Bioenergy Expansion and Economic Sustainability from Environment‑Energy‑Food Security Nexus: A Review
Bibliographic record
Abstract
Bioenergy could have deep effects on economic, social, and environmental sustainability.Thus, the present research aims to review the potential risks and benefits of bioenergy production and consumption.For this purpose, we follow the approach of a systematic review and collect the 105 studies on bioenergy from the Scopus database.The literature suggests that bioenergy is the largest source of replacement of fossil fuels compared to other renewable energy sources and helps to conserve the environment.However, bioenergy production targeted at forest land could have environmental problems as forests are a big source of carbon sinks and biodiversity.Nevertheless, bioenergy consumption is environmentally friendly and releases the least emissions compared to all types of fossil fuels.Moreover, the installation and operational costs of bioenergy are lesser compared to other renewable energy sources.Thus, bioenergy is a cost-effective solution to replace fossil fuels compared to other renewable energy sources.However, bioenergy production replacing the existing crops could reduce the availability of land and water for other agricultural products, which can be responsible for food shortages and rising food prices.Thus, bioenergy production could cause food insecurity with the rapidly growing population worldwide.However, bioenergy could have many other benefits from economic and social dimensions.Thus, the literature has suggested government intervention to achieve net positive benefits from bioenergy production and consumption.Particularly, the literature has suggested public and private spending on R&D activities to find better sources and technologies for bioenergy production and to improve biomass and overall agriculture productivity.Moreover, literature has suggested using marginal lands, other unutilized lands, crop and forest residues, and wastes for biomass production to reduce the pressure on forests and croplands to ensure both food security and environmental conservation.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".