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.

This statistic shows the population density in urban areas of China in 2023, by region. In 2023, cities in Heilongjiang province had the highest population density in China with around ***** people living on one square kilometer on average. However, as the administrative areas of many Chinese cities reach beyond their contiguous built-up urban areas - and this by varying degree, the statistical significance of the given figures may be limited. By comparison, the Chinese province with the highest overall population density is Jiangsu province in Eastern China reaching about 7956 people per square kilometer in 2023.

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.

Urban infrastructure resilience is an important perspective for measuring the development quality of resilient cities and an important way to measure the level of infrastructure development. This paper uses the kernel density estimation, exploratory spatial data analysis, and spatial econometric models to analyze the characteristics of dynamic evolution and the spillover effects of the infrastructure resilience levels in 283 prefecture-level and above cities in China from 2010 to 2019. Our results are as follows. (1) The overall level of urban infrastructure resilience increased. The eastern region had a higher level than the national average. In contrast, the central, western and north-eastern regions had a slightly lower level than the national average. (2) The areas with high and higher resilience levels were mostly cities with more developed economic and social conditions in Eastern China. The areas below moderate resilience levels show a certain degree of clustering and mainly include some cities in Central, Western, and Northeast China. (3) The national level of urban infrastructure resilience shows significant spatial clustering characteristics, and the spatial pattern from coastal to inland regions presents a hotspot-subhotspot-subcoldspot-coldspot distribution. (4) There is a differential spatial spillover effect of national urban infrastructure resilience, which is gradually strengthened under the role of the economy, financial development, population agglomeration and government funding and weakened under the role of urbanization, market consumption and infrastructure investment. By exploring the dynamic evolution of infrastructure resilience in cities at the prefecture level and above and its spatial spillover effects, we provide a scientific basis for avoiding the siphoning effect among cities, improving the level of infrastructure resilience, and guiding the construction and development of resilient cities.

This is a nationwide survey of adults in mainland China that explored attitudes towards health care, including how people evaluate their health system and their trust in doctors and health care providers. It also includes data on respondents’ health-related behaviours and utilization of preventive and other health services, as well as trust in political institutions, cultural values, economic status, social capital and standard demographic variables.This interdisciplinary project establishes a new collaboration among UK researchers and a leading Chinese social research team, to conduct the first major study of Chinese people's attitudes towards their health care. The project's core theoretical contribution is to understanding the relationships between attitudes and health-related behaviours, focussing particularly on how people evaluate their health system, their trust in doctors and the health system, and their utilization of preventive and curative health services. Previous quantitative research on health in China has examined the influence on utilization of age and gender, incomes, insurance protection, distance to health service providers and perceived health care needs. Yet work done in other countries has shown that attitudes, including performance evaluations and trust, can impact on people's decisions about when and where to use health services. At the same time, qualitative studies in China have suggested that people are often critical of performance and that there is a crisis of trust in doctors and the health care system. Our project is the first systematic study of these attitudes and how they influence utilization. The Research Center for Contemporary China at Peking University carried out fieldwork from 1 November 2012 to 17 January 2013. The target population was mainland Chinese citizens age 18 to 70 residing for more than 30 days in family dwellings in all 31 provinces. The survey used the GPS Assisted Area Sampling Method (Landry & Shen, 2005) to project a grid onto 2855 counties, county-level cities or urban districts of the same status. Stratification took place in stages. At the first stage, the country was divided into three official macro-regions, Eastern, Central and Western; each macro-region was divided into urban and rural administrative areas, giving six layers in total; 60 primary sampling units (PSU) corresponding to county-level administrative divisions were selected at random across the six layers with probability proportionate to population (see map below). Within each PSU, three half-square minutes (HSM) of latitude and longitude were chosen with probability proportionate to population density, within each of these, again proportionate to population density, a number of spatial square seconds (SSS) corresponding to 90m x 90m squares was selected at random. Within each SSS, all dwellings were enumerated, and 27 were selected in each HSM by systematic sampling. Within each dwelling respondents were identified by the Kish method. The result was a sample of 5,424 dwellings in which 3,680 valid interviews were completed, giving a response rate of 67.9 per cent.

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China’s Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

Changes of urban infrastructure resilience global Moran’s I index.

Comprehensive evaluation index system of urban infrastructure resilience.

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China’s Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.

Detection of influencing factors of spatial distribution of national agricultural cultural heritage.