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Forest-based mitigation certificates: blockchain integration of ecosystem claims
Prompt CC2. The combination of remote sensing data (thermal, radiometric, laser, and imagery) and machine learning can be used to calculate above-ground biomass in forest projects as well as proxies for below-ground carbon content. Blockchain-based records can be used to immutably track ecosystem state over time; a key process to quantify and assetize ecosystem services, such as nature-based carbon capture. Integrate near real-time biomass data of the Yale-Myers Forest (YMF) into structured ecosystem claims posted on a blockchain.
Prompt Hosts: Pachama | Regen Network | Yale Openlab
This prompt requires general knowledge of machine learning and remote sensing, as well as the Regen Network platform. It does provide different contribution opportunities for experts in forest conservation and carbon credits.
Contribution opportunities:
  • Search, compile and clean historic biomass data of the Yale-Myers forest to be used in a baseline scenario
  • Work with the Pachama team to leverage their data and analysis ecosystem to calculate near real-time biomass content of the Yale-Myers Forest.
  • Work with the Regen Network team to integrate this as a structured claims posted on their instance of the Cosmos blockchain
  • Propose a simulation environment for the creation of ‘test’ carbon credits (or broader ecosystem service credits) from YMF through mechanisms of enhancing sinks (eg. reforestation, ecosystem nutrient boost, biodiversity growth etc). Consider historic baseline biomass scenarios, existing regulations, standard and methodologies that would be applicable for the test project.
  • Create code that can use an API or a web scraping system to request information of land-use restrictions per jurisdictional regulations (eg. regulation that prevents or allows forest exploitations —this is necessary to analyse additionality)
  • Work with the Pachama and Regen Network team to simulate this pilot project, embedding structured claims into ecosystem service credits.

Background & Opportunity

Digital transformation of forest-based carbon credits: R&D roadmap & demonstrative pilots

Introduction

Land-based climate mitigations hold a central role in achieving the Paris agreement’s global climate targets. Certified carbon credits are valuable instruments used by different organizations (eg. companies, large non-profits) to ‘offset’ their direct and indirect emissions with compensatory activities outside their internal operations. Not only are these useful for private organisations to meet their voluntary climate pledges, they could directly contribute to subnational pledges (eg. regional government) as well as nationally determined contributions —as long as credits are properly retired and accounted in their nested jurisdictions whilst preventing double counting. Forest-based carbon credits, representing a nature-based solution, have a large potential to meet the growing demand of carbon offsetting markets. However, traditional mechanisms to audit and certify forest initiatives can be costly, often changing the economic feasibility in project financing; preventing credits to ever be minted from eligible small and medium scale forest conservation and reforestation actions.
With the onset of emerging technologies, the opportunity for a deep digital transformation in the process of monitoring, reporting and verifying (MRV) forest carbon credits can bring about a disruptive change in the sector. If properly integrated, leveraging remote sensing (both lidar and high fidelity imagery), drones with spatial computing capabilities, IoT sensors (i.e. internet-of-things), big data, machine learning and blockchain can produce a radical cost reduction in MRV; essentially through processes of automation and disintermediation. The use of state-of-the-art machine learning, for example, cannot only be used to perform low cost calculation of forest biomass using remote sensing data, it can be employed to detect ‘leakage’ and ‘permanence’ within conventional certification standards. Furthermore, blockchain and modern cryptographic techniques bring significant value to: the creation of digital climate assets (i.e. carbon credits and complementary metadata), their trading within and across climate market jurisdictions, their role within smart contracts, and in mechanisms for nested accounting once assets are retired.
reference sketch for the prompt
Additional resources:
-Resources for deep learning with satellite & aerial imagery https://github.com/robmarkcole/satellite-image-deep-learning