Your broader point is a fair one, and I appreciate that you've raised it. This is generally hard to do, and speaks to a larger question about how to measure across different "benefits" - how do you measure freedom versus DALYs, or climate effects, or animal welfare. Of course that doesn't mean we shouldn't do it - with or without social resistance! Cross-cause work is something we'd like to do more at RP.

In the case you've mentioned above, I wonder if QALYs might be relevant. (This may be your point about quantitative vs qualitative). Your example calculations directly compare the 1.2 minutes of extra driving to 0.7 minutes of being alive. But how does the time driving compare to the additional time you would spend at your destination? I would imagine that the gap between those two states is smaller than "alive (at some average level of happiness) vs dead". As you note, the calculations are rough enough that it's hard to work out what the overall conclusion is, but I think we'd probably need to apply another factor to the 1.2 minutes to capture that the time spent in the car is unlikely to be worse than death.

Separately - and more as a point of interest - I wonder what would actually happen if speed limits were drastically reduced in the way that you mention. Yes, there would definitely be negative effects like you describe, but I think peoples' habits would also change so that they can avoid sitting around driving all day. (To clarify, I'm not in favour of such a drastic change, I just find it interesting to consider)

This is some great detective work, and thanks for drawing it to our attention. These two specific examples don't affect our conclusions, but I agree with your point re. the need for more skepticism about the WHO data.  

Our suggestions regarding specific countries that could benefit from policy change are very tentative (in part because we expect this WHO data to be out of date, coming from 5+ years ago). I think your comment here underlines the importance of getting country-specific context for anyone doing further research on this. We didn't have time during our report, but I believe CE is following up on some specific examples!

Great point, Joel. This is something that we discussed while writing the report, as it feels relevant to Thailand and Pakistan. Traffic jams come into play here - not only because they might limit the de facto speed, but because they're so unpopular that politicians could be concerned about proposing a policy that could be linked to making these worse. That being said, we don't know how "urban" is being defined here - it's possible that there are periurban areas further out in cities that really would benefit from a lower speed limit. 

Enforcement is a different issue altogether, and one we didn't have time to look into. I think our general take from speaking to AIPF was that any policymaking efforts on this should plan for a level of enforcement advocacy as well to achieve effective change. 

Hi Vasco - sorry for the slow response! I don't think I have a particularly satisfactory answer for you on this topic, but I think my instinctive response is that the potential could be limited, depending on whether we think the patterns that underly the algorithms for forecasting now will be the same/ be able to be quickly adapted for these scenarios. My understanding is that forecasting depends on predicting the physics in the atmosphere (which may not change), as well as interactions with the land/oceans (which may be affected, e.g. changing landscapes due to death of flora/fauna). If this scenario is one that we haven't experienced before, maybe we could simply update the parameters based on new observations and the models would work, but maybe we would need to do research to improve the methods that are in use, which could take time. Another limitation could be that I'm not sure how well models could predict the evolution over several years, as you mention - our focus was on <6 months and we found limited accuracy.

Perhaps just one other thing to flag is that doing forecasting at somewhere like ECMWF uses a huge amount of energy, so we'd want to think about the trade-offs with other needs!

Hey Ben - thanks for your question. Unfortunately we didn't come across anything in our research that would quickly explain the decrease for 2004-2006 (or the increase in 2012-2016). It could have been differences in the atmospheric conditions at that time that the model was less able to handle, or changes to the ECMWF forecasting methods.

Re. machine learning, it does seem likely that there's scope for that here. While we didn't look into this in detail, we mention two ideas briefly in this spreadsheet of potential interventions. 

1. Use machine learning to downscale (i.e. get better resolution) of existing forecasts: Tim Palmer at Oxford is a proponent of this.
2. Use machine learning to improve forecasts: this seems plausible and interesting, and I don't know that we should expect that rich countries will drive improvements that close the gap in forecast quality between the tropics and NH. It also seems likely that the quality/ quantity of data from the tropics will put some limits on how successful ML can be for some locations.

Would be interested to hear if you have any further thoughts on either topic!