Akter, Nipa and Mohammed, Awais and Almunshi, Muhammad Yousuf (2024) Advanced Low-Carbon Concrete for Sustainable Infrastructure Development in the United States. American Journal of Engineering, Mechanics and Architecture, 2 (11). pp. 199-208. ISSN 2993-2637
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Abstract
Background: The construction industry stands as a major source of worldwide carbon emissions because standard concrete manufacturing generates about seven to eight percent of all global CO2 emissions. The construction industry now receives advanced low-carbon concrete (LCC) technology which functions as an environmentally friendly building material solution. The United States has not accepted these technologies because they face multiple obstacles which include technical problems and economic barriers and regulatory restrictions. Methods: This study employs a quantitative cross-sectional design based on a structured survey of 175 construction industry professionals. A Likert-scale questionnaire was used to assess four key dimensions: environmental awareness, technical performance, economic feasibility, and policy support. The research team used descriptive statistics and Pearson correlation and multiple regression analysis to study the connections between variables which affect LCC adoption. Results: The research data shows that people have strong environmental knowledge with a score of 4.18 and they trust system performance at a level of 4.05 which indicates positive views about LCC technologies. The economic viability assessment shows a score of 3.72 which creates a moderate obstacle for project implementation. The regression model shows that environmental awareness and technical performance and policy support have major effects on adoption rates through R² = 0.64 and p = 0.001 but cost-related elements create adverse effects on decision-making processes. Conclusion: The technical and environmental capabilities of LCC technologies show great potential but their wide adoption faces two major obstacles which include financial restrictions and government policy barriers.
| Item Type: | Article |
|---|---|
| Subjects: | A General Works > AI Indexes (General) |
| Depositing User: | admin eprints |
| Date Deposited: | 28 Jun 2026 23:14 |
| Last Modified: | 28 Jun 2026 23:14 |
| URI: | http://eprints.umsida.ac.id/id/eprint/16653 |
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