The mechanism of the ornamental plant variety rights value formation and enhancement strategy based on SEM-SD
Bibliographic record
Abstract
With the acceleration of global urbanization, ornamental plants are playing an increasingly critical role in urban greening and climate regulation. Beyond improving urban landscapes, they mitigate the urban heat island effect through transpiration and enhance overall environmental quality. In developed countries, the surge of home gardening during the pandemic greatly stimulated the ornamental plant market, heightening interest in new variety development and the commercialization of plant variety rights. However, many newly bred ornamental varieties have failed to be effectively translated into productive resources, or their value has been underestimated, thereby constraining marketization. This study investigates the value formation mechanism of the Ornamental Plant Variety Rights (OPVR) and proposes optimization pathways for value enhancement. An OPVR value evaluation index system was first constructed through systematic literature review, expert consultation, and value chain analysis. A structural equation model (SEM) was then applied to examine the interrelationships among influencing factors, followed by a system dynamics (SD) model to explore the dynamic evolution of OPVR value and factor sensitivities. The SEM results indicate that four categories of factors contribute to OPVR value formation in the following order: variety value (Var)> technological level (Tec)> market and brand (Mar)> intellectual property protection (IPP). Specifically, variety value exerts both direct and indirect effects through its positive influence on technology and market performance, while IPP, though less influential, positively reinforces variety and market factors. The SD simulations further reveal that factor impacts are comparable during the first four years but diverge thereafter, with sensitivity ranking as follows: market and brand > technological level > variety value > intellectual property protection. Overall, this study clarifies the mechanisms underlying OPVR value formation and offers actionable insights for enterprises to design evidence-based strategies that enhance variety value, strengthen market positioning, and improve long-term competitiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".