Until the 1800s, population growth was incredibly slow on a global level. The global population was estimated to have been around 188 million people in the year 1CE, and did not reach one billion until around 1803. However, since the 1800s, a phenomenon known as the demographic transition has seen population growth skyrocket, reaching eight billion people in 2023, and this is expected to peak at over 10 billion in the 2080s.

The statistic shows the development of the world population from 1950 to 2050. The world population was around 7.38 billion people in 2015.

The global population

As shown above, the total number of people living on Earth has more than doubled since the 1950s, and continues to increase. A look at the development of the world population since the beginning of the Common Era shows that such a surge in numbers is unprecedented. The first significant rise in population occurred during the 14th century, after the Black Death had killed approximately 25 million people worldwide. Subsequently, the global population increased slowly but steadily until it reached record numbers between 1950 and 2000.

The majority of the global population lives on the Asian continent, as a statistic of the world population by continent shows. In around 100 years, it is estimated that population levels on the African continent will have reached similar levels to those we see in Asia today. As for a forecast of the development of the world population, the figures are estimated to have reached more than 10 billion by the 22nd century.

Growing population numbers pose an increasing risk to the planet, since rocketing numbers equal increased consumption of food and resources. Scientists worry that natural resources, such as oil, and food resources will become scarce, endangering the human race and, even more so, the world’s ecosystem. Nowadays, the number of undernourished / starving people worldwide has decreased slightly, but forecasts paint a darker picture.

This database represents the historic, current and future estimates and projections with number of inhabitants for the world's largest urban areas from 1950-2050. The data covers cities and other urban areas with more than 750,000 people.

India Population Projection: Single Year data was reported at 1,667,873,933.000 Person in 2050. This records an increase from the previous number of 1,658,330,351.000 Person for 2049. India Population Projection: Single Year data is updated yearly, averaging 1,394,461,787.000 Person from Mar 2001 (Median) to 2050, with 50 observations. The data reached an all-time high of 1,667,873,933.000 Person in 2050 and a record low of 1,019,001,911.000 Person in 2001. India Population Projection: Single Year data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Demographic – Table IN.GAI001: Population Projection: Single Year.

This statistic shows the twenty countries with the projected largest urban populations worldwide in 2050. Forecasts estimate that the urban population of China will be **** billion people in 2050.

50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

Before 2025, the world's total population is expected to reach eight billion. Furthermore, it is predicted to reach over 10 billion in 2060, before slowing again as global birth rates are expected to decrease. Moreover, it is still unclear to what extent global warming will have an impact on population development. A high share of the population increase is expected to happen on the African continent.

projected population total, broken down by age and sex, for 2030, 2040, and 2050. They are benchmarked on the 2020 Decennial Census Count data from the U.S. Census Bureau to reflect the effect of the latest demographic trends on the future population. These projections were produced and released by the Cooper Center on July 1, 2024.

The Cooper Center projections research is widely used and well received; this data has been cited by a diverse range of organizations including many federal agencies, state legislatures, businesses, non-profits, think-tanks, academic institutions, and the media. The last vintage of projections for 2020 were found to be highly accurate when evaluated and compared to the actual Census Count data.

This Excel spreadsheet is the primary output from the "county controls" development process. It includes forecasts of overall population for counties in the MORPC 15-county region in five-year intervals in the forecast horizon, as well as forecasts of housing, jobs, workers, and other variables related to these. It is updated approximately every four years in conjunction with updates to the Metropolitan Transportation Plan land use model (1), however it is also made available to the public as a standalone resource via the MORPC Population Hub (2). Referring to the MORPC Data User Personas (3), it includes summary tables intended for use by Engaged Elaine, Decisive Delaney, and Hopeful Hadiya, and a machine-readable long-form table intended for use by Savvy Sonja, Specialist Samir, and Coding Corey. The forecasts are produced using a proprietary time series model using historical population data from the U.S. Census Population Estimates Program (4) and various sources of contemporary data. A detailed methods document has not been released as of March 2023, however more information is available upon request.(1) MORPC Metropolitan Transportation Plan Appendix A: Future Land Use, p. v, https://www.morpc.org/wordpress/wp-content/uploads/2020/10/A_LandUse.pdf(2) MORPC Population Resource Hub: County Forecasts, https://www.morpc.org/popforecast(3) MORPC Data User Personas, https://www.morpc.org/tool-resource/data-user-personas/(4) Population Estimates Program, U.S. Census, https://www.census.gov/programs-surveys/popest.html

This dataset contains tabular data at county, municipal/planning district, and zonal levels for the Adopted 2050 v1.0 Population & Employment Forecasts. DVRPC uses 2050 v1.0 to as the forecast vintage nomenclature, as it is the first forecast to use 2050 as a horizon year. Analytical Data Report (ADR 21014) documents the forecasting process and methodologies.

As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon, or to the horizon year of the long-range plan. Allocation of growth is forecasted using a land use model, UrbanSim, and working closely with member county planning staffs. DVRPC has prepared regional, county, and municipal-level population and employment forecasts in five-year increments through 2050, using 2015 Census population estimates and 2015 National Establishments Time Series (NETS) employment data as the base.

Note: while 2019 land use model results are provided, the forecast was only adopted for 2015, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.

While the forecast is not adopted at the transportation analysis zone (TAZ) level, nor for intervening years between those ending in "0" or "5", it is allocated to these zones for use in DVRPC’s travel demand model in our UrbanSim land use model. TAZ data conforms to municipal/district level adopted totals for population and employment. It also generates a number of other attributes required for the travel demand model.

United States US: Population Projection: Mid Year: Growth data was reported at 0.450 % in 2050. This stayed constant from the previous number of 0.450 % for 2049. United States US: Population Projection: Mid Year: Growth data is updated yearly, averaging 0.700 % from Jun 2001 (Median) to 2050, with 50 observations. The data reached an all-time high of 0.980 % in 2006 and a record low of 0.450 % in 2050. United States US: Population Projection: Mid Year: Growth data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.US Census Bureau: Demographic Projection.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

This data package includes nine population proximity index layers for 2005, 2030 and 2050, for rural, urban and total populations. The layers are distributed as 1km GeoTIFFs and GeoJPGss at 1km. The aim of these layers is to describe the population which may be likely to visit a specific locality where access is determined by Euclidean distance. By using the layers alongside other geographic datasets relating to disease risk it may help identify where people may come into contact with a disease. Human population layers are often used in models to identify risk areas where humans and viruses interact, however most pathogens are not restricted to areas of human habitation: many are found in lesser populated areas such as forests. This dataset will help identify less populated areas that may well still receive high visitor numbers. The layers have been projected to 2030 and 2050 to enable projections of human/disease interfaces in the medium-term which are required to inform policy makers at country and continental level. Urban and rural populations have been separated into individual layers as in some cases it is useful to distinguish between the behaviour and associated risks attributed to the different population segments. There may be a different risk of contacting diseases in rural habitats for rural workers than for than urban visitors.

TAZ Population and Employment Forecasts for the DVRPC region, 2015 - 2050. To be used for planning purposes.

As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon, or to the horizon year of the long-range plan. Allocation of growth is forecasted using a land use model, UrbanSim, and working closely with member county planning staffs. DVRPC has prepared regional, county, and municipal-level population and employment forecasts in five-year increments through 2050, using 2015 Census population estimates and 2015 National Establishments Time Series (NETS) employment data as the base. A forthcoming Analytical Data Report will document the forecasting process and methodologies.

While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC’s travel demand model and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request. Note: while 2019 land use model results are provided, the forecast was only adopted for 2015, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.

This statistic displays a timeline with estimations of the total population in Chile from 2010 to 2050, broken down by gender. By 2050, the source projected a total male population of 10.3 million people in the South American country.

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

County Population and Employment Forecasts for the DVRPC region, 2015 - 2050. To be used for planning purposes.

As a part of DVRPC’s long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon, or to the horizon year of the long-range plan. Allocation of growth is forecasted using a land use model, UrbanSim, and working closely with member county planning staffs. DVRPC has prepared regional, county, and municipal-level population and employment forecasts in five-year increments through 2050, using 2015 Census population estimates and 2015 National Establishments Time Series (NETS) employment data as the base. A forthcoming Analytical Data Report will document the forecasting process and methodologies.

This data provides the county-level forecast. Note: while 2019 land use model results are provided, the forecast was only adopted for 2015, 2020, 2025, 2030, 2035, 2040, 2045, and 2050.

Projections estimate that the population in Italy will decrease in the following years. In January 2025, the Italian population added up to 59 million people, but in 2030 Italians will be 58 million individuals. Twenty years later, the population will be around 52 million people. Low birth rate and old population The birth rate in Italy has constantly dropped in the last years. In 2023, 6.4 children were born per 1,000 inhabitants, three babies less than in 2002. Nationwide, the highest number of births was registered in the southern regions, whereas central Italy had the lowest number of children born every 1,000 people. More specifically, the birth rate in the south stood at 7 infants, while in the center it was equal to 5.9 births. Consequently, the population in Italy has aged over the last decade. Between 2002 and 2024, the age distribution of the Italian population showed a growing share of people aged 65 years and older. As a result, the share of young people decreased. The European exception Similarly, the population in Europe is estimated to decrease in the coming years. In 2024, there were 740 million people living in Europe. In 2100, the figure is expected to drop to 586 million inhabitants. However, projections of the world population suggest that Europe might be the only continent experiencing a population decrease. For instance, the population in Africa could grow from 1.41 billion people in 2022 to 3.92 billion individuals in 2100, the fastest population growth worldwide.

This data set defines boundaries of oil and gas project areas, greater sage-grouse (Centrocercus urophasianus) core areas, and non-core and non-project areas within the Wyoming Landscape Conservation Initiative (WLCI; southwestern Wyoming). Specifically, the data represents results from the manuscript “Combined influences of future oil and gas development and climate on potential Sage-grouse declines and redistribution” for high oil and gas development, low population size, and with effects of climate change under an RCP 8.5 scenario (2050) . The oil and gas development scenario were based on an energy footprint model that simulates well, pad, and road patterns for oil and gas recovery options that vary in well types (vertical and directional) and number of wells per pad and use simulation results to quantify physical and wildlife-habitat impacts. I applied the model to assess tradeoffs among 10 conventional and directional-drilling scenarios in a natural gas field in southwestern Wyoming (see Garman 2017). The effects climate change on sagebrush were developed using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM, version 4) climate model and representative concentration pathway 8.5 scenario (emissions continue to rise throughout the 21st century). The projected climate scenario was used to estimate the change in percent cover of sagebrush (see Homer et al. 2015). The percent changes in sage-grouse population sizes represented in these data are modeled using an individual-based population model that simulates dynamics of populations by tracking movements of individuals in dynamically changing landscapes, as well as the fates of individuals as influenced by spatially heterogeneous demography. We developed a case study to assess how spatially explicit individual based modeling could be used to evaluate future population outcomes of gradual landscape change from multiple stressors. For Greater sage-grouse in southwest Wyoming, we projected oil and gas development footprints and climate-induced vegetation changes fifty years into the future. Using a time-series of planned oil and gas development and predicted climate-induced changes in vegetation, we re-calculated habitat selection maps to dynamically modify future habitat quantity, quality, and configuration. We simulated long-term sage-grouse responses to habitat change by allowing individuals to adjust to shifts in habitat availability and quality. The use of spatially explicit individual-based modeling offered an important means of evaluating delayed indirect impacts of landscape change on wildlife population outcomes. This process and the outcomes on sage-grouse population changes are reflected in this data set.

This database presents population and other demographic estimates and projections from 1960 to 2050. They are disaggregated by age-group and gender and cover approximately 200 economies.