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Outsmarting Climate Change: Artificial Intelligence and the Future of Greenhouse Gas Regulation | JD Supra

For nearly a decade, there has been a downward trend in greenhouse gas (GHG) emissions from industry…that is, until now. According to the World Meteorological Organization, atmospheric levels of the three main greenhouse gases – carbon dioxide, methane and nitrous oxide – all reached new records in 2021. Moreover, these emissions have increased by nearly an additional 1% in the United States for 2022. Although the reason for this increase is not entirely clear, it is likely the result of biological and human-induced processes, including an increase in gasoline consumption and a rebound in air travel following the COVID-19 pandemic.

While the United States has made progress in reducing GHG emissions, one of the challenges it faces today is identifying the sources of these emissions and quantifying their production. Traditionally, regulators have relied on various industries and other actors in the regulated community to gather GHG emissions information, such as air emissions inventories and other self-reported data, for each individual source. Unfortunately, due to limited reporting requirements, incomplete and/or outdated inventories, and underestimates of reported emissions, these information sources do not accurately describe what is actually happening with GHG emissions.

In fact, new data suggests that among major countries reporting their oil and gas production emissions to the United Nations, these emissions are actually up to three times higher than self-reported data. Where does this seemingly “new” information come from? The answer lies in artificial intelligence (AI). Using satellite coverage, remote sensing, machine learning and AI, it is now possible to identify and analyze sources of GHG emissions that were previously invisible to the human eye and undetectable by methods. traditional modeling.

For example, Climate TRACE, a global nonprofit coalition created to independently track GHG emissions, uses more than 300 satellites, more than 11,100 air, land, and sea sensors, and additional public information to build models. GHG emissions estimates. These patterns are then used to train the AI ​​to spot even subtle differences in satellite images and data patterns.

Last week, Climate TRACE released the most detailed global inventory of facility-level GHG emissions to date, including emissions data from more than 70,000 individual sources around the world. These sources include power plants, steel mills, urban road networks, oil and gas production and refining, shipping, aviation, mining, waste, agriculture, transportation and production of steel, cement and aluminum. With the ability to access and track information on millions of major sources of GHG emissions at your fingertips, the next question is whether and how to use this data in the future to regulate GHG emissions. In the end, here in the United States, we may not have to wait long for the answer.

On the heels of the Climate TRACE findings, the Biden-Harris administration announced a proposal to reduce methane pollution by 87% below 2005 levels by 2030. As part of their proposal, the administration would establish a “super-emitter response program” that uses data from regulators or approved third parties with expertise in remote methane sensing technology to identify large-scale emissions for immediate action .

Therefore, it is very likely that regulators will soon utilize the use of AI developed by third parties, such as Climate TRACE, to some extent to monitor and/or enforce GHG emissions. At the very least, others across the country and around the world will almost certainly use this information to keep a close eye on the companies they currently work with or plan to engage in business activities in the future.

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