Decarbonize

Decarbonizing online shopping, one purchase at a time.

Inspiration

Most people are unaware of how their purchasing behaviors affect the environment. With the rise of online shopping during COVID, we want to help you make better consumer decisions that are mindful of the carbon footprint the products you order create.

What it does

DeCarbonize, your very own carbon delivery calculator.

DeCarbonize is a chrome extension that estimates the carbon footprint of each Amazon product you visit. It contains a built-in dashboard that allows users to track their purchasing habits and carbon footprint over time, providing an opportunity for users to reflect on their consumer behavior and its impact on the environment.

How we built it

The chrome extension was built using HTML, CSS, and JavaScript. In building the dashboard, it was a combination of React, plot.ly, Python, HTML, CSS, and JavaScript.

Challenges we ran into

Our biggest challenge was finding a reliable way to quantify the carbon footprint. We came across Amazon's official statement that detailed their carbon footprint per USD, and deduced that this was a legitimate way of estimating each Amazon purchase's carbon footprint.

Accomplishments that we're proud of

We were able to successfully create a chrome extension, inject code in Amazon's UI that looks natural on the webpage, and create a visual dashboard that can display relevant carbon footprint data specific to each user's purchasing behavior. Additionally, the way we worked as a team in regards to our communication, check-ins, and strategies to complete and prioritize features in the project by the deadline went very well!

What we learned

  • Git
  • React
  • Data Visualization
  • Web scraping with JavaScript
  • How to build a chrome extension

What's next for DeCarbonize

  • Generalizing our service to other e-commerce sites such as eBay, Etsy, Alibaba, AliExpress, and others.
  • Generate recommendations for alternative delivery options or sustainable sellers that are either data-driven or utilizing the predictive power of machine learning.
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