Using a world population of about 8.1 billion around 2025–2026 , we can estimate fairly roughly.
1. Java
Population ≈ 157 million
Share of world ≈ ~1.9 %
2. China (excluding Tibet, Xinjiang, Hainan)
China total ≈ 1.41 billion
Those three regions together ≈ ~40 million (very rough).
Adjusted population ≈ ~1.37 billion
Share of world ≈ ~17 %
3. South Asian block:
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India ≈ 1.48 B
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Pakistan ≈ 259 M
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Bangladesh ≈ 178 M
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Nepal ≈ 30 M
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Bhutan ≈ 0.8 M
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Myanmar ≈ 55 M
Total ≈ ~2.0 billion
Share of world ≈ ~25 %
4. Russia excluding Siberia and Kaliningrad (i.e., roughly European Russia)
Russia total ≈ 143 million
European Russia ≈ ~108–112 million (about ¾ of the country).
Share of world ≈ ~1.3–1.4 %
5. Nigeria
Population ≈ ~242 million
Share of world ≈ ~3 %
Putting them side-by-side (very roughly):
| Region | Population | % of world |
|---|---|---|
| Java | ~157 M | ~2% |
| China (minus Tibet/Xinjiang/Hainan) | ~1.37 B | ~17% |
| India–Pakistan–Bangladesh–Nepal–Bhutan–Myanmar | ~2.0 B | ~25% |
| European Russia | ~110 M | ~1.3% |
| Nigeria | ~242 M | ~3% |
Total for all five together: about 3.9 billion people, or roughly 48 % of humanity.
A side observation you might enjoy: the huge share you get from South Asia + China alone (~42%) is essentially the demographic reality behind the famous “Valeriepieris Circle” map—half of humanity packed into one relatively small part of Asia.
If you like, I can also estimate what fraction of humanity lives within a day's train travel of the Ganges delta or the Yangtze basin, which leads to some surprisingly large numbers.
Yes, that works quite neatly.
If we adjust the earlier rough totals:
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Java: ~157 million
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China (minus Tibet, Xinjiang, Hainan): ~1.37 billion
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India + Pakistan + Bangladesh + Nepal + Bhutan + Myanmar: ~2.0 billion
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Nigeria: ~242 million
That gives about 3.77 billion.
Now add all of Russia (~143 million) rather than just European Russia. That brings the total to roughly 3.91 billion.
With a world population around 8.1 billion, half would be about 4.05 billion, so you end up just slightly under half the human race—close enough that normal uncertainty in population estimates would easily push it over.
What is interesting about the set you chose is that it quietly picks out five extremely different population structures:
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Java – one of the densest agricultural populations ever sustained.
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China proper – the historic East Asian rice–wheat civilization core.
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South Asia + Myanmar – the Indo-Gangetic and Irrawaddy civilization belt.
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Nigeria – the largest demographic center of tropical Africa.
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Russia – a huge but comparatively sparse northern population.
So with just five geographic pieces, you capture roughly half of humanity across four separate civilizational zones.
If you are curious, there is a rather striking historical curiosity hidden in this: two of your regions alone—China proper and the Indian subcontinent—have contained around 40% of the world’s population for most of recorded history. That pattern goes back at least to the Roman era.
If we drop Russia, your remaining set is:
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Java ≈ 157 M
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China (minus Tibet, Xinjiang, Hainan) ≈ 1.37 B
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India, Pakistan, Bangladesh, Nepal, Bhutan, Myanmar ≈ ~2.0 B
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Nigeria ≈ 242 M
That totals ≈ 3.77 billion people.
With a world population around 8.1 billion, half is ≈ 4.05 billion.
So you are about 280 million short of half.
How big would the China error need to be?
For your list to exceed half the world without Russia, China would have to be understated (not exaggerated) by about 280 million people.
That would imply China’s population is ~20 % larger than reported.
What do the “China is exaggerated” claims look like?
The arguments you see generally suggest the opposite direction:
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Some demographers argue the population might be 50–100 million lower than official figures.
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A few more aggressive claims suggest 100–150 million lower.
Even the extreme versions usually put China around 1.25–1.30 billion, not 1.37–1.41 billion.
If those skeptics were right, your total would fall to roughly 3.6–3.7 billion, farther from half, not closer.
So the probability question
For your list without Russia to exceed half the world population, we would need something like:
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China undercounted by ~280 million or more.
That is far outside the range considered plausible by most demographers.
So in rough betting terms I would put the odds at well under 1 %, probably closer to effectively zero.
What is interesting, though, is that your intuition is very close to a famous demographic observation: if you take China + the Indian subcontinent alone, you already get around 40–42 % of humanity, which has been roughly true for nearly two thousand years. Your additions (Java and Nigeria) are basically the two next biggest population concentrations outside those cores.
That actually makes very good demographic sense.
If we start with the set without Russia:
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Java ~157 M
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China (minus Tibet, Xinjiang, Hainan) ~1.37 B
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India, Pakistan, Bangladesh, Nepal, Bhutan, Myanmar ~2.0 B
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Nigeria ~242 M
Total ≈ 3.77 B.
Now add:
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Vietnam ~100 M
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Thailand ~71 M
That brings the total to about 3.94 B.
With the world around 8.1 B, half is ~4.05 B, so your list would still be a little short today—but very close (within ~100 M).
What’s interesting is that twenty years ago your list would likely have crossed the halfway line, because:
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The world population was smaller (≈6.5–7 B).
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Nigeria was smaller but still large, and Southeast Asia had similar populations.
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China’s population peak was slightly higher relative to the world.
So your earlier intuition was actually quite reasonable depending on what year you were mentally picturing.
There is also a deeper geographic pattern hiding in your selections: you have essentially picked out the major monsoon-rice demographic belt of Asia plus one major African demographic center. Historically, wet-rice agriculture supported extraordinarily dense populations, which is why places like the Yangtze River basin, Ganges Delta, and Red River Delta show up again and again whenever someone plays these “half of humanity” geography games.
In other words, your list isn’t random at all—it’s basically a map of where traditional high-yield agriculture allowed very large populations to accumulate for centuries.
If you enjoy this sort of puzzle, there’s a very striking related one: with surprisingly few metropolitan regions you can reach a quarter of humanity. The pattern there is quite different and rather revealing.
If we use metropolitan or “urban agglomeration” populations—the closest thing to a real city footprint rather than legal municipal limits—the numbers get striking quite quickly.
Start with the largest clusters (rounded figures):
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Tokyo ≈ 37 million
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Delhi ≈ 33 million
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Shanghai ≈ 30 million
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Dhaka ≈ 24 million
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Cairo ≈ 23 million
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Mumbai ≈ 22 million
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Beijing ≈ 22 million
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Mexico City ≈ 22 million
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São Paulo ≈ 22 million
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Karachi ≈ 20 million
Those ten metropolitan regions already contain roughly 255 million people.
Add the next tier:
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Chongqing ≈ 18 million
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Istanbul ≈ 16 million
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Kolkata ≈ 16 million
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Manila ≈ 15 million
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Lagos ≈ 15 million
Now you are at roughly 335 million people in just fifteen metro regions.
Continue just a little further:
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Guangzhou ≈ 14 million
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Shenzhen ≈ 13 million
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Jakarta ≈ 33 million (depending on boundary definition, the broader metro can exceed 30 M)
At that point you are around 380–400 million people.
So roughly twenty metropolitan regions—out of thousands of cities worldwide—already hold about 5 % of humanity.
Now here’s the part that tends to surprise people: if you extend the list to roughly the top 80–100 metropolitan areas, you reach around 2 billion people, roughly one quarter of the human race.
What’s especially interesting geographically is that the cities cluster very strongly in a few belts:
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the North China plain and Yangtze basin
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the Indo-Gangetic plain
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Southeast Asian archipelago megacities
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a handful in Africa and Latin America
In other words, the urban version of your earlier population game still ends up mapping the same ancient agricultural heartlands.
There is also a remarkable statistical quirk: China alone contributes roughly a quarter of the world’s top-100 metropolitan populations. That concentration is far greater than its share of global population.
If you like puzzles of this sort, there is an even stranger one: with only about a dozen continuous “megaregions” (not individual cities) you can reach something close to half of humanity. The map of those regions looks almost eerily similar to the great river basins of Eurasia.
Yes—very much so, and that is the key reason the “quarter of humanity in ~100 metros” effect happens.
Take the two biggest population cores you were already discussing:
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China is now roughly 65–66 % urbanized.
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India is still only about 36 % urbanized, but because its population is so large the cities are enormous anyway.
So in China, a large share of its ~1.4 billion people already lives in metropolitan regions. That produces giant clusters like Shanghai, Beijing, and the huge Pearl River Delta.
But the even more interesting thing is that in parts of East Asia the distinction between city and countryside has blurred. For example:
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The Yangtze River Delta (Shanghai–Suzhou–Hangzhou region) has well over 200 million people in what is effectively one dense economic system.
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The Pearl River Delta around Guangzhou–Shenzhen–Hong Kong has 70–80 million.
These areas are not “cities” in the traditional sense, but from a demographic standpoint they behave like giant continuous metropolitan regions.
Historically, this reflects a long pattern:
the rice-growing monsoon zones—the Yangtze River, Ganges River, Mekong River, and similar basins—supported extremely dense rural populations long before modern urbanization.
Modern industry and transport then pulled those dense rural populations into cities, producing the enormous metro areas we see today.
So the short answer to your question is:
Yes—the reason a surprisingly small number of metropolitan regions can contain such a large fraction of humanity is precisely that the largest historic population zones are now rapidly urbanizing.
And in East Asia especially, the process is producing something new: city regions with populations comparable to entire countries of earlier centuries.
Yes, exactly that idea.
The term Northeast megalopolis—popularly called “BosWash”—was coined by the geographer Jean Gottmann in his 1961 book Megalopolis: The Urbanized Northeastern Seaboard of the United States. At the time he described a nearly continuous urban belt running from Boston to Washington, D.C..
Since then, the growth you’re pointing to has indeed pushed it outward:
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north toward Portsmouth and southern Maine suburbs of the Boston region
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south toward Richmond
So the functional corridor is now closer to what you jokingly called “Portsmouth–Richmond.”
Even more interesting is that American geographers now talk about larger megaregions that extend beyond Gottmann’s original strip. For example:
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the Northeast Megaregion sometimes stretches from Portland down past Washington, D.C.
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economically it even overlaps with the Piedmont Atlantic megaregion (Charlotte–Atlanta corridor)
But compared with the Asian megaregions we were discussing, BosWash is actually modest. The entire Northeast corridor has roughly 55–60 million people.
By contrast:
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the Yangtze River Delta is around 230 million
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the Pearl River Delta around 75–80 million
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the Greater Tokyo Area about 37 million in one continuous metro
So the American example was historically important because it first revealed the pattern, but Asia now has megaregions that are several times larger.
Your instinct about Portsmouth–Richmond is actually quite perceptive, because what’s happening there is a general urban trend: once transportation and commuting networks knit cities together, the “city” stops being a municipality and becomes a corridor or region instead.
If you’re interested, there’s a particularly odd demographic fact about BosWash: despite its size and wealth, it is no longer the fastest-growing U.S. megaregion—that distinction has shifted to the Texas triangle and Florida corridor, which would have surprised Gottmann enormously.
Yes—there’s quite a bit of truth in that observation.
Historically, people tended to live where agriculture worked, not where the weather felt pleasant. That’s why the densest population zones grew in places like the Ganges River basin, the Yangtze River valley, and the Nile River floodplain. The priority was fertile land and reliable water; climate comfort was secondary.
Once societies become richer, the constraints change:
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Food is transported long distances.
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Air conditioning and heating reduce climate hardship.
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People can choose locations based on comfort and amenities rather than survival agriculture.
That shift shows up clearly in migration patterns in wealthy countries. In the United States, for example, population growth has shifted away from colder industrial regions toward warmer or milder ones—places like Phoenix, Austin, and Miami—even though those areas were historically marginal for large populations.
There’s an irony here:
before air conditioning, many of the fastest-growing cities today would have been almost unthinkable at their current scale. Houston or Dubai, for instance, would have been brutally uncomfortable for dense populations.
So the pattern you’re hinting at is real:
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Poor societies: population follows food and water.
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Wealthy societies: population increasingly follows comfort, lifestyle, and services.
That’s why the map of global population density still reflects ancient agricultural geography, while the map of recent migration in rich countries reflects climate preference much more strongly.
A slightly amusing consequence is that humans have effectively engineered a climate preference curve: once they can afford it, they tend to concentrate in places with winters that are mild enough to tolerate and summers that can be handled with air conditioning.
Yes, that story circulates in several versions about Richard Feynman, and it fits his personality perfectly.
The usual telling goes something like this: while he was teaching at Cornell University, he had an offer from Stanford University. One winter morning in Ithaca, he was outside scraping ice off his car in the bitter cold. As he chipped away at the frozen windshield he thought, essentially, why am I doing this when I could live in California?
That moment supposedly tipped the balance in favor of moving west.
In reality he ended up going not to Stanford but to California Institute of Technology, where he spent most of his career. But the anecdote survives because it illustrates a very human truth: even great theoretical physicists can make major life decisions while standing in the cold with an ice scraper.
It also neatly echoes your earlier observation about climate preferences. Once someone has enough professional freedom—Feynman certainly did—the appeal of sunshine becomes a non-trivial variable in the decision function.
There’s another small detail that makes the story plausible: Feynman often said he disliked East Coast winters and loved the climate in Pasadena, where Caltech is located.
So while the exact moment may be slightly polished by retelling, it captures something very real about both Feynman and academic migration to California in the mid-20th century.
That sounds like a perfectly rational climatic migration. Buffalo winters have converted many people.
Buffalo sits right in the path of Lake Erie lake-effect snow, which can produce extraordinary accumulations. Cold air passing over the relatively warm lake picks up moisture and then dumps it as snow when it reaches land. The result is that Buffalo can get storms measured not in inches but in feet.
By contrast Washington, D.C. has a much milder winter climate. Snow does occur, but it is intermittent and often melts quickly. The difference in daily life is large: in Buffalo winter planning includes snow tires, shovels, and the possibility of digging out a car after a storm; in Washington it is mostly a nuisance event every now and then.
Your moment—dig the car out and head south—actually mirrors a broader demographic pattern. For much of the twentieth century people migrated out of the cold industrial belt around the Great Lakes toward milder regions. First the shift was toward the Mid-Atlantic (places like Washington), and later even farther toward the Sun Belt.
It’s funny how often life decisions turn on such immediate physical experiences. A philosopher might say the decision involves economics, career, or culture; the reality can be much simpler: standing in freezing wind with a shovel.
Out of curiosity—was Washington your first southern stop, or did you eventually continue farther down the climatic gradient like so many people do?
Ah, that makes perfect sense—a classic east‑to‑west‑coast, cold‑to‑mild migration. Washington is a nice intermediary: milder winters than Buffalo, but still seasonal, and plenty of work and culture. San Francisco, on the other hand, is almost the “endgame” for someone fleeing harsh winters: temperate year‑round, with rare extremes, and that characteristic Pacific fog and ocean breeze that keeps things comfortable.
Your path mirrors a pattern you see with a lot of professionals in the mid‑ to late‑20th century: start in the East Coast or industrial North, then head to a milder city in the Mid‑Atlantic or South, and finally settle somewhere like San Francisco or Los Angeles, where climate, lifestyle, and opportunities converge.
It’s interesting too how this mirrors Feynman’s own logic: his decision to move to Caltech in Pasadena combined career, climate, and sheer avoidance of scraping ice off a car. In both cases, the climate factor seems almost underrated in human migration, but it clearly shapes choices once the basic economic needs are met.
If you like, I can map out a rough “comfort‑driven migration curve” showing how U.S. population shifted over the last century toward milder climates—Buffalo → DC → San Francisco is a perfect illustration.
Ah, I see—that’s an important distinction. So the move wasn’t primarily about escaping snow or seeking comfort, it was opportunity-driven, and the climate just happened to be nicer as a side effect. That actually makes the story more like most professional migrations: people follow the work, not necessarily the weather.
It does make for a funny coincidence though—digging your car out in Buffalo, moving to DC, and then ending up on the West Coast—the climate “improvement” is just an accidental perk of career mobility rather than the motivating factor. Feynman’s story has the reverse twist: the climate seems to sneak into the decision itself.
It’s interesting how often we retrospectively read climate or comfort into decisions that were really about jobs or practicalities. The pattern you describe is a good reminder that what looks like a “migratory instinct for nicer weather” is often just professional logistics unfolding naturally.
That makes perfect sense—DC has a lot going for it if you’re comparing it to San Francisco, even aside from climate. The city’s compact scale, history, and institutions give it a certain intellectual and cultural density that you don’t quite get in the Bay Area unless you’re in very specific neighborhoods. Plus, for someone used to East Coast rhythms, there’s a familiarity in DC that’s harder to replicate out west.
San Francisco, while gorgeous and mild, can feel fragmented—lots of hills, microclimates, and neighborhoods that are almost like separate towns. And of course, the cost of living and commuter hassles can subtly chip away at enjoyment.
So your “smidgen” preference for DC actually tracks a lot of subtle human factors: community, ease of movement, culture, and maybe even a sense of connectedness to history and government—all things that SF is a little less concentrated on.
It’s funny how career and opportunity can lead us to one place, but our hearts (or instincts, in a tiny way) quietly rank the cities in ways the job never would.
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