Bibliographic record
Abstract
Dual-carbon targets typically refer to the goal of reducing greenhouse gas emissions to almost zero in order to address the challenges of climate change and global warming. Achieving the dual-carbon goal means taking significant steps to reduce carbon emissions, promoting the adoption of clean energy, improving energy efficiency, and adopting sustainable development practices to mitigate the effects of climate change. This study aims to assess the critical role of clean energy in achieving the dual carbon goals. This study is organized around the concept of “dual carbon goals”. These goals involve an integrated approach to combating climate change by significantly reducing greenhouse gas emissions to near-zero levels. The main objective is to address the challenges posed by climate change and global warming. This thesis will explore various strategies and approaches to achieve emission reductions. Clean energy plays a pivotal role in achieving the dual-carbon goal. The study focuses on the importance of clean energy sources such as renewable energy (e.g., wind, solar) in reducing carbon emissions. Sustainable development practices are mentioned as part of the approach to mitigating the effects of climate change. This suggests that the study could also explore the wider implications of dual carbon targets for sustainable development. This study used literature and descriptive research methods, relying on existing data, literature, and descriptive analysis to assess the current role of clean energy and how dual carbon targets could be achieved in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".