The Role of Green Patents in Innovation: An fsQCA Study of Chinese Listed Agricultural Enterprises
Bibliographic record
Abstract
This study employs a comparative fuzzy-set qualitative comparative analysis (fsQCA) to examine the combined effects of traditional factors and green patents on innovation performance in Chinese listed agricultural enterprises, offering insights into sustainability in agriculture through innovation. By analyzing 84 valid cases from 107 agricultural companies, we conduct two fsQCA analyses to compare innovation pathways with and without green patents as a conditional factor. The first analysis investigates the impacts of five factors—firm size, executives’ educational background, return on net assets, ownership concentration, and government subsidies—on non-green innovation performance, identifying four distinct pathways: executive-dispersed, employee-financed, executive-centralized, and executive-profitable. In the second analysis, green patents are introduced as an independent variable. The overall solution coverage remains stable, but the configurational landscape shifts, with two original pathways persisting and two new pathways emerging—both involving green patents. The findings suggest that the impact of green patents on innovation is condition-dependent rather than universally beneficial. Green patents amplify innovation performance only when supported by strong managerial education, financial stability, and policy incentives, particularly in the executive green synergy pathway, where raw coverage reaches 0.41, underscoring their role as a conditional multiplier in sustainable innovation. These results provide theoretical and empirical evidence for balancing economic benefits with environmental responsibility in agricultural enterprises and emphasize the need for targeted policy subsidies, enhanced managerial education, and optimized shareholder structures to drive sustainable innovation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".