What Drives the Household's Pro-environmental Behavior? Dfferences in what People Say and Do
Bibliographic record
Abstract
"Promoting changes in behavior related to natural resources and environmental concerns requires to understand the factors associated. In a context of common pool resources, cooperation is required to overcome social dilemmas, but the understanding of the factors that underlie the behavior is an urgent task. There is considerable evidence on what move the individuals to perform pro-environmental actions. However, since the information about private behaviors is self-reported, produces challenges to analyze in a causal fashion. This paper intends to analyze what intrinsic and extrinsic motivations could explain pro-environmental behaviors. I collect information of households in eight small-urban villages in Colombia and combine it with information from a previous randomize field experiment conducted in these villages. With this data set, there is self-declared behaviors and objective measures of water consumption. I use a propensity score matching to analyze how the motivations affects both. The heterogeneous findings show a contradiction between what the households affirm that they are doing where they are asked and what they are actually doing. Moreover, the anticipated feelings of guilt and anticipated feelings of pride are important drivers to explain pro-environmental behaviors in self-declared behaviors. While the perceived control of behavior and monetary incentives in the observable 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".