Achieving Green Building in Qatar through Legal and Fiscal Tools
Bibliographic record
Abstract
In the midst of both a multi-State blockade of Qatar and the urgency to complete major building projects in time to host the 2022 FIFA World Cup, the limits of Qatar’s resource sustainability have been tested. The State of Qatar is the world’s highest per capita consumer of water and emitter of CO2 emissions. Qatar is also at considerable risk of becoming an unlivable nation if the global temperature change targets of the Paris Agreement are breached. National law and policy seek to address this by promoting sustainability and focusing on reducing consumption, though such efforts are commonly overwhelmed by the enormity of the construction projects. This article considers how the advancement of green building can provide multiple dividends in Qatar by enabling reduced resource consumption and producing less waste. LEED® certified “green” buildings consume between 10% and 25% less energy and 11% less water and emit 34% lower greenhouse gases than similar conventional buildings. The article analyses Qatar’s law and policy approaches and available options. It further examines comparative law and policy models in the UK to explore how compatible such measures would be in Qatar. It concludes with possible legal and policy options available, assessing how effective such measures may work if transplanted into and/or adapted by Qatar.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".