Gaussian Process Regression as a Replicable, Streamlined Approach to Inventory and Uncertainty Analysis in Life Cycle Assessment

Life
cycle assessment plays a critical role in quantifying environmental
impacts, but its credibility remains challenged when data and uncertainty
analysis are lacking. In this study, we propose a data compilation
framework to address these two issues. The framework first quantifies
the correlations of production activities among existing data in temporal,
geographical, and taxonomic dimensions. The framework then introduces
covariance functions to convert these correlations to a similarity
matrix, and the Gaussian process regression model is adopted to predict
new data based on these covariance functions. The associated uncertainty
is automatically characterized using the posterior distribution of
predictions. The framework is demonstrated on the nitrogen fertilizer
application rate for food productionan activity recognized
for its environmental burdenwith results capable of reflecting
temporal and geographical variations. By introducing the concept of
phylogenetic distance as a correlation of taxonomy, the framework
provides a quantitative basis for predictions in a proxy data usage
scenario. The framework can be used in developing temporally and regionally
representative life cycle inventories and databases and can facilitate
consistent uncertainty quantification in future life cycle assessment
methodologies.