November 20, 2024
European neighbors Portugal and Spain are currently neck-and-neck in the race to roll out solar and wind power.
On the chart, you can see the share of electricity from the combination of solar and wind in each country. Their rate of progress has been very similar.
In 2023, both countries generated around 40% of their electricity from these sources. Wind power is more prevalent in Portugal, while solar is more used in Spain.
This data comes from Ember.
Explore more data on the rollout of clean energy across the world →
Today
America’s most popular type of meat is chicken. In Argentina, chicken is tied with beef. And in Japan, it’s fish and seafood.
There are large differences in the popularity of meat types across the world.
In the chart above, you can see the share of supply that comes from different types of meat: poultry, beef, pork, goat, and seafood. I’ve picked just a selection of countries that highlight some of the variation across the world.
Of course, countries also eat very different amounts of meat; this chart focuses on the relative amounts in national diets.
This data comes from the Food and Agriculture Organization of the United Nations.
Explore the most popular types of meat in your country in the global dataset →
Yesterday
Global emissions of local air pollutants have probably passed their peak.
The chart shows estimates of global emissions of pollutants such as sulphur dioxide (which causes acid rain), nitrogen oxides, and black and organic carbon.
These pollutants are harmful to human health and can also damage ecosystems.
It looks like emissions have peaked for almost all of these pollutants. Global air pollution is now falling, and we can save many lives by accelerating this decline.
The exception is ammonia, which is mainly produced by agriculture. Its emissions are still rising.
These estimates come from the Community Emissions Data System (CEDS).
Air pollution has not peaked everywhere in the world — explore the data for your country →
January 27
One way to understand how far international migrants move is to measure the shortest distance between the borders of their origin and destination countries.
The chart above shows these distances for all international migrant populations worldwide. It includes the total number of people living outside their home country rather than yearly migration flows.
Most migration journeys are short, with neighboring countries (shown as “0 km” on the chart) the most common destinations. Nearly half of all migrants — about 47% — move less than 500 kilometers, roughly the distance from the Netherlands to Switzerland. The median distance between origin and destination countries is just under 600 kilometers.
24% of migrants travel over 3,000 kilometers, about the distance from Ukraine to Portugal. Only a small fraction — less than 4% — move more than 10,000 kilometers, roughly equivalent to a journey from Madagascar to the United Kingdom.
Read our full article on how far migrants travel from their home countries →
January 24
Papua New Guinea has 840 living languages — more than any other country.
A living language is one that is spoken by at least one person as their first language. The chart shows the ten countries with the most living languages as of 2024. This data is from the Ethnologue dataset produced by the Summer Institute of Linguistics International.
There are over 7,000 living languages globally, meaning that more than 10% of the world’s living languages are spoken in Papua New Guinea.
Papua New Guinea was initially settled by humans around 50,000 years ago, allowing a long time for languages to be established. Around 3,500 years ago, people speaking a different family of languages (Austronesian) arrived and settled in Papua New Guinea, bringing additional diversity to the country.
Unlike many nations, Papua New Guinea did not experience historical events such as the establishment of an early centralized authority, which often led to the dominance of a single language. Instead, its rugged mountainous terrain isolated communities, fostering the independent development of numerous languages.
Explore the number of living languages in other countries →
January 23
Cardiovascular disease mortality has fallen massively since the 1950s.
This chart shows annual age-standardized death rates from cardiovascular diseases in four countries: the United States, France, the United Kingdom, and Italy.
The decline is substantial. In the United States, the death rate dropped from over 500 per 100,000 people in 1950 to under 150 in 2021 — a four-fold decline. The reduction in France and the United Kingdom was even greater, with death rates falling five-fold.
This progress comes from advancements in medical science, surgeries, emergency care, public health efforts, and dietary changes, improving cardiovascular health.
A dramatic reduction in smoking rates, better screening and monitoring for conditions like high blood pressure, and the development of life-saving treatments such as stents, statins, and clot-busting drugs have all contributed.
Explore trends in cardiovascular mortality in more countries →
January 22
In the 1970s, the UN General Assembly adopted a resolution asking developed countries to contribute at least 0.7% of their national income to foreign aid. Most countries accepted this target, except for Switzerland and the United States.
But very few countries have met this target in the fifty years since then. Even today, only a handful of countries do.
Just five countries met this target in 2023: Norway, Luxembourg, Sweden, Germany, and Denmark. You can see them in blue on the map.
Every other developed country gave less than 0.7% of their national income.
Explore more of our new charts on foreign aid: who contributes, and where it goes →
January 21
Artificial intelligence has advanced rapidly over the past 15 years, fueled by the success of deep learning.
A key reason for the success of deep learning systems has been their ability to keep improving with a staggering increase in the inputs used to train them — especially computation.
Before deep learning took off around 2010, the amount of computation used to train notable AI systems doubled about every 21 months. But, as you can see in the chart, this has accelerated significantly with the rise of deep learning, now doubling roughly every six months.
As one example of this pace, compared to AlexNet, the system that represented a breakthrough in computer vision in 2012, Google’s system “Gemini 1.0 Ultra” just 11 years later used 100 million times more training computation.
To put this in perspective, training Gemini 1.0 required roughly the same amount of computation as 50,000 high-end graphics cards working nonstop for an entire year.
Read more about how scaling up inputs has made AI more capable in our new article by Veronika Samborska →
