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💡 Active projects and challenges as of 06.06.2026 00:54.
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zuerich.ch
Circularity - Team 1
Scaling Circular Solutions in Zurich
GreenTechHackathon.pptx by Paola Castillo
Challenge presented by Sara Graf
zuerich.ch
Circularity - Team 2
Scaling Circular Solutions in Zurich
GenAI - Team 1
GenAI for Earth: Innovating for Impact and Efficiency
Challenge
GenAI for Earth: Innovating for Impact and Efficiency
Generative AI has shifted from a niche technical field to a foundational tool for global industry in record time. However, this revolution comes with a significant environmental cost: the energy required to serve model queries (inference) and the water cooling needed for data centers are substantial. At the same time, GenAI offers unprecedented capabilities in processing complex climate data, optimizing supply chains, and communicating sustainability concepts to the public.
The industry is at a crossroads: we must find ways to make AI itself "greener" while simultaneously leveraging its power to solve the planet’s most urgent ecological crises.
Problem Statement
The rapid adoption of GenAI often prioritizes performance and speed over environmental sustainability. Developers frequently lack the tools or incentives to measure and reduce the carbon intensity of their AI-driven applications. Conversely, many high-impact climate solutions—such as localized weather prediction, circular material identification, or complex environmental policy analysis—are bottlenecked by a lack of accessible, intelligent automation that can scale these efforts to the general population.
Existing Solutions & Market Insights
The landscape is evolving quickly, with two primary areas of focus:
- Green AI Tools: Emerging frameworks like CodeCarbon or Carbon Tracker help developers monitor emissions, and "Small Language Models" (SLMs) are gaining traction as energy-efficient alternatives to massive LLMs for specific tasks.
- Ecologits: An open source emission calculator for per-prompt emissions
Existing Solutions & Market Insights
- AI for Climate Applications: We see GenAI being used for "synthetic data" generation in climate modeling, AI-powered chatbots for waste sorting (e.g., helping citizens navigate circularity), and automated auditing for corporate ESG reports.
- The "Black Box" of Inference: It is currently difficult for a developer to know exactly how many grams of CO^2 are used in the use of AI models.
This challenge outlines the urgency of addressing environmental concerns in GenAI while harnessing its potential to address the climate crisis. Solutions need to focus on green tooling, sustainable AI applications, and transparency in inference costs to balance innovation and responsibility.
Join the Discussion:
- Which of these applications do you think holds the most potential, and why?
- How can we address the challenges of data quality, bias, and scalability in AI for sustainability?
- What role do you envision for open source and community-driven initiatives in advancing sustainable AI?
- How can we in general better align AI development with environmental goals?
Helbling - Team 1
Radical visions for future Consumer Products: Initial Question. How might we design consumer products that people love, keep, repair, and pass on – while minimizing their environmental footprint? Background & Current Situation Consumer products such as coffee machines, kitchen appliances, or small household devices are often designed for short lifecycles. Driven by cost pressure and fast-changing trends, many of these products are built with limited durability, low repairability, and materials that are difficult to recycle. As a result, they are frequently discarded after a relatively short period of use, contributing significantly to waste and CO₂ emissions. At the same time, affordable and convenient products remain highly attractive to consumers. This creates a key challenge: how to reconcile sustainability, durability, and circularity with cost and user expectations.
Challenge document (PDF)
Home - Community
Lowering your carbon impact using smart home technologies might be rewarding. But what if we started optimizing on a community level rather than an individual level. We are applying the principles of regenerative communities to the topic of electricity production. What is the difference of organizing electricity production in a community of 50 houses rather than everybody on his own. We created a simulation of both scenarios. Is uses 4 different personas and AI generated consumption/reduction data. Comparing the two scenarios we see a community driven way to optimize is both cheaper and more effective due to Shared Battery Usage, EV Load Balancing, Virtual Net Metering, V2G and Bulk Purchasing.
Lowering your carbon impact using smart home technologies might be rewarding. But what if we started optimizing on a community level rather than an individual level?
We are applying the principles of regenerative communities to the topic of electricity production. What is the difference of organizing electricity production in a community of 50 houses rather than everybody on his own.
We created a simulation of both scenarios: https://tasteroute.ch/
Is uses 4 different personas and AI generated consumption/reduction data. Comparing the two scenarios we see a community driven way to optimize is both cheaper and more effective due to Shared Battery Usage, EV Load Balancing, Virtual Net Metering, V2G and Bulk Purchasing.
Smart Home - Smart Green Key
👋 Meet Challenge 1 - Smart Homes for All 💡 🏡 In partnership with ecoinvent How might we design a digital tool that helps homeowners or installers quickly visualize the carbon 'break-even' point of a smart home installation? The smart home market is booming, driven by the promise of convenience, security, and energy efficiency. Homeowners and installers are increasingly adopting IoT devices like smart thermostats, automated lighting, and energy monitors to reduce their energy bills and environmental footprint. However, every smart device comes with its own "carbon debt"—the emissions generated during raw material extraction, manufacturing, and global shipping. Currently, the ecosystem is focused primarily on operational efficiency (saving electricity today) while often ignoring embodied carbon (the environmental cost of the hardware).