? Grid intensity view:

Issue 1

Issue 2

Issue 3

Issue 4

Issue 5

Issue 6

Issue 7

Issue 8

Unknown grid intensity

The perfect data paradox

Sunlight filters through trees along a forest path
Image by Brett Duboff. CC-BY-NC 4.0.

It’s a simple paradox. We’ll wait for a solution to be perfect before we invest in adopting it, but every solution creates its own issues and can only be improved with adoption. And so forms the biggest argument against adopting new emissions data solutions: the problem’s too complex to solve, and the solutions aren’t perfect yet.

It makes sense – it’s true, isn’t it?

Ultimately, at the end of every well-intentioned policy or piece of regulation, there’s a human trying to plan what on earth they need to do when they get to work. If you’re in a sustainability role, this means grappling with acronyms, scientific terms, accounting principles and emotional pressure from dealing with climate crisis rhetoric.

You’re balancing the needs of the company, budgets, the regulators, your customers and your own career. If you’re juggling all this, and someone chucks you something else to catch that’s imperfect, dodging it is a likely strategy to avoid dropping all the other stuff you’ve spent so long getting off the ground. 

Adopting an imperfect tool means that you’re also adopting its snags. Solving yesterday’s problem usually means you’ve got a new task for tomorrow. In other words, solutions never gave anyone a day off.

Imperfect progress

New technologies solve problems that stem from the imperfections of the tech that precedes them.

We have wireless communication because wires break, and they keep you in one location, but now they run out of battery. We have planes because ships aren’t fast enough, but those planes are more expensive. We have highly efficient wind turbines floating in the North Sea because fossil fuels are running out and killing the planet, but the wind doesn’t always blow.

All of those technologies were embraced by early adopters. Looking back, the early imperfections that came with wireless, wind and aviation would be laughable reasons to not adopt them when compared to what they’ve unlocked.

There’s a few simple principles to liberate data from the perfection paradox. They should work for everyone if you keep them at the back of your mind:

  • The biggest problems are best solved by chipping away at them.
  • Adopt new tech when it’s 85% perfect. The final 15% never happens.
  • New solutions cause new issues, but that’s progress.

Digital emissions data is not so binary

When it comes to emissions data from the digital and cloud sector, I see a few common reasons given to justify delaying adoption and action.

“The tech solutions in this field are improving every few months. If we measure now, it risks being outdated in future.”

It’s a good point. This probably – if the person saying it was totally honest – comes from a place of fear. Getting things wrong is not something that’s embraced in most companies. So to stick your neck out and pursue an innovative sustainability project that might involve data that is soon up or out-dated could well be seen negatively by those who decide whether you’re doing your job right. 

This then becomes an issue of messaging. Be clear that this is an innovative project. Use the fact that you’ve moved your company to the frontline as a point of strength, not weakness. 

“The grid should be renewable soon, so we’ll wait for that.”

Fantastic! And then we just need to wait until we’ve discovered limitless electricity, too. No – sarcasm is not the way to answer this one, however tempting. There are countless reasons why this is not a good basis to ignore sustainability data. 

For one, we’re likely decades away from this scenario. When we finally achieve neutral energy worldwide, we’ll be entering the post-carbon era. And in this new era we’ll be faced with the constraints of the grid, and energy demand management will come to define it. And for that, you’ll need data. 

“Digital is net positive, so it’s not a priority.”

It’s almost a cliche. The digital economy – and particularly cloud computing in the corporate world – has made society efficient beyond all belief of our previous generations. 

But positive impact doesn’t mean immunity from evidence. Something can only be net positive if the gross problem has been measured. 

“It’s impossible to perfectly measure digital emissions.”

This statement is completely true. There’s no argument about it. It is impossible to perfectly measure digital emissions. It’s impossible to perfectly quantify pretty much all emissions from all sources. As solutions providers, data companies need to keep their clients at the sharpest possible edge of research and development in the field. 

The closest you can get to perfection is to place yourself at that sharp edge.

If you don’t want to be a pioneer, these reasons for avoiding using imperfect data will serve you well. They are answers that are routinely used when lobbying, when answering customer demands for emissions data and when satisfying your investors.

But, if you hear them, or you’re thinking about speaking them – remember that there are others who have already chosen solutions over avoidance, and there are many more lining up behind them. 

The frontline

For those that are responsible for the frontline measurement and reporting of emissions data, we ask a lot. We ask for the world, and provide imperfect tools for them to deliver it with.

Telling a company or an individual to just get on with it is a sure-fire way to wind people up the wrong way, and disincentivise adoption.

The global Greenhouse Gas Protocol, responsible for guiding companies through emissions data disclosures, is built in the image of financial reporting standards. This is why we call it ‘carbon accounting’, and it makes a lot of sense. We’re balancing the books – tipping the scales.

But unlike in finance, embracing imperfections in emissions data doesn’t need to make things harder, and using force means that those on the frontline simply crack. The frontline is thin and so is our window of time to limit the impacts of the climate crisis. Cracking the frontline seems like a short-sighted solution.

The long-sighted policymaker might just reimagine our frontline workers as part of the innovation process. Regulation and policy must clear the way for adoption and allow for natural mistakes to be made on the job – these mistakes build the solutions.

Embracing the imperfections in the data we have is what is going to solve tomorrow’s crises, and so on it will go until the solutions for this particular challenge are just enough to tip the scales back in the planet’s favour.

This is imperfect progress.

Rory Brown is the Head of Innovation at Greenpixie – a global cloud emissions data provider. Greenpixie develops methodology and technology to measure the impact of cloud computing at scale. Rory coordinates cross-sector collaboration between government, commercial, non-profit and academic stakeholders in digital sustainability.