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More Boo In CO2 – Energy Institute Blog

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New paper suggests carbon dioxide causes three times the damage previously used by the US government.

The third floor of the new earthquake-resistant renovation Giannini Hall at Berkeley is a great place, if you care about one of the more obscure numbers out there: the social cost of carbon. SCC is the damage of one ton of CO2 done throughout its long lifespan in all industries and across the planet. The sheer number of nerds obsessing over how this number is calculated, permanently locating, or visiting this unique hallway makes it a super fun place – for those nerds. The level of excitation in response to the output of a new paper led by the young german economist on this floor was significantly higher than when the Warriors deservedly won the NBA championship last year. Sorry Boston, but you know that’s true.

The Keeling Curve

One of the greatest (or so I think) contributions of economists to public policy is the required application of cost-benefit analysis to a significant portion of federal regulations since the 1970s (or 1993 according to how you feel). If federal agencies want to impose new regulations, they often have to show, as part of a regulatory impact analysis, that the benefits of the regulations (for example, pollution damage avoided through reduced energy consumption thanks to the rules of energy efficiency) are higher than the costs (increased manufacturing cost known as more efficient gadgets).

Since virtually all human activities cause greenhouse gas emissions, the damage caused by one tonne of CO2 fact is the key. There is a long history of use of this number dating back to the Bush II presidency when NHTSA, EPA and DOE applied three very different numbers for the same emitted gas. During the Obama presidency, a interagency working group produced an official figure, which was then $42 per tonne emitted in 2020 using a 3% discount rate.

Forget the bad economy that happened under President Trump. President Biden in the first month of office implemented a slightly updated SCC, which was $52/ton and ordered a major update, which was supposed to take into account the improvements suggested by the National Academies of Science and Engineering.

New article edited by David Anthoff and Kevin Rennert presents the results of this immense work carried out mainly during the Trump years with a team organized in RFF composed of a team of future all stars (congratulations to Frank Errickson, Lisa Rennels and Cora Kingdon) and all the current stars (who don’t need a shoutout). Because we’ve all read too much of The Lord of the Rings, the world’s press has focused on the number that rules them all: The new central estimate of $185 per ton of CO2 which is more than three times the current number (at a 2% risk-free rate used for discounting). Extra, extra, read all about it! Bigger numbers! Immense ! RaRaRa. Sure. But here’s what I love about this paper:

  1. He lives in the light. i mean that’s all Open source. If you can program in Julia (which you can learn in a weekend if you know a bit of Python or R), you can modify the model however you like. You can add items. Subtract stuff. Change stuff. Personalize it as you wish. While Richard Tol will remind me that the new model’s great-grandparents (DICE and FUND) also lived out in the open and Fran Moore (currently in the White House) dragged the third (PAGE) into the light, I would like to recall that most of the components of these older models were calibrated when Counting Crows was at the top of the charts. Count who? Exactly.
  2. The damage functions are updated and take into account a certain adaptation. There are four sectors in the model: agriculture, energy, sea level rise and mortality. Damage functions, which translate a changing climate into a change in well-being, have been the focus of recent empirical economic literature and this effort draws heavily from that literature (the Climate Impact Lab at Berkeley/Chicago/Rhodium/Rutgers has a parallel effort with a different approach). Why is this important? For some of the older models, damage to the agriculture and energy sectors did not match the more recent literature. The update changed the relative contributions of these sectors.
  3. The document brings an important update to the refresh. I won’t go into technical details here, but there are two things. First, the article uses a risk-free rate of 2%, which is lower than the lowest rate previously used and is in line with recent expert elicitation from the Yadda Yadda journal. The second update is that discounting now takes into account the rate of economic growth, which is key if you’re the Ramsey type (sorry for the nerd jargon) and care about pricing risk correctly.
  4. The coolest thing though, IMHO, is the full characterization of uncertainty. Nut Soup. These models require a lot of things. The first thing you need are future emissions based on income and population assumptions. Previously, we were just putting together a handful of scenarios that seemed reasonable and didn’t really attach probabilities to lands with different levels of wealth and population. This article went crazy about it by combining interesting new econometrics on very long term forecasts with expert elicitation to characterize future states of the economy probabilistically! It sounds simple, but it’s not and that alone would have been a big step forward.
  5. They don’t stop there. The authors update the climate model to reflect current scientific knowledge, a damage component and a discount module. Each of them has its own source of uncertainty – some related to assumptions about the future, others related to assumptions about parameters. But this new model (GIVE) makes it possible to take into account the evolution of uncertainty and to translate it into a distribution of the SCC taking into account these different sources of uncertainty. So. Turnaround. Cool.

There’s a bunch of other cool stuff here. And some Debbie or Donald Downers in the comments and on Twitter are going to be spreading hate about what the authors should have done to satisfy their own backgrounds. But. I’m going to highlight a few things we should focus on to better characterize the social cost of carbon.

  1. There are literally hundreds of sectors that could be affected by climate change. This model contains four. Granted, these are probably the most climate-sensitive that we know anything about. Yet I want us to push for non-market and non-market damages like species loss, forests, water availability, conflict, migration. There are really cool people like Hannah Druckenmiller working on these issues, but it’s a situation where everyone is on deck. Go for it.
  2. I still don’t understand why we don’t put the equity-weighted social cost of carbon front and center? We keep talking about environmental justice, like we know what we’re talking about and care about it, and then we focus on the non-equity-weighted number. I’ve written about this before and my inbox was full of “Woah. That’s cool. How do I do that?” We know that the Germans use an equity-weighted SCC, which David Anthoff helped calculate. So I really hope that David isn’t distracted by the disaster currently unfolding in our favorite Fussball team and provides us with an equity weighting module.
  3. We have little to no control over fat tail risks (really big catastrophic events with non-zero probabilities. Pick your poison.) I keep thinking we might want to consider a “Weitzman disclaimer” for everything CCS that we deploy in the analysis of benefits and costs. . To quote one of my heroes of his superb paper 2009“Perhaps in the end, the climate change economist can help the most by not presenting a cost-benefit estimate for what is inherently a fat-tailed situation with potentially unlimited downside exposure. as if it were precise and objective? […] Even more openly acknowledging the incredible magnitude of deep structural uncertainties that are involved in climate change analysis and better explaining to policy makers that the artificial sharpness conveyed by conventional IAM-based CBAs here is uniquely and unusually misleading compared to more ordinary non-climatic analyses. Changing the ABC’s situations could go a long way to raising the level of public discourse regarding what to do about global warming.

Martin Weitzman in 2014. He was best known for his research on the potentially catastrophic economic risks of global warming.

This last point is what keeps me awake at night. A graduate student walked into my office and asked, “How do I model and learn something that hasn’t happened in the past but might happen in the future?” Our friends in climatology have literally spent billions of dollars on this issue. We climate landscape economists have been primarily focused on learning measurable things in the rear view mirror, which is what today’s toolkit is geared toward. But I fear that in a world without the great Marty Weitzman, we are not encouraged enough to encourage this graduate student and her peers to pursue these all-important issues.

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