How does climate change affect biomass production and rural poverty?
Bibliographic record
Abstract
The interrelation between climate change, biomass production, and rural poverty is an area of growing concern, as these factors are intricately linked and often exacerbate one another. The objective of this critical review is to investigate existing knowledge, identify research gaps, and explore how climate-induced disruptions affect biomass production, exacerbate rural poverty, and increase vulnerability. High-quality peer-review publications were sourced via Web of Science, Scopus, and Google Scholar to include the most relevant papers in line with the objective. A bibliometric analysis yielded three key concepts: (i) biofuel innovations and sustainable development, (ii) climate dynamics and biomass environmental impact, and (iii) rural poverty and energy challenges. The review delves into the complex interplay of factors influencing biomass production, climate change, and rural/remote poverty. Climate change intensifies the challenges rural communities face, enhancing their vulnerability to poverty. For these communities, biomass production not only offers a sustainable energy alternative but also a pathway to economic upliftment. Addressing climate change through sustainable biomass production emerges as a vital strategy, providing a dual solution by mitigating environmental degradation and offering a robust framework for poverty alleviation in rural areas. The review emphasizes the urgent need to integrate climate action, sustainable energy production, and rural economic development.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".