<b>Exploring the Challenges of Integrating Geothermal Energy </b><b>in Sport Spots: A Multi Method Research Design</b>
Bibliographic record
Abstract
Recently, the global need for renewable and sustainable energy reservoirs has gained significant momentum. Among the multiple renewable energy options, geothermal energy stands a particularly advantageous and promising option. This research provides useful information to support sustainable development via clean energy use in sports facilities. A mixed-methods is used, including a qualitative section (interview with experts and reviewing the previous papers), and two survey sections to validate the challenges. 23 experts participated in the qualitative section and initial variables were identified. There were 123 participants in survey one, and the data were analyzed by exploratory factor analysis (EFA) and there were 137 participants in survey two, and the data were used for confirmatory factor analysis (CFA). to ensure adherence to subject-to-item ratio guidelines (5:1 for EFA, 10:1 for CFA) and statistical power for robust factor analysis. The results identified six main challenges and 18 sub-challenges. It was found that financial aspects pose a key challenge, as the upfront costs of installing geothermal systems can be higher compared to traditional energy sources. Another significant challenge is the scarcity of awareness and comprehension of geothermal technology. Technical complexities, as drilling and heat exchange system design, require specialized knowledge and expertise. Some practical options are presented for establishing geothermal energy use in sport facilities, including (1) financial incentives (e.g., government subsidies, public-private partnerships), (2) awareness campaigns targeting facility managers and policymakers, (3) technical training programs for geothermal system maintenance, and (4) streamlined permitting processes to reduce bureaucratic barriers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".