JOURNAL ARTICLE
An AHP and BEES model-based sustainability assessment of small-scale marble processing industries: Indian prospects.
Published In: Mining Technology (2572-6668), 2025, v. 134, n. 1. P. 30 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Sodha, Dharmanshu Singh; Mali, Harlal Singh; Singh, Amit Kumar 3 of 3
Abstract
The article focuses on assessing energy consumption and sustainability in small-scale ornamental marble stone processing industries in Rajasthan, India. It presents a detailed energy analysis of various stone cutting and transferring techniques across four processing plants, using life-cycle assessment (LCA) with gate-to-gate boundaries to evaluate environmental impacts such as global warming potential, abiotic depletion, acidification, and human toxicity via the GaBi® tool. The study employs the Analytical Hierarchy Process (AHP) and the Building for Environmental and Economic Sustainability (BEES) model to rank the industries based on environmental, economic, and manufacturing criteria, finding consistent results between the two methods. It also proposes practical strategies to reduce energy consumption, manage waste, and improve sustainability, including adopting renewable energy, optimizing machinery use, and better waste slurry management. The research highlights the significance of energy management in Rajasthan's marble sector while noting limitations due to the small sample size and suggesting broader future studies for more generalizable conclusions.
Additional Information
- Source:Mining Technology (2572-6668). 2025/03, Vol. 134, Issue 1, p30
- Document Type:Article
- Subject Area:Chemistry
- Publication Date:2025
- ISSN:2572-6668
- DOI:10.1177/25726668241311555
- Accession Number:185154338
- Copyright Statement:Copyright of Mining Technology (2572-6668) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.