The future is a cruel mistress. She beckons, urging us to innovate and adapt. She also punishes, for moving too slowly or too quickly. There are few arenas that demonstrate this more perfectly than transition risk.
Transition risk is most frequently associated with firms moving too slowly: failing to divest a coal mine before regulations make it a stranded asset, or falling behind competitors that adapt their offerings to suit climate-conscious buyers.
Recent events, however, have seen two incidents of transition risk from moving too quickly. The first comes from the world of oil and gas, where climate leaders like BP and Shell have seen their valuations fall compared with climate laggards like Chevron and ExxonMobil. The second comes from the automotive industry, where automakers set EV production targets that overestimated consumers’ actual demand.
This latter trap is one that technologists know well, as they race towards the future. The dot-com boom, blockchain and augmented reality all involved tech innovators making bold bets on the future that turned out to be years or decades ahead of their time, and thus fell flat on their faces.
To investors, the climate transition represents more than one opportunity to fall flat on their faces; it represents dozens, if not hundreds. In the energy space alone, batteries, hydrogen (green, blue, grey, brown, pink), linear generators, thermal energy storage and carbon capture are all areas of huge future uncertainty.
The answer, of course, is to gather data and make calculated, dispassionate bets. Our recent Green Quadrant on climate financial data and analytics vendors highlights the platforms that help investors do this best (see below). Specifically, we assessed transition risk quantification capability in terms of:
- Transition risk financial impact quantification
- Climate scenario coverage
- Methodological rigour
- Number of organizations and asset classes covered
- Unique climate transition risk insights
Leaders in the space go a long way towards giving investors the data and tools they need to make the right bets. For example, Ortec Finance, which scored joint highest for transition risk quantification alongside Clarity AI, quantifies transition impacts at the country and sector level using Cambridge Econometrics’s non-equilibrium E3ME model. Among other things, this approach allows analysts to assess technology transitions like renewable power uptake and EV adoption.
That transition risk will create winners and losers – among both investors moving too quickly and too slowly – is inevitable. Fortunately, there are tools to be on the right side of history.
For an interactive deep-dive into our analysis of climate financial data and analytics vendors, see the recent webinar featuring our Green Quadrant benchmarking analysis.
