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Emergent wayfinding for a flourishing planet

Across disciplines and sectors, many are reaching a moment of reconsideration, contemplating new ways to think and work. Collaborative Earth Labs gather diverse areas of knowledge and applied skills, cultivating collaboration that leads to scientifically informed and locally attuned regenerative action.

how Collaborative Earth labs work

lifecycle of a lab

Each lab focuses on a well-defined scientific problem or technological need that stands in the way of planetary regeneration. Through data-analysis and modeling, labs forecast alternate futures, revealing the ecological, social, and economic impacts of potential paths.


A community member identifies a scientific question or technological need that stands as a key barrier to social-ecological regeneration. They make a pitch. First, their proposal is circulated for peer review. Next, they present their ideas at a virtual event and on our website.

weeks 0-8


Lab leaders identify the skills and knowledge needed to realize their research and development plans. Collaborative Earth members offer their expertise to meet the new lab’s needs. The team commits to working together.

weeks 9-12


Research and development are carried out and documented. Frontline communities are early participants in directing R & D. The team consults with other Collaborative Earth members as needed.

weeks 13-40


The Collaborative activates the lab's discoveries through publication, media, open-science assets, and on-the-ground regenerative work in partnership with land stewards.

weeks 40+

Coastal Wetland Forests
Elliott White Jr

The goal of our lab is to create a high-spatial resolution map of coastal forested wetlands at global scale. If we know precisely where these ecologically critical but fragile forests are located, we can manage freshwater flows to counteract saltwater introgression due to rising sea levels, and we can assist in their migration inland, preserving their critical function in protecting coastlines and sequestering carbon.

Jason Baldes
Gisel Booman

Across the continent, a number of first nations are in the process of reintroducing bison to the grasslands in which they were once the primary grazer and an ecologically vital species. Initial experiences and evolutionary considerations suggest that this may be ecologically beneficial in terms of grassland biodiversity, carbon cycle, and resilience to climate change. However, these questions have not yet been studied at scale. In this lab, we will leverage remote sensing to scale up from ground measurements, establishing the large-scale patterns of bison impact.

Riparian Ecosystems
Forrest Pound

Beaver dams are known to result in greener, more drought-resilient waterways in semi-arid environments. We are using computer vision to spot dams in satellite imagery, generating a large dataset that we can use to train models that will tell us what the ecological effects of a dam will be at any point on a waterway. The goal is to create a tool to guide efficient restoration through the introduction of small dams.

Bundled Ecological NFT
Philip Taylor

Markets in voluntary carbon credits are increasingly providing a flow of capital for regenerating ecosystems. The problem is, thriving and resilient ecosystems are not just carbon. We need to find ways to structure credits to incentivize the diverse and functional ecosystems we want, not merely high-concentrations of carbon. We will design the technological tools to support a market in bundled ecological credits.

Global Forests
Aron Boettcher

We are building an accurate and global model for predicting potential rates of reforestation and resulting carbon sequestration. Such a model could have a transformational impact on global reforestation efforts by opening new streams of financing in the form of carbon credit futures.

Impact & Risk
Aaron Hirsh
Valérie Lechêne

Leveraging The Earthshot Institute’s broad scientific and technical expertise, the Impact and Risk Lab helps investors and governments who earnestly want to forecast, measure, and address the socio-ecological risks to and/or impacts from their work. For a given system, we build simple process-based models to identify key socio-ecological risks and outcomes. We then draw on big data to improve and train our models, generating quantitative predictions and developing measurement systems for verification.

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