Decarbonization strategies for the building sector: A comparative study of Qatar and global case studies
Bibliographic record
Abstract
Objectives This study examines decarbonization strategies in the building sectors of oil- and gas-producing countries, focusing on Qatar and a selection of global cities, including Oslo, Stockholm, Yokohama, Vancouver, Berlin, London, Seattle, Washington, DC, New York, and Portland. It aims to identify transferable practices and evaluate how local and global approaches can inform context-specific carbon reduction in the built environment. Methods A comparative case study approach was used, drawing on peer-reviewed literature, policy documents, and institutional reports published between 2010 and 2023. The study emphasized strategies applied in urban building sectors, including regulatory measures, retrofitting programs, and renewable energy integration. Cities were selected for their varying energy profiles, climate action plans, and relevance to the Qatari context. Findings Key findings highlight the importance of setting clear emissions targets, establishing energy performance benchmarks, and aligning urban planning with climate policy. Effective strategies observed include the adoption of stringent building codes, large-scale retrofitting, and coordinated public engagement. While global cities demonstrate measurable progress, oil- and gas-producing nations face challenges such as economic dependency on hydrocarbons and governance limitations. Conclusions Qatar's decarbonization prospects depend on political commitment, regulatory enforcement, and investment in sustainable building practices. Tailoring global lessons to national conditions—particularly through standards like GSAS—can advance its climate goals. The study contributes practical insights for policymakers seeking to reduce building-sector emissions in fossil-fuel-reliant economies.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".