Proposed solutions to anthropogenic climate change: A systematic literature review and a new way forward
Bibliographic record
Abstract
Humanity is now facing what may be the biggest challenge to its existence: irreversible climate change brought about by human activity. Our planet is in a state of emergency, and we only have a short window of time (7-8 years) to enact meaningful change. The goal of this systematic literature review is to summarize the peer-reviewed literature on proposed solutions to climate change in the last 20 years (2002-2022), and to propose a framework for a unified approach to solving this climate change crisis. Solutions reviewed include a transition toward use of renewable energy resources, reduced energy consumption, rethinking the global transport sector, and nature-based solutions. This review highlights one of the most important but overlooked pieces in the puzzle of solving the climate change problem - the gradual shift to a plant-based diet and global phaseout of factory (industrialized animal) farming, the most damaging and prolific form of animal agriculture. The gradual global phaseout of industrialized animal farming can be achieved by increasingly replacing animal meat and other animal products with plant-based products, ending government subsidies for animal-based meat, dairy, and eggs, and initiating taxes on such products. Failure to act will ultimately result in a scenario of irreversible climate change with widespread famine and disease, global devastation, climate refugees, and warfare. We therefore suggest an "All Life" approach, invoking the interconnectedness of all life forms on our planet. The logistics for achieving this include a global standardization of Environmental, Social, and Governance (ESG) or similar measures and the introduction of a regulatory body for verification of such measures. These approaches will help deliver environmental and sustainability benefits for our planet far beyond an immediate reduction in global warming.
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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".