U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Philos Trans R Soc Lond B Biol Sci
  • v.364(1532); 2009 Oct 27

Human population growth and the demographic transition

The world and most regions and countries are experiencing unprecedentedly rapid demographic change. The most obvious example of this change is the huge expansion of human numbers: four billion have been added since 1950. Projections for the next half century expect a highly divergent world, with stagnation or potential decline in parts of the developed world and continued rapid growth in the least developed regions. Other demographic processes are also undergoing extraordinary change: women's fertility has dropped rapidly and life expectancy has risen to new highs. Past trends in fertility and mortality have led to very young populations in high fertility countries in the developing world and to increasingly older populations in the developed world. Contemporary societies are now at very different stages of their demographic transitions. This paper summarizes key trends in population size, fertility and mortality, and age structures during these transitions. The focus is on the century from 1950 to 2050, which covers the period of most rapid global demographic transformation.

1. Introduction

After centuries of very slow and uneven growth, the world population reached one billion in 1800. The modern expansion of human numbers started then, rising at a slow but more steady pace over the next 150 years to 2.5 billion in 1950. During the second half of the twentieth century, however, growth rates accelerated to historically unprecedented levels. As a result, world population more than doubled to 6.5 billion in 2005 (United Nations 1962 , 1973 , 2007 ). This population expansion is expected to continue for several more decades before peaking near 10 billion later in the twenty-first century. Around 2070, the world's population will be 10 times larger than in 1800.

The recent period of very rapid demographic change in most countries around the world is characteristic of the central phases of a secular process called the demographic transition . Over the course of this transition, declines in birth rates followed by declines in death rates bring about an era of rapid population growth. This transition usually accompanies the development process that transforms an agricultural society into an industrial one. Before the transition's onset, population growth (which equals the difference between the birth and death rate in the absence of migration) is near zero as high death rates more or less offset the high birth rates typical of agrarian societies before the industrial revolution. Population growth is again near zero after the completion of the transition as birth and death rates both reach low levels in the most developed societies. During the intervening transition period, rapid demographic change occurs, characterized by two distinct phases. During the first phase, the population growth rate rises as the death rate declines while the birth rate remains high. In the second phase, the growth rate declines (but remains positive) due to a decline in the birth rate. The entire transition typically takes more than a century to complete and ends with a much larger population size.

The plot of world population size over time in figure 1 (top solid line) shows the typical S-shaped pattern of estimated and projected population size over the course of the transition. Population growth accelerated for most of the twentieth century reaching the transition's midpoint in the 1980s and has recently begun to decelerate slightly. Today, we are still on the steepest part of this growth curve with additions to world population exceeding 75 million per year between 1971 and 2016.

An external file that holds a picture, illustration, etc.
Object name is rstb20090137f01.jpg

Population size estimates, 1900–2005 and projections 2005–2050. High, medium and low variants.

Contemporary societies are at very different stages of their demographic transitions. Key trends in population size, fertility and mortality during these transitions are summarized below. The focus is on the century from 1950 to 2050, covering the period of most rapid global demographic change. The main source of data is the United Nation's 2006 world population assessment, which provides estimates for 1950–2005 and projections from 2005 to 2050 ( United Nations 2007 ).

2. Future population trends

The projected rise in world population to 9.2 billion in 2050 represents an increase of 2.7 billion over the 2005 population of 6.5 billion. Nearly all of this future growth will occur in the ‘South’—i.e. Africa, Asia (excluding Japan, Australia and New Zealand), and Latin America—where population size is projected to increase from 5.3 to 7.9 billion between 2005 and 2050 ( table 1 ). In contrast, in the ‘North’ (Europe, Northern America, Japan and Australia/New Zealand), population size is forecast to remain virtually stable, growing slightly from 1.22 to 1.25 billion between 2005 and 2050. The difference in trends between these two world regions reflects the later stage of the transition in the North compared with the South.

Table 1.

Population estimates (1950–2005) and projections (2005–2050), by region. Adapted from United Nations (2007) .

The global demographic transition began in the nineteenth century in the now economically developed parts of the world (the North) with declines in death rates. Large reductions in birth rates followed in the early part of the twentieth century. These transitions are now more or less complete. But, as shown in table 1 , trends for the two principal regions in the North are expected to diverge between 2005 and 2050: an increase from 0.33 to 0.45 billion in Northern America, and a decline from 0.73 to 0.66 billion in Europe. In fact, several countries in Europe (e.g. Russia) and East Asia (e.g. Japan) face significant population declines as birth rates have fallen below death rates.

The demographic transitions in Africa, Asia and Latin America started later and are still underway. In 2005, Asia had a population of 3.94 billion, more than half of the world total, and its population is expected to grow by 34 per cent to 5.27 billion by 2050. Africa, with 0.92 billion inhabitants in 2005, is likely to experience by far the most rapid relative expansion, more than doubling to 2.0 billion by 2050. Latin America, with 0.56 billion in 2005, is the smallest of the regions of the South; its projected growth trend is similar to that of Asia.

It may seem surprising that population growth continues at a rapid pace in sub-Saharan Africa, where the AIDS epidemic is most severe. This epidemic has indeed caused many deaths, but population growth continues because the epidemic is no longer expanding and the birth rate is expected to remain higher than the elevated death rate in the future ( UNAIDS 2007 ; Bongaarts et al . 2008 ). The epidemic's demographic impact can be assessed by comparing the standard UN population projection (which includes the epidemic's effect) with a separate hypothetical projection in which AIDS mortality is excluded ( United Nations 2007 ). In sub-Saharan Africa, the former projects a 2050 population of 1.76 billion and the latter a population of 1.95 billion. The difference of 0.2 billion in 2050 between these projections with and without the epidemic is due to deaths from AIDS as well as the absence of the descendents from people who died from AIDS. According to these projections, the population of sub-Saharan Africa will grow by one billion between 2005 and 2050 despite the substantial impact of the AIDS epidemic. In fact, no country is expected to see a decline in its population size between 2005 and 2050 due to high AIDS mortality. Most populations in sub-Saharan Africa will more than double in size, several will triple and Niger is expected to quadruple by 2050 ( United Nations 2007 ).

Transitions in the developing world have generally produced more rapid population growth rates in mid-transition than historically observed in the North. In some developing countries (e.g. Kenya and Uganda), peak growth rates approached four per cent per year in recent decades (implying a doubling of population size in two decades), levels that were very rarely observed in developed countries except with massive immigration. Two factors account for this very rapid expansion of population in these still largely traditional societies: the spread of medical technology (e.g. immunization, antibiotics) after World War II, which led to extremely rapid declines in death rates, and a lag in declines in birth rates.

Population sizes for the 10 largest countries in 2005 and in 2050 are presented in table 2 . In 2005, China (1.31 billion) and India (1.13 billion) were by far the largest countries, together accounting for nearly half the South's total. The top 10 include six Asian countries and only one country each in Latin America and Africa. By 2050, the ranking is expected to have shifted substantially, with India's population exceeding China's, and with Ethiopia and DR Congo rising to the top 10, replacing Japan and the Russian Federation.

Table 2.

Ten largest countries by population size in 1995 (estimate) and 2050 (medium projection). Adapted from United Nations (2007) .

To simplify the presentation of results, all projections discussed in this study are taken from the medium variant of the UN projections ( United Nations 2007 ). The UN has a good record of making relatively accurate projections ( National Research Council 2000 ), but the future is of course uncertain and actual population trends over the next half century will likely diverge to some extent from current projections. The UN makes an effort to capture this uncertainty by publishing separate high and low projections. For the world, the high and low variants reach 7.8 and 10.8 billion, respectively, in 2050, indicating a rather wide range of possible outcomes (see dashed lines in figure 1 ).

3. Drivers of population growth: fertility and mortality

The world's population increases every year because the global birth rate exceeds the death rate. For example, in 2000–2005 population size increased at a rate of 1.17 per cent per year, which equals the difference between a birth rate of 2.03 per cent and a death rate of 0.86 per cent. At the country level, population growth is also affected by migration, but for the regional aggregates of population used in this analysis, migration is usually a minor factor, and it will therefore not be discussed in detail.

The annual birth and death rates of populations are in turn primarily determined by levels of fertility and mortality experienced by individuals. The most widely used fertility indicator is the total fertility rate (TFR), which equals the number of births a woman would have by the end of her reproductive years if she experienced the age-specific fertility rates prevailing in a given year. Mortality is often measured by the life expectancy (LE) at birth, which equals the average number of years a newborn would live if subjected to age-specific mortality rates observed in a given year.

(a) Fertility

The UN's past estimates and future projections of fertility levels by region for the period 1950–2050 are presented in figure 2 . In the 1950s, the TFR in the South was high and virtually stable at around six births per woman on average. This high level of fertility reflects a near absence of birth control, a condition that has prevailed for centuries before the middle of the twentieth century. In the late 1960s, a rapid decline in fertility started nearly simultaneously in Asia and Latin America. In contrast, Africa has experienced only limited reproductive change. As a result of these divergent past trends, fertility levels in 2000–2005 differed widely among regions from as high as 5 births per woman (bpw) in Africa, to 2.5 bpw in Asia and Latin America. Average fertility in the North was already low in the early 1950s and has since declined to 2.0 bpw in Northern America and to 1.4 bpw in Europe.

An external file that holds a picture, illustration, etc.
Object name is rstb20090137f02.jpg

Trends in the total fertility rate by region.

The decline in the average fertility in the South from 6 to 3 bpw over the past half century has been very rapid by historical standards. This reproductive revolution is mainly due to two factors. First, desired family size of parents has declined as the cost of children rose and child survival increased. Second, government intervention played a key role. In China this took the form of a coercive and unpopular one-child policy, but most other countries implemented voluntary family planning programmes. The aim of these programmes is to provide information about and access to contraceptives at subsidized prices so that women who want to limit their childbearing can more readily do so.

UN projections for the South assume that the TFR will eventually reach and then fall slightly below the so-called ‘replacement’ level in all regions. Replacement fertility is just above 2 bpw and it represents the level at which each generation just replaces the previous one, thus leading to zero population growth (in the absence of mortality change and migration). Below-replacement fertility produces, in the long run, population decline. As is evident from figure 2 , the TFRs in Asia and Latin America are expected to reach the replacement level around 2020. Africa is assumed to be on a much slower trajectory towards replacement fertility because of its lower level of socio-economic development. High fertility therefore remains a key cause of future population growth in this region. In contrast, the already low fertility of the North is expected to remain below replacement and is no longer driving population growth.

(b) Mortality and life expectancy

Mortality levels have also changed rapidly over the past several decades ( figure 3 ). The South experienced exceptional improvements in LE from an average of 41 years in 1950–1955 to 64 years in 2000–2005. By the early 2000, Latin America reached mortality levels similar to those prevailing in the North in the 1970s, and Asia was just a few years behind. Africa experienced the highest mortality and improvements in LE stalled in the 1990s due to the AIDS epidemic. As a result, Africa's LE, at 52 years in 2000–2005, was still substantially below that of Asia (68) and Latin America (72). As expected, Europe and Northern America already achieved relatively low levels of mortality by 1950, but they have nevertheless seen significant further improvements since then. Europe's LE (74) is now lower than North America's (78) because of a rise in mortality in Eastern Europe after the break-up of the Soviet Union.

An external file that holds a picture, illustration, etc.
Object name is rstb20090137f03.jpg

Trends in LE by region.

Projections of future LEs by the UN assume continued improvements over time in all regions. The North is expected to reach 82 years in 2050 despite the increasing difficulty in achieving increments as countries reach ever higher levels of LE. Asia and Latin America are expected to continue to close the gap with the North, and Africa will continue to lag, in part because the continent remains affected by the AIDS epidemic.

It should be noted that the assumptions made by the UN about future trends in fertility and mortality are not based on a firm theoretical basis. Instead, the UN relies on empirical regularities in past trends in countries that have completed their transitions, mostly in the North, where fertility declined to approximately the replacement level, and increases in LE became smaller over time. This is a plausible approach that unfortunately leaves room for potential inaccuracies in projection results.

4. Changing population age composition

Over the course of the demographic transition, declines in fertility and mortality cause important changes in a population's age composition. In general, countries in the early stages of the transition have a younger age structure than countries in the later stages.

Figure 4 presents the distribution of the 2005 population in four broad age groups: 0–14, 15–24, 25–64 and 65+ by region. Most of the regions in the South—Africa, Latin America, South Asia and West Asia—have very young age structures with about half of the population under age 25 (62% in Africa). The exception is East Asia (mostly China) where this proportion is 37 per cent. In the North, the population under 25 is still smaller: 35 per cent in North America and just 30 per cent in Europe. The reverse pattern is observed for the proportion 65+, which is much higher in the North than in the South, ranging from as high as 15 per cent in Europe to as low as just 3 per cent in Africa.

An external file that holds a picture, illustration, etc.
Object name is rstb20090137f04.jpg

Distribution of population by age, by region, 2005.

(a) The age-dependency ratio

A changing age distribution has significant social and economic consequences, e.g. for the allocation of education, healthcare and social security resources to the young and old. Assessments of this impact often rely on the so-called age-dependency ratio (DR) that summarizes key changes in the age structure. The DR at a given point in time equals the ratio of population aged below 15 and over 65 to the population of age 15–64. This ratio aims to measure how many ‘dependents’ there are for each person in the ‘productive’ age group. Obviously, not every person below 15 and over 65 is a dependent and not every person between ages 15 and 65 is productive. Despite its crudeness, this indicator is widely used to document broad trends in the age composition.

Over the course of a demographic transition, the DR shows a characteristic pattern of change. Figure 5 presents this pattern as observed in the South from 1950 to 2005 and projected from 2005 to 2050. Early in the transition, the DR typically first rises slightly as improvements in survival chances of children raise the number of young people. Next, the DR falls sharply as declines in fertility reduce the proportion of the population under age 15. This decline has important economic consequences because it creates a so-called ‘demographic dividend’, which boosts economic growth by increasing the size of the labour force relative to dependents and by stimulating savings ( Birdsall et al . 2001 ). Finally, at the end of the transition, the DR increases again as the proportion of the population over age 65 rises. Figure 5 also plots the DR of the North from 1950–2050. From 1950 to 2010 it showed a slight decline, but after 2010 it rises steeply as very low fertility and increasing longevity increases the proportion 65+. This ageing of the North poses serious challenges to support systems for the elderly (OECD 1998 , 2001 ).

An external file that holds a picture, illustration, etc.
Object name is rstb20090137f05.jpg

Dependency ratio estimates, 1950–2005.

(b) Population momentum

At the end of the demographic transition natural population growth reaches zero once three conditions are met:

  • Fertility levels-off at the replacement level of about 2.1 bpw (more precisely, the net reproduction rate should be 1). If fertility remains above replacement, population growth continues.
  • Mortality stops declining. In practice, this is not likely to happen because improvements in medical technology and healthcare as well as changes in lifestyles, etc. will probably ensure continued increases in LE.
  • The age structure has adjusted to the post-transitional levels of fertility and mortality.

The adjustment of the age structure at the end of the transition takes many decades to complete. A key implication of this slow adjustment process is that population growth continues for many years after replacement fertility is reached if, as is often the case, the population is still relatively young when fertility reaches the replacement level. The tendency of population size to increase after a two-child family size has been reached is referred to as population momentum ; it is the consequence of a young population age structure (‘young’ is defined relative to the age structure in the current life table) ( Bongaarts & Bulatao 1999 ).

The population momentum inherent in the age structure of a particular population at a given point in time can be estimated with a hypothetical population projection in which future fertility is set instantly to the replacement level, mortality is held constant and migration is set to zero. Since such a variant is not directly available from UN projections, it will not be presented here. However, the UN does provide ‘instant replacement’ projections in which mortality and migration trends are the same as in the standard projection. This projection gives an approximation of the combined effect on future growth of population momentum and declining mortality in the South because the role of migration is small. The difference between this hypothetical projection and the standard medium UN projection is a measure of the impact of high fertility on future population growth.

Results of these two projections are presented in figure 6 , which compares the per cent growth between 2005 and 2050 for regions in the South. The black bars give the growth in the standard (medium variant) projection and the grey bars give the growth in the ‘instant replacement’ projection. Three results are noteworthy. First, the two projections differ most in Africa (+117% versus +50%) which is as expected because fertility is still very high in this region. Second, in all regions of the South outside China, populations would be expected to rise by 50 per cent (62% in West Asia) if fertility were set to replacement in 2005. This implies that momentum and declining mortality are responsible for nearly half of the projected future population growth in Africa and for the large majority of growth in Latin America, and South and West Asia. Third, in East Asia and in Latin America the replacement projection exceeds the medium UN projection. This finding is explained by the fact that fertility in these regions is assumed to average below the replacement level over the next half century.

An external file that holds a picture, illustration, etc.
Object name is rstb20090137f06.jpg

Percentage increase in population 2005–2050, by region, alternative projections. Black bars denote medium UN projection; grey bars denote instant replacement projection (hypothetical).

5. Conclusion

The world and most countries are going through a period of unprecedentedly rapid demographic change. The most obvious example of this change is the huge expansion of human numbers: four billion have been added since 1950. Other demographic processes are also experiencing extraordinary change: women are having fewer births and LEs have risen to new highs. Past trends in fertility and/or mortality have led to very young populations in high fertility countries in the South and to increasingly older populations in the North. Still other important demographic changes which were not reviewed here include rapid urbanization, international migration, and changes in family and household structure.

Global population growth will continue for decades, reaching around 9.2 billion in 2050 and peaking still higher later in the century. The demographic drivers of this growth are high fertility in parts of the South, as well as declining mortality and momentum. This large expansion in human numbers and of the accompanying changes in the age structure will have multiple consequences for society, the economy and the environment as discussed in the subsequent chapters in this issue.

One contribution of 14 to a Theme Issue ‘ The impact of population growth on tomorrow's world ’.

  • Birdsall N., Kelley A., Sinding S.2001 Population matters: demographic change, economic growth and poverty in the developing world Oxford, UK: Oxford University Press [ Google Scholar ]
  • Bongaarts J., Bulatao R.1999 Completing the demographic transition . Popul. Dev. Rev. 25 , 515–529 ( doi:10.1111/j.1728-4457.1999.00515.x ) [ Google Scholar ]
  • Bongaarts J., Buettner J., Heilig G., Pelletier F.2008 Has the AIDS epidemic peaked? Popul. Dev. Rev. 34 , 199–224 ( doi:10.1111/j.1728-4457.2008.00217.x ) [ Google Scholar ]
  • National Research Council 2000 Beyond six billion: forecasting the world's population (eds Bongaarts J., Bulatao R.). Washington, DC: National Academy Press [ Google Scholar ]
  • OECD 1998 Maintaining prosperity in an ageing society Paris: OECD Publications [ Google Scholar ]
  • OECD 2001 The fiscal implications of ageing: projections of age-related spending . OECD Economic Outlook 69 , 145–167 [ Google Scholar ]
  • UNAIDS 2007 AIDS Epidemic Update Geneva: UNAIDS [ Google Scholar ]
  • United Nations 1962 Demographic yearbook New York, NY: United Nations [ Google Scholar ]
  • United Nations 1973 The determinants and consequences of population trends New York, NY: Department of Economic and Social Affairs, Population Studies 50, United Nations [ Google Scholar ]
  • United Nations 2007 World population prospects: the 2006 revision New York, NY: United Nations Population Division [ Google Scholar ]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • My Account Login
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Open access
  • Published: 11 February 2022

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

25k Accesses

66 Citations

13 Altmetric

Metrics details

  • 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.

Similar content being viewed by others

case study about population growth

Making cities mental health friendly for adolescents and young adults

Pamela Y. Collins, Moitreyee Sinha, … Lian Zeitz

case study about population growth

Real-world time-travel experiment shows ecosystem collapse due to anthropogenic climate change

Guandong Li, Torbjörn E. Törnqvist & Sönke Dangendorf

case study about population growth

Comparing the carbon footprints of urban and conventional agriculture

Jason K. Hawes, Benjamin P. Goldstein, … Nevin Cohen


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%.

figure 1

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 ).

figure 2

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.

figure 3

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.

figure 4

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.

figure 5

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.

figure 6

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.

Seto, K. C., Fragkias, M., Güneralp, B. & Reilly, M. K. A meta-analysis of global urban land expansion. PLoS ONE 6 , e23777 (2011).

Article   CAS   Google Scholar  

Grimm, N. B. et al. Global change and the ecology of cities. Science 319 , 756–760 (2008).

Seto, K. C. et al. Climate Change 2014: Mitigation of Climate Change. In Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Edenhofer, O. et al.) 923–1000 (Cambridge University Press, 2014).

McDonald, R. I. et al. Research gaps in knowledge of the impact of urban growth on biodiversity. Nat. Sustain. 3 , 16–24 (2020).

Article   Google Scholar  

Güneralp, B. & Seto, K. C. Futures of global urban expansion: uncertainties and implications for biodiversity conservation. Environ. Res. Lett. 8 , 014025 (2013).

d’Amour, C. B. et al. Future urban land expansion and implications for global croplands. Proc. Natl Acad. Sci. 114 , 8939–8944 (2017).

Pandey, B. & Seto, K. C. Urbanization and agricultural land loss in India: Comparing satellite estimates with census data. J. Environ. Manage. 148 , 53–66 (2015).

Frank, L. D. & Engelke, P. Multiple impacts of the built environment on public health: walkable places and the exposure to air pollution. Int. Reg. Sci. Rev. 28 , 193–216 (2005).

Vernon Henderson, J. Understanding knowledge spillovers. Reg. Sci. Urban Econ. 37 , 497–508 (2007).

IRP. The weight of Cities: Resource Requirements of Future Urbanization (IRP, 2018).

Li, X., Zhou, W. & Ouyang, Z. Forty years of urban expansion in Beijing: What is the relative importance of physical, socioeconomic, and neighborhood factors? Appl. Geogr. 38 , 1–10 (2013).

Aguayo, M. I., Wiegand, T., Azócar, G. D., Wiegand, K. & Vega, C. E. Revealing the driving forces of mid-cities urban growth patterns using spatial modeling: a case study of Los Ángeles, Chile. Ecol. Soc . https://doi.org/10.5751/ES-01970-120113 (2007).

Duranton, G. Determinants of city growth in Colombia. Pap. Reg. Sci. 95 , 101–131 (2016).

Fang, Y. & Pal, A. Drivers of urban sprawl in urbanizing China—a political ecology analysis. Environ. Urban. 28 , 599–616 (2016).

Oueslati, W., Alvanides, S. & Garrod, G. Determinants of urban sprawl in European cities. Urban Stud. 52 , 1594–1614 (2015).

Cheshire, P. & Magrini, S. Urban growth drivers in a Europe of sticky people and implicit boundaries. J. Econ. Geogr. 9 , 85–115 (2009).

Angel, S., Parent, J., Civco, D. L., Blei, A. & Potere, D. The dimensions of global urban expansion: estimates and projections for all countries, 2000–2050. Prog. Plan. 75 , 53–107 (2011).

Shlomo, A., Chabaeva, A. & Gitlin, L. The dynamics of global urban expansion (English) . https://documents.worldbank.org/en/publication/documents-reports/documentdetail (2005).

Thapa, R. B. & Murayama, Y. Drivers of urban growth in the Kathmandu valley. Appl. Geogr. 30 , 70–83 (2010).

Li, G., Sun, S. & Fang, C. The varying driving forces of urban expansion in China: Insights from a spatial-temporal analysis. Landsc. Urban Plan. 174 , 63–77 (2018).

Zhang, Q., Wallace, J., Deng, X. & Seto, K. C. Central versus local states: Which matters more in affecting China’s urban growth? Land Use Policy 38 , 487–496 (2014).

Bettencourt, L. M. A. The origins of scaling in cities. Science 340 , 1438–1441 (2013).

Mahendra, A. & Seto, K. C. Upward and Outward Growth: Managing Urban Expansion for More equitable Cities in the Global South. Working Paper . (World Resources Institute, 2019).

Brueckner, J. K. & Sridhar, K. S. Measuring welfare gains from relaxation of land-use restrictions: the case of India’s building-height limits. Reg. Sci. Urban Econ. 42 , 1061–1067 (2012).

Reilly, M. K., O’Mara, M. P. & Seto, K. C. From Bangalore to the Bay area: comparing transportation and activity accessibility as drivers of urban growth. Landsc. Urban Plan. 92 , 24–33 (2009).

Ustaoglu, E. & Williams, B. Determinants of urban expansion and agricultural land conversion in 25 EU Countries. Environ. Manage. 60 , 717–746 (2017).

Reba, M. & Seto, K. C. A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change. Remote Sens. Environ. 242 , 111739 (2020).

UN, DESA. World Urbanization Prospects 2018 (UN, 2019).

Cruz, N. F., da, Rode, P. & McQuarrie, M. New urban governance: a review of current themes and future priorities. J. Urban Aff. 41 , 1–19 (2019).

Tonkiss, F. City government and urban inequalities. City 24 , 286–301 (2020).

Ahluwalia, I. J. Urban governance in India. J. Urban Aff. 41 , 83–102 (2019).

Haggard, S., MacIntyre, A. & Tiede, L. The rule of law and economic development. Annu. Rev. Polit. Sci. 11 , 205–234 (2008).

Fragkias, M., Lobo, J., Strumsky, D. & Seto, K. C. Does size matter? Scaling of CO 2 emissions and U.S. urban areas. PLoS ONE 8 , e64727 (2013).

Bettencourt, L. M. A. & Lobo, J. Urban scaling in Europe. J. R. Soc. Interface 13 , 20160005 (2016).

Fragkias, M. & Seto, K. C. Evolving rank-size distributions of intra-metropolitan urban clusters in South China. Comput. Environ. Urban Syst. 33 , 189–199 (2009).

Solow, R. M. Technical change and the aggregate production function. Rev. Econ. Stat. 39 , 312–320 (1957).

Mahtta, R., Mahendra, A. & Seto, K. C. Building up or spreading out? Typologies of urban growth across 478 cities of 1 million+ cities. Environ. Res. Lett. 14 , 124077 (2019).

Bloom, D. E., Canning, D. & Fink, G. Urbanization and the Wealth of Nations. Science 319 , 772–775 (2008).

Chen, M., Zhang, H., Liu, W. & Zhang, W. The global pattern of urbanization and economic growth: evidence from the last three decades. PLoS ONE 9 , e103799 (2014).

Turok, I. & McGranahan, G. Urbanization and economic growth: the arguments and evidence for Africa and Asia. Environ. Urban. 25 , 465–482 (2013).

African Development Bank, OECD, & United Nations Development Programme. African Economic Outlook 2016: Sustainable Cities and Structural Transformation (OECD, 2016).

Colsaet, A., Laurans, Y. & Levrel, H. What drives land take and urban land expansion? A systematic review. Land Use Policy 79 , 339–349 (2018).

Whitehead, C., Chiu, R. L. H., Tsenkova, S. & Turner, B. in Urban Land Markets: Improving Land Management for Successful Urbanization (eds. Lall, S. V. et al.) 51–69 (Springer, 2009).

Ortigoza, A. F. et al. Characterising variability and predictors of infant mortality in urban settings: findings from 286 Latin American cities. J. Epidemiol. Commun. Health (2020) https://doi.org/10.1136/jech-2020-215137 . (2020).

Güneralp, B., Reba, M., Hales, B. U., Wentz, E. A. & Seto, K. C. Trends in urban land expansion, density, and land transitions from 1970 to 2010: a global synthesis. Environ. Res. Lett. 15 , 044015 (2020).

Kaufmann, D., Kraay, A. & Mastruzzi, M. The Worldwide Governance indicators: methodology and analytical issues. Hague J. Rule Law 3 , 220–246 (2011).

R Core Team. R: A Language and Environment for Statistical Computing . (R Foundation for Statistical Computing, 2020).

Oxford Economics. (Database.) Global Cities 2030. (Oxford Economics, 2016).

National Bureau of Statistics. China Statistical Yearbook . (National Bureau of Statistics of P. R. China, various years).

Reserve bank of India. Report on Currency and Finance 2000-01 . https://www.rbi.org.in/scripts/AnnualPublications.aspx?head=Report+on+Currency+and+Finance (2002).

Feenstra, R. C., Inklaar, R. & Timmer, M. P. The next generation of the penn world table. Am. Econ. Rev. 105 , 3150–3182 (2015).

Download references


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

Author information

Authors and affiliations.

Yale School of the Environment, Yale University, New Haven, CT, USA

Richa Mahtta, Meredith Reba & Karen C. Seto

Department of Economics, College of Business and Economics, Boise State University, Boise, ID, USA

Michail Fragkias

Department of Geography, Texas A&M University, College Station, TX, USA

Burak Güneralp

World Resources Institute, WRI Ross Center for Sustainable Cities, Washington, DC, USA

Anjali Mahendra

School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USA

Elizabeth A. Wentz

You can also search for this author in PubMed   Google Scholar


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.

Corresponding author

Correspondence to Richa Mahtta .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 18 May 2021

Accepted : 11 January 2022

Published : 11 February 2022

DOI : https://doi.org/10.1038/s42949-022-00048-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Habitability of low-lying socio-ecological systems under a changing climate.

  • Tom Spencer
  • Alexandre K. Magnan
  • Colette C. C. Wabnitz

Climatic Change (2024)

Examining the Spatiotemporal Dynamics and Determinants of Land Urbanization in Prefecture-level Cities, China

  • Xiangjun Ou

Chinese Geographical Science (2024)

A systematic review of Earthquake Early Warning (EEW) systems based on Artificial Intelligence

  • Pirhossein Kolivand
  • Peyman Saberian
  • Seyed Mohammad Ayyoubzadeh

Earth Science Informatics (2024)

Territorial dynamics of spatial growth in Kathmandu Valley, Nepal: understanding geographical notion of urban sustainability

  • Shobha Shrestha
  • Bikash Kumar Karna
  • Narendra Raj Paudel

GeoJournal (2024)

Measuring efficiency of public hospitals under the impact of Covid-19: the case of Türkiye

  • Seher Nur Sülkü
  • Alper Mortaş

Cost Effectiveness and Resource Allocation (2023)

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

case study about population growth

Search form

Population and environment case studies: local approaches to a global challenge.

Chih-Hsien (Michelle) Lin, Detbra Rosales, Melanie Jackson

It is apparent that we now live in a new epoch, the Anthropocene (IGBP, 2001), in which Earth’s environment and climate is mainly controlled by human activity. Environmental damage is accelerating on a global scale. As the world’s population increases, improving standards of living without destroying or degrading the natural environment becomes a challenge. Water shortages, sea-level rise, air pollution and degradation of coastline afflict many areas all over the world.

The larger the population, the more complex the environmental problems become (Fig. 1). The challenge is to build synergies between members of separate disciplines and between scientists, policymakers and the public within and between nations that can accomplish collaboratively what none are capable of doing alone for global climate change. A number of case studies in the coastal zone, based on population density gradients, from Palau , Maryland Coastal Bays, Moreton Bay in Australia and Chesapeake Bay to Pearl River in China will be reviewed to understand the population dynamics, environment issues, and management services. Importantly, through this case study discussion, we can learn from different perspectives between nations and the mistakes in terms of the environment and quality of living.

The relationship between population size between complexity among case studies.

Palau is not letting the overwhelming climate change impacts slow them down. The Pacific country of Palau (with a population of only 21,000) has made significant environmental inroads to a pristine ecosystem protection and a sustainable tourism-based economy. They are looking for ways to increase the resilience of their diverse mangroves, seagrasses and coral reefs to promote high-end ecotourism and manage development to protect its unique ecosystem. However, managing conflicts between conservation, tourism and traditional practices are inevitable in Palau. For example, how do we push to develop education awareness of ecological processes and sustainable development to the public? How do we overcome the knowledge gap, culture differences and language barriers before educating local people about global climate change? How do we spread awareness of the environmental problems when it brings people into closer contact with nature via ecotourism? It is obvious that Palau needs international support and lessons from different experiences and perspectives for management, monitoring and research. A comparison between regions (such as tropical versus temperate environments) is necessary, but it should be careful not to extrapolate too much. Culture bias on nutrient pollution and marine impacts on different systems must be taken into account when making environmental decisions.

The Maryland coastal bay, Chincoteague Bay lagoon system is a wave-dominated environment. The changes impacting water quality, land use and the ecosystem have been associated with intensification of anthropogenic stressors (Fertig et al. 2013) Non-linear ecosystem level changes are due to the complexity of the phenomena occurring in this system. Therefore, management of coastal ecosystems requires a strong interaction between managers and researchers (Dennison 2008). Problem-oriented research is an effective way to examine the sustainable use of coastal zones, and targeting proper species that can affect human health directly is also important for implementing research. The aim of research is to translate it into meaningful information for the decision-making process or its evaluation.

The Moreton Bay system in Australia is known for seagrasses, mangroves and coral diversity. The bay is special in that wildlife is close to city skyline. The health of the bay had worsened over the past year due to the growing population along the coastlines. A significant component of nitrogen pollution from sewage discharge leads to marine eutrophication (Costanzo et al. 2001). Scientists researching water quality issues have developed an ecosystem health index for assessing the health of Moreton Bay. Functional zones based on habitats are also well defined to process effectiveness assessment. On the other hand, the scientists working in Moreton Bay have had good support from politicians, which has enhanced the communication with the public. The Queensland government and mayor are big advocates of the idea that the more people hear the problems, the more they get behind the actions. Currently, they yield good result: the receiving sewage discharge used to be seven times higher than the water quality standards in Queensland; however, it is currently only about two times the standard.

Fig. 2. The drivers-pressures-state-impacts-responses (DPSIR) framework scheme.

The drivers-pressures-state-impacts-responses (DPSIR; Fig. 2) framework provides a standard framework for site assessment and evaluation on the effect of human activity on environment. The framework has been applied to study the complex interactions in Chesapeake Bay and China’s megacity around Pearl River. The rapid rate of population growth around Chesapeake Bay watershed has changed the land use and expanded urban areas. Harmful algal blooms, declines in oyster population, land erosion and invasive species have become major environmental issues here. Although the Chesapeake Bay is extremely well studied; effective communication between science and management is required to bridge the barriers to integration (Boesch 2006). While the Chesapeake Bay is extensively managed with multiply branches; the community involvement and partnership are commonly separated. People do not feel a sense of ownership for the bay.

Population growth in China is formidable. The economic imbalances within the country itself result in a huge and constant influx of migrants to the coastal megacity (defined as a city with more than 10 million people in search of better jobs and quality of living). China’s Pearl River Delta region has overtaken Tokyo as world’s largest megacity. Large population pressures on resources cause devastating effects on natural environments and human health. As megacities grow, the boundaries expand. It is difficult to manage efficiently when cities reach unprecedented scales and complexity beyond population models. Although the urbanization rate of this coastal megacity has been slowing down, there are a number of uncertainties in terms of nutrient contaminants and future climate change.

Governments around the world are moving to integrate their efforts to address complex environmental issues, such as the Kyoto Protocol . However, there are many challenges we must face in order to make this possible, and to working together across-boundaries can range from technological applications, such as data to culture bias in science and organization. Science may have good networking through peer review, but integrative management is not easy to conduct.

References :

  • IGBP (2001) Global Change and the Earth System: a Planet Under Pressure . In: IGBP Science, No. 4. International GeosphereeBiosphere Programme, Stockholm, Sweden, p. 32.
  • Boesch DF (2006) Scientific requirements for ecosystem-based management in the restoration of Chesapeake Bay and Coastal Louisiana . Ecological Engineering 26:6-26 [ pdf ]
  • Costanzo SD, O’donohue MJ, Dennison WC, Loneragan NR, Thomas M (2001) A new approach for detecting and mapping sewage impacts. Marine Pollution Bulletin 42:149-156
  • Dennison WC (2008) Environmental problem solving in coastal ecosystems: A paradigm shift to sustainability . Estuarine, Coastal and Shelf Science 77:185-196 [ pdf ]
  • Fertig B, O'Neil JM, Beckert KA, Cain CJ, Needham DM, Carruthers TJB, Dennison WC (2013) Elucidating terrestrial nutrient sources to a coastal lagoon, Chincoteague Bay, Maryland, USA . Estuarine, Coastal and Shelf Science 116:1-10
  • Sekovski I, Newton A, Dennison W (2011) Megacities in the coastal zone: Using a driver-pressure-state-impact-response framework to address complex environmental problems. Estuarine, Coastal and Shelf Science xxx (2011) 1-12

Next Post > Kick-starting Collective Impact in Five Easy Report Card Steps

Stephanie Siemek 9 years ago

I agree with the statement, “The larger the population, the more complex the environmental problems become…”, as this idea can be supported within our own personal experiences on how difficult it is to accommodate a large group of people. For instance, how difficult is it for a large group of friends to all agree on what to do on Friday night? Each person has different ideas, needs, and opinions. Therefore, how can it be possible for multiple leaders, states, countries etc. to form an agreement that will restore and sustain the Earth’s ecosystems?

Worldwide collaboration and understanding is necessary for conservation and recovery of ecosystems, as nature has no boundaries. Therefore, it will be up to our leaders to enforce policies that will not lead us into total destruction as human population continues to grow. This may mean that they will eventually have to take measures that will not make every big corporation “happy” and cause burden on the economy and society, but it will keep us from completely destroying our resources, planet, and ourselves.

Whitney Hoot 9 years ago

I think you bring up some really excellent points. You've made me start thinking a lot about the relationships among human population size, growth, and density and how these factors influence conservation and management of marine resources. For instance, Palau actually has a higher population growth rate than China (0.8 percent per year vs. 0.5 percent per year), but we are talking about population sizes that are almost incomparable (21,000 vs. 1.36 billion) and hugely different land masses (458 sq km vs. 9.6 million sq km). Even though China is a huge country, it is much more densely populated than Palau; the density in Palau is about 45 people per sq km vs. 142 per sq km in China. (That being said, I imagine that population density is a more useful figure when managing marine resources in Palau than in China, because the density will inevitably be less variable in a small island country.)

We think a lot about population growth in large countries such as China and India - but what about tiny nations like Palau and the Federated States of Micronesia? Obviously, the global implications of population growth in these small countries are much less significant, but locally, population growth can place serious pressure on resources. Should global concerns always outweigh local concerns? China's per capita fish consumption is over 26 kg per year (http://www.greenfacts.org/en/fisheries/l-2/06-fish-consumption.htm) - not a small amount if you multiply it by more than a billion. So, we have to think a lot about Chinese fisheries. But what if there's an endemic species in Palau that could be wiped out by adding just a few more people to the island who are eating reef fish every day? As always, in conservation, we have to prioritize. And there are two ways to look at it - we could spend a lot of money and a lot of time addressing a massive issue (e.g. China's impact on fish abundance) or a lot less time and a lot less money (and we might even be successful) addressing a smaller, locally-scaled conservation issue in Palau. Just fish for thought.

Atika 6 months ago

Worldwide collaboration and understanding is necessary for conservation and recovery of ecosystems, as nature has no boundaries. Therefore, it will be up to our leaders to enforce policies that will not lead us into total destruction as human population continues to grow.

Post a comment

Your E-Mail

E-Mail Notifications: Off All

National Academies Press: OpenBook

Growing Populations, Changing Landscapes: Studies from India, China, and the United States (2001)

Chapter: chinese case studies: an introduction, chinese case studies: an introduction.

Zhao Shidong Institute of Geographic Science and Natural Resources, Chinese Academy of Sciences

With the rapid development of China's economy over the last decades, its land use patterns have changed significantly, especially since the central government's adoption of socioeconomic reform policies, beginning in the late 1970s. Across China, the speed and scale of land use change have varied because of the country's diverse natural and socioeconomic conditions. In order to understand the process and the mechanism of land use change, and then provide a solid basis for the future sustainable planning of land use in China's many different regions, the Chinese research team chose the Jitai Basin, a typical rural area, and the Pearl River Delta, characterized by rapid urbanization, as its study sites (see map , p. 178).


The Jitai Basin, located in Jiangxi Province in south-central China, is made up of four counties that contain two cities. At the end of 1995, the Jitai Basin was home to 2.47 million people; its population density was 198 persons per square kilometer.

Historically, the Jitai Basin was a relatively developed area for agricultural production and handcraft industries such as shipbuilding and textiles, because the Ganjiang (Gan River) served as a main transportation artery between north and south. But with the development of modern industry and communications, the opening of foreign trade ports (Guangzhou, Shanghai, Fuzhou, Xiamen, and Ningbo) in the late nineteenth century, and the building of the Guangzhou–Wuhan and Wuhan–Beijing

railways, the direction of the flow of goods changed rapidly, weakening the transportation function of the Ganjiang River. From then on, China saw its economy grow rapidly in coastal areas, and the Jitai Basin gradually lost its dominant position in communications and the economy and slipped into a declining state.

After the founding of the People's Republic of China in 1949, the central government began to promote the development of the more rural regions of the country. As a result, in the 1950s and 1960s the Jitai Basin was the beneficiary of significant investment in an industrial program, technological assistance, and an influx of trained migrants from the more developed regions. Development of the country as a whole, however, was at a very low level, and cultural, political, and economic restrictions hampered the assistance efforts. In the end, then, no significant socioeconomic development occurred in the Jitai Basin from 1949 to 1978, and, indeed, population pressure and extreme economic policies resulted in serious damage to the region's natural resources. For example, overcutting of forests to provide fuel for steel smelters caused deforestation and soil erosion. And the expansion of agriculture to marginal hilly and mountainous areas in order to meet the subsistence demands of the rapidly growing population for food and fuel further accentuated the serious problems of environmental degradation.

Since the introduction of government reforms in 1978, the Jitai Basin has achieved relatively remarkable economic development in absolute terms. With implementation of the “household responsibility” system in 1982, agricultural productivity increased and the transition from cereal production to cash crop production (such as fruits and vegetables) accelerated. Meanwhile, the local government, aware of the damage to the ecosystem generated by deforestation and soil erosion, successfully implemented a series of policies to reforest the hills and mountains. Despite these achievements, the Jitai Basin still lags behind the coastal regions in economic development and urbanization. In fact, the gap between its socioeconomic development and that of developed regions (for example, the Pearl River Delta) is widening. One important reason is that the central government's economic development strategy tends to favor coastal areas. Other reasons are the Jitai Basin's location in China's hinterlands and its limited access to investment, technology, and the markets in metropolitan areas. In addition, because the region had a surplus of agricultural laborers stemming from the significant lack of development of the nonagricultural sectors, the massive out-migration of young laborers from the Jitai Basin to developed regions such as the Pearl River Delta increased. This development relieved the pressure on local employment, but also weakened agricultural production.


Formed by the alluvium delivered by the West, North, and East Rivers, the Pearl River Delta is located in southern China's Guangdong Province. The study region, which lies in the central part of Pearl River Delta, consists of 13 counties or cities, which belong to six municipalities and are distributed on either side of the Pearl River estuary. The Pearl River Delta is one of the most heavily populated regions of China. In 1995 its permanent population density was 743 persons per square kilometer, compared with 378 for all of Guangdong Province and 126 for China as a whole.

Historically, the Pearl River Delta was known nationally for its production of grain, sugar, silk, freshwater fish, and fruits. Indeed, the region was referred to as the “Fish and Rice County.” The Delta also was one of the places in China where modern industry first appeared. However, from 1866, when industry first arrived, to 1949, when the new China was founded, the region's economy developed very slowly, and many residents of the Delta left to earn a living abroad. One factor in its slow growth was its location; because the Delta is situated at the frontier of the national defense, very few of the important industries were allowed to set up operations in the region.

After implementation of socioeconomic reforms in 1978, the Delta quickened its pace of development and now is one of the richest areas in China. But rapid industrialization and urbanization also have produced dramatic changes in the Pearl River Delta's landscape, as well as environmental pollution. Overall, within less than 20 years the Delta area was transformed from a rural agricultural area into a highly developed region through rapid industrialization and urbanization. Within this process, the interactions between population growth, land use change, and the relevant economic and environmental problems are complex and unique.

Image: jpg

As the world’s population exceeds an incredible 6 billion people, governments—and scientists—everywhere are concerned about the prospects for sustainable development.

The science academies of the three most populous countries have joined forces in an unprecedented effort to understand the linkage between population growth and land-use change, and its implications for the future. By examining six sites ranging from agricultural to intensely urban to areas in transition, the multinational study panel asks how population growth and consumption directly cause land-use change, and explore the general nature of the forces driving the transformations.

Growing Populations, Changing Landscapes explains how disparate government policies with unintended consequences and globalization effects that link local land-use changes to consumption patterns and labor policies in distant countries can be far more influential than simple numerical population increases. Recognizing the importance of these linkages can be a significant step toward more effective environmental management.

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.



The Effect of Population Growth on the Environment: Evidence from European Regions

  • Published: 09 April 2018
  • Volume 35 , pages 379–402, ( 2019 )

Cite this article

  • Hannes Weber   ORCID: orcid.org/0000-0002-1163-9219 1 &
  • Jennifer Dabbs Sciubba 2  

4259 Accesses

92 Citations

26 Altmetric

Explore all metrics

There is a long-standing dispute on the extent to which population growth causes environmental degradation. Most studies on this link have so far analyzed cross-country data, finding contradictory results. However, these country-level analyses suffer from the high level of dissimilarity between world regions and strong collinearity of population growth, income, and other factors. We argue that regional-level analyses can provide more robust evidence, isolating the population effect from national particularities such as policies or culture. We compile a dataset of 1062 regions within 22 European countries and analyze the effect from population growth on carbon dioxide (CO 2 ) emissions and urban land use change between 1990 and 2006. Data are analyzed using panel regressions, spatial econometric models, and propensity score matching where regions with high population growth are matched to otherwise highly similar regions exhibiting significantly less growth. We find a considerable effect from regional population growth on carbon dioxide (CO 2 ) emissions and urban land use increase in Western Europe. By contrast, in the new member states in the East, other factors appear more important.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Note Thick black lines denote the median, box limits are 25th and 75th percentile, respectively, red marks are mean values, and jitter points are regions ( N = 96 in high population growth group and N = 96 in control group). (Color figure online)

The EU classifies its territory into four layers according to the Nomenclature des Unités Territoriales Statistiques (NUTS). The lowest level consists of NUTS-3 regions, designed to usually host between 150,000 and 800,000 people. France, for instance, consists of 100 NUTS-3 regions (départements), 20 NUTS-2 regions (régions), 8 NUTS-1 regions (groups of régions), and one NUTS-0 region (metropolitan France).

These countries are Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, France, Germany, Hungary, Italy, Ireland, Latvia, Lithuania, Luxembourg, Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, and Spain. For CO 2 emissions, no data were available for Croatia. As a result of a reform of regional boundaries in the German state of Saxony, most regions in Saxony are missing from the analysis (note the white area on the maps).

For the models explaining urban growth which is measured between 1990 and 2006, population growth is averaged for this period. However, population data are not available for all regions since 1990 in the source dataset; for these regions the values refer to average population growth between the earliest available year since 1990 and 2008. Figure  1 displays average annual population growth rates between 2000 and 2008 for all regions.

Since urban land use is measured as a percentage of total land use and therefore 0–1 bounded, we use the logit transformation on this variable.

A random effects model was initially considered (providing similar results to the fixed effects model), but a Hausman test suggested superiority of the fixed effects estimator. Since we are not interested in estimating country-level predictors, we went without random effects (or multilevel) models.

Optimal matching and genetic matching were used as alternative algorithms. Since the results do not differ substantially, we only report the findings from propensity score matching here.

An, L., Lindermann, M., Qi, J., Shortdridge, A., & Liu, J. (2005). Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers, 95 (1), 54–79.

Article   Google Scholar  

Angus, I., & Butler, S. (2011). Too many people? Population, immigration, and the environmental crisis . Chicago, IL: Haymarket.

Google Scholar  

Basten, S., Huinink, J., & Klüsener, S. (2012). Spatial variation of sub-national fertility trends in Austria, Germany and Switzerland. Comparative Population Studies, 36 (2–3), 615–660.

Becker, G. S., Murphy, K. M., & Tamura, R. (1990). Human capital, fertility, and economic growth. Journal of Political Economy, 98 (5), S12–S37.

Benfield, K. (2011). How Hamburg became Europe’s Greenest City. Citylab. Accessed 1 Apr 2016.

Bijak, J., Kupiszewska, D., Kupiszewski, M., Saczuk, K., & Kicinger, A. (2007). Population and labour force projections for 27 European countries, 2002–2052: Impact of international migration on population ageing. European Journal of Population, 23, 1–31.

Bivand, R., & Piras, G. (2015). Comparing implementations of estimation methods for spatial econometrics. Journal of Statistical Software, 63 (18), 1–36.

Bloom, D. E., Canning, D., & Sevilla, J. (2003). The demographic dividend. A New Perspective on the Economic Consequences of Population Change . Santa Monica: RAND.

Book   Google Scholar  

Bongaarts, J. (1992). Population growth and global warming. Population and Development Review, 18 (2), 299–319.

Bookchin, M. (1996). Which way for the ecology movement? . San Francisco, CA: AK Press.

Boserup, E. (1965). The condition of agricultural growth . London: Allen & Unwin.

Caldwell, J. C. (1976). Toward a restatement of demographic transition theory. Population and Development Review, 2 (3/4), 312–366.

Carson, R. T. (2010). The environmental Kuznets curve: Seeking empirical regularity and theoretical structure. Review of Environmental Economics and Policy, 4 (1), 3–23.

Catalán, B., Saurí, D., & Serra, P. (2008). Urban sprawl in the Mediterranean? Patterns of growth and change in the Barcelona Metropolitan Region 1993–2000. Landscape and Urban Planning, 85 (3), 174–184.

City of Aarhus (2016). Aarhus CO 2 neutral in 2030. https://stateofgreen.com/files/download/135 . Cited 1 April 2016.

Conca, K., Princen, T., & Maniates, M. F. (2002). Confronting consumption . Cambridge, MA: The MIT Press.

Coole, D. (2013). Too many bodies? The return and disavowal of the population question. Environmental Politics, 22 (2), 195–215.

Couch, C., Karecha, J., Nuissl, H., & Rink, D. (2005). Decline and sprawl: An evolving type of urban development–observed in Liverpool and Leipzig. European Planning Studies, 13 (1), 117–136.

Cramer, J. (2002). Population growth and local air pollution: Methods, models, and results. Population and Development Review, 28 (Supplement), 22–52.

De Ridder, K., Lefebre, F., Adriaensen, A., Arnold, U., Beckroege, W., Bronner, C., et al. (2008). Simulating the impact of urban sprawl on air quality and population exposure in the German Ruhr area. Part II: Development and evaluation of an urban growth scenario. Atmospheric Environment, 42, 7070–7077.

De Sherbinin, A., Carr, D., Cassels, S., & Jiang, L. (2007). Population and Environment. The Annual Review of Environment and Resources, 32, 5.

Dietz, T., & Rosa, E. A. (1997). Effects of population and affluence on CO 2 emissions. Proceedings of the National Academy of Sciences of the USA, 94, 175–179.

Dublin City Council. (2016). Dublin City development plan 2016–2022 written statement (Vol. 1). Dublin: Dublin City Council.

Dyson, T. (2010). Population and development: The demographic Transition . London/New York: Zed Books.

Ehrlich, P. R. (1968). The population bomb . New York: Sierra Club/Ballantine Books.

Environmental Protection Agency (2006). Ireland’s Greenhouse Gas Emissions in 2006. Count Wexford: Environmental Protection Agency.

European Commission (1995). Eurobarometer 44.0. INRA, Brussels. GESIS Data Archive, Cologne. ZA2689 Data file Version 1.0.1. https://doi.org/10.4232/1.10916 .

European Commission (2009). Environment: Stockholm and Hamburg win first European Green Capital awards. Brussels, European Commission 23 Feb.

European Commission. (2011). Roadmap to a resource efficient Europe, Communication COM (2011) 571 of 20 September 2011 . Brussels: European Commission.

European Commission. (2015). The 2015 ageing report: Economic and budgetary projections for the 28 EU Member States (2013–2060) . Brussels: European Commission.

European Environmental Agency. (2007). CLC2006 technical guidelines. EEA Technical report No 17/ 2007 . Luxembourg: Office for Official Publications of the European Communities.

European Spatial Planning Observation Network (2012). Corine land cover, third level of the nomenclature (CLC_AGG3). http://database.espon.eu/db2 . Cited 9 March 2015.

European Spatial Planning Observation Network (2014). CO 2 emissions from ground transport. http://database.espon.eu/db2 . Cited 11 March 2015.

European Union (2016). Brussels. http://ec.europa.eu/environment/europeangreencapital/winning-cities/previous-finalists/brussels/index.html . Cited 1 April 2016.

Eurostat (2015a). Population on 1 January by broad age group, sex and NUTS 3 region (demo_r_pjanaggr3). http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=demo_r_pjanaggr3&lang=en . Cited 17 March 2015.

Eurostat (2015b). Gross domestic product (GDP) at current market prices by NUTS 3 regions (nama_r_e3gdp). http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_r_e3gdp&lang=en . Cited 13 March 2015.

Gorrenflo, L. J., Corson, C., Chomitz, K. M., Harper, G., Honzák, M., & Özler, B. (2011). Exploring the association between people and deforestation in Madagascar. In R. P. Cincotta & L. J. Gorenflo (Eds.), Human population: Its influences on biological diversity . Berlin/ Heidelberg: Springer.

Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2 (2), 111–120.

Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis, 15 (3), 199–236.

Höhn, C., Avramov, D., & Kotowska, I. E. (Eds.). (2008). People, Population Change and Policies. Lessons from the Population Policy Acceptance Study: Demographic knowledge—gender—ageing. Data CD-ROM (Vol. 2). Berlin: Springer.

Holdren, J. P., & Ehrlich, P. R. (1974). Human population and the global environment: Population growth, rising per capita material consumption, and disruptive technologies have made civilization a global ecological force. American Scientist, 62 (3), 282–292.

Honaker, J., King, G., & Blackwell, M. (2011). Amelia II: A program for missing data. Journal of Statistical Software, 45 (7), 1–47.

Huang, P. (2012). Over-breeders and the population bomb. the reemergence of nativism and population control in anti-immigration policies. In L. Mazur (Ed.), A pivotal moment. Population, justice, and the environmental challenge . Washington, D.C./Covelo (CA): Island Press.

Huang, B., Zhang, L., & Wu, B. (2009). Spatiotemporal analysis of rural–urban land conversion. International Journal of Geographical Information Science, 23 (3), 379–398.

Hummel, D., Adamo, S., de Sherbinin, A., Murphy, L., Aggarwal, R., Zulu, L., et al. (2013). Inter-and transdisciplinary approaches to population–environment research for sustainability aims: A review and appraisal. Population and Environment, 34 (4), 481–509.

Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual Review of Environment and Resources, 28 (1), 205–241.

LeSage, P., & Pace, R. (2009). Introduction to spatial econometrics . London/New York: CRC Press.

Liddle, B. (2013). Population, affluence, and environmental impact across development: evidence from panel cointegration modeling. Environmental Modelling and Software, 40, 255–266.

Liddle, B. (2014). Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses. Population and Environment, 35 (3), 286–304.

Lutz, W., & Qiang, R. (2002). Determinants of human population growth. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 357 (1425), 1197–1210.

Lutz, W., Scherbov, S., Prskawetz, A., Dworak, M., & Feichtinger, G. (2002). Population, natural resources, and food security: Lessons from comparing full and reduced-form models. Population and Development Review, 28, 199–224.

MacKellar, F. L., Lutz, W., Prinz, C., & Goujon, A. (1995). Population, households, and CO 2 emissions. Population and Development Review , 21 (4), 849–865.

Mayda, A. M. (2010). International migration: A panel data analysis of the determinants of bilateral flows. Journal of Population Economics, 23, 1249–1274.

Mazur, L. (2012). Introduction. In L. Mazur (Ed.), A pivotal moment. Population, justice, and the environmental challenge . Washington, D.C./Covelo (CA): Island Press.

O’Neill, B. C., Dalton, M., Fuchs, R., Jiang, L., Pachauri, S., & Zigova, K. (2010). Global demographic trends and future carbon emissions. Proceedings of the National Academy of Sciences, 107 (41), 17521–17526.

O’Neill, B. C., Liddle, B., Jiang, L., Smith, K. R., Pachauri, S., Dalton, M., et al. (2012). Demographic change and carbon dioxide emissions. The Lancet, 380 (9837), 157–164.

Palomba, R., Menniti, A., & Mussino, A. (1998). Attitudes towards demographic trends and policy. European Journal of Population, 4, 297–313.

Patacchini, E., Zenou, Y., Henderson, J. V., & Epple, D. (2009). Urban sprawl in Europe. Brookings-Wharton Papers on Urban Affairs (pp. 125–149).

R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org . Cited 30 September 2013.

Rasmussen, U.V. & Christensen, A.M.H. (2010). Danish EcoCities: Six cutting-edge climate and energy cities. In 2010 aceee summer study on energy efficiency in buildings . http://aceee.org/files/proceedings/2010/data/papers/2264.pdf . Cited 1 April 2016.

Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys . Hoboken, NJ: Wiley.

Satterthwaite, D. (2009). The implications of population growth and urbanization for climate change. Environment and Urbanization, 21 (2), 545–567.

Schrodt, P. A. (2014). Seven deadly sins of contemporary quantitative political analysis. Journal of Peace Research, 51 (2), 287–300.

Schulp, C. J., Nabuurs, G. J., & Verburg, P. H. (2008). Future carbon sequestration in Europe—effects of land use change. Agriculture, Ecosystems & Environment, 127 (3), 251–264.

Schultz, T. P. (1993). Returns to women’s education. In E. M. King & M. A. Hill (Eds.), Women’s education in developing countries: Barriers, benefits, and policies . Baltimore, MD: Johns Hopkins University Press. (for the World Bank) .

Schumpeter, J. A. (1994 [1954]). History of economic analysis. London/New York: Routledge.

Seto, K. C., Fragkias, M., Güneralp, B., & Reilly, M. K. (2011). A meta-analysis of global urban land expansion. PLoS ONE, 6 (8), e23777.

Shi, A. (2003). The impact of population pressure on global carbon dioxide emissions, 1975–1996: evidence from pooled cross-country data. Ecological Economics , 44 (1): 29–42.

Siedentop, S., & Fina, S. (2012). Who sprawls most? Exploring the patterns of urban growth across 26 European countries. Environment and Planning A, 44 (11), 2765–2784.

Simon, J. L. (1993). Economic thought about population consequences: Some reflections. Journal of Population Economics, 6 (2), 137–152.

Simon, J. L. (1994). More people, greater wealth, more resources, healthier environment. Economic Affairs, 14 (3), 22–29.

Spengler, J. J. (1998). History of population theories. In: Simon, J. L. (Ed.) The economics of population: Classic writings (pp. 3–15). New Brunswick, NJ: Transaction Publishers.

Teitelbaum, M. S., & Winter, L. M. (1985). The fear of population decline . New York: Academic Press.

United Nations (2015). World Population Projections. The 2015 Revision. Volume I: Comprehensive Tables. New York: United Nations.

Wackernagel, M., & Rees, W. (1996). Our ecological footprint: Reducing human impact on the earth . Gabriola Island, BC: New Society Publishers.

Ward, M. D., & Gleditsch, K. S. (2008). Spatial regression models . Los Angeles: Sage.

York, R., & McGee, J. A. (2016). Understanding the Jevons Paradox. Environmental. Sociology, 2 (1), 77–87.

York, R., Rosa, E. A., & Dietz, T. (2003). STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46 (3), 351–365.

Zhu, Q., & Peng, X. (2012). The impacts of population change on carbon emissions in China during 1978–2008. Environmental Impact Assessment Review, 36, 1–8.

Download references

Author information

Authors and affiliations.

Department of Sociology, University of Mannheim, A5, 6, 68159, Mannheim, Germany

Hannes Weber

Department of International Studies, Rhodes College, 2000 North Parkway, Memphis, TN, 38112, USA

Jennifer Dabbs Sciubba

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Hannes Weber .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

See Tables  3 , 4 , and 5 .

Rights and permissions

Reprints and permissions

About this article

Weber, H., Sciubba, J.D. The Effect of Population Growth on the Environment: Evidence from European Regions. Eur J Population 35 , 379–402 (2019). https://doi.org/10.1007/s10680-018-9486-0

Download citation

Received : 27 July 2016

Accepted : 26 March 2018

Published : 09 April 2018

Issue Date : 15 May 2019

DOI : https://doi.org/10.1007/s10680-018-9486-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Population growth
  • CO 2 emissions
  • NUTS-3 regions
  • Find a journal
  • Publish with us
  • Track your research

Geo for CXC

Case Study – The USA

The United States of America (USA) is a large country with an area of 9,826,630 square kilometers and a population estimated at 311,591,917 in July 2011. Click the link below to view a map of the USA.

Map of the USA  

Population Growth in the United States of America (USA)

Unlike many developed countries, where the population is stable or even decreasing, the population of the USA is growing fairly quickly. Let us examine some of the factors influencing population growth in the USA:

The death rate is fairly high due to the large proportion of elderly people in the population. A high death rate tends to slow down the rate of population growth.

The fertility rate is only slightly above 2.0. This cannot account for the rate of increase in the population as a fertility rate of about 2.0 is believed to have a stabilizing effect on population growth.

Every year, the USA receives large numbers of immigrants. Over 1 million people enter the country legally each year. Large numbers also enter illegally. These immigrants are often young people who start families soon after settling in the USA. They often have larger families than people born in the United States. Immigration is the main reason for the fairly rapid growth in the population of the USA. The graph below illustrates this very well.

It is clear in the graph above that the number of immigrants and their descendants is growing much faster than the rest of the population. According to the graph, within the next few decades, the population of the USA will be made up mostly of people who have migrated to the country since 1970 and their descendants.

Comparing Population Growth in Jamaica and the USA

The CXC/CSEC syllabus requires students to compare the factors affecting population growth in one Caribbean territory and one developed country. You can easily compare population growth in Jamaica and the United States.

  • The fertility rates in both countries are quite low. The fertility rate in the USA is just above 2.0. The fertility rate in Jamaica is about 2.4, a little higher than that of the USA.
  •  Jamaica experiences a net outflow of migrants. This means that there are more emigrants than immigrants. The situation in the USA is reversed. There is a net inflow of migrants with very large numbers of immigrants and relatively few emigrants. The large number of immigrants which pour into the USA and their descendants are the main reason for the current rate of growth in the US population.
  • Due to reductions in the fertility and birth rates, population growth in Jamaica has slowed down quite a bit in the last few decades. However the population of the USA continues to grow fairly quickly.

Take the Population Quiz!

Pin It on Pinterest

  • StumbleUpon

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

  • We're Hiring!
  • Help Center

paper cover thumbnail

Population Growth and Associated Problems: A Case Study Of Darjeeling Town

Profile image of bishal chhetri

A growth in population implies a change between two given points in time. The net change in population between two points in time is expressed in percentage and is described as the growth rate of population. Population growth takes place due to migration and natural increase, which if unchecked accounts for multifarious problems. These problems manifest themselves in development of slums, shortages of water supply, traffic congestion, problems of waste disposal, etc. This paper studies the growth of population in Darjeeling town and highlights the problems associated with it. It further attempts to evaluate the policies adopted by Darjeeling Municipality to solve such problems and suggest some remedial measures for it. I. GEOGRAPHICAL ACCOUNT OF THE STUDY AREA The District of Darjeeling lies between 26°31’N and 27°13’N latitude and between 87°59’E to 88°53’E longitude. The total area is about 3,149 sq.Kms. It is bounded on the north by Sikkim, in the south by Bangladesh, West Dinajp...

Related Papers

Dr.Narendranath Guria

Growth of population in recent times has caused much concern, living in a period of unparalleled population growth. Although there are signs indicating that this unusual rate of increase is coming to an end in some parts of the Bilaspur City. Bilaspur City mostly migrate people being one of the factors determining population growth, has attracted enough attention. In the rural context livelihoods depends more directly on natural resources than in the urban context where cash-based income streams and assets are more significant. And, poor people impact less on the forces causing environmental degradation in urban areas. Urban environmental degradation is primarily associated with health impacts. As a result, the causes, consequences and distributional costs of urban deprivation are commonly more adequately addressed via political and economic policies rather than through direct intervention into environmental processes.

case study about population growth


It is right that population is one of the essential components of economic activities of any society. Different power holders like Cooch Behar Kingdom, Sikkim, Nepal, East India Company etc controlled Terai region in Darjeeling district in different periods. Once upon a time this region was vacant or very less populated. This area started to attract the people to settle here largely when the British East India Company acquired it from Sikkim. After that, this area started to become filled with people coming from different parts of this state, and other states of India and from other nations also. This growth of population has brought many changes in economic activities in this region. This paper aims to highlight the economic changes that are resulted due to the growth of population. This study is mainly based on secondary data.

IOSR Journals

In this paper an attempt has been made to identify the nature and present scenario of urbanization and their constraints to economic development and future prospect. Urbanization is usually thought to be a consequence of the growth of large scale industries, expansion of administrations, development of transport and communication and growth of trading activities. As a process, urbanization has not been deeply involved in the economic emancipation of this hill region, but there is no denying the fact that acting as major nodes, they have always taken a leading role in its economic reorganization. The current paper is based on the analysis of secondary data from Census of India (1981-2011) and other Government publications and some observation in the area. These throw light on the vice-versa relationship of the urbanization in this Himalayan region and its impact on development. The study revealed that the Darjeeling Himalaya experience the fluctuation of urban growth in compare to rural areas due to demographic factor. The population is increasing tremendously and as a result the urban problems are mounting over the local authority especially in town areas where the infrastructure facility is limited. Demographic planning for the region must include both population limitation and migration control, through a combination of socioeconomic incentives and legal controls. Hence the urban centres of this region need to be controlled and directed in a sustainable path.

Dr. Jiban Krishna Mandal*

Publisher ijmra.us UGC Approved

The South 24 Parganas is the largest and the second populated district is originated on 1 st March in 1986 in West Bengal state in India. The paper primarily explains about the history of population growth and gives an overview of the trends of population growth over hundred years of the district and patterns of population growth has been discussed in block level for last three decades. It explains the differential population growth for both rural and urban areas. It also suggests that the rural unemployment and diminishing agricultural productivity are responsible for the rural urban migration and resultant very high population growth in urban areas than rural areas. This paper attempts to identify the accelerating population growth in the northern part of the study area which is contiguous to Kolkata due to the urbanisation, industrialization and commercialization etc. whereas declining trends of population growth has been observed in the rural agrarian littoral deltaic southern part which is contiguous to the Sundarbans mangrove forest.

Ashis Sarkar

English Bazar and Puruliya are two important Class I towns of West Bengal. They are also the district headquarter of their respective districts. Both the towns are located far away from the metropolitan influence of Kolkata in relatively backward part of the state. Inspite of their locational disadvantage, these two towns have witnessed a steady increase in their population growth during the recent past. In this paper an attempt has been made to analyze the factors which contributed to the steady increase in population of these towns.

Shriya Mukherjee

Population growth refers to the increase or decrease of inhabitants in a particular area during a specific period of time. West Bengal is one of the populous states of India and Paschim Medinipur is a highly populated district of West Bengal. The western part of this district is formed by Jhargram Subdivision which is economically very backward in nature compared to the other subdivisions of the district. This paper analyses the population growth of Jhargram Subdivision during a period of fifty years (1961 to 2011) and finds out that the region had very high population growth rate during 1961 which has come down considerably in 2011. But during each decade a certain undulating trend of population growth in each block can be seen as a result of natural increase and socio-econo-political scenario of the district. The analysis reveals that the study area has entered the late second stage of demographic transition in 2011 from first stage in 1961.

Abhik Dasgupta

Indian Journal of Spatial Science, Spring Issue, 12(1)

North 24-Parganas is the 2nd-most populous district in the 2nd-largest populated nation of the world. It bears unique dualities as well as diversities in different aspects. One of the significant challenges it faces is that it has to cater to the needs of about 11% of the total population of West Bengal within about 4.6% of the state's total area. High population density and a high rate of increase in population are indeed a problem for the district. In 2021, the problem may outbreak in all situations. Therefore, the pattern of trends in growth and spatial distribution needs analysis in terms of centrality. For this, like progressive growth rates, concentration index and mean centers of population in different periods have been used. It is found that the population may grow to about 20 million by 2069. Between 1951 and 2011, the population of the district grew more than three times and in 2021, it would be about 11,282,156. The district shows four distinct stages of population growth based on the average annual growth rate of population, i.e., pre-1931, 1931-1961, 1961-2001, and post-2001. During 1971-81, the rural growth rate was negative whereas, the urban growth rate was more than 90%. The weighted mean centers of population shifted significantly within the Habra-I block. The analysis will be more significant for the upcoming periods to chalk out the development and planning for the district.

Dr. Rozina Khatun

Jiaur Rahaman

Rapid Population growth is now a big critical problem across the world. Rapid population growth refers to the aggressive increase of population brought about by increased birth rates with decreased death rates and increase immigration. In other words rapid population growth means more demand for food, clothes and Habitation. It means more space to grow food and provide accommodations to millions of people. Apart these there is also need of better quality of life means of transport, communication, industry, services, education and entertainment. That's For the outcome is always hampered on environment like deforestation, increased pressure on arable land ,loss of biodiversity, pollution(air, soil, water & noise) and results exploited of natural resources and the climatic change. In terms of total population in India West Bengal (Study area) occupies 4 th position and in terms of area placed 13 th position in India. The decadal population growth 13.84% (World average 1.25%) and in terms of population density of West Bengal is 1029 persons/sq.km, ranked 2 nd (India average 382 persons/sq.km). This study is to find out how over population became threaten to natural resources and cause of environmental degradation. All the data are collected mainly from secondary and methods include calculation, presentation and graphical representation of collected secondary data like population density and decadal growth, land use category, forest cover and urbanization). It is essential to prevent environmental degradation and losses. Conservation of natural resources and control the pollutions is only way to prevent environmental degradation. It is possible by reducing quantity of population and increasing quality and awareness among the population. Apart from these the balance with the carrying capacity of support system; need to education and public awareness (individual, organizations, and nations) and also need of recycling modified modern technology and government policy.


Annals of Laparoscopic and Endoscopic Surgery

Iranian Journal of Cancer Care

Nader Hajloo


vivi yuskianti

Ariela Santana

Apivut Veeravinantanakul

Montenegrin Journal of Economics

Dr. Alexandrina Maria Pauceanu

… Nacional de Pilas …

Journal of the American Oil Chemists' Society

Norberto Roveri

Ámbitos. Revista Internacional de Comunicación

Mercedes Medina

Ocean and Coastal Research

Lee-Ann Hayek

Ekawati Septiana

Pesquisa Florestal Brasileira

Carlos Sanquetta

Nature Sustainability

Abdrahmane Wane

Journal of Electroceramics

Paul Muralt



Christian Prip

Gastrointestinal Endoscopy

Pierre Teixeira

Pabrik Bio Septic Tank Sumur kabupaten Pandeglang

Physical Review E

Stefan Scheidl

Environmental Research

Journal of Optical Communications and Networking

Vittorio Curri

Communications in Algebra

Jonathan Dixon

Jurnal Ilmiah Ekonomi Islam

Khavid Normasyhuri

Karahaber diyalogları 02 (Eylül-Ekim 2006)

Clinical medicine. Oncology

Pankaj Taneja

Journal of Enam Medical College

Dipanwita Saha

Journal of Water, Sanitation and Hygiene for Development

Christoph Hoeser

International Journal of Wireless & Mobile Networks

Adib Abdellah

Edgardo Manero

Tatiana Codreanu

Manuel Rial


  •   We're Hiring!
  •   Help Center
  • Find new research papers in:
  • Health Sciences
  • Earth Sciences
  • Cognitive Science
  • Mathematics
  • Computer Science
  • Academia ©2024

Read our research on: Immigration & Migration | Podcasts | Election 2024

Regions & Countries

A look at black-owned businesses in the u.s..

The owner of Marcus Book Store, the oldest Black-owned bookstore in the U.S., talks with her employee about a shop display in Oakland, California, in December 2021. (Amy Osborne/The Washington Post via Getty Images)

More than one-in-five Black adults in the United States say owning a business is essential to financial success, according to a September 2023 Pew Research Center survey . While Black-owned businesses have grown significantly in the U.S. in recent years, they still make up a small share of overall firms and revenue, according to our analysis of federal data.

Pew Research Center conducted this analysis to examine the characteristics of Black-owned businesses in the United States. The analysis relies primarily on data from the 2022  Annual Business Survey  (ABS), conducted by the U.S. Census Bureau and the National Science Foundation’s National Center for Science and Engineering Statistics.

The survey – conducted annually since 2017 – includes all non-farm U.S. firms with paid employees and receipts of $1,000 or more in 2021. Firms are defined as businesses “consisting of one or more domestic establishments under its ownership or control.” Majority business ownership is characterized in the survey as having 51% or more of the stock or equity in the firm. The Census Bureau counts multiracial firm owners under all racial categories they identify with; Hispanic firm owners may be of any race. Read more about the ABS methodology .

A bar chart showing that about 3% of U.S. businesses were Black-or African American-owned in 2021.

In 2021, there were 161,031 U.S. firms with majority Black or African American ownership , up from 124,004 in 2017, according to the latest estimates from the Annual Business Survey  (ABS), conducted by the U.S. Census Bureau and the National Science Foundation. Black-owned firms’ gross revenue soared by 43% during this timespan, from an estimated $127.9 billion in 2017 to $183.3 billion in 2021.

Despite this growth, majority Black-owned businesses made up only about 3% of all U.S. firms that were classifiable by the race and ethnicity of their owners in 2021. And they accounted for just 1% of gross revenue from all classifiable companies that year. By comparison, in 2021, roughly 14% of all Americans were Black.

As has  long been the case , White majority-owned businesses made up the greatest share of classifiable firms (85%) and their revenue (93%) in 2021. About one-in-ten classifiable firms (11%) were majority-owned by Asian Americans, and no more than 7% had majority ownership by someone from another racial and ethnic group.

The Annual Business Survey classifies businesses as “majority Black- or African American-owned” if a Black owner has at least 51% equity in the firm. The same standard holds for business owners of other racial and ethnic backgrounds. The U.S. Census Bureau counts multiracial firm owners under all racial categories they identify with; Hispanic firm owners may be of any race. 

Not all U.S. businesses are classifiable by the race or ethnicity of their owners. In 2021, about 4% of all businesses in the U.S. were  not  classifiable by the race and ethnicity of their owners – though these firms accounted for 61% of total revenue. Ownership and revenue figures in this analysis are based on the roughly 5.7 million firms that  were  classifiable by the race and ethnicity of their owners in 2021, most of which are smaller businesses.

How many workers do Black-owned businesses employ?

Black or African American majority-owned firms provided income for roughly 1.4 million workers in 2021. Their annual payrolls were estimated at $53.6 billion.

Still, most Black-owned firms tend to be smaller businesses. Two-thirds had fewer than 10 employees in 2021 ; 13% had 10 to 49 employees and just 3% had 50 or more. Another 16% reported having no employees. (The ABS determines employment size by the number of paid workers during the March 12 pay period.)

What’s the most common sector for Black-owned businesses?

By far, health care and social assistance. About 45,000 of the roughly 161,000 U.S. companies with majority Black or African American ownership, or 28% of the total, were part of this sector in 2021.

Looked at a different way, 7% of  all  classifiable U.S. businesses in the health care and social assistance sector were majority Black-owned that year .

A chart showing that health care and social assistance is the most common sector among Black-or African American-owned businesses.

Other common sectors that year included:

  • Professional, scientific and technical services (comprising 14% of all Black-owned businesses)
  • Administrative and support and waste management and remediation services (8%)
  • Transportation and warehousing (8%)
  • Retail trade (6%)
  • Construction (6%)

Where are Black-owned businesses located?

A map showing that Black- or African American-owned businesses made up greatest share of firms in District of Columbia, Georgia and Maryland in 2021.

Most Black or African American majority-owned businesses (87%) are located in urban areas. Just 5% are in rural areas – that is, places with fewer than 2,500 inhabitants, under  the Census Bureau’s definition .

Some of the most populous states also have the greatest number of Black majority-owned businesses. Florida had 18,502 such businesses in 2021, California had 15,014 and Georgia had 14,394.

Black majority-owned businesses made up the greatest  share  of all classifiable firms in the District of Columbia (15%), Georgia and Maryland (8% each).

Who are Black business owners?

  • They’re more likely to be men than women. Some 53% of Black-owned firms in 2021 had men as their majority owners, while 39% had women majority owners. Another 8% had equal male-female ownership. The gender gap is larger among classifiable U.S. firms overall: 63% were majority-owned by men in 2021, 22% were majority-owned by women and 14% had equal male-female ownership.
  • They tend to be middle-aged. Roughly half (49%) of Black or African American business owners who reported their age group were ages 35 t0 54 in 2021. Another 28% were 55 to 64, and just 7% were younger than 35.
  • A majority have a college degree. Among owners who reported their highest level of education completed, 27% had a bachelor’s degree and 34% had a graduate or professional degree in 2021.

What motivates Black entrepreneurs?

When asked to choose from a list of reasons why they opened their firm, about nine-in-ten Black or African American majority owners who responded said an important reason was the opportunity for greater income; a desire to be their own boss; or wanting the best avenue for their ideas, goods and services. Balancing work and family life (88%) and having flexible hours (85%) were also commonly cited.

For most Black or African American majority owners, their business is their primary source of income . Seven-in-ten of those who reported income information in 2021 said this was the case.

Note: This is an update of a post originally published on Feb. 21, 2023.

case study about population growth

Sign up for our weekly newsletter

Fresh data delivered Saturday mornings

8 facts about Black Americans and the news

Key facts about the nation’s 47.9 million black americans, facts about the u.s. black population, african immigrants in u.s. more religious than other black americans, and more likely to be catholic, across religious groups, a majority of black americans say opposing racism is an essential part of their faith, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

We've detected unusual activity from your computer network

To continue, please click the box below to let us know you're not a robot.

Why did this happen?

Please make sure your browser supports JavaScript and cookies and that you are not blocking them from loading. For more information you can review our Terms of Service and Cookie Policy .

For inquiries related to this message please contact our support team and provide the reference ID below.

U.S. flag

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

JavaScript appears to be disabled on this computer. Please click here to see any active alerts .

The Case of DDT: Revisiting the Impairment

The fact that DDT (or dichloro-diphenyl-trichloroethane) played a role in the decline of bald eagle and other bird-of-prey populations (e.g., ospreys, brown pelicans) is now commonly appreciated among most biologists. However, the link between DDT and the eggshell thinning that caused reproductive failure in these birds was not initially recognized. Ultimately, the connection was made by re-examining the description of the impairment.

Photo of a bold eagle in flight.

The first link between DDT and diminishing bald eagle and other birds of prey populations was the consistent observation of high body burdens of DDT metabolites. In other words, there was co-occurrence of the declining bird populations and the candidate cause, DDT. There was also evidence of a complete exposure pathway to birds based on body burden of DDT. However, extensive toxicity testing of DDT on adult bird mortality revealed no relationship. This suggested that the proposed mechanism, toxicity, was implausible. However, lethality was not the impairment; decline of birds-of prey was the impairment. A new conceptual model was required that considered other mechanisms that could result in declines in bird populations. In a reexamination of the overall analysis, it became apparent that the species chosen for testing had been relatively tolerant of DDT exposure compared to those that were affected in the wild, and that the endpoint observed in these tests (lethality) would not reflect reproductive success or failure resulting from DDT exposure.

Field observations eventually revealed a potential plausible mechanism of reproductive failure due to eggshell thinning among bald eagles and other birds of prey. Laboratory experiments showed that DDE could cause eggshell thinning. Field studies showed that field exposures to DDE, a metabolite of DDT, were sufficient to cause effects in many species of birds based on the stressor-response relationship. Together these findings provided lines of evidence by which DDT might cause eggshell thinning and reduce reproductive success, a more specific impairment than declines in bird population.

In 1972, DDT was banned from most uses in the United States. In the years following the ban, bald eagle and other bird-of-prey populations slowly recovered. The recovery of bird populations after the use of DDT was banned, is an example of mitigation of the effect following manipulation of the cause, and is very strong evidence that the use of DDT was, in fact, the true cause of bald eagle and other bird-of-prey population declines.

  • Grier JW (1982) Ban of DDT and subsequent recovery of reproduction in bald eagles. Science 218:1232-1234.
  • CADDIS Home
  • Volume 1: Stressor Identification
  • Volume 2: Sources, Stressors and Responses
  • Volume 3: Examples and Applications
  • Volume 4: Data Analysis
  • Volume 5: Causal Databases
  • 0 Shopping Cart

Internet Geography

A country with a low rate of population growth or decline - Japan

case study about population growth

A country with a low rate of population growth or decline – Japan

According to the World Bank, the population of Japan as of 2018 is at 126.5 million, including foreign residents. The population of only Japanese nationals was 124.8 million in January 2019.

Japan was the world’s tenth-most populous country as of 2018. Total population had declined by 0.8 per cent from the time of the census five years previously, the first time it had declined since the 1945 census.

Since 2010, Japan has experienced net population loss due to falling birth rates and minimal immigration, despite having one of the highest life expectancies in the world, at 85.00 years as of 2016 (it stood at 81.25 as of 2006). Using the annual estimate for October of each year, the population peaked in 2009 at 128,570,000.

Why is Japan’s population declining?

Fewer women in Japan are having babies, leading to a reduction in birth rates. There are a number of reasons for this:

  • Many Japanese women work in high-tech industries
  • Their careers may be affected by being a mother
  • Children are becoming increasingly expensive due to increased childcare costs
  • Couples and women can afford a better standard of living if they have fewer children to support
  • People are putting off having children until later in life to focus on careers and enjoy a better standard of living

Significant improvements in Japan’s health care have led to people living much longer than before. However, despite this, death rates are increasing.

As you can see from the graph above, death rates in Japan are increasing. Despite improvements in medical care, Japan has an ageing population which has resulted in an increased death rate . As death rates are now higher than birth rates, the population is in decline.

What are the consequences of Japan’s ageing and declining population?

Japan’s declining population will result in a shortage of workers in the future. This will lead to reduced economic growth and the closure of some services. Industrial development may also reduce as there are fewer people available to innovate in the country’s high-tech sector.

There will also be a higher dependency ration, which means there will be fewer young people to support the ageing population. This will lead to younger people paying higher taxes to support the elderly population.

Some post-industrial towns will become derelict due there not being enough workers to support some industries.

What is being done to tackle Japan’s declining population?

Japan needs to incentivise having children and will need to attract migrants in the future. Japanese Prime Minister Shinzo Abe wants to prevent the population from dropping below 100 million by 2060. In 2017, the government announced a 2 trillion yen ($18 billion) spending package to expand free preschool for children aged 3 to 5 — and for children aged 2 and under from low-income families — and cut waiting times at daycare centres.

Internet Geography Plus

Premium Resources

Please support internet geography.

If you've found the resources on this page useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.

Related Topics

Use the images below to explore related GeoTopics.

A country which is under-populated – Australia

Topic home, igcse units, share this:.

  • Click to share on Twitter (Opens in new window)
  • Click to share on Facebook (Opens in new window)
  • Click to share on Pinterest (Opens in new window)
  • Click to email a link to a friend (Opens in new window)
  • Click to share on WhatsApp (Opens in new window)
  • Click to print (Opens in new window)

If you've found the resources on this site useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.

Search Internet Geography

Top posts and pages.


Latest Blog Entries

Statistical Techniques in Geography Poster

Pin It on Pinterest

  • Click to share
  • Print Friendly


  1. Socioeconomic Stratification: A Case Study on Sustainable Growth in a

    case study about population growth

  2. (PDF) A simple way to study the global population growth

    case study about population growth

  3. (PDF) Population and Economic Growth: A Review Essay

    case study about population growth

  4. ≫ Human Population Growth Free Essay Sample on Samploon.com

    case study about population growth

  5. Population Growth Essay

    case study about population growth

  6. (PDF) Impact of Population Growth

    case study about population growth


  1. Analyse the impact of population growth on education

  2. The Impact of Population Growth: How It Affects Major Cities in the US

  3. fact of population #population #fact #study #short


  1. Case Studies of Population Change and Economic Growth

    Case Studies of Population Change and Economic Growth 45 The East Asian demographic transition was one of the critical factors in the region's spectacular economic growth (Bloom and Williamson, 1998; Bloom, Canning, and Malaney, 2000). Between 1965 and 1990, per capita income rose annually by more than 6 percent. One expla-

  2. The Effect of Population Growth on the Environment: Evidence from

    Hamburg, in northern Germany, is a case of low population growth and low emissions. With around 1.7 million inhabitants, Hamburg is one of the European Union's largest cities and its population grew at a modest 0.48% per annum during the study period. The city won the European Union's award for "Europe's Green Capital" in 2011.

  3. Major Trends in Population Growth Around the World

    The world's population continues to grow, reaching 7.8 billion by mid-2020, rising from 7 billion in 2010, 6 billion in 1998, and 5 billion in 1986. The average annual growth rate was around 1.1% in 2015-2020, which steadily decreased after it peaked at 2.3% in the late 1960s.

  4. An Introduction to Population Growth

    Why Study Population Growth? Population ecology is the study of how populations — of plants, animals, and other organisms — change over time and space and interact with their environment ...

  5. U.S. Case Studies: An Introduction

    U.S. Case Studies: An Introduction. M. Gordon Wolman. Department of Geography and Environmental Engineering, Johns Hopkins University. Although in very different settings, the U.S. study sites—South Florida in the southeastern United States and Chicago in the American Midwest—demonstrate the dynamic changes in population that have taken place over short periods of time in their histories ...

  6. Indian Case Studies: An Introduction

    Indian Case Studies: An Introduction. P. S. Ramakrishnan School of Environmental Sciences, Jawaharlal Nehru University. With its rapidly growing population touching the 1 billion mark, its food security a concern, and its other developmental needs begging answers, India has seen its land use dynamics undergo rapid changes during the last few decades.

  7. Human population growth and the demographic transition

    Population growth is again near zero after the completion of the transition as birth and death rates both reach low levels in the most developed societies. ... as is often the case, the population is still relatively young when fertility reaches the replacement level. ... Population Studies 50, United Nations [Google Scholar] United Nations ...

  8. How Populations Grow: The Exponential and Logistic Equations

    The Exponential Equation is a Standard Model Describing the Growth of a Single Population. The easiest way to capture the idea of a growing population is with a single celled organism, such as a ...

  9. Urban land expansion: the role of population and economic growth for

    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 ...

  10. Population Growth and Economic Development: Theoretical Arguments and

    The relationship between population growth and economic development has remained a controversial topic since the time of Malthus. Opinion among the scholars on this issue is sharply divided. ... Population growth and economic development in low-income countries: A case study of India's prospects. Princeton University Press. Google Scholar ...

  11. The Role of Population in Economic Growth

    In a meta-analysis of studies of economic growth and population growth, Heady and Hodge (2009) ... In any case, economic growth will remain important in the 21st century for at least two reasons. First, if Piketty's analysis is correct, slow economic growth may continue to be a factor in rising inequalities in the distribution of income and ...

  12. Population Case Study: Kerala, India

    Population Case Study: Kerala, India. India's population is estimated to be around one billion. India has one of the highest population growth rates in the world. In the last ten years, its population has increased by 181 million. If this growth rate continues it could become the world's most populated country by 2020.

  13. Population Growth and its Effects on Environment: A Case Study of

    Studies are being done upon population growth in order to reflect on economic growth, employment, savings and environment, conservation of assets, investments and environmental impacts [Richard ...

  14. Population Growth and Economic Development: A Case Study of Yugoslavia

    1995-2000, 5.9. According to the medium variant of the population projection, Yugoslavia, in the year 2000, could expect 25653 thousand inhabitants with the following age structure: the age group up to 19 years would diminish to 22.9%; the group of 20 to 59 would increase to 60.7% and over 60 would rise to 16.4%.

  15. Population and Environment Case Studies: Local Approaches to a Global

    A number of case studies in the coastal zone, based on population density gradients, from Palau, Maryland Coastal Bays, ... The rapid rate of population growth around Chesapeake Bay watershed has changed the land use and expanded urban areas. Harmful algal blooms, declines in oyster population, land erosion and invasive species have become ...

  16. Chinese Case Studies: An Introduction

    Within this process, the interactions between population growth, land use change, and the relevant economic and environmental problems are complex and unique. Page 178 Share Cite Suggested Citation: "Chinese Case Studies: An Introduction."

  17. The Effect of Population Growth on the Environment: Evidence from

    Hamburg, in northern Germany, is a case of low population growth and low emissions. With around 1.7 million inhabitants, Hamburg is one of the European Union's largest cities and its population grew at a modest 0.48% per annum during the study period. The city won the European Union's award for "Europe's Green Capital" in 2011.

  18. Case Study

    Case Study - The USA. The United States of America (USA) is a large country with an area of 9,826,630 square kilometers and a population estimated at 311,591,917 in July 2011. ... Due to reductions in the fertility and birth rates, population growth in Jamaica has slowed down quite a bit in the last few decades. However the population of the ...

  19. (PDF) Population Growth and Associated Problems: A Case Study Of

    This paper studies the growth of population in Darjeeling town and highlights the problems associated with it. It further attempts to evaluate the policies adopted by Darjeeling Municipality to solve such problems and suggest some remedial measures for it. I. GEOGRAPHICAL ACCOUNT OF THE STUDY AREA The District of Darjeeling lies between 26°31 ...

  20. Case study 2: Niger

    Uneven population distribution: Case Study 2: Niger. The Subject Guide Population distribution and economic development at the national scale including voluntary internal migration, core-periphery patterns and mega-city growth. Two detailed and contrasting examples of uneven population distribution.

  21. A country with a rate of high population growth

    The graph below illustrates the impact of the One Child Policy on fertility rates, expressed as babies per woman. The fertility rate in China has dropped, reducing future strain on resources. The average number of children per woman in China dropped from 6 to 2.5 between 1950 and 2005. An estimated 300-400 million births were avoided.

  22. Environmental Science 4.02: Case Study: Population Growth

    Environmental Science 4.02: Case Study: Population Growth. The human population is in an exponential growth phase. Click the card to flip 👆. The human population did not change much from the dawn of civilization to about the mid-eighteenth century. From then on, the human population has grown rapidly. In this case study, you will look at how ...

  23. A look at Black-owned businesses in the U.S.

    In 2021, there were 161,031 U.S. firms with majority Black or African American ownership, up from 124,004 in 2017, according to the latest estimates from the Annual Business Survey (ABS), conducted by the U.S. Census Bureau and the National Science Foundation. Black-owned firms' gross revenue soared by 43% during this timespan, from an estimated $127.9 billion in 2017 to $183.3 billion in 2021.

  24. UK Population Growth Is Weighing on Jeremy Hunt's Spending Plans

    Britain's fast-growing population is ramping up pressure on Chancellor Jeremy Hunt's spending plans, requiring a £25 billion ($31.7 billion) top-up to stop fresh cuts to some public services ...

  25. The Case of DDT: Revisiting the Impairment

    Together these findings provided lines of evidence by which DDT might cause eggshell thinning and reduce reproductive success, a more specific impairment than declines in bird population. Outcome. In 1972, DDT was banned from most uses in the United States. In the years following the ban, bald eagle and other bird-of-prey populations slowly ...

  26. A country with a low rate of population growth or decline

    According to the World Bank, the population of Japan as of 2018 is at 126.5 million, including foreign residents. The population of only Japanese nationals was 124.8 million in January 2019. Japan was the world's tenth-most populous country as of 2018. Total population had declined by 0.8 per cent from the time of the census five years ...