Breaking the Data Barrier in Food LCAs: Minimal Proxy Variables for Robust Climate-Change Assessment in Wine Production via SVD and Stability Analysis

Reducing the number of input variables required for Life Cycle Assessment (LCA) is essential for improving the practicality of environmental analyses, as data collection remains one of the main barriers to LCA implementation. However, LCA datasets often lack the statistical properties required by conventional dimensionality reduction techniques such as Principal Component Analysis and Factor Analysis, including substantial shared variance and favourable observation to variable ratios. This study proposes an alternative analytical framework that combines Singular Value Decomposition (SVD) with algebraic indicators of impact concentration and structural stability. The methodology was evaluated using a dataset of Italian wine production systems for the Climate Change impact category. Preliminary diagnostics (KMO = 0.36) confirmed the unsuitability of covariance-based latent variable methods. The proposed framework instead analyses the concentration of environmental impacts through the Gini index and the recurrence of the most influential variables across observations. The results show that five original environmental impact contribution variables preserve more than 90% of the information contained in the original impact matrix, while four of them are independently confirmed as a structurally conserved core by both SVD and recurrence analysis. These findings suggest that conserved algebraic structures can support the identification of proxy variables for streamlined LCA, providing an assumption light methodology for reducing data collection requirements while preserving most of the information contained in the original dataset.