< back to projects

Trusted Carbon Credits

(2020 - )

The Cambridge Centre for Carbon Credits is an initiative I started with Andrew Balmford, David Coomes, Srinivasan Keshav and Thomas Swinfield, aimed at issuing trusted and verifiable carbon credits towards the prevention of nature destruction due to anthropogenic actions. We are using a combination of large-scale data processing (satellite and and sensor networks) and decentralised Tezos smart contracts to build a carbon marketplace with verifiable transactions that link back to trusted primary observations.

In around 2019 Srinivasan Keshav joined the Cambridge Computer Lab from Waterloo, and we got chatting about ways that computer science could be applied to helping our colleagues in conservation with their quest to improve nature-based solutions (both deforestation and rewilding efforts). We wrote down our initial thoughts with some colleagues in “How Computer Science Can Aid Forest Restoration” which summarises some of the literature survey and thoughts that we had. We ran a summer UROP set of projects (writeups here) which had to be done remotely due to the 2020 lockdown.

Later during the pandemic, Srinivasan Keshav and I joined up with Andrew Balmford from Zoology and David Coomes and Thomas Swinfield from Plant Sciences and the Cambridge Conservation Institute to establish a centre dedicated to combining classic topics from computer science (big data processing, reproducible computation and distributed consensus) to the pressing problem of issuing trusted and verifiable carbon credits towards the prevention of nature destruction due to anthropogenic actions. Our centre will be announced later in 2021 officially, so for now I shall leave some student projects here for anyone interested in Part II/III projects. Just get in touch with me directly to discuss them during term time.

Potential Student projects

These are of an appropriate difficulty/time level for Part II/III projects in Cambridge:

Trusted image capture

  • Description: The goal of this project is to link image capture from trusted hardware devices, such as Azure Sphere, to a global file store, such as IPFS, with summaries posted to a blockchain. This would allow us to trace an image to its creator with an unbroken chain of trust. Students who have some background working with microcontroller-based single-board devices, such as a Raspberry Pi, would be preferred.
  • Supervisor(s): Srinivasan Keshav and Anil Madhavapeddy

Building nextgen forest simulators using multicore OCaml

  • Description: The state of the art in forest simulation is an agent-based model, such as TROLL. The goal of this project is to reimplement the core framework behind TROLL in multicore OCaml, which is a cutting-edge functional programming language with support for algebraic effects and parallel computation. The project will evaluate the performance of multicore OCaml agent simulators vs the existing state of the art, and form a more qualitative view of how effective a groundup approach might be. Students should have a basic familiarity with functional programming.
  • Supervisor(s): Anil Madhavapeddy

Durable storage of primary observational data

  • Description: Observational data from trusted sources, such as satellites and trusted cameras are voluminous (terabytes to petabytes) but need to be stored for decades. This project explores storage and indexing of such data. Ensuring immutability of the data through a blockchain link would also be desirable.
  • Supervisor(s): Srinivasan Keshav and Anil Madhavapeddy

Geolocation in a rainforest

  • Description The goal here is to design a geolocation service in a rainforest, where GPS is unavailable and the environmental conditions are harsh. Previous work has tried to use smartphone sensors, and we would like to continue this exploration: tinyurl.com/nx4cp4xx
  • Supervisor(s): Srinivasan Keshav and Anil Madhavapeddy

Related publications

How Computer Science Can Aid Forest Restoration
Gemma Gordon, Amelia Holcomb, Tom Kelly, Srinivasan Keshav, Jonathan Ludlam and Anil Madhavapeddy.
Technical report in the arXiv (2109.07898) on Jun 2021.