| Autor: |
Sayali Sandbhor, Sayali Apte, Vaishnavi Dabir, Ketan Kotecha, Rajkumar Balasubramaniyan, Tanupriya Choudhury |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
AIMS Environmental Science, Vol 10, Iss 6, Pp 894-910 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2372-0352 |
| DOI: |
10.3934/environsci.2023048?viewType=HTML |
| Popis: |
The cement industry's carbon emissions present a major global challenge, particularly the increase in atmospheric carbon dioxide (CO2) levels. The concrete industry is responsible for a significant portion of these emissions, accounting for approximately 5–9% of the total emissions. This underscores the urgent need for effective strategies to curb carbon emissions. In this work, we propose to use artificial intelligence (AI) to predict future emission trends by performing a detailed analysis of cement industry's CO2 emissions data. The AI predictive model shows a significant increase in overall carbon emissions from the cement sector which is attributed to population growth and increased demand for housing and infrastructure. To address this issue, we propose a framework that emphasizes on implementing carbon sequestration through reuse of construction and demolition (C & D) waste by using recycled aggregates. The paper proposes a framework addressing carbon sequestration through use of C & D waste. The framework is applied specifically to Maharashtra State in India to calculate the potential reduction in carbon emissions by construction industry resulting from recycled aggregates. The study reveals a projected saving of 24% in carbon emissions by adopting the suggested framework. The process and outcomes of the study aim to address the concerns of climate change through reduced carbon emissions in the construction industry promoting recycle and reuse of construction waste. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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