Transitioning to low-carbon suburbs in hot-arid regions: A case-study of Emirati villas in Abu Dhabi
Bibliographic record
Abstract
The continued and global popularity of single-family homes indicates that a scalable, yet regionally appropriate strategy for achieving zero-carbon suburban development is needed in the coming decades. This need is especially critical in the hyper-arid region around the Arabian Gulf where per-person carbon emissions are among the highest in the world. Using geometrically sensitive simulation models for a household's building energy, water, and automobile use, this paper uses Emirati neighborhoods in Abu Dhabi, UAE as a case study to estimate emissions for a baseline and set of future possible scenarios towards transitioning single-family households in the region to low, and eventually near-zero operational carbon emissions. An analysis of the combined impact of energy efficiency gains through (1) technology adoption and better design, (2) carbon intensity reduction from renewable energy transitions, and (3) carbon sequestration from parcel scale tree planting is presented. From a baseline CO 2 emissions of 64.3 tons per household per year for new construction, the study finds future emissions potential reductions of 33.7%–49.0% from improved house design, 98.1% from electrification and solar energy sourcing, and 99.4% from combined design and technology improvements. This study also finds that if the water-energy nexus in Abu Dhabi transitioned to solar-powered, reverse-osmosis desalination, trees would become net carbon sinks (0.6–1.9 kg CO 2 /m 2 /yr). As a result, in the future, low-density neighborhoods with dense areas of tree planting may become a sustainable housing typology when measured by net operational emissions. This fact would upend multiple current assumptions by Western planners about sustainable transitions for arid regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".