After decades of research, we now have a clear understanding of the actions needed to tackle climate change.

From phasing out fossil fuels to expanding renewable energy to developing carbon capture – we know what we need to do and we a good idea of how quickly we need to do them.

But what happens when things go wrong, or new information comes to light challenging previous assumptions? What if we learnt that renewable energy technologies weren’t being scaled up faster enough, or if forests stopped absorbing carbon dioxide?

How should we change tack?

A new paper published in Nature Communications has explored pathways to limit warming to 1.5°C in response to new ‘adverse information.’

Using computer modelling, the research essentially maps a ‘plan b’ pathway when things go pear-shaped.

To learn more, I caught up with two of the researchers from the Grantham Institute for Climate Change and the Environment at Imperial College London behind the study – Dr Shivika Mittal and Dr Ajay Gambhir.

Let’s jump back to the 2015 Paris Agreement. Can you briefly remind us why 1.5°C of warming is so important?

Ajay: At the Paris UN conference in 2015, a number of countries including small island developing states urged the international community to set in place an agreement to limit global warming to 1.5°C above pre-industrial levels.

Before Paris, there was an understanding that the target should be 2°C, but these countries – supported by negotiators from a number of other regions – argued that 2°C would pose an existential threat, in terms of impacts such as sea level rise. Subsequent research, as summarised by the Intergovernmental Panel on Climate Change (IPCC), has shown that the expected impacts of a warming world at 1.5°C are far lower than at 2°C. So, the 1.5°C target is worth fighting for.

Shivika : The IPCCC’s special report on 1.5°C highlights the importance of limiting global warming to 1.5°C to avoid the negative impacts on our ecosystem like drought, famine, and heat stress, which are still projected at 2°C.

So, how are things going? Are we on track to limiting warming to 1.5°C?

Shivika: By most estimations, we aren’t on track. We would need to be – approximately – halving global emissions of CO₂ and other greenhouse gases by around 2030, even if we only wanted an approximate 50/50 chance of achieving 1.5°C. Yet emissions are still rising.

Your research explores the arrival of ‘new adverse information’ that affects ‘pathways’ to keep warming to 1.5°C – can you elaborate on these concepts?

Ajay: It’s almost inevitable that things won’t go according to plan if or when the world finally embarks on global rapid emissions reductions towards net-zero by around mid-century, which is required to give us a fair chance of limiting global warming to 1.5°C.

There could be some nice, positive surprises such as those we’ve seen in the last decade with rapidly falling renewables and battery costs. But we should also plan on the basis of some not-so-nice, adverse, surprises. We explore these in the paper.

Tens of thousands of the world’s top scientists, engineers, and thinkers are working together to rapidly reduce climate warming emissions. Why do we need this research? Could something seriously unexpected actually happen?

Ajay: Sadly, yes. We saw with the pandemic how unexpected surprises can happen, and we should not embark on a strategy to achieve something so important as addressing climate change without considering many eventualities.

So, things could go wrong. What adverse information did you look at?

Shivika: Our study considered technology risks like slower than hoped for rates of scale up of key technologies like solar, wind and carbon capture and storage.

We also looked at the possibility that forests could start emitting, rather than absorbing CO_2, which is a serious concern given the drying of forests around the world due to hotter temperatures.

Finally, we considered a reduced 1.5°C carbon budget, which is the amount of CO₂ from human activities that will lead to 1.5°C of warming. 

How did your study use modelling to understand how we can respond to new adverse information?

Ajay: Our modelling framework calculates what is termed a “least-cost” path to a given emissions or climate target. It considers current and future energy demand, costs of technologies and measures to reduce emissions both today and in the future, and other key factors to work out the lowest cost path to meeting the given target.

We were able to fast-forward in time by looking at what the models calculated for a given future year (we look at 2025 and 2030) and then tell the models  things had changed, such as a slower scale-up of new technologies from that point, “tell” the models that things had changed, such as scale-up rates of key technologies were slower, the carbon budget had reduced, and so on. By re-starting the model from those years with this new information, we could simulate what we call the course adjustment.

What did you find out? What do we need to do when things go wrong? What actions will be most effective and why?

Shivika: We found out if we consider adverse information like slower than hoped-for rates of scale up of key mitigation technologies such as  solar, wind and carbon capture and storage then we need to rely on other sources of low carbon power like nuclear, hydro and geothermal as well as increasing the electrification of our end use sectors . We also found out that implementing energy demand management strategies is an effective way to reduce the cost of achieving our goal when things aren't going as planned.

Is this a hopeful piece of a research? What should people take away from your study?

Ajay: Robust decision making and planning calls for a consideration of many possibilities of things that might go well or (more importantly) not-so-well. There’s lots more to be done but we hope we’ve demonstrated how this planning can usefully be done in existing modelling frameworks.

Shivika: We want to emphasise that importance of considering and managing potential risks posed by both technology and socio-economic factors while making the decision about the transition pathways. By doing so, we can create pathways that are more resilient to disruptions of all kinds and ensure that our plans remain viable over the long term.