Urban configurations of carbon neutrality: Insights from the Carbon Neutral Cities Alliance
Bibliographic record
Abstract
This paper examines configurations of carbon neutrality in the building and energy sector as expressed in the urban governance documents of the members of the Carbon Neutral Cities Alliance (CNCA). ‘Carbon neutrality’ is a mutable idea, which makes it unclear what kinds of future urban systems are imagined. As self-identified pioneers of deep decarbonization, the CNCA members are constructing ideas about what carbon neutral means and how urban systems should be changed to reduce greenhouse gas emissions. In this paper, climate governance policy documents provide a window to understand how these carbon neutral imaginaries are being constructed. The analysis draws on discourse analysis and textual network analysis to unpack the sociotechnical configurations that are planned to be mobilized to constitute carbon neutral built environments. Concept map visualizations are used to scrutinize planned configurations of objects (e.g. solar photovoltaics, district energy and energy efficiency technology) and policy instruments (e.g. energy use benchmarking and urban planning tools). The analysis shows three key building and energy configurations: (1) The District Energy City, (2) The Zero Net Energy City and (3) The Natural Gas Transition City. Furthermore, the findings demonstrate that urban imaginaries of carbon neutrality are incorporating complex configurations of socio-technical objects while, at the same time, distinct socio-technical configurations are being favoured in individual places. These configurations inform socio-technical imaginaries that will continue to drive policy outcomes over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".