QSAR Models for Climate Impact Assessment: Born to Be Sustainable.

The Global Warming Potential (GWP) is among the metrics used as midpoints in the Life Cycle Impact Assessment (LCIA) phase of the Life Cycle Assessment. However, GWP values are not always available. In recent years, predictive modelling approaches have been increasingly developed to address existing data gaps. In this study, new Quantitative Structure-Activity Relationship (QSAR) models were developed to support climate impact assessment by predicting GWP and Radiative Efficiency of chemicals from the molecular structure. These models, which are built using Multiple Linear Regression, are designed to comply with the OECD (Organization for Economic Co-operation and Development) principles for QSAR use for regulatory purposes: they are transparent, validated, characterized by a quantitative domain of applicability and based on interpretable molecular descriptors. These QSARs are characterized by lower complexity, in terms of the number and type of descriptors, and easier applicability than other models with similar performance available in the literature for the same endpoints. A dedicated case study shows how the new QSARs can be applied to screen a large dataset including more than 13,000 possible alternatives to harmful chemicals. These results show that the new models may be suggested as valuable support in LCIA procedure and for the screening of environmentally preferable and safer alternatives.