Extending the Life Cycle Methodology to Cover Impacts of Land Use Systems on the Water Balance (7 pp)
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- Heuvelmans, G., Muys, B. & Feyen, J. Int J Life Cycle Assessment (2005) 10: 113. doi:10.1065/lca2004.05.159
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Goal, Scope and Background
Whilst initially designed for industrial production systems, environmental life cycle assessment (LCA) has recently been increasingly applied to agriculture and forestry projects. Several authors suggested that the standard LCA methodology needs to be refined to cover the particularities of agri- and silvicultural production systems. Until now, water quantity received little attention in these methodological revisions, notwithstanding the well-known impact of agriculture and forestry on issues like water availability, drought and flood risk. This paper proposes an add-on to existing LCA methods in the form of an indicator set that integrates water quantity impacts of agri- and silvicultural production.
First, system boundaries are discussed in order to identify the water flows between the production system and the environment. These flows are attributed to impact categories, linked to environmental burdens and to the areas of protection. Appropriate indicators are selected for each potential burden.
Results and Discussion
At the present, two input related impact categories deal with water quantity: Abiotic resource depletion and land use. The list of output related impact categories presented by Udo de Haes et al. (1999) does not include water quantity impacts like flood and drought risk. A new impact category “regional water balance” is introduced to cover these risks. Exceedance probabilities are used as indicators for these temporal variations in streamflow.
Conclusion and Outlook
The method presented in this paper can bring a life cycle assessment closer to real world concerns. The main drawback, however, is the increasing data requirement that might hinder the feasibility of the method. Future research should focus on this problem, for instance by applying a relatively simple numerical model that can calculate the indicator scores from more easily accessible data.