Deep Decarbonization in Practice: Solutions and Challenges for Low-Carbon Building Retrofits
Bibliographic record
Abstract
This paper examines efforts taking place in London, San Francisco and Stockholm to implement deep greenhouse gas emission cuts—‘deep decarbonization’—through the transformation of buildings and urban energy infrastructure for increased energy efficiency and low/zero carbon energy supply. Drawing on interviews, policy document analysis, and site tours to buildings and energy infrastructure, this paper analyzes how deep decarbonization is being embedded into urban buildings, energy systems, and institutions. It argues that practitioners are finding ways to create new low/zero carbon future buildings, but are having difficulty correcting the historical development path through retrofitting. This paper examines solutions and challenges brought to light by urban decarbonization in practice targeting existing buildings from which other cities can learn. Four key lessons for low/zero carbon retrofits are highlighted: 1) shift primary targets from homeowners to owners of multiple buildings, 2) expand the suite of resources available to support zero carbon retrofits, 3) experiment and teach using public investment, and 4) institutionalize energy and carbon reporting linked to municipal department targets. Given the necessity of low-carbon, efficient, and climate resilient building retrofits to address the climate crisis, action can be scaled up by considering buildings and energy infrastructure an infrastructure priority for public investment.
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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 | 0.001 | 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 | 0.000 |
| 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".