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and interactions with feedbacks and constraints. Future research should focus on
modeling methods that can translate across the various categories of metrics we
have outlined here. This is important is because FEW constraints are primarily
expressed by extensive metrics, both biophysical and socioeconomic, but many
decisions and investments are made based upon establishment-level intensive
improvements.
We hope that the framework outlined here might provide a useful lens by which
to categorize metrics and inform future discussion and planning efforts. Proper metrics can be used to expand the impact categories (economic, social, environmental)
to include impacts on other community capitals, including political, cultural, built,
and human. See Chap. 3 to consider how various metrics might relate the indicators
identified for the UN Sustainable Development Goals.
If data or models identify critical thresholds or leverage points, then these
“boundaries” can be used to create relative metrics that inform resilience of a system of interest. For example, there seems to be a strong connection between the
percentage of GDP spending on energy commodities and economic recession. We
might already be reaching a fundamental constraint on current FEW interdependencies at the overall economic (meso/macro) scale. Therefore, understanding FEW
constraints is becoming more important as many long-term trends now indicate
diminishing returns (King 2015).
Key Points
• Metrics provide an important bridge between data, models, policy, and ultimately
behavior.
• Our choice of metric can constrain which data we collect, how our models are
framed, and thus our ability to understand the world.
• There is a trade-off between comprehensiveness (more metrics) and comprehensibility (fewer metrics).
• The chapter presents a taxonomy of metric dimensions across which metrics can
vary: intensive vs. extensive and absolute vs. relative.
• FEW metrics can be applied at different system spatial and temporal scales.
• Different stakeholders can prefer different metrics and system scales depending
upon their strategic objectives.
• Life cycle assessment provides a useful framework within which to understand
interactions among food–energy–water systems and thus guide our choice of
metrics.
Discussion Points and Exercises
1. Describe why the identification of metrics is an important step in action toward
building a more sustainable food–energy–water nexus.
2. Identify a historical example within the FEW nexus which follows the
data → models → metrics → actions process outlined in Fig. 12.3. Was this
example successful in your view (i.e., the example might or might not have produced an action that you agree with)?
3. Describe how data (un)availability might impact our choice of metrics (with
reference to examples) and determine an approach to mitigate this problem.
M. Carbajales-Dale and C. W. King
and interactions with feedbacks and constraints. Future research should focus on
modeling methods that can translate across the various categories of metrics we
have outlined here. This is important is because FEW constraints are primarily
expressed by extensive metrics, both biophysical and socioeconomic, but many
decisions and investments are made based upon establishment-level intensive
improvements.
We hope that the framework outlined here might provide a useful lens by which
to categorize metrics and inform future discussion and planning efforts. Proper metrics can be used to expand the impact categories (economic, social, environmental)
to include impacts on other community capitals, including political, cultural, built,
and human. See Chap. 3 to consider how various metrics might relate the indicators
identified for the UN Sustainable Development Goals.
If data or models identify critical thresholds or leverage points, then these
“boundaries” can be used to create relative metrics that inform resilience of a system of interest. For example, there seems to be a strong connection between the
percentage of GDP spending on energy commodities and economic recession. We
might already be reaching a fundamental constraint on current FEW interdependencies at the overall economic (meso/macro) scale. Therefore, understanding FEW
constraints is becoming more important as many long-term trends now indicate
diminishing returns (King 2015).
Key Points
• Metrics provide an important bridge between data, models, policy, and ultimately
behavior.
• Our choice of metric can constrain which data we collect, how our models are
framed, and thus our ability to understand the world.
• There is a trade-off between comprehensiveness (more metrics) and comprehensibility (fewer metrics).
• The chapter presents a taxonomy of metric dimensions across which metrics can
vary: intensive vs. extensive and absolute vs. relative.
• FEW metrics can be applied at different system spatial and temporal scales.
• Different stakeholders can prefer different metrics and system scales depending
upon their strategic objectives.
• Life cycle assessment provides a useful framework within which to understand
interactions among food–energy–water systems and thus guide our choice of
metrics.
Discussion Points and Exercises
1. Describe why the identification of metrics is an important step in action toward
building a more sustainable food–energy–water nexus.
2. Identify a historical example within the FEW nexus which follows the
data → models → metrics → actions process outlined in Fig. 12.3. Was this
example successful in your view (i.e., the example might or might not have produced an action that you agree with)?
3. Describe how data (un)availability might impact our choice of metrics (with
reference to examples) and determine an approach to mitigate this problem.
M. Carbajales-Dale and C. W. King