Why complexity matters when measuring sustainability

This is part of a series examining the pitfalls of sustainability measurements, drawing on lessons from outside the business world. For more, see parts 1, 2 and 3.

In the last two months, our atmosphere has passed the 400-ppm carbon threshold. Unlike the sound barrier, this number doesn’t mark an abrupt physical change of state. It’s a symbol of the now increasingly likely future when climate change substantially affects life on earth.

Still, even as a negative marker, 400 ppm tells us just how urgent it is that even useful and popular tools such as metrics take more leaps. Metrics aren’t just markers. In order to guide us, metrics should take account of the reality of the complex world in which we actually operate, no matter how unfamiliar or uncomfortable that may sometimes seem. Systems thinking needs to be better understood, taken to a higher level and brought into the world of metrics. 

We note that systems thinking is starting to come up in sustainable business conversations, but remain concerned about persistent mindsets that ignore complexity. Without it, sustainability practitioners are unequipped to grapple with a point that should come up early in metrics work: "Did we just miss something very important?"

Too many articles implicitly distill big problems into linear, simpler solutions. While the latter have an important place, the large reliance on them fails the global test -- in all senses of the term -- that our world’s challenges demand.

Complexity is a close kin to systems thinking. However, this is mostly understood only at the glazed-look level. A working definition might help: Complexity is about understanding how interdependent, nonlinear systems actually act in the wild. That's not how we’d like them to act, or how parts can be tested in the laboratory, but as whole entities.

Here are a couple of pitfalls for sustainability metrics; you can review pitfalls 1-7 here, here and here.

Pitfall 8: Overlooking nonlinearities

Exceedances of thresholds, sometimes called tipping points, are where the underlying state of something actually changes its physical condition. It becomes something qualitatively different from what was just there before. These events may or may not be known ahead of time.

In a GreenBiz article, Sissel Waage describes how natural ecosystems can leap directly from forest to grassland -- not the usual sequence -- under the right pressures, in nonsynchronous leaps. This instructs us no longer to count on the common assumption of straight-line realities when developing and projecting the results of metrics.  

Pitfall 9: Failure to embrace uncertainty, seeing data as sufficient without wizened interpretation, and pervasive human biases

The old joke goes that everyone talks about the weather but no one does anything about it. Post-Superstorm Sandy, we’d argue that a lot more people are finally starting to act and are seeking connections, if not causes, as it relates to climate change. “Did climate change cause Superstorm Sandy?” has jumped off the weather map and into national political discussions.

Taken in this light, we should understand more about where weather forecasts and climate models are coming from. Nate Silver’s book, “The Signal and the Noise,” published a scant month before Sandy, does just that.

Let's put three conventional "wisdoms," or at least common practices, on the table:

  1. Weather forecasters are often wrong (hence the jokes).
  2. Uncertainty is a bad thing and, regardless, does not need to be worked into predictions or even much talked about.
  3. To the degree that chaos theory is at all popularly understood, it has the reputation for making predictions impossible or meaningful, as any departure in practice from what is expected can reset the system on a new, unexpected path.

Image credit: Garden maze by Mopic via Shutterstock.

Next page: Put conventional 'wisdom' to rest