Welcome to the era of big data for Climate

Good intentions are no longer enough

It’s time to get real on the road to net zero – and we can’t change what we don’t measure

“Life is a journey, not a destination.”

This quotation has come to mind a lot in the last year, as we seem finally to have arrived at the age of climate commitments. Governments spent last year circling their calendars in red, eager to announce their net-zero birthdays before COP Glasgow. Over 130 countries have chosen 2050; laggards China and Russia have committed to 2060; leaders Sweden and Germany have declared for 2045.

Meanwhile, the trickle of businesses committing to decarbonisation has turned into a flood; nearly one in threeof the world’s 2000 largest companies had committed to net-zero by the end of 2021.

All of this is great news on the surface, of course. But dig deeper and questions emerge. Walmart, for example, has released its official strategy to become carbon-neutral by 2040. But research by Net Zero Tracker revealed that this excludes Scope-3 emissions – those generated along its supply chains, or by consumers in using its products. No small matter when these Scope 3 emissions account for 95% of the company’s estimated footprint. How can Walmart meaningfully decarbonise if it doesn’t even monitor the majority of its own impact?

Net-zero is more than just a destination. It’s time to get serious about the journey.

Mapping the route to net-zero

“I think to make any progress in any domain, you have to be able to measure what you’re doing,” explainsRamez Nasser, entrepreneur and board member of cleantech firm Energisme. I had invited Nasser to join me on Conversations on Climate – our podcast hosted by United Renewablesand the London Business School Energy Alumni Club – to address just this topic. What tools do we need to turn our net-zero dreams into concrete realities?

Drawing from his experience across commodities, energy, big data and AI, Nasser didn’t disappoint. His work, and that of Energisme, are at the forefront of the effort to map the reality of the carbon economy in our hyperconnected, globalised world – because you can’t change what you can’t measure.

“With the development of technology and IoT [Internet-of-Things], we’re equipping buildings, cities, and factories with a whole host of electronic components and people, to measure their efficiency at using energy effectively,” Nasser says. With this wealth of new data, companies like Walmart will be able to track energy and emissions not only from their own plant and property, but across the full life-cycle of their products. From supply chains to the homes of their customers, numerous new data points can be gathered and centrally aggregated by the modern firm.

Eventually, like a mosaic, the result will be a highly detailed and accurate image of reality – one that can be turned into action. If firms can locate their emissions pain points, both upstream and downstream, they can more properly target and measure their reduction efforts. As Nasser put it, “quantifiability is accountability.’

Is AI the answer to TMI?

This wealth of new data creates its own challenges – the most immediate being the sheer scale of it all. “You’re talking about IoT data, scatter systems, BMS data, contract data, meteorological data [and so on] which is all very heterogeneous and increasingly complex,” Nasser explains. To aggregate this data at scale, and in real time, is beyond our ability to cope with alone.

The answer is to apply the tools of big data, AI and automation. This is no longer a nice-to-have; in a world that produces 50 zetabites of data a year (rising to 2,000 by 2050) these technologies are a business born of necessity. But get it right, and AI promises more concrete maps towards our net-zero destination.

Perhaps the first task is to make sure that it is actually useable. As Nasser pointed out 80% of data scientist’s time is ‘looking for data, cleaning up data, making it available for their algorithms.’ In the land of good intentions, it is fine to leave diligent scientists to build datasets over months and years. But for organisations who need to transform in real-time, there is an opportunity here to leverage machines to radically improve the efficiency of their data handling. For example, robotic process automation (RPA) is one such area where there have been huge improvements in processing heterogenous data from multiple inputs and using it to run central processes.

Who watches the watchers?

I agree with Nasser that there is a huge opportunity here, to use these technologies to map out concrete paths to decarbonisation. But I am also wary, because these tools come with risks which to my mind have not yet been fully addressed.

Firstly, this flood of new data raises issues of privacy and power. As we have found out with tech giants like Facebook and Google, whomever controls the wellsprings of data gathers an inordinate amount of control over our digital lives. Big Tech already knows where we live, who we know, what we buy, what will make us angry, and a thousand other details about us. Once we become truly networked into smart cities and homes enlivened by the internet of things, the risks to our privacy and self-determination will increase exponentially. The last thing we want is for our personal footprint to become a tool of control, be it to sell us or sanction us.

Second, the digital economy still has questions to answer about its own impact upon the planet. Datacentres account for 2% of the world’s carbon emissions – equivalent to the global aviation industry – and training a large AI machine generates five times as much CO2 as that emitted by a car over a human lifetime. It’s all very well to talk about the improvements in efficiency in data storage and handling; but the Jevons Paradox (aka the rebound effect) is one of the cornerstones of environmental economics, and there is no reason to assume that lower data costs won’t just mean more traffic. Individual firms and countries are making strides towards renewably-powered data centres, and this will need to become industry standard before too long.

Finally, there is a question around talent. How do we draw the brightest tech minds now emerging from MIT and LBS towards this space? My sense is that Silicon Valley, and the blockchain technologies, are still the brightest lights for new graduates, and that will need to change.

Start your (data) engines

There will be combinations of regulation and innovation which can answer these critiques, and it is good to see different countries experimenting already with different approaches.

Ultimately, I think Nasser is right when he says that, “big data, today, is almost a race.” If we don’t build out these solutions quickly, we will become dependent on those who do – and we may not like the values they embody within their algorithms.

And if no-one makes it work, then net-zero may remain just a dream some of us have. Because in order to take the first concrete step, we need to be able to see the ground beneath our feet.

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