Non-marginal AWARE

Non-marginal AWARE factors are to be used to characterise the environmental impacts of large water consumptions (application context for marginal versus non-marginal is discussed in the first two papers below). Two sets of non-marginal AWARE factors have been derived (first and third paper below). They are not the result of a consensus-building process and WULCA makes no recommendations regarding their use at this point. What is important for the validity of the study is to use the factors for the context of application for which they were developed.

1. Marginal and non-marginal approaches in characterization: how context and scale affect the selection of an adequate characterization model. The AWARE model example

by Anne-Marie Boulay, Lorenzo Benini & Serenella Sala.

Article in The International Journal of Life Cycle Assessment · August 2019 DOI: 10.1007/s11367-019-01680-0

This work was supported by the WULCA Sponsors 2016-2018


LCA traditionally has been founded on the ceteris paribus principle, by which the assessed contribution is assumed not to affect the background state, i.e., being marginal. As LCA is increasingly used to assess interventions at larger scales (e.g., territory, sectors), it becomes necessary to provide adequate characterization factors. Applying this concept to the water scarcity footprint AWARE model, this paper has for main objective to provide guidance on the use of different characterization approaches; the resulting interpretation, including in relation to normalization; and the implication for decision making.


The specific case of AWARE is taken, and average factors are calculated by integrating the characterization factor’s equation of the AWARE model with respect to local water consumption, and dividing the total impacts obtained per each cubic meter consumed. The resulting average factors are applied (at the country scale) to European Union countries for the total water consumption, and the results are compared with the same assessment performed using the traditional marginal factors.

Results and discussion

Average CF at the watershed level for AWARE are provided for the country scale. Differences, sometimes significant, are observed between the two sets, with the average factors always being lower than (or equal to) the marginal ones. The rank correlation coefficient (correlation between the watershed values’ rank with both approaches) is of 0.965, and the mean difference coefficient is 0.16 (the larger the value, the more different the datasets, out of a maximum value of 2). For countries presenting areas with potentially more extreme water scarcity, the difference between the two normalization sets is higher, reflecting that there can be significant differences in applying the marginal or average CFs. A set of points for attention for methodological choices are presented and specific recommendations discussed from the perspective of the practitioner. In particular, by building on the shortcomings shown of marginal and average characterization factors, a broader application of LCIA is proposed to large-scale, non-marginal, and prospective assessments.


In conclusion, as goals and scopes of life-cycle-based studies are expanding, it is important to ensure that methodologies used reflect the new applications and the specific context for which LCA is needed. This paper provides the average CF for the AWARE model, which will now allow practitioners to assess water scarcity footprint of large interventions coherently, providing guidance on the implication of the selection of marginal or average CFs and the interpretation thereof. It also provides important guidance for practitioner to apply when using characterization factors of any methods in order to ensure coherence of their interpretation and consistency within their study.

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2. Comment to “Marginal and non-marginal approaches in characterization: how context and scale affect the selection of an adequate characterization factor. The AWARE model example”

by Silvia Forin, Markus Berger & Matthias Finkbeiner

3. Water scarcity footprint of hydropower based on a seasonal approach - Global assessment with sensitivities of model assumptions tested on specific cases

by Stephan Pfister, Laura Scherer, Kurt Buxmann

According to ISO 14046 the quantification of the water scarcity footprint (WSFP) of hydropower reservoirs has to consider (1) the evaporation of water from the surface of the reservoir, (2) the baseline evaporation of water of the same area before the reservoir has been built, and (3) the water scarcity index of the location of the reservoir on a spatially and temporally explicit level.
When a reservoir has a storing function, e.g., for irrigation in the dry season, monthly water scarcity indexes have to be used in order to calculate the WSFP, since storage in wet seasons and release in dry seasons can counteract water scarcity and lead to a reduction of overall water scarcity in the watershed.
This paper builds on previous research regarding detailed hydropower modeling and extends the water scarcity assessment to include and advance new methods for identifying sensitivities in monthly WSFP of hydropower due to the choice of impact assessment methods. We applied the global analysis to 1473 hydropower plants covering >100 countries, and added a detailed assessment for a subset of important power plants to discuss the limitations of global assessments. We thereby provide the most complete WSFP of global hydropower with state-of-the-art methods, assess the robustness of the global model and different methodological choices, and provide new monthly average AWARE CFs on watershed level.
The results show that water scarcity can often be mitigated if the net evaporation is compensated by the storage effects. The two water scarcity metrics applied lead to larger differences than expected, since the monthly dynamics of dams can lead to stronger differences than the differences in the applied water scarcity factors. The new insights help to better understand the WSFP of hydropower and its uncertainties.

Link to paper and data