Framework for the inter-comparison of ecological footprint of universities

• Is the ecological footprint of universities comparable?
• How can model and parameter uncertainties affect the estimated ecological footprint?
• Uncertainty analysis can help in assessing the relevancy of parameters and in making the distinction.
• Results showed that model uncertainties have the largest impact on the estimates.
• Conclusion is that uncertainty analysis provides a robust framework for inter-comparison of universities.

The ecological footprint (EF) method represents the suitability of a given population on the carrying capacity of the total system. It was developed in order to measure the relationship between nature and humans, being supported on the premise that each individual requires a surface area that provides goods and services essential to life. In this article only in EF for universities is studied, but most of the underlying concepts and methods are valid for any other human activity for which EF may be applied.In this study an uncertainty analysis of EF of universities is made. This is, to the authors’ knowledge, the first time such a study is published on the subject. The intention is to demonstrate the usefulness of uncertainty analysis in the evaluation of results, inter-comparability, and on communication of EF outcomes.Results showed that EF model uncertainties have large impact on EF estimates, in particular in what regards the decision about accounting or not the contribution of key parameters. Inclusion or not of very sensitive parameters, for which there is also high uncertainty, in the estimation of EF may have a strong impact on the estimated values and also in the inter-comparability of EF estimates. This is the case of mobility.Uncertainty analysis, by studying model uncertainty, parameter uncertainty and variability, can provide a robust framework for the inter-comparison of ecological footprints of universities. In fact, the method may prove useful for the assessment of ecological footprints of any kind.

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