Grabbing “Green”: Markets, Environmental Governance and the Materialization of Natural Capital
Bibliographic record
Abstract
Over the past two decades, the incorporation of market logics into environment and conservation policy has led to a reconceptualization of “nature.” Resulting constructs like ecosystem services and biodiversity derivatives, as well as finance mechanisms like Reducing Emissions from Deforestation and Forest Degradation, species banking, and carbon trading, offer new avenues for accumulation and set the context for new enclosures. As these practices have become more apparent, geographers have been at the forefront of interdisciplinary research that has highlighted the effects of “green grabs”—in which “green credentials” are used to justify expropriation of land and resources—in specific locales. While case studies have begun to reveal the social and ecological marginalization associated with green grabs and the implementation of market mechanisms in particular sites, less attention has been paid to the systemic dimensions and “logics” mobilizing these projects. Yet, the emergence of these constructs reflects a larger transformation in international environmental governance—one in which the discourse of global ecology has accommodated an ontology of natural capital, culminating in the production of what is taking shape as “The Green Economy.” The Green Economy is not a natural or coincidental development, but is contingent upon, and coordinated by, actors drawn together around familiar and emergent institutions of environmental governance. Indeed, the terrain for green grabbing is increasingly cultivated through relationships among international environmental policy institutions, organizations, activists, academics, and transnational capitalist and managerial classes. This special issue of Human Geography brings together papers that draw on a range of theoretical perspectives to investigate the systemic dimensions and logics mobilizing green grabs and the creation of new market mechanisms. In inverting the title – “grabbing green” instead of the more conventional green grabs – we explore how “the environment” is being used instrumentally by various actors to extend the potential for capital accumulation under the auspices of “being green.” Using a diversity of empirical material that spans local to global scales, the papers reveal the formation of the social relations and metrics that markets require to function. They identify the “frictions” that inhibit the production of these social relations, and they link particular cases to the scalar configurations of power that mobilize and give them shape.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".