Yeah that's fair enough re that part of the comment. 

Yeah I suppose I would disagree with how a lot of researchers view the strength of evidence provided by cross-sectional studies. I think a lot of researchers seem to endorse the proposition 'if this could be confounded, it provides no evidence of causation', which I don't think is right. It depends on one's prior on how plausible the confounder is. I think this is why a lot of economics has stopped trying to focus on some of the more important macro questions, and I think this is a mistake. 

eg consider the potential effects of climate change on economic performance. I do think cross-sectional evidence is highly relevant and should update one's view. If economic performance were very strongly climatically determined, I would expect this to show up strongly in the cross-section. I wouldn't expect to see California being way richer than Baja California. I wouldn't expect gross state product for US states to look like this as a function of state average temperature:

I would expect growth rates to be uniformly low in climatically exposed places like Vietnam, Bangladesh, Indonesia, India etc, which is not what we see. So, I do think this sort of evidence should update one's view, even though there are obviously loads of potential confounders. 

In climate economics, people don't like this, so they have started using panel data approaches which aim to test the exogenous effects of weather changes on economic performance in particular periods of time. This supposedly provides better evidence of causation, but I think should be completely ignored because of huge researcher degrees of freedom, reporting bias and political bias. I think they leave the door open for econometric skullduggery to provide inflated estimates. In part because the cross-sectional evidence is more transparent, I think it is more reliable. 

Re confounding, the headline estimate that James uses is adjusted for various potential confounders. 

"Aside from controlling for all time-invariant factors using FE models, we control for a variety of moment-specific factors, including: what people were doing (40 activities), who they were doing it with (7 types), time of day (three-hour blocks split by weekday vs. weekend/bank holiday), location Page 16 (inside/outside/in vehicle and home/work/other), and how many responses the participant has previously given. OLS estimates also include time-invariant controls for gender, employment status, marital and relationship status, household income, general health, children, single parent status, region, age and age squared at baseline. Derivations/descriptive statistics are given in Web Appendix S5.

I don't have a strong view on what effect the potential selection effect would point. 

the economic damage inflicted by natural disasters is much higher now than it was in the 1960s (as a share of GDP)

You cannot infer trends in the climate-related economic impact of natural disasters from trends in the total damage of natural disasters or trends in the per capita economic impact of natural disasters. The economic costs of natural disasters is influenced by the increasing economic value of areas that are vulnerable to climate change.  Eg here is a Miami beach a century ago compared to today

You need to adjust for this by producing an estimate of normalised damages (discussed here by one of the most cited climate researchers). For example, here is the normalised cost to the US of hurricanes since 1900. i.e. suggestive of increased losses on the order of $5-10bn over the course of a century, which is about 0.2% of US GDP. 

Despite what you read in the media, according to the IPCC, for the vast majority of extreme events, it is not yet possible to attribute with confidence any change to climate change. A white entry in the table means that a signal cannot yet be noticed with confidence. A blue entry means the signal is increasing. An orange entry means the signal is decreasing.

There is as yet no clear evidence of a climate signal for precipitation, flooding, drought, fire weather, wind speed, storms, cyclones, and coastal flooding.

I don't agree with your points on natural disasters. I am going to post below the charts from OWID on weather-related deaths, including absolute numbers and per capita numbers. Some comments:

• These numbers are (now thankfully) small, falling well well short of catastrophe. 
• There have been massive declines in the absolute and per person risk from the most threatenting risks (droughts, floods). This is due to economic development. 
• To characterise the trend in flood deaths as anything other than a dramatic downward trend seems clearly wrong. 
• For all the media discussion of wildfires and a world on fire, we have passed 1 degree and wildfire deaths are 140 per year, which is far exceeded by the number of people who die falling off ladders. Perhaps not today, but at some point the media and the scientific community are going to face scrutiny for exaggerating on climate change. 
• Extreme temperature deaths are increasing, but this would (I assume) include cold-related deaths and heat-related deaths. According to Zhao et al, cold-related deaths are 9x heat-related deaths today, so one would expect the 1C we have already experienced to have reduced the death toll. In the absence of climate change, the increase would be more pronounced. (I haven't looked into the data source though)
• There is large net migration (i.e. in the millions of people) to low lying coastal areas in Asia that are most vulnerable to coastal storms. This, rather than climate change significantly confounds trends in per capita or total storm deaths. Nevertheless, storm deaths are at historic lows for any ten or twenty year period in the 20th Century. 
• As indicated by your comment, if your concern is reducing deaths from climate change, the main thing to do seems to be to increase economic growth in poor countries given that is what drove the massive decline in weather-related deaths over the last 200 years. 

I think it is a very hard area to provide an accurate outline of, and I think to do that you need to go beyond reading the abstracts of papers and to look at the assumptions in those paper which typically combine very pessimistic warming, very pessimistic economic growth, limited or no adaptation. I think a lot of your analysis errs in a pessimistic direction. 

1. [edit: misread the first point]: "The IPCC’s 6th Assessment Report predicts that, even if we fail to undertake significant further action, it’s very unlikely that we’ll reach 3°C or more of warming." I'm not sure where you are getting this from given that you direct the reader to a >3000 page report, but this is now widely accepted to not be true. As I discussed in my report on climate change, warming of 2.5C is widely accepted to be the most likely outcome on current policy by a range of modelling studies. Since this does not account for changes in policy, this is pessimistic. 
2. It is notable that you ignore climate economics in your overview. This is the field tasked with estimating the aggregate costs of climate change, and most models find that the costs of warming for 2-3C are a 0-5% reduction of GDP relative to a world without warming. Since GDP per capita will increase several fold up to 2100, average living standards will still be higher. These models do not find that the costs of 1.5C would be 'huge', rather that they are close to a 0% reduction in GDP. Given that we are already pretty close to 1.5C, it is pretty obvious that the effects will not be 'huge' (but I'm not sure what you mean by that). 
   1. Do you disagree with these models? If so, why? The best ones include the impacts you talk about here, including food, flooding drought, heat stress etc, and find the same results. I don't think you should be selective in accepting the expert consensus on things without evidence or argument. 
   2. You say that climate change will increase food insecurity. True, but studies that incorporate the effects of climate change, economic growth and agricultural progress find reductions in food insecurity on nearly all socioeconomic scenarios. I think it would be worth providing this context. 
3. Drought 
   1. You cite the carbonbrief article on people subject to drought which, for those who can be bothered to check, cites this article, which does not consider adaptation in its estimates and so is not realistic. 
   2. You cite the study saying that 132 million extra people will be exposed to drought in a 1.5C world. We are already nearly in that world and this decade a total of 662 people have died from drought per year, according to Our World in Data. This would be useful context. 
   3. Water scarcity is mainly driven by mispricing of water and especially subsidies of water for farmers. 
4. Temperature-related deaths
   1. You say that at 1.5C "nearly 14% of the world’s population could experience severe heatwaves at least every five years". Yes, but we are nearly in that world today, and today cold-related deaths are 9x heat-related deaths (Zhao et al 2019). In the near-term at least we should expect temperature-related deaths to decline due to climate change. I think you should at least note this in your overview. 
   2. You cite Bressler et al (2023). Bressler et al claim that assuming limited adaptation, very pessimistic economic growth (SSP3) and very pessimistic warming (RCP8.5), deaths increase by 5 million per year by 2100. Per the IPCC, it is not actually permitted to combine SSP3 and RCP8.5, but studies often do this. Bressler et al also doesn't consider many important forms of adaptation, which have led to declining heat-related deaths in rich countries. Consequently, I think the estimates in Bressler et al are not accurate, at least biased high by several orders of magnitude and probably have the wrong sign. 
5. Tipping points 
   1. You note the release of vast amounts of methane from the Arctic. There is one study asserting without evidence that this would happen, but it is extremely controversial and expert elicitation suggests that methane clathrates would increase 2100 temperatures by at most 0.1C on an insanely pessimistic emissions scenario (RCP8.5). 
   2. You cite the Kemp et al study claiming "sudden, severe, and potentially irreversible changes to the climate" but they don't provide any evidence for this and the IPCC denies this, if you mean "an shift in global climate that happens due to warming before 4C that adds more than 2C to global temperatures". The main argument provided in the Kemp paper is a causal loop diagram with a lot of arrows running between climate change and other problems. It is notable that one could apply the same analysis to other things such as 'bad economic policy' without arriving at the conclusion that this is a serious catastrophic risk. 
   3. In general, if you are defining 'catastrophic risk' in the usual way, could you explain how you get to >800 million deaths from climate change via a non-conflict pathway? The biggest death estimate for a particular impact I have seen is the Bressler et al claim that assuming limited adaptation, very pessimistic economic growth (SSP3) and very pessimistic warming (RCP8.5), deaths increase by 5 million per year by 2100. (Per the IPCC, it is not actually permitted to combine SSP3 and RCP8.5, but studies often do this.) On more plausible assumptions, I would expect temperature-related deaths to decline. Even if you take the Bressler estimate at face value, where do you get the remaining >795 million deaths? For all other impact pathways I have seen, deaths from weather-related events are set to decline relative to today due to economic growth and adaptation. 
6. Conflict 
   1. In support of the claim that climate change is an important contributor to conflict risk you cite an 80,000 Hours article. The IPCC is highly equivocal on the effects of climate change on civil conflict. It basically says nothing about the effects on interstate conflict and no scholars of great power war think it is an important driver of 21st century potential great power conflicts. 

I'm not sure the phone study has the traditional weaknesses as cross-sectional studies. It's a bit more like a panel study where you can track in very fine-grained detail what events are happening and what happens to subjective wellbeing at the time. Because the event data is so fine-grained and there are so many contiguous datapoints, it provides very good evidence of causation. These sorts of studies also give intuitively plausible results for all other events. People don't like work, getting divorced, being unemployed, being widowed; people like sex, seeing their friends etc. 

It's true that people opt in, but I don't see any particular reasons to think that this would have a bias towards 'happy drinkers'. The same is true for other life events. Like maybe there is some bias such that people who enjoy sex are more likely to opt in to phone-based subjective wellbeing studies, but I don't think that is what is driving the results. 

(this was a joke)

Hi Nick, yeah I get that there are costs to alcohol, I just think it is important to consider the benefits when deciding what to do. A lot of public health policies are defended by only focusing on the costs of doing something, not the benefits. So, I think it is important to consider the benefits. 

My own intuition is that if there were no alcohol, happiness would go down for the vast majority of people and would go up for a small minority. From my own experience, people often seem close to their happiest when drinking, and it is a very important social lubricant. 

I think the best study in James' review was the subjective wellbeing one because it measures what, in my view, actually matters, which is people's moment to moment wellbeing. It's also a very large sample. I wouldn't class the benefits found in the study as 'tiny'. The increase in wellbeing while drinking is nearly as large as that produced during spending time with friends. And this is a benefit which would be spread across billions of people. 

Yeah wrt to your botec, I wasn't sure whether you were implicitly writing off time spent asleep. 

I suppose you would also have to do the same for measuring the effects of money on wellbeing. Do you do that?

Hangovers are a myth