A web-based software application helps universities easily identify best opportunities to reduce emissions, together with well-defined populations to target, thanks to a unique approach to data visualization and data-preserving anonymization.
Find the demo link further down.
The goal at Zurich University of Applied Sciences (ZHAW) is to measurably reduce their own emissions through an evidence-based, motivational governance.
Measurable reductions, motivational governance
- For measurable reductions, we need to be able to easily understand which emission-generating behavior is worth amending for what populations.
- For motivational governance, we have to get staff and students on board. They need insight into what interventions are created for which reason, and become empowered to contribute to the decision-making.
We introduce a public, web-based platform to visualize all emission data with helpful additional context. The platform is accompanied with a data loader that cleans, merges, and anonymizes all emission data before they enter the platform.
The platform solves 2 key needs:
- It's easy to explore and find emission-related behavior worth amending.
- The behavior to amend is linked to well-defined populations. Therefore, a policy or intervention can be targeted at a precise audience.
Powerful data science made easy to use
We do this by providing unique data visualizations that are deeply informative, but are easy to use at the same time. Decision-makers without a data science background can now answer questions like:
- „How many tons CO2 would we save if everyone switched from plane to train for certain distances or destinations?“
- „Which populations are flying overseas, for what reasons?“
- „How does travel depend on age, department, travel reason, and gender?“
The companion data loader application merges data from various sources and solves issues such as staff names spelled differently across various databases. Most importantly, it anonymizes all data with an algorithm that protects the privacy of all individuals without giving up important details like age, gender, and more. Check out a more detailed description about this anonymization at the Mondrian page.
The first version of the product is now live. It has already delivered important, previously unknown insights into the ZHAW emission behavior to decision-makers. Click here to play the flights demo!
“Evidence-based Sustainability Governance”
This product is part of a larger project called Evidence-based Sustainability Governance.
As a next big feature, we plan to support "intervention tracking". This feature will allow decision makers to introduce experimental interventions for reducing emissions, and test them before making them permanent policy.
Finally, we will combine this with existing gigmade technology for dynamic decision making, to enable a decentral forum where experimental interventions are negotiated and prioritized.