Understanding stakeholder perceptions of environmental justice: a study of tourism in the Erhai Lake basin, Yunnan province, China
Bibliographic record
Abstract
Environmental justice is an important component of sustainable tourism, but stakeholder perspectives related to environmental justice may vary. Using Q-methodology, we investigated different stakeholder perceptions related to environmental justice within the context of tourism and ecological restoration. Specifically, in the Erhai Lake basin, China, we explore perspectives around an ecological restoration effort that included the government mandated closure of 1900 establishments (inns and restaurants) in response to environmental degradation. We identify and explore four environmental justice perspectives: the togetherness, protection, operator loss, and local loss perspectives. These four perspectives are contextualized within three dimensions of environmental justice (i.e., distribution, recognition, and participation). Our findings highlight differing views related to who is affected most by the inn closures (e.g., future generations, local residents, inn owners), and general consensus related to the outcomes of the process being more important than the process itself. Finally, we discuss potential reasons for these differing perspectives and recommend ways to improve environmental justice among different stakeholders. This research can facilitate sustainable development of tourism by highlighting the facets of ecological restoration policy implementation most important to stakeholders, including recognition of diverse stakeholder concerns and identities, clear and well supported rationale for policy design, and increased equity in the distribution of costs and benefits of policies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| 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".