The production, use and disposal of goods consumed in Switzerland are linked to land uses that can damage biodiversity. The “biodiversity footprint” indicator shows the extent of this damage. It is based on the potential species loss (i.e. the probability of a species becoming globally extinct) caused by specific types of land use, such as agriculture and settlements, compared to the natural state. It is calculated differently depending on the region of the world: If forest is turned into agricultural land in Europe, the potential biodiversity losses are lower than if this occurred in a rain forest region.
The indicator includes land uses abroad that are caused by imported products (footprint perspective).
Per capita, the pressure on biodiversity caused by Swiss consumption increased by around 8% from 2000 to 2018. It amounted to 7.2 species-years per trillion species (pico-PDF∙a, see Method) in 2018. In fact, the pressure abroad continued to rise sharply, while the domestic share of the biodiversity footprint fell from 42% to 30%. Foodstuffs and animal feed account for the largest share of the imported biodiversity footprint.
Due to the growth of the resident population in Switzerland, the absolute biodiversity footprint has increased even more than the biodiversity footprint per capita, i.e. from 48 to 61 micro-PDF∙a.
This increase between 2000 and 2018 will result long term in the additional loss of around 13 species per million, or an annual extinction rate of 0.7 species per million. This means that species loss caused by Swiss consumption alone is occurring at a similar rate as the observed natural global loss of one species per million annually (Steffen et al. 2015). A comparison with the natural extinction rate shows that Switzerland’s biodiversity footprint is far in excess of the threshold value – the value which, when extrapolated to the world’s population, is in line with the planet’s capacity. Because of this and the increase, the current state and trend are assessed as negative.
The pressure on biodiversity has increased to the same extent as Swiss final demand; in other words, prosperity and pressure on the natural world have not been decoupled at all, and biodiversity efficiency has not improved.
It is not possible to make an international comparison is not possible at this time for reasons of methodology.
The method corresponds to the interim recommendation of the UNEP-Life Cycle Initiative. It is based on Chaudhary et al. (2016) and quantifies the long-term expected potential loss caused by a specific land use (such as agriculture or settlements) compared to an untouched, natural reference state and takes into account that different land uses affect biodiversity with varying degrees of intensity. It also takes the vulnerability of species into consideration and converts the regional decline of commonly occurring species and the global extinction of endemic species into “completely globally extinct species”. Thus, it subsumes – similar to the way the greenhouse warming potential uses the kg of CO2-equivalent unit for greenhouse gases – varying impact intensities under one indicator. The equivalents of potentially globally extinct species are integrated over the years (a) and quantified per million species (micro-PDF∙a) or per trillion species (pico-PDF∙a) . It describes the likelihood that species will become irreversibly extinct due to land use.
Relationship to Switzerland’s Red Lists: The biodiversity footprint indicates the long-term potential species loss on a global level. Its approach differs substantially from that of the Red Lists and the corresponding data on biodiversity in Switzerland. This is why the biodiversity footprint cannot be compared with the latter. In addition, the biodiversity footprint covers only the main cause of species loss, i.e. land use. Other drivers of biodiversity loss such as climate change, nitrogen and pesticide inputs are not taken into account.
 Pico-PDF·a = 10-12 PDF·a (i.e. one trillionth PDF·a); PDF = potentially disappeared fraction of species; the term ‘species-years’ refers to this integration over time.
|Targeted trend||Initial value||Final value||Variation in %||Observed trend||Assessment|
|Decrease||Average 2000-2002||Average 2016-2018||9.36%||Growth||negative|