Environment Centric Sustainable Management Framework Integrating Lifecycle Analysis and Material Flow for Industrial Applications

Abstract

Environmental sustainability management has grown in significance as resource depletion climate change and ecological degradation worsen. Rapidly expanding industries and shifting consumption patterns have made data-driven policy frameworks and strategic environmental governance more crucial than ever. Due to fragmented stakeholder coordination lax enforcement of regulations and poor data utilization many organizations struggle to implement sustainable practices despite international initiatives. This research aims to develop a comprehensive framework for managing environmental sustainability that enhances decision-making optimizes resource utilization and reduces environmental impact through empirical analysis and workable solutions. The study employs a mixed-methods approach to collect primary and secondary data from industrial sectors and environmental monitoring organizations in different regions as well as sustainability reports. Stakeholder interviews and reviews of policy documents yield qualitative information while carbon footprint energy efficiency ratios water consumption measurements and waste generation indicators are examples of quantitative data. As part of data measurement standardized environmental performance indicators (EPIs) are applied in compliance with ISO 14001 guidelines and Global Reporting Initiative (GRI) standards. The proposed model combines lifecycle assessment (LCA) material flow analysis (MFA) and sustainability balanced scorecard (SBSC) techniques to evaluate performance in-depth. Predictive modeling for environmental risk assessment using AI and machine learning as well as the development of an industry-specific Sustainability Integration Index (SII) of best practices for sustainable operations are some of the primary results. A robust adaptable and flexible environmental sustainability management system that can manage complex ecological problems in a variety of industrial contexts is made possible by this study.