Feasibility and Design of Distributed Carbon Dioxide Capture Networks in Urban Environments
Bibliographic record
Abstract
Cities are hotspots for carbon dioxide (CO 2 ) emissions due to their increased rates of human activity. This results in the accumulation of CO 2 within urban infrastructure, including in buildings, traffic tunnels, and the ambient atmosphere. Using the City of Boston as an example, this perspective article assesses the technical and logistic feasibility of deploying a distributed network of small-scale CO 2 capture systems to targeted urban locations with elevated concentrations of CO 2, as this may provide advantageous energetic and kinetic conditions that can reduce the resource consumption of the capture system. It is estimated that a theoretical energy savings between 2 and 15% per tonne of captured CO 2 can be realized. Criteria for technologies that are appropriate for urban areas, including sorbent materials, system architectures, and suitable energy sources for populated areas, are proposed, primarily focused on safety, space efficiency, and whether they produce excess noise and vibration. Operational and logistic considerations regarding downstream CO 2 handling and transport are discussed. Research pathways necessary for the design of effective urban CO 2 capture systems are highlighted, as well as potential economic incentives and policy interventions that can encourage their adoption. At the nexus of public health, global warming mitigation, and community climate adaptation, urban CO 2 removal can be important in realizing net zero goals while providing a range of additional societal and economic benefits.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".