Chemistry-Informed Ensemble Modeling for Sustainable Infrastructure Risk Management

Journal of Business Intelligence & Decision Science Review

Mowma Mazumder, Shahedul Islam, MD Rasel Ul Alam

Department of Cyber Security (MSc), Daffodil International University (DIU); Department of Data Science, United International University (UIU); University of the Cumberlands, Department of Computer and Information Sciences, Williamsburg, Kentucky, USA

Journal of Business Intelligence & Decision Science ReviewVol. 1, Issue 1August 28, 2026

View Full PDF

Abstract

Rising regulatory pressure and investor demand for transparent sustainability reporting have pushed organizations to integrate environmental performance data into core business decision-making. This demonstration study examines how business intelligence (BI) dashboards that combine carbon-emission tracking, resource-efficiency metrics, and financial indicators influence strategic decision quality across 48 mid-sized manufacturing firms over a 24-month period. Using a mixed-methods approach that pairs quarterly performance data with structured management surveys, the analysis shows that firms adopting integrated environmental-financial dashboards achieved a 17% faster response time to compliance risks and a measurable improvement in resource-allocation efficiency compared to firms using siloed reporting systems. The findings suggest that embedding environmental indicators directly within existing BI infrastructure, rather than maintaining separate sustainability reports, produces more actionable and timely decisions.

Keywords

Enterprise Risk Management (ERM) Structural Anisotropy Machine Learning Non- Destructive Testing (NDT) Infrastructure Governance Business Intelligence

Article Information

Published
August 28, 2026
Journal
Journal of Business Intelligence & Decision Science Review
Volume / Issue
1 / 1
Article No.
JBIDSR-2021001
Year
2021

Browse

All published articles · Journal archive