Just a decade and a half as it turns out! (though things definitely felt dizzyingly fast back then - think Google was launched just 5 years after HTML)
We are already the densest OECD city by quite a margin! (22,000 per sqkm in the inner 20 district, twice that of Manhanttan and 3 times that of Tokyo - and still 8,600 in the Petite Couronne, which includes 8 million people)
It did not - the population inhabiting the current French territory had represented 20% of Europe's population quite consistently since medieval times. E.g. France numbered 19.7 million inhabitants as early as 1457 [1]
The population explosion that's discussed in the article (the population dividend of the demographic transition) just never happened in France - which is the whole point of why its relative standing dropped so much over the past two centuries compared to other hitherto much smaller countries such as England and Germany.
Absolutely! Also “town” seems rather uncharitable as Marseille has been a city without interruption for the past 2600 years since it’s founding as Massalia by Greek colonists - making it almost as old as Rome. It’s home to 1.8 million today, and a beautiful place to visit!
To enjoy modern standards of living, medium to high density urban arrangements are much less resource intensive than suburban sprawl, which the relevant comparison point.
The figure you quote, aggregated at the global level, entirely results from a comparison of city lifestyle with pre-industrial countryside lifestyles in developing countries. By contrast, living in the countryside or in suburbia in rich nations is much more environmentally damaging than in dense urban areas, which is what gp was referring to.
The impact of a tax is measured as a share of your income - if a tax represents a growing share of your income as you grow richer, it’s called progressive, otherwise it’s regressive (ie falls harder on the poor).
This is the case for consumption taxes in general and carbon taxation in particular. This is not contradictory with the fact that the top income decile emits more per capita in absolute than the lowest one.
This is especially manifest for gasoline consumption: richer households consume significantly more of it, but it represents less than 2-3% of their income, while it can reach more than 10% for poorer households.
Indeed, this can and should be offset with a full redistribution of the tax proceeds - either flat or targeted at lower income households - to compensate the inherent regressivity of carbon taxation.
Source: my PhD was on the distributional consequences of carbon pricing.
This is actually due to the inclusion of suburbs and exurbs in the “urban” category for US, Canada & Australia.
Half of the US population lives in the suburbs (see above posters). What we would recognize as a real urban environment only accounts for less than a third of the US population (98M people).
One really underappreciated aspect of R is that it's a lisp at heart. This enables the user (and enterprising package writer) to build really clean abstractions for the task at hand.
The tidyverse suite of Hadley Wickham is a great example of this, notably with the pipe operator %>% (similar to |> in F#) which is not part of the base language and yet could be very easily implemented. Julia's macros probably enables the same type of implementation, but I don't see how one would achieve it as easily in Python for example. Non-standard evaluation is another example of R's lispiness in action [0].
Also, consider how easy it is to walk R's S-exp. Expressions in R can only be one of four things: an atomic value, a name, a call or a pairlist. Wickham's Advanced R has a great intro on this [1].
I believe Wickham's amazing work with tidyverse (which really changes the way you code in R) is just the beginning of a rediscovery of R's inner lisp power, a kind of "R: the good parts" moment.
In terms of loss of civilians and military personnel though, it does not compare: the Soviet Union accounted for more than 40% of all WW2 deaths (both civilian and military) worldwide - including deaths on the Pacific front.
The Soviet Union lost a total of 26.6 million people, or 13.8% of their 1939 population. Germany suffered tremendously as well of course, with 5.7 million dead, or 8.2% of their 1939 population [0].
This is obviously not a contest, and I'm sure we can all agree that the suffering was tremendous on all sides. However, when it comes to the perception of war in Russia, the fact that the brunt of the Allied war effort in terms of casualties was borne out by the Soviet Union during WW2 (a fact often little known or recognized in Western countries) is especially relevant.
The surprising fact here is that this can mostly be explained pretty well as the lessons of WW2 still being vivid in the memory of many countries...except that Russia suffered by far the most of any countries on the Western front, and still a majority there would fight a war for their own country.
The persistent glorification of Russia's role in WW2 (although historically justified) might have something to do with it. The amputation of a quarter of Russia's territory right after the end of the Cold War is also used to great effect by nationalists (just like Germany's territory was reduced, by the same proportion, in 1919 - with the same effects).
Even in Shibuya? That's interesting, because depending on the size of the apartment you consider middle-class, this would make it cheaper than Paris. Definitely not what I remember from living in Japan 10 years ago.
Shouldn't this be exactly the contrary? If you have 10 independent coin flips, the chance of getting 10 heads is < 0.1%, while if they are perfectly correlated, then it simply becomes 50%.
Having 10 independent causes of death going up simultaneously is very unlikely to be caused by random chance. Rather it's an indication of an external phenomenon influencing all causes simultaneously.
How about Paris to Marseille (775km vs 615km for LA to SF) in 3 hours 15 min, Paris to Bordeaux (584km) to be 2 hours by 2017, or Brussels to Lyon (734km) in 3 hours 43 min?