Abstract：This paper presents a systematic approach, named as CLCD-Q method, to assess and control data quality of LCA studies. The method starts with the uncertainty assessment of raw data and mathematical relations based on pedigree matrix. Afterwards, the uncertainties of process data and LCA results can be derived from two Monte Carlo simulations. For each LCA result, key process data and raw data with high uncertainty and high sensitivity in LCA model can be identified, which indicates the "hot spot" for data quality improvement. CLCD-Q is supported by LCA software (eBalance) and CLCD database. The case study of Chinese grid power shows that this method can guide the selection of raw data and the mathematical relations with the uncertainty assessment extending on the raw data. It also provides a guide for efficient data quality improvement by revealing the most relevant data in the life cycle model.