https://www.nature.com/articles/s41558-020-00985-5
and follow up works.
The methods are fairly straightforward. Panel regression with temperature effects on top of a per region and per year background.
As I understand it, there is some disagreement for how to exactly aggregate the growth effects over time. But to me what was shocking about this is just how clearly you already see the impacts in existing _macroeconomic_ data, once it's sufficiently high spatial resolution. I am in the "who really cares about GDP" camp generally, it's a very crude measure. I was not expecting to see climate impacts to show up in GDP data for quite some time.
It seems clearly absurd, though. They claim that 1 degree of temperature variance reduces GDP by 5pp, which is an insanely huge effect. They're saying that variability (not growth) of a size you can't even feel would eliminate the entirety of US economic growth and push the rest of the world into a large recession.
This kind of paper where you regress things against each other with no attempt to understand or explain the underlying causalities, nor have any ability to validate the results, is the sort of thing that yields false positive results all the time, it's textbook replication crisis stuff.
The statement about 5 percent per 1 degree variability gives you the sensitivity, it's not meant to extrapolate to an actual increase of 1 degree. A 1 degree increase in variability would be absolutely massive. Your intuition that you "can't even feel this" is just wrong. Just to illustrate: The variability in historical data is roughly 3° as far as I know. A 1° increase means a massively wider distribution of daily temperatures within each month. More heat days, more extremes, more wild swings. I couldn't immediately find how much it has increased in the last century, unfortunately, but if I read this [2] right, then an increase in mean temperature by 4-5° in the SSP chosen is projected to come with an increase in day to day variability of 0.5° in the most affected regions. That's really the worst case scenario as far as climate change is concerned.
[1] https://www.nature.com/articles/s41597-023-02323-8 [2] https://www.pnas.org/doi/pdf/10.1073/pnas.2103294118
Again, when it comes to the comprehensive assessment of the cost of climate change produced by the same team there is a debate going on (that I don't know the details of, so I don't have an opinion either way), let's see what comes out of that:
https://www.nature.com/articles/s41586-024-07219-0
But I haven't heard anyone raise any real issues with the paper we are discussing, and it's well cited, and there are prominent economists who have staked their careers and reputation on climate change not being all that bad (e.g. Nordhaus), who are willing and able to push back against exactly this type of analysis if there are holes to be found...
Maybe they address all of these obvious problems in their paper, but I doubt it:
1. The low quality and wide CIs of the temperature datasets themselves. Look at the recent revelations about the CIs on the UK's weather data for an example (and the total lack of care from the Met Office about fixing the situation). It's one country but similar problems are found everywhere. One of the most depressing conclusions I reached when studying this topic a few years ago was that we don't really know if the world is getting warmer or not. It's not even a well posed question to begin with, and the datasets are subject to massive historical revisions, but even ignoring those fatal problems the error bars on the underlying data are wider than the claimed increases.
2. Dubious nature of the gridded GDP dataset they're using. Much of the data comes from sources merely cited as "literature". Of those, many are themselves estimated and modeled data dubious on their face, e.g. the Australia data comes from a paper with only two citations (one of which is this paper) and which claims to accurately reconstruct GDP back to the 1850s! This is a jenga tower of estimates and there doesn't seem to be any attempt to measure or think about error in a systematic way: they explain that they looked for outliers in the data to do manual validation (unmentioned, did they remove outliers?), and they admit that the dataset has serious problems, but just blithely hand wave it all away. There is no attempt to estimate the accuracy of their data beyond observing it isn't 100% accurate.
3. Inability to validate the resulting model ("robustness tests" aren't the same thing as validation, at all). Unvalidated models aren't useful for anything.
As for Nordhaus, I wouldn't try to outsource decisions about the reliability of work to old names. They've been pointing out serious problems in the literature for years, some of them decades, and there have never been reforms. If someone warns of problems 100 times and is ignored every time, the lack of a warning the 101st time doesn't mean anything. They could just be tired. I would be, in their shoes!
