In 2023, the annual per capita gross domestic product (GDP) in different provinces, municipalities, and autonomous regions in China varied from approximately 200,300 yuan in Beijing municipality to roughly 47,900 yuan in Gansu province. The average national per capita GDP crossed the threshold of 10,000 U.S. dollars in 2019 and reached around 89,400 yuan in 2023. Regional economic differences in China The level of economic development varies considerably in different parts of China. Four major geographic and economic regions can be discerned in the country: The economically advanced coastal regions in the east, less developed regions in Northeast and Central China, and the developing regions in the west. This division has deep historical roots reflecting the geography of each region and their political past and present. Furthermore, regional economic development closely correlates with regional urbanization rates, which closely resembles the borders of the four main economic regions. Private income in different parts of China Breaking the average income figures further down by province, municipality, or autonomous region reveals that the average disposable income in Shanghai or Beijing is on average more than three times higher than in Tibet or Gansu province. In rural areas, average disposable income is often only between one third and one half of that in urban areas of the same region. Accordingly, consumer expenditure per capita in urban areas reaches the highest levels in Shanghai, Beijing, and the coastal regions of China.

In 2024, the average annual per capita disposable income of rural households in China was approximately 23,119 yuan, roughly 43 percent of the income of urban households. Although living standards in China’s rural areas have improved significantly over the past 20 years, the income gap between rural and urban households is still large. Income increase of China’s households From 2000 to 2020, disposable income per capita in China increased by around 700 percent. The fast-growing economy has inevitably led to the rapid income increase. Furthermore, inflation has been maintained at a lower rate in recent years compared to other countries. While the number of millionaires in China has increased, many of its population are still living in humble conditions. Consequently, the significant wealth gap between China’s rich and poor has become a social problem across the country. However, in recent years rural areas have been catching up and disposable income has been growing faster than in the cities. This development is also reflected in the Gini coefficient for China, which has decreased since 2008. Urbanization in China The urban population in China surpassed its rural population for the first time in 2011. In fact, the share of the population residing in urban areas is continuing to increase. This is not surprising considering remote, rural areas are among the poorest areas in China. Currently, poverty alleviation has been prioritized by the Chinese government. The measures that the government has taken are related to relocation and job placement. With the transformation and expansion of cities to accommodate the influx of city dwellers, neighboring rural areas are required for the development of infrastructure. Accordingly, land acquisition by the government has resulted in monetary gain by some rural households.

In 2021, the highest per capita gross domestic product (GDP) of major cities in China had been reached in Beijing, amounting to about 183,980 yuan per person. Per capita GDP of cities may vary largely in China, from 20 to 30 thousand yuan in smaller and remote cities in the countryside to nearly 200,000 yuan in large cities.

Regional gross domestic product (GDP) in China varies tremendously across the country. In 2024, the GDP of Guangdong province amounted to around 14.2 trillion yuan, whereas that of Tibet only reached about 276.5 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 3.5 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.

GDP: per Capita: Xinjiang: Tacheng data was reported at 86,627.000 RMB in 2023. This records an increase from the previous number of 79,453.000 RMB for 2022. GDP: per Capita: Xinjiang: Tacheng data is updated yearly, averaging 40,587.000 RMB from Dec 2005 (Median) to 2023, with 17 observations. The data reached an all-time high of 86,627.000 RMB in 2023 and a record low of 11,113.000 RMB in 2005. GDP: per Capita: Xinjiang: Tacheng data remains active status in CEIC and is reported by Tacheng Municipal Bureau of Statistics. The data is categorized under China Premium Database’s National Accounts – Table CN.AH: Gross Domestic Product: per Capita: Prefecture Level Region.

In 2024, the average annual per capita disposable income of households in China amounted to approximately 41,300 yuan. Annual per capita income in Chinese saw a significant rise over the last decades and is still rising at a high pace. During the last ten years, per capita disposable income roughly doubled in China. Income distribution in China As an emerging economy, China faces a large number of development challenges, one of the most pressing issues being income inequality. The income gap between rural and urban areas has been stirring social unrest in China and poses a serious threat to the dogma of a “harmonious society” proclaimed by the communist party. In contrast to the disposable income of urban households, which reached around 54,200 yuan in 2024, that of rural households only amounted to around 23,100 yuan. Coinciding with the urban-rural income gap, income disparities between coastal and western regions in China have become apparent. As of 2023, households in Shanghai and Beijing displayed the highest average annual income of around 84,800 and 81,900 yuan respectively, followed by Zhejiang province with 63,800 yuan. Gansu, a province located in the West of China, had the lowest average annual per capita household income in China with merely 25,000 yuan. Income inequality in China The Gini coefficient is the most commonly used measure of income inequality. For China, the official Gini coefficient also indicates the astonishing inequality of income distribution in the country. Although the Gini coefficient has dropped from its high in 2008 at 49.1 points, it still ranged at a score of 46.5 points in 2023. The United Nations have set an index value of 40 as a warning level for serious inequality in a society.

In 2023, the annual per capita gross domestic product (GDP) of Chongqing municipality in China amounted to about 94,100 yuan, up from approximately 89,000 yuan in the previous year. As the administrative area of Chongqing municipality is quite huge and includes many smaller cities and villages, the per capita GDP of the central urban areas is much higher than total per capita GDP of the municipality.

In 2023, Shanghai was the city with the largest GDP in China, reaching a value added of approximately 4.7 trillion yuan. The four Chinese first-tier cites Beijing, Shanghai, Shenzhen, and Guangzhou had by far the strongest economic performance. Development of Chinese cities Rapid urbanization and economic growth have reshaped all Chinese cities since the economic opening up of China. While the first-tier cities have overall benefitted most from this development, the last two decades have seen many second-tier cities catching up. For many years already, growth rates in Qingdao, Hangzhou, Changsha, and Zhengzhou have been higher than in Shanghai or Beijing.This development was driven by lower costs in smaller cities, a specialization of their economies, and political measures to support inland cities and ease the pressure on the largest municipalities. Today, per capita GDP in cities such as Suzhou, Nanjing, and Shenzhen is already higher than in Beijing or Shanghai. Future perspectives Competition between cities will further change China’s urban landscape in the future. Medium-sized cities that can provide an attractive economic environment have the potential to grow their economy at a faster pace, attract immigration, and further increase their relative importance. Cities that are losing their competitive edge, however, like Shenyang, Dalian, and other cities in the northeastern rustbelt, are increasingly confronted by economic stagnation and demographic decline.

IntroductionThe combined populations of China and India were 2.78 billion in 2020, representing 36% of the world population (7.75 billion). Wheat is the second most important staple grain in both China and India. In 2019, the aggregate wheat consumption in China was 96.4 million ton and in India it was 82.5 million ton, together it was more than 35% of the world's wheat that year. In China, in 2050, the projected population will be 1294–1515 million, and in India, it is projected to be 14.89–1793 million, under the low and high-fertility rate assumptions. A question arises as to, what will be aggregate demand for wheat in China and India in 2030 and 2050?MethodsApplying the Vector Error Correction model estimation process in the time series econometric estimation setting, this study projected the per capita and annual aggregate wheat consumptions of China and India during 2019-2050. In the process, this study relies on agricultural data sourced from the Food and Agriculture Organization of the United States (FAO) database (FAOSTAT), as well as the World Bank's World Development Indicators (WDI) data catalog. The presence of unit root in the data series are tested by applying the augmented Dickey-Fuller test; Philips-Perron unit root test; Kwiatkowski-Phillips-Schmidt-Shin test, and Zivot-Andrews Unit Root test allowing for a single break in intercept and/or trend. The test statistics suggest that a natural log transformation and with the first difference of the variables provides stationarity of the data series for both China and India. The Zivot-Andrews Unit Root test, however, suggested that there is a structural break in urban population share and GDP per capita. To tackle the issue, we have included a year dummy and two multiplicative dummies in our model. Furthermore, the Johansen cointegration test suggests that at least one variable in both data series were cointegrated. These tests enable us to apply Vector Error Correction (VEC) model estimation procedure. In estimation the model, the appropriate number of lags of the variables is confirmed by applying the “varsoc” command in Stata 17 software interface. The estimated yearly per capita wheat consumption in 2030 and 2050 from the VEC model, are multiplied by the projected population in 2030 and 2050 to calculate the projected aggregate wheat demand in China and India in 2030 and 2050. After projecting the yearly per capita wheat consumption (KG), we multiply with the projected population to get the expected consumption demand.ResultsThis study found that the yearly per capita wheat consumption of China will increase from 65.8 kg in 2019 to 76 kg in 2030, and 95 kg in 2050. In India, the yearly per capita wheat consumption will increase to 74 kg in 2030 and 94 kg in 2050 from 60.4 kg in 2019. Considering the projected population growth rates under low-fertility assumptions, aggregate wheat consumption of China will increase by more than 13% in 2030 and by 28% in 2050. Under the high-fertility rate assumption, however the aggregate wheat consumption of China will increase by 18% in 2030 and nearly 50% in 2050. In the case of India, under both low and high-fertility rate assumptions, aggregate wheat demand in India will increase by 32-38% in 2030 and by 70-104% in 2050 compared to 2019 level of consumption.DiscussionsOur results underline the importance of wheat in both countries, which are the world's top wheat producers and consumers, and suggest the importance of research and development investments to maintain sufficient national wheat grain production levels to meet China and India's domestic demand. This is critical both to ensure the food security of this large segment of the world populace, which also includes 23% of the total population of the world who live on less than US $1.90/day, as well as to avoid potential grain market destabilization and price hikes that arise in the event of large import demands.

Using air purifiers is an intervention to reduce exposure to fine particulate matter (PM2.5) for health benefits. We performed a comprehensive simulation in urban China to estimate the cost-effectiveness of long-term use of air purifiers to remove indoor PM2.5 from indoor and ambient air pollution in five intervention scenarios (S1–S5), where the indoor PM2.5 targets were 35, 25, 15, 10, and 5 μg/m3, respectively. In scenarios S1 to S5, 5221 (95% uncertainty interval: 3886–6091), 6178 (4554–7242), 8599 (6255–10,109), 11,006 (7962–13,013), and 14,990 (10,888–17,610) thousand disability-adjusted-life-years (DALYs) can be avoided at the cost of 201 (199–204), 240 (238–243), 364 (360–369), 522 (515–530), and 921 (905–939) billion Chinese Yuan (CNY), respectively. A high disparity in per capita health benefits and costs was observed by city, which expanded with the decrease of the indoor PM2.5 target. The net benefits of using purifiers in cities varied across scenarios. Cities with a lower ratio of annual average outdoor PM2.5 concentration to gross domestic product (GDP) per capita tended to achieve higher net benefits in the scenario with a lower indoor PM2.5 target. Controlling ambient PM2.5 pollution and developing the economy can reduce the inequality in air purifier use across China.

Key information about China Gross Savings Rate

• China Gross Savings Rate was measured at 44.3% in Dec 2023, compared with 44.3% in the previous year.
• China Gross Savings Rate is updated yearly, with data available from Dec 1952 to Dec 2023, and an average rate of 44.3%.
• The data reached an all-time high of 44.3% in Dec 2010 and a record low of 16.5% in Dec 1962.
• CEIC calculates Gross Domestic Savings Rate from annual Gross Domestic Savings and annual Nominal GDP. Gross Domestic Savings is calculated as Nominal GDP less Final Consumption Expenditure. The National Bureau of Statistics provides Final Consumption Expenditure in local currency and Nominal GDP in local currency.
• In the latest reports, China GDP expanded 4.5% YoY in Mar 2023.
• China Nominal GDP reached 4,166,759.1 USD mn in Mar 2023. Its GDP deflator (implicit price deflator) increased 0.4% in Mar 2023.
• China GDP Per Capita reached 12,621.7 USD in Dec 2023.

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Urban Planning Software Market Size 2024-2028

The Urban Planning Software Market size is estimated to grow by USD 4.05 billion at a CAGR of 7.81% between 2023 and 2028. Infrastructure development is a priority area for many governments and organizations worldwide, driven by increasing investments and a growing focus on building smart cities. This trend is fueled by several factors, including the expanding middle-class population and the need for efficient, modern infrastructure to support economic growth and improve quality of life. Infrastructure projects encompass various sectors, such as transportation, energy, water supply, and telecommunications, and require significant capital investment and advanced technology. As a result, the infrastructure industry is poised for continued growth and innovation, offering opportunities for businesses and investors alike.

What will be the size of the Market During the Forecast Period?

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Market Dynamics

The market is witnessing significant growth due to the increasing number of non-residential construction projects and infrastructure development activities in response to the growing urban population. City planners are leveraging technology to efficiently manage and design urban spaces. The market is segmented into components, which include software and services, and segments, such as the cloud-based segment and web-based segment. Government bodies are also investing in urban planning software to optimize budgets and implement smart city technologies. Emerging countries are leveraging technology advancements and cloud software to enhance construction processes and infrastructure development, with a focus on designing residential buildings, roads, bridges, and rail systems, supported by skilled professionals and real estate companies, while government agencies and service companies implement training programs and resource management solutions to optimize engineering and architectural plans. The latest trends include the integration of 5G technology and data centers to enhance the functionality and efficiency of these tools. Open-source software is gaining popularity due to its cost-effectiveness and flexibility. The United Nations (UN) has emphasized the importance of urban planning to address the challenges of urbanization and sustainability. Urban planning software plays a crucial role in this regard, enabling city planners to create harmonious and livable urban spaces. The market is expected to continue its growth trajectory in the coming years, driven by the increasing demand for efficient and technologically advanced urban planning solutions.

Key Market Driver

One of the key factors driving the market growth is the growing middle-class population. The increasing middle-class population in developing countries in APAC, South America, and MEA is expected to significantly contribute to the market growth. In addition, there is an increase in per capita income due to the rapidly increasing economic activities in developing economies such as China, India, Argentina, Indonesia, and South Africa.

Moreover, the rise in the gross domestic product (GDP) per capita in these countries is also fuelling the rise in the disposable income of the population. In addition, a majority of the population is opting for long-term investment opportunities due to factors such as rapid industrial, manufacturing, and economic developments in these countries, fuelled by urbanization. As a result, there is an increasing adoption of software for different real-estate projects. Hence, such factors are positively impacting the market which, in turn, will drive the growth during the forecast period.

Significant Market Trend

A key factor shaping the market growth is the use of blockchain technology in software. There is a rapid advancement in technologies that can resolve the challenges associated with the openness of data and procedures in the market. The advent of blockchain technology enables transparency at all levels of activity in urban planning making it effective.

Moreover, the main advantage of using blockchain in urban planning is that there is a reduction in fraud and transaction duplication as every record is encrypted. Furthermore, the implementation of blockchain offers smooth and quick transactions by doing away with the necessity for a middleman. Hence, such factors are positively impacting the market trends which in turn will drive the market growth during the forecast period.

Major Market Challenge

The threat of open-source urban planning software is one of the key challenges hindering growth. There is a growing popularity for open-source software which poses a significant threat to the market. There is an increasing preference for open-source software as it is widely available on the Internet and can be downloaded easily.

Moreover, open-source software

Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population; not only is it better able to describe the change pattern of population and biomass, but it is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of changing industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.

Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita),the corresponding industrial structure scenarios in each period were set, and each industry’s output value was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP, and, therefore, it was adjusted according to the need of the future industrial structure scenarios of the research area.

According to the age distribution of China's population in 2024, approximately 68.6 percent of the population were in their working age between 15 and 64 years of age. Retirees aged 65 years and above made up about 15.6 percent of the total population. Age distribution in China As can be seen from this statistic, the age pyramid in China has been gradually shifting towards older demographics during the past decade. Mainly due to low birth rates in China, the age group of 0 to 14 year-olds has remained at around 16 to 17 percent since 2010, whereas the age groups 65 years and over have seen growth of nearly seven percentage points. Thus, the median age of the Chinese population has been constantly rising since 1970 and is forecast to reach 52 years by 2050. Accompanied by a slightly growing mortality rate of more than 7 per thousand, China is showing strong signs of an aging population. China's aging society The impact of this severe change in demographics is the subject of an ongoing scientific discussion. Rising standards of living in China contain the demand for better health care and pension insurance for retirees, which will be hard to meet with the social insurance system in China still being in its infancy. Per capita expenditure on medical care and services of urban households has grown more than ninefold since 2000 with a clear and distinctive upward trend for the near future. As for social security spending, public pension expenditure is forecast to take up approximately nine percent of China's GDP by 2050.

To promote collaborative governance of PM2.5 and O3 pollution, understanding their spatiotemporal patterns and determining factors is crucial to control air pollution in China. Using the ground-monitored data encompassing PM2.5 and O3 concentrations in 2019 across 337 Chinese cities, this study explores the spatiotemporal patterns of PM2.5 and O3 concentrations, and then employed the Multi-scale Geographically Weighted Regression (MGWR) model to examine the socioeconomic and natural factors affecting PM2.5 or O3 concentrations. The results show that PM2.5 and O3 concentrations exhibit distinct monthly U-shaped and inverted U-shaped temporal fluctuation patterns across Chinese cities, respectively. Spatially, both pollutants manifest spatial clustering characteristic and a certain degree of bivariate spatial correlation. Elevated PM2.5 concentrations are predominantly concentrated on north and central China, as well as the Xinjiang Autonomous Region, whereas higher O3 concentrations are distributed widely across north, east, and northwest China. The MGWR model outperforms traditional OLS and global spatial regression models, evidenced by its enhanced goodness-of-fit metrics. Specifically, the R2 values for the PM2.5 and O3 MGWR models are notably high, at 0.842 and 0.861, respectively. Socioeconomic and natural factors are found to have multi-scale spatial effects on PM2.5 and O3 concentrations in China. On average, PM2.5 concentrations show positively correlations with population density, the proportion of the added value of secondary industry in GDP, wind speed, relative humidity, and atmospheric pressure, but negatively relationship with per capita GDP, road density, urban greening, air temperature, precipitation, and sunshine duration. In contrast, O3 concentrations are also positively associated with population density, the proportion of the added value of secondary industry in GDP, energy consumption, precipitation, wind speed, atmospheric pressure, and sunshine duration, but negatively correlated with per capita GDP, road density, and air temperature. Our findings offer valuable insights to inform the development of comprehensive air pollution management policies in in developing countries.

Explore gender statistics data focusing on academic staff, employment, fertility rates, GDP, poverty, and more in the GCC region. Access comprehensive information on key indicators for Bahrain, China, India, Kuwait, Oman, Qatar, and Saudi Arabia.

academic staff, Access to anti-retroviral drugs, Adjusted net enrollment rate, Administration and Law programmes, Age at first marriage, Age dependency ratio, Cause of death, Children out of school, Completeness of birth registration, consumer prices, Cost of business start-up procedures, Employers, Employment in agriculture, Employment in industry, Employment in services, employment or training, Engineering and Mathematics programmes, Female headed households, Female migrants, Fertility planning status: mistimed pregnancy, Fertility planning status: planned pregnancy, Fertility rate, Firms with female participation in ownership, Fisheries and Veterinary programmes, Forestry, GDP, GDP growth, GDP per capita, gender parity index, Gini index, GNI, GNI per capita, Government expenditure on education, Government expenditure per student, Gross graduation ratio, Households with water on the premises, Inflation, Informal employment, Labor force, Labor force with advanced education, Labor force with basic education, Labor force with intermediate education, Learning poverty, Length of paid maternity leave, Life expectancy at birth, Mandatory retirement age, Manufacturing and Construction programmes, Mathematics and Statistics programmes, Number of under-five deaths, Part time employment, Population, Poverty headcount ratio at national poverty lines, PPP, Primary completion rate, Retirement age with full benefits, Retirement age with partial benefits, Rural population, Sex ratio at birth, Unemployment, Unemployment with advanced education, Urban population

Bahrain, China, India, Kuwait, Oman, Qatar, Saudi Arabia

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Wages in China increased to 120698 CNY/Year in 2023 from 114029 CNY/Year in 2022. This dataset provides - China Average Yearly Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Taking 2005 as the base year, the future population scenario was predicted by adopting the Logistic model of population. It not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted by using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation by nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data adopted the non-agricultural population. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of GDP per capita),the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.

China Snack Bar Market size was valued at USD 16 Billion in 2024 and is projected to reach USD 34 Billion by 2032, growing at a CAGR of 10% from 2026 to 2032.

Key Market Drivers:

Rapid Urbanization and Changing Consumer Lifestyles: Increasing urban population and busy lifestyles driving convenient snack consumption. According to the National Bureau of Statistics of China, the urbanization rate reached 64.7% in 2021, up from 60.6% in 2018. The World Bank notes that China's rapid urbanization has fundamentally transformed consumption patterns, with urban consumers showing a growing preference for convenient, ready-to-eat food options.

Rising Disposable Income and Middle-Class Expansion: Growing middle-class purchasing power increasing snack market potential in China. The World Bank reported China's per capita GDP reached $12,556 in 2021, indicating significant economic growth.