A review on clean energy solutions for better sustainability
Bibliographic record
Abstract
This paper focuses on clean energy solutions in order to achieve better sustainability, and hence discusses opportunities and challenges from various dimensions, including social, economic, energetic and environmental aspects. It also evaluates the current and potential states and applications of possible clean-energy systems. In the first part of this study, renewable and nuclear energy sources are comparatively assessed and ranked based on their outputs. By ranking energy sources based on technical, economic, and environmental performance criteria, it is aimed to identify the improvement potential for each option considered. The results show that in power generation, nuclear has the highest (7.06/10) and solar photovoltaic (PV) has the lowest (2.30/10). When nonair pollution criteria, such as land use, water contamination, and waste issues are considered, the power generation ranking changes, and geothermal has the best (7.23/10) and biomass has the lowest performance (3.72/10). When heating and cooling modes are considered as useful outputs, geothermal and biomass have approximately the same technical, environmental, and cost performances (as 4.9/10), and solar has the lowest ranking (2/10). Among hydrogen production energy sources, nuclear gives the highest (6.5/10) and biomass provides the lowest (3.6/10) in ranking. In the second part of the present study, multigeneration systems are introduced, and their potential benefits are discussed along with the recent studies in the literature. It is shown that numerous advantages are offered by renewable energy-based integrated systems with multiple outputs, especially in reducing overall energy demand, system cost and emissions while significantly improving overall efficiencies and hence output generation rates. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".