This statistic shows the 20 countries with the highest population growth rate in 2024. In SouthSudan, the population grew by about 4.65 percent compared to the previous year, making it the country with the highest population growth rate in 2024. The global population Today, the global population amounts to around 7 billion people, i.e. the total number of living humans on Earth. More than half of the global population is living in Asia, while one quarter of the global population resides in Africa. High fertility rates in Africa and Asia, a decline in the mortality rates and an increase in the median age of the world population all contribute to the global population growth. Statistics show that the global population is subject to increase by almost 4 billion people by 2100. The global population growth is a direct result of people living longer because of better living conditions and a healthier nutrition. Three out of five of the most populous countries in the world are located in Asia. Ultimately the highest population growth rate is also found there, the country with the highest population growth rate is Syria. This could be due to a low infant mortality rate in Syria or the ever -expanding tourism sector.

The impacts of climate change on forest community composition are still not well known. Although directional trends in climate change and community composition change were reported in recent years, further quantitative analyses are urgently needed. Previous studies focused on measuring population growth rates in a single time period, neglecting the development of the populations. Here we aimed to compose a method for calculating the community composition change, and to testify the impacts of climate change on community composition change within a relatively short period (several decades) based on long-term monitoring data from two plots—Dinghushan Biosphere Reserve, China (DBR) and Barro Colorado Island, Panama (BCI)—that are located in tropical and subtropical regions. We proposed a relatively more concise index, Slnλ, which refers to an overall population growth rate based on the dominant species in a community. The results indicated that the population growth rate of a majority of populations has decreased over the past few decades. This decrease was mainly caused by population development. The increasing temperature had a positive effect on population growth rates and community change rates. Our results promote understanding and explaining variations in population growth rates and community composition rates, and are helpful to predict population dynamics and population responses to climate change.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

In 2024, the population of Taiwan decreased by around 0.86 people per thousand inhabitants (per mill). Taiwan's population declined in 2020 for the first time in decades and population decrease is expected to accelerate in the future. High fluctuations between 2020 and 2023 were mainly due to strong net migration during the COVID-19 pandemic. The natural growth rate, which denotes population growth not including the effects of net migration, was at around -2.87 per mill in 2024.

When comparing somatic growth thermal performance curves (TPCs), higher somatic growth across experimental temperatures is often observed for populations originating from colder environments. Such countergradient variation has been suggested to represent adaptation to seasonality, or shorter favorable seasons in colder climates. Alternatively, populations from cold climates may outgrow those from warmer climates at low temperature, and vice versa at high temperature, representing adaptation to temperature. Using modelling, we show that distinguishing between these two types of adaptation based on TPCs requires knowledge about (i) the relationship between somatic growth rate and population growth rate, which in turn depends on the scale of somatic growth (absolute or proportional), and (ii) the relationship between somatic growth rate and mortality rate in the wild. We illustrate this by quantifying somatic growth rate TPCs for three populations of Daphnia magna where population growth s...

Chart and table of India population from 1950 to 2025. United Nations projections are also included through the year 2100.

In 2023, the annual population growth in the Philippines increased by 0.1 percentage points (+6.85 percent) compared to 2022. In total, the population growth amounted to 1.53 percent in 2023. This increase was preceded by a declining population growth.Population growth refers to the annual change in population, and is based on the balance between birth and death rates, as well as migration.

1. Overall impacts of targeted conservation interventions on population growth rate (λ) will depend on within-year and among-year variation in exposure of target individuals to interventions, and in intervention efficacy in increasing vital rates of exposed individuals. Juvenile survival is one key vital rate that commonly varies substantially within and among years, and consequently drives variation in λ. However, within-year, among-year and overall impacts of targeted interventions on population-wide survival probabilities of potentially mobile juveniles are rarely quantified, precluding full evaluation and evidence-based refinement of interventions.

2. We applied multi-state mark-recapture models to eight years of ring-resighting data from a threatened red-billed chough (Pyrrhocorax pyrrhocorax) population to quantify within-year and among-year variation in juvenile exposure to a targeted intervention of supplementary feeding and parasite treatment, and to estimate efficacy in increasing juvenile survival probability. We then combined and up-scaled these estimated effects to evaluate the impact of the eight-year intervention on overall population-wide survival probability and resulting population size.

3. High proportions of surviving juveniles (>70%) were exposed to the intervention across the annual biological cycle in all years. Exposure was associated with higher short-term survival probabilities through the full annual cycle. Consequently, management increased estimated population-wide annual juvenile survival by approximately 0.14. However, such effects were only evident in cohorts with low overall annual survival.

4. Population models projected that these impacts on annual juvenile survival substantially reduced population decline, such that population size at the end of the eight-year intervention was approximately double that without management.

5. Synthesis and applications. Our results show how complex patterns of within-year and among-year variation in exposure and efficacy of targeted conservation interventions can arise and scale up to affect population-level outcomes. We demonstrate positive effects of a major intervention, but also highlight potential routes to improve efficacy, for example through more precise targeting of agricultural management actions in the context of among-year variation in environmental conditions.

Studies using matrix models to study plant demography. (0.11 MB PDF)

The statistic shows the natural rate of population growth by continent in the middle of 2014. The natural rate of population growth in Africa was 2.5 percent in the middle of 2014.The natural rate of population growth arises from the birth rate minus the death rate and without including the effects of migration.Population growthAs shown in the statistic above, the natural rate of population growth continues to increase on almost every continent in 2013.Due to medical advances, better living conditions and the increase of agricultural productivity the world population is continuously rising. The development of the world population from 1950 to 2030 is estimated to be tripled according to United Nations’ data.The majority of the world population lives in Asia, but the population in Africa is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100. This forecast is based on the rapid growth of the developing countries, such as Africa. Developing countries are well known for its urban residents living in slum conditions. A slum is defined as a thickly populated, metropolitan area with bad living conditions and people living below the poverty line.The urban population in developing countries, who lived in slums has increased steadily for the last decades. In 1990, around 656.7 million people were living in slums in developing countries, while this number rose to 827.7 million people living in slums of developing countries in 2010.The number of people living in slums worldwide is estimated to grow from 1,145,984 in year 2010 to 1,477,291 in year 2020 by the UN-HABITAT. In some countries the population living in slums grows faster than in others, naturally. The percentage of urban slum dwellers in Morocco for example nearly doubled from 13 percent to 24 percent between 2000 and 2010, while the same rate in Turkey only grew moderately from 13 to 18 percent.

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the extent that fluctuations in age structure were useful for predicting annual population growth rates using models which account for density-dependence. 3- Fluctuations in age structure were of a similar magnitude to fluctuations in abundance. For three populations (moose, elk, owls), the mean and the skew of the age distribution fluctuated without stabilizing over the observed time periods. More precisely, the sample variance (interannual variance) of age structure indices increased with the length of the study period which suggests that fluctuations in age structure were non-stationary for these populations – at least over the 15-48 year periods analysed. 4- Fluctuations in age structure were associated with population growth rate for two populations. In particular, population growth varied from positive to negative for moose and from near zero to negative for elk as the average age of adults increased over its observed range. 5- Non-stationarity in age structure may represent an important mechanism by which abundance becomes non-stationary – and therefore difficult to forecast – over time scales of concern to wildlife managers. Overall, our results emphasize the need for vertebrate populations to be modelled using approaches that consider transient dynamics and density-dependence, and that do not rely on the assumption that environmental processes are stationary.

Existing studies show how population growth and rising incomes will cause a massive increase in the future global demand for food. We add to the literature by estimating the potential effect of increases in human weight, caused by rising BMI and height, on future calorie requirements. Instead of using a market based approach, the estimations are solely based on human energy requirements for maintenance of weight. We develop four different scenarios to show the effect of increases in human height and BMI. In a world where the weight per age-sex group would stay stable, we project calorie requirements to increases by 61.05 percent between 2010 and 2100. Increases in BMI and height could add another 18.73 percentage points to this. This additional increase amounts to more than the combined calorie requirements of India and Nigeria in 2010. These increases would particularly affect Sub-Saharan African countries, which will already face massively rising calorie requirements due to the high population growth. The stark regional differences call for policies that increase food access in currently economically weak regions. Such policies should shift consumption away from energy dense foods that promote overweight and obesity, to avoid the direct burden associated with these conditions and reduce the increases in required calories. Supplying insufficient calories would not solve the problem but cause malnutrition in populations with weak access to food. As malnutrition is not reducing but promoting rises in BMI levels, this might even aggravate the situation.

Understanding the influence of environmental variability on population dynamics is a fundamental goal of ecology. Theory suggests that, for populations in variable environments, temporal correlations between demographic vital rates (e.g., growth, survival, reproduction) can increase (if positive) or decrease (if negative) the variability of year-to-year population growth. Because this variability generally decreases long-term population viability, vital rate correlations may importantly affect population dynamics in stochastic environments. Despite long-standing theoretical interest, it is unclear whether vital rate correlations are common in nature, whether their directions are predominantly negative or positive, and whether they are of sufficient magnitude to warrant broad consideration in studies of stochastic population dynamics. We used long-term demographic data for three perennial plant species, hierarchical Bayesian parameterization of population projection models, and stochastic simulations to address the following questions: (1) What are the sign, magnitude, and uncertainty of temporal correlations between vital rates? (2) How do specific pairwise correlations affect the year-to-year variability of population growth? (3) Does the net effect of all vital rate correlations increase or decrease year-to-year variability? (4) What is the net effect of vital rate correlations on the long-term stochastic population growth rate (λS)? We found only four moderate to strong correlations, both positive and negative in sign, across all species and vital rate pairs; otherwise, correlations were generally weak in magnitude and variable in sign. The net effect of vital rate correlations ranged from a slight decrease to an increase in the year-to-year variability of population growth, with average changes in variance ranging from -1% to +22%. However, vital rate correlations caused virtually no change in the estimates of λS (mean effects ranging from -0.01% to +0.17%). Therefore, the proportional changes in the variance of population growth caused by demographic correlations were too small on an absolute scale to importantly affect population growth and viability. We conclude that in our three focal populations and perhaps more generally, vital rate correlations have little effect on stochastic population dynamics. This may be good news for population ecologists, because estimating vital rate correlations and incorporating them into population models can be data-intensive and technically challenging.

Data for "Climate change impacts on population growth across a species’ range differ due to nonlinear responses of populations to climate and variation in rates of climate change "

Natural and anthropogenic disturbances co-occur in most systems, but how they interact to shape demographic outcomes remains poorly understood. Such interactions may alter dynamics of populations in non-additive ways, making demographic predictions challenging when focusing on only one disturbance. Thus, understanding the interactive effects of such disturbances is critically important to determine the population viability of most species under a diversity of stressors. We used a hierarchical integral projection model (IPM), parameterized with 13 years of field data across 20 populations, encompassing 2435 individuals of an endangered herb, Liatris ohlingerae. We examined interactive effects of vertebrate herbivory, fire and anthropogenic activities (sand roads) on vital rates (e.g. survival, growth, reproduction, recruitment) and ultimately on population growth rates (λ), to test the hypothesis that interactions amplify or dampen differences in λ depending on environmental contexts. We constructed megamatrices to determine coupled dynamics in individuals damaged vs. not damaged by herbivores in roadsides and in Florida scrub with different times since fire. We identified strong interactive effects of fire with herbivory and habitat with herbivory on vital rates and on population growth rates in the IPM model. We also found different patterns of variation in λ between habitat and time-since-fire scenarios; population growth rates were higher in roadside populations compared to scrub populations and declined with increasing time since fire. Herbivory had interactive effects with both fire and human disturbances on λ. Herbivory resulted in decreased differences in λ due to anthropogenic disturbance and slightly increased differences in λ due to time since fire. Synthesis. The co-occurrence of various disturbances may both amplify and dampen the effects of other disturbances on population growth rate, thus shaping complex population dynamics that are neither linear nor additive. These realistic nonlinearities represent challenges in understanding and projecting of population dynamics. Here, we examined the effects of various sources of disturbance on the population dynamics of an endangered plant species, finding complex interactions affecting population growth rates. We argue that integration of multiple, interacting stressors in IPMs will allow more accurate estimation of the overall effects of ecological processes on species viability.

Climate change can severely impact species that depend on temporary resources by inducing phenological mismatches between consumer and resource seasonal timing. In the winter moth, warmer winters caused eggs to hatch before their food source, young oak leaves, became available. This phenological mismatch changed the selection on the temperature sensitivity of egg development rate. However, we know little about the fine-scale fitness consequences of phenological mismatch at the individual level and how this mismatch affects population dynamics in the winter moth. To determine the fitness consequences of mistimed egg hatching relative to timing of oak budburst, we quantified survival and pupation weight in a feeding experiment. We found that mismatch greatly increased mortality rates of freshly hatched caterpillars, as well as affecting caterpillar growth and development time. We then investigated whether these individual fitness consequences have population-level impacts by estimating the effect of phenological mismatch on population dynamics, using our long-term data (1994–2021) on relative winter moth population densities at four locations in the Netherlands. We found a significant effect of mismatch on population density with higher population growth rates in years with a smaller phenological mismatch. Our results indicate that climate change-induced phenological mismatch can incur severe individual fitness consequences that can impact population density in the wild.

Population growth rates will respond to an environmental driver only if the driver impacts demographic rate(s) and the population is sensitive to impacted demographic rate(s). If populations vary in the sensitivity of population growth rate to demographic rates, the effect of an environmental driver on population growth rate could vary across populations, even if the effect of the driver on demographic rates does not vary across populations. Here, we use five years of demographic data of a common alpine plant, including data from a neighbor removal experiment and a climate warming experiment, to quantify the relative contribution of neighbor effects on demographic rates vs. sensitivity of population growth rate to demographic rates to across-population variation in neighbor effects on population growth rate. We find neighbor effects on population growth rate vary significantly across populations, and this effect is driven primarily by variation in sensitivity of population growth rate t...

About this dataset

Context

The dataset tabulates the United States population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of United States across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of United States was 340.11 million, a 0.98% increase year-by-year from 2023. Previously, in 2023, United States population was 336.81 million, an increase of 0.83% compared to a population of 334.02 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of United States increased by 57.95 million. In this period, the peak population was 340.11 million in the year 2024. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the United States is shown in this column.
• Year on Year Change: This column displays the change in United States population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Year. You can refer the same here

How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual ‘realized’ growth rates have gained in popularity. Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. We extend realized LTREs in two ways. First, we furt...

Trend-based projections

Four variants of trend-based population projections and corresponding household projections are currently available to download. These are labelled as High, Central and Low and differ in their domestic migration assumptions beyond 2017. The economic crisis has been linked to a fall in migration from London to the rest of the UK and a rise in flows from the UK to London. The variants reflect a range of scenarios relating to possible return to pre-crisis trends in migration.

High: In this scenario, the changes to domestic migration flows are considered to be structural and recent patterns persist regardless of an improving economic outlook.

Low: Changes to domestic migration patterns are assumed to be transient and return to pre-crisis trends beyond 2018. Domestic outflow propensities increase by 10% and inflows decrease by 6% as compared to the High variant.

Central: Assumes recent migration patterns are partially transient and partially structural. Beyond 2018, domestic outlow propensities increase by 5% and inflows by 3% as compared to the High variant.

Central - incorporating 2012-based fertility assumptions: Uses the same migration assumptions as the Central projeciton above, but includes updated age-specific-fertility-rates based on 2011 birth data and future fertility trends taken from ONS's 2012-based National Population Projections. The impact of these changes is to increase fertility by ~10% in the long term.

GLA 2013 round trend-based population projections:
Borough: High
Borough: Low
Borough: Central
Borough: Central - incorporating 2012-based NPP fertility assumptions
Ward: Central

GLA 2013 round trend-based household projections:
Borough: High
Borough: Low
Borough: Central

GLA 2013 round ethnic group population projections:
Borough: Central

Updates:
Update 03-2014: GLA 2013 round of trend-based population projections - Methodology
Update 04-2014: GLA 2013 round of trend-based population projections - Results
Data to accompany Update 04-2014
Update 12-2014: GLA 2013 round ethnic group population projections
Data to accompany Update 12-2014

Housing linked projections

Two variants of housing-linked projections are available based on housing trajectories derived from the 2013 Strategic Housing Land Availability Assessment (SHLAA). The two variants are produced using different models to constrain the population to available dwellings. These are referred to as the DCLG-based model and the Capped Household Size model. These models will be explained in greater detail in an upcoming Intelligence Unit Update.

Projection Models:

DCLG-Based Model

This model makes use of Household Representative Rates (HRR) from DCLG’s 2011-based household projections to convert populations by age and gender into households. The models uses iteration to find a population that yields a total number of households that matches the number of available household spaces implied by the development data. This iterative process involves modulating gross migration flows between each London local authority and UK regions outside of London. HRRs beyond 2021 have been extrapolated forward by the GLA. The model also produces a set of household projections consistent with the population outputs.

Capped Household Size Model

This model was introduced to provide an alternative projection based on the SHLAA housing trajectories. While the projections given by the DCLG-Based Model appear realistic for the majority of London, there are concerns that it could lead to under projection for certain local authorities, namely those in Outer London where recent population growth has primarily been driven by rising household sizes. For these boroughs, the Capped Household Size model provides greater freedom for the population to follow the growth patterns shown in the Trend-based projections, but caps average household size at 2012 levels. For boroughs where the DCLG-based SHLAA model gave higher results than the Trend-based model, the projections follow the results of the former.

Household projections are not available from this model.

Development assumptions:

SHLAA housing data

These projections incorporate development data from the 2013 Strategic Housing Land Availability Assessment (SHLAA) database to determine populations for 2012 onwards. Development trajectories are derived from this data for four phases: 2015-20, 2021-25, 2026-30, and 2031-36. For 2012-14, data is taken from the 2009 SHLAA trajectories. No data is included in the database for beyond 2036 and the 2031-36 trajectories are extended forward to 2041. This data was correct as at February 2014 and may be updated in future. Assumed development figures will not necessarily match information in the SHLAA report as some data on estate renewals is not included in the database at this time.

GLA 2013 round SHLAA-based population projections:
Borough: SHLAA-based
Borough: capped SHLAA-based
Ward: SHLAA-based
Ward: capped SHLAA-based

GLA 2013 round SHLAA-based household projections:
Borough: SHLAA-based

GLA 2013 round SHLAA-based ethnic group population projections:
Borough: SHLAA-based

Zero-development projections

The GLA produces so-called zero-development projections for London that assume that future dwelling stocks remain unchanged. These projections can be used in conjunction with the SHLAA-based projections to give an indication of the modelled impact of the assumed development. Variants are produced consistent with the DCLG-based and Capped Household Size projections. Due to the way the models operate, the former assumes no development beyond 2011 and the latter no development after 2012.

GLA 2013 round zero development population projections:
Borough: DCLG zero development
Borough: capped zero development
Ward: DCLG zero development
Ward: capped zero development

Frequently asked question: which projection should I use?

The GLA Demography Team recommends using the Capped Household Size SHLAA projection for most purposes. The main exception to this is for work estimating future housing need, where it is more appropriate to use the trend-based projections.

The custom-age population tool is here.

To access the GLA's full range of demographic projections please click here.