Detection and characterisation of pollutant assets with AI and EO to prioritise green investments: the geoasset framework

Rossi, Cristian, Tkachenko, Nataliya, Bayaraa, Maral, Voulgaris, Georgios, Foster, Peter, Reece, Steven, Scott, Kimberly, Christiaen, Christophe, McCarten, Matthew and Voulgaris, Georgios (2022) Detection and characterisation of pollutant assets with AI and EO to prioritise green investments: the geoasset framework. International Geoscience and Remote Sensing Symposium (IGARSS), Kuala Lumpur, Malaysia, 17th - 22nd July 2022. Published in: IGARSS Proceedings. IEEE ISBN 9781665427920 (Accepted)

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Abstract

Detailed and complete data on physical assets are required in order to adequately assess environment-related risk and impact exposure and the diffusion of these risks and impacts through the financial system. Investors need to know where the physical assets (e.g., power plant, factory, farm) are located of companies in their portfolios, and what their polluting characteristics are. This is essential to manage these environment-related risks and to channel investments to more sustainable alternatives. At present, data on physical assets is typically incomplete, inaccurate, or not released in a timely manner. As a result, key stakeholders including asset owners, asset managers, regulators and policymakers are frequently forced to make crucial decisions with incomplete information. Accurate and comprehensive global asset-level databases are a prerequisite for meaningful innovation in green and digital finance. They provide the link between the financial system and the “real economy” and allows the wealth of EO datasets and insights that we have available to be made actionable for sustainable finance decision making. We created a framework to derive a global database of pollutant plants, such as cement, iron, and steel, which represent about 15% of the global CO2 emissions. Our solution makes use of state-of-the-art deep learning architectures coupled with Earth observation data.

Item Type: Conference Proceedings
Keywords: Spatial finance, Deep Learning, Pollutant industry, Remote Sensing
Schools and Departments: School of Engineering and Informatics > Informatics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 03 Aug 2022 09:11
Last Modified: 03 Aug 2022 09:11
URI: http://sro.sussex.ac.uk/id/eprint/107235

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