Decarbonizing the Cement Industry: A Comprehensive Analysis of Renewable Energy Pathways and GHG Emissions in the UAE
Bibliographic record
Abstract
This study presents a comprehensive analysis of decarbonization strategies for the cement industry, with a particular focus on the United Arab Emirates (UAE). The cement sector contributes approximately 6-7% of global CO₂ emissions, with 60% originating from limestone decomposition during clinker production and 40% from fossil fuel combustion. Using the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) tool, we evaluated six renewable energy pathways. Three renewable natural gas (RNG) sources (landfill gas, Canadian natural gas, and North American natural gas for Fischer-Tropsch plants) and three renewable electricity mixes (Alberta, alumina reduction, and wind). Ridge regression modeling was employed to analyze emission trends in the UAE cement industry from 2018 to 2022. Our findings reveal that wind electricity pathways can reduce emissions by approximately 75% per ton of cement, while RNG pathways offer significant reductions where electricity infrastructure is limited. Integration of carbon capture technologies with renewable energy sources could further reduce the carbon footprint. These insights provide actionable guidance for policymakers and industry stakeholders to implement effective decarbonization strategies aligned with the UAE’s sustainability goals.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".