By Mathew Carr
Feb. 18, 2021 — LONDON: The problem with existing carbon markets is that they are tainted by complication.
It’s hard to know if you are getting good value for money or even if you understand what the heck you’re buying.
Market Stability Reserves (MSRs), Certified Emission Reductions (CERs), corresponding adjustments (CAs), additionality, carbon border adjustment mechanisms (CBAMs), Reducing Emissions from Deforestation and Forest Degradation (REDD), sustainable development goals (SDGs), Verified Carbon Standard (VCS) and one of my personal favorites: common but differentiated responsibilities and respective capabilities (CBDR-RCs).
Here are eight pages of acronyms – don’t be shy:
https://www.ipcc.ch/site/assets/uploads/sites/2/2019/05/SR15_AnnexII_Acronymes.pdf
Now, artificial intelligence is going to bring a new level of transparency and simplicity that will make the markets work better for the corporate buyer, for the politician in almost 200 nations seeking cost-effective policy under the Paris climate deal and even potentially for the mum-and-dad investor.
One of the big problems currently is that a buyer just can’t tell how beneficial a carbon credit is. OK, it’s apparently worth one ton of carbon dioxide, but has it helped alleviate poverty in a least-developed nation? Are there human rights issues linked to the project? Has it helped improve water quality for the people living around the project that created it?
Digitization will help the market thrive by putting carbon credit buyers in charge of making sure they get what they want.
Viridios Capital, a Sydney, Australia-based asset manager, has written a paper to show how some of it might work.
See the report, with charts, in this Linked In post: https://www.linkedin.com/posts/viridios-capital_ai-paves-the-way-for-valuing-impact-activity-6766567269800792064-awdA
This one shows how a credit generated in Cambodia worth on the surface $3.71 a ton actually has a fair value of $28.02 a ton, taking into account other UN sustainable development goal metrics.
As corporates seek to set and meet net zero targets without damaging their reputation and environmental lobby groups try to make their lobbying is more sophisticated and targetted, this AI will help tremendously.
Another example. Here’s why a solar credit from India might be worth less than the forestry credit in Cambodia, even when all the sustainable development goals are not ascribed a value:
The machine learning is from a database of past transactions, part of Viridios Capital’s intellectual property.
The machine learning is just getting started, says Eddie Listorti, the CEO of Viridios, speaking by phone from Australia. The note was in response to the final report by the Taskforce to Scale the Voluntary Carbon Market, which is seeking to cut the risk for corporates taking on aggressive emission-reduction, or “net-zero” targets.
See this: http://carrzee.org/2021/01/28/net-zero-companies-from-google-to-shell-see-a-way-forward-and-want-it/
It’s difficult to overstate the importance of cutting these risks, especially for high-emitting companies. It’s easy to forget that much of the climate damage caused during the past 100 years or so has been by just a few hundred huge commercial business groups around the world.
Even with the AI, there’ll still be a strong need for smart, tough regulation overseeing it.
See also some of the key trends for corporates seeking to burnish their green credentials, including another cool way of ensuring a buyer might check fair value using price tension provided by auctions of put options: http://carrzee.org/2021/02/09/the-era-of-climate-brinkmanship-is-ending-as-carbon-free-cashflow-rises