An Ecological Efficiency-Based Framework for Environmental Risk Assessment in Manufacturing

With the accelerating global transition toward green manufacturing and ecological sustainability, establishing an efficient environmental risk assessment framework has become a critical requirement for sustainable industrial transformation. The study develops an environmental risk assessment system for the manufacturing industry based on ecological efficiency. By combining the Analytic Hierarchy Process (AHP)and the Fuzzy Comprehensive Evaluation (FCE) method. Additionally, the Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm (GA) were integrated into a unified multi-objective optimization framework, enabling the coordinated enhancement of both economic and environmental performance. Experimental results demonstrate that the proposed system achieves the shortest running time under small-scale enterprise conditions, approximately 9 seconds initially and under 20 seconds at 30 enterprises, with stable growth across larger scales. For a scale of 90-enterprises, the runtime remains about 42 seconds. It also exhibited the fewest convergence iterations across industries at around 30 for electronics and new energy sectors, with as few as 28 for new energy manufacturing, and the highest risk classification accuracy, exceeding 0.95. These findings emphasize the novelty of integrating multiple decision-making and optimization techniques into a cohesive eco-efficiency optimization framework, demonstrating significant potential for green decision-making, environmental risk control, and sustainable industrial development.