A participant in the Collaborative 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+
The Earthshot Institute is gathering teams to conduct research and build tech that will catalyze ecological regeneration. Together, we are cultivating a research ecosystem that grows across disciplinary and institutional boundaries.
Four labs are already working toward our goal. Three more launched Earth Day: Bison, Coastal Wetland Forests, and Bundled Ecological NFT. Learn more about the institute and match with a lab by watching our Earth Day event! Diverse skills are needed—and all are welcome. Come lend your unique genius to the planet!
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If you are interested in contributing more significantly to the Collaborative Earth community, the form will guide you in providing information about your knowledge areas, skills, and background. Once you submit, you will receive an email to join our slack workspace.
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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.
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.
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.
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.
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.
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.
