As members of the Energy Futures Policy Collaborative, we spend a lot of time thinking about the future (it’s right in the name!). One way of thinking about policy is as a stated direction for how we might collectively be in the future, alongside the boundaries and incentives that we think might help us get there. To carve out a successful path using policy, we try to imagine and understand what pitfalls and opportunities might be just over the horizon.
To help us figure that out, we use models: technological adoption models, climate models, energy system models. Of course, that’s easier said than done. The last 18 months of pandemic policy-making have been a powerful lesson in both the fallibility of models and the challenges of interpreting them correctly and acting. Does that mean we should consign models and modelling to the waste bin? No! But we need to be thoughtful about the kinds of models we’re embracing and how we use them.
First, it’s important to differentiate between two kinds of models at play in the climate and energy space.
Most commonly when people think of models, they think of predictive models. These are models that aim to predict the trajectory of a set of factors. Typically, predictive models work better with physical systems (like the rolling of a ball down a hill, or the operation of a machine) than they do with sociotechnical systems. Given what we know about the inputs, outputs, and processes related to global climate, as well as the variety and robustness of the modelling that has evolved around it, we can feel fairly confident about the predictive quality of climate models. Energy system modelling, on the other hand, is much more closely bound up in a myriad of (often irrational) human decisions, from consumer behaviour to political movements to the decisions of financial elites. Some energy system modellers opt to look at the rate of tech adoption and improvement as the solution here. However, change in the technology space may not be linear: technologies may lurk beneath the surface for years before sudden changes in their affordability or uptake. For many technologies, the shape of the graph is a longer period of time in plateau, then sudden inflection points. Solar and wind are a great example here, where the rate of cost decline outstripped the consensus predictions; on the inverse, the decline of coal has outstripped predictions. How do we make sense of this unpredictability?
Here’s where our other kind of model comes in: scenario modelling. Rather than attempt to predict the unpredictable, we instead look to models for a range of possible futures, or scenarios. Smart scenario work covers a wide range, including outlier, unlikely scenarios as well as a set of more-likely possibilities. There are two watch-outs we need to be careful to avoid. First, there’s a human cognitive short-cut that encourages us to assume that things will continue in the way that they have before, what we might call our “business as usual” scenario. It’s a common short-cut because it’s often the case! However, while business-as-usual is a useful starting place for scenario work, it can’t be our end point: we need to look at outlier signals and possibilities as well. The second pitfall we need to be aware of is playing “pick your favourite scenario”; it can be tempting to anchor on the scenarios that look best for our particular mix of assets and stakeholders. As planners and policy makers, working with a variety of scenarios enables us to put boundary markers around the decisions we’re considering, reckoning with a gradient of plausible and possible outcomes while avoiding these two cognitive traps.
So, by predicting what makes sense to predict and leveraging scenarios for those things that are too complex or variable, what does that enable us to do? In industry, it allows decision-makers to hedge their bets, building a portfolio that covers the range of possible outcomes while considering the timing of emerging opportunities. For government, its utility is twofold, allowing governments to understand the potential impact of their policy choices, while also enabling policy-making that is more flexible and durable to external forces for a wider variety of stakeholders in a more diverse set of futures, encouraging the development of policies that enable adaptation rather than path dependence.
It’s in the middle space between these two kinds of model-based decision-making that the EFPC aims to sit, with the goal of answering the question: how might we use the levers of policy to enable industry and citizens in Alberta to achieve good outcomes across a range of climate and energy futures? Models help us do that, with both greater confidence and a deeper appreciation for the risks and opportunities that might lie ahead.
Dr. Sara Hastings Simon is Assistant Professor Department of Physics and Astronomy, and the School of Public Policy Director MSc in Sustainable Energy Development (SEDV). and Director MSc in Sustainable Energy Development.