Climate risk analysis from space: remote sensing, machine learning, and the future of measuring climate-related risk- Report, July 2018

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Executive Summary

• Accurate asset-level data can dramatically enhance the ability of investors, regulators, governments, and civil society to measure and manage different forms of environmental risk, opportunity, and impact. Asset-level data is information about physical and non-physical assets tied to company ownership information.

• Remote sensing (and related technological developments such as machine learning) can help secure better asset-level data and at higher refresh rates. In particular remote sensing can help identify the features and use of assets relevant to determining asset-level GHG emissions.

• We expect that the development of a global catalogue of every physical asset in the world to be already within the reach of technical feasibility. The process of identifying and tagging assets (e.g. power generating stations, mines, farms, industrial sites) and asset-level features (e.g. cooling technologies, air pollution control technologies) can be automated through the use of machine learning.

• It is possible to train learning algorithms to recognise an asset and its features in remote imagery and then scan global imagery corpuses to identify all assets of that type. Human error rates are sufficiently low on these classification tasks that it is reasonable to expect these problems to be entirely automatable.

• With the exponential increase in space-based sensing, computing power, and algorithmic complexity, end-to-end learning systems are becoming increasingly available to academic researchers and the private sector alike.

• There are also viable methods using remote sensing data that could be implemented to measure asset-level GHG emissions. These methods are: (1) a direct method, which involves the use of various sensors on spaceborne and airborne instruments to measure emissions directly; and (2) an indirect method, which utilises various identifiable asset characteristics to model GHG emissions.

• The direct method of monitoring emissions requires the use of satellite or airborne instruments.
Accurately monitor GHGs from space is challenging because of their relatively small signal in comparison to other atmospheric constituents, but advances in both sensor technology and retrieval models are leading to more precise detection.

• Direct emission monitoring is currently feasible for a relatively limited scope of assets (such as assets that are situated in regions with very few other sources of emissions in the surrounding area). The launch of the CarbonSat satellite in 2020 as well as some already scheduled sunsynchronous sensors offer the potential for more precise observation of GHG concentrations and emissions at the asset-level.

• A complementary approach to direct measurement is to model GHG emissions indirectly using identifiable asset characteristics. This requires the identification of key characteristics that are associated with GHG emissions. For example, asset utilisation rates are inherently linked to the level of GHG emissions. Using some of the spaceborne instruments in combination with real asset-level production data it is possible to model an asset’s utilisation rate. Employing this projection of the utilisation rate an estimate of the emissions can then be obtained using a standardised model. The indirect approach represents a more feasible method of measuring GHG emissions based on currently available technology.

• Through future research projects undertaken over multiple phases we plan to make asset-level data (including various technical features) and GHG emissions monitoring for each asset (using both direct and indirect methods) available for every physical asset in every sector globally, beginning with the most GHG intensive assets. We hope to create platforms for various users to access and use this data. This endeavour has the potential to transform how different actors in different parts of society measure and manage environmental risks, impacts and opportunities. It is enabled by significant public (and private) investment in data capture and remote sensing, which can now be brought together and processed in novel ways for direct application.