You have any questions?
All right, okay.
Thanks very much for putting me on the program.
It's an honor to be here.
I should self disclose I'm an economist.
Is there any other economist in the room?
That's what I fear.
So I'm going to talk to you about what economists do about climate change and how we figure out if it's a serious problem.
The very serious problem, not such a serious problem, which tends to make economists amongst the most hated people in the space.
But don't fear.
I think we have good things to contribute.
And today I want to talk to you about how we incorporate tipping points in the climate system into economic impact estimates of climate change.
And my co-first and I, we've built a model.
We call a meta stands for model for economic tipping point analysis.
And I'm going to show you what this model does, how it works, and maybe get you interested in using it or working with it.
And it's in Julia.
Okay, so it's a, it's a collaboration that's not just me.
The core team has seven deets in James Rising Simon.
I would say it's the core architect of the model.
James is the reason we could manage to put this into Julia.
And we've partnered with Frustian Delphor project.
He's a methane scientist.
And we've gone on blackness.
A climate economist.
It's kind of between climate science and climate economics largely speaking.
So tipping points are climate systems.
What are they?
Well, there's different definitions around that the definition we like to use is that you do a little small push in terms of emissions or temperature change and something large this continuous happens.
Or it doesn't even have to be discontinuous. Something very large happens.
So there's this proportionality of impact to output.
Other people have a more strict definition.
But if you take this wider definition, you get a lot of things that you might have heard about.
Let's just look at one.
For example, the green and I sheet here.
It's a big ice sheet.
That as the earth warms melt.
And as it melts, it adds to sea level rice.
And so for additional warming, you get additional sea level rice.
More than you would have gotten just from thermal expansions.
And there are the tipping points that add emissions.
For example, the permafrost.
If the permafrost unfreezes top zone Siberia, that leads to organic matter coming to contact with surface air.
You get fluxes of methane and fluxes of emissions, the warming accelerates.
So these are the kind of things we think about.
Perhaps the most pressing one or the most lowering one that keeps the scientists up at night today is the Atlantic marion overturning circulation.
That transports heat around the Atlantic.
And there's signs that slowing down.
But we don't really have longstanding time series.
So we don't quite know if that's just a fluctuation or is that the start of a collapse.
And there's no good way of putting probabilities to that.
So if you risk averse, you should say we should probably not play around with this.
Because if it does collapse, we have a big problem.
On the other hand, maybe it doesn't.
And then what should we do with that?
So what does economics have to say about this?
What can we learn from that in economics?
Well, we back in 2017, we started work that came out in 2021.
And we did a literature survey.
We thought, okay, what do economists think about these stepping points?
And we discovered to our surprise that there were more than 50 papers that modeled or estimated some kind of tipping point.
And economic impacts of climate change, which is very good.
But then we realized many of these were at-hawks studies.
And it sends that, you know, you have a study that's carefully modeled.
And then you assume tipping points exist.
So something bad happens.
But that's something bad.
Was not calibrated to anything in the natural sciences.
So it's basically what economists thought might happen.
It wasn't any quantitative calibration and then underlying those.
So we thought these studies aren't really useful for getting policy relevant quantifications.
But the good thing is, there were about 20 studies that did have some kind of geophysical realism.
So if you think about the green and ice sheet, they would do a small ice sheet model,
emulator model or an emulator model that would emulate the larger ice sheet models.
And you couple that with an economic model and you have some kind of geophysical realism.
You push on, on emissions, you push on temperature and something happens.
And that's something is calibrated to the science fully.
And there were about 21 studies, which are about, often they're about the same tipping points.
About eight, nine tipping points, half in modeled and climate economics.
Now the problem we have is that these studies come from different research teams.
They come from different models.
Even if they come from the same model, they run under different parameters for different scenarios.
So there's no way to do a normal meta analysis,
where you take the individual estimates and then you do something to some of them into one big estimate,
which is what we wanted to do.
Instead, we quickly realized that you really needed to produce what we call an integrated assessment model.
It's a model that has a climate model and an economic model,
where we can emulate each single one of these studies precisely.
And then put them into the same framework into the same structure,
so that we control the assumptions that go into that.
So we can run that under consistent assumptions and the different assumptions,
and see what the overall effect of all these tipping points is on economic impacts, estimates of climate change.
The model we created is called the model for economic tipping point,
the analysis meta, which is why I put this in the talk.
It's freely available on GitHub.
You can do whatever you want with it.
And it belongs to something economists, a climate economist,
called Social Cost Integrated Assessment Models.
The bigger ones out there are the gift model by resources for the future
that's been used to advise the US government,
and the climate impact labs de-SIM model.
I think it's dynamic, spatial, climate impacts models,
I'm feeling like that.
And both of these use are used to inform policy makers.
So if someone in government wants to know what's the economic impact of climate change,
in 2050, they probably use one of these two models.
There are the ones out there, but that's the latest generation.
It's modular, they tend to be quite good.
It's been calibrated to recent climate signs,
and they've had a features, but for an economist, it's not as interesting.
So a work on software.
So when we created meta in the paper in 2021, I'm very embarrassed,
but it wasn't Excel.
It was not just an Excel.
It used to commercial plug-in, because we needed to run
onto color simulations.
And I'm very sorry.
It's very embarrassing.
We knew that at the time.
It makes it very hard to check code.
I mean, we had to put it on GitHub.
But it makes it much harder to check code.
It makes it much harder to access.
You notice this plug-in?
I think it costs a thousand euros to run it.
I mean, it's...
When we released it, the company contacted us and said,
how are you using a plug-in?
Would you like to do a webinar?
So more people subscribe to our services.
And we thought, no, we'd rather work on putting this into open software.
But the reason we had it in Excel is because climate economists had worked
with this software before.
So it's just a legacy issue.
You start.
And then you have some skills in that.
And you put that into the next version.
But we made a choice to part it into open software.
I should also say there is a person Dominic from the Fundman's
program, I think, who just took the whole thing from Excel and
ported it fully into Gams, which is incredible.
I mean, Gams is not my preferred software.
But he checked everything we did.
And he found very few code errors.
Of course, there always is something in there that was
influential for the results in any way.
So we are very thankful for him for checking all of that.
But still we thought it was a lot of value in putting that into
Julia.
The reason we went for Julia and not Python, say it's because
there is a big community of climate economists that live in
Julia.
And that's thanks to the Mimmy framework, or Mimmy JL.
That's the effect or standard in climate economics.
Now, it comes out of a Berkeley group.
Mostly David, Anta's group.
These are Randall's, Frank Garrick's, and many more people
involved.
And they're very, very helpful, very nice people.
And the reason this started to be created is that there was
some standardization in the United States as a result of a report
by the National Academy of Sciences of the United States on
how to best advice government on an economic impact of climate change.
And Mimmy JL takes some of these recommendations, or is the basis
to making some of these recommendations more readily usable.
For example, this is a framework that allows you to build
integrated assessment models in a way that they can talk to each other,
in a way that you can swap out different modules and components.
And I show you in a second what I mean with that.
So this structure of meta is twofold.
There's a climate model, and then they're economic impacts of climate change.
For economists who might watch this, maybe this is an economist.
It's not an optimization model.
It's a model that runs scenarios.
You feed the models scenarios to have emissions of CO2,
of methane, and of other forces.
And CO2 and methane are explicitly modeled.
So what you do is you take emissions, you convert it into concentrations.
Greenhouse gas is near atmosphere.
You convert it into radiated forcing,
that's to change in Earth's energy balance as a result of that concentration.
And then you convert that radiated forcing change into global means of
a temperature change.
So you go from emissions to concentrations to radiated forcing to warming.
Very standard stuff.
So far, this is basically only climate science.
And the neat thing is, thanks to the Mimmy framework,
we can take the model that was created to standardize
how economists and other emulator models use climate science
to convert emissions to concentrations ready at the forcing.
That's called fair.
And people involved with the Mimmy framework built Mimmy fair,
which is an exact version of fair, but in Mimmy.
So instead of us having to re-program that,
we can just take that off the shelf and put it into our model,
which is great because it minimizes errors.
It minimizes different climate modules,
because the economists should agree on the climate science.
We should disagree on the economics and check what that changed.
So that's very good.
So what we do is, as I said, we take emissions.
We convert it into global means of a temperature change.
But this is about tripping points.
So how do the tripping points come in?
Well, you can add the tripping points.
You can switch them on or off.
Each of these modules is independent.
You can switch them on.
You can switch them off.
I discussed the permafrost current feedback before.
So what happens when you switch that on is that
global means surface temperature change gives you
more permafrost melting.
And then you have more trumps all in Siberia and other places in the north.
It comes into contact with the surface air.
And from organic decomposition mostly,
to get fluxes of methane and to get fluxes of CO2.
And they go back up there and generate additional warming estigo back through the climate model.
The same happens.
First one, dissociation of ocean emission hydrates.
You can think of those as kind of submerged permafrost
on the continental slopes and the oceans.
But this is a very uncertain tipping point.
So for the most part, we don't include it in the standard runs.
We used to do that, but we decided not to do it anymore
because the science has become less clear rather than more clear.
And there are other ones, like the Amazon forest,
rainforest, dive back, but no time to go through the mob.
So as I said, we produce global means surface temperature change.
This is the climate change variable.
The drives and damages and impacts in this model.
And so on the damage aside, we start with global means surface temperature change.
And there's two damages channels, one from temperature.
And one from sea level rise.
Now you could say that's incomplete.
There are might be other ones.
You know there might be things like ocean acidification.
Also it's a thing.
But economists haven't been able to create
mappings from global means surface temperature change to these outcomes.
So that's working progress.
I think these people working on biodiversity loss as well.
And this is just a snapshot of very our economics.
It's not us saying other channels are important.
So keep this in mind.
Missing channels usually means your underestimate damages of climate change.
Because most of the time, these things tend to be negative.
Not always, but any time is there.
All right.
So we take global means surface temperature change.
We do something called statistical downscaling or pattern scaling.
That gets us national means surface temperature change.
And then we have damages at the national level that calibrated
to a study in climate economics, which we think is still the best out there.
Despite a lot of flaws.
So the difficult part is in climate economics is to map geophysical change
like temperature change to economic outcomes.
We do this, and we think it's better than not doing it at all.
But it has a lot of caveats.
It's very uncertain.
So keep that in mind.
We don't think a precise quantification is possible at this point.
But a quantification, we believe, is better than no quantification.
Similarly, on the sea level rise damages channel.
So you have without any tipping points, you have thermal expansion.
So the oceans just become larger as the water heats, needs more space.
And then you have melting of small ice caps and glaciers.
That contributes to water and sea level rise.
Now you can add tipping points to this.
So if you have the disintegration of the large ice sheets in Greenland and in the Antarctic,
these melt and give you additional sea level rise.
Very simple.
That's implemented here.
That gets us total sea level rise.
That gets us national sea level rise damages.
Which in the end affect consumption per capita.
That's the variable economists like to use to think about
how much do people suffer or how much do people gain from climate change.
Aimock, as I said before, is the Atlantic meridional overturning circulation that
transports heat across the Atlantic.
If that were to fully disappear, the climate of the Sun Europe would be very, very different.
And not very nice.
And then we have a tipping point that just affects India.
So it's a bit outside of the channel.
There is a change to India and some amongst the reliability.
So these are all the tipping points we can model at the moment in climate economics.
But this is by no means an exhaust of list of tipping points.
It's also by no means us saying this is the right implementation.
This is a metal study for the moment for the most part.
We have taken what people in climate economics thought was the right thing to do.
In some cases we change the calibration a little bit.
But mostly this is trying to assess what climate economists believe.
And you might disagree.
You might say, well, maybe this is a component on aimot.
It needs a different calibration.
It needs a different implementation.
And there is indeed work being done.
So there is a really good work by two PhD students in Hamburg.
They took the aimock module here and added a different channel and refined the calibration.
Which improved the estimates.
And they used the model for this purpose.
So they forked the model.
They changed this aimock component and they were in everything.
With this change component, which I think is exactly why we wanted to put this on
the first place.
Okay.
I think that's on the model structure and on what we have done now.
I'll show you a few results before we get to the Q&A.
So the first thing, the main result of this paper we published in 2021 was that if you look at damages that you come
from a meeting one ton of CO2 today.
We want to know what's the additional damage you get if they're tipping points in the climate system.
So what if no percent of this ton on economists?
So the social cost of carbon is the damage from a meeting one ton of CO2 today.
You sum up all the damage over the lifetime of this ton of CO2.
It might be near atmosphere for hundreds of years.
And you take the present value of that in terms of how much it affects humans around the world.
Very complex but basically think of this as damages from a meeting CO2, but just for one ton.
Okay.
But I didn't even tell you what the absolute level is.
It's just an estimate of we estimate these damages without tipping points and with tipping points.
And this graph what it tells you is what's the difference between the estimate with tipping points.
And the estimate without tipping points and the different model configurations.
That's why you have this distribution.
There's a lot of parameters to some scenarios we run there.
And so a positive number means that tipping points run has a higher damage than the non-tipping points run.
Okay.
And so you can see that there's basically no run in which tipping points help us.
They almost always make damages worse.
Our best estimate is about 25% increase in damages.
But we think this is quite conservative.
So we try to be under conservative side of things.
If you take the full uncertainty you get something like a 40% increase in damages from having tipping points.
From considering that they exist.
And we calculate about a 0.1 probability of them doubling.
So you just sum up everything here.
Or a 0.02 probability of troubling.
So it's more than 200%.
That's the first result.
So they make damages worse and the way that we can measure.
The second is they're almost worse for almost everyone around the world.
So no good news here.
And this is work not by us, but again by the people who've used our model.
And they used it to quantify future impacts and stock indices.
And they find that climate change.
They create a stock market returns and tipping points worse and that.
And lastly that's the paper we're working on now.
It's its submission at the moment where we asked okay.
What can we learn from tipping points in terms of climate policy?
So if we do ambitious climate policy does this decrease the likelihood of tipping points occurring.
And we've done a paper off on global methane action.
So cutting methane around the world.
And quickly does that change tipping points?
And indeed we find that if you do ambitious a methane action.
You decrease tipping point intensity across all the tipping points.
Something between depending on where you are and 2050 between two and 10%.
Depending on which tipping points you're looking at.
That's unpublished.
So numbers might still change, but roughly that's where we are.
Okay, let me conclude.
So we've made quite a lot of progress in economics and in on how we model tipping points.
There's people who do different work in this.
It's starting to be integrated in conversations with US authorities.
So they're taking part of meta at the moment to put that into the other model.
Stay using.
And we think quantification is useful because people do do benefit cost analysis.
And if you don't quantify tipping points, the estimate that's used is zero.
Other people widely disagree to say, well, if you're trying to do a partial quantification.
Because you might not have the ocean acidification or you might underestimate worst case scenarios.
That's dangerous because you make it seem like not such a big problem and it's really a gigantic problem.
That's a fair criticism.
So you're in this tension.
Should we quantify?
Should we not quantify?
I'm large.
And certain she's remain in particular about how economists think about impacts of certain physical change.
And what has happened recently is that we have improved calibration of some of these tipping points.
Likely hoots.
The Antarctic model is a recent paper by Macau Frisam and the son of his PhD students.
Sorry if one of his master students I think.
We've recorded this thing in Julia.
So it's accessible to community now.
And the different things you could do if you interested.
First you just fork it and run it yourself under different scenarios.
You write your own research paper.
That's perfectly fine.
You could try to maintain or contribute or improve calibration of some of the components or add a new component.
Or you could even change other parts of the model.
You could add different damages functions.
So you want to study not national consumption per capita,
but maybe financial stability or something like that.
So you could produce a component that does that.
So if you're interested, do reject.
Very happy to chat.
And you find everything on GitHub.
Thanks.
Thank you.