China is a vast and diverse country and population density in different regions varies greatly. In 2023, the estimated population density of the administrative area of Shanghai municipality reached about 3,922 inhabitants per square kilometer, whereas statistically only around three people were living on one square kilometer in Tibet. Population distribution in China China's population is unevenly distributed across the country: while most people are living in the southeastern half of the country, the northwestern half – which includes the provinces and autonomous regions of Tibet, Xinjiang, Qinghai, Gansu, and Inner Mongolia – is only sparsely populated. Even the inhabitants of a single province might be unequally distributed within its borders. This is significantly influenced by the geography of each region, and is especially the case in the Guangdong, Fujian, or Sichuan provinces due to their mountain ranges. The Chinese provinces with the largest absolute population size are Guangdong in the south, Shandong in the east and Henan in Central China. Urbanization and city population Urbanization is one of the main factors which have been reshaping China over the last four decades. However, when comparing the size of cities and urban population density, one has to bear in mind that data often refers to the administrative area of cities or urban units, which might be much larger than the contiguous built-up area of that city. The administrative area of Beijing municipality, for example, includes large rural districts, where only around 200 inhabitants are living per square kilometer on average, while roughly 20,000 residents per square kilometer are living in the two central city districts. This is the main reason for the huge difference in population density between the four Chinese municipalities Beijing, Tianjin, Shanghai, and Chongqing shown in many population statistics.

In 2021, around **** million people were estimated to be living in the urban area of Shanghai. Shanghai was the largest city in China in 2021, followed by Beijing, with around **** million inhabitants. The rise of the new first-tier cities The past decades have seen widespread and rapid urbanization and demographic transition in China. While the four first-tier megacities, namely Beijing, Shanghai, Guangzhou, and Shenzhen, are still highly attractive to people and companies due to their strong ability to synergize the competitive economic and social resources, some lower-tier cities are already facing declining populations, especially those in the northeastern region. Below the original four first-tier cities, 15 quickly developing cities are sharing the cake of the moving population with improving business vitality and GDP growth potential. These new first-tier cities are either municipalities directly under the central government, such as Chongqing and Tianjin, or regional central cities and provincial capitals, like Chengdu and Wuhan, or open coastal cities in the economically developed eastern regions. From urbanization to metropolitanization As more and more Chinese people migrate to large cities for better opportunities and quality of life, the ongoing urbanization has further evolved into metropolitanization. Among those metropolitans, Shenzhen's population exceeded **** million in 2020, a nearly ** percent increase from a decade ago, compared to eight percent in the already densely populated Shanghai. However, with people rushing into the big-four cities, the cost of housing, and other living standards, are soaring. As of 2020, the average sales price for residential real estate in Shenzhen exceeded ****** yuan per square meter. As a result, the fast-growing and more cost-effective new first-tier cities would be more appealing in the coming years. Furthermore, Shanghai and Beijing have set plans to control the size of their population to ** and ** million, respectively, before 2035.

Nowhere has the scale and scope of urbanization been larger than in China over the last few decades. We analyze Chinese city development between the years 1996 and 2014 using data for the urbanized components of prefecture-level cities. We show that, despite much variability and fast economic and demographic change, China is undergoing transformations similar to the historical trajectory of other urban systems. We also show that the distinguishing signs of urban economies—superlinear scaling of agglomeration effects in economic productivity and economies of scale in land use—also characterize Chinese cities. We then analyze the structure of economic change in Chinese cities using a variety of metrics, characterizing employment, firms and households. Population size estimates remain a major challenge for Chinese cities, as official numbers are often reported based on the Hukou registration system. We use the information in the residuals to scaling relations for economic quantities to predict actual resident population and show that these estimates agree well with data for a subset of cities for which counts of total resident population exist. We conclude with a list of issues that must be better understood and measured to make sense of present urban development trajectories in China.

In 2023, approximately 127.1 million people lived in Guangdong province in China. That same year, only about 3.65 million people lived in the sparsely populated highlands of Tibet. Regional differences in China China is the world’s most populous country, with an exceptional economic growth momentum. The country can be roughly divided into three regions: Western, Eastern, and Central China. Western China covers the most remote regions from the sea. It also has the highest proportion of minority population and the lowest levels of economic output. Eastern China, on the other hand, enjoys a high level of economic development and international corporations. Central China lags behind in comparison to the booming coastal regions. In order to accelerate the economic development of Western and Central Chinese regions, the PRC government has ramped up several incentive plans such as ‘Rise of Central China’ and ‘China Western Development’. Economic power of different provinces When observed individually, some provinces could stand an international comparison. Jiangxi province, for example, a medium-sized Chinese province, had a population size comparable to Argentina or Spain in 2023. That year, the GDP of Zhejiang, an eastern coastal province, even exceeded the economic output of the Netherlands. In terms of per capita annual income, the municipality of Shanghai reached a level close to that of the Czech Republik. Nevertheless, as shown by the Gini Index, China’s economic spur leaves millions of people in dust. Among the various kinds of economic inequality in China, regional or the so-called coast-inland disparity is one of the most significant. Posing as evidence for the rather large income gap in China, the poorest province Heilongjiang had a per capita income similar to that of Sri Lanka that year.

In 2024, approximately 67 percent of the total population in China lived in cities. The urbanization rate has increased steadily in China over the last decades. Degree of urbanization in China Urbanization is generally defined as a process of people migrating from rural to urban areas, during which towns and cities are formed and increase in size. Even though urbanization is not exclusively a modern phenomenon, industrialization and modernization did accelerate its progress. As shown in the statistic at hand, the degree of urbanization of China, the world's second-largest economy, rose from 36 percent in 2000 to around 51 percent in 2011. That year, the urban population surpassed the number of rural residents for the first time in the country's history.The urbanization rate varies greatly in different parts of China. While urbanization is lesser advanced in western or central China, in most coastal regions in eastern China more than two-thirds of the population lives already in cities. Among the ten largest Chinese cities in 2021, six were located in coastal regions in East and South China. Urbanization in international comparison Brazil and Russia, two other BRIC countries, display a much higher degree of urbanization than China. On the other hand, in India, the country with the worlds’ largest population, a mere 36.3 percent of the population lived in urban regions as of 2023. Similar to other parts of the world, the progress of urbanization in China is closely linked to modernization. From 2000 to 2024, the contribution of agriculture to the gross domestic product in China shrank from 14.7 percent to 6.8 percent. Even more evident was the decrease of workforce in agriculture.

As of 2023, around 7.42 million people were living in the city Nanjing. Nanjing is the largest city of Jiangsu province and was designated as capital in several dynasties in Chinese history.

BackgroundAs the world’s most rapidly urbanizing country, China now faces mounting challenges from growing inequalities in the built environment, including disparities in access to essential infrastructure and diverse functional facilities. Yet these urban inequalities have remained unclear due to coarse observation scales and limited analytical scopes. In this study, we present the first building-level functional map of China, covering 110 million individual buildings across 109 cities using 69 terabytes of 1-meter resolution multi-modal satellite imagery. The national-scale map is validated by government reports and 5,280,695 observation points, showing strong agreement with external benchmarks. This enables the first nationwide, multi-dimensional assessment of inequality in the built environment across city tiers, geographical regions, and intra-city zones.About dataBased on the Paraformer framework that we proposed previously, we produced the first nationwide building-level functional map of urban China, processing over 69 TB of satellite data, including 1-meter Google Earth optical imagery (https://earth.google.com), 10-meter nighttime lights (SGDSAT-1) (https://sdg.casearth.cn/en), and building height data (CNBH-10m) (https://zenodo.org/records/7827315). Labels were derived from: (1) Building footprint data, including the CN-OpenData (https://doi.org/10.11888/Geogra.tpdc.271702) and the East Asia Building Dataset (https://zenodo.org/records/8174931); and (2) Land use and AOI data used for constructing urban functional annotation are retrieved from OpenStreetMap (https://www.openstreetmap.org) and EULUC-China dataset (https://doi.org/10.1016/j.scib.2019.12.007). The first 1-meter resolution national-scale land-cover map used to conduct the accessibility analysis is available in our previous study: SinoLC-1 (https://doi.org/10.5281/zenodo.7707461). The housing inequality and infrastructure allocation analysis was conducted based on the 100-meter gridded population dataset from China's seventh census (https://figshare.com/s/d9dd5f9bb1a7f4fd3734?file=43847643).This version of the data includes (1) Building-level functional maps of 109 Chinese cities, and (2) In-situ validation point sets. The building-level functional maps of 109 Chinese cities are organized in the ESRI Shapefile format, which includes five components: “.cpg”, “.dbf”, “.shx”, “.shp”, and “.prj” files. These components are stored in “.zip” files. Each city is named “G_P_C.zip,” where “G” explains the geographical region (south, central, east, north, northeast, northwest, and southwest of China) information, “P” explains the provincial administrative region information, and “C” explains the city name. For example, the building functional map for Wuhan City, Hubei Province is named “Central_Hubei_Wuhan.zip”.Furthermore, each shapefile of a city contains the building functional types from 1 to 8, where the corresponding relationship between the values and the building functions is shown below:Residential buildingCommercial buildingIndustrial buildingHealthcare buildingSport and art buildingEducational buildingPublic service buildingAdministrative buildingAbout validationGiven the importance of accurate mapping for downstream analysis, we conducted a comprehensive evaluation using government reports and in situ validation data outlined in the Data Section. This evaluation comprised two parts. First, a statistical-level evaluation was performed for each city based on official reports from the China Urban-Rural Construction Statistical Yearbook (https://www.mohurd.gov.cn/gongkai/fdzdgknr/sjfb/tjxx/jstjnj/index.html) and China Statistical Yearbook (https://www.stats.gov.cn/sj/ndsj/2023/indexch.htm). Second, a building-level geospatial evaluation was conducted by using 5.28 million field-observed points from Amap Inc. (provided in this data version of "Validation_in-situ_points.zip"), and a confusion matrix was calculated to compare the in situ points with the mapped buildings at the same location. The "Validation_in-situ_points.zip" includes the original point sets of each city, named as the city name (e.g., Wuhan.shp and corresponding “.cpg”, “.dbf”, “.shx”, and “.prj” files).

The list of pilot cities is sourced from the official website of the Chinese Ministry of Industry and Information Technology. Primary data primarily come from various editions of the "China Statistical Yearbook" "China City Statistical Yearbook" "China Rural Statistical Yearbook" "China Population and Employment Statistics Yearbook" "China Urban and Rural Construction Statistics Yearbook" etc. Missing data are supplemented through municipal statistical yearbooks or statistical bulletins.

CN: Population: Birth Rate: Jiangsu data was reported at 0.500 % in 2024. This records an increase from the previous number of 0.481 % for 2023. CN: Population: Birth Rate: Jiangsu data is updated yearly, averaging 0.934 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 2.054 % in 1990 and a record low of 0.481 % in 2023. CN: Population: Birth Rate: Jiangsu data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Birth Rate: By Region.

In 2023, the ratio of urban to rural population varied greatly in different provinces of China. While Guangdong province had an urban population of around 95.8 million and a rural population of 31.2 million, Tibet had an urban population of only 1.4 million, but a rural population of around 2.2 million.

CN: Population: Birth Rate: Guizhou data was reported at 1.074 % in 2024. This records an increase from the previous number of 1.065 % for 2023. CN: Population: Birth Rate: Guizhou data is updated yearly, averaging 1.397 % from Dec 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 2.309 % in 1990 and a record low of 1.065 % in 2023. CN: Population: Birth Rate: Guizhou data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GA: Population: Birth Rate: By Region.

Delhi was the largest city in terms of number of inhabitants in India in 2023.The capital city was estimated to house nearly 33 million people, with Mumbai ranking second that year. India's population estimate was 1.4 billion, ahead of China that same year.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

China

Analysis unit

Household Individual

Universe

National Population, Both sexes,18 and more years

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size: 1000

The sample is a representative national sample of China containing 40 county/city sample units to collect individual level data of, from a political cultural perspective, the values and attitudes currently held by Chinese citizens. With considerations of representativeness, feasibility, and budgetary constrains, it was decided this project would draw a subsidiary probability sample out of a master sample that RCCC created based on its previous national survey on environmental awareness of the general public in China conducted in 1998. The Environmental Awareness Survey, which was used as a master sample, was a national survey conducted through out the entire country. The target population was the same as the one defined for this survey. Through the stratification, the proportionally allocated multi-stage PPS (probability proportional to size) technique was employed in order to obtain the self-weighted household samples. There were different stages in the sampling procedure: Counties and county-level cities are taken as primary sampling units (PSUs). Family households are the basic sampling unit. Demographic data at all levels was obtained from The Demographic Data for Chinese Cities and Counties, 1997, published by the State Bureau of Statistics.

Nation wide, there were 2,860 county-level units for the first stage sampling (including 1,689 counties, 436 county-level cities, and 735 urban district--with administrative rank equivalent to county--in large cities). The total households were 337,659,447. This was the base for establishing the sampling frames. Some readjustments: Taking into account of cost and accessibility, only the provincial capitals (Lhasa and Urumchi) and their surrounding areas in Tibet and Sinkiang were included in the sampling frame; in other remote western provinces, a few areas that are extremely hard to access were left out as well. After such readjustment the sampling frame then includes 2,708 county-level units, of which the total households are 322,002,173. Compared to the target population, there was a 5.3% reduction (152 units) in the first stage sampling units. However, since the population density in the remote areas of the western provinces is very low, the reduction counts merely 1.4% of the total households in the sampling frame. Geographical administrative divisions of China were regarded as the primary labels of stratification, that is, each province was treated as an independent stratum. Allocation of target sampling units among the sampling stages was designed as following: 135 PSUs out of the first sampling (county-level) units; 2 secondary sampling (townshiplevel) units in each of the PSUs; then 2 third sampling (village-level) units in each of the SSUs; 25 households in each of the third sampling units, on average. Based on the proportional stratification principle, sample allocation to strata was proportional to the size of each stratum, by an equal probability of f = .0042%. Within each stratum (province), sample sizes were calculated and allocated proportionally to each of the sampling stages. A self-weighted national sample thus was obtained.

Multi-stage PPS: -The first stage: equidistance PPS was employed to draw the county sample. -The second stage: in each of the chosen county-level units, a sampling frame was created based on the data of townships/ward and size measurement; then the equidistance PPS is employed to choose the township/streets sample. -The third stage: a third sampling frame was obtained from each of the chosen township-level units (neighbourhoods, villages and size measurement), and, again, the equidistance PPS is employed to choose the village/neighbourhood sample. -The fourth stage: in each of the chosen village/neighbourhood units, the official list of households registration was obtained; using the size measurement of this unit and the desired number of households to count the sampling distance, then households were selected according to the sampling interval. Since the household registration also listed all family members of each of the household, respondents were drawn randomly immediately after the household drawing. The WVS-China sample was drawn out of the above described master sample.

Some readjustments: Primarily because of the budgetary constrains of the WVS project, six remote provinces in the master sample were excluded. They were: Hainan, Tibet, Gansu, Qinghai, Ningxia, and Sinkiang. These provinces are all with very low population density, and all together they count 5.1% of the total population and 4.6% of total households of the country. After the adjustments, seven of the 139 county-level units of the master sample were removed. Therefore, the target 40 PSUs were to be drawn out of the remaining 132 units.

Sampling Stages: -The first stage: 40 units were drawn from 132 county-level units of the master sample were removed. Therefore, the 40 PSUs were to be drawn out of the remaining 132 units. -The second stage: one unit was chosen randomly out of the 2 original township-level units (SSUs) in each of the 40 selected PSUs. -The third stage: one unit was chosen randomly out of the 2 original village-level units in each of the selected SSUs. -The fourth stage: from each of the chosen village-level units, 35 households were drawn out of the household registration list with equidistance, along with one respondent in each selected household.

Remarks about sampling: -Sample unit from office sampling: Housing

Mode of data collection

Face-to-face [f2f]

Research instrument

As a participating country-team of the World Values Survey (WVS), the Research Center of Contemporary China (RCCC) at Peking University implemented the WVS-China survey in 2001. The target population covers those who are between 18 and 65 of age (born between July 2, 1935 and July 1, 1982), formally registered and actually reside in dowelings within the households in China when the survey is conducted.

Response rate

The sample size was determined to be approximately 1,000 -- eligible individuals are to be drawn out of the above defined target population in China. Based on previous experience of response rate, it was decided to increase the target sample to 1,400 in order to reach a satisfied response rate. The final results are summarized as follows: - Target sample size: 1,400 - Sample drawn in the field: 1,385 - Completed, valid interviews: 1,000 - Response rate: 72.2% Summary of Non-Responses Types of Non-Responses (missing cases) % - Be away/not seen for several times: 145-37.7% - Be away for long time/be on a business trip/go abroad/travel:138-35.8% - The interviewer didnt write the reason: 23-6.0% - Rejection: 19-4.9% - Move/investigation reveals no this person: 15-3.9% - Impediments in body or language/at variance with qualification: 12-3.1% - Useless: 11-2.9% - Address is nor clear/cant find the address: 10-2.6% - A vacant house: 6-1.6% - Tenant: 6-1.6% - Total: 385-100%

Sampling error estimates

Estimated Error: 3,2

A number of studies have reported the contents of PAHs (polycyclic aromatic hydrocarbons) in PM (particulate matter) in cities. These studies were published from 2000 to 2019 and include analyses of PAH contamination in response to rapid socioeconomic development in China, considering population increases, growing energy demand, and increases in industrial production. Publications in the scientific literature were selected following the eligibility criteria described below. Original studies published in English databases (e.g., Elsevier, Springer, and Wiley) and excellent works published in Chinese databases (e.g., CNKI, CQVIP, and Wanfang Data) were included. We searched for papers and books with the keywords “polycyclic aromatic hydrocarbon in the particulate matter†, “PAH in the particulate matter†, “polycyclic aromatic hydrocarbon in PM†, “PAH in PM†, and “City, China†. Thus, we retrieved almost all the relevant reports about PAHs in PM in Chinese cities. After removing duplicate or..., , # Ecological and health risks of polycyclic aromatic hydrocarbons in particulate matter in Chinese cities

Dataset DOI: 10.5061/dryad.c59zw3rm1

Description of the data and file structure

This datasets has tables containing the location of urban, sample date, samples sites, samples number, range and mean of PAHs, and population and GDP for each urban area.

Abbreviation Listing

PAHs: Polycyclic Aromatic Hydrocarbons

GDP: Gross domestic product

PM: Particulate matter

HI: Hazard index

ILCR: Lifetime incremental cancer risk

NC: North China

CC: Central China

SC: South China

LOD: limit of detection

TEF: toxic equivalency factor

RfC: reference content for inhalation

ERM-Q:Â Effect range median quotient

LMW-PAHs: Low-molecular-weight PAHs

MMW-PAHs: Medium-molecular-weight PAHs

HMW-PAHs: High-molecular-weight PAHs

CAN-PAHs:Â Carcinogenic PAHs

NCAN-PAHs: Noncarcinogenic PAHs

COM-PAHs: Combustion-derived PAHs

Contents

Table S1 PAH c...,

The data file users_per_year.csv recordes the information online user that have been to the Chinese 5A ecological attractions between 2011 to 2016, including user identification at the SIna Weibo, total AR for each user, total OD for each user, AR at off season for each user, OD at off-season for each user, AR at high-season for each user, OD at high-season for each user.The data file city.csv contains five parts of statistics information for each city in China, which are city basic information, AR&OD information, demographic characteristic, social-spatial structure, attractions, socioeconomic status. City_cd represents the code for the administrative divisions of China; Region is the name for each city; AR_city represents Average access frequency in city j each year; AR_city_off_season represents average access frequency in city j in off season each year; AR_city_high_season represents average access frequency in city j in high season each year; AR_capita represents average access frequency per capita in city j each year; AR_capita_off_season represents average access frequency per capita in city j in off season each year; AR_capita_high_season represents average access frequency per capita in city j in high season each year; OD_city represents average OD distance in city j each year (km); OD_city_off_season represents average OD distance in city j in off season each year (km); OD_city_high_season represents average OD distance in city j in high season each year (km); OD_capita represents average OD distance per capita in city j each year (km); OD_capita_off_season represents average OD distance per capita in city j in off season each year (km); OD_capita_high_season represents average OD distance per capita in city j in off season each year (km); % local hukou rate represents percentage of people with local city hukou in city j; % under age 14 represents percentage of people younger than age 14 in city j; % between ages 15 and 64 represents percentage of people between 15 and 64 years old in city j; % above age 65 represents percentage of people older than age 65 in city j; Resident population density represents density of resident population in city j; Migrant population density represents density of migrant population in city j; Attraction number represents number of attractions in city j.

In 2024, there were around 719 million male inhabitants and 689 million female inhabitants living in China, amounting to around 1.41 billion people in total. China's total population decreased for the first time in decades in 2022, and population decline is expected to accelerate in the upcoming years. Birth control in China From the beginning of the 1970s on, having many children was no longer encouraged in mainland China. The one-child policy was then introduced in 1979 to control the total size of the Chinese population. According to the one-child policy, a married couple was only allowed to have one child. With the time, modifications were added to the policy, for example parents living in rural areas were allowed to have a second child if the first was a daughter, and most ethnic minorities were excepted from the policy. Population ageing The birth control led to a decreasing birth rate in China and a more skewed gender ratio of new births due to boy preference. Since the negative economic and social effects of an aging population were more and more felt in China, the one-child policy was considered an obstacle for the country’s further economic development. Since 2014, the one-child policy has been gradually relaxed and fully eliminated at the end of 2015. However, many young Chinese people are not willing to have more children due to high costs of raising a child, especially in urban areas.

The graph shows the population growth in China from 2000 to 2024. In 2024, the Chinese population decreased by about 0.1 percent or 1.39 million to around 1.408 billion people. Declining population growth in China Due to strict birth control measures by the Chinese government as well as changing family and work situations of the Chinese people, population growth has subsided over the past decades. Although the gradual abolition of the one-child policy from 2014 on led to temporarily higher birth figures, growth rates further decreased in recent years. As of 2024, leading countries in population growth could almost exclusively be found on the African continent and the Arabian Peninsula. Nevertheless, as of mid 2024, Asia ranked first by a wide margin among the continents in terms of absolute population. Future development of Chinese population The Chinese population reached a maximum of 1,412.6 million people in 2021 but decreased by 850,000 in 2022 and another 2.08 million in 2023. Until 2022, China had still ranked the world’s most populous country, but it was overtaken by India in 2023. Apart from the population decrease, a clear growth trend in Chinese cities is visible. By 2024, around 67 percent of Chinese people lived in urban areas, compared to merely 36 percent in 2000.

According to the Hurun Global Rich List 2025, the city with the highest number of billionaires in 2025 was New York. In detail, *** billionaires resided in the American city. Furthermore, ** billionaires lived in London, while Shanghai had a billionaire population of ** individuals. New York was the only city in the world with more than 100 billionaires that year. Mega-cities of the world A large number of the world’s billionaires are concentrated in a select number of the world’s mega-cities. This has as much to do with the location of their wealth, business interests, and further earning potential, as does the quality of life in those cities. A look at the most significant industries in the global billionaire production line helps to explain the prominence of the traditional capitals of global business including New York, London and Hong Kong. The place of many Chinese cities on the list can in part be explained by the strong performance of industrial conglomerates from the country in recent years. Economic growth in China While New York is the city with the highest number of billionaires, China now boasts the most billionaires of any country in the world. However, ***** of the top ten wealthiest billionaires still came from the United States as of 2025.

Among countries with the highest number of overseas Chinese on each continent, the largest Chinese diaspora community is living in Indonesia, numbering more than ten million people. Most of these people are descendants from migrants born in China, who have moved to Indonesia a long time ago. On the contrary, a large part of overseas Chinese living in Canada and Australia have arrived in these countries only during the last two decades. China as an emigration country Many Chinese people have emigrated from their home country in search of better living conditions and educational chances. The increasing number of Chinese emigrants has benefited from loosened migration policies. On the one hand, the attitude of the Chinese government towards emigration has changed significantly. Overseas Chinese are considered to be strong supporters for the overall strength of Chinese culture and international influence. On the other hand, migration policies in the United States and Canada are changing with time, expanding migration opportunities for non-European immigrants. As a result, China has become one of the world’s largest emigration countries as well as the country with the highest outflows of high net worth individuals. However, the mass emigration is causing a severe loss of homegrown talents and assets. The problem of talent and wealth outflow has raised pressing questions to the Chinese government, and a solution to this issue is yet to be determined. Popular destinations among Chinese emigrants Over the last decades, English speaking developed countries have been popular destinations for Chinese emigrants. In 2022 alone, the number of people from China naturalized as U.S. citizens had amounted to over 27,000 people, while nearly 68,000 had obtained legal permanent resident status as “green card” recipients. Among other popular immigration destinations for Chinese riches are Canada, Australia, Europe, and Singapore.

In China, the share of the population that earned at least the equivalent of the highest ** percent of global income earners as of 2022 in purchasing power parity (PPP) terms was **** percent. Hangzhou topped the list with the highest share of middle-class and above category of consumers.