CARBON FOOTPRINT TRACKER

Inspiration

Hackathon challenge from Inks and Metal

What is Carbon Footprint?

Carbon footprint accounting can be carried out at a variety of levels (e.g., national, per person, product, service, etc.). Despite the high level of interest in carbon footprinting, there is a surprising lack of agreed-upon definitions as to what a carbon footprint is.

Your carbon footprint is the sum of CO2e emissions that your activities produce.

How it was built

- Research
- Design
- Coding
- Documentation

🔍Intensive research was carried out on

Carbon footprint,
Carbon dioxide equivalent (CO2e)
Datasets on regards Carbon footprints
Conversion rates and values affecting various emission sources

🛠 Tools used

HTML
SASS/CSS
Material table
React Js
Next Js
Next Internal API
Vercel for hosting
And surely GitHub

Challenges faced

Gathering conversion rates was not as simple as it may seem. Various sources of information have various values of energy input, output, and consumption for various entities and so getting a middle ground for system calculation was seriously tasking.

*Images are not fully optimized and would take some time to load up *Not all intended features are complete. Features and design updates would be pushed into production later.

What was learned

Calculations of Carbon footprint can only be estimated with deeper levels of research, complex conversions, and intertwined calculations increasing the efficiency of each estimate value.

What's next for Metal Carbon Footprint

Converting the Project into a standard system that would be usable. This would require more research and code refactoring with page redesigns to fit into everyday usage. Metal carbon footprint intends to be a source of quick carbon footprint adviser to an everyday user who doesn't have deep knowledge of conversion rates and technical energy consumptions. In other words...... A simple and more efficient user-friendly CO2e emission advisor

Continuous integration. The carbon footprint tracker would continuously check the carbon footprint and give personalized suggestions of what to do and a better approach to everyday activities based on specific user input without having to get the data every time.

Integration to hardware devices and control system to get direct power consumption without the user having to input data and still being able to give efficient readings and personalized suggestions within the home and on the platform.