Per capita gross domestic product (GDP) of cities in China varies tremendously, mainly depending on the location of the city. Cities with the highest per capita GDP are mainly to be found in coastal provinces in East China and in South China, like Guangdong province. The poorest cities are located in the still less developed western parts of China, like Gansu province, or in the Chinese rust belt in Northeastern China, like Heilongjiang province.

Since 2000, the share of people living in extreme poverty in rural China has been constantly decreasing. In February 2021, the Chinese government announced that - based on the current definition of poverty - all residents in China have been relieved from extreme poverty. In the past, extreme poverty had been more common in western and central parts of China, and in these regions the number of poor households is still considerably higher today.

This study investigates the alarming rise of urban poverty in China; in particular the patterns of urban poverty and the institutional causes are examined. The researchers look for evidence of institutional innovations that have emerged as individuals and organisations seek to negotiate more secure access to vital civic goods and services. A case study approach was used due to the complexity of the issue and the size of the Chinese urban population. Six cities were chosen and four neighbourhoods in each city were investigated. These cities were distributed in the costal, central and western region respectively, including Guangzhou, Nanjing, Harbin, Wuhan, Kumin, and Xi’an.

Further information is available from the ESRC Award webpage.

In 2024, the average annual per capita disposable income of rural households in China was approximately ****** yuan, roughly ** 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 *** 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.

This dataset is used to investigates the local variations, determinants, effects and influence mechanisms of Health Poverty Alleviation Policy in China. The dataset contains policy data at city and provincial levels, city charateristics data, nationally survey data (CHARLS) and qualitative data.

This article compares the population agglomeration characteristics of the Xi’an metropolitan area in western China with those of metropolitan areas in other regions officially approved by the Chinese government. The kernel density estimation method and Markov chain model were used to conduct the study. The results revealed that from 2010 to 2020, the population agglomeration level of the Xi’an metropolitan area showed a trend of first increasing and then decreasing. The absolute gap in the population agglomeration level between cities within the metropolitan area gradually narrowed, and the polarization phenomenon of population agglomeration was not obvious. Compared with metropolitan agglomerations such as Nanjing, Wuhan, Fuzhou, Changsha-Zhuzhou-Xiangtan, Chongqing, and Chengdu, the Xi’an metropolitan agglomeration had a lower population agglomeration level, with a significant gap. Moreover, there was an obvious “club convergence” phenomenon in the population agglomeration levels of different urban agglomerations. The probability of the population agglomeration level remaining stable was at least 53.85%, indicating that there was a “Matthew effect” in which the rich become richer and the poor become poorer. Through the convergence models of α and β, the analysis suggested that there was no significant α convergence between the population agglomeration level of the Xi’an metropolitan agglomeration and that of other metropolitan agglomerations. Instead, there was a significant β divergence, indicating that the gap between the Xi’an metropolitan agglomeration’s population agglomeration level and that of other metropolitan agglomerations is gradually widening. An integrated theoretical framework of population agglomeration was constructed from three dimensions: producers, consumers, and social people. An empirical analysis was conducted on the causes of population agglomeration in the Xi’an metropolitan area and other metropolitan areas. The multiple regression results showed that the income level, public consumption expenditure level, education level, comfortable living environment, and educational level were important factors leading to differences in population agglomeration. The geographic detector results showed that factors in the consumer dimension were the main reasons for population agglomeration in metropolitan areas.

With rising social inequality in China, housing insecurity in informal migrant settlements known as chengzhongcun (urban villages) became a significant issue. In the suburbs of Beijing, former villages are turned into migrants' production sites; in the peri-urban areas of Shanghai, co-renting in the same room has become highly controversial; in Guangzhou, urban villages are becoming a 'thriving' world of their own, lacking basic infrastructure. Are these migrant settlements slums? According to the operational definition by UN-HABITAT, these settlements can indeed be viewed as slums. How different are they from slums in other developing countries? The project investigates the dynamics of migrant village formation, examine redevelopment practices and policies, and to identify the scope for progressive upgrading as an alternative approach. The project samples 15 migrant villages in three major cities (Beijing, Shanghai and Guangzhou) in China and applies qualitative and quantitative methods to identify the housing tenure, socioeconomic profiles, landlords' self construction tactics, migrants' coping strategies, and existing and new institutions as appropriate vehicles for in-situ redevelopment. The project aims to inform Chinese policy makers and provide learning feedback to the wider international development community, offering new experiences in coping with the 'challenge of slums'.

Harvesting of Chinese caterpillar fungus, one of the most expensive biological commodities in the world, has become an important livelihood strategy for mountain communities of Nepal. However, very little is known about the role of Chinese caterpillar fungus in household economy. We estimated the economic contribution of Chinese caterpillar fungus to the household income, quantified the extent of “Chinese caterpillar fungus dependence” among households with different economic and social characteristics, and assessed the role of cash income from the Chinese caterpillar fungus harvest in meeting various household needs including education, debt payments, and food security. Results show that Chinese caterpillar fungus income is the second largest contributor to the total household income after farm income with 21.1% contribution to the total household income and 53.3% to the total cash income. The contribution of Chinese caterpillar fungus income to total household income decreases as the household income increases making its contribution highest for the poorest households. There is significant correlation between Chinese caterpillar fungus dependency and percentage of family members involved in harvesting, number of food-sufficient months, and total income without Chinese caterpillar fungus income. Income from Chinese caterpillar fungus is helping the poorest to educate children, purchase food, and pay debts. However, reported decline of Chinese caterpillar fungus from its natural habitat might threaten local livelihoods that depend on the Chinese caterpillar fungus in future. Therefore, sustainable management of Chinese caterpillar fungus through partnership among local institutions and the state is critical in conserving the species and the sustained flow of benefits to local communities.

In the face of increasing global unsustainable risks such as poverty, hunger, and pollution. Building sustainable agriculture (SA) in the digital age is a fundamental task for human survival. Based on the coupled coordination perspective, this paper constructs an SA system that takes into account more stakeholders by considering poverty alleviation and income increase (PI), food security (FS), and green agriculture (GA) as subsystems. The impact of digital technology on SA is systematically analyzed through data from 276 prefecture-level and above cities in China from 2005 to 2020. The study shows that digital technology has a significant upgrading effect on SA and its subsystems. And digital technology is more likely to promote SA in the developed eastern region and peripheral cities. Moreover, agricultural productivity and labor productivity play a mediating mechanism in the process of digital technology for SA. Digital financial inclusion fuels the high input process of digital technology for incentivizing SA, PI, and GA, but it cannot affect the highly technical process of digital farming. Further research found that the incentives of digital technology for SA and GA are characterized by the nonlinear characteristic of increasing marginal effects. Due to the second digital divide, there is a U-shaped incentive process of digital technology for PI to fall and then rise. Finally, the spillover nature of digital technology leads to spatial spillovers in its contribution to SA development.

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.

According to the monitoring data from the Embassy of the United States, there was on average 39 micrograms of PM2.5 particles per cubic meter to be found in the air in Beijing during 2023. The air quality has improved considerably since 2013.

Reasons for air pollution in Beijing

China’s capital city Beijing is one of the most populous cities in China with over 20 million inhabitants. Over the past 20 years, Beijing’s GDP has increased tenfold. With the significant growth of vehicles and energy consumption in the country, Beijing’s air quality is under great pressure from the economic development. In the past, the city had a high level of coal consumption. Especially in winter, in which coal consumption increased due to heating, the air quality could get extremely bad on the days without wind. In spring, the wind from the north would bring sand from Mongolian deserts, resulting in severe sandstorms in Beijing. The bad air quality also affected the air visibility and threatened people’s health. On days with very bad air quality, people wearing masks for protection can be seen on the streets in the city.

Methods to improve air quality in Beijing

Over the past years, the government has implemented various methods to improve the air quality in Northern China. Sandstorms, which were quite common 15 years ago, are now rarely seen in Beijing’s spring thanks to afforestation projects on China’s northern borders. The license-plate lottery system was introduced in Beijing to restrict the growth of private vehicles. Large trucks were not allowed to enter certain areas in Beijing. Above all, the coal consumption in Beijing has been restricted by shutting down industrial sites and improving heating systems. Beijing’s efforts to improve air quality has also been highly praised by the UN as a successful model for other cities. However, there is also criticism pointing out that the improvement of Beijing’s air quality is based on the sacrifice of surrounding provinces (including Hebei), as many factories were moved from Beijing to other regions. Besides air pollution, there are other environmental problems like water pollution that China is facing. The industrial transformation is the key to China’s environmental improvement.

Reducing multidimensional relative poverty is one of the important issues in the current global poverty governance field. This article takes 12 ethnic regions in China as the research object and constructs a multidimensional relative poverty measurement system. The calculated multidimensional relative poverty index is decomposed according to provinces, cities, dimensions, and indicators. Then, the Dagum Gini coefficient and convergence analysis are used to analyze spatiotemporal heterogeneity and convergence characteristics. The results show that the multi-dimensional relative poverty situation of various provinces in ethnic minority areas has improved from 2012 to 2021, among which Tibet province is the most serious and Shaanxi is the best. According to the analysis of convergence, it was observed that there is no σ-convergence of multidimensional relative poverty in ethnic areas in general, and there is absolute β-convergence in general and in the southwest and northwest regions, and there is no absolute β-convergence in the northeast region. Based on this, policy recommendations for reducing multidimensional relative poverty are proposed at the end of the article. Compared with previous studies, this article focuses on ethnic regions that are easily overlooked. Starting from the dimensions of economy, social development, and ecological environment, the poverty measurement system has been enriched.

Comparison of the two results (statistics of the number of counties and cities with four change patterns of pollutants).

Comparison of the number of counties and cities by CEDI and CEDI’.

Comparative analysis of changes in counties and cities based on the EDI and EDI with EC corrected.