Permutable AI, an award-winning UK start-up in the field of ESG data, has been awarded a grant by the UK’s innovation agency Innovate UK, to greatly improve accuracy in predicting the world’s CO2 emissions trajectory.
Permutable will use its world-class AI technology – specifically Natural Language Processing (NLP) – and data from Permutable’s global data partners, to fill in data gaps caused by companies’ failure to disclose carbon emissions and use this to improve prediction accuracy.
The UK was the first G20 country to make it mandatory for Britain’s largest businesses to disclose their climate-related risks, but this omits small-to-medium sized enterprises (SMEs). Most large company carbon emissions come from their supply chains and the SMEs in them. Supply chain emissions are on average 11 times higher than those produced by a corporation’s own direct activity, according to CDP.
The project’s aim is to greatly improve the accuracy of estimates of carbon emissions generated by the highest polluting industries and the many companies in their supply chains. With access to best-in-class data, Permutable’s machine learning team will provide better and more accurate estimates for every industry and country. Resolving the problem of lack of high-quality, reliable, and comparable data on CO2 emission levels hinges to a large extent on detecting and addressing persistent data gaps.
The funding enables Permutable to combine the latest machine learning technology with Permutable’s knowledge of changing carbon emission trends to help governments and companies address the urgent sustainability issues exemplified by the current climate crisis, achieve net-zero emissions globally, and avoid greenwashing. This includes setting a clearer decarbonisation trajectory, as well as identifying carbon emission data gaps by industry.
Wilson Chan, Permutable Founder & CEO said: “The Paris Agreement set long-term goals to guide all nations to reduce global greenhouse gas emissions greatly and to limit the global temperature increase in this century to 2 °C while pursuing efforts to limit the increase even further to 1.5 °C. However, to do this, we urgently need to address the data gap, thought to be in the region of 90 – 95% of emissions data from companies. Using our NLP technology, we aim to greatly improve the accuracy of estimates of carbon emissions as the drive to decarbonise intensifies and the carbon footprint of the corporate world comes under tougher scrutiny. We are grateful to Innovate UK for this grant, which allows us to dedicate our machine learning technology and knowledge to a critical global issue.”
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