The life cycle climate impacts of industry-specific machinery technologies: A meta-analysis

Arias-Castillo A., Viere T. The life cycle climate impacts of industry-specific machinery technologies: A meta-analysis. Resources Conservation and Recycling Advances, Volume 31, September 2026, 200354, https://doi.org/10.1016/j.rcradv.2026.200354

Abstract

Machinery technologies enable industrial production and societal development but can contribute substantially to greenhouse gas emissions and are often underrepresented in environmental assessments. This meta-analysis identified 86 life cycle assessment (LCA) studies of industry-specific machinery and synthesized the life cycle climate impacts of 39 machinery technologies from 10 categories (n = 150 datapoints). Median cradle-to-grave emissions ranged from 89.2 t CO2e/unit for additive manufacturing machinery to 1790 t CO2e/unit for lifting and handling equipment. Median cradle-to-gate emissions ranged from 4.7 to 336 t CO2e/unit. Across most technologies, the use-phase dominated total impacts; however, manufacturing impacts were not negligible for multiple technologies and can become relatively more important under low utilization and decarbonization pathways such as electrification. To address drivers beyond normalization, we applied rank-based correlation analyses to quantify how key parameters (mass, lifetime, and use intensity) relate to phase-specific climate impacts. Mass showed the strongest associations with both cradle-to-gate and operation impacts, while operating-time proxies were most informative for explaining burden allocation rather than consistently predicting absolute operational impacts across heterogeneous technologies. Finally, we developed an evidence-based, exploratory archetype map that positions technologies in a stage-resolved space defined by cradle-to-gate share, annual operating hours, and mass (embodied scale), and translates technology placement into prioritized circular economy strategies (R0–R9; narrowing, slowing, and closing resource loops). This can provide technology-linked guidance for bottom-up modeling and for prioritizing resource-efficiency and decarbonization interventions across machinery technologies. We also identified major evidence gaps, particularly limited manufacturing inventory transparency and underrepresentation of several machinery categories.