Forecasting not Predicting

Projections for Decarbonisation Roadmaps

In the world of business, sustainability, and climate action, the terms forecasting and predicting are often used interchangeably. However, they have distinct meanings that, when not understood clearly, can lead to confusion—and even missteps in decision-making. This blog post explores the key differences between these two concepts, dives into their semantic implications, and discusses how cognitive biases can distort our approach to both forecasting and predicting the future.

Defining Forecasting and Predicting

At their core, forecasting and predicting are both about estimating future outcomes. However, the methods and expectations behind them differ significantly.

• Forecasting is a systematic process that uses historical data, trends, and models to estimate what will happen in the future. It’s inherently grounded in uncertainty and involves creating multiple potential scenarios to account for different variables. Forecasting, particularly in contexts like climate change and greenhouse gas emissions, often involves long-term analysis and integrates external forces (e.g., technological advancements, policy changes, societal shifts) into the projections.

  • Example: A company might forecast its carbon emissions through 2030 based on trends like grid decarbonisation, energy consumption patterns, and EV adoption.

• Predicting, on the other hand, implies a more definitive or singular outcome. Predictions are often seen as more deterministic, focusing on the exact future state rather than a range of possibilities. While predictions may be based on data, they are typically narrower in scope and less flexible than forecasts.

  • Example: A prediction might be made about a specific event, like whether a particular product will succeed in the market, based on current data points. It implies certainty in the outcome, rather than exploring a range of potential scenarios.

The key distinction lies in the certainty and scope. Forecasting deals with a range of potential outcomes, acknowledging uncertainty and variability, while predicting often aims for a more fixed result, sometimes without fully accounting for the inherent uncertainty.

The Role of Uncertainty in Forecasting

Uncertainty is central to forecasting, and this is something that many people overlook. Whether in the context of climate models, financial planning, or emissions projections, forecasts don’t claim to know the future—they provide a likely range of outcomes based on current knowledge and assumptions.

Viable Pathway’s forecasting engine is a perfect example of this approach. It integrates historical data, exogenous trends (e.g., EV adoption, grid decarbonisation), and evolving technologies to model emissions pathways, considering both business-as-usual scenarios and potential disruptions.

Forecasting tools like this are designed to be trend-anchored, meaning they are shaped by long-term patterns rather than fleeting trends or one-off events. This allows organizations to prepare for multiple scenarios, rather than fixating on a single prediction that may quickly become obsolete.

Cognitive Biases in Forecasting and Predicting

Cognitive biases are the mental shortcuts that our brains use to make quick decisions, but these can lead to errors, especially when we try to forecast or predict future events. The decision-making process can be distorted by biases like availability bias, confirmation bias, and anchoring bias, all of which can shape the way we approach forecasting and predicting.

Here’s how cognitive biases come into play:

• Availability Bias: We tend to make judgments based on information that is most readily available to us, often focusing on recent or vivid examples. For example, if a company has recently invested heavily in energy efficiency, they may overestimate the impact of those improvements on their future emissions, ignoring broader external factors like grid decarbonisation.

• Confirmation Bias: We search for data that supports our pre-existing beliefs and dismiss data that contradicts them. When forecasting GHG emissions, if a sustainability team is particularly focused on reducing emissions through internal efforts, they might neglect the long-term influence of external trends like policy shifts or the adoption of renewable energy technologies that could significantly reduce emissions without direct intervention.

• Anchoring Bias: People often rely too heavily on the first piece of information they receive. In forecasting, this could lead to overestimating the significance of a single trend or event. For instance, a company might anchor its emissions forecast on early-stage EV adoption data, without considering how broader factors (e.g., grid decarbonisation, consumer behavior changes) will affect emissions in the coming decades.

These biases can easily distort predictive models as well, which often fail to account for uncertainty and overestimate the precision of their outcomes. In both forecasting and predicting, it’s crucial to be aware of these biases and actively mitigate their impact.

Why Forecasting Is Superior for Decision-Making

When we forecast, we acknowledge uncertainty and provide a range of potential future scenarios. This is a much more reliable approach than predicting a singular outcome because it allows for adjustments as new information becomes available. Forecasting supports adaptive decision-making and long-term strategic planning, especially when the future is as unpredictable and complex as the climate crisis or GHG emission targets.

• Example: Viable Pathway’s forecasting engine, which integrates a range of trends and scenarios, helps sustainability managers understand where emissions might be reduced by external trends and where internal action is necessary. This is a far more comprehensive and flexible approach than simply predicting that emissions will reduce by a certain percentage based on a single data point.

Forecasting and Predicting in Practice: Real-World Implications

Let’s put this into a real-world context: A sustainability manager must decide whether to implement internal carbon reduction strategies or rely on external trends, such as the anticipated rise in renewable energy adoption. If they predict that the energy grid will decarbonise quickly and drastically, they may not invest in necessary internal changes, missing out on opportunities to reduce emissions further. But if they forecast using a range of scenarios, they can make more informed decisions about where to focus their efforts, including recognizing areas where external trends may not be enough, and proactive internal initiatives are needed.

The key takeaway here is that forecasting gives organizations flexibility, ensuring that they are prepared for multiple outcomes, whereas predicting can narrow the decision-making scope and create a false sense of certainty.

Conclusion: Avoiding Cognitive Biases and Embracing Forecasting

The key to effective decision-making in sustainability lies in understanding the semantics of forecasting vs predicting and the cognitive biases that distort our thinking. By embracing forecasting—an inherently uncertain and dynamic process—organizations can avoid the cognitive traps that lead to poor decision-making.

Tools like Viable Pathway’s forecasting engine are designed to account for uncertainty, integrate trends, and provide a range of possible future outcomes. This allows sustainability teams to make more informed, adaptive decisions that align with long-term emissions reduction goals.

As we move towards a future where data-driven decision-making is paramount, it’s crucial to understand that forecasting is the path to understanding a complex, uncertain future—not predicting it with false certainty.