Energy Efficiency and Decarbonisation Pathways in Injection Moulding: A Life Cycle Assessment of End-of-Life Allocation Methods

Life Cycle Assessment (LCA) is extensively employed to support sustainability evaluation in waste management and manufacturing systems; however, outcomes are highly sensitive to methodological decisions, particularly end-of-life (EoL) allocation approaches. This study examines how cut-off and substitution approaches affect the energy performance and decarbonisation potential of high-density polyethylene (HDPE) injection moulding systems. A dual framework is adopted: first, a literature review examines methodological sensitivities in EoL modelling; second, a quantitative case study assesses industrial-scale primary data for the production of durable HDPE bottles (300 mL). The LCA model integrates specific technical parameters, including a 220 °C melt temperature and a 36 s cycle time, ensuring a realistic representation of manufacturing conditions. The results indicate that allocation choices significantly influence calculated impacts, sometimes reversing the relative ranking of configurations. Substitution-based approaches report higher benefits by crediting avoided primary production, while cut-off logic provides more conservative estimates. Quantitative analysis shows that transitioning from open-loop to fully closed-loop configurations reduces cumulative energy demand by 3.2% and freshwater emissions per functional unit by 2.8%. Furthermore, the study identifies a ‘landfill paradox’ specific to HDPE waste within transitional energy systems: due to the carbon sequestration effect of landfilled polymers and current grid emission factors, landfilling exhibits a lower net carbon footprint (0.03 kg CO2-eq./kg) than high-efficiency incineration (1.54 kg CO2-eq./kg). These findings highlight that circular economy evaluations are strongly shaped by methodological assumptions, with direct implications for energy policy. Bridging the gap between specific industrial processing parameters and end-of-life allocation logic underscores the need to incorporate primary industrial data and transparent allocation frameworks to support reliable decision-making in the transition toward low-carbon and energy-efficient manufacturing systems.