Positioning infrastructure and technologies for low‐carbon urbanization
Bibliographic record
Abstract
Abstract The expected urbanization of the planet in the coming century coupled with aging infrastructure in developed regions, increasing complexity of man‐made systems, and pressing climate change impacts have created opportunities for reassessing the role of infrastructure and technologies in cities and how they contribute to greenhouse gas ( GHG ) emissions. Modern urbanization is predicated on complex, increasingly coupled infrastructure systems, and energy use continues to be largely met from fossil fuels. Until energy infrastructures evolve away from carbon‐based fuels, GHG emissions are critically tied to the urbanization process. Further complicating the challenge of decoupling urban growth from GHG emissions are lock‐in effects and interdependencies. This paper synthesizes state‐of‐the‐art thinking for transportation, fuels, buildings, water, electricity, and waste systems and finds that GHG emissions assessments tend to view these systems as static and isolated from social and institutional systems. Despite significant understanding of methods and technologies for reducing infrastructure‐related GHG emissions, physical, institutional, and cultural constraints continue to work against us, pointing to knowledge gaps that must be addressed. This paper identifies three challenge themes to improve our understanding of the role of infrastructure and technologies in urbanization processes and position these increasingly complex systems for low‐carbon growth. The challenges emphasize how we can reimagine the role of infrastructure in the future and how people, institutions, and ecological systems interface with infrastructure.
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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 | 0.000 |
| 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".