Natural capital must be defended: green growth as neoliberal biopolitics
Bibliographic record
Abstract
This contribution addresses the growing global trend to promote ‘natural capital accounting’ (NCA) in support of environmental conservation. NCA seeks to harness the economic value of conserved nature to incentivize local resource users to forgo the opportunity costs of extractive activities. We suggest that this represents a form of neoliberal biopower/biopolitics seeking to defend life by demonstrating its ‘profitability’ and hence right to exist. While little finance actually reaches communities through this strategy, substantial funding still flows into the idea of ‘natural capital’ as the basis of improving rural livelihoods. Drawing on two cases in Southeast Asia, we show that NCA initiatives may compel some local people to value ecosystem services in financial terms, yet in most cases this perspective remains partial and fragmented in communities where such initiatives produce a range of unintended outcomes. When the envisioned environmental markets fail to develop and benefits remain largely intangible, NCA fails to meet the growing material aspirations of farmers while also offering little if any bulwark against their using forests more intensively and/or enrolling in lucrative extractive enterprise. We thus conclude that NCA in practice may become the antithesis of conservation by actually encouraging the resource extraction it intends to combat.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".