Authors
Ben Caldecott, Lucas Kruitwagen, Matthew McCarten, Dr Xiaoyan Zhou, Xiaoyan Zhou, David Lunsford, Phanos Hadjikyriakou, Valentin Bickel, Torsten Sachs & Niklas Bohn
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. Remote sensing can help identify the features and use of assets relevant to determining asset-level GHG emissions. With the exponential increase in space-based sensing, computing power, and algorithmic complexity, end-to-end learning systems are becoming increasingly available that could be implemented to measure asset-level GHG emissions. This has the potential to transform how different actors in different parts of society measure and manage environmental risks, impacts and opportunities.