Categories
1. Home
The Net-Zero Home Sprint: Quantifying the Carbon Cost of Smart Living
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).
Background & Current Situation
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).
Problem Statement
While the technology to make homes "smarter" and more efficient is widely available, there is a significant lack of transparency regarding the total lifecycle impact of these devices. Homeowners and installers operate in an information vacuum where the "green" benefits of a device (energy saved) are rarely weighed against its "carbon debt" (embodied carbon). Without a clear way to calculate the carbon break-even point, users risk "over-automating," leading to hardware-heavy installations that may never achieve a net-positive environmental impact.
Existing Solutions & Market Insights Most current energy-tracking solutions only look at the "use phase" of a product. Open Source Ecosystems: Platforms like Home Assistant provide the infrastructure for automation but often lack built-in "Carbon Intelligence." LCA Barriers: Professional databases like ecoinvent contain the gold standard of environmental data, but this information is rarely accessible to the average smart home installer or DIY enthusiast in a user-friendly format. Market Friction: * Time-to-Value: Installers need quick answers during a consultation; they cannot spend hours manually calculating carbon footprints. Data Silos: Device specifications are disconnected from the carbon-intensity data of the materials used to build them. Verification: There is a growing demand for data-backed sustainability claims to avoid "greenwashing" in the construction and home-renovation sectors.
The Challenge "In a 1-day sprint, how might we design a digital tool that helps homeowners or installers quickly visualize the carbon 'break-even' point of a smart home installation?" Your solution could, for example: The Environmental Insight Tool: Develop a prototype (which could utilize the ecoinvent API) to pull specific material impact data and calculate the embodied carbon of a standard smart home "starter kit." The 'Green ROI' Calculator: Build a rapid-input web tool where an installer can select 3–5 devices and get an immediate estimate of the carbon debt vs. the projected monthly savings. Hardware Lifecycle Visualizer: Create a dashboard that shows which smart home components (e.g., batteries vs. plastic casings) contribute most to the footprint, helping users choose lower-impact hardware. Decision Matrix: Design a simple logic-based tool that advises users on priority: "For this specific home, a smart thermostat pays for its carbon debt in 6 months, but automated blinds will take 12 years—focus on the thermostat first."
Hackathon Note for Participants:
Time Constraint: Focus on a functional Minimum Viable Prototype (MVP). Prioritize the core logic and user value over a polished UI/UX. Data Access: You have the option to use API access to ecoinvent, allowing you to pull real-world emissions factors for electronics, plastics, and energy grids. However, use of the API is not mandatory; you may use other verified data sources or creative approaches to solve the challenge.
2. Circularity
Scaling Circular Solutions in Zurich
Enabling more people in Zurich to participate in circular behaviors (such as sharing, repairing, reusing, and redistributing goods) by making existing solutions more visible, accessible, and attractive. Zurich has a fragmented ecosystem of initiatives that promote circular economy practices, ranging from second-hand marketplaces and sharing platforms to repair services and donation networks. These services exist across multiple physical and digital touchpoints, including mobile apps, websites, messaging groups, and community-driven platforms. Despite their diversity and potential impact, most of these solutions operate in silos and remain largely unknown to the broader population.
While the infrastructure for reuse, repair, sharing, and redistribution already exists, it is highly fragmented and lacks visibility, accessibility, and scale. Users often face high search costs, inconsistent user experiences, and limited trust or awareness. As a result, many reusable goods are discarded, and participation in circular practices remain limited to niche communities or restricted to specific use cases (e.g. design classics, collectors’ items).
Problem Statement
While the infrastructure for reuse, repair, sharing, and redistribution already exists, it is highly fragmented and lacks visibility, accessibility, and scale. Users often face high search costs, inconsistent user experiences, and limited trust or awareness. As a result, many reusable goods are discarded, and participation in circular practices remain limited to niche communities or restricted to specific use cases (e.g. design classics, collectors’ items).
Existing Solutions & Market Insights
Globally and locally, platforms such as peer-to-peer marketplaces, sharing economy apps, and repair networks have demonstrated strong potential to reduce waste and extend product lifecycles. A few examples in Zurich are:
- Online peer-to-peer marketplaces: Tutti, Ricardo, marko, World of plenty, messenger chats
- Physical peer-to-peer marketplaces: MARTA, Kreisflohmi, public book shelves, public libraries, Madame Frigo (for food)
- Physical second-hand marketplaces/donation networks: Brockenhäuser, Second-hand shops, Josy, Äss-Bar (for food)
- Sharing economy apps: Sugar cup, Züri teilt, Sharley, Pumpipumpe, Too Good To Go (for food)
- Repair services: Platform by the city of Zurich in development
Market research in the circular economy space highlights key barriers to adoption, including:
- Lack of centralization or interoperability between platforms
- Low user awareness and engagement
- Proximity to desired service/marketplace
- Friction in user journeys (e.g., too many steps, unclear value proposition, inconsistent availability)
- Trust and safety concerns in peer-to-peer exchanges
At the same time, studies show that users are increasingly motivated by sustainability, cost savings, and community belonging, indicating strong latent demand if solutions are made more accessible and user-friendly.
The Challenge
How might we design a scalable, user-centric digital solution that connects, amplifies, and simplifies access to existing circular economy services in Zurich?
Your solution could, for example:
- Aggregate or integrate multiple platforms into a unified experience
- Improve discoverability of local initiatives
- Reduce friction in participating (e.g., listing, finding, or repairing items)
- Build trust and engagement among users
- Use data, AI, or network effects to scale impact
3. Helbling
Sustainable Consumer Product Design
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.
Problem Statement
Despite increasing awareness of sustainability, most consumer devices are still not designed to last, be repaired, or remain desirable over time. Products often fail prematurely, are difficult to fix, or become obsolete due to design and business model limitations. The central question is: What does it take to design a consumer product that is robust, repairable, timeless, and at the same time affordable and competitive? And how must product design and business models evolve to make this possible?
Existing Solutions & Market Insights
Approaches such as modular design, repair initiatives, and product-as-a-service models demonstrate that longer lifecycles and circularity are possible. However, these solutions are often limited to niche markets or higher price segments. Helbling will contribute to this challenge by introducing practical tools and a simplified methodology based on Ecodesign principles and Life Cycle Assessment (LCA). These tools enable participants to directly qualitatively assess the CO₂footprint of current products and compare different and new visionary design strategies.
The Challenge
Design a consumer product vision for the year 2040 that combines market success with a radically reduced environmental footprint.
Participants will select a product (from provided suggestions or their own choice), analyze its current state using Helbling’s methodology, and identify key weaknesses regarding durability, repairability, and sustainability. Based on these insights, teams will develop a future-oriented product vision that integrates circular design principles, improved materials and architecture, and potentially new business models. The goal is to create a solution that is not only environmentally sustainable (“enkeltauglich”), but also attractive, usable, and scalable in the market. Throughout the process, participants will be supported by Helbling coaches.
Outcome
Teams will present a Product Vision 2040, outlining their concept, key innovations, and expected sustainability impact. The solution should demonstrate how future consumer products can achieve both low environmental footprint and high user acceptance.
Author: Roland Lehmann
4. GenAI
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.
- 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: How can we better align AI development with environmental goals?
- 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?
