Alisa Holme

SUSTAIN Cohort 1 Student

University

University of Aberdeen

SUSTAIN Project

Transparent Carbon Footprint Quantification and Reporting in Agriculture

Supervisors

Dr Milan Markovic (University of Aberdeen)

Dr Paul Williams (Queen's University Belfast)

Research Interests

Knowledge graphs, LLMs, Carbon accounting, MRV systems, Semantic web

I joined the SUSTAIN CDT to be a part of the collaboration between academia and industry to combat climate issues in the agri-food sector. I’ll be investigating practical solutions to enhance the functionality, accessibility, and credibility of carbon accounting, and to explore the use of AI for such tasks. I'm looking forward to working with the stakeholders of carbon accounting systems for this research - the farmers and the food industry - to make sure the project is relevant to real life problems and to offer practical solutions and knowledge to reduce the climate impact of agriculture.

I’ll be based at the School of Natural and Computing Science at the University of Aberdeen and working with supervisors from both biological sciences and computing to enhance the interdisciplinary aspects of this research. I am originally from Finland but have lived in Aberdeen for five years now, having completed my undergraduate and master’s degrees here in Biological Sciences (environmental modelling). In my free time I write and play volleyball for the university and the local premier team.

Transparent Carbon Footprint Quantification and Reporting in Agriculture

Dr Milan Markovic, University of Aberdeen; Dr Paul Williams, Queen’s University Belfast; Dr Matthias Kuhnert, University of Aberdeen; plus an Industry Supervisor

Agriculture, responsible for 10% of global greenhouse gas emissions, holds significant potential for negative emissions, particularly in soil carbon sequestration. However, existing Monitoring, Reporting, and Verification MRV) systems lack standardization, transparency, and face challenges in data availability. Symbolic AI technologies (e.g., knowledge graphs) can be used to capture additional information such as geospatial context and data provenance of the data analysis which could enhance the transparency and thus support novel MRV systems.

This PhD project explores practical applications of AI technologies to address the challenges in one or a combination of the following topics:

AI-based quality and trust assessments of carbon footprint and sequestration reporting (e.g., using provenance knowledge graphs documenting calculation processes)
AI-based approaches to automate carbon footprint and sequestration reporting (e.g., based on semantic digital twins representing business assets)
AI-based enhancements to resilience of carbon footprint and sequestration techniques (e.g., accounting for extreme events)
Automated validations of local data in federated Machine Learning infrastructures for privacy preserving soil-based carbon capture analysis

While the PhD is in the field of computer science, Alisa is be expected to apply an interdisciplinary approach to solving these challenges. Alisa is developing skills in designing, applying, and testing semantic applications in the context of real-world challenges.

Welcome to the UKRI AI Centre for Doctoral Training in Sustainable Understandable agri-food Systems Transformed by Artificial INtelligence (SUSTAIN).