Phenomenon window

Can AI Forecast Your City's Carbon Footprint?

You've seen the headlines: cities are going green, but how do they actually know where to start? Imagine if you could look at your own neighborhood—the buildings, the traffic, the weather—and predict exactly how much carbon it's pumping into the air. That's what you'll explore here. Using real public data from cities, you'll train a machine learning model to forecast CO2 emissions block by block. You'll see patterns you never noticed: why some areas emit way more than others, and how a simple change—like adding a bus lane or planting trees—might shift the numbers. But here's the twist: AI models are only as good as the data they're fed. You'll dig into the messy reality of urban data—missing values, weird outliers, and hidden biases—and decide if you can trust the predictions. By the end, you'll have your own model and a map that shows what a greener future could look like. Ready to see your city in a whole new light?

• “

  Think about the neighborhood you live in. What do you think are the top three sources of carbon emissions there—cars, buildings, industry, something else?

• “

  Have you ever noticed a place in your city that feels especially polluted or smoggy? What did you see or smell that made you think that?

• “

  If you could change one thing about your daily routine to reduce your carbon footprint, what would it be and why?