Natural and constructed evaluation criteria measure impacts on an objective directly. However, when neither can be found or developed in a practical way, proxy attributes are used to measure impacts indirectly. Proxy criteria are those that correlate well with objectives that are otherwise difficult to measure or estimate. A common example of this is the use of habitat area as a proxy for the degree of welfare of an endangered species. Whereas it is almost impossible to estimate with any degree of certainty the number of (say) marbled murrelets the British Columbia, it is much easier to estimate the number of hectares of old growth forest on which marbled murrelets depend. Such proxy criteria can be a very attractive option, but there are several potential pitfalls.

When a proxy attribute is used, the decision maker must implicitly consider the relationship between the proxy attribute and the endpoint it represents. As long as there is a one-to-one relationship between a fundamental objective and a proxy attribute, the situation is reasonably manageable for decision makers (Keeney, 1992). However, in ecological systems, the prevalence of non-linear phenomena makes reliance on proxy attributes without exploration of their implications a dangerous practice. There are three interrelated problems:

They hide non-linear relationships between proxy and endpoint. For example, the area of available spawning habitat serves as a good proxy indicator for the productive potential of wild salmon. However, this proxy only holds for as long as spawning habitat limits the productive capacity of salmon; after a point, adding more and more spawning habitat does not necessarily provide any benefit at all. Any such threshold effects can result in decision makers placing inappropriately high or low weights on a proxy.

They mask uncertainty in the relationship between proxy and endpoint. Proxy indicators can hide the uncertainty in the relationship between the proxy and the true endpoint, even while the proxy itself can be estimated with confidence. For example, although we might be quite certain of being able to produce 25% more bugs to serve as food for fish, we may have no idea whether this is what is limiting fish production. If what we really care about is fish rather than bugs, which seems likely, it is important that decision makers know that there is great uncertainty about whether an action that produces more bugs will in turn produce more fish. By choosing the proxy as the evaluation criterion, subsequent analytical effort is focused on developing accurate estimates of the proxy, leaving potentially gaping holes in understanding about how the endpoint will respond.

They obscure important value judgments. When one proxy criterion serves as a proxy for multiple endpoints and the relationship with one endpoint is different than with another, important trade-offs between the endpoints may not be exposed. For example, when dust control, visual quality and wildlife are all fundamental objectives in managing a reservoir drawdown zone, it seems easy and intuitive to use the area of vegetation as a proxy for them all. However, the type of vegetation that maximizes dust control (achieved by policies that produce large areas sparsely vegetated with simple non-native grass communities) is not the same as the type of vegetation that maximize wildlife value (achieved by policies that produce relatively smaller areas vegetated with complex shrub and cottonwood communities and a diversity of native grasses). Thus overemphasis on the proxy can obscure dialogue about what really matters.

These lead to important process concerns:

Proxies reinforce reliance on technical experts.

In environmental risk decisions, the ability to assess the relationship between a proxy and its endpoint usually requires detailed technical knowledge. As a result, non-technical stakeholders will be forced to rely on technical specialists to provide insight into this relationship. It can become difficult to separate the technical judgment about the relationship between the attribute and the endpoint from the value judgment about how much weight (importance) to give the endpoint. This tends to increase the power or perceived power of technical stakeholders, who should have no more legitimacy (and sometimes less) than non-technical participants for making value judgments.

They lead to reification which can hinder communication and collaboration.

Related to this is the problem of reification. Sometimes a proxy indicator is so closely associated with the objective it is intended to measure that it mistakenly is treated as an objective in its own right. GDP growth, for example, is generally interpreted as an indicator of the overall health of a country’s economy. However, some government policies may become aimed at increasing GDP growth even though the policy may actually harm the economy’s health (for example by artificially introducing regulations that increase the transaction volume of domestic trade). This process is called reification, and can happen when proxies are used. In the above example, a group can easily become so obsessed with producing dense vegetation in the drawdown zone (through the introduction of non-native grasses for example) that they fail to notice that their innovative new alternative – introduction of non-native grasses – does not support the fundamental objective of supporting native wildlife.

Key Ideas

• Only use proxies when natural and constructed criteria are not available
• Proxies can hide non-linear relationships; mask uncertainty; and obscure value judgments
• Proxies can reinforce reliance on technical experts
• Proxies that are closely associated with objectives tend to be mistakenly treated as objectives themselves