Aims and Principles
Data science for climate intelligence. ClimateNode aims to make smart and innovative use of data science to research and promote understanding of climate risks and impacts. Its current focus is on natural language processing and knowledge graphs. ClimateNode's algorithms can extract information on hazards and impacts from a large number of newspaper articles, reports and other texts (subject to permissions) and link them to places, assets, organisations, sectors and commodities.
Public benefit outlook. There is a growing recognition that information which helps societies adapt to climate change should not only be available to those with the deepest pockets. ClimateNode seeks projects and partners which share this belief. CN’s concept of ‘climate risk’ encompasses that of the business and financial community, but is not limited to it. Furthermore, it is taken as self-evident that risks to human welfare encompass risks to natural resources and processes, health, security and other social and public goods.
Respect for science. There is a need for good quality information which neither exaggerates nor downplays the risks of climate change. However, we all know that objectivity can be difficult for such an emotive subject. ClimateNode aims to treat scientific research relevant to its work accurately and objectively. Please note that ClimateNode does not credit itself with or offer scientific expertise.
Multicausal approach. Understanding climate risks and impacts in a particular location often means understanding how both climate and non-climate drivers interact. Examples of non-climate drivers of environmental change include subsidence, deforestation and river modification. Risks and impacts could be amplified if they take place in a context of political instability, or dampened by adaptation efforts. Impacts may also themselves lead to knock-on or unforeseen consequences, or be caused by multiple factors interacting in complex ways (as with wildfire). ClimateNode seeks to understand factors which could exacerbate, dampen or even simulate climate risks and impacts, taking a multicausal approach by default.
Helen Jackson is Director of ClimateNode. Helen is an environment and natural resource economist with many years’ experience in climate change, energy and environmental policy and economics, working on projects for multilateral organisations, energy companies and governments. She was one of the first people to work for leading climate and energy consultancy Vivid Economics (now part of McKinsey). She has also worked for green finance pioneers the Climate Bonds Initiative on assessing asset-level climate resilience, as well as for the Economist Intelligence Unit. Originally trained as a physicist, Helen started out expecting to be a climate scientist, picking up her coding skills during space and atmospheric physics research projects as a student. She has an Advanced Diploma in data modelling, incorporating database design, from Oxford University. Her research has been cited by The Rough Guide to Economics and the leaders of both the UK Conservative and Labour parties. linkedin twitter
Dr Elliot Christou is a data scientist with a background in theoretical physics at University College London. He is interested in using artificial intelligence to solve complex real world problems. He provided the foundations of ClimateNode's natural language processing capacity as part of the Faculty AI Fellowship programme in autumn 2020. He is now a data scientist at the Connected Places Catapult. linkedin