No worries! I’m glad you found the paper useful and interesting!
The mortality cost of carbon is just the number of excess deaths from temperature-related mortality in units of excess deaths from emitting one metric ton of CO_2. So it’s just excess deaths and nothing else. The social cost of carbon is the full monetized value of all climate impacts from emitting one ton of CO_2, which includes the monetized value of those excess deaths in addition to other sources of climate damages. You can see that before the model accounted for temperature-related mortality, the social cost of carbon was $37, but after accounting for temperature-related mortality, it is $258. However, note my caveat from the conclusion: “It is important to note that recent literature has identified other shortcomings in the DICE model including other issues with the climate-economy damage function and the climate module. Besides adding the effect of climate change on mortality and subsequent feedbacks, DICE-EMR takes the rest of the DICE model as given without updating other factors.”
It’s hard for me to determine how much the different simplifying assumptions from your back-of-the-envelope formula are affecting your estimate. The linearity assumption is certainly causing a big difference because the system is highly convex. Also, the DICE-EMR model has the DICE climate model built into it that can show the climatic effect of changes in emissions. I’m not sure how much error you’re introducing with the back-of-the envelope climate assumptions, but that could also be an issue.
All this to say, if estimating the marginal impact (either the mortality cost of carbon or the full social cost of carbon) were as simple as a back-of-the envelope calculation, then there wouldn’t be a need to give William Nordhaus the Nobel Prize for his work on the original DICE model (the first one for environmental economics), nor for me to do this work. I think Louis Dixon’s original post is basically all you need to do for this exercise (at least for leveraging my paper’s results). Or as @jh suggested above, a $1/ton estimate just gets you to $4.4K per life saved using my paper’s results.
Also, see this one quote from the end of the paper: “Separate from policy, the MCC and SCC can be useful in informing the decision-making of individuals, households, companies, charities, and other organizations in determining the social impact of the emissions generated by their activities. The emissions contributions of these groups are usually marginal relative to the aggregate emissions of the world economy from the industrial revolution through the twenty-first century. Therefore, the social impact of changes in their activities that either reduce or increase emissions should be quantified using estimates of marginal impacts: i.e. the SCC and the MCC.”
Thank you for your responses! I added edits to the essay to reflect this.
Overall, as I noted in the edits, this exercise has made me shift from being skeptical about all climate change interventions to considering shifting some donations from global poverty to climate change interventions. Not entirely convinced, but it seems a lot more plausibly effective than I first suspected.
Some things I don’t understand though:
It makes sense that with a convex harms curve, marginal harms will be worse than this back of the envelope linear calculation suggests. But it’s surprising that they’re 10 times higher. I guess it’s just very nonlinear, as you say, but that’s surprising to me.
The $1/ton estimate comes from CATF, which is a lobbying organization. Their cost effectiveness calculations account for money they spend lobbying, but not deadweight loss caused by taxes and regulations. How reasonable is it accept that sort of accounting?
1. To your question on accounting for deadweight losses etc., it is true that this is not included, rather this is an estimate of marginal changes from donations. But the factors not included in the calculation are not only deadweight losses (and other costs), but also lots of benefits, e.g. economic benefits from technological leadership. This is parallel to GiveWell analyses which only focus on mortality/direct income gains and ignore a lot of other follow-on benefits and costs.
2. The air pollution benefits of clean energy advocacy are plausibly in the same ballpark as climate benefits (depends on how severe climate change turns out) and benefits from overcoming energy poverty are also very significant (though hard to causally pin-down given the relationship between energy demand growth and human welfare is bidirectional, I explore this a bit more here).
3. One thing that is very different between GiveWell recommendations on global health and FP recommendations on climate is the attitude towards uncertainty—GiveWell recs have a high uncertainty avoidance whereas CATF and other estimates are meant to be risk-neutral estimates leveraging a fairly indirect theory of change (policy advocacy > policy change > technological change > changed emissions trajectory). So, in that sense the absence of risk-neutral global health recommendations biases the argument in favor of climate.
No worries! I’m glad you found the paper useful and interesting!
The mortality cost of carbon is just the number of excess deaths from temperature-related mortality in units of excess deaths from emitting one metric ton of CO_2. So it’s just excess deaths and nothing else. The social cost of carbon is the full monetized value of all climate impacts from emitting one ton of CO_2, which includes the monetized value of those excess deaths in addition to other sources of climate damages. You can see that before the model accounted for temperature-related mortality, the social cost of carbon was $37, but after accounting for temperature-related mortality, it is $258. However, note my caveat from the conclusion: “It is important to note that recent literature has identified other shortcomings in the DICE model including other issues with the climate-economy damage function and the climate module. Besides adding the effect of climate change on mortality and subsequent feedbacks, DICE-EMR takes the rest of the DICE model as given without updating other factors.”
It’s hard for me to determine how much the different simplifying assumptions from your back-of-the-envelope formula are affecting your estimate. The linearity assumption is certainly causing a big difference because the system is highly convex. Also, the DICE-EMR model has the DICE climate model built into it that can show the climatic effect of changes in emissions. I’m not sure how much error you’re introducing with the back-of-the envelope climate assumptions, but that could also be an issue.
All this to say, if estimating the marginal impact (either the mortality cost of carbon or the full social cost of carbon) were as simple as a back-of-the envelope calculation, then there wouldn’t be a need to give William Nordhaus the Nobel Prize for his work on the original DICE model (the first one for environmental economics), nor for me to do this work. I think Louis Dixon’s original post is basically all you need to do for this exercise (at least for leveraging my paper’s results). Or as @jh suggested above, a $1/ton estimate just gets you to $4.4K per life saved using my paper’s results.
Also, see this one quote from the end of the paper: “Separate from policy, the MCC and SCC can be useful in informing the decision-making of individuals, households, companies, charities, and other organizations in determining the social impact of the emissions generated by their activities. The emissions contributions of these groups are usually marginal relative to the aggregate emissions of the world economy from the industrial revolution through the twenty-first century. Therefore, the social impact of changes in their activities that either reduce or increase emissions should be quantified using estimates of marginal impacts: i.e. the SCC and the MCC.”
Thank you for your responses! I added edits to the essay to reflect this.
Overall, as I noted in the edits, this exercise has made me shift from being skeptical about all climate change interventions to considering shifting some donations from global poverty to climate change interventions. Not entirely convinced, but it seems a lot more plausibly effective than I first suspected.
Some things I don’t understand though:
It makes sense that with a convex harms curve, marginal harms will be worse than this back of the envelope linear calculation suggests. But it’s surprising that they’re 10 times higher. I guess it’s just very nonlinear, as you say, but that’s surprising to me.
The $1/ton estimate comes from CATF, which is a lobbying organization. Their cost effectiveness calculations account for money they spend lobbying, but not deadweight loss caused by taxes and regulations. How reasonable is it accept that sort of accounting?
(Working at Founders Pledge)
1. To your question on accounting for deadweight losses etc., it is true that this is not included, rather this is an estimate of marginal changes from donations. But the factors not included in the calculation are not only deadweight losses (and other costs), but also lots of benefits, e.g. economic benefits from technological leadership. This is parallel to GiveWell analyses which only focus on mortality/direct income gains and ignore a lot of other follow-on benefits and costs.
2. The air pollution benefits of clean energy advocacy are plausibly in the same ballpark as climate benefits (depends on how severe climate change turns out) and benefits from overcoming energy poverty are also very significant (though hard to causally pin-down given the relationship between energy demand growth and human welfare is bidirectional, I explore this a bit more here).
3. One thing that is very different between GiveWell recommendations on global health and FP recommendations on climate is the attitude towards uncertainty—GiveWell recs have a high uncertainty avoidance whereas CATF and other estimates are meant to be risk-neutral estimates leveraging a fairly indirect theory of change (policy advocacy > policy change > technological change > changed emissions trajectory). So, in that sense the absence of risk-neutral global health recommendations biases the argument in favor of climate.