Operational Efficiency, Resource Utilization, Waste, and Environmental Performance in Bangladesh Apparel Manufacturing: A Data-Driven Sustainability Model

Abstract

Bangladesh’s apparel sector faces pressure to improve production efficiency while reducing resource consumption, textile waste, and environmental burdens. Yet prior research often embeds these issues within broad sustainability indices, obscuring the operational mechanisms connecting them. This study develops a quantitative sustainability decision model for Bangladesh apparel manufacturing using four tightly connected constructs. The main objective is to explain how operational efficiency may improve environmental performance through resource utilization and waste reduction. Three questions assess upstream relationships, downstream effects, and the mediating roles of resource utilization and waste reduction. Six hypotheses predict positive direct relationships across these paths and positive indirect effects through both proposed mediators. The Natural-Resource-Based View grounds the model because it explains how resource-related capabilities can generate measurable environmental outcomes. The factory-level scope excludes household behavior and macroeconomic indicators while focusing on qualified managers and technical professionals. The study adopts a post-positivist, deductive, explanatory, quantitative, and cross-sectional design for organization-level analysis. A primary survey of 350–450 respondents are proposed, supported by stratification across factory size and geographic concentration. Covariance-based structural equation modeling tests theory, while partial least squares structural equation modeling assesses predictive robustness. Artificial neural network analysis provides a secondary nonlinear predictive extension after structural relationships are established. External Bangladesh benchmarks report 88 percent lighting efficiency adoption, 72 percent efficient motors, and 51 percent waste segregation. However, no common respondent-level dataset currently measures all four constructs, so the six hypotheses remain unestimated. This evidence boundary prevents claims about original path coefficients, mediation sizes, predictive accuracy, or causal relationships. The study contributes a parsimonious theoretical mechanism and an auditable decision framework for managers, policymakers, and researchers. Future research should collect harmonized longitudinal firm-level data linked with objective plant metrics to strengthen causal testing and policy design.