Most of us have at least one moment in our lives when we wish we’d made a different choice.
It’s easy to recognize what went wrong in hindsight, but key factors that could have made all the difference at the time are often easily missed as we experience them.
Understanding those pivot points becomes even harder for complex systems at global scales. Predictive modeling, however, is the one thing that can get us remotely close to pinning down those important factors before they pass us by.
To that end, environmental policy researcher Frances Moore from the University of California, Davis and colleagues across the US used computer simulations to predict and analyze 100,000 climate change futures.
By running scenarios over and over again, while tweaking different factors like a climate groundhog day, we can begin to understand potential linchpins that our collective fates revolve around.
With global commitments still falling far short of meeting the Paris Agreement, and planned carbon dioxide emissions set to rapidly blast through our remaining carbon budget, finding these points of focus is more important now than ever.
Most climate modeling to date has focused on the technical aspects of the problem – the climate itself, or mitigating technologies. Previous research has demonstrated we have the technical capabilities to make the changes we need, and that they are economically possible.
But time and time again these are thwarted by other factors that modeling has so far mostly neglected – human social and political systems – despite the fact that what we humans choose to do with our emissions swamps any other climate variable by magnitudes.
Moore and team searched across many different disciplines to include social, economic, and political factors that will influence our emission rates, to feed into their computer simulations of warming levels by 2100.
“We’re trying to understand what it is about these fundamental socio-political-technical systems that determine emissions,” says Moore.
They added constraints to their variables using historical data, and identified several social factors – including how the public views climate change – as key to determining which group of scenarios are most likely to play out.
“It has been hypothesized that this emerging signal of climate change in people’s everyday experience of weather might lead to widespread acknowledgement of the existence of global warming and possibly, by extension, support for mitigation policy,” the researchers explain in their paper.
“A tendency towards social conformity can lead to tipping-point-type dynamics in which a system transitions suddenly from a previously stable state given a sufficient critical mass of proponents of the alternate norm.”
This is why factors like our society’s perceptions remain so important. Moore and colleagues also considered how cognitive biases like the shifting-baseline effect may affect the social factors.
Moore previously led a study into this bias, which found people tend to compare current weather anomalies against what they remember in the last eight years, rather than more historic weather, so as time goes by, this comparison baseline changes too.
Whether or not this comes into play is one of the many things that will influence which of the future paths we’ll find ourselves on.
Then of course the social factors are also tightly interwoven with the costs and effectiveness of mitigation technologies and how quickly political institutions respond.
“Almost all of our identified clusters have distinguishing parameters from more than one [discipline], implying that the interaction between these subsystems is key in driving variance in potential… emissions pathways,” the team writes.
The good news is that the model suggests a high likelihood of accelerating emission reductions once everything is factored in. Over 90 percent of their simulations showed we are at least on track to reduce the business-as-usual scenario of 3.9°C of warming by at least 0.5°C, even when factoring in higher ends of the uncertainty ranges.
In these worst-case scenarios, the team notes: “Populations are highly fragmented by policy opinion, preventing the diffusion of support for climate policy. Unresponsive political institutions that are biased towards the status quo delay climate policy until after 2080”.
The simulations suggest it is now highly unlikely we can remain below 1.5°C, even under an ‘aggressive action scenario’, just like other studies have already warned.
This is not surprising, Moore and team explain, as 1.5°C now requires widespread use of negative emissions technologies that were not included in the model, because such technologies do not even exist at the scale and efficiency required yet. That doesn’t necessarily mean they may not be more helpful in the future though.
However, the future scenarios demonstrate that we still do have a decent shot at keeping emissions below 2°C. In 30 percent of the scenarios the future plays out like this:
“Rapid diffusion of support for climate policy leads to a rapid increase in policy ambition over the 2020s. Effective emissions-reduction technologies and rapid diffusion around the world reduce global emissions to zero by 2060.”
There are still many unknowns not considered by the models, the team acknowledges, but their work gives us an overview into how existing climate models connect to the human world they’re embedded in – at individual, national, and global scales.
The researchers conclude that our social attitudes, improvements and cost reductions of technologies, and responsiveness of our political systems are the strongest drivers of future emissions and may provide the best targets for potential positive tipping points.
“Understanding how societies respond to environmental change, and how policy arises from social and political systems, is a key question in sustainability science,” says Moore. “I see this as pushing that research, and also being useful for climate adaptation and impact planning.”
This is the closest thing we will ever have to hindsight. So the question is, will we make use of it? Because, despite all our marvelous technological advances, we still can’t go back in time to fix this if we get it wrong.
This research was published in Nature.