How machine learning will help us fight climate change

A CarbonCancel blog by
Wilbert Osmond - Carbon Data Analytics and Machine Learning intern | 23/07/2020

We constantly make decisions that affect the climate crisis, and if you’re reading this, chances are that you want to reduce your carbon footprint. That’s a good first step, but what to do next? The next steps often seem abstract and daunting, as we can’t easily find out the climate impact of our daily choices. The information required is often not accessible. Even when it is, it’s too confusing, abstract and generic. This frustrates those of us who want to take responsibility but are not given the tools to do so. It’s a myth that individuals like us can’t meaningfully contribute to solving the climate crisis. The changes we make individually can create big cumulative effects that can significantly mitigate global emissions. But of course, we need to know how we can help. 

The CarbonCancel calculator currently allows you to manually input your housing situation, consumption behavior and travel patterns, to match them with their respective emission factors and give you your total estimated carbon footprint. In the future, we’d like to incorporate machine learning techniques to such digitization of our consumer habits. This way, we stand an even better chance to make significant contributions to climate change and close the information gap. CarbonCancel has a two-step plan to guide people towards a more sustainable lifestyle: (1) Understanding your carbon footprint, and (2) Facilitating behavioral change.

For the first half of the plan, our goal is to help people understand their  personal carbon footprint in a more dynamic way, on top of the current static calculator. One of the ideas that we are thinking of is using computer vision to determine specific grocery items purchased by scanning the bill. This would make it possible to quickly and easily discover the associated emissions. Your weekly carbon footprint can be tracked regularly, and machine learning could be used to flag small changes you could make to reduce it. It may tell you which products you could avoid or replace this week. We could also estimate the carbon emissions of your purchases before buying them, enabling us to plan the best actions to take in advance.

The second half of the plan is to provide insights into behavioral change. Machine learning is highly effective at modelling human preferences, and this can be leveraged to help balance the climate scales. Our line of thinking is to model and cluster individuals based on their preferences and consumption characteristics. From that, we can predict which actions simultaneously could be prioritized to decrease their carbon footprint the most, and who might be most amenable to sustainable behavioral change.

"Essentially, we could design tailor-made plans to carry out personalized data-driven climate actions."

CarbonCancel can help you understand and reduce your carbon footprint, and guide you on your path to a climate-neutral existence. We’d like this to be as effortless as possible. With machine learning, we can automatically track and calculate your carbon footprint to give you intuitive insights into the climate impact of every choice you make. By identifying behaviors that lead to the highest emissions and providing constructive opportunities from modelling behavior, we design your next steps towards cancelling your carbon. 

When we reminisce on how the battle against the climate crisis was won, who will we recall as the key player who turned the tides? We might remember that it was each and every one of us.

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