Life Cycle Assessment of Central Solar Heating Plants with Seasonal Storage
Bibliographic record
Abstract
Renewable energies can play a very important role in the development of a new energy model contributing effectively towards a more sustainable development in the mid and long term. In this context Central Solar Heating Plants with Seasonal Storage (CSHPSS) are able to provide space heating and Domestic Hot Water (DHW) to residential buildings with high solar fractions (>50%). These systems are already being used in Central and Northern Europe, as well as in Canada, where there is an important experience in district heating systems. Life Cycle Assessment (LCA) is an objective methodology that evaluates the environmental loads associated with a product, process, or activity, identifying and quantifying the use of mass and energy as well as environmental emissions over its life cycle. It provides a comprehensive view of the environmental aspects of a product or process and a more accurate picture of the true environmental trade-offs in product and process selection. In this paper is presented a LCA of a CSHPSS, which should cover the space heating and DHW demand of 500 dwellings of 100 m 2 , located in Zaragoza, Spain. Environmental burdens through the life cycle of the system are estimated based on relevant emissions to the atmosphere, e.g. greenhouse gases, NO x , SO x , and comprehensive environmental indicators as, for instance, the IMPACT 2002+ and CED (Cumulative Energy Demand). These indicators allow to evaluate the reduction of the environmental load achieved by the CSHPSS analyzed with respect to conventional space heating and DHW systems, as well as to identify the most critical aspects since an environmental perspective.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".