SAFE uses fuzzy logic which does not require an explicit mathematical model to calculate aggregate indicators, and it can process quantitative as well as qualitative information. Fuzzy logic avoids the use of weights which are often arbitrary or cannot be easily extracted from a decision maker. Moreover, SAFE is a rather simple model that respects the non-compensability property, while it is the only approach that evaluates sustainability taking into account the time dimension.

On the other hand SAFE has certain shortcomings that are found in other models as well:

1. It is subjective to an extent and it doesn’t possess a mechanism whereby the number of inputs is limited to the absolutely necessary ones. A certain overlap among indicators exists. For example, the number of hospital beds overlaps with public health expenditure or urban total particulates and urban NO₂ concentration overlaps with mortality from respiratory diseases. However, it is next to impossible to find causal models connecting such indicators.
2. The rule bases and the membership functions reflect the values, knowledge and biases of those who devise them. The rule bases of SAFE put equal weights of importance to the input variables, as is done in other aggregation methods after consulting with experts. Given that sustainability is not a concept amenable to a rigorous definition, subjectivity in its modeling is not surprising. This is the case with all other models of sustainability.
3. More work remains to be done to refine the weights and membership functions of certain indicators such as CO₂ emissions, nuclear and hazardous waste, loss of biodiversity, central government debt, etc., in order to capture emerging sustainability issues as reality changes.