Comparative Analysis of the Construction Industry in the EU and the Arab States of the Persian Gulf (GCC): Its Impact on Economic Growth
Bibliographic record
Abstract
The construction industry plays a pivotal role in propelling global economic growth. This research examines the economic contributions of the construction sectors in the European Union (EU) and the Gulf Cooperation Council (GCC) region. The EU's approach is characterised by a focus on sustainability-driven policies, including energy efficiency and circular economy practices. This is intended to foster economic resilience and alignment with international commitments such as the Paris Agreement. In contrast, the construction sector in the Gulf Cooperation Council (GCC) is driven by large-scale infrastructure projects and urbanisation, which are in turn fuelled by oil revenues. This emphasises the importance of diversification as a means of reducing economic volatility. By analysing regulatory frameworks, investment trends and technological innovations, the study demonstrates how the construction industry contributes to GDP growth, job creation and industrial development in both regions. Moreover, it investigates potential avenues for interregional collaboration, underscoring the capacity of sustainable construction practices and innovation to bolster economic stability and competitiveness within the global construction landscape. The study also highlights how circular economy principles can redefine sustainable construction practices, enhancing resource efficiency and promoting resilience.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".