Quantifying energy consumption variability in injection molding: A meta-regression analysis

• Conducted a meta-analysis of injection molding energy consumption. • Quantified variability in specific energy consumption (SEC) across polymers, machines, and process parameters. • Identified material theoretical heat energy, injection utilization, and machine type as key predictors of SEC. • Proposed a standardized checklist to improve cross-study comparability. Injection molding dominates global thermoplastic production, making its energy performance critical to sustainability assessments. Specific energy consumption (SEC, kWh/kg) is a key metric for comparing manufacturing efficiency and is widely used in decision-support tools such as life cycle assessment (LCA). However, for injection molding, SEC is often represented by a single generic value, with limited consideration of variability across material types, process parameters, and system configurations, potentially compromising the robustness of LCA results. Here, we conduct a meta-analysis of 160 energy-use data points from 15 peer-reviewed studies, spanning 20 material types and three machine types. We find a mean SEC of 3.13 kWh/kg, which is 2 to 2.5 times higher than default values in commonly used databases, and observe that acrylonitrile butadiene styrene (ABS) exhibits a distinct high-SEC cluster. A mixed-effects meta-regression further identifies material theoretical heat energy, machine type, and injection utilization as the primary drivers of variability, collectively explaining 74.2 % of the observed variance. Selecting polymers with low theoretical heat energy and maximizing injection utilization can reduce SEC by up to 50 % across machine types. This study provides a data-driven model to improve LCA accuracy and guide energy-efficiency strategies in sustainable materials manufacturing.