For nearly a decade, there has been a downward trend in greenhouse gas (GHG) emissions from the industry… that is, until now. According to the World Meteorological Organization, atmospheric levels of the three major greenhouse gases—carbon dioxide, methane, and nitrous oxide—reached new all-time highs in 2021. Additionally, these emissions have increased by nearly an additional 1% in the United States over the 2022. Although the reason for this increase is not entirely clear, it is likely the result of both biological and human processes, including an increase in gasoline use and a rebound in air travel following the COVID-19 pandemic. .
While the US has made progress in reducing GHG emissions, one of the challenges it faces today is identifying the sources of these emissions and quantifying how much they are producing. Traditionally, regulators have relied on various industries and others in the regulated community to collect information on GHG emissions, 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 Unfortunately, due to limited reporting requirements, incomplete and/or outdated inventories, and underestimates of reported emissions, these data sources do not accurately describe what actually happens with GHG emissions.
In fact, new data suggests that among the top countries that report their emissions from oil and gas production to the United Nations, these emissions are actually up to three times higher than self-reported data. Where did this seemingly “new” information come from? The answer lies in artificial intelligence (AI). Using satellite coverage, remote sensing, machine learning, and artificial intelligence, it is now possible to identify and analyze sources of GHG emissions that were previously invisible to the human eye and undetectable using traditional modeling methods.
For example, Climate TRACE, a global nonprofit coalition created to independently track GHG emissions, uses more than 300 satellites, more than 11,100 air, ground, and sea sensors, and additional public information to create model estimates. of GHG emissions. These models are then used to train the AI to detect even subtle differences in the satellite imagery and data patterns.
Last week, Climate TRACE released the most detailed global inventory of facility-level GHG emissions to date, which includes 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 steel, cement, and aluminum production. With the ability to access and track information on millions of major sources of GHG emissions at our fingertips, the next question is whether and how to use this data in the future to regulate GHG emissions. Turns out, here in the US, 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 87% below 2005 levels by 2030. As part of its proposal, the administration would establish a “reduction program Super Emitter Response” that uses data from regulatory agencies or approved third parties with expertise in methane remote sensing technology to identify large-scale emissions for immediate action.
Therefore, it is very likely that regulators will soon employ the use of AI developed by third parties, such as Climate TRACE, in some capacity to monitor and/or enforce GHG emissions. At the very least, others across the country and around the world will surely use this information to keep a close eye on the companies they are working with now or considering doing business with in the future.