Peer review of: Air pollution control strategies directly limiting national health damages in the US (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

I was asked to review the energy system aspect of this manuscript. The paper uses the GCAM-USA 
model which is a well-established and well-respected model in the energy modeling world, so there is 
not a concern from my perspective in relying on that model for this analysis. The energy system 
parameters used in GCAM-USA are generally aligned with EIA’ 2018 Annual Energy Outlook (AEO). I 
assume the aligning was done against the reference scenario, but that was not specified. Aligning 
against AEO assumptions is common in the energy modeling community. 

My primary concern with this paper is that only one future energy trajectory is presented, and that 
energy trajectory is not well documented. From what I can tell of that future energy trajectory, I do 
not believe it to be representative of what an actual future will look like (even a business as usual 
future). For example, Figure S1(a) shows that electricity generation from coal is flat over time, but 
given the many drivers of clean energy (e.g., the many 100% clean energy standards, the increased 
RPS requirements, etc.), this doesn’t seem reasonable. As one example, I’ve plotted here the coal 
generation projections from two other organization to compare against the AEO 2018 reference case 
by EIA. These projections are the NREL 2018 Standard Scenarios Mid-Case and the BNEF 2018 New 
Energy Outlook. Both of these organization tend to have more bullish projections related to clean 
energy potential, so I am not advocating that your reference case show similar trends, but I do think 
that discussing the implications of your findings in the face of more of a clean energy transition is 
warranted. Would your finding still hold if the coal generation declines by half, with the associated PM 
reduction that would entail? 

[Figure here included in attachment] 

Relatedly, the limited change in the power sector evolution does give me pause as to what is 
represented in the model. Do you have the many renewable and clean energy standards in the model 
that I referenced above? If so, then I have fewer concerns about your ability to represent any 
transitions here. Or what about the CAFE standards? And California’s SB-100 requirement? I didn’t see 
mention of those. And from what I could tell, the cost and performance input assumptions for 
electricity technologies were Iyer et al. (2017) which used the AEO 2016 for input assumptions. For 
solar in particular, those projections are relatively old (the AEO 2016 reference case PV prices never 
get below $1500/kW by 2040, but current prices are already at or below that point – see slide 18 at 
https://emp.lbl.gov/sites/default/files/lbnl_utility_scale_solar_2018_edition_slides.pdf), and using 
high renewable energy cost would lead to less of a transition away from coal. 

As you can probably tell from the comments above, I am interested in more documentation about 
your assumptions. One in particular that I think should be reported in the PM emission rates from the 
various technology types you represent in the energy sector. I don’t feel like anything is “wrong” with 
the modeling, only that some assumptions might be dated and might not be reflective of the transition 
that is already happening in the energy sector, so ensuring that the findings are robust against a 
transition would make this paper more meaningful to me. 

Reviewer #2 (Remarks to the Author): 

One of the consequences of Rachel Carson’s Silent Spring was that air pollution control programs that 
had previously been part of the US Public Health Service were moved into the newly formed 
Environmental Protection Agency. Air pollution control thus came to be driven not by measures of its 
effects on human health, but rather by a legal and regulatory framework that was focused on 
compliance with emissions standards and meeting pollution levels. This framework has had significant 
successes and has greatly reduced levels of air pollution in the United States, although more than 
100,000 deaths are still ascribed each year in the United States to ambient air pollution. 
The authors of this very interesting manuscript propose a new approach to modeling efficacy of air 
pollution control standards, an approach that is focused specifically on seeking the most cost-effective 
ways to achieve reductions in pollution related mortality costs. In a sense, this proposed approach 
brings air pollution control back to its beginnings in public health. 
To assess the relative efficacy of various pollution control strategies, the authors examine reductions 
in pollution related mortality costs that could be achieved under a reference scenario, as well as under 
5 alternate scenarios of increasing stringency. In the reference scenario, no additional future 
regulations are assumed, whereas each of the other scenarios apply increasingly strict national 
reduction targets focused on reduction in mortality costs. 
The findings are very interesting. Most importantly, they find that very large reductions in pollution 
related mortality costs could be achieved by focusing on a small number of high emission intensity 
sources, such as industrial coal and building biomass. They acknowledge that this approach will do 
little to reduce CO2 emissions or to slow the pace of climate change, but they emphasize that these 
“end-of-pipe” approaches will be highly effective in achieving short-term reductions in morbidity and 
mortality and thus in reducing the costs of pollution related disease and death.