Environmental justice and REDD+ safeguards in Laos: Lessons from an authoritarian political regime
Bibliographic record
Abstract
Balancing agendas for climate mitigation and environmental justice continues to be one of the key challenges in climate change governance mechanisms, such as Reducing Emissions from Deforestation and Degradation (REDD+). In this paper we apply the three-dimensional environmental justice framework as a lens to examine the REDD+ process in the Lao People's Democratic Republic (Laos) and the REDD+ social safeguards. We focus particularly on challenges to justice faced by marginalized communities living in forest frontier areas under an authoritarian regime. Drawing on policy analysis and open-ended interviews across different policy levels, we explore procedural, distributional, and recognitional justice across the REDD+ policy levels in Laos. We find that REDD+ social safeguards have been applied by both donors and state actors in ways that facilitate external control. We underscore how authoritarian regime control over civil society and ethnic minority groups thwarts justice. We also highlight how this political culture and lack of inclusiveness are used by donors and project managers to implement their projects with little political debate. Further obstacles to justice relate to limitations inherent in the REDD+ instrument, including tight schedules for dealing with highly sensitive socio-political issues under social safeguards. These findings echo other research but go further in questioning the adequacy of safeguards to promote justice under a nationally driven REDD+. We highlight the importance of recognition and political context, including aspects such as power relations, self-determination and self-governance of traditional or customary structures, in shaping justice outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".