Energy and Environment Group

Lent term

• 28Mar
  Democratizing Carbon Markets: A Blockchain-Based Emission Trading System for Small and Large-Scale Stakeholders in Brazil
  Jean Martina, Universidade Federal de Santa Catarina

  *Abstract*

  The integration of blockchain technology into carbon markets offers a unique opportunity to create more transparent, inclusive, and efficient trading mechanisms. This presentation introduces a novel Blockchain Emission Trading System (BETS) model designed to align with Brazil’s new carbon market legislation (Law 15042/2024), ensuring that both large landholders and small rural producers can participate fairly. Our approach leverages official land registries, such as SICAR, to create spatially and temporally verifiable carbon credits, preventing fraud and double counting while enabling greater accessibility for smaller stakeholders who often struggle to enter regulated markets. By decentralizing the issuance and trading of carbon credits, our model aims to reduce intermediaries, lower costs, and promote broader participation, ultimately fostering a more equitable environmental and economic transition. Through a systematic mapping study, we identify key challenges and research directions for blockchain-based carbon markets and propose a framework that ensures compliance with national and international standards while prioritizing social and economic inclusivity.

  *Bio*

  Jean is a professor at the Federal University of Santa Catarina (UFSC) in Brazil, specializing in information security, blockchain technology, and electronic documents. He holds a PhD in Computer Science from the University of Cambridge, where his research focused on cryptographic protocols and secure execution of code. Over the years, he has worked extensively on the development of blockchain-based solutions, particularly in the areas of digital identity, electronic signatures, and regulatory compliance. His recent work explores the use of blockchain to improve transparency, security, and inclusivity in digital ecosystems, including its application in carbon markets and sustainable finance.

Easter term

• 04Apr
  Title to be confirmed
  Onkar Gulati, University of Cambridge

  Abstract not available

• 11Apr
  Towards Global-scale Species Distribution Modelling
  Oisin Mac Aodha, University of Edinburgh

  *Abstract*

  Estimating the geographical range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to the availability of multi-modal data sources such as paired images and natural language descriptions. In this talk, I will present recent work from my group where we have developed deep learning-based solutions for estimating species' ranges from sparse presence-only data. I will also discuss some of the open challenges that exist in this space.

  *Bio*

  Oisin Mac Aodha is a Reader in Machine Learning in the School of Informatics at the University of Edinburgh. He is also an ELLIS Scholar and former Turing Fellow. He obtained his PhD from University College London and was a postdoc at Caltech prior to his current role. His current research interests are in the areas of self-supervised learning, 3D vision, fine-grained learning, and human-in-the-loop learning. In addition, he works on questions related to AI for conservation and biodiversity monitoring.

• 08May
  Machine Learning for Building-Level Heat Risk Mapping
  Andrea Domiter, University of Cambridge

  *Title*

  Machine Learning for Building-Level Heat Risk Mapping

  *Abstract*

  Climate change is intensifying the frequency and severity of heat waves, increasing risks to public health and energy systems worldwide. However, many existing heat vulnerability assessments focus primarily on outdoor temperatures, overlooking indoor conditions that directly affect occupants. Although building simulations can reveal the types of buildings whose occupants are most at risk, they rarely pinpoint the exact locations of these vulnerable buildings. In this presentation, I will present a data-driven workflow that locates high-risk buildings and discuss the labeling strategies we have explored for classifying real-world structures using satellite imagery.

  *Bio*

  Andrea is a first-year PhD student in the Department of Computer Science and Technology at the University of Cambridge. She is supervised by Prof Srinivasan Keshav. Her research bridges machine learning with civil and environmental engineering, focusing particularly on its applications within the built environment.