Enhancing Renewable Energy Systems, Contributing to Sustainable Development Goals of United Nation and Building Resilience Against Climate Change Impacts
Bibliographic record
Abstract
Climate change impacts due to unprecedented rising concentrations of greenhouse gas (GHG) are intensifying and widespread, making extreme climate events more widespread, frequent, and severe. To mitigate the worst consequences of climate warming, herein it is investigated how the global community can collectively achieve a large‐scale, sustained reduction in GHG emissions, and how to effectively move away from a predominantly fossil fuel‐based economy to one dominated by renewable energy? This transition is necessary to achieve the sustainable development goals (SDGs) of United Nations (UN) to ensure resilient and healthy environment for present and future generations, especially the SDG 7 of UN, “Affordable and Clean Energy”, set up to achieve global development of modern renewable energy systems. Investment policies and patterns of developed and developing countries in transitioning to energy productions primarily from renewable sources and obstacles such as scale‐up challenges, innovations in new energy systems, policies, financing mechanisms, and implementation strategies are examined. Furthermore, a comprehensive overview of the present global status of hydropower, wind, and solar, the three most significant renewable electricity technologies, as well as their basic operating principles, costs, and potential is conducted. Hydroelectric, wind, and solar power had grown from 3429, 346, and 34 TWh yr−1 in 2010 to 4274, 1598, and 846 TWh yr−1 in 2020, a growth of about 1.25, 4.60, and 24.9 times in a decade, respectively. Strategies to achieve energy systems that are of or near net zero GHG emissions by 2050s through the deployment of renewable energy systems are also investigated.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".