uneven development case study

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• Published: 23 October 2020

Dramatic uneven urbanization of large cities throughout the world in recent decades

• Liqun Sun   ORCID: orcid.org/0000-0002-2637-7151 1 ,
• Ji Chen   ORCID: orcid.org/0000-0003-3060-0827 2 ,
• Qinglan Li   ORCID: orcid.org/0000-0002-6912-1156 1 &
• Dian Huang   ORCID: orcid.org/0000-0002-4833-2376 3  

Nature Communications volume  11 , Article number:  5366 ( 2020 ) Cite this article

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• Developing world
• Sustainability

The world has experienced dramatic urbanization in recent decades. However, we still lack information about the characteristics of urbanization in large cities throughout the world. After analyzing 841 large cities with built-up areas (BUAs) of over 100 km 2 from 2001 to 2018, here we found an uneven distribution of urbanization at different economic levels. On average, large cities in the low-income and lower-middle-income countries had the highest urban population growth, and BUA expansion in the upper-middle-income countries was more than three times that of the high-income countries. Globally, more than 10% of BUAs in 325 large cities showed significant greening ( P  < 0.05) from 2001 to 2018. In particular, China accounted for 32% of greening BUAs in the 841 large cities, where about 108 million people lived. Our quantitative results provide information for future urban sustainable development, especially for rational urbanization of the developing world.

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Introduction.

According to the latest report from the United Nations (UN), the global population in 2018 was 7.6 billion and the urban population was 4.2 billion 1 . By 2050, the global population will reach 9.7 billion, and 68% of the population (i.e., 6.6 billion people) will live in urban areas. Undoubtedly, urban sustainable development is highly related to the future of humanity 2 . However, due to the different levels of socioeconomic development, the process of urbanization is uneven across countries 3 . Usually, in developed countries, urban expansion is adaptable to the population growth. The residents are served by good public services 4 and have access to adequate urban infrastructure, such as water and energy supplies, sanitation 5 , education, and green space or parks. For many developing countries, the national economic growth and development are inadequate to meet the needs of a growing urban population 6 , 7 . In most cases, these cities lack basic infrastructure, and face overcrowding, pollution, and other urban environmental problems 3 . At present, urban sustainable development is an increasing concern worldwide, and it has been enshrined in the 2030 Agenda for the UN’s Sustainable Development Goals 8 , 9 . To achieve the goals, it is essential to track the urbanization progress of cities at different development stages in as detailed a way as possible. A large number of studies have focused on multiple aspects of urbanization from the regional scale 10 , 11 , 12 , 13 , 14 to the global scale 15 , 16 , 17 . However, a global perspective that clarifies the urbanization characteristics of large cities around the world is still lacking.

Our work was mainly motivated by a desire to address the following questions: (1) how have global large cities developed in recent decades in terms of urban expansion, population growth, and urban greenness change? (2) What are the relationships between the urbanization features and the economic levels? To address both questions, we used remote sensing data and economic data for the period 2001−2018 and the gridded population data from 2000 to 2015 to quantify the urbanization characteristics of 841 large cities throughout the world. A quantitative and comparative study of the urbanization of global large cities has not yet been made available, but it is vital to combat poverty and achieve urban sustainable development in the near future 8 , 9 , 18 .

Built-up area (BUA) expansion

From 2001 to 2018, the global BUA increased from 7.47 × 10 5  km 2 to 8.0 × 10 5  km 2 (Supplementary Fig.  1a ), which is equivalent to an increase in the area of 1,130 standard football fields (7,140 m 2 ) per day. During this period, the top 10 countries with the greatest BUA expansion (BUAE) were China (47.5% of the global BUA increase), the United States (9%), Nigeria (5.0%), India (3.6%), Indonesia (2.8%), Russia (1.8%), Mexico (1.7%), Malaysia (1.6%), Vietnam (1.5%), and Ghana (1.3%) (Table  1 and Supplementary Fig.  1b ). Except for the United States, the other nine countries are developing and emerging countries.

Using the MCD12Q1 product 19 , we merged the BUA pixels adjacent to each other into a united urban patch (see Methods). Our analysis focused on the urban patches larger than 100 km 2 (large cities, hereafter) around the world. The advantage of defining large cities in this way is that we were able to include more cities with large BUA areas but relatively smaller populations, such as many low-density cities in the United States and other developed countries. It is worth noting that the large cities studied in this paper included clusters of cities; for example, the Yangtze River Delta (YRD) in China is considered one city, but actually includes four cities: Shanghai, Suzhou, Wuxi, and Changzhou. From 2001 to 2018, the number of the large cities increased from 777 to 841 (Fig.  1a ), and the total BUA of the large cities increased from 2.7 × 10 5  km 2 to 3.08 × 10 5  km 2 , which are 36.1% and 38.5% of the global BUA in 2001 and 2018, respectively. Once the observations from all the 841 large cities were compared, strong economic patterns emerged from the seemingly diverse BUAE across those large cities (Fig.  1a ). According to the country economic classification issued by the World Bank 20 , the 841 large cities were divided into four groups: high income (H, gross national income (GNI) more than $12,375 per capita), upper middle income (UM, GNI between $3996 and $12,375 per capita), lower middle income (LM, GNI between $1026 and $3995 per capita), and low income (L, GNI < $1025 per capita) (Supplementary Fig.  2 ). The large cities are mainly located in high-income (353 large cities) and upper-middle-income (340 large cities) countries, while there are 127 large cities in the lower-middle-income countries and 21 in the low-income countries. The top 10 countries with the largest BUA of large cities are the United States (27.0% of the BUA in 841 large cities), China (19.0%), Japan (6.0%), Brazil (4.0%), Germany (2.9%), India (2.8%), Canada (2.5%), Australia (2.0%), Mexico (1.9%), and Russia (1.8%) (Table  1 ).

a Distribution of the 841 large cities at different levels of the BUA expansion (BUAE) from 2001 to 2018. E1: 0 ≤ BUAE ≤ 1 km 2 , blue points; E2: 1 km 2  < BUAE ≤ 50 km 2 , green points; E3: BUAE > 50 km 2 , magenta points. The top 10 cities with BUAE are labeled with their names. YRD Yangtze River Delta, PRD Pearl River Delta. b Bars are the percentages of the total 841 large cities for the large cities at the different levels of BUAE and 4 economic levels. Purple lines are the average BUAE values of the large cities at different economic levels. c Distribution of 841 large cities at different levels of population growth (PopG). Ng: negative population growth, orange points; L1: 0 < PopG ≤ 100K, blue points; L2: 100K < PopG ≤ 1M, green points; L3: PopG > 1M, magenta points. The 22 large cities with a PopG of more than 2 million are labeled with their names. d Bars are the percentages of all the large cities for PopG at four different economic levels. Purple lines are the values of average population growth of large cities at different economic levels. All large cities are classified according to the World Bank’s estimates of 2018 gross national income (GNI) per capita, H high income, UM upper middle income, LM lower middle income, and L low income; see Methods for details. e The ratio ( R PB ) of the urban population growth rate to the BUAE rate for the 105 large cities with BUAE of over 50 km 2 from 2001 to 2018 (magenta points in ( a )). For each city, the urban population growth rate was calculated using the data for the period from 2000 to 2015. In ( b , d ), the error bars show SEM (standard error of the mean) for each economic level.

From 2001 to 2018, the average BUAE in high-income countries was only 12.6 km 2 per city, which significantly differs from that of the other three groups ( P  < 0.01; purple lines in Fig.  1b ). On average, the BUAE in the upper-middle-income countries is the highest, reaching 38.0 km 2 per city (purple lines in Fig.  1b ), which is more than three times that in the high-income countries. In the upper-middle-income countries, there are 61 large cities with BUAE of over 50 km 2 , and 51 of them are located in China (magenta points in Fig.  1a ). Moscow is the only city in Europe with a large BUAE (>50 km 2 ). In contrast, most of the cities in Europe and South America have BUAE of <50 km 2 (green points in Fig.  1a ), and urban expansions in some cities are almost stagnant (<1 km 2 ) (blue points in Fig.  1a ).

Urban population growth

Urban population growth is one of the driving forces of urban expansion. Using the global population grid data sets 21 in 2000, 2005, 2010, and 2015 (see Methods), we found that 80% of the large cities (676 out of 841) experienced population growth. Among them, 22 large cities experienced population growth of more than 2 million (magenta points labeled with the cities’ names in Fig.  1c ). These large cities are mainly located in Asia (15 large cities) and Africa (5 large cities), and there is 1 in South America (Sao Paulo) and 1 in Europe (Moscow) (Fig.  1c ). The average population growth rates of the large cities in the low-income and lower-middle countries from 2000 to 2015 are almost the same, ~5.0 × 10 5 per city, which is more than five times that in the high-income countries (1.0 × 10 5 per city) and 1.7 times that in the upper-middle-income countries (3.0 × 10 5 per city) (Fig.  1d ). We calculated the ratio ( R PB ) of the urban population growth rate (UPGr) to the BUAE rate (BUr) for the 841 large cities (see Methods), and found that the R PB is larger than 1 in 543 large cities (64.5%). In particular, for the 105 large cities with BUAE of over 50 km 2 , the UPGr in 55% of the large cities (58 out of 105) is faster than the urban expansion rate (Fig.  1e ), even though these large cities have already experienced a significant BUAE. This result indicates that in most parts of the world, without considering the vertical urban growth through the construction of high-rise apartments and condominiums, urban expansion has lagged behind the population growth since the advent of the new century. This is exactly opposite to the trend in the urbanization before 2000 reported by a previous study, which found that urban land was expanding faster than the urban population growth 22 .

In 2015, the 20 cities with the highest population density were located in Asia (15 large cities) and Africa (5 large cities) (Fig.  2a ). The average population density of the large cities in the low-income countries was 6.2 × 10 3 per km 2 , which is ~3.2 times that in the high-income countries (2.0 × 10 3 per km 2 ) (purple lines in Fig.  2b ). Among these large cities, Dhaka, Kathmandu, Manila, Karachi, Istanbul, and Cairo are the top 6 large cities with the highest population density (Fig.  2c ). These cities experienced rapid population growth (>2 million) from 2000 to 2015 (Fig.  2c ), and limited urban expansion from 2001 to 2018 (<200 km 2 , Fig.  2c ). In contrast, during the same period, there were only four large cities experiencing population growth of more than 5 million, and they are the YRD, Pearl River Delta (PRD), Beijing, and Jakarta. Due to rapid urban expansion, the population densities of these four cities were relatively low (Fig.  2c ).

a Distribution of the 841 large cities at four different levels of the population density (PopD, unit: 1/km 2 ) in 2015. D1: 0 < PopD ≤ 1K, blue points; D2: 1K < PopD ≤ 5K, green points; D3: 5K < PopD ≤ 10K, orange points; D4: PopD > 10K, magenta points. b Bars are the percentages of the 841 large cities for four PopD levels in 2015 at four economic levels. Error bars show SEM for each economic level. c Relationship between PopD in 2015 and the BUA expansion from 2001 to 2018 of the 22 large cities with population growth larger than 2M. YRD Yangtze River Delta, PRD Pearl River Delta. The size of the dots refers to the population growth at different levels.

Urban greenness change

To obtain a rational perspective of global urban greenness change, we assumed that if the greenness of a BUA pixel increased, some new parks, green spaces, and/or green roofs might have been developed in the pixel, or, at a minimum, the street vegetation in the pixel experienced growth. To avoid interference from the vegetation phenology, we chose the annual maximum greenness of the vegetation to represent the best state of urban greening.

Using the enhanced vegetation index (EVI) 23 , 24 as a greenness indicator and BUA of 2018 as the urban extent, we applied the Mann–Kendall method to evaluate the trend of the annual maximum EVI (EVI max ) for each urban pixel of the 841 large cities from 2001 to 2018 (see Methods). Then, we calculated the ratio ( R greening ) of the area of the pixels with a significant increasing EVI max trend ( P  < 0.05) to the BUA in 2018 of the corresponding large city. Using this method, we evaluated urban greening change since 2001. Most of the pixels with significant greening trends were located in stable BUAs (i.e., pixels that were BUAs throughout the study period, 2001–2018; see Methods). Finally, all 841 large cities were divided into three levels according to the values of R greening (R1–R3, Fig.  3a ). There are 325 large cities where more than 10% of the urban pixels (i.e., R greening  > 0.1) indicated significant greening from 2001 to 2018 (magenta points in Fig.  3a ). These large cities are predominantly distributed in East Asia, Europe, and North America, and sparsely distributed in South America, Africa and Oceania. In particular, China, the United States, and Japan are the top 3 countries contributing to the greening from 2001 to 2018, accounting for 32%, 19%, and 7.7% of the total greening BUA in the 841 large cities, respectively (Table  1 ). Moreover, there are 159 large cities with R greening larger than 0.1 in the upper-middle-income countries, and 143 large cities in high-income countries (magenta bars in Fig.  3b ). Among the four economic classifications, the average value of the R greening is the largest in the upper-middle-income countries, reaching 0.12 per city, followed by the high-income countries with 0.1 per city. The average value of R greening in the low-income countries is the smallest, ~0.03 per city (purple lines of Fig.  3b ).

a Distribution of the 841 large cities at different levels of greening BUA ( R greening , the ratio of the BUA pixels with significant greening trend to the total BUA for a large city). R1: R greening  ≤ 0.01, blue points; R2: 0.01 <  R greening  ≤ 0.1, green points; R3: R greening  > 0.1, magenta points. The top 10 greening BUA of large cities are labeled with their names. The large cities with no pixel greening are overlapped with black dots (i.e., the cities in the dash box of the Gulf of Guinea in Africa). b Percentages of the 841 large cities for three different R greening levels and four economic levels. Purple lines are the average values of R greening at different economic levels. Error bars show SEM for each economic level. c An example of greening BUA in YRD of China. YRD Yangtze River Delta. YRD mainly includes four cities: Shanghai (SH), Suzhou (SuZ), Wuxi (WX), and Changzhou (CZ).

From the spatial pattern of the greening pixels in 325 large cities ( R greening  > 0.1), we found that many greening areas of large cities have a shape like a “fried egg” where the yolk-shaped downtown area has been turning green significantly ( P  < 0.05); the “egg white” area refers to the area of browning or no significant greening outside the yolk-shaped area, and these areas are usually the suburbs. As one of the ten large cities with the largest greening BUA from 2001 to 2018, the YRD is a typical example of the “fried egg” with a cluster of the yolk-shaped areas of greening BUA (Fig.  3c ). The other nine large cities with the largest greening BUA are PRD, Tokyo, Miami, Beijing, Chicago, Seoul, Tianjin, Sao Paulo, and Osaka (Fig.  3a , Supplementary Fig.  3 , and Supplementary Table  1 ). The yolk-shaped greening BUA is also obvious in Beijing, Chicago, Seoul, Tianjin, Sao Paulo, and Osaka (Supplementary Fig.  3d–i ). These yolk-shaped greening areas are usually located in the downtown of the cities. Normally, the main characteristics of these areas include a high building density and high population density 25 . In these areas, the majority of the urban construction would have already been completed before 2001, so any newly established parks or green spaces, even the growth of street vegetation, would increase the greenness of the BUA pixels year by year. Notably, although the PRD has the largest area of greening BUA (Supplementary Table  1 ), there are many hills and mountains in that region (Supplementary Fig.  4a ); thus, the greening area distributed in the PRD is rather diffuse compared to that in the YRD (Supplementary Fig.  4b ).

Population in greening BUA

According to the latest gridded population data in 2015, there are ~1.16 billion urban residents in the 841 large cities, and 192 million urban population are living in the BUA pixels with significant greening trends (Fig.  4a ). The average population living in BUA with significant greening trends in the upper-middle-income countries is the largest, ~4.0 × 10 5 per city, followed by the high-income countries with a value of 1.5 × 10 5 per city (Fig.  3b ). In contrast, the average numbers for the urban population living in the greening BUA in the lower-middle-income and low-income countries are only 1.1 × 10 5 per city and 0.24 × 10 5 per city, respectively (Fig.  4b ). As the most populous country, China has 150 large cities, and there are about 108 million urban population living in greening BUAs. This accounts for 56.2% of the total population in greening BUAs among the 841 large cities (Fig.  4c ). The United States has 151 large cities, with 10.98 million people living in greening BUA (Fig.  4c ). As an emerging economy and the second most populous country in the world, India has 34 large cities, in which 5.2 million people live in greening BUA (Fig.  4c ). The 20 cities with the largest population living in greening BUA (see Supplementary Table  2 ) are marked with black points in Fig.  4a . Among them, Urumqi (UQ, in Fig.  4a ), the capital of the Xinjiang Autonomous Region in the semi-arid region of north-western China, has the highest value of R greening (0.53) (Supplementary Table  2 ).

a Distribution of the 841 large cities at different levels of population living in greening BUA (noted as PGB). N: No PGB; P1: 0 < PGB ≤ 100K, blue points; P2: 100K < PGB ≤ 1M, green points; P3: PGB > 1M, magenta points. The top 20 PBG large cities are marked with black dots, and 10 of them are labeled with their names (see Supplementary Table  2 for details). YRD Yangtze River Delta, PRD Pearl River Delta, UQ Urumqi. b Bars are the percentages of the total 841 large cities for PGB at four economic levels. Purple lines are the average PGB at four economic levels. Error bars show SEM for each economic level. c The top 10 countries with largest percentages of the total PGB in the 841 large cities.

In fact, the greenness of many cities in upper-middle-income countries increased significantly from 2001 to 2018, which was due to the relatively low greenness of these cities before 2001. In contrast, in the high-income countries, there were quite possibly already many parks and green spaces in the cities 4 , 26 , 27 before 2001 (Supplementary Fig.  5a ). Even though the urban greenness of upper-middle-income countries increased significantly in recent decades, cities in the high-income countries in 2018 were still “greener” compared to those in developing countries. To test this understanding, we calculated the average EVI max values of all the large cities (EVI city ) in 2018, and then ranked them into four quarters (Q1–Q4 in Supplementary Fig.  5b ). Notably, due to the semi-arid or arid climate, the urban greenness of large cities located in the southwest of the United States and Australia is usually low (magenta points of Q1 in Supplementary Fig.  5a ). However, our results reveal that the economic impacts on urban greenness are significant (Supplementary Fig.  5c ). The average EVI city in high-income countries is 0.37, which is the highest among the four economic classifications. The average EVI city in the low-income countries is 0.28. Furthermore, according to the gridded population data in 2015, only 12% (135 million) of the large cities’ total population lived in cities in Q4; moreover, over 71% (157 out of 211) of these large cities in Q4 were located in high-income countries (Supplementary Fig.  5d ). By contrast, ~69% of the large cities’ total populations lived in cities in Q1 and Q2 (Supplementary Fig.  5d ).

Using the results presented above, we identified the urbanization characteristics for large cities in terms of urban expansion, population growth, and greening BUA changes. We observed that global urbanization in the countries with different income classifications in recent decades exhibited a dramatic unevenness. Below, we highlight three urbanization features obtained in this study.

The first is the unevenness between the BUAE and urban population growth. From 2001 to 2018, the urban expansion and urban population growth in the high-income countries were the lowest (12.6 km 2 per city; 1.2 × 10 5 per city) among the four classifications (Fig.  1b, d ). Conversely, the upper-middle-income countries had the largest urban expansion (38.0 km 2 per city) (Fig.  1b ), while the low-income countries had the highest urban population growth (5.0 × 10 5 per city) (Fig.  1d ). From the ratio of the UPGr to the BUA growth rate, we found the urban population increased faster than the urban expansion in most parts of the world in recent decades. This result could be attributed to the fastest-growing urban population since the 1950s, which has completely changed the urbanization state from mid-1980s to 2000 reported by a previous study 22 . Moreover, due to the different growth rates of the urban population and urban area, the unevenness of the population pressure in the high-income countries (2.0 × 10 3 per km 2 ) and low-income countries (6.2 × 10 3 per km 2 ) is also significantly different ( P  < 0.01) (Fig.  2b ). This result indicates that compared with the faster-growing urban population (Fig.  1d ), even though the BUAE of the low-income countries is relatively fast (Fig.  1b ), the urban expansion still lags far behind the urban population growth (Fig.  2b ). Hence, the cities with a rapid population growth and relatively slow BUAE in the lower-middle-income and low-income countries could undergo serious urban problems, such as slums and crowding 15 , 28 . In addition, due to the fast urban expansion, the urbanization in the upper-middle-income countries may meet the needs of their population growth.

The second feature is unevenness between rapid urban population growth and slow urban greening increase. We used the annual maximum greenness (EVI max ) to represent the best state of urban greening. According to the average EVI max for each city (EVI city ) in 2018, we found that one quarter of the greenness with the highest range of EVI city , Q4, only encompassed 12% of the total population in the large cities (Supplementary Fig.  5d ). In contrast, ~69% of the total population lived in the areas with a lower greenness (Q1 and Q2). However, with the economic development of the upper-middle-income countries in recent decades, urban greening has increased significantly, where the average R greening is 0.12 (Fig.  3b ). In the high-income countries, due to “greener” urbanization (Supplementary Fig.  5a ) before 2001, the greening increase of these cities is not the largest (Fig.  3b ). Nevertheless, in the low-income countries, not only is the greenness of cities the lowest (EVI city  = 0.28; Supplementary Fig.  5c ) but the ratio of greening BUAs from 2001 to 2018 is also the smallest ( R greening  = 0.04; Fig.  3b ). These results indicate that on the way to equitable and sustainable urban development 29 , many large cities in the upper-middle income countries initially made substantial progress, but there is still a long way to go for the large cities in lower-middle-income and low-income countries. In addition, as the world’s largest source of emissions 30 , large cities with a significant greening trend may also benefit the health of local urban residents 31 , 32 , and, to a certain extent, mitigate the impact of global climate change 33 .

The third feature is rapid urbanization of the large cities in China. From 2000 to 2015, the urban population growth of China was the largest in the world 1 . The urban expansion from 2001 to 2018 was also the largest, accounting for 47.5% of the total urban expansion in the world (Supplementary Fig.  1b ). Due to the rapid economic growth in the study period 34 , China invested a large amount of resources into infrastructure construction for advancing the urban living environment 35 . Among the 325 large cities in the world with significant urban greening increase ( R greening  > 0.1) (magenta points in Fig.  3a ), 101 of them are located in China, and most of them have remarkable yolk-shaped greening areas (Fig.  3a ). These greening urban areas account for 32% of the total greening BUAs in the 841 large cities, where 108 million people were living (Fig.  4c ). The greening of cities would have resulted from more urban parks or green spaces, which are likely to have multiple benefits for the urban environment and human health/well-being, including improving cardiovascular conditions 31 , 36 , and mental health 32 and reducing crime and violence 37 .

Overall, we observed that urbanization in the large cities has progressed rapidly in recent decades; moreover, our results indicate there are some economic-related global urbanization characteristics. However, due to the scarcity of the economic data (income levels) 20 , the interpretation of the results still has certain limitations. For example, with a large economic aggregate and large land area, China has a significant difference in the economic development in the eastern and western regions. In the YRD and PRD in eastern China, the gross domestic product (GDP) per capita in 2018 (see Methods) has reached the level of the high-income countries. Thus, we should be cautious in elucidating the impacts of country income levels on urbanization. In addition, in order to provide a global estimate of urbanization, we used 100 km 2 as a criterion to screen large cities throughout the world. Consequently, only dozens of the cities in Africa and India were selected, which may not represent the reality of the urbanization process in those countries. In a future study, it will be necessary to adjust the criterion for large cities to get a more comprehensive understanding of the urbanization characteristics in those countries or regions.

The systematic monitoring of the global urbanization process, especially in the low-income countries where the data are normally scarce, is essential to achieving the UN’s Sustainable Development Goals 8 , 9 . Therefore, our research using remote sensing data associated with limited demographic and economic data indicates a viable way of synoptically and repeatedly monitoring urbanization process worldwide. Our quantitative results, combined with previous policy studies 3 , 15 , 38 and case studies 14 , 39 , 40 , make it possible to assess the unevenness of the urbanization from more perspectives, such as the urban heat island effect 41 , water security 42 , and structure 25 . For the majority of the developing countries, understanding the dramatic uneven urbanization of the past several decades can provide a scientific reference to improve their urban governance 43 , thereby achieving a virtuous circle among urban expansion, population growth, and urban greening changes. This is vital for achieving urban sustainable development.

MODIS land cover type product

The urban and built-up pixels used in this study were extracted from the International Geosphere-Biosphere Programme classification types provided by the collection 6.0 MODIS yearly product known as MCD12Q1. The spatial resolution of the land cover product was 500 m and had a reported 73.6% overall land cover classification accuracy and relatively high accuracy for urban areas 19 . Any pixel classified as urban and BUA had at least a 30% impervious surface area, including building materials, asphalt, and parking lots. We used the tool “Raster to Polygon” in ArcGIS software to extract the BUA polygon from the raster layer. In this process, only the edge-adjacent (four connectivity) pixels were merged into a united urban patch.

In this study, there were three levels of BUAE (Fig.  1a ): E1 refers to very small or no urban expansion (≤1 km 2 ), E2 is less than half of the minimum BUA of the large city (1 km 2  < BUAE ≤ 50 km 2 ), and E3 is larger than half of the minimum BUA of the large city (>50 km 2 ). The reason for using 50 km 2 as the threshold was that when excluding 12 large cities with BUAE of more than 250 km 2 , we found that the value of the average urban expansion of the remaining 829 large cities, adding 1 standard deviation to those expansions, was close to 50 km 2 .

In addition, there were three levels of BUA (Fig.  1a ). The BUA of the first level was between 100 and 580 km 2 , the second level was between 580 and 2000 km 2 , and the third level was larger than 2000 km 2 . For BUAs in 2018, more than 19 large cities had areas over 2000 km 2 . Excluding those 19 cities, we found that the value of the average BUA of the remaining 822 large cities, adding 1 standard deviation to those areas, was close to 580 km 2 . This is why we divided the 841 large cities into three different sizes (i.e., the point sizes in Fig.  1 ).

EVI data and EVI max trends

The vegetation index data sets from the MOD13A1 version 6 product were used in this study to match the spatial resolution of the land cover data (500 m). These data sets include two primary vegetation layers: the normalized difference vegetation index (NDVI) and the EVI. Due to the greater sensitivity of the EVI relative to that of the NDVI in the high-biomass regions, our results were based on the use of the EVI. For each pixel, the annual maximum EVI (EVI max ) was generated from all the acquisitions over a 16-day period.

We used a Mann–Kendall test, which is a nonparametric test, to detect monotonic trends in the time-series data and evaluate the trends of EVI max from 2001 to 2018. For a comprehensive and systematic analysis of global urban greenness change, we used the BUA in 2018 to detect urban greening change. Then, if a pixel changed to an urban area in 2002 or later, the greenness change in the pixel was calculated. Notably, if the expended BUA was developed from cropland and natural vegetation land, the greenness of the expended BUA would possibly decrease. In fact, we calculated the BUA where greenness showed a significant increasing trend ( P  < 0.05). Using this method, we could exclude those pixels that had changed from rural vegetation to BUA (urban expansion).

In this study, the spatial resolution of the MOD13A1 data was 500 m; thus, we wrote a Python program to calculate the Mann–Kendall trend of EVI max from 2001 to 2018 for each pixel in a raster file. Then, the Geospatial Data Abstraction Library ( https://gdal.org ) was used to mosaic all the raster files (212 files) of the trends into a global raster map of the EVI max trend. Finally, we extracted the EVI max trend for each urban pixel within the urban extent (BUA in 2018) and calculated the ratio ( R greening ) of the area of the pixels with a significant increasing EVI max trend ( P  < 0.05) to the entire BUA of the corresponding urban extent.

Global country administrative area

The global country administrative areas were downloaded from the Database of Global Administrative Areas (GADM). The GADM data consisted high-resolution shapefiles at all administrative levels, such as at the country, state, and provincial levels. We used the latest version (v 3.4) in this study.

Population and economic data

The country population data were obtained from World Urbanization Prospects: The 2018 Revision . The 1 km resolution Gridded Population of the World collection (fourth version; GPWv4), which was adjusted to the national-level population counts estimated by the UN, was obtained from the Socioeconomic Data and Applications Center. The World Bank classifies economies as low income (<$1025), lower middle income ($1026–$3995), upper middle income ($3996–$12,375), or high income (more than $12,375) based on the GNI per capita in 2018. The GDP data of YRD and PRD in 2018 were obtained from the Statistics Bureau of Jiangsu Province and Guangdong Province, respectively.

In this study, we calculated the ratio ( R PB ) of the UPGr to the BUr for the 841 large cities. For each large city, the UPGr and BUr were defined as follows:

where Pop 2015 and Pop 2000 are the urban populations of a large city in 2015 and 2000, respectively. BUA 2018 and BUA 2001 are the BUAs of the large city in 2018 and 2001, respectively.

Reporting summary

Further information on research design is available in the  Nature Research Reporting Summary linked to this article.

Data availability

The MODIS land cover type products are available from https://e4ftl01.cr.usgs.gov/MOTA/MCD12Q1.006/ . The MODIS EVI data sets are available from https://e4ftl01.cr.usgs.gov/MOLT/MOD13A1.006/ . The global country administrative areas are available from the Database of Global Administrative Areas (GADM) ( https://www.gadm.org/ ). The country population data were obtained from World Urbanization Prospects: The 2018 Revision ( https://population.un.org/wup/ ). The 1 km resolution Gridded Population of the World (GPW) collection (fourth version; GPWv4) is available from the Socioeconomic Data and Applications Center ( https://sedac.ciesin.columbia.edu/data/collection/gpw-v4/united-nations-adjusted ). The World Bank classifies economies based on the gross national income (GNI) per capita in 2018 ( https://blogs.worldbank.org/opendata/new-country-classifications-income-level-2019-2020 ). The GDP data of YRD in 2018 were downloaded from the Statistics Bureau of Jiangsu Province ( http://tj.jiangsu.gov.cn ), and the GDP data of PRD in 2018 were downloaded from the Statistics Bureau of Guangdong Province ( http://stats.gd.gov.cn/ ).

Code availability

All computer code used in the data analysis is available from the corresponding author upon reasonable request.

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Acknowledgements

This study was supported by the Science and Technology Department of Guangdong Province with Grant of 2019B111101002, the Innovation of Science and Technology Commission of Shenzhen Municipality Ministry with Grants of JCYJ20170413164957461 and GGFW2017073114031767, and National Natural Science Foundation of China (Grant No. 91747205).

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L.S. and J.C. designed the research; L.S., Q.L., and D.H. conducted data analysis and calculation; L.S. and J.C. wrote the paper; all the authors contributed to the interpretation of the results.

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Correspondence to Ji Chen or Qinglan Li .

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Digital Commons @ USF > Office of Graduate Studies > USF Graduate Theses and Dissertations > USF Tampa Theses and Dissertations > 3010

USF Tampa Graduate Theses and Dissertations

Placing reedy creek improvement district in central florida: a case study in uneven geographical development.

Kristine Bezdecny , University of South Florida Follow

Graduation Year

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Dissertation

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Geography and Environmental Science and Policy

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Kevin Archer, Ph.D.

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M. Martin Bosman, Ph.D.

Rebecca Johns, Ph.D.

Ambe Njoh, Ph.D.

Steven Reader, Ph.D.

urban geography, Celebration, urban revitalization, tourism, Walt Disney World

This study is primarily about the theory of uneven geographical development. In an era when it is proclaimed that, through globalization, the world has become flat, the unevenness of economic and social development is often overlooked or suppressed. As the nexus between global and local processes, the urban space often becomes the site of conflict between those defining the hegemonic narrative of the space, from a global and flat perspective; and those experiencing heterogenous local narratives, whose uneven positions are reinforced by this hegemonic narrative.

This study explores the conditions of uneven geographical development in the urban space of central Florida. Focusing primarily on the Reedy Creek Improvement District (RCID), better known by much of the world as Walt Disney World, and on Celebration, the community developed by the Disney Corporation in the 1990s, the relationship between urban development and tourism, the defining economic sector in the region, are explored in the context of space-place, global-local narratives.

This is done using the four conditions of David Harvey's Theory of Uneven Geographical Development. First, the history of sociopolitical processes within the urban space are explored as creating a framework upon which contemporary uneven geographical development could be built. Second, the development and continued power of the RCID in central Florida are examined within the context of accumulation by dispossession. Third, Celebration as a consumed company town is examined in the context of accumulation across space-time. Finally, the relationships between the RCID and Celebration, and the rest of the central Florida region, are developed in the context of struggles occurring simultaneously across multiple scales.

This study shows that the theory of uneven geographical development applies well to a region that is heavily dependent upon the tourist sector for its economy, and thereby works to control the narrative of that space to continue attracting consumers. It also shows that, while the theory of uneven geographical development works well for a space that is a primary global tourist sink, it needs additional theoretical sophistication in order to better suit rapidly changing global processes.

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Bezdecny, Kristine, "Placing Reedy Creek Improvement District in Central Florida: A Case Study in Uneven Geographical Development" (2011). USF Tampa Graduate Theses and Dissertations. https://digitalcommons.usf.edu/etd/3010

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How and why is development across India in uneven?

India is an emerging country with a rapidly growing economy in Southern Asia. It has the second-largest population in the world with 1.3 billion people. Until 1947 India was a British colony. It now has its own democratically elected government.

India has a diverse cultural background. One of India’s most famous cultural exports is Bollywood films which are exported around the world.

India has a varied landscape from mountains to great plains and desert to lush forests. These natural environments and rich culture and traditions make India a popular tourist destination.

Ports, such as those in Mumbai, have developed along India’s extensive coastline, providing significant trade opportunities for India.

Development across India is very uneven. This uneven development can be explained by the core-periphery model. Industrialised, urban areas which are centres for economic growth are core areas. The periphery is the surrounding, mainly rural areas where there is little economic development and few jobs.

Core areas developed around raw-materials. For example, the Damodar valley became a centre for heavy engineering following the discovery of coal and iron ore. There are three major steelworks in the area and the location has access to large rivers and ports which means transportation of goods and people is easy.

Once a large industry moves into an area there is a multiplier effect . This means people have better jobs and a higher income which leads to increased wealth in the area. This means there is more investment in the infrastructure, which in turn attracts more businesses to the area. In addition to this, there is also a growth in smaller industries either providing goods and services to the main industry or, using the products of the main industry in manufacturing.

In India, the south and western states have the highest GDP per capita. Maharashtra is the most urbanised state in India. Greater wealth means more money can be spent on healthcare and education, improving people’s quality of life.

States that are peripheral to Maharashtra, such as Bihar, have higher levels of poverty. States such as this still rely on agriculture for much of their income, however, crop yields and prices are variable. Due to low incomes, economic investment in peripheral areas such as Maharashtra is relatively low, making it challenging for development.

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Gentrifying rural community development: A case study of Bama Panyang River Basin in Guangxi, China

• Published: 13 July 2022
• Volume 32 , pages 1321–1342, ( 2022 )

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• Huayun Tan 1 , 2 , 3 &
• Guohua Zhou 1 , 4  

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Rural gentrification is deeply characterized by institutional context and spatiotemporal heterogeneity. Based on a diachronic field investigation, this paper constructs an analytical framework for gentrifying rural community development (GRCD) with a “community” theoretical perspective and analytical approach, defines the concept of GRCD, and analyzes the main characteristics, formation mechanism and regulation path of a typical gentrifying rural community in the Panyang River Basin of Bama County, Guangxi. Driven by factors such as the complex flow and heterogeneous living space practices of the host-guest community, the longevity “myth” led by commercial capital and consumption demand, and multiple action logics and desertification community governance, great changes have occurred in the social space and material landscape of the rural longevity community. Such changes include comprehensive reconstruction of the resident population, surface interaction and social separation of the host-guest community, residential structure change and settlement landscape renewal, and delocalization of the healing landscape and lifestyle changes. We propose policy insights in three areas: public and localization institutional arrangements, shared and comfortable gentrifying rural community making, and inclusive and synergistic gentrifying rural community governance. Through these aspects, we provide insights from the Chinese case for the gentrification and community development of rural areas in the Global South.

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Foundation : National Natural Science Foundation of China, No.42001205, No.41971224

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School of Geographic Science, Hunan Normal University, Changsha, 410081, China

Huayun Tan & Guohua Zhou

School of Geography and Planning, Nanning Normal University, Nanning, 530001, China

Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning, 530001, China

Hunan Key Laboratory of Geospatial Big Data Mining and Application, Changsha, 410081, China

Guohua Zhou

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Tan Huayunn (1981–), PhD and Associate Professor, specialized in tourism geography and rural geography. E-mail: [email protected]

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Tan, H., Zhou, G. Gentrifying rural community development: A case study of Bama Panyang River Basin in Guangxi, China. J. Geogr. Sci. 32 , 1321–1342 (2022). https://doi.org/10.1007/s11442-022-1999-0

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Tourism Causes Uneven Development: A Case Study of Natural and Cultural Heritage Tourism in Pakistan

• Masood UR Rehman Azhar Research Scholar, Development Planning and Management School of Social Sciences Universiti Sains Malaysia, Penang, Malaysia
• Nor Malina Malek Associate Professor, School of Social Sciences Universiti Sains Malaysia, Penang, Malaysia
• Saima Masood Lecturer, Social Sciences Programme Department of Management Sciences COMSATS University Islamabad, Wah Campus, Pakistan

Tourism is a global phenomenon and considered as a rescuer of the underprivileged and disadvantaged, providing opportunities and monetary benefits, encouraging social exchange and enhancing livelihood. It is a major source of foreign exchange earnings and development in the developing countries. On the other hand, it is also argued that tourism creates uneven development in society. It creates a dependency of the country on an unreliable source of income. It increases inequality in income and resource distribution in society. Tourism has flourished to a level of an industry worldwide, but it has hegemony of elite class. This essay aims to analyze the concept that “Tourism causes uneven development in society” according to the theoretical framework of tourism and development by discussing a case study of tourism in Pakistan.

Author Biography

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Dimoska, T. (2008). Sustainable Tourism Development as a Tool for Eliminating Poverty. Economics and Organization, 5(2), 173-178.

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Malik, S., Chaudhry, I. S., & Sheikh, M. R. (2010). Tourism, Economic Growth and Current Account Deficit in Pakistan: Evidence from Co-integration and Causal Analysis. European Journal of Economics, Finance and Administrative Sciences(22), 21-31.

Mujahida, N. (2002). Tourism in Pakistan: problems and prospects (1 ed.). Islamabad: Quaid-i-Azam University.

Pakistan, G. o. (2010). Pakistan Economic Survey 2008-09. Islamabad: Government of Pakistan Retrieved from http://www.finance.gov.pk/survey_0809.html .

Robinson, M., & Picard, D. (2006). TOurism, Culture and Sustainable Development. Retrieved from

Scheyvens, R. (2007). Exploring the Tourism-Poverty Nexus. Current Issues in Tourism, 10(2), 231 - 254.

Simpson, M. C. (2008). Community Benefit Tourism Initiatives--A conceptual oxymoron? Tourism Management, 29(1), 1-18. doi:DOI: 10.1016/j.tourman.2007.06.005

Ullah, Z., Johnson, D., Micallef, A., & Williams, A. (2009). Coastal scenic assessment: unlocking the potential for coastal tourism in rural Pakistan via Mediterranean developed techniques. Journal of Coastal Conservation, 1-9. doi:10.1007/s11852-009-0078-3

UNESCO. (2017a). Pakistan. Retrieved from http://whc.unesco.org/en/statesparties/pk

UNESCO. (2017b). The World Heritage Convention. Retrieved from http://whc.unesco.org/en/convention/

UNWTO. (2016). Tourism Highlights Retrieved from http://www.worldtourism.org/states/index.php

UNWTO. (2017). Member States. Retrieved from http://www.worldtourism.org/states/index.php

WTO. (2009). International Tourism Challenged by Deteriorating World Economy. UNWTO

World Tourism Barometer, 7(1). Retrieved from World Tourism Organization website: http://www.world-tourism.org/facts/wtb.html

WTO. (2017). Why tourism. Retrieved from http://www.worldtourism.org/aboutwto/why/en/why.php?op=1

Zain, M., & Badar, M. U. (2009). A study on factors promoting tourism in Pakistan. Retrieved from Karachi:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Impact of Uneven Development ( Edexcel IGCSE Geography )

Revision note.

Geography Lead

Example Case Study: Impacts of Uneven Development - China

• China has a wide variation in GDP per capita by province
• Beijing and Shanghai have GDP per capita of over US$23,000
• Four provinces all along the east coast have GDP per capita over US$13,000
• The province of Gansu has the lowest GDP of US$4936

China GDP per Capita by Province

• The provinces along the east coast can be regarded as the economic core of China
• Most secondary and tertiary economic activity is located there
• Includes the Gobi and Takla Makan Deserts
• Plateau of Tibet - mountainous region including the Himalayas

Social Impacts

• 17 million people are still living on about US$2.30 a day
• Over 90% of people living in poverty live in rural areas
• The standard of housing is often poor
• Increasing numbers of people are being moved to apartment blocks to free up land for factories
• Over 25% of rural households have access to piped water
• Literacy rates in rural areas are 65% whereas in urban areas they are 84%
• Regional inequality has led to significant rural-urban migration in China

Economic Impacts

• Leads to increased poverty
• Difficult to attract businesses to these regions
• The periphery is more dependent on primary economic activities

Environmental Impacts

• This leads to water contamination and disease
• Use of biomass for cooking and heating increases indoor air pollution
• Lead, zinc and other mining waste increase water pollution
• Small industries such as those that process electronic waste don't dispose of the waste safely
• More than 40% of China's land is degraded from over-cultivation, overgrazing and erosion
• 19% of farmland is contaminated by heavy metals and chemical pollutants

Worked example

Suggest one impact of uneven development within a country (3).

• Your answer needs to identify an impact, explain it and then outline its impact to gain the full three marks. Below are two examples of how you could answer the question.
• People may have a low quality of life (1) due to the lack of access to clean water (1) this can lead to people drinking unclean water which makes them ill/spreads diseases (1)
• People may have little access to schools (1) this means that children do not learn to read and write (1) which means they are not able to get better jobs and increase their income (1)

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Author: Bridgette

After graduating with a degree in Geography, Bridgette completed a PGCE over 25 years ago. She later gained an MA Learning, Technology and Education from the University of Nottingham focussing on online learning. At a time when the study of geography has never been more important, Bridgette is passionate about creating content which supports students in achieving their potential in geography and builds their confidence.

GDP Center Round-up: The 2024 Summit of the Forum on China-Africa Cooperation

By Akanksha Goyal and Angie Ye

The 2024 Summit of the Forum on China-Africa Cooperation (FOCAC) will be held in Beijing from September 4-6, 2024, with African leaders and members of FOCAC scheduled to attend.

Since the inception of the FOCAC in 2000 and the China-Africa Development Fund in 2006, China’s economic ties with Africa has deepened significantly. Over the past two decades, what have been the impacts and implications of China’s growing economic presence on the continent?

Ahead of the FOCAC, the Boston University Global Development Policy Center has produced a suite of research investigating the trends, outcomes, benefits and risks of China’s economic engagement with Africa. Below, see a summary and highlights of the latest research:

Relative Risk and the Rate of Return: Chinese Loans to Africa Database, 2000-2023

A new update to the  Chinese Loans to Africa (CLA) Database , managed by the Boston University Global Development Policy Center, estimates that from 2000-2023, Chinese lenders provided 1,306 loans amounting to $182.28 billion to 49 African governments and seven regional borrowers.

In 2023, Chinese lenders issued 13 new commitments with a value of $4.61 billion to eight countries and two regional financial institutions. This represents the first time the annual loan amount to Africa has risen since 2016 but is far below the early years of the Belt and Road Initiative (BRI), in which cumulative commitments surpassed $10 billion annually.

A new policy brief analyzes the state of Chinese lending to Africa ahead of the 2024 FOCAC Summit. Their findings suggest that, going forward, China will likely continue to pursue a bifurcated strategy of risk-averse experiments with borrowers that received fewer loans in past years and decidedly riskier forms of engagement with its longtime partners. Additionally, the size of future individual loans is expected to shrink, even in countries that have a long history of engagement with China, as the pre-pandemic pipeline of big-ticket projects empties out. Explore the data and read the policy brief .

Africa Takes the Wheel: The Role of Host States in Shaping Outcomes of Chinese-Supported Power Generation Projects in Africa

Chinese companies have installed over 25 GW of generation capacity in Africa, making up more than 15 percent of sub-Saharan Africa’s installed generation capacity. Despite their undeniable contribution to the power sector in sub-Saharan Africa, the price and investment outcomes of these projects have varied. Chinese companies have been observed to construct both low- and high-quality infrastructure projects in Africa, with some projects seen as affordable and others more expensive.

Why have the outcomes of these projects varied and how do African host states exercise agency to influence the outcomes of Chinese-supported power generation projects?

A new working paper by Naa Adjekai Adjei examines how African host states exercise agency to influence the outcomes of Chinese-supported hydropower plants in Ghana and Uganda. It analyzes the financing and construction of hydropower plants financed by the Export-Import Bank of China and Sinohydro in Uganda and Ghana using semi-structured interviews and document analysis. Read the working paper   and  read the blog summary .

Chinese Economic Ties and Low-carbon Industrialization in Africa

Since the establishment of the FOCAC in 2000 and the China-Africa Development Fund in 2006, China’s economic ties with Africa have grown and deepened significantly.   However, China’s deepening connections and in particular, its foreign direct investment (FDI), in Africa have been the subject of discussion regarding their composition, goals, nature and implications for the continent’s industrial and economic development.  

How does Chinese FDI influence the carbon intensity of the manufacturing sector in African recipient countries?

A new working paper by Solomon Owusu, Keyi Tang and Gideon Ndubuisi examines the impact of Chinese FDI on low-carbon industrialization in Africa, within the context of China’s growing economic ties with the continent.   Read the working paper   and  read the blog summary .

Evaluating Regional Prefeasibility Facilities: Expanding Renewable Energy and Energy Access in the SADC Region

The Southern African Development Community (SADC) region has one of the highest solar irradiation and great wind energy potential in sub-Saharan Africa. The falling costs of both solar photovoltaic and wind energy technologies and the discovery of transition minerals essential for the shift to low-carbon economies in several SADC countries makes the region a favorable destination for renewable energy project developers. However, so far, only 1 percent of solar and wind energy potential has been tapped.

Structures and policies to promote investment in renewable energy in the SADC region exist. Why then, is the total contribution of solar and wind energy still low in most SADC countries, despite abundant renewable energy sources and supporting structural frameworks?

A new report by the Boston University Global Development Policy Center, the Southern African Development Community Development Finance Resource Centre and the SADC Centre for Renewable Energy and Energy Efficiency highlights the inadequacy of regional and global prefeasibility facilities for expanding renewable energy and energy access in the SADC region. Read the report   and  read the blog summary .

Direct Impacts and Spatial Spillovers: The Impact of Chinese Infrastructure Projects on Economic Activities in sub-Saharan Africa

Amid a weak and uneven economic recovery in the Global South, regional development and infrastructure investment seem to be critical to sustainable development. In particular, infrastructure provision is seen as an “unmissable driver for development” in sub-Saharan Africa. Aside from institutional reasons, physical infrastructure is critical in facilitating market integration and freeing lagging regions from the poverty trap. In many sub-Saharan African countries, regional development goals are often associated with the public infrastructure provision to increase productivity and promote welfare.

Over the past 20 years, China has grown to be the largest financier and constructor of infrastructure in the region. Have Chinese investment projects directly impacted local economic activities in sub-Saharan African countries? And are there spatial spillover effects from those infrastructure projects across regional borders?

In a   new working paper, Yan Wang and Yinyin Xu use aspatial and spatial econometrics analysis to gauge the impact of Chinese infrastructure projects on economic activities in local jurisdictions in sub-Saharan Africa. They find that the existence of Chinese infrastructure projects leads to increased economic activity proxied by nighttime luminosity and notable positive spillovers in neighboring jurisdictions in the region. Read the working paper   and  read the blog summary .

China-Africa Economic Bulletin, 2024 Edition

African countries have and are shaping development goals in alignment with the United Nations 2030 Sustainable Development Goals and the African Union Agenda 2063. Potential sources of financing for energy and transition materials have become increasingly important for devising strategies that will allow Africa to achieve these goals.

Over the past three decades, China-Africa economic engagement has deepened across trade, development finance and FDI, contributing to African countries’ development and bringing about economic benefits and environmental risks. 

A new report by the Boston University Global Development Policy Center and the African Economic Research Consortium analyzes China-Africa trade, finance and FDI from 2000-2022 to evaluate trends, reveal gaps and identify pathways for China to support Africa’s energy access and transition amidst economic challenges and energy opportunities. Read the report   and  read the blog .

Elevating ESG: Empirical Lessons on Environmental, Social and Governance Implementation of Chinese Projects in Africa

China is an important partner in providing critically needed finance and infrastructure development capacity for African countries. With a growing awareness of environmental, social and governance (ESG) risks associated with infrastructure development, it is important to consider to what degree development projects cause ESG-related harm to host countries and communities.

Are Chinese-financed development projects in Africa meeting China’s stated ESG guidelines?

A new report by the Boston University Global Development Policy Center, the Fudan University Green Finance and Development Center, the South African Institute of International Affairs and LSE IDEAS uses a consistent ESG framework based on stated Chinese and local ESG requirements and recommendations to analyze five development projects. The Chinese-supported projects are in three African countries that are recipients of large amounts of Chinese financing on the African continent – Egypt, Nigeria and Ethiopia – and two sectors: energy and industrial parks, which are aligned with China’s ambitions to enhance energy access and support manufacturing in Africa. The report also assessed the case studies’ potential risks to biodiversity and Indigenous lands, based on their spatial locations. Read the report   and  read the blog summary .

Never miss an update: Subscribe to the Global China Initiative Newsletter . 

UK studies highlight prevalence, severity, and impact of long-COVID symptoms

nito100 / iStock

Two new studies conducted in the United Kingdom describe the prevalence, severity, and impact of long COVID. One of the studies found fatigue, tiredness, and shortness of breath were the most common persistent symptoms among UK healthcare workers (HCWs) and had a significant impact on their work and life. The other suggested that pain is the most prevalent and severe symptom among long-COVID patients and that demographic factors such as age and ethnicity play a role in symptom severity. 

Long-COVID prevalence in HCWs

In the first  study , published yesterday in the Journal of Infection, researchers from the UK Health Security Agency (UKHSA) and Public Health Scotland analyzed data collected through the SARS-CoV-2 Immunity and Reinfection Evaluation (SIREN) study, a large prospective cohort study of UK HCWs participating in frequent polymerase chain reaction (PCR) and antibody testing for SARS-CoV-2 since June 2020. 

Because HCWs in the UK and elsewhere have been disproportionately affected by COVID-19, the researchers wanted to determine the prevalence of persistent symptoms in SIREN participants and the impact of those symptoms on work and life. 

An electronic survey was sent to SIREN participants who had reported a SARS-CoV-2 infection by September 12, 2022. The survey provided a list of 35 symptoms and asked about the severity of the initial infection, number of infections, prevalence of and healthcare consultations for persistent symptoms (lasting more than 12 weeks), impact on work-related and daily activities, and days absent from work.

Of the 16,599 participants eligible to complete the survey, 6,677 responded and 5,053 (median age, 49 years; 84.3% female; 90.7% White) were included in the final analysis. The prevalence of persistent symptoms differed by infection episode but was highest for first infections (32.7%) compared with second (21.6%) and third infections (21.6%). The most frequently reported symptoms were fatigue and tiredness, shortness of breath, and difficulty concentrating. 

A higher prevalence of persistent symptoms was reported during the wild-type variant period than in the other variant periods (52.9% wild-type vs 20.9% Omicron for any symptom reported), and in general, almost every reported persistent symptom became less prevalent from the wild-type to Delta to Omicron periods. 

A substantial proportion of UK healthcare workers in our cohort experienced persistent symptoms following their initial SARS-CoV-2 infection, and this has impacted their life and work.

A higher prevalence of persistent symptoms was also found among those who were unvaccinated (38.1%) than among those who were vaccinated (22.0%). Multivariable analysis showed that participants were less likely to report persistent symptoms in infections occurring after vaccination compared with those with an infection before vaccination in the Alpha/Delta and Omicron periods (Alpha/Delta adjusted odds ratio [aOR], 0.66; 95% confidence interval [CI], 0.51 to 0.87; Omicron aOR, 0.07; 95% CI, 0.01 to 0.65).

Of the participants who reported persistent symptoms, 51.8% and 42.1% said those symptoms impacted their day-to-day and work-related activities "a little," respectively, and 24.0% and 14.4% reported that the impact was "a lot." When asked about work adjustments, 8.9% said they had reduced their working hours, and 13.9% reported changing their work pattern. The median number of days taken off from work due to persistent symptoms was 14. 

"A substantial proportion of UK healthcare workers in our cohort experienced persistent symptoms following their initial SARS-CoV-2 infection, and this has impacted their life and work," the study authors wrote. "This persistence of ill-health following acute infection in a substantial proportion of healthcare staff infected with SARS-CoV-2 has implications for workforce planning, and occupational health teams in designing supportive return to work policies."

Pain the most prevalent self-reported symptom 

In the second  study , published yesterday in JRSM Open, researchers from University College London (UCL) analyzed self-reported symptoms from 1,008 people in England and Wales who had been referred to a National Health Service post-COVID clinic and had reported their symptoms on an app—the Living with COVID Recovery Digital Health Intervention—from November 30, 2020, to March 23, 2022. 

Their aim was to identify the prevalence of self-reported symptoms and the relationship between demographic factors and symptom intensity, which app users ranked on a scale of 0 ("not at all intense") to 10 ("extremely intense").

Of the 1,008 participants, 77% reported symptoms multiple times, and 23% reported symptoms only once. A total of 1,604 unique symptoms were reported, which researchers grouped into 109 symptom categories. The most prevalent symptoms reported were pain (26.5% of all symptoms reported), neuropsychological issues (18.4%), fatigue (14.3%), and dyspnea (shortness of breath, 7.4%). The intensity of the symptoms increased by 3.3%, on average, each month since participants initially registered on the app.

Our study highlights pain as a predominant self-reported symptom in long Covid, but it also shows how demographic factors appear to play a significant role in symptom severity.

Multiple linear regression analysis revealed that age, sex, ethnicity, education, and deprivation level were significantly associated with symptom intensity. For example, participants aged 68 to 77 and 78 to 87 experienced higher symptom intensity (32.8% and 86% higher, respectively) than those aged 18 to 27, women reported 9.2% more intense symptoms than men, and non-White individuals reported 23.5% more intense symptoms than White individuals. 

Higher education levels were associated with less symptom intensity than the least educated, and people in less deprived areas had less intense symptoms than those in the most deprived areas. But the number of symptoms did not vary by deprivation level.

The study authors say treatment for long COVID should focus on the most prevalent symptoms and that understanding the observed associations can inform healthcare policies and strategies aimed at minimizing the burden of long COVID, which they say will remain a "pressing concern" for as long as new SARS-CoV-2 variants continue to emerge.

"Our study highlights pain as a predominant self-reported symptom in long Covid, but it also shows how demographic factors appear to play a significant role in symptom severity," lead author David Sunkersing, PhD, MSc, of the UCL Institute of Health Informatics, said in a UCL press release . "Our findings can help shape targeted interventions and support strategies for those most at risk."

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