An investigation into neighborhood residents’ cognition of and participation in low-carbon behavior: a case study in Chengyang district of Qingdao, China
Bibliographic record
Abstract
To discover potential approaches for meeting increasingly stringent emission controls, a questionnaire was launched in a newly built gated neighborhood in Chengyang district of Qingdao, China, to examine individual residents' cognition of and participation in low-carbon behaviors, which play a pivotal role in the construction of low-carbon neighborhoods.Statistical analysis of the questionnaires indicated that resident individuals' cognition regarding the paths to low-carbon neighborhood construction still centered on the traditional aspects of energy saving and emission reduction.The popularization of low-carbon lifestyles in all areas, such as "adoption of central cooling system" and "acceptance of laddering electricity price", in which potential low-carbon behaviors lie, is still an important mission of low-carbon transition in the near future.Furthermore, due to the relatively high level of local economic development and good traffic conditions in the surveyed neighborhood, a high proportion of residents were engaged in low-carbon behaviors from the perspective of transportation, such as "public transportation", "public bicycles or electromobiles" and "walking".Thus, to date, local residents have achieved good results in low-carbon mobility.The use of energy-efficient cars is also a potential field for emission reduction.Finally, suggestions were proposed to encourage residents' participation in low-carbon behaviors.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".