‘CLOSING THE CARBON LOOP’: Climate Policy Discourses and the Material Politics of Municipal Waste‐to‐Biofuel Programs
Bibliographic record
Abstract
Abstract Waste‐to‐biofuel (WTB) programs have gained popularity as a municipal circular economy and an emissions reduction strategy. The upgrading of biofuels to renewable natural gas (RNG) has drawn particular interest, as RNG can displace conventional fossil fuels in any existing natural gas end use and be delivered through existing pipeline infrastructure. This article examines RNG produced at the City of Toronto’s waste facilities in partnership with Enbridge Distribution Inc. Toronto has framed its program as a strategy to ‘close the carbon loop’, recirculating waste as a new energy resource and, by extension, the carbon embodied in municipal waste. The article, first, examines the construction of the carbon loop policy narrative that draws from the technical work of emissions accounting. Second, it discusses how and why choices that shape energy systems are made as part of such programs. In Toronto, distributing through the Enbridge pipeline network has enabled the production of flexible environmental attributes that can be virtually assigned to a range of end uses and users. Understanding how policy narratives are constructed to describe municipal policy experimentation and situate municipal experiments within wider energy systems and energy system politics is critical to ensure experiments contribute to long‐term net zero pathways.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.075 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| 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".