Carbon Capture Systems for Building-Level Heating Systems—A Socio-Economic and Environmental Evaluation
Bibliographic record
Abstract
The energy consumption of buildings contributes significantly to global greenhouse gas (GHG) emissions. Energy use for space and water heating in buildings causes a major portion of these emissions. Natural gas (NG) is one of the dominant fuels used for building heating, emitting GHG emissions directly to the atmosphere. Many studies have been conducted on improving energy efficiency and using cleaner energy sources in buildings. However, implementing carbon capture, utilization, and storage (CCUS) on NG building heating systems is overlooked in the literature. CCUS technologies have proved their potential to reduce GHG emissions in fossil fuel power plants. However, their applicability for building-level applications has not been adequately established. A critical literature review was conducted to understand the feasibility and viability of adapting CCUS technologies to co-function in building heating systems. This study investigated the technical requirements, environmental and socio-economic impacts, and the drivers and barriers towards implementing building-level CCUS technologies. The findings indicated that implementing building-level CCUS technologies has significant overall benefits despite the marginal increase in energy consumption, operational costs, and capital costs. The information presented in this paper is valuable to academics, building owners and managers, innovators, investors, and policy makers involved in the clean energy sector.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".