Households’ Risk Perceptions in Response to Shale Gas Exploitation: Evidence from China
Bibliographic record
Abstract
In 2014, China became the world’s third country to realize shale gas commercial development, following the United States and Canada. So far, there has been a lack of comprehensive discussion on risk perception related to shale gas in China. This paper aims to understand Chinese residents’ risk perceptions toward shale gas exploitation. A survey was conducted with 730 interviewed participants in two counties of Sichuan province (Weiyuan County and Gong County). This study shows that, in China, an elderly female tends to perceive lower risks, and a higher education level is commonly associated with lower risk perception. Besides the socio-demographic characteristics, two major findings are also explored in this study. First, household’s perceived benefits from shale gas exploitation do not statistically significantly affect their risk concerns. Second, the respondents’ environmental consciousness, including their anticipation of environmental impacts and their perceptions about environmental degradation, plays a crucial role in their perception of the risks of shale gas exploitation. This implies that local residents’ judgments on the severity of environmental impacts significantly contribute to their risk perceptions. These findings therefore contribute to local authorities’ policy making in protecting local residents from the risks of shale gas exploitation and in better communicating about risk with the residents.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".