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The nitrogen footprint has been proposed as an environmental indicator to quantify and highlight how individuals, organizations, or countries contribute to nitrogen pollution. While some footprint indicators have been successful in raising awareness of environmental pressures among the public and policy-makers, they have also attracted criticism from members of the life cycle assessment (LCA) community who find some footprints confusing and misleading as they measure substance and energy flows without considering their environmental impacts. However, there are also legitimate reasons to defend footprints as a useful class of indicators despite their incompatibility with LCA principles. Here, in light of this previous research and debate, we critically assess models and proposed uses for the nitrogen footprint, and explore options for further development. As the nitrogen footprint merely quantifies gross nitrogen emissions irrespective of time, location, and chemical form, it is a crude proxy of environmental and health impacts compared to other, more sophisticated environmental impact indicators. However, developing the nitrogen footprint toward LCA-compatible impact assessment would imply more uncertainty, more complexity, and more work. Furthermore, we emphasize that impact assessment has an unavoidable subjective dimension that should be recognized in any development toward impact assessment. We argue that the nitrogen footprint in its present form is already fit for some purposes, and therefore further development towards impact assessment may be unnecessary or even undesirable. For some uses it seems more important that the footprint has a clear physical meaning. We conclude that the best way forward for the nitrogen footprint depends crucially on what story it is used to tell.
This article was published in the following journal.
Name: Journal of environmental management
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Theoretical models simulating behavior or activities in nursing, including nursing care, management and economics, theory, assessment, research, and education. Some examples of these models include Orem Self-Care Model, Roy Adaptation Model, and Rogers Life Process Model.
Theoretical representations that simulate the behavior or activity of biological processes or diseases. For disease models in living animals, DISEASE MODELS, ANIMAL is available. Biological models include the use of mathematical equations, computers, and other electronic equipment.
Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.
Statistical models of the production, distribution, and consumption of goods and services, as well as of financial considerations. For the application of statistics to the testing and quantifying of economic theories MODELS, ECONOMETRIC is available.
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