Building sustainable circular agriculture in China: economic viability and entrepreneurship
Bibliographic record
Abstract
Purpose In the context of China, the purpose of this paper is to empirically answer three related questions: Could circular agriculture (CA) attain economic, ecological and social benefits simultaneously? What is key to a successful CA business in emerging economies? And who plays the vital role in building and sustaining a circular business? Design/methodology/approach The paper is based on a field study and looks at a farm in China. It uses a triangulation methodology to collect information. Besides longitudinal filed work at the farm, the researchers have also interviewed multiple stakeholders and conducted field research at the local markets. Findings With concrete performance data, the study proves that a circular approach can help achieve ecological, economic and social goals together. It shows that economic viability is essential to succeeding in circular operation, sufficient production pathways are required to make such operation sustainable, and entrepreneurship is key to build and grow a circular business. Research limitations/implications The findings point to the crucial role of entrepreneurship in promoting the circular model in emerging economies. These findings, however, may not be readily generalizable, given the limitations of the case study approach. Practical implications The study highlights a few areas in which government assistance can make a difference, including financial incentives, information provision, technical support and most importantly the creation of a positive environment for entrepreneurial development. Originality/value While prior research emphasizes the role of government in promoting circular economy in developing and emerging markets, the study proves that entrepreneurship is key to turning government initiatives into economically viable and sustainable circular operation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".