It was the Australian bush fire that finally did it.
For 12 years Adam Hearne had worked at companies that represented some of the world’s largest sources of greenhouse gas emissions. First at Rio Tinto, one of the largest industrial miners, and then at Amazon, where he handled inbound delivery operations across the EU, Hearne was involved in ensuring that things flowed smoothly for companies whose operations spew millions of tons of carbon dioxide into the environment.
Amazon’s business alone was responsible for emitting 51.17 million metric tons of carbon dioxide last year — the equivalent of 13 coal-burning power plants, according to a report from the company.
Then, Hearne’s home country burned.
In 2019 wildfires erupted that engulfed more than 46 million acres of land, destroyed over 9,000 buildings, and killed over 400 people and untold numbers of animals — driving some species to the brink of extinction.
Hearne, along with an old friend from his business school rugby days (Roheet Shah) and computer science and machine learning experts from Imperial College of London (Yuri Oparin and Jeremiah Smith), launched CarbonChain that year. The company, now poised to graduate from the latest Y Combinator cohort, is pitching a service that can accurately account for emissions from the commodities industry — which is responsible for 50% of the world’s greenhouse gas emissions.
The company’s services are coming at the right time. Countries around the globe are poised to adopt much more stringent regulations around carbon dioxide and greenhouse gas emissions. The European Union is slowly working toward passage of sweeping new regulations on climate change that are mirrored in the region’s local economies. Even petrostates like Russia are poised to enact new climate regulations (at least according to Russian officials).
What’s missing in all of this are ways for companies to accurately track their emissions and technologies that can adequately monitor how well emissions offsets are working.
CarbonChain tackles this problem by going to the sectors that are responsible for the largest percentage of greenhouse gas emissions, Hearne said.
“The world needs hard accounting and hard numbers of what commodities companies are producing,” said Hearne in a July interview.
To ensure that emissions reductions and regulations are working, regulators need to go after oil and gas and commodities and minerals producers, according to Hearne. “Those sectors are uniform and carbon intensive and that’s how you quantify them,” he said.
CarbonChain has built models for every single asset in the supply chain for these industries, according to Hearne. The company has created digital twins of every piece of equipment used in heavy industry. If CarbonChain can’t get the information about the equipment from the companies that use it, they go to the engineering firms that built the equipment or facility for the company.
“In order to get a number that doesn’t get laughed out of the room we have to go down to the aluminum smelter that has a power station right next to it,” said Hearne. “Ninety percent of its footprint is its electrical usage.”
According to Hearne, CarbonChain’s system is so precise that it can tell users how much carbon emissions are embedded in a cup of coffee or a glass of wine (which is two pounds of carbon dioxide for imported wine, by the way).
CarbonChain is already selling its services to commodities producers and carbon traders who are operating in existing carbon trading schemes.
So far, the company has received roughly $500,000 from the U.K. government and an investment from one of its (undisclosed) commodities customers.
But CarbonChain’s technology seems to have the most rigorous methodology of any of the companies that’s purporting to do emissions monitoring. Other startups purporting to provide carbon emissions data for companies include Persefoni, which raised $3.5 million for its solution, and another Y Combinator graduate, SINAI Technologies.
If the company can actually measure the embedded emissions of materials down to a single piece of rebar, it could have huge consequences for industry broadly.
The company also slots nicely into the trend of entrepreneurs with deep industry experience building vertical solutions based on the collection of massive data sets using machine learning.