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How data science can help save the planet

Jeremy Bradley, Chief Data Scientist at Datasparq

With current carbon offsetting processes being dubiously effective, we suggest a data-driven approach that companies can use to become reliably sustainable and protect themselves from punitive investor and government action.

Approximately 120 stacked fans of roughly diameter 2m, situated in a desert used in the direct air capture of CO2
An array of fans blow air through a carbon dioxide-absorbing material in this mock-up of a direct air capture plant. © Carbon Engineering

The Paris climate agreement was important — vitally important — not because it meant that it solved the problem of climate change — it did not. Nor did it mean that the world’s governments were able to agree on a mechanism for greenhouse gas reduction, that is clearly a path much longer in the making. It was important because it represented a global, multi-nation-state backed, statement of intent to try to limit average global warming to 1.5C.

Now that has happened it is up to us: citizens, employees, colleagues, companies, organisations… to take a lead. Even if successive COPs seem woefully inadequate in ambition and outcome, it actually doesn’t matter — we now have a global call-to-arms and a goal that needs to be met. And increasingly as many cities and states across the world are finding out, it matters less and less whether national governments are able to reach a consensus or not.

As data scientists and engineers, we have our challenge — and in the (nearly) immortal words of Matt Damon as Martian astronaut, Mark Watney:

“You solve one problem… and you solve the next one… and then the next. And If you solve enough problems, you get to save the planet!” — Mark Watney

That sounds like something we can get behind.

So how can this happen and why isn’t it happening yet?

The challenge for companies that quite genuinely want to reduce their impact through their operations or services is that they have no easy way of understanding the real costs and downside risk of continuing as they are. Most companies have everyday pressures to maintain or improve their financial bottom line and those are easily understood and accountancy practices are put in place to monitor and support that goal. There are very few companies who monitor and report openly on their carbon bottom line and it is not clear how one could even convert between carbon and cash, especially in jurisdictions where there is no hard requirement to do so.

There is no easy or consistent way of computing the financial downside of pumping millions of tonnes of CO₂ into the atmosphere.

Even for companies who are engaging in offset activities such as carbon credits, there are many pitfalls and no guarantees that carbon is actually being sequestrated and removed from the atmosphere [5].

It is very clear that where there is not now specific environmental legislation governing the release of greenhouse gases in a country, it is a safe assumption that there will be such legislation in many countries in the next 10 years. (Nevertheless without legislation, the pressure today from institutional investors to reach net zero is significant, as evidenced by one of the largest global funds, Norway’s Sovereign Wealth fund [1,6]).

What form any legislation takes will of course vary: carbon credits, carbon trading, carbon taxes, carbon limits, possibly consumer carbon rationing if things get really bad. Each of these options carries a financial impact on companies as well as being variously effective in reducing greenhouse gases.

Although legislation is rarely retrospective it is also a significant possibility that some key polluting industries, e.g. the oil and gas sector, energy generation sectors, may well be asked to be, not just carbon neutral but significantly carbon negative in the future; this will allow those industries to unwind some of their historic carbon debt if done correctly.

And the data science in this?

Readers of this series will know how much we use and value reinforcement learning (RL) and RL thinking in our approach to data science.

The reinforcement learning paradigm applies to business operation as much as it does to data science execution. A business is every bit an RL machine — it has levers it can pull, decisions it can take, projects it can try. From those decisions a well-run business will try to quantify their success and from that learnt success or failure, future decisions and projects are initiated.

If that success quantification process (in RL terms, the reward function) excludes a major element of the operation of the company — the cost of removing huge amounts of CO₂ from the atmosphere — then a company will never learn the correct behaviours and decisions to operate in a sustainable way. Never!

What is needed is an accurate quantification of the cost of removing CO₂ from the atmosphere (so-called CDR) that can be established as a baseline measure for carbon removal.

Hard to quantify costs around CDR

There is a good reason for not currently being able to quantify the carbon cost easily— because carbon mitigation is complex and a fast changing picture: do you plant trees which require land and long periods of time, do you invest in sustainability projects, do you pay someone for a carbon certificate? What do these mean and do they even achieve the goal of removing carbon from the environment?

An organisation that generates greenhouse gases now and wants to reduce that impact, has broadly two options:

  • change the operation so that they reduce or don’t emit carbon in the first place
  • acknowledge that some carbon is part of their current operational cycle and attempt to mitigate it through various removal approaches

Each of these possibilities carries implementation challenges. But for a business to know whether its worth changing its operation it needs to accurately understand the actual cost of carbon mitigation.

Direct Air Capture of CO₂

So now we have established the need for a standard cost metric for atmospheric carbon removal or CDR, we need a standard process that can be costed. Enter Direct Air Capture or DAC.

The process behind Direct Air Capture is relatively simple and starts with a similar chemical reaction to the one used on the Apollo moon missions to remove CO₂ from the various manned space capsules.

As shown below, ingested atmospheric air (with usually only 450 parts per million of CO₂ as of 2023!) is passed through a potassium hydroxide solution. This effectively scrubs the air of CO₂ but leaves a solution of potassium carbonate. Fortunately potassium hydroxide can be recovered from the carbonate salt by means of a pellet reactor and a calcium hydroxide solution. While the potassium hydroxide can be reused directly, the resulting calcium carbonate needs to be further treated in order to extract the CO₂ (for compression and storage) and turned back into calcium hydroxide via a final slaking process.

A process diagram representing the chemical process behind direct air capture of CO2. It shows air being ingested on the left and CO2 being produced on the right.
The chemical extraction process behind Direct Air Capture of CO₂ (from Keith et al. [3])

This all looks very neat — the circularity of the two central processes obviates the need for large amounts imported chemicals. Just as well considering we will ideally need to extract in excess of 37 billion tonnes of CO₂ per year [4] by one means or another (including vegetation growth and oceanic absorption) just to keep at the position we are today.

But where’s the catch?

However this seemingly saviour process comes with a big catch — it is enormously energy intensive.

According to [3], it takes 67MWh of energy to capture 170 tonnes of atmospheric carbon dioxide using this process. This may seem like a huge negative and it is indeed a practical challenge of implementation.

For our purposes however, it is exactly what we need to know. Electricity is sold on the wholesale market by the MWh. Currently, as I write, the price of electricity on the UK’s National Grid is retailing £207 per MWh. This gives us a means of pricing CDR both in the short term, and over the longer term. At that price it would cost an eye watering £81 per tonne of CO₂ removed. For context, this figure has regularly dropped to £30-£40 per tonne and the average cost per tonne of CO₂ removal has been £61.85 in the last week in the UK.

Actually it’s worse than that because the UK Grid is also currently operating at a carbon intensity of 134gCO₂/kWh, but this can be taken into account (as it clearly ought to be). Removing the carbon from the electricity generation as well, gives an adjusted price of £86.13/tonne.

In general...

For a generic CDR technology (t) that takes Eₜ MWh to extract Mₜ kg of CO₂ with an energy cost of Cₛ $/MWh where the energy source (s) has a carbon intensity of Iₛ gCO₂/kWh, the $ cost per kg of CO₂ removal is:

\frac{E_t C_s}{M_t — I_s E_t} where I_s E_t is much less than M_t
Formula for cost per kg of extracting carbon dioxide from the environment

Constructing the Carbon metric

You would of course not want to sample the spot market price for electricity to give you your carbon metric, you would for instance:

  • take a rolling average over a period of time
  • find a market where electricity was cheap and itself low-carbon
  • perhaps only pay for DAC at times when the wholesale price of electricity has dropped below a certain point

All these are achievable and can be reflected in a suite of metrics to conform to a gold-standard actual cost of carbon removal for an organisation that has no other way of reducing its operational carbon output but wants to do the right thing!


There we have it. As my colleague James Tawton more succinctly put it:

It’s complicated, but we can now expose the costs and savings implications of CDR via direct air capture as part of this data-driven model. With this, organisations and companies can ensure the financial, legal and ethical integrity of their sustainability strategy.

For now an idea, but one we are looking to implement. We work with companies who generate carbon dioxide in various parts of the world through heavy fleet movements, flights, supply chains, etc. We will now be able to introduce realistic cost metrics into our optimisation algorithms that can take into account carbon capture and removal (after first minimising the carbon generated).

I am sure it will generate some interesting discussions but as companies realise that they are going to be liable for this carbon removal cost at some point, it’s probably a good idea to set that money aside now. Even better, start investing in carbon removal technology and plants now and ensure that it is in a form they can be 100% sure actually does the job.

Postscript: Saving the planet?

To be clear I am not advocating using DAC as the core technology for carbon reduction in the future — it is more by way of a upper bound on what we may have to do [7]. If other approaches involving increasing global vegetation cover, rewilding, restoring peat bogs, and just burning less stuff can be used and importantly verified [5] then they will likely be easier and much cheaper. DAC of some variety is part of the mix of approaches that we will need to rescue our planetary ecosystem [2].

But let’s play the thought experiment. If we were to put aside a sum of money and an energy budget to reverse carbon dioxide concentrations in the atmosphere — what would that look like?

Using the UK’s grid statistics for the last year, 2022, (which was a high cost year for lots of reasons): £194.79 per MWh at an intensity of 178gCO₂/kWh gives a cost metric of £94.61 per tonne of CO₂ extracted.

If we plan to remove all annual CO₂ emissions and start to reverse atmospheric concentration levels we probably want to plan to remove around 40 billion tonnes annually. That would be £3.78tn or $4.58tn per year of OPEX cost (roughly 4.1% of annual global GDP). To be clear though this would need us to divert around 55% of current globally generated electricity (based on 2021 generation [8]) into carbon sequestration, so more efficient and cheaper techniques are definitely needed as well.

And finally…

If you are interested in joining us at Datasparq for exciting projects like this or just finding out how your company can benefit from our sustainable and ethical approaches to Data Science and Engineering, please get in touch. We’ll be delighted to hear from you.


[1] S. Feingold Norway’s massive sovereign-wealth fund sets net-zero goals. World Economic Forum, September 2022

[2] A. Owen, J. Burke, E. Serin. Who pays for greenhouse gas removal in the UK? Designing equitable policy to fund BECCS and DACCS? Grantham Research Institute, LSE, August 2022

[3] D.W. Keith, G. Holmes, D. St-Angelo, K. Heidel. A Process for Capturing CO₂ from the Atmosphere. Joule. Volume 2(8), pp. 1573–1594, 15 August, 2018

[4] I. Tiseo, Annual carbon dioxide (CO₂) emissions worldwide from 1940 to 2021. 3 January, 2023

[5] P. Greenfield. Revealed: more than 90% of rainforest carbon offsets by biggest certifier are worthless, analysis shows. The Guardian, 18 January 2023

[6] R. Neate. World’s biggest investment fund warns directors to tackle climate crisis or face sack. The Guardian, 3 February 2023

[7] S.M. Smith, et al. The State of Carbon Dioxide Removal: A global, independent scientific assessment of Carbon Dioxide Removal. 1st Edition. University of Oxford. January 2023.

[8] Electricity generation worldwide from 1990 to 2021. 16 Jan 2023.

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