City-level sustainable development impacts on environmental literacy: feelings toward nature, environmental knowledge, and pro-environmental behavior
Bibliographic record
Abstract
Research in environmental education adopts a broad concept of environmental literacy, recognizing the multifaceted nature that encompasses cognitive, affective, and behavioral components. However, minimal research has examined how these components interact with each other across various cities. The present study aims to fill this gap by investigating the interplay between the components of environmental literacy, such as affiliation with nature, environmental knowledge, and environmental behavior. More importantly, the study explores how these relationships vary across different cities that differ in sustainable development levels. Children (N = 979, Mage = 11.13) from 29 cities in China completed measures of affiliation with nature, environmental literacy, and pro-environmental behavior (PEB). We classified the cities into high versus low sustainable development levels using the Low-Carbon and Green Index – a comprehensive measure of sustainable development, including energy consumption, carbon emissions, and public policy. A moderated mediation analysis revealed that environmental knowledge mediated the relation between affiliation with nature and PEB only in cities with higher sustainable levels. As a result, environmental education should be tailored to a city’s sustainable development level to better encourage pro-environmental behavior.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".