Challenge background
The world and Germany, in particular, are currently facing drastic consequences of the global climate crisis. In the last few years, we have faced extreme heat and devastating floods. All as a result of climate change.
Climate change is to a high degree caused by CO2 (Carbon Dioxide) emissions. According to Our World in Data (https://ourworldindata.org/food-ghg-emissions) 26% of all GreenHouse Gas emissions are produced by the agricultural sector and food industry. This industry is very cost-sensitive and demand-driven, therefore just the consumers have the power to shape this industry by making them. At the same time, for an average person, there is no easy way to find out and compare different products and their impact on the environment.
In this project, we will make the first step towards solving this problem! We will aggregate environmental impact information, learn more about different factors contributing to it and as a result help, people make smart environmentally friendly choices that could turn around the world!
The problem
The world and Germany in particular are currently facing drastic consequences of the global climate crisis. In the last few years, we have faced extreme heat and devastating floods. All as a result of climate change. Climate change is to a high degree caused by CO2 (Carbon Dioxide) emissions. 26% of all GreenHouse Gas emissions are produced by the agricultural sector and food industry. This industry is very cost sensitive and demand driven, therefore just the consumers have the power to shape this industry by making their. Educating people by providing them with easily available environmental impact information could have a large impact on their choices and as a consequence trigger a change in the production chains.
Goal of the project
We will:
- Aggregate environmental impact information.
- Build a model that predicts CO2 emissions.
- Learn more about different factors contributing to CO2 emissions of the common products.
- Deploy the model.
- Create a visual interface (dashboard) and make it publicly available.
Project timeline
- 1
Week 1
Initial research of possible data sources and problem relevant information
- 2
Week 2
Data Collection through web-scrapping
- 3
Week 3
Data Collection through web-scrapping (cont)
- 4
Week 4
Data cleaning and augmentation
- 5
Week 5
Model Development
- 6
Week 6
Model deployment
- 7
Week 7
Visualisation and publication
- 8
Week 8
Brainstorming possibilities for project extension
What you'll learn
- Research of the available literature and data sources
- Data Collection through Web-Scrapping
- Data Pre-processing
- Exploratory-Data Analysis
- Modeling
- Model Deployment into possible API
- Visualisation and Publication