The UNEP/SETAC Life Cycle Initiative

Author(s):  
Guido Sonnemann ◽  
Sonia Valdivia
2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Bradley Ridoutt ◽  
Danielle Baird ◽  
Kimberley Anastasiou ◽  
Gilly Hendrie

AbstractThe food system is responsible for around 70% of global freshwater use. Pathways toward responsible consumption and production of food are therefore critically needed to ensure the planetary boundary for freshwater use is not transgressed. There is also an uneven spatial distribution of freshwater resources and human water demands, meaning that water-scarcity is acute in some regions but a lesser concern in others. Quantifying the water-scarcity impacts associated with food consumption is therefore a complex challenge due to the diversity of individual eating patterns, the very large number of individual food products available, and the many different regions where food is grown or processed. To our knowledge, this is the first study to calculate water footprints for a large number of self-selected diets. Life cycle assessment was used to model the water-scarcity footprints of 9,341 individual Australian adult diets obtained through 24-hour recall as part of the most recent Australian Health Survey. Three water-scarcity indicators were used, including the AWARE model recently developed by a project group working under the auspices of the United Nations Environment Programme (UNEP) / Society of Environmental Toxicology and Chemistry (SETAC) Life Cycle Initiative (www.lifecycleinitiative.org). In addition, a diet quality score was calculated for each of these diets. Our objective was to identify pathways toward healthier diets with lower water-scarcity impacts. Dietary water-scarcity footprints averaged 362 L-eq person-1 day-1 and were highly variable (sd. 218 L-eq person-1 day-1), reflecting the diversity of eating habits in the general community. The largest water-scarcity impacts were related to the excessive consumption of discretionary foods (alcoholic beverages, processed meat products, dairy desserts, cream, butter, muesli bars, confectionery, chocolate, biscuits, cakes, waffles, fried potato and extruded snacks, etc.). The potential to reduce dietary water-scarcity impacts is large, although the opportunity to intervene through amended dietary guidelines is not straightforward due to the large variations in water-scarcity footprint intensity between individual foods within a food group, and the inability of consumers to identify lower water-scarcity footprint products without food labeling. Reductions in the water-scarcity footprint of Australian food consumption are likely best achieved through reductions in food waste, technological change to improve water-use efficiency in food production, as well as the implementation of product reformulation and procurement strategies in the food manufacturing sector to avoid higher water-scarcity footprint intensity ingredients.


2002 ◽  
Vol 7 (4) ◽  
pp. 192-195 ◽  
Author(s):  
Helias A. Udo de Haes ◽  
Olivier Jolliet ◽  
Greg Norris ◽  
Konrad Saur

2021 ◽  
Author(s):  
Victoria Tokareva ◽  
Igor Bychkov ◽  
Andrey Demichev ◽  
Julia Dubenskaya ◽  
Oleg Fedorov ◽  
...  

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 56 ◽  
Author(s):  
Igor Bychkov ◽  
Andrey Demichev ◽  
Julia Dubenskaya ◽  
Oleg Fedorov ◽  
Andreas Haungs ◽  
...  

Modern large-scale astroparticle setups measure high-energy particles, gamma rays, neutrinos, radio waves, and the recently discovered gravitational waves. Ongoing and future experiments are located worldwide. The data acquired have different formats, storage concepts, and publication policies. Such differences are a crucial point in the era of Big Data and of multi-messenger analysis in astroparticle physics. We propose an open science web platform called ASTROPARTICLE.ONLINE which enables us to publish, store, search, select, and analyze astroparticle data. In the first stage of the project, the following components of a full data life cycle concept are under development: describing, storing, and reusing astroparticle data; software to perform multi-messenger analysis using deep learning; and outreach for students, post-graduate students, and others who are interested in astroparticle physics. Here we describe the concepts of the web platform and the first obtained results, including the meta data structure for astroparticle data, data analysis by using convolution neural networks, description of the binary data, and the outreach platform for those interested in astroparticle physics. The KASCADE-Grande and TAIGA cosmic-ray experiments were chosen as pilot examples.


2002 ◽  
Vol 6 (1) ◽  
pp. 11-13 ◽  
Author(s):  
Helias A. Udo Haes

2015 ◽  
Vol 20 (8) ◽  
pp. 1045-1047 ◽  
Author(s):  
Julia Martínez-Blanco ◽  
Atsushi Inaba ◽  
Ana Quiros ◽  
Sonia Valdivia ◽  
Llorenç Milà-i-Canals ◽  
...  

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