Bibliographic record
Abstract
One of the significant, perhaps momentous, developments in the effort to mitigate climate change has been the convergence around the target of Net Zero Emissions (NZE). The path to NZE envisions a massive increase in the installed capacity of renewable energies (REs)accompanied by a radical reduction in fossil energies. The paper outlines the main technologies required to make renewables a reliable alternative. Storage mechanisms such as batteries, hydrogen, and pumped storage are reviewed. Major impediments to the replacement of fossil fuels with renewable sources include technological, political, and social lock ins. Since NZE emissions are unlikely to be realized without installing negative emission devices, carbon dioxide removal methods and their concomitant challenges are discussed. Demand side mitigation in the transport, buildings, and industry sectors, relevant technological solutions, and adjustments to social preferences are briefly addressed. In addition to analyzing the challenges to realize an NZE world, the paper cautions against a single metric focus while ignoring concerns over adaptation, rising inequalities, water and food availability, and biodiversity. The financial implications of the various mitigation technologies are highlighted along with the need to transfer technologies and funds to developing nations to avoid and/or reduce emissions.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".