Rawls in the mangrove: Perceptions of justice in nature‐based solutions projects
Bibliographic record
Abstract
Abstract Adapting to and mitigating against climate change requires the protection and expansion of natural carbon sinks, especially ecosystems with exceptional carbon density such as mangrove forests (an example of ‘blue carbon’). Projects that do this are called ‘nature‐based solutions’ (NbS). International norms regulating NbS stipulate the importance of justice, in contrast with some of the history and practice in wider conservation. However, what justice means and how it manifests in practice remain contentious. Selling carbon credits on the voluntary market is a growing source of funding for NbS. A large literature examines the ethics, economics, science and politics of such payments for ecosystem services (PES), including for blue carbon. The interpretations of justice in this context are particularly contentious, but operational blue carbon projects have not been examined from a justice perspective. Here we report on a case study involving the first blue carbon project, Mikoko Pamoja, and its sister project Vanga Blue Forest, both based in Kenya. We consider how justice is conceived by local participants and beneficiaries, using interviews, focus groups and participant observation to collect data, as well as by international stakeholders and in relevant governing documents and policy. We compare these perceptions with expectations and critiques derived a priori from the literature, including a classic thought experiment that influential justice philosopher John Rawls called the ‘original position’. In contrast to high‐level policy and much of the literature, but in common with Rawls, local stakeholders emphasised distributional aspects of justice. Locally situated interpretations of contentious issues such as elite capture and commodification differed markedly from common interpretations in the literature. Our work emphasises the importance of situating abstract concepts in their local contexts when evaluating justice in NbS projects. It shows how narratives advocating technical precision and economic efficiency in NbS can militate against transparency and agency at a local level and emphasises the critical importance of benefit sharing that is perceived to be fair. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".