A Review of Systems Thinking Perspectives on Sustainability in Bioresource Waste Management and Circular Economy
Bibliographic record
Abstract
A plethora of sustainability-related challenges plague the modern world, among which is residue management. The significant implications of waste management on local populations and the global climate system have propelled research efforts toward residue management. Improved understanding and predictions in biomass residue management can help identify opportunities to advance residue management to address these complex challenges. In recent years, sustainability science has gained momentum and is viewed as the most effective approach to addressing wicked problems. For instance, the release of greenhouse gases into the atmosphere is a major contributor to climate change. This review examines how a greater knowledge of human–environment interaction and the value of ecological services could facilitate the recycling of agricultural and forestry wastes for their uses in bioenergy production and soil protection. In addition, it highlights the connection between biomass residual management and the United Nations Sustainable Development Goals, thereby strengthening the circular and ecological economy. Additionally, this review also discusses how interdisciplinary and systems thinking can contribute to the advancement of biomass residue management. This review aims to explore how the principles of sustainability science and systems thinking can help enhance the reutilization of agricultural and forest residues through biomass residue management. It also aims to assess their potential in reducing environmental and social impacts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".