By John White, Managing Director EMEA, Riskthinking.AI

We are all in this greenhouse together

In 1985, Carl Sagan testified in front of the US Congress for 15 unbroken minutes. He spoke clearly about what was causing climate change, what the consequences would be, and shared views on some of the conditions that would need to be true for society to address it.

Almost forty years on we are amidst the biggest industrial revolution in human history.

Over the next twenty years, we must redesign and rebuild much of what we have come to depend upon over the last 200. Our energy systems and the way we think about energy will change. Our resource consumption and the value we place upon natural resources will change. Our physical assets will be built and operate differently.

There will be more people (by some estimates almost 2 billion more, very unevenly distributed), living in higher concentrations, placing increasing demand on urban infrastructure that in many cases was never designed to handle this amount of use.

All of these things, and more, are going to happen amidst the most unstable climatic period in modern times. There will be more frequent and more severe climate-driven natural events, and the chronic effects of long-term (read “permanent” within our lifetimes) changes in our atmospheric, hydrologic and other natural systems will predicate wholesale reorganisation of global supply chains, migration and potentially geopolitical conflict.

It certainly seems like a mountain to climb (it is), however it is not all doom and gloom. As our exposure to the negative consequences of climate change increases so does our awareness of them. Awareness drives action, and action drives change.

One of the many positive changes this awareness has driven is a significant leap forward in the availability and reliability of forward-looking climate data. Our team here at Riskthinking.AI has been leading innovation in this space for many years, and we’re keen to share some of the things we’ve learned and some that we’re still learning about.

In order to solve a problem, you first need to understand it

For many years the prevailing approach to managing natural disasters within the private and financial sectors has been to deploy a combination of insurance, business continuity and crisis management. These “black swan” events were so unpredictable in terms of location, time and magnitude that the only place to put them was on the right hand tail of the risk register:

  • Impact = High
  • Likelihood = Unknown
  • Response = Insure, plan to respond

Recent advances in climate science have shifted this paradigm. It is now possible to look forward in time and predict where and when climate-driven natural events may occur to a sufficient level of confidence that they can (and should in many cases) be relocated on the risk register, leading to a more appropriate risk management approach:

  • Impact = High
  • Likelihood = Probable at this location within the next 20 years
  • Response = Adapt / defend / build redundancy / relocate / dispose (and insure and plan to respond)

Achieving this level of confidence in forward-looking climate predictions is a formidable challenge. Sitting beneath this new data are hundreds of individual climate models individually weighted for optimal geographic performance, thousands of real-world observed events used to validate and bias correct the outputs, over fifty different types of acute and chronic perils, and hundreds of millions of locations collectively comprising trillions of calculations. Nevertheless, it can, and has, been achieved, and is now being used by some of the world’s most progressive organisations to get in front of the climate challenges ahead.

Balancing theoretical and applied climate action

Simply knowing how fast the wind might blow or how high the floodwater might come anywhere on earth is only part of the solution. What investors, companies and governments around the world really want to know is what the consequences of these events will be, and when they are most likely to occur.

To know this, you need to know where your assets are, where your suppliers assets are, where the commodities and natural resources your company depends upon are, and where possible, how big, how much, what type, and more.

For some of these data points we can use technology to automate the collection process; for others we can use technology to make the collection process easier. As a practical example:

  • Finding assets: Most large companies will – with some level of effort – be able to tell you where their own assets are. Far fewer will be able to tell you where their suppliers' assets are. This is where technology can automate and expedite; with over 50 million corporate assets mapped and geotagged we can now undertake over 80% of this asset discovery work in less than 1% of the time.
     
  • Valuing assets: Using technology to automate the asset valuation process is fraught with risk when looking ahead in time. Average values can be used and industry standards applied; however, small changes in local land use or business operations can significantly impact the current and future value of an asset to its owner. Here is where technology can be used to facilitate the modification of asset replacement and disruption values to suit modelling purposes.

The challenge with all of the above is to find the point at which the value of the additional data being collected is equivalent to the cost of collection. With poor planning it is easy to create burdensome processes that take in vast amounts of data but ultimately yield little in terms of additional decision-making support.

Message sent is not message received

Ask any board “who thinks climate change is important?” and you will see many hands go up. Ask the same board “who is responsible for climate change?” and you are much more likely to see the occasional sideways glance. This is not because boards don’t care about climate – of course they do – it is because climate change does not neatly fit into the existing board accountability framework.

Climate change is not a new “thing” that you can buy, sell, do or don’t. It is, moreover, a context within which all the things you already buy, sell, do or don’t are affected.

To ensure our message is received we need to look at the form in which it is sent. Within any boardroom there are people responsible for protecting cashflow and balance sheet, for keeping employees safe, for maintaining regulatory permission to operate in different jurisdictions, for ensuring the company acts in ethical and responsible ways, for maintaining efficient supply chains, etc.

When we present climate change in the form of these metrics we make it accessible in the boardroom. When we say that climate change could cost anywhere from £20 million to £100 million dollars a year by 2040, the CFO sits up. When we say that 30% of our workforce are located in areas that will experience so much water stress as to challenge the viability of long-term habitation, the CRO sits up. When we say that we have only one supplier for a critical component and that supplier is in a location that is likely to experience potentially catastrophic events every five years from 2030 onwards, the COO sits up.

It is the translation of theoretical climate science to the applied language of the boardroom that makes it accessible.

In Summary

A resilient organisation is one that has the ability and will to act before the need to act becomes acute.

When we step out of the immediacy of the climate discussion and look at the bigger picture, what we realise is that if we want to drive greater decision making with greater data we need to think about more than just the data itself.

It is only when climate data is well prepared, curated and communicated that it lands well – where the consequences of both action and inaction are clear and accessible to the decision makers. This means placing as much emphasis on the human elements of the equation as the technical ones; as much emphasis on the context of the problem as the cure for it.

In the world of Riskthinking.AI, this means placing equal emphasis on all parts of the risk equation (stochastically modelling exposure, integrating physical and transition risk, providing easy access to asset data, outputs that are boardroom ready) and not over-engineering expensive, complex climate models that are functionally not suited to the real-world decisions they are designed to support.

There has never been a more important time for society to invest in and use the best of technology to accelerate progress in the climate space as we are, after all, in this greenhouse together.