Probability-Based Decision Tool Helps Clarify Climate Change Policy Questions
Uncertainty doesn’t necessarily imply a lack of clarity. We can be uncertain about the outcome of a particular event or question, but we can be very clear about the nature of the questions and the likely outcome of a sequence of those events. The study of global climate change is fraught with uncertainty, as is much of science in general. This isn’t unusual. By its very nature, science must be conservative. Findings and conclusions should have a high degree of statistical significance (that is, you wouldn’t expect them to happen by chance), yet at the same time those conclusions can never be announced as “certain” or “settled science.”
By Michael Cochrane
Uncertainty doesn’t necessarily imply a lack of clarity. We can be uncertain about the outcome of a particular event or question, but we can be very clear about the nature of the questions and the likely outcome of a sequence of those events.
The study of global climate change is fraught with uncertainty, as is much of science in general. This isn’t unusual. By its very nature, science must be conservative. Findings and conclusions should have a high degree of statistical significance (that is, you wouldn’t expect them to happen by chance), yet at the same time those conclusions can never be announced as “certain” or “settled science.”
But that uncertainty doesn’t mean we can’t be clear about the range of possible outcomes of events tied to global warming. Clarity regarding these questions is especially important for policymakers. Given the massive monetary and non-monetary costs to society associated with some policy decisions regarding anthropogenic (human caused) global warming (AGW), it’s important such decisions not be made in the current cauldron of politically heated rhetoric about AGW.
One way for policymakers to gain needed clarity and think rationally about governmental responses to the risks of global warming is to model the uncertainty surrounding the key questions that must be addressed in any response.
In “Modeling Climate Change Policy Decisions Using a Probability Tree,” we propose a probability-tree-based model designed to explore a range of scenarios associated with the answers to a sequence of yes/no questions fundamental to the issue of global warming. The questions are:
- Is the earth actually warming?
- If the earth is warming, is this actually a problem?
- If the earth is warming, and this is a problem, is human activity causing the warming?
- If human activity is the primary cause of global warming, will reducing this activity also reduce global warming?
- If human activity is not the primary cause of global warming, is it still possible to stop it?
You’ll notice that the first three questions are in direct sequence, each one conditioned on the probability of the previous question being a yes or a no. The last two questions are each conditioned on the positive or negative outcome of question three. It’s clear that such a sequence of questions can be modeled in the form of a tree with branches representing the outcomes. Following the branches sequentially through the tree results in six unique paths, or scenarios.
As we show in this paper, each of these scenarios corresponds to a general policy response ranging from “do nothing, because global warming doesn’t exist or is not really a problem” to the need for taking action, whether such action is a reduction of carbon-dioxide emissions or defensive measures to protect vulnerable regions of the world from the inevitable effects of global warming.
If you think this approach to modeling global warming policy outcomes and possible responses seems a bit naïve, you may be surprised. Tweaking the model and experimenting with various probabilities suggests that, even granting that global warming is real, is a problem, and humans are responsible, the probability that we can undo it by reducing carbon-dioxide emissions is very sensitive to changes in probabilities between yes and no.
Perhaps another reason to be careful when formulating policy under high degrees of uncertainty.
Michael Cochrane, Ph.D., Engineering Management and Systems Engineering, is Founder of Value Function Analytics, a consulting firm that helps clients achieve their objectives by helping them to think about values. Also a writer with World News Group and a Contributing Writer with the Cornwall Alliance, he has expertise in statistical modeling and analysis.
Originally published at Earth Rising.
