production. In addition, we provide an overview of relevant technologies at the industrial level, such as site identification, power production methods, and direct use. Finally, we discuss the geothermal power production prospects for 2050, the classification of production capacity on the technology side, and existing roadmaps for points of interest concerning technological development. We hope this review helps to identify existing gaps, future challenges, and areas needing further attention and investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".