In 2024, approximately 127.8 million people lived in Guangdong province in China. That same year, only about 3.7 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 2024. 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.

The population proportion in the provinces of western China residing in potential high-risk areas.

Population: Hunan: West Hunan data was reported at 2,432.000 Person th in 2023. This records a decrease from the previous number of 2,461.200 Person th for 2022. Population: Hunan: West Hunan data is updated yearly, averaging 2,548.800 Person th from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 2,850.000 Person th in 2010 and a record low of 2,432.000 Person th in 2023. Population: Hunan: West Hunan data remains active status in CEIC and is reported by West Hunan Municipal Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GN: Population: Prefecture Level Region.

Throughout the Common Era, until the 18th century, Japan's average population growth rate was significantly higher that those of Europe or China. Japan's relative isolation meant that it was not subjected to the same devastating pandemics during this period (especially plague), which caused regular spikes in mortality across Eurasia. During the period between 1700 and 1850, China and Western Europe's growth rates rose significantly due to improvements in food supply, water treatment, and more infrequent pandemics; as well as the spread of vaccination in Europe. In the late-19th and 20th centuries, population growth was high in all three regions, due to the onset of the demographic transition.

In 2023, the natural growth rate of the population across China varied between 7.96 people per 1,000 inhabitants (per mille) in Tibet and -6.92 per mille in Heilongjiang province. The national total population growth rate turned negative in 2022 and ranged at -1.48 per mille in 2023. Regional disparities in population growth The natural growth rate is the difference between the birth rate and the death rate of a certain region. In China, natural population growth reached the highest values in the western regions of the country. These areas have a younger population and higher fertility rates. Although the natural growth rate does not include the direct effects of migration, migrants are often young people in their reproductive years, and their movement may therefore indirectly affect the birth rates of their home and host region. This is one of the reasons why Guangdong province, which received a lot of immigrants over the last decades, has a comparatively high population growth rate. At the same time, Jilin, Liaoning, and Heilongjiang province, all located in northeast China, suffer not only from low fertility, but also from emigration of young people searching for better jobs elsewhere. The impact of uneven population growth The current distribution of natural population growth rates across China is most likely to remain in the near future, while overall population decline is expected to accelerate. Regions with less favorable economic opportunities will lose their inhabitants faster. The western regions with their high fertility rates, however, have only small total populations, which limits their effect on China’s overall population size.

Regional gross domestic product (GDP) in China varies tremendously across the country. In 2024, the GDP of Guangdong province amounted to around **** trillion yuan, whereas that of Tibet only reached about ***** billion yuan. While Guangdong has a thriving economy and is densely populated, Tibet is located in a remote mountain area and has a population of only around *** million people. Regional economic differences in China China can generally be divided into four different economic macro-regions: the economically well-developed coastal parts in Eastern China, the less-developed Central and Northeastern China, and the developing region of Western China. This division is reflected in the figures for regional per capita GDP. The coastal parts of China are not only economically more advanced, but also have a considerably higher population density. This is the result of climatic conditions on the one hand and China's firm integration into the global economy on the other. International companies were initially attracted by special economic zones set up in coastal areas during China's market opening, and well-connected, highly developed urban areas of Eastern China are still favored by international businesses. Prospects for future development The Chinese government has long since been aware of the economic disparities in the country and the political unrest they might stir. Major efforts have been made to improve the conditions in less developed regions. The situation in Central and Western China has improved considerably in the last two decades, and rural poverty decreased on a striking scale. In recent years, growth rates in the west of China have even been higher than in coastal areas. However, the constraints of the global economy remain, and it is very likely that Eastern China will stay ahead in international markets in the foreseeable future.

Based on the ecological-economic-social system, green development efficiency is divided into green ecological efficiency, green economic efficiency and green social efficiency. Their corresponding indicator systems are constructed, and the Super-SBM model, Super-SBM-Undesirable model and kernel density estimation are applied to measure and analyze green development efficiency and its dynamic evolution in western China from 2007 to 2019. Tobit model is constructed and used to empirically analyze the influencing factors of the green development efficiency in western China. The study shows that: (1) green ecological efficiency and green economic efficiency in western China are generally at a low level, and mainly dragged by northwest China, while green social efficiency in western China is generally at a high level, and mainly dragged by southwest China; (2) green ecological efficiency, green economic efficiency and green social efficiency in western China all show a slight trend of first decreasing and then increasing; (3) all three sub-efficiencies of green development in western China have a decreasing trend of absolute difference, right trailing and polarization; (4) the lower green ecological efficiency in western China is due to the negative impacts from the level of government intervention, the level of economic development, and foreign direct investment. The lower green economic efficiency is due to the positive impacts from population density, the level of government intervention, the level of financial development, and foreign direct investment. The higher green social efficiency is due to the positive impacts from population density, the level of financial development, the level of economic development, and the green technological innovation. The study is based on countermeasure recommendations focusing on improving green social efficiency in southwest China, as well as green ecological efficiency and green economic efficiency in northwest China, which are of reference value to promote green development more comprehensively in western China.

Population: Hunan: West Hunan: Fenghuang data was reported at 351.700 Person th in 2022. This records a decrease from the previous number of 353.600 Person th for 2021. Population: Hunan: West Hunan: Fenghuang data is updated yearly, averaging 353.300 Person th from Dec 2004 (Median) to 2022, with 19 observations. The data reached an all-time high of 386.300 Person th in 2009 and a record low of 327.500 Person th in 2016. Population: Hunan: West Hunan: Fenghuang 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.GJ: Population: County Level Region.

About this dataset

Context

This list ranks the 3 cities in the West Baton Rouge Parish, LA by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Chinese Population: This column displays the rank of city in the West Baton Rouge Parish, LA by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total West Baton Rouge Parish Chinese Population: This tells us how much of the entire West Baton Rouge Parish, LA Chinese population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Based on the ecological-economic-social system, green development efficiency is divided into green ecological efficiency, green economic efficiency and green social efficiency. Their corresponding indicator systems are constructed, and the Super-SBM model, Super-SBM-Undesirable model and kernel density estimation are applied to measure and analyze green development efficiency and its dynamic evolution in western China from 2007 to 2019. Tobit model is constructed and used to empirically analyze the influencing factors of the green development efficiency in western China. The study shows that: (1) green ecological efficiency and green economic efficiency in western China are generally at a low level, and mainly dragged by northwest China, while green social efficiency in western China is generally at a high level, and mainly dragged by southwest China; (2) green ecological efficiency, green economic efficiency and green social efficiency in western China all show a slight trend of first decreasing and then increasing; (3) all three sub-efficiencies of green development in western China have a decreasing trend of absolute difference, right trailing and polarization; (4) the lower green ecological efficiency in western China is due to the negative impacts from the level of government intervention, the level of economic development, and foreign direct investment. The lower green economic efficiency is due to the positive impacts from population density, the level of government intervention, the level of financial development, and foreign direct investment. The higher green social efficiency is due to the positive impacts from population density, the level of financial development, the level of economic development, and the green technological innovation. The study is based on countermeasure recommendations focusing on improving green social efficiency in southwest China, as well as green ecological efficiency and green economic efficiency in northwest China, which are of reference value to promote green development more comprehensively in western China.

The aging population is a common problem faced by most countries in the world. This study uses 18 years (from 2002 to 2019) of panel data from 31 regions in China (excluding Hong Kong, Macao, and Taiwan Province), and establishes a panel threshold regression model to study the non-linear impact of the aging population on economic development. It is different from traditional research in that this paper divides 31 regions in China into three regions: Eastern, Central, and Western according to the classification standard of the National Bureau of Statistics of China and compares the different impacts of the aging population on economic development in the three regions. Although this study finds that the aging population promotes the economy of China’s eastern, central, and western regions, different threshold variables have dramatically different influences. When the sum of export and import is the threshold variable, the impact of the aging population on the eastern and the central region of China is significantly larger than that of the western region of China. However, when the unemployment rate is the threshold variable, the impact of the aging population on the western region of China is dramatically higher than the other regions’ impact. Thus, one of the contributions of this study is that if the local government wants to increase the positive impact of the aging population on the per capita GDP of China, the local governments of different regions should advocate more policies that align with their economic situation rather than always emulating policies from other regions.

In 2022, the total permanent resident population of Xinjiang autonomous region in China amounted to around ***** million inhabitants. Xinjiang autonomous region is located in Western China.

In 2022, the total permanent resident population of Qinghai province in China amounted to around **** million inhabitants. Qinghai province is located in Western China.

In 2022, the estimated population density of China was around 150.42 people per square kilometer. That year, China's population size declined for the first time in decades. Although China is the most populous country in the world, its overall population density is not much higher than the average population density in Asia. Uneven population distribution China is one of the largest countries in terms of land area, and its population density figures vary dramatically from region to region. Overall, the coastal regions in the East and Southeast have the highest population densities, as they belong to the more economically developed regions of the country. These coastal regions also have a higher urbanization rate. On the contrary, the regions in the West are covered with mountain landscapes which are not suitable for the development of big cities. Populous cities in China Several Chinese cities rank among the most populous cities in the world. According to estimates, Beijing and Shanghai will rank among the top ten megacities in the world by 2030. Both cities are also the largest Chinese cities in terms of land area. The previous colonial regions, Macao and Hong Kong, are two of the most densely populated cities in the world.

Population: Hunan: West Hunan: Yongshun data was reported at 405.300 Person th in 2022. This records a decrease from the previous number of 407.300 Person th for 2021. Population: Hunan: West Hunan: Yongshun data is updated yearly, averaging 446.400 Person th from Dec 2004 (Median) to 2022, with 19 observations. The data reached an all-time high of 503.700 Person th in 2009 and a record low of 405.300 Person th in 2022. Population: Hunan: West Hunan: Yongshun 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.GJ: Population: County Level Region.

Population: Rural: Hunan: West Hunan: Jishou data was reported at 107.300 Person th in 2022. This records a decrease from the previous number of 109.300 Person th for 2021. Population: Rural: Hunan: West Hunan: Jishou data is updated yearly, averaging 93.400 Person th from Dec 2004 (Median) to 2022, with 19 observations. The data reached an all-time high of 183.000 Person th in 2007 and a record low of 83.300 Person th in 2014. Population: Rural: Hunan: West Hunan: Jishou 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.GJ: Population: Rural: County Level Region.

About this dataset

Context

This list ranks the 55 counties in the West Virginia by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each county over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Chinese Population: This column displays the rank of county in the West Virginia by their Chinese population, using the most recent ACS data available.
• County: The County for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the county is shown in this column.
• % of Total County Population: This shows what percentage of the total county population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total West Virginia Chinese Population: This tells us how much of the entire West Virginia Chinese population lives in that county. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

About this dataset

Context

This list ranks the 150 cities in the West Virginia by Chinese population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Chinese Population: This column displays the rank of city in the West Virginia by their Chinese population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Chinese Population: The Chinese population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Chinese. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total West Virginia Chinese Population: This tells us how much of the entire West Virginia Chinese population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Compositions of green development efficiency indicators in western China.

Distribution intervals of green social efficiency in western China.