In the world of business, sustainability, and climate action, the terms projecting, forecasting and predicting are often used interchangeably. But they mean different things—and misunderstanding those differences can lead to confusion and poor decisions.

This blog post clarifies the three concepts, explains why Viable Pathway follows the Australian Bureau of Statistics (ABS) definitions, and shows how cognitive biases can distort the way we think about the future.

Defining Projecting, Forecasting and Predicting

At their core, all three terms deal with the future. But the expectations and methods behind each differ.

Projecting is conditional. According to the ABS:

A projection simply indicates a future value if a set of underlying assumptions occur.
Projections answer the question: If these assumptions hold, what would the future look like? They do not carry any claim about likelihood or confidence.

Example: A projection might show that a company’s emissions will double by 2050 if its revenue continues to grow at 3% per year and no mitigation measures are taken.

Forecasting is probabilistic. Again, the ABS explains:

A forecast speculates future values with a certain level of confidence, based on current and past values as an expectation (prediction) of what will happen.
Forecasts are built from historical data and trends, and they aim to present a most likely range of outcomes.

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

Predicting, by contrast, implies a single definitive outcome. Predictions often suggest certainty, even when that certainty is not justified.

Example: A prediction might claim that emissions will fall by exactly 20% by 2030.

The distinction matters: projections are what-if statements, forecasts are most-likely scenarios, and predictions are often treated as certainties.

Why We Use Projections

Viable Pathway’s modelling work is best described as projections. We build models using a defined set of assumptions about business growth, external trends, and emissions factors, and we calculate outcomes if those assumptions occur.

We do not claim to know the future or provide probabilities. That means our results are not forecasts, and they are certainly not predictions. Instead, they show clients how their emissions might evolve if specific conditions hold true.

This distinction is important. Treating projections as forecasts—or worse, predictions—creates a false sense of certainty and can lead to inaction.

Cognitive Biases at play

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 project, 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 projecting 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 projecting 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 projecting , this could lead to overestimating the significance of a single trend or event. For instance, a company might anchor its emissions projection on early-stage EV adoption data, without considering how broader factors (e.g., grid decarbonisation, consumer behaviour 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 projecting and predicting, it’s crucial to be aware of these biases and actively mitigate their impact.

Why Projections are Superior for Decision-Making

When we project, 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. Projections support 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 projection 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.

Projections 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 project 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 projecting 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 Projecting

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

Tools like Viable Pathway’s projecting 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 projecting is the path to understanding a complex, uncertain future—not predicting it with false certainty.