case study of infrastructure growth over time in one city

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Case study of infrastructure growth: Hong Kong introduction

By Matt Burdett, 13 February 2019

On this page, we introduce Hong Kong as a case study of infrastructure growth over time in one city.

Hong Kong as seen from Lugard Road on The Peak. The homes of approximately five million people are visible in this photograph with over two million more spread over the rest of the territory.

Background to Hong Kong

Hong Kong is a unique place: it has a very high population density, over 7 million people, a very high GDP per capita, a relatively recent experience of colonialism (ending in 1997), a mixture of English and Chinese languages, and it is the world’s third largest financial hub. Most importantly, the city has a semi-independent status as a Special Autonomous Region in China. It has its own currency, legal system, immigration laws, and control over social and economic policies under the mini-constitution known as Hong Kong’s ‘Basic Law’ which will end in 2047 when the former colony is formally and fully integrated into China.

Hong Kong is made up of three regions, as shown below.

Regions of Hong Kong. Source: An, Liu and Liu, n.d.

Hong Kong’s infrastructure challenges are also due to the mountainous and coastal nature of the city. The map below shows the relief of the territory in relation to other features.

Large physical map of Hong Kong with roads, railroads, relief and parks. Source: Maps of the World, n.d.

Soft infrastructure

Hong Kong is a top-five world city according to the AT Kearney Global Cities Report ( Mendoza Peña et al., 2018 ). The soft infrastructure includes some of the best educational opportunities in Asia (three of Asia’s top ten universities are in the city), a highly respected judicial system, and free public healthcare for everyone backed up by many private health insurers. A 2011 report by LSE Cities found Hong Kong topped an index of 129 major cities in several areas of social and economic development, which indicates the soft infrastructure of education services, healthcare and so on are successful:

Hong Kong’s successful wealth, health and education – topping the list of 129 cities for the quality of its services. Source: Burdett, Taylor and Kaasa, (eds.), 2011, p13 .

Another key aspect of the soft infrastructure is the way that people can access government services. Hong Kong’s government services are some of the most accessible in the world with almost all of them online – you can do your taxes, book sporting facilities, make a noise complaint, request information about recycling, renew your library books and read the formal business of the Legislative Council (Hong Kong’s parliament) all through the government e-portal ( GovHK, 2019 ).

Furthermore, Hong Kong’s secure Smart ID Card system allows people to access personal data from the government securely. The system is so secure it is used by Hong Kong residents to enter and leave Hong Kong instead of the use of a passport.

The challenge for hard infrastructure

Hard infrastructure is also world class. Transport, water, sanitation, energy, and telecommunications are all globally recognised.

However, as Hong Kong is one of the world’s most densely populated cities, this has been hard to achieve. In 2018 there were 6830 persons per square kilometre ( Census, 2019 ), and the population density is increasing over time.

Population densities (persons per square kilometre) in the different regions of Hong Kong. Source: Census, 2019

The map below shows the population distribution across Hong Kong. The density of population in the northern section of Hong Kong Island and Kowloon should contribute to significant savings in infrastructure development, because of the distance of cabling, pipes and so on is lower because people live so close together. However, the density is so great that it can be difficult to avoid disruption on the existing networks when upgrading the systems or adding new parts to the network. The photograph at the top of the page shows a typical street in the centre of Kowloon, where closing roads to develop infrastructure has an obviously large impact.

Hong Kong’s population distribution. Source: LSE Cities, via Byte Sized Investments, 2017 .

International links: an unusual urban problem

In most cities, international connections are not a major concern. However, Hong Kong’s limited size means it has to include international links as part of its regular urban development. In the 1990s by the lack of capacity at the airport, which was located inside the harbour between Kowloon and Hong Kong Island, led to the planning for a new airport. In 1998, Chek Lap Kok airport – more commonly known as Hong Kong airport – was opened and allowed massive expansion of commercial flights. In 2012 a third runway was given the go-ahead to be built in the sea next to the existing airport, including a new terminal, with a due date for completion by 2024 ( Lee, 2018 ). All of this development occurred through land reclamation. The photograph below shows the airport in 2010.

Chek Lap Kok airport in 2010 prior to the extra development of the third runway and terminal that are being built in the foreground of the picture. Source: Chan, 2010

Another international link for Hong Kong is that with Zhuhai and Macau. These two cities are located approximately 40 km to the west of Hong Kong across the Pearl River Delta. To expand the cross-border trade and links with these two cities, the Hong Kong, Macau and Zhuhai authorities have built the world’s longest sea-bridge which includes highway junctions on stilts in the middle of the sea. The bridge carries vehicles on the upper level and a rail link on the lower level. It opened in 2018.

The Hong Kong-Macau bridge. Source: Gibbs, 2018.

An, Liu and Liu, n.d. Regional Map of Hong Kong. Geog 351 SFU. http://www.sfu.ca/geog/geog351spring09/group04/map.html Accessed 27 January 2019.

Burdett, Taylor and Kaasa, (eds.), 2011. Cities, Health and Well-being. https://lsecities.net/wp-content/uploads/2012/06/Cities-Health-and-Well-being-Conference-Report_June-2012.pdf Accessed 27 January 2019.

Census [Census and Statistics Department of the Hong Kong Government], 2019. Hong Kong in Figures (Latest Figures): Population density by area. https://www.censtatd.gov.hk/hkstat/hkif/index.jsp Accessed 26 January 2019.

Chan, 2010. This photo was taken on Flight CA102, an A321-200 of Air China towards Beijing. https://en.wikipedia.org/wiki/Hong_Kong_International_Airport#/media/File:A_bird%27s_eye_view_of_Hong_Kong_International_Airport.JPG Accessed 9 February 2019

Gibbs, 2018. Hong Kong-Macau bridge to open on Oct. 23. https://calvinayre.com/2018/10/19/casino/hong-kong-macau-bridge-open-next-tuesday/ Accessed 9 February 2019.

GovHK, 2019. Government’s ICT Strategy & Initiatives. https://www.gov.hk/en/residents/communication/government/governmentpolicy.htm Accessed 14 February 2019.

Lee, 2018. Hong Kong airport looking to speed up expansion work to cut impact on flight numbers.https://www.scmp.com/news/hong-kong/economy/article/2142490/hong-kong-airport-looking-speed-expansion-work-cut-impact Accessed 9 February 2019.

LSE Cities, via Byte Sized Investments, 2017. MTR Corp’s First Competitive Advantage. http://www.bytesizedinvestments.com/mtr-corps-first-competitive-advantage/ Accessed 26 January 2019.

Mendoza Peña et al., 2018. 2018 Global Cities Report – Learning from the East: Insights from China’s Urban Success. https://www.atkearney.com/2018-global-cities-report Accessed 13 February 2019.

Case study of infrastructure growth: Hong Kong – an introduction: Learning activities

Describe the physical features of Hong Kong. [2]
Identify the three regions of Hong Kong. [1]
Describe the soft infrastructure of Hong Kong. [2]
Create a sketch map showing the population densities of different parts of Hong Kong. [4] Note: 2 marks for BOLTS (Border, Orientation north arrow, Legend, Title and Scale) and 2 marks for correct use of shading or similar method to show density.
‘High population density can lead to savings in the cost of infrastructure.’ Evaluate the accuracy of this statement in relation to Hong Kong. [6]
Describe Hong Kong’s infrastructure regarding its links to places outside of Hong Kong. [4]
Suggest reasons why Hong Kong’s infrastructure has developed to a highly advanced stage compared to other world cities. [4]

Other tasks

On a blank map of Hong Kong, add the major features of infrastructure that are shown on this page. Continue to add to your map using the other pages on this site.

Going further

Hong Kong is the fifth most globalised city of 2018. It has held this position for a number of years. Look at the AT Kearney Global Cities Report 2018 or the most recent available and conduct research to identify the common features of all the top five cities (London, New York, Paris and Tokyo) in terms of their general infrastructure.

All secondary material on this site is clearly referenced and may be subject to copyright restrictions by the original authors. All original material on this page is subject to copyright.

Urban land expansion: the role of population and economic growth for 300+ cities

Richa Mahtta ORCID: orcid.org/0000-0001-6126-4200 1 ,
Michail Fragkias 2 ,
Burak Güneralp ORCID: orcid.org/0000-0002-5825-0630 3 ,
Anjali Mahendra 4 ,
Meredith Reba 1 ,
Elizabeth A. Wentz 5 &
Karen C. Seto 1

npj Urban Sustainability volume 2 , Article number: 5 ( 2022 ) Cite this article

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Developing world
Environmental impact

Global urban populations are projected to increase by 2.5 billion over the next 30 years. Yet, there is limited understanding of how this growth will affect urban land expansion (ULE). Here, we develop a large-scale study to test explicitly the relative importance of urban population and Gross Domestic Product (GDP) growth in affecting ULE for different regions, economic development levels and governance types for 300+ cities. Our results show that population growth, more than GDP, is consistently the dominant determinant of ULE during 1970–2014. However, the effect of GDP growth on ULE increases in importance after 2000. In countries with strong governance, economic growth contributes more to ULE than population growth. We find that urban population growth and ULE are correlated but this relationship varies for countries at different developmental stages. Lastly, this study illustrates that good governance is a necessary condition for economic growth to affect ULE.

Higher immigration and lower land take rates are driving a new densification wave in European cities

Chiara Cortinovis, Davide Geneletti & Dagmar Haase

Updating global urbanization projections under the Shared Socioeconomic Pathways

Shiyin Chen, Qingxu Huang, … Lei Zhu

Spatial structure of city population growth

Sandro M. Reia, P. Suresh C. Rao, … Satish V. Ukkusuri

Introduction

Urbanization is fundamentally a process including both urban population growth and urban land change 1 . However, there is very little understanding about the relationship of urban population growth and urban land expansion. What explains the physical expansion of cities? Does having more people in urban areas lead to the expansion of urban land? Or does economic activity drive urban land use change? With forecasts of global urban population growth of 2.5 billion between 2018 and 2050, there is an urgent need to understand how this massive demographic shift may affect the expansion of urban land areas.

Urban land change affects biogeochemical cycles, regional to global climate, hydrological systems, and biodiversity 2 . Expansive urban growth is strongly linked to higher per capita urban greenhouse gas emissions 3 , habitat fragmentation and biodiversity loss 4 , 5 , inefficient use of natural resources 5 , and loss of agricultural lands 6 , 7 . Compact urban growth is positively correlated with improved human health outcomes 8 , economic growth 9 , energy and resource efficiency 10 .

Studies on the determinants of ULE have typically focused on a single city 11 , 12 , cities in a single country 13 , 14 or cities within a region 15 , 16 . Only three studies 1 , 17 , 18 have examined drivers of urban expansion for cities globally. Each of these studies have focused on either one specific year or one static time period with country-scale GDP data. All these studies (local or global) examine potential determinants of ULE (e.g., slope, arable land, temperature, population etc.) and have shown that ULE is driven by many factors, with demographic or economic growth as the primary drivers 19 , 20 , 21 . These findings support theory from urban economics and urban science that posit population and income as the primary drivers of ULE. For example, urban economics identifies demand for land as a derivative demand that is shifted by exogenous factors such as population and income. A more recent theoretical development—the science of cities—points to scaling laws relating urban population, wealth and land area 22 . Detailed case studies also highlight the effects of local policies and regulations such as zoning and housing policies 23 , floor area ratios 24 , subsidies for transport infrastructure and foreign direct investment 25 as additional drivers of ULE.

While local or regional studies provide insight into the drivers of urban expansion for a particular place, it is difficult to generalize the results for other places. Moreover, majority of these studies on ULE focus on cities in Europe, North America, and China 26 , 27 . Herein lies a scale and geographic mismatch between scientific knowledge about urban expansion and contemporary trends of global urbanization: most of the urban population growth in the next three decades will be in developing countries with relatively lower levels of economic development and yet there is limited understanding of ULE processes in these places. The United Nations (UN) estimates that, nearly 70% of the urban population growth will take place in just 20 countries (Supplementary Fig. 1 , UN, DESA 28 ), with all but one in either developing or least developed countries.

This is important because there is a strong correlation between the level of urbanization and national average income level. In 2018, high-income countries had a level of urbanization of 81% on average, while low-income countries had an urbanization level on average of 32% (UN, DESA 28 ). Although the relationship between urbanization and national income is complex, there is strong empirical evidence that as countries urbanize, national incomes also rise.

However, there is much variation in national incomes for countries with similar levels of urbanization. Some of this variation may be attributable to differences in governance and institutions. There is much evidence that effective institutions and governance are preconditions for cities to deliver municipal services and create vibrant, equitable and livable places 29 , 30 . Rule of Law and effective governance are necessary to create an environment attractive for private capital investments, which are necessary for infrastructure, industry, and innovation 31 , 32 . Well-governed cities, those with safe roads, clean water, and health services generally have functioning institutions.

Collectively, the literature points to urban population growth, economic development, governance, and institutions as important factors that shape urban expansion. However, because the majority of the existing literature tends to focus on single case studies, and testing various potential exploratory variables driving ULE, there is very little understanding of how the level of economic development and demographic change affect ULE across different contexts, or in particular regions, countries, or cities. This study fills these knowledge gaps. Our study is different from past studies because we focus only on population and economic growth as the dominant drivers of ULE and examine how geographic region, stage of economic development, and quality of national governance affect the relative importance of these factors. To this end, we explicitly test the relative importance of population growth and GDP growth in shaping ULE across 300 cities and over 45 years in two different time periods (1970–2000 and 2000–2014). We also consider the role of governance, which was not considered in any of the previous studies, as a factor that mediates the effects of economic and population growth on ULE.

The central question we ask is: What matters more for ULE under different geographic, development and institutional contexts: population or GDP growth? Our analysis answers the following questions: (1) What are the city-scale patterns of population growth, economic growth, and ULE across world regions for the period 2000–2014? (2) What drives ULE more: population or economic growth? (3) How does the relative importance of urban population growth and economic growth change across geographic regions, national income levels, and institutional settings?

This analysis is grounded theoretically on the concept of urban scaling and a derivative urban expansion accounting framework (presented in the Supplementary Note 1 ). Urban scaling refers to the idea that major urban properties, such as urban greenhouse gas emissions and urban area extent, show scaling relationships with urban population 33 , 34 , 35 . We formulate a growth accounting model of urban land expansion, based on urban scaling theory. Growth accounting is a tool developed by economists 36 to breakdown the growth of a variable of interest into several components. Our model breaks down the growth of urban land into two major factors in the theoretical framework: the growth in urban population and the growth of gross metropolitan product.

Trends in ULE, population, and economic growth rates

Our results show large variability in average annual growth rates of ULE with population and GDP per capita at the city scale (Fig. 1 ). On average, urban land is expanding at much lower rates than population or GDP per capita growth rates for cities with populations greater than one million. The average annual ULE rate in a million-plus city is 1.08%, whereas the average annual growth in population is 1.58% and in GDP per capita is 4.21%.

Regional variations in percent growth of a ULE with population, b ULE with economic growth (vertical dotted line shows mean percent growth in population/GDP per capita and horizontal dotted line shows mean percent growth in urban land), c ULE, d population, and e GDP per capita (Box plots represents 1st and 3rd quartiles, median and outliers). Regional color coding is consistent in scatterplots and boxplots. Percent growth in urban land area have been calculated from Mahtta et al. 37 . Population and GDP per capita growth rates have been calculated from the Oxford Economics 2016 database 48 . Note: The trends for the pre-2000 period are not shown due to the data unavailability of GDP at city-scale.

There is no single dominant trend across regions (Fig. 1 ). Cities where population growth rate is more than ULE rate are concentrated in Africa, Middle East, India, Central, and South America (hence CS America), and North America (Fig. 1a ). In contrast, cities with higher ULE rates than population growth rates are concentrated in China and East and Southeast Asia (hence E & SE Asia) and Europe. The majority of cities in India and Africa show higher population growth rate than ULE. As expected, cities in Europe and North America exhibit the lowest urban land and population growth rates.

We observed clear geographic patterns in the ULE and economic growth rate for selected regions (Fig. 1b ). With few exceptions, cities with higher ULE growth rates than GDP per capita are concentrated in Africa. Higher economic than ULE growth rate, however, follows a trend with the highest in cities of China (6–15%) followed by India (2–7%) and E & SE Asia.

In Africa, most cities have a higher population growth rate than economic growth rate with few exceptions in cities of Nigeria, Ethiopia, Mozambique, and South Africa. A few cities in these countries (e.g., Benin city, Ibadan, Kano, Addis Ababa, etc.) have doubled the rates of GDP per capita from 2000 to 2014. Similarly, we found higher economic growth rates than population growth rates in all 81 Chinese cities. However, in the East & SE Asia region, higher GDP per capita rates than population growth rates are observed only in the cities of Indonesia (e.g., Ujung Pandang, Surabaya, Jakarta) and Taiwan (e.g., Taipei, Taichung), cities in Japan and South Korea show less population growth rates from 2000 to 2014. A few cities in the Middle East region—Doha (Qatar), Sharjah (UAE), Dubai (UAE)—have exceptionally high population growth rates.

There are considerable variations in growth rates of GDP per capita, population and ULE within regions (Fig. 1c–e ). Regions where we found more variability (i.e., low to high) in GDP per capita growth rates at city scale are E & SE Asia, Africa, and CS America. Cities in Middle East show the maximum variability in population growth rates followed by Africa. Middle East is the only region where population growth rates are much higher than economic or ULE growth rates. Significant variability in ULE rates is exhibited by Africa, India, and China regions. However, ULE rates are much lower than population or GDP per capita growth rates in both India and Africa.

ULE is driven more by population than economic growth

Our regression model shows that the urban population growth rate has more influence in driving ULE than economic growth in both pre-2000 and post-2000 periods (Table 1 , Model I). In the pre-2000 period, a unit increase in population growth rate is associated with an increase in annual ULE rate by 16%, whereas a unit increase in GDP per capita growth rate is associated with a 7.3% increase in the ULE rate. Similarly, in the post-2000 period—a unit increase in population growth rate is associated with a 23% increase in the ULE rate, and a unit increase in GDP per capita growth is associated with a 12.4% increase in the ULE rate. Further, our analysis shows that the effect sizes of GDP per capita growth and population growth have increased from pre-2000 to post-2000. For instance, a city’s ULE rate has increased from 0.16 to 0.23 with one unit increase in population between pre-2000 to post-2000. These results are robust even after controlling for regions, income groups, and institutional factors (Table 1 , Model II–V). The interactions between explanatory variables are statistically insignificant in all models.

Average annual ULE rates in high-income (HI) countries are significantly different from all other income groups when controlled for population and GDP per capita in pre-2000 (Table 1 , Model II). However, in post-2000, we found no significant differences in ULE rates of HI and upper middle-income (UMI) countries. Similar trends were observed with the addition of a regional dummy variable. In pre-2000, after controlling for GDP per capita and population, average levels of ULE rate are highest in India as compared to North America—followed by China and Africa (Table 1 , Model III). Africa shows highest average ULE rates compared to North America region in the post-2000 period. Average ULE rates in India shows less significant differences from North America compared to pre-2000. In contrast, China shows no significant differences in ULE rates compared to North America in post-2000. Taken together, these trends show increased convergence in ULE rates over time across the world.

The goodness-of-fit measures of our regression models (measured by the R 2 statistic) increased slightly from 0.21 to 0.28 (Table 1 , Model I), for the pre-2000 and post-2000 periods, respectively. This increase is consistent across all the models (Table 1 , Model II–V). We expand on the interpretation of the R 2 statistic in the Supplementary Note 2 . Even after controlling for regional dummies, GDP per capita and population variables can only explain about 40% of the variation in ULE at the city scale.

ULE in lower to higher-income countries

To examine the varying influence of economic and population growth on ULE, we used averages across income groups and geographic regions. We found an inverted U-shape curve for the relationship between GDP per capita growth rate and ULE (Fig. 2 ). The contribution of annual growth in GDP per capita towards ULE is the lowest in low-income (LI) countries and is consistent in the cross-section analysis across both time periods. However, the percent contribution of economic growth towards urban expansion increases many-fold for LI and middle-income countries. At the same time, it decreases significantly for the HI countries (Fig. 2a ).

a Country income categories, and b region groups in pre-2000 and post-2000.

The change from pre-2000 to post-2000 contribution of economic growth to ULE occurs across LI to HI regions. In HI regions, the decrease in the relative contribution of economic growth from pre-2000 to post-2000 is concentrated in North America (Fig. 2b ). The contribution of GDP per capita to urban growth declined from 38 to 26%, while that of population growth increased from 63 to 74%. In contrast, the contribution of GDP per capita rates increased in the other global regions, from the pre-2000 to post-2000 period. The largest increase in the contribution of GDP per capita rates occurred in China, followed by India, CS America, and Africa. This suggests that in countries undergoing economic development, GDP per capita growth could be an important factor that shapes how urban expansion unfolds. In LI countries, while the GDP per capita growth rate has become an important predictor in the post-2000 period, population growth’s relative contribution remained high in urban growth.

Urban population, urban land, and income levels

Although countries with comparable national incomes vary significantly in terms of their level of urbanization, there is a clear correlation between percent urban population and national income (Fig. 3a ). As urbanization levels rise, national incomes also tend to rise. However, the same does not hold for urban land (%), where we find very little correlation between urban land and national income (Fig. 3b ). With few exceptions, the percentage of urban land varies between 25 and 75%, irrespective of national income. Furthermore, in some LI countries such as in Africa (e.g., Liberia, United Republic of Tanzania, Mozambique, and Congo), percent urban land is at similar levels as countries with much higher national incomes. This represents a critical challenge. Even though some African countries have percentage urban land levels comparable to high income countries, their per-capita national income remains low. This suggests that African countries, with some exceptions, are not benefitting from agglomeration economies. These conditions intersect with inadequate infrastructure services, owing to inefficient urban land use.

a Urbanization and per capita national income (adapted from UN DESA 28 ), and b Urban land and per capita national income (GNI). Urban definitions used to calculate percent urbanization are country specific and listed on the UN website ( https://population.un.org/wup/ ). Each dot represents a country, and the size of the dot is shown by the population for the same in 2018. Percent urban land is calculated from Global Human Settlement Layer (GHSL 2014) dataset as the share of impervious surface to the total urban footprint.

Governance and ULE

Based on the average institutional score over the study period and difference in the score over the study period, we identified four categories (Supplementary Table 1 ) for each of the governance indicators, Rule of Law and Governance Effectiveness. Strong and Getting Stronger category represents countries with average high governance scores (mean > 2.5) during the study period and increase in governance scores (difference is positive) over the study period. Strong and Getting Weaker category is characterized by countries with high average governance scores during the study period but with declining scores over the study period. Weak and Getting Stronger category represents countries with low average governance scores during the study and getting higher over the studied time-period. Similarly, Weak and Getting Weaker category has countries with low governance scores during the study period and then further lowering scores over the study period.

With few regional trends, we found distinct variations between the countries in different regions as we moved from pre-2000 to post-2000 (Supplementary Table 2 ). We found that Strong and Getting Stronger category is dominated by countries in Europe, North America, and Middle East regions for both the governance indicators in pre-2000. However, Governance Effectiveness has weakened for few countries in Europe (e.g., Netherlands, France, Germany) and North America regions moving them to Strong and Getting weaker category in post-2000. Contrary to that, for Rule of Law category, few countries in Middle East region (e.g., Israel, Saudi Arabia) have moved from Strong and Getting Weaker in pre-2000 to Strong and Getting Stronger category in post-2000. Whereas countries in Africa are concentrated in Weak and Getting Stronger and Weak and Getting Weaker categories both the indicators except South Africa, Ghana, and Tunisia countries.

Our analysis of the relative contribution of population and economic growth rate on ULE across these categories suggests that strong national governance allows economic growth to contribute more to ULE in countries as compared to the population growth (Fig. 4 ). Our results suggest that from the pre-2000 to post-2000 period, for countries with more robust governance ( Weak and getting stronger and Strong and getting Stronger categories), ULE can be attributed more to GDP per capita growth (Fig. 4 ). An exception to this observed trend is the countries in the Strong and getting Weaker category under Government Effectiveness indicator.

a Rule of Law and b Government effectiveness.

Further, over 70% of ULE can be attributed to GDP per capita growth rate under the Weak and Getting Stronger category in the post-2000 period for Rule of Law indicator. This result suggests that for this period, an increase in the Rule of Law has helped GDP per capita to predominantly drive ULE in the Weak and Getting Stronger category, whereas it had a negligible effect on the Strong and Getting Stronger category where the Rule of Law was already strong.

We found that the rise in the Government Effectiveness indicator—which generally captures the quality of policy formulation and its implementation by the national government—has a profound effect on Strong and Getting Stronger countries. The contribution of GDP per capita in explaining ULE has increased substantially in these countries suggesting even strong initial states of Government Effectiveness can be improved and allow urbanization processes to be more closely linked to economic development.

Taken together, there are two key takeaways from the results (Fig. 5 ). First, the importance of population growth in affecting ULE is consistent over time in context of regions, income levels and governance. This can be interpreted as more urban dwellers equals the need for more urban land. Second, in post-2000, only in a few cases has GDP become more important than population growth in affecting ULE: for instance, China which has stronger governance effectiveness and rule of law and is an upper middle-income country (as highlighted in the Fig. 5 ). These two results corroborate a recent study by Mahtta et al. 37 , which shows that the predominant urban growth pattern is outward expansion. That is, most urban growth worldwide is characterized more by outward low-rise development than upward high-rise development 37 . The primary exceptions to this global trend are countries with strong governance, such as China, South Korea, and few middle eastern countries like Saudi Arabia, Qatar, and the UAE. In these countries there has been a significant increase in the number of high-rise buildings.

Color on the bars represents the types of contexts (region, income, governance). First bar for each category represents the pre-2000 period and the one with dashed lines represents the post-2000 period.

Across different geographic regions and levels of economic development, ULE is driven more by population than economic growth. The implications of this for multiple dimensions of sustainability and global environmental change are significant given the projected urban population growth in countries with low levels of economic development. High ULE rates with low economic growth can result in negative impacts on the environment. Previous studies have shown a weakening relationship between urbanization and economic growth 38 , 39 in Africa and Asia 40 , compared to observed patterns in Europe and North America. Our results suggest a similar decoupling between ULE and economic growth, especially for urban areas in the lowest income regions such as South Asia and Africa.

Our analysis show that the relative contribution of GDP in explaining ULE has increased significantly in LI, LMI, and UMI countries from the pre-2000 to post-2000 period. We observe that the increase in the contribution of economic growth to the expansion of urban land occurs up to a point. When a country enters the highest income category, population growth again becomes an important predictor. Several factors may be driving this trend: we hypothesize that at earlier stages of economic development, GDP per capita growth drives the development of urban infrastructure, providing the foundation for agglomeration economies. This development makes cities attractive to rural populations and thus encourages migration. There are exceptions, however. For example, in Africa, factors like the natural increase in urban population due to higher fertility rates, dissatisfaction with local public services, agricultural distress, natural disasters, etc., push rural dwellers to urban areas 41 .

HI countries tend to have well-developed markets with relatively higher labor mobility, significant and established agglomeration economies, high quality urban infrastructure and services, and capitalization of amenities in land and real estate markets. In such settings, migration between cities in search of better amenities explains the relative importance of population growth in shaping ULE. The results also show that strong governance is an important factor for shaping ULE. We found that governance has become weaker over time for most of the low-income and middle-income countries. The results show that effective governance is necessary for GDP growth to affect ULE.

Our understanding of the process of physical urban expansion is enriched if we examine both supply and demand for land simultaneously. On the demand side, households and firms are part of local, regional, and national real estate markets. It is thus not surprising that population and employment growth are major drivers of the demand for urban land. Naturally, preferences of all types of economic agents (households and firms) for space and location as well as public policy are also primary drivers of demand. Similarly, supply is affected by policies such as land use planning or zoning that increase or constrain the amount of buildable land along with geography, demographic factors and market forces 42 . The context in which these market forces operate is important: countries at a higher level of economic development and with a stronger Rule of Law or higher Government Effectiveness will have better functioning and highly efficient markets, thus leading to planned ULE 43 .

Similarly, in cities with weak national governance, ULE is primarily attributed to population growth. This is intuitive, considering that with the weak and weakening Rule of Law and Government Effectiveness, we observe uneven economic development within countries, typically favoring cities as locations that concentrate political power and significant rent-seeking activities. Still, even in those cities, physical and other types of infrastructure will either be lacking or in poor maintenance; thus, economic development opportunities will be stunted. Migrants who arrive in these cities are escaping worse rural living conditions—real or perceived—and can most easily occupy undeveloped lands around the metropolitan area in an unplanned and sprawled fashion. As governance quality improves, a more suitable environment that is conducive for economic development emerges, which reduces the relative effect of population growth on ULE.

The contextual factors within which population and economy drive ULE are dynamic and may change in unexpected ways. These include sudden shocks such as a global economic crisis, a pandemic or a natural disaster that may occur as various impacts of ongoing climate change unfold. Therefore, the general trajectory of development we lay out here based on our findings might change with the onset of these sudden shocks. For example, it is highly likely that the current COVID-19 pandemic proves to be simply the first in a succession of pandemics for the foreseeable future. A pandemic can compel national travel restrictions, which make cross-border migration much less likely as seen in the current one.

Similarly, the current pandemic is illustrative of how a large-scale outbreak may reduce within-country mobility and slow down economic activity, which may result in lower rates and levels of ULE, at least temporarily. Cities with limited basic urban infrastructure will undoubtedly be affected more adversely during such a shock. Nevertheless, the slower urban development rates may offer opportunities for a re-assessment of policies in formerly rapidly urbanizing places that might affect public spaces, including public transport, housing, and retail. These realignments along with modern technologies that allow for remote work and autonomous driving may lead to transformational changes in how urban areas grow and function. Such changes will undoubtedly be reflected in real estate markets as we witnessed with COVID-19 where companies relocated offices to cheaper suburban areas or smaller cities (e.g., San Francisco Bay Area firms moving to cities in Texas in the US), creating the opportunity to convert existing office space to housing inside the city. The combined effects of losses in the retail and hospitality sectors, rising real estate vacancy rates, and declining use of public transit, especially in central urban areas will take shape over multiple years as we emerge from the pandemic, and the long-term impacts of these changes on ULE remain uncertain. This would be a valuable area for further research.

Methodologically, our study shows the importance of utilizing city-level statistics to understand urban expansion. While national-level analysis provides useful insights, it also aggregates data such that the variability among large, medium, and small cities is lost. Thus, it is essential to understand the underlying variability because the heterogeneity among cities, even in one region, is high 37 , 44 and is similar to heterogeneity levels between countries. Our understanding of urban processes such as land expansion can be advanced if we shift our attention towards the city rather than the country as the unit of analysis. Furthermore, understanding the joint dynamics of the urban population, ULE, economic development, and governance quality is also important for identifying a robust suite of policies to manage rates of ULE. Our study indicates that urban growth can be better understood by considering both urbanization (urban demographic share) and the physical expansion of urban areas. The association between urbanization and income growth can vary depending upon how we conceptualize the urban growth process: as a demographic process or as a land change process.

Our findings can be used to inform urban land development policies across distinct geographies, economies, and governance structures. Understanding the urban expansion factor attribution mix can lead to policy interventions that target either population growth or GDP growth differentially. In cities and contexts where economic growth primarily accounts for ULE rates, policies that target local economic growth will have a significant effect on urban expansion; these could involve spatial economic planning (aiming at establishing agglomeration economies through the location choices of firms and infrastructure within the city), investments in human and social capital and expanding opportunities for human interaction and exchange of ideas.

In cities and contexts where population growth primarily accounts for ULE rates, policies that target local population growth will affect rates of urban expansion. Such policies include the establishment or removal of population migration incentive schemes (relocation payments or tax incentives), metro tax exemption schemes for large employers, and urban growth boundaries. Naturally, a mix of instruments can be utilized in cities and contexts, where economic and population growth account for approximately equal portions of ULE. A main takeaway of our analysis is that policies to manage ULE can be implemented indirectly through local policies affecting population and economic growth. This can help in facilitating a transition towards urban sustainability (SDG 11) through participatory, integrated, and sustainable planning.

A main takeaway of our analysis is that policies to manage ULE can be implemented indirectly through local policies affecting population and economic growth. This implies that regional/urban growth and economic development strategies must incorporate ULE considerations and be aligned with participatory, integrated, and sustainable spatial planning processes. This can help facilitate a transition towards urban sustainability (SDG 11).

Over the next thirty years, an additional 2.5 billion urban dwellers will require the construction of more towns and cities, which in turn will require new urban land. The implications for land resources are enormous. Without policies and strategies in place to protect various land ecosystems—farmland, wildlife corridors, sensitive habitats—we can expect to see significant urban-induced land changes that will have negative consequences for both the environment and livelihoods. However, the results also point to the importance of governance in affecting ULE. Whether ULE is driven differentially by population growth or economic growth will be affected by geographic region, the stage of economic development, and the quality of governance. This much is clear: the combination of economic and urban population growth in the next 30 years will result in substantive new urban expansion. The patterns of urban expansion that emerge will depend much on institutions and governance; our results show that much can be done to shape how urban expansion is manifested in the coming decades.

We collated ULE and socioeconomic data at city scale from various sources from 1970 to 2014 (Table 2 ). We selected 2000 as the break year, as city-level data for economic indicator (GDP) was only available after 2000. We refer the two time periods as pre-2000 and post-2000. We combined the data on ULE from two published peer-reviewed papers 37 , 45 . Güneralp et al. 45 use a bottom-up approach to calculate ULE rates through a meta-analysis of published studies and inputs from a previously published meta-analysis by Seto et al. 1 . In contrast, Mahtta et al. 37 use a top-down approach utilizing the built-up area from the GHSL dataset. Thus, for the pre-2000 period, we calculated the average annual ULE rates by averaging the decadal rates from Güneralp et al. 45 . Here, we selected only city-based studies (251) from the database. Approximately 185 out of 251 cities have more than one million population. GDP data was calculated at the country scale except for China, India, and the United States, where GDP data are at sub-national levels (province, state, and state, respectively). Average annual GDP per capita growth rate was calculated for each of them for 1970–2000 period. We used population data from World Cities database by J. Vernon Henderson ( http://www.econ.brown.edu/Faculty/henderson/worldcities.html ) to calculate average annual population growth rate for each city.

For the post-2000 period, we used 478 cities with a population threshold of one million as described in Mahtta et al. 37 . We further computed annual ULE rates at the city scale as, (Urban area in t 1 /Urban area in t 2 ) 1/n −1) * 100, where t 1 is the final period, t 2 is the initial period, and n is the time interval between these two time periods. Next, we calculated the population and GDP per-capita rates (% annual) at the city scale using the Oxford Economics database. After combining the datasets on these three variables, our sample size was reduced to 363 cities. We assumed that the population and economic development contribute to ULE only during the growth stage. Accordingly, we capped negative values of both population and economic growth rates at zero. Still, our results are consistent across restricted and unrestricted scales of the two variables. For urban expansion variable, however, we consider only positive rates.

Except for Asia, we labeled each city using the UN defined world macro-regions. For cities in Asia, we considered China, India, and the Middle East as distinct regions and kept the rest of Asia as a separate region (Fig. 6 ). Due to a smaller number of cities in the Middle East, Oceania, E & SE Asia, and the rest of the Asia region, we represented them as one region named “Others” for regression analysis.

Numbers on the bars represent the cities in each region. The total number of cities in the pre-and post-2000 period are 251 and 363, respectively.

Model specifications

We developed descriptive statistical models (Model I to Model V) of urban growth for both the periods (1970–2000 and 2000–2014). In Model I, our baseline specification, we use GDP per capita and population growth as the only independent variables. Models II to V expands the baseline specification by adding dummy variables—region, income level, the Rule of Law indicator, and Government Effectiveness indicator—respectively.

For our income-based dummy variables, we used data from the World Bank. The World Bank classifies countries based on gross national product per capita as HI, UMI, LMI, and LI. The categorization is available annually from 1987 to 2020. We selected the year 2014 country level income-based categorization for both pre-2000 and post-2000 models. Categorization of countries from this study is shown in Supplementary Table 2 .

We used the Worldwide Governance Indicators which assesses countries based on institutional qualities to create governance dummy variables. These indicators include six dimensions of governance for 215 countries and territories from 1996 to 2018 time-period: (i) Voice and accountability; (ii) Political stability and absence of violence; (iii) Government effectiveness; (iv) Regulatory quality; (v) Rule of law; and (vi) Control of corruption 46 . For this analysis, we chose two indicators based on both conceptual and statistical factors: Rule of Law and Government Effectiveness. Conceptually, we decided the governance indicators that closely match with fundamental governance attributes of service delivery, policy making and implementation, public confidence in institutional setup, and quality of conflict mitigation and resolution mechanisms; statistically, our exploratory analysis of the set of available indicators revealed a significant correlation between the measures. The Rule of Law indicator measures the perceptions of the extent to which agents have confidence in and abide by society’s rules, particularly as they relate to contract enforcement, property rights, the police, and the courts, and the likelihood of crime and violence. Similarly, the Government Effectiveness indicator captures “Perceptions of the quality of public and civil services and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies”. Based on the average state during the study period and change in the governance indicators over the study period, we categorized individual countries into four quadrants with weak and strong governance categories. We did this categorization for both the pre-2000 and post-2000 period. In our analysis of the institutional effects, we estimated the impact of institutional quality for a specific time frame by taking average scores across years within each of the two periods.

Relative contribution

The main predictor variables are population growth rate and GDP per capita growth rate across all models; we employ a distinct set of dummies to formulate a multiplicity of regression specifications for our attribution analysis. To calculate the proportion of urban expansion attributed to the growth rate of GDP and the population growth rate, we devise the following technique that relies on our regression’s fitted values. We examine the following regression specification in our datasets:

where rtch is the urban expansion rate of change, potrtc is the population rate of change, gdprtc is the growth in gdp per capita, and dummyvar can be one of the three following dummy variables: a regional dummy variable; a national income category dummy variable; or a governance quality dummy variable—either regarding Government Effectiveness or the Rule of Law.

Applying ordinary least squares regression to our dataset, we arrive at the following estimated fitted regression line:

For example, when dummyvar is the set of regional dummies, the fitted line statistically accounts for the effect of each region. We then subsample the above results for each region ( j = China, India, etc.) and create the following two indicators for of the proportion of urban expansion attributed to either population or GDP growth rate for the j th region.

Each indicator measures the mean fitted values of the variable of interest (population rate of change or GDP per capita rate of change) over a specific region as a proportion of the sum of mean fitted values of population and GDP per-capita rate of change. In other words, we extract the average fit emerging from each variable for each region as a proportion to the average fitted value with the two variables. We calculate these indicators for all j regions and with n j observations of cities within each region. Thus, ULE rate attributed to

We repeat the same analysis for all sets of dummy variables, capturing the attribution of both population growth rate and GDP per capita growth rate for all categories included in the dummy set. The method is further elaborated in SI section.

Statistical analysis

Statistical analyses were conducted in R programming language v. 4.0.3 47 . R packages used for data processing, analysis and visualization were: plyr, RColorBrewer, tidyverse, ggpubr, hrbrthemes, gridExtra, ggrepel, psych, sandwich, and stargazer. lm function was used to conduct the linear regressions. For analyzing gridded GHSL data, we used raster, rgdal and sf packages.

Data availability

The datasets aggregated and/or analyzed during the current study are available from the corresponding author on reasonable request. GHSL dataset are available at https://ghsl.jrc.ec.europa.eu/ghs_bu2019.php . City-level population data are available at ( http://www.econ.brown.edu/Faculty/henderson/worldcities.html ). City-level Oxford Economic database is proprietary and is thus not freely available.

Code availability

The code used to generate the results in this study is available from the authors upon reasonable request.

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Acknowledgements

This study was supported by NASA LCLUC grant NNX15AD43G and NASA grant 80NSSC18M0049.

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R.M., K.C.S., and M.F. designed the research; R.M. led the study and performed the analyses with contribution from M.F. and K.C.S.; R.M., K.C.S., and M.F. interpreted the results and wrote the paper; and A.M., B.G., E.W., and M.R. commented on the draft and final manuscript and provided additional significant edits. All authors approved the manuscript for submission.

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Mahtta, R., Fragkias, M., Güneralp, B. et al. Urban land expansion: the role of population and economic growth for 300+ cities. npj Urban Sustain 2 , 5 (2022). https://doi.org/10.1038/s42949-022-00048-y

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Urban economic development in Africa: A case study of Nairobi city

Subscribe to africa in focus, jacob nato , jn jacob nato policy analyst - kenya institute for public policy research and analysis @jacobnato1984 humphrey njogu , hn humphrey njogu principal policy analyst - kenya institute for public policy research and analysis @humnjogu rose ngugi , rn rose ngugi executive director - kenya institute for public policy research and analysis aloysius uche ordu , and aloysius uche ordu director - africa growth initiative , senior fellow - global economy and development @aloysiusordu ede ijjasz-vasquez ede ijjasz-vasquez nonresident senior fellow - global economy and development , africa growth initiative @ede_wbg.

February 17, 2023

Below is a viewpoint from the Foresight Africa 2023 report, which explores top priorities for the region in the coming year. Read the full chapter on Africa’s cities .

In most countries, urbanization leads to substantial productivity gains supported by scale, density, and agglomeration. Better connected people and firms lead to savings in transport and logistics, technological and information spillovers, and more efficient labor markets. However, Africa’s urbanization has not realized the full potential and benefits of such agglomeration. The economic transformation and benefits of urbanization, observed in other regions, are yet to be achieved in sub-Saharan Africa.

To understand the barriers, and unlock the economic opportunities of urbanization, the Africa Growth Initiative (AGI) at the Brookings Institution developed an “ Urban Economic Growth Framework for African cities .” The framework focuses on the three primary constraints limiting a city’s ability to benefit from agglomeration and generate productive jobs: Accessibility, the business environment, and public sector governance. The framework provides specific indicators and ways to identify these three critical constraints, with a view to inform and guide policymakers on specific actions and appropriate policies.

As a start, the AGI framework was applied to the city of Nairobi (Kenya’s capital), to analyze Nairobi’s key challenges and possible solutions for growth and employment.

Unemployment and underemployment in Nairobi are a top concern, especially as youth makeup 48 percent of the total unemployed workforce (15 to 64 years). While the labor force in Kenya has been growing at an average annual rate of about 3 percent, Nairobi needs to generate many more (and better) jobs to offer improved livelihood opportunities to its large youth demographic. At the national level, Kenya has registered good progress in creating jobs, especially in the digital and gig economy. The report recommends two areas of focus. First, in coordination with the national government, Nairobi City County needs to support the gradual formalization of the large number of informal jobs and enterprises by easing business registration and motivating registration through targeted support programs. Second, better education and skills in targeted economic sectors are required to enhance productivity and earnings. Nairobi city should ensure that tertiary institutions provide training and skills consistent with emerging technologies.

[Nairobi] city has enormous potential to achieve the benefits of urban agglomeration and create productive jobs by paying particular attention to its challenges in accessibility and infrastructure, business environment, as well as public sector governance and finance.

Furthermore, enterprise data in Nairobi shows that businesses are likely to transition from micro- to medium-, and to large enterprises as the owners’ levels of education attainment rises.

Accessibility within the city: Accessibility is vital for connecting workers to firms and firms to markets. Despite the excellent progress made on infrastructure development, there is a high concentration of unpaved roads in Nairobi’s high-density informal settlements.

Consequently, as shown in the report, most jobs are not accessible within one hour of public transport commute i.e., commuting time by bus, matatu (shared taxi), or foot. The city also has a mismatch in zoning and land use. Nairobi therefore needs a new approach to urban planning that considers population growth, infrastructure, housing, and land use. Equally important is updating the land appraisal system and creating more public spaces.

Business environment: Many businesses in the city face several challenges, including complex processes to access licenses and permits, insufficient finance, expensive land, rigid labor regulations, inefficiency in tax administration, and crime risk. For example, a business takes about 92 days to secure an electricity connection. A firm loses about KSh 2.3 million per year due to power outages on average. These are critical areas for Nairobi to enhance its business environment. Furthermore, it is essential to coordinate the implementation of business policy reforms between the national and county governments.

Public sector governance and finances: The devolution process in Kenya has given Nairobi City County a total of 14 constitutional functions. The city faces important challenges in terms of financing, despite the commendable increase in revenues and fiscal transfers from KSh 9.51 billion in FY 2013/14 to KSh 19.42 billion in FY 2020/21. Still, the city faces several financing shortfalls, from high levels of pending bills and fiscal deficits, to delays in receipt of equitable fiscal transfers. These challenges call for proper budget planning, improved budget execution, and higher levels of the city’s source revenue.

The application of the AGI Urban Economic Growth Framework to Nairobi City County shows that the city has enormous potential to achieve the benefits of urban agglomeration and create productive jobs by paying particular attention to its challenges in accessibility and infrastructure, business environment, as well as public sector governance and finance.

Cities use innovation to get smarter about infrastructure

With $1.2 trillion in federal infrastructure funding about to hit U.S. cities of all sizes, local leaders everywhere are looking for new ways to maximize this once-in-a-lifetime opportunity to improve roads, parks, bus fleets, renewable energy, flood defenses, internet access—and, most importantly, residents’ everyday lives.

Leveraging innovation tools like data, collaboration, and resident engagement will be critical to this effort—both to ensure that investments solve real problems and that they meet White House guidelines and local goals around equity, climate change, economic development, and more.

And while the funding opportunity is unprecedented, a growing number of cities in the Bloomberg Cities Network are already demonstrating how innovation can supercharge infrastructure advancements. Here, we dig into some of these already-underway projects to uncover ways they can inspire further infrastructure endeavors in the months and years to come.

Orlando: Fostering a culture of infrastructure collaboration

Chris Castro, the Director of Sustainability and Resilience in Orlando , Fla., convened a remarkable meeting a few weeks ago. He gathered in one room about 70 people representing just about every government agency or entity in the Orlando area that is eyeing infrastructure dollars—from city, county, regional, and state agencies to public schools, colleges, and universities, the airport, a local expressway authority, and others.

Castro says the convening tapped into a collaborative culture that local leaders have been carefully building in the Orlando region, and hope to leverage to bring in federal dollars. “Partnerships are the only way forward,” he says. “If we approach these things in silos, we’ll never fundamentally transition toward a cleaner future.”

Castro points to Orlando’s push for electric buses as a good example of local collaboration, which accelerated through the city’s participation in the Bloomberg Philanthropies American Cities Climate Challenge . The city, the local transit authority, and the city-owned electric utility teamed up to cover the cost of charging infrastructure through an innovative financing scheme that has Orlando ahead of schedule on its quest to roll out a zero-emission bus-rapid transit system downtown . Castro calls the setup, aided in part by federal grants, a public-public-public partnership.

“This partnership allows us to share the upfront costs of this transition,” he says. “Secondly, this is complex stuff. Especially with electrification, if you’re not engaging your utility, you’re not going to electrify your fleet.”

Syracuse: Using data to make safer sidewalks

The state of the sidewalks in Syracuse , N.Y., has been a big problem for a long time. Actually, it’s been two problems.

Dunham used that data—including details on pedestrian traffic and vehicle speeds and roadway characteristics—to determine where impassable sidewalks were most likely to force pedestrians to walk in an unsafe street. To begin scaling up a response, she developed a new municipal sidewalk maintenance program , which the city council passed last year .

Key to the program is that the city takes responsibility for fixing broken sidewalks. And while property owners are still on the hook for snow removal, city-paid teams will ensure that the most critical sidewalks get cleared. To pay for the first year, the city is tapping into federal dollars through the American Rescue Plan. After that, property owners will pay an annual fee that starts at $20 for a residence and eventually grows to $100 as the program scales up.

In a city where a quarter of residents don’t own a car, city leaders see taking better care of sidewalks as key to running a more equitable city. And the fact that the program is data-driven, Dunham says, makes it easy to explain to residents that pedestrian safety is the number one priority. “There are so many decisions we have to make with limited resources,” she says. “It helps me sleep better at night to know that I can defend the decisions we’ve made because they’re supported by data.”

Minneapolis and St. Paul: Using residents’ insights to build a better carshare

Carsharing services are nothing new. But with spotty car coverage in many cities, and corporate players who come and go, carsharing has never quite lived up to its potential.

Now, the Twin Cities are flipping the script on carsharing by partnering with HOURCAR , a local nonprofit that has run a limited carsharing service for 15 years, and expanding the service into a more ambitious initiative that tackles local leaders’ climate and equity goals.

The carsharing hubs also include a pair of on-street parking spots where neighbors can charge their own EVs. Stark likens these spots to a down payment on the big job ahead of wiring street parking for EV charging. The electricity comes from wind power, making the whole system as clean and green as it can get.

Evie Carshare was developed in part through the Twin Cities’ participation in the American Cities Climate Challenge , using funds from federal, city, state, regional, and philanthropic sources. And in many ways, it really was built by Twin Cities residents. Especially in neighborhoods new to carsharing, residents were paid to participate in focus groups to get feedback on how the service could work best for them. Stark says that feedback directly shaped key aspects of the program such as the pricing structure and the need for vehicles to be located close by.

“Residents told us, ‘I’m not going to walk blocks and blocks to find a car, especially not in winter,’” Stark says. “And so in designing the overall system, we kept it relatively compact in terms of the geography, to try to make sure that there’s a really good chance that there’s a vehicle and a charging station close to you.”

Norfolk: Using data to ‘live with water’ and reduce flooding

As climate change accelerates sea-level rise, scientists expect the coastal city of Norfolk , Va., to experience some of the most severe flooding impacts in the U.S. City leaders are preparing, in part, by becoming global experts at using data to drive decisions around development and flood protection.

The project builds on a lot of other data-driven flood-protection work happening in Norfolk, which earned What Works Cities Certification for its data practices. For example, the city’s network of tide gauges sends updated information to a public data dashboard every six minutes. Norfolk also built an online tool that helps residents visualize the flood risks at their homes and understand steps they can take to reduce water damage. Thanks to this and other outreach efforts, Norfolk residents enjoy a 25-percent discount off flood insurance premiums, translating to roughly $200 in annual savings per household.

San Antonio: Collaborating across sectors to close the digital divide

A lot of federal dollars are flowing into expanding broadband internet access right now, both through the federal infrastructure law and the American Rescue Plan before it. San Antonio has been preparing for this moment for a while.

It started back in 2019, with an effort to understand the local digital divide better. Federal data on broadband access is notoriously spotty, so the city teamed up with Bexar County and researchers at the University of Texas at San Antonio to do their own data deep dive . They found that 20 percent of households lack broadband connectivity, with the biggest gaps in neighborhoods that are predominantly Black and Latino.

The effort will require $600 million to meet its goal of eliminating the digital divide entirely in Bexar County. That’s a lot, even in times like these when funding is abundant. Dillard believes the cross-sector collaboration the region has put in place is well situated to get there. “We don’t want this plan to just sit on a shelf,” he says. “With the plan, we have a mode of attack that we’re going to execute on.”

Have questions about federal recovery funding? Get them answered here . Bloomberg Philanthropies, in partnership with the U.S. Conference of Mayors, has expanded the Federal Assistance e311 program to field local leaders’ questions on the federal infrastructure law, in addition to questions about ARP, FEMA, and CARES funds. The program will host topic-specific workshops to provide expert guidance on how to apply for funds, the need for strong controls on federally funded projects, and strengthening partnerships to bolster innovation capacity.

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COMMENTS

Case study of infrastructure growth: Hong Kong introduction
By Matt Burdett, 13 February 2019. On this page, we introduce Hong Kong as a case study of infrastructure growth over time in one city. Hong Kong as seen from Lugard Road on The Peak. The homes of approximately five million people are visible in this photograph with over two million more spread over the rest of the territory.
2. Changing Urban Systems
Case study of infrastructure growth over time in one city 4. The causes of urban deindustrialization and its economic, social and demographic consequences ... For non IST students, please click on the tab below to be taken to our case study page on gentrification in Berlin, Germany. The work can be found 2/3 of the way down the page.
New York City Infrastructure Case Study
Case study of infrastructure growth over time in one city. The picture used in the agenda and below is a mindmap of ideas created by my current grade 12 students about what they would expect to learn about NYC in this case study. New York Infrastructure Case Study (Urban Environments)
Infrastructure Growth And Sustainable Development: Review Of Lagos City
Lagos City is one of the fastest-growing megacities in the world, with a population of over 20 million people. Over the past few decades, the city has experienced significant infrastructure growth ...
PDF The Impact of Infrastructure on Development Outcomes
2.1 Studies included in the review: Search and selection criteria . This meta-analysis concentrates on the impact of infrastructure on a large array of economic and social outcomes. Infrastructure is understood here as covering three main sectors: • Transport, comprising roads, railroads, ports, and airports.
The Impact of Road Infrastructure Development Projects on Local
The paper uses a mixed-methods and a comparative case study approach. It integrates qualitative, quantitative and spatial methods where the data is collected and analyzed in a concurrent way to reach completeness and a more comprehensive account (Bryman 2006).We selected two case studies (Kisumu in Kenya and Accra in Ghana) based on their geographic locations regarding road infrastructure ...
Green infrastructure in cities for the achievement of the un ...
Cities, in continuous growth, are at a critical moment to guarantee economic, social and environmental stability, as recognised by the UN 2030 Agenda for Sustainable Development. This study aims to demonstrate how Green Infrastructure (GI) can contribute, as spaces of opportunity in cities, to achieving Sustainable Development Goals (SDGs) in metropolitan areas. And by doing that, more ...
PDF NYC Infrastructure Case Study
change over time to urban systems Objectives: -Urban system growth including infrastructure improvements over time, such as transport, sanitation, water, waste disposal and telecommunications • Case study of infrastructure growth over time in one city Activities: -Welcome -Recap -The Big Idea -Objectives
Urban Sustainability Transitions in the Global South: a Case Study of
The existing literature is replete with examples of how sustainable urbanism unfolds in cities in the developed world. However, there is limited insight into how this concept is exemplified in the developing world. This paper poses the question, what strategies are appropriate for transitioning developing world cities towards greater sustainability. Drawing on insights from Curitiba and Accra ...
Sustainable urban infrastructure: A review
Sustainability is an all-inclusive concept designating a process of achieving human development over time and ... Bishop et al. (2000) argued that spatial data on infrastructure could aid the management of urban infrastructure growth. Hinks ... Sahely et al. (2005) offered one of the first case studies on urban metabolism analysis applied to ...
Urban land expansion: the role of population and economic growth for
To this end, we explicitly test the relative importance of population growth and GDP growth in shaping ULE across 300 cities and over 45 years in two different time periods (1970-2000 and 2000 ...
The vital role of infrastructure in economic growth and development
The case for infrastructure investment as a stimulus Infrastructure investment has a strong impact on economic growth, as evidenced by a 2020 GI Hub study that found the economic multiplier for public investment (including infrastructure) is 1.5 times greater than the initial investment in two to five years - much higher than other forms of ...
Urban economic development in Africa: A case study of Nairobi city
Public sector governance and finances: The devolution process in Kenya has given Nairobi City County a total of 14 constitutional functions. The city faces important challenges in terms of ...
Assessing transformative change of infrastructures in urban area
The case study is used to judge how assessment practices can provide more clarity on the potential for transformative change by identifying the strengths and limitations of measures applied in sustainable urban redevelopment. Section 2 of this paper presents the conceptual framework, which is applied in an empirical setting in Section 3.
Cities use innovation to get smarter about infrastructure
With $1.2 trillion in federal infrastructure funding about to hit U.S. cities of all sizes, local leaders everywhere are looking for new ways to maximize this once-in-a-lifetime opportunity to improve roads, parks, bus fleets, renewable energy, flood defenses, internet access—and, most importantly, residents' everyday lives.. Leveraging innovation tools like data, collaboration, and ...
CASE STUDY: infrastructure growth over time in one city ...
describe rail transport in Shanghai. - developed in less than 20 years. - over 400km long. - 13 metro lines. - carry over 5 million passengers daily. - serves 40% of the population. examples of the efficiency of the rail transport system. - journey to Hangzhou shortened from 90 to 30 minutes. - Journey to Nanjing from 180 to 70 mins.
Case study
To understand urban system growth including infrastructure improvements over time, such as transport, sanitation, water, waste disposal and telecommunications. To know a case study of infrastructure growth over time in one city (Shanghai). To explore the causes of urban deindustrialization and its economic, social and demographic consequences.
Project Case studies
Case studies. The USD 508.62 million CLSG Interconnector Project is a landmark cross-border project involving the construction of a transmission line of over 1,300km, with the aim to interconnect the CLSG countries' energy systems into the West Africa Power Pool (WAPP) regional energy network.
Case study of infrastructure growth over time in one city
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Mega infrastructure projects and their contribution to ...
This paper examines the critical role of Mega Infrastructure Projects in sustainable urban and peripheral development by presenting a Sustainable Infrastructure Serum Analysis supported by primary field research. In the Athens Metro case study, we examined the project's impact on sustainable development by analysing the opinions of the project's users. As a result, the Athens Metro serves as a ...
Case study of infrastructure growth over time in one city (Shanghai
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PDF Mega infrastructure projects and their contribution to ...
of the project's users. As a result, the Athens Metro serves as a case study to help us better understand sustainable infrastructure as a framework for green growth from the standpoint of society. The three pillars of sustainable development are inextri-cably linked. This study focuses on the social comprehension and acceptance of a