Two economic concepts every sustainability professional should know
BY VALENTINA PRADO, SENIOR SUSTAINABILITY ANALYST and PETER DUNN, MARKETING DIRECTOR for EARTHSHIFT GLOBAL
This article was first published by GreenBiz on MAY 8th 2018
As sustainability scientists, we are faced on a daily basis with complex, multi-dimensional problems that challenge our own analytical skills and our organizations’ decision-making processes. In the typical corporate sustainability environment, it’s essential that we provide management teams with robust, well-aggregated information and proposed solutions that take into account multiple priorities and constituencies.
Fortunately, the fields of economics and decision analysis offer helpful methods for achieving these goals — multi-criteria decision-making methods such as the analytical hierarchy process, multi-attribute utility, outranking and TOPSIS, among others. But it’s important to keep in mind that while all these methods are capable of aggregating data (indicating economic, environmental and social performance) into a single metric, it doesn’t mean it properly reflects the objectives of any given analysis. Understanding each method’s underlying assumptions, strengths and weaknesses helps us apply them appropriately to the problem at hand and provide our colleagues with the most useful information.
In this article, we’ll take a broader perspective on two underlying concepts of these methods that are most important when dealing with sustainability problems: transitivity and compensation. We’ll explore some opportunities they provide, pitfalls to be mindful of and recommendations for applying decision-support principles in a sustainability context.
It stems from the “Economic Man,” a fundamental concept of classical economics that underlies mathematical models used in optimization and decision-making. Economic Man is always rational; his behavior follows specific rules dictated by a particular view of the world. Hence, any decision that does not follow these rules is deemed “irrational,” even though it may still be (by other standards) a valid decision.
Economic Man’s two main rules (or axioms) are: more utility is better than less; and options are evaluated in an absolute manner, independently from one other. In other words, Economic Man does not compare. This independent evaluation gives solutions the property of transitivity: If Economic Man prefers A to B and B to C, A is preferred over C. Transitivity allows for a robust mathematical solution.
However, once we enter the realm of sustainability, problems become less tidy and more complex — there is generally no universal, best solution for all environmental, social, and economic aspects of a situation. This means that solutions are intransitive, much like a rock-paper-scissor game where each option has strengths and weakness. So how can we make use of a model that assumes transitivity?
Recall that achieving transitivity requires that each option be evaluated independently so that the evaluation of alternatives is unchanged as we introduce or remove options. This avoids inconveniencies such as “rank reversal” but can undermine the decision-making process, because these methods rely on information that is often unavailable. Searching for this input data makes the process cumbersome and troubling, and can even reach a point where the decision-making process is focused on feeding the model as opposed to evaluating the tradeoffs and the decision at hand. To assess options in an independent way, we need a reference — and how do we choose a reference given the varying scales of sustainability decisions?
Consider, for example, metrics of human toxicity, jobs, or climate change relative to a consumer product — how would we establish a threshold for the scale of individual projects or consumer products?
One recent trend is the incorporation of planetary boundaries into environmental assessments such as Life Cycle Assessment (LCA). These have the limitation of covering only the environmental dimension, and moreover, it’s difficult to incorporate these absolute-threshold references into the widely varying scales of sustainability problems. How can planetary boundaries be scaled to the level of a community project in Costa Rica while covering all three sustainability dimensions in a meaningful way?
In the absence of an absolute measure, we fall back on what humans naturally do best: comparing and choosing the best option out of multiple possibilities. This means that transitivity and its mathematical convenience can be lost, and forces us to acknowledge that the term “best” is relative and implies comparison.
The field of decision analysis is divided in this debate. On one side, there are normative approaches to decision analysis which follow Economic Man’s rationale. These methods are prescriptive and mathematically robust, but mathematical robustness comes at a high cognitive price. On the other side, a descriptive approach, closer to behavioral economics, proposes methods that align with how we naturally make decisions, that is, by comparison, in a context-dependent manner. These methods are easier to apply and understand but can be subject to mathematical discrepancies.
So what is a sustainability practitioner supposed to do? It’s generally best to use methods that evaluate alternatives with respect to each other to find the best overall compromise. Note that “best compromise” implies that it may not be the best at everything, but also not the worst. In sustainability, there may not be a perfect solution, so we try to find the most balanced one.
That brings us to the second concept we need to take into account: compensation, which refers to a method’s ability to compensate for poor performance with good performance. In a sustainability context, this can imply justifying environmental losses for economic gains. In fully compensatory methods, this trade can occur indefinitely — for example, as long as there are economic gains, we can continue to justify environmental degradation.
In sustainability science, compensation between forms of capital (social, economic and environmental) is known as a weak sustainability perspective. The danger of full compensation is that it can lead to extreme solutions that overemphasize a single performance criterion. Imagine that your grades in school could offset one another, so that your extraordinary ability in one subject would compensate for deficiencies in others to the extent that your overall grade was the best in the class. Would it be sensible to say that the best student in the class is failing all but one subject?
Clearly, full compensation is not suitable for finding a balance. But partially compensatory methods can be a good decision-making alternative. These methods use non-linear aggregation functions that limit compensation past a certain point. In our example of trading off environmental losses for economic gains, a partial compensation approach would say that some environmental loss is acceptable, but past a certain point, no money can adequately compensate.
The resolution to this decision analysis debate is that there is no perfect method. The practitioner must choose based on the specific problem at hand, the objectives and the available information.
However, by understanding the properties we’ve discussed, it is possible to make general recommendations for sustainability scientists looking to apply decision support methods:
- Make yourself aware of a method’s underlying assumptions.Just because it has been used before doesn’t mean it always will fit.
- Economic Man’s perspective seldom fits a sustainability problem. Understand how the method evaluates, rates, ranks alternatives and what type of information is needed for this process (the cognitive load).
- Inquire about how methods deal with trade-offs and handle compensation, and whether you find this appropriate for the problem. Overall, in cases dealing with more than one pillar of sustainability, where you would encounter trade-offs, a descriptive approach can be more suitable for supporting decisions as it recognizes intransitivity and context, and is more conscious of the cognitive efforts of the decision-maker.