acesfull86
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https://www.theunseenandtheunsaid.com/p/the-climate-damage-function-problem
One of the most striking features of modern climate economics is not consensus, it’s dispersion. Depending on which paper, model, or administration you consult, the economic damages from climate change range from modest to catastrophic. The “social cost of carbon” alone has swung wildly, from roughly $190 per metric ton of emissions under the Biden administration to effectively zero under Trump.
A new paper by Finbar Curtin and Matthew Burgess, “The Empirically Inscrutable Climate-Economy Relationship,” argues that this dispersion is not a temporary problem awaiting better data or cleverer econometrics. It is, instead, a fundamental and irreducible feature of the enterprise. Their conclusion is uncomfortable: we cannot reliably estimate the macroeconomic damage from climate change using historical data.
Most empirical climate-economy models follow a similar structure. Researchers estimate how temperature (or other climate variables) has historically affected GDP, then feed projected future warming into that relationship to generate estimates of future economic damages.
The problem, Curtin and Burgess argue, is that this relationship is not stable or uniform, but varies dramatically across both space and time.
Countries with the same average temperature can have entirely different economic responses to climate variation. El Salvador and Iraq, for example, may share similar climates but have radically different economic structures, institutions, and adaptive capacities. Pooling them into a single regression implicitly assumes a common “damage function” that simply doesn’t exist.
…
If the identification problem weren’t enough, the paper highlights another issue that should make anyone uneasy: extreme sensitivity to a handful of observations.
In one prominent study (Burke et al. 2015), removing just six data points out of more than 6,000 reduces the estimated climate effect by about 25 percent.
Even more striking is the example of a now-retracted Nature paper, where erroneous data from a single country, Uzbekistan, accounted for roughly two-thirds of the estimated global climate damage effect. Mistakes do, of course, happen, but the deeper question is: how can one small country plausibly drive global conclusions in the first place?
…
If the underlying relationship cannot be reliably identified, then there is no single “correct” social cost of carbon. The wide range of estimates is not a temporary inconvenience but reflects a deep uncertainty that cannot be eliminated with better data or more sophisticated models.
One of the most striking features of modern climate economics is not consensus, it’s dispersion. Depending on which paper, model, or administration you consult, the economic damages from climate change range from modest to catastrophic. The “social cost of carbon” alone has swung wildly, from roughly $190 per metric ton of emissions under the Biden administration to effectively zero under Trump.
A new paper by Finbar Curtin and Matthew Burgess, “The Empirically Inscrutable Climate-Economy Relationship,” argues that this dispersion is not a temporary problem awaiting better data or cleverer econometrics. It is, instead, a fundamental and irreducible feature of the enterprise. Their conclusion is uncomfortable: we cannot reliably estimate the macroeconomic damage from climate change using historical data.
Most empirical climate-economy models follow a similar structure. Researchers estimate how temperature (or other climate variables) has historically affected GDP, then feed projected future warming into that relationship to generate estimates of future economic damages.
The problem, Curtin and Burgess argue, is that this relationship is not stable or uniform, but varies dramatically across both space and time.
Countries with the same average temperature can have entirely different economic responses to climate variation. El Salvador and Iraq, for example, may share similar climates but have radically different economic structures, institutions, and adaptive capacities. Pooling them into a single regression implicitly assumes a common “damage function” that simply doesn’t exist.
…
If the identification problem weren’t enough, the paper highlights another issue that should make anyone uneasy: extreme sensitivity to a handful of observations.
In one prominent study (Burke et al. 2015), removing just six data points out of more than 6,000 reduces the estimated climate effect by about 25 percent.
Even more striking is the example of a now-retracted Nature paper, where erroneous data from a single country, Uzbekistan, accounted for roughly two-thirds of the estimated global climate damage effect. Mistakes do, of course, happen, but the deeper question is: how can one small country plausibly drive global conclusions in the first place?
…
If the underlying relationship cannot be reliably identified, then there is no single “correct” social cost of carbon. The wide range of estimates is not a temporary inconvenience but reflects a deep uncertainty that cannot be eliminated with better data or more sophisticated models.
