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When everything comes down to data, it makes sense that data will lead the charge in the green transition – but is all data good data?

WRITTEN BY: CAMERON SAUNDERS

We live in an age when most all human behaviour is reducible to data, when everything we do is filtered through some algorithm and turned into datasets that is then filtered through some more algorithms to be understood by the ever-burgeoning field of data science. But, with the climate crisis upon us and in full swing, how can data help us reduce our emissions?

In myriad ways, as it turns out. And, sophisticated though our models might be, we are at the very beginning of understanding its capabilities and implications.

Take something as simple as a building. Currently, most operate on a rather antiquated on/off system, in which the power is turned on and used as people are inside the structure. This leads to wasted energy. In fact, with the proper approach to building use, up to 10 million tonnes of C20 could be kept out of our atmosphere every year.

PROCESSING DATA ...

With the proper application and execution of data, we could know when people are in (when rooms are booked), when they’re not in, and data will know – automatically and unbeknownst to us – when a building should be at whatever power, whatever energy usage, is optimal. Furthermore, the data provided by data collection systems will provide us with information that will be a powerful influence on our behaviour, for the data will let us know the actions that lead to the highest emissions.

Data, in so few words, provides us with the information which then guides our decisions, while using technology like AI to reduce our emissions helps us without us knowing about it at all. This approach can, and has, and increasingly will, be applied to businesses at large.

An important starting point is that without the right kind of information – the kind of information that comes from data – no business can begin to make the transition to zero carbon. Emitwise is a Londonbased company that calls itself a carbon management platform, advising companies on their transitions to carbon neutrality.

Co-Founder and Chief Design Officer

Eduardo Gómez noted the importance of data in this transition: “Without data, there is no visibility, and without visibility, it’s extremely difficult to make good decisions (or track the impact of these decisions). Analytics that leverage various data sources, including primary data about what companies consume, how they act and the impact of said actions, will greatly accelerate and improve decision making around emission reductions.”

But it’s not just efficient management. Data – as long as it is accessible and accurate

– can tell companies what their competitors are doing, and, in this age of ESG compliance, that can make a world of difference. A client will indeed gravitate towards a company with a solid record on the environment.

“Carbon data can also act as an industrylevel catalyst to reduce emissions by benchmarking activities,” Gómez goes on.

“For example, if you’re a large furniture manufacturer, and suddenly you have all this rich data around the emissions of other furniture manufacturers, the reduction initiatives they’ve previously carried out, and the impact they’ve had, you’d be more inclined to do the same.”

AI and accuracy

Accuracy in data is, of course, essential. If the information provided to a company is not accurate, how is the company to make the right moves in terms of reducing carbon footprint? In the quest for accuracy, artificial intelligence tools –machine learning, advanced analytics, integration, open-data standards, blockchains and APIs – are fast becoming essential. Among the benefits in terms of accuracy are better precision surrounding a company’s analysis of energy and the identification of inefficiencies. And, most importantly, the more data that

AI can accumulate and sift through, the more accurate it is as a tool.

You could even say that AI turns data analytics into high art.

At the Boston, MA-based digital transformation consultancy Publicis Sapient, they track these changes and advise companies on how best to adapt.

Joseph Tabita, the company’s Senior Vice President on energy and commodities, spoke to us about the possibilities embedded in AI data monitoring. It can, he says, “drive pattern recognition and innovation in a way that traditional data analytics cannot emulate.”

“These can be smaller use cases such as predicting consumer consumption based on smart metre data, spending patterns and other demographics, to complex multivariable optimisation of complex farm to fork, or well to pump supply chains. Companies like Microsoft and Google are already providing elements of this architecture for companies to build on top of.”

But as the pace of carbon data analytics grows and becomes widely available, it can be difficult to keep up and sift through what is accurate data and what is not. With so much riding on data for green transition, measures must be taken to ensure that it is on point.

JOSEPH

One such measure – Climate Trace (climatetrace.org) – was launched by former US Vice President and current climate advocate Al Gore at COP27, the United Nations’ meeting on climate change, which was held in November 2022 in Sharm El Sheikh, Egypt. This online database uses satellites and AI models to measure emissions as the source; think, a map of the world with darker clusters over the most populous cities and industrial bases. Some of the discrepancies this new effort has identified are that many self-reported emissions (especially of the oil and gas sector) are completely off – in some cases being three times higher than reported. Such accurate data assessments as Climate Trace will be essential as the world increasingly relies on data to power its energy transition.