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13.1.1 The Importance of Metrics
We try to measure what we value. We come to value what we measure.
—Donella Meadows (Meadows 1998)
Metrics are useful to measure what we value and facilitate effective stakeholder
communication, engagement, and decision-making around FEW activities, regulations, and targets. As suggested by the quote above, there are two primary intents of
metrics:
First, metrics attempt to capture what society values.
Second, society is itself molded by the ongoing effort to bestowing the measured quantities with greater value.
There is, therefore, a purposeful dimension to this act of measurement: a hope to
mold society in a specific way, in an attempt to better align societal actions to specific values. An example familiar to most people is the use of atmospheric carbon
dioxide concentrations as a metric by which to discuss and guide action on climate
change mitigation (Chap. 11). Well-defined metrics are crucial for the ability of
stakeholders and decision-makers to sift through competing arguments for and
against different FEW nexus policies. However, different stakeholders might
emphasize one set of metrics over another to focus attention on what they deem
most important. Figure 12.3 shows how metrics enable conversations regarding how
data we measure translate to values of different stakeholders. Metrics are more reliable when more stakeholders agree that they accurately summarize both data and
values, and vice versa.
In the economic sector, gross domestic product (GDP) is a widely used metric of
economic development and a significant driver of economic policies. However,
GDP is deficient as a measure of other important societal concerns such as quality
of life and environmental conditions (see Sect. 3.4). As a result, excessive use of
GDP can result in policies which increase GDP but have detrimental effects on
other areas (Heun et al. 2015). In recent decades, efforts have been made to extend
the assessment of economic activities beyond the simple accounting of market
prices (see Chap. 5).
Certain computational frameworks help quantify societal energy and material
investments and environmental impacts associated with the provision of goods and
services. These computational methods help translate data from one domain (e.g.,
water) to another (e.g., energy), and can enable the definition of metrics useful in
both domains. Examples of such computational frameworks include Net Energy
Analysis (NEA) (Slesser 1974) and Life Cycle Assessment (LCA) (ISO 2006) (see
Sect. 13.2.1) which seek to comprehensively quantify the net impact of all activities
and impacts of a process. The State of California for example has incorporated LCA
into evaluating its Low Carbon Fuel Standards. Such frameworks can be implemented at both the establishment (micro) and meso/macro scales.
M. Carbajales-Dale and C. W. King
13.1.1 The Importance of Metrics
We try to measure what we value. We come to value what we measure.
—Donella Meadows (Meadows 1998)
Metrics are useful to measure what we value and facilitate effective stakeholder
communication, engagement, and decision-making around FEW activities, regulations, and targets. As suggested by the quote above, there are two primary intents of
metrics:
First, metrics attempt to capture what society values.
Second, society is itself molded by the ongoing effort to bestowing the measured quantities with greater value.
There is, therefore, a purposeful dimension to this act of measurement: a hope to
mold society in a specific way, in an attempt to better align societal actions to specific values. An example familiar to most people is the use of atmospheric carbon
dioxide concentrations as a metric by which to discuss and guide action on climate
change mitigation (Chap. 11). Well-defined metrics are crucial for the ability of
stakeholders and decision-makers to sift through competing arguments for and
against different FEW nexus policies. However, different stakeholders might
emphasize one set of metrics over another to focus attention on what they deem
most important. Figure 12.3 shows how metrics enable conversations regarding how
data we measure translate to values of different stakeholders. Metrics are more reliable when more stakeholders agree that they accurately summarize both data and
values, and vice versa.
In the economic sector, gross domestic product (GDP) is a widely used metric of
economic development and a significant driver of economic policies. However,
GDP is deficient as a measure of other important societal concerns such as quality
of life and environmental conditions (see Sect. 3.4). As a result, excessive use of
GDP can result in policies which increase GDP but have detrimental effects on
other areas (Heun et al. 2015). In recent decades, efforts have been made to extend
the assessment of economic activities beyond the simple accounting of market
prices (see Chap. 5).
Certain computational frameworks help quantify societal energy and material
investments and environmental impacts associated with the provision of goods and
services. These computational methods help translate data from one domain (e.g.,
water) to another (e.g., energy), and can enable the definition of metrics useful in
both domains. Examples of such computational frameworks include Net Energy
Analysis (NEA) (Slesser 1974) and Life Cycle Assessment (LCA) (ISO 2006) (see
Sect. 13.2.1) which seek to comprehensively quantify the net impact of all activities
and impacts of a process. The State of California for example has incorporated LCA
into evaluating its Low Carbon Fuel Standards. Such frameworks can be implemented at both the establishment (micro) and meso/macro scales.
M. Carbajales-Dale and C. W. King