Evaluating Green Environmental Performance Through Multi-Stakeholder Governance: A Comparative Analysis of NCA and fsQCA in the New Energy Vehicle Industry
Bibliographic record
Abstract
The emphasis on green development in China underscores the critical role of green environmental performance in achieving sustainability. This study introduces a multi-stakeholder governance perspective to evaluate enterprises’ green environmental performance, utilizing a framework with five factors categorized into firm, government, and societal dimensions. Employing a combination of Necessary Condition Analysis (NCA) and Fuzzy Set Qualitative Comparative Analysis (fsQCA), it investigates specific conditions enhancing green environmental performance in new energy vehicle enterprises, drawing on a sample of 49 companies in 2021. Results indicate that high levels of green environmental performance are influenced by multiple factors, including incentives, pressures, resources, and media attention. Additionally, the study identifies a potential complementary relationship between government subsidies and green technology innovation in driving high-level green environmental performance, subject to specific conditions. These findings contribute to our understanding of corporate green environmental performance and provide practical implications for fostering sustainability in the new energy vehicle industry.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".