Leveraging Design Thinking in Park Planning to Promote Low-Carbon Behavior: A Case of Changsha Yanghu Wetland Park in China
Bibliographic record
Abstract
This study explores the role of behavioral stimulation, environmental attraction, and facility experience in influencing low-carbon park design, visitor awareness, sustainable behavior, and design satisfaction.Using Changsha Yanghu Wetland Park in China as a case study, the research examines how urban parks can promote sustainability through effective design strategies.A cross-sectional survey was conducted among 510 participants, including park visitors, local community members, and online respondents.Data was collected using a structured questionnaire with validated measurement scales from past research.structural equation modeling (SEM) was performed using AMOS to analyze the relationships between key variables and assess the impact of behavioral, environmental, and facility-related factors on low-carbon park design.Results confirm that behavioral stimulation, environmental attraction, and facility experience significantly enhance low-carbon park design, fostering visitor awareness, sustainable behavior, and overall design satisfaction.The study highlights how sustainable infrastructure, interactive experiences, and environmental aesthetics contribute to eco-conscious engagement in urban green spaces.This research contributes to urban sustainability and environmental psychology by integrating design thinking principles into low-carbon park planning.The findings offer practical insights for urban planners, policymakers, and environmental organizations, emphasizing the need for behaviorally informed, eco-friendly park designs to promote sustainable urban development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".