November 11, 2020 4:41PM

What We Do Not Know about Climate Change and Why That Matters

The New York Times recently summarized the environmental regulatory legacy of President Trump as well as some more recent maneuvering with respect to future climate change reports. Not surprisingly the articles painted neither a pretty nor subtle picture:

… as Mr. Biden works to enact domestic climate change rules and rejoin the Paris accord, emissions attributable to Mr. Trump’s actions will continue, tipping the planet further into a danger zone that scientists say will be much harder to escape.

“Donald Trump has been to climate regulation as General Sherman was to Atlanta,” said Michael Gerrard, director of the Sabin Center for Climate Change Law at the Columbia Law School, referring to the Union general who razed the city during the Civil War. “Hopefully it won’t take as long to rebuild.”

Who should one turn to for a less heated (pardon the phrase) view of the science and policy decisions we face? The author who has informed my thinking the most about climate change is Robert Pindyck, professor of economics at MIT’s Sloan School of Management. In a recent paper, he explains clearly how little we know, why we know so little, and how that lack of knowledge matters for policy.

He first looks at the science of climate change and projections of the likely effect of carbon emissions on future temperatures. He reviews the 140 studies published since 1970 on climate sensitivity—the increase in the global average temperature that would result from a doubling of atmospheric carbon concentration. The bulk of the studies (115 of the 131) have “best estimates” between 1.5 and 4.5C. That is a wide range, and if we include the outlier 16 studies’ “best estimates,” that range expands to between 0.5 and 8C. The uncertainty in the estimates is increasing slightly over time: the standard deviation in post‐​2010 studies is 1.13 as compared to 1.03 in pre‐​2010 studies.

Why is there such uncertainty? The short answer is feedback loops: changes in the underlying physical processes arising from initial temperature increases created by growing carbon concentration. We do not know if feedback is normally distributed nor do we know its mean and standard deviation. An important article in Science argued uncertainty about climate sensitivity is in the realm of the “unknowable” and that the uncertainty will remain for decades.

What economic damages result from temperature increases? We have some sense of how higher temperatures might affect agriculture. But those estimates are from short‐​term changes in weather, not long‐​term changes in climate. The latter will occur slowly. We will adapt. Even to the extent we don’t, losses of agricultural output in some regions of the world (near the equator) might be offset by increased output in other regions (northern Canada and Russia). And agriculture is only 1%–2% of gross domestic product for industrialized countries and 3%–20% of GDP for developing countries. For the rest of the economy, economic activity is not related to temperature.

For Pindyck, the key for policy is the possibility of a catastrophic loss of GDP in the future. How much should we pay currently for carbon abatement to avoid catastrophe in the future? In Pindyck’s formulation, future generations may deeply regret irreversible environmental damage. But those generations also could find such preservation less valuable than we currently expect, in which case they would regret the irreversible expenditure that we made on preservation. He writes:

Should we hold back on emissions abatement because of the sunk cost, or should we accelerate abatement because of the irreversible environmental damage caused by emissions? And by how much should we hold back or accelerate? Sorry, but I can’t answer these questions. Why not? Because we simply don’t know enough about the climate system and about the impact of varying amounts of climate change.

Ironically, the lack of knowledge that makes “climate insurance” valuable prevents us from determining exactly how large that value is.