Factory farm gas: Rendering industrial animal waste as renewable energy on North Carolina pig farms
Bibliographic record
Abstract
This article critically examines Factory Farm Gas (FFG) generated from industrial animal waste from North Carolina's (US) hog sector, revealing how this corporate-driven, government-subsidized sustainability initiative, aimed at addressing the sector's methane emissions, ultimately serves to expand and entrench industrial animal production. FFG depends on the large-scale concentration and confinement of farmed animals, whose waste is managed in lagoon sprayfield systems now retrofitted with an anaerobic digester to promote the production and capture of biogas. These techno-political objects illuminate how waste management governance and technologies become central to processes of spatial and economic fixing, power consolidation, and capital accumulation. By challenging the framing of biogas as a renewable energy source, this article argues that FFG is more than just a greenwashing tactic by situating it within critical literature on agro-energy networks to highlight how it can lead to unanticipated factory farm expansion and new sites for accumulation. In North Carolina, this emerging economy unfolds amid a decades-long struggle to phase out the lagoon sprayfield system. Using a political economy framework and drawing from fieldwork and interviews, this article traces how anaerobic digesters entrench the interests of agribusinesses and energy sectors, sustaining polluting and emission-intensive practices while generating new geographies of factory farming, including centralized gas upgrading facilities and pipelines. By critically examining these dynamics, this article underscores the need for interdisciplinary research and coalition-building across environmental justice, energy, and food justice movements to contest the entrenchment of industrial animal agriculture within renewable energy transitions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".