357
(e.g., total evapotranspiration for crops and irrigated lawns, evaporation from
thermoelectric power plant cooling). Therefore, the impact of water use increases as
the limit is approached, that is, as A → D.
It is the knowledge of how close a system is to reaching one or more boundaries
that provides feedbacks for prompting operational changes or new actions. Chapter
19 discusses three case studies of large watersheds. These case studies demonstrate
how each basin is experiencing effects from one or more specific limits, or boundaries, that influence both the withdrawal and consumption of water from the basin
as a source as well as the discharge of pollutants into the basin as a sink. Other
non- water limits, such as deforestation goals, also affect water infrastructure and
use, such as via hydropower dams in the Amazon basin.
Thus, the use of relative metrics is crucial in discussing the management and
distribution of limited resources. A pure focus on absolute metrics avoids discussion
of the distribution of resources because there is no conceptual limit on the absolute
quantity of that resource (i.e., there is no need to discuss how to distribute an infinite
quantity).
Ultimately, the definition of establishment-level performance targets raises
issues regarding the distribution of resources, impacts, and accountability within the
larger context of the network or region being analyzed. This distribution of access
and accountability is often the focus of decision-making and planning and often
makes consensus difficult, particularly in the context of historic access rights, such
as those to water. This now prompts a discussion of how metrics—both absolute and
relative, both intensive and extensive—fit into the context of informing action.
13.2.3.3 Summary of Intensive–Extensive and Absolute–Relative Metric
Combinations
Given the two categories of metrics from A and B, there are effectively four possible
combinations of metrics at the establishment or meso/macro scale.
Table 13.1 shows examples of each combination of metrics in the context of the
FEW nexus.
Absolute–intensive metrics are those that have the form of “unit q/unit y.”
Generally, but not always the numerator (unit q) quantifies an input to a process, and
the denominator (unit y) quantifies an output from the process (as depicted in
Fig. 13.1 and Eq. (13.1)), but an output/input framework can also be used, as in
efficiency metrics. In the context of biophysical units within FEW systems, there
can be nine combinations of metrics (energy–water, water–food, food–energy, etc.).
There can also be socioeconomic metrics with non-FEW units, such as financial
cost. An example of an absolute–intensive metric is the electricity input per unit of
treated water [kWh/m
3
] produced by a reverse osmosis desalination plant.
An absolute–intensive metric can be dimensionless, and energy return on
investment (EROI), which has units of energy output per unit of energy invested,
is an example. These types of metrics help understand how a technology or management practice might impact an input-output relationship: What type of water
13 Metrics
(e.g., total evapotranspiration for crops and irrigated lawns, evaporation from
thermoelectric power plant cooling). Therefore, the impact of water use increases as
the limit is approached, that is, as A → D.
It is the knowledge of how close a system is to reaching one or more boundaries
that provides feedbacks for prompting operational changes or new actions. Chapter
19 discusses three case studies of large watersheds. These case studies demonstrate
how each basin is experiencing effects from one or more specific limits, or boundaries, that influence both the withdrawal and consumption of water from the basin
as a source as well as the discharge of pollutants into the basin as a sink. Other
non- water limits, such as deforestation goals, also affect water infrastructure and
use, such as via hydropower dams in the Amazon basin.
Thus, the use of relative metrics is crucial in discussing the management and
distribution of limited resources. A pure focus on absolute metrics avoids discussion
of the distribution of resources because there is no conceptual limit on the absolute
quantity of that resource (i.e., there is no need to discuss how to distribute an infinite
quantity).
Ultimately, the definition of establishment-level performance targets raises
issues regarding the distribution of resources, impacts, and accountability within the
larger context of the network or region being analyzed. This distribution of access
and accountability is often the focus of decision-making and planning and often
makes consensus difficult, particularly in the context of historic access rights, such
as those to water. This now prompts a discussion of how metrics—both absolute and
relative, both intensive and extensive—fit into the context of informing action.
13.2.3.3 Summary of Intensive–Extensive and Absolute–Relative Metric
Combinations
Given the two categories of metrics from A and B, there are effectively four possible
combinations of metrics at the establishment or meso/macro scale.
Table 13.1 shows examples of each combination of metrics in the context of the
FEW nexus.
Absolute–intensive metrics are those that have the form of “unit q/unit y.”
Generally, but not always the numerator (unit q) quantifies an input to a process, and
the denominator (unit y) quantifies an output from the process (as depicted in
Fig. 13.1 and Eq. (13.1)), but an output/input framework can also be used, as in
efficiency metrics. In the context of biophysical units within FEW systems, there
can be nine combinations of metrics (energy–water, water–food, food–energy, etc.).
There can also be socioeconomic metrics with non-FEW units, such as financial
cost. An example of an absolute–intensive metric is the electricity input per unit of
treated water [kWh/m
3
] produced by a reverse osmosis desalination plant.
An absolute–intensive metric can be dimensionless, and energy return on
investment (EROI), which has units of energy output per unit of energy invested,
is an example. These types of metrics help understand how a technology or management practice might impact an input-output relationship: What type of water
13 Metrics