Economic and CO2 Emissions Comparison of District Energy System Using Geothermal and Solar Energy Resources
Bibliographic record
Abstract
District energy (DE) systems provide an important means of mitigating greenhouse gas (GHG) emissions and the significant related concerns associated with global climate change. DE systems can use fossil fuel, renewable energy and waste heat as energy sources, and facilitate intelligent integration of energy systems. In this paper, solar thermal and geothermal energy are compared as energy sources for a district energy system which serves a community including commercial and educational buildings. The DE system is assessed for the considered energy resources in two main ways, by considering CO2 emissions and economic aspects. The results obtained for the solar and geothermal energy sources are compared to detect trends. The results indicate that solar thermal energy is the most advantageous energy technology for a DE system from an environmental perspective, while geothermal energy is more beneficial from a financial point of view. An examination of the cost distribution for the technologies shows that when solar thermal energy is the main energy supply for a DE system, the system exhibits the highest loan payments and the lowest fuel costs (FCs) and insurance and maintenance (I&M) payments. With geothermal systems, loan payments are lower while the total cost over the life of the technology is higher for the DE system. Using solar thermal and geothermal technologies as the energy supply for a DE system also yields environmental benefits which can lead to financial advantages through such instruments as tax breaks. The research reported here is intended to allow energy technology suppliers to work with communities while accounting appropriately for economic issues and CO2 emissions associated with these energy technologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".