• • The proposed method reduces carbon footprint deviation from 0.05% to 3.03% and uncertainty from 2.65% to 5.45% as data missing deepens, demonstrating robust accuracy under incomplete data.
• • Under mixed data missing scenarios, carbon footprint uncertainties for five electromechanical products (including transformers, high-speed diesel engines, wind turbines) remain below 10%, validating industrial applicability.
• • Integration of data quality indicators and Monte Carlo simulation quantifies uncertainty, enabling identification of critical data inputs and improving reliability of carbon footprint results.
• • The method addresses data gaps from suppliers, manufacturers, and databases via substitution algorithms and correction coefficients, overcoming limitations of existing single-product LCA studies.