title: Trends in sensitivity analysis practice in the last decade.
abstract: The majority of published sensitivity analyses (SAs) are either local or one factor-at-a-time (OAT) analyses, relying on unjustified assumptions of model linearity and additivity. Global approaches to sensitivity analyses (GSA) which would obviate these shortcomings, are applied by a minority of researchers. By reviewing the academic literature on SA, we here present a bibliometric analysis of the trends of different SA practices in last decade. The review has been conducted both on some top ranking journals (Nature and Science) and through an extended analysis in the Elsevier's Scopus database of scientific publications. After correcting for the global growth in publications, the amount of papers performing a generic SA has notably increased over the last decade. Even if OAT is still the most largely used technique in SA, there is a clear increase in the use of GSA with preference respectively for regression and variance-based techniques. Even after adjusting for the growth of publications in the sole modelling field, to which SA and GSA normally apply, the trend is confirmed. Data about regions of origin and discipline are also briefly discussed. The results above are confirmed when zooming on the sole articles published in chemical modelling, a field historically proficient in the use of SA methods.
authors.affiliation: European Commission, Joint Research Centre (JRC), Unit of Econometrics and Applied Statistics, via Enrico Fermi 2749 TP 361, Ispra, 21027 VA, Italy.
authors.forename: Federico
authors.initials: F
authors.lastname: Ferretti
authors.affiliation: Centre for the Study of the Sciences and the Humanities (SVT), University of Bergen (UIB), Spain; Institut de Ciència i Tecnologia Ambientals (ICTA), Universitat Autonoma de Barcelona, Spain.
authors.forename: Andrea
authors.initials: A
authors.lastname: Saltelli
authors.affiliation: European Commission, Joint Research Centre (JRC), Institute for Energy and Transport, via Enrico Fermi 2749 TP 690, Ispra, 21027 VA, Italy.
authors.forename: Stefano
authors.initials: S
authors.lastname: Tarantola
keywords: Bibliometric analysis; Chemical modelling; Global sensitivity analysis; Sensitivity analysis