The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

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.

Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

Effective population size (Ne) is a particularly useful metric for conservation as it affects genetic drift, inbreeding and adaptive potential within populations. Current guidelines recommend a minimum Ne of 50 and 500 to avoid short-term inbreeding and to preserve long-term adaptive potential, respectively. However, the extent to which wild populations reach these thresholds globally has not been investigated, nor has the relationship between Ne and human activities. Through a quantitative review, we generated a dataset with 4610 georeferenced Ne estimates from 3829 unique populations, extracted from 723 articles. These data show that certain taxonomic groups are less likely to meet 50/500 thresholds and are disproportionately impacted by human activities; plant, mammal, and amphibian populations had a <54% probability of reaching = 50 and a <9% probability of reaching = 500. Populations listed as being of conservation concern according to the IUCN Red List had a smaller median than unlisted populations, and this was consistent across all taxonomic groups. was reduced in areas with a greater Global Human Footprint, especially for amphibians, birds, and mammals, however relationships varied between taxa. We also highlight several considerations for future works, including the role that gene flow and subpopulation structure plays in the estimation of in wild populations, and the need for finer-scale taxonomic analyses. Our findings provide guidance for more specific thresholds based on Ne and help prioritize assessment of populations from taxa most at risk of failing to meet conservation thresholds. Methods Literature search, screening, and data extraction A primary literature search was conducted using ISI Web of Science Core Collection and any articles that referenced two popular single-sample Ne estimation software packages: LDNe (Waples & Do, 2008), and NeEstimator v2 (Do et al., 2014). The initial search included 4513 articles published up to the search date of May 26, 2020. Articles were screened for relevance in two steps, first based on title and abstract, and then based on the full text. For each step, a consistency check was performed using 100 articles to ensure they were screened consistently between reviewers (n = 6). We required a kappa score (Collaboration for Environmental Evidence, 2020) of ³ 0.6 in order to proceed with screening of the remaining articles. Articles were screened based on three criteria: (1) Is an estimate of Ne or Nb reported; (2) for a wild animal or plant population; (3) using a single-sample genetic estimation method. Further details on the literature search and article screening are found in the Supplementary Material (Fig. S1). We extracted data from all studies retained after both screening steps (title and abstract; full text). Each line of data entered in the database represents a single estimate from a population. Some populations had multiple estimates over several years, or from different estimation methods (see Table S1), and each of these was entered on a unique row in the database. Data on N̂e, N̂b, or N̂c were extracted from tables and figures using WebPlotDigitizer software version 4.3 (Rohatgi, 2020). A full list of data extracted is found in Table S2. Data Filtering After the initial data collation, correction, and organization, there was a total of 8971 Ne estimates (Fig. S1). We used regression analyses to compare Ne estimates on the same populations, using different estimation methods (LD, Sibship, and Bayesian), and found that the R2 values were very low (R2 values of <0.1; Fig. S2 and Fig. S3). Given this inconsistency, and the fact that LD is the most frequently used method in the literature (74% of our database), we proceeded with only using the LD estimates for our analyses. We further filtered the data to remove estimates where no sample size was reported or no bias correction (Waples, 2006) was applied (see Fig. S6 for more details). Ne is sometimes estimated to be infinity or negative within a population, which may reflect that a population is very large (i.e., where the drift signal-to-noise ratio is very low), and/or that there is low precision with the data due to small sample size or limited genetic marker resolution (Gilbert & Whitlock, 2015; Waples & Do, 2008; Waples & Do, 2010) We retained infinite and negative estimates only if they reported a positive lower confidence interval (LCI), and we used the LCI in place of a point estimate of Ne or Nb. We chose to use the LCI as a conservative proxy for in cases where a point estimate could not be generated, given its relevance for conservation (Fraser et al., 2007; Hare et al., 2011; Waples & Do 2008; Waples 2023). We also compared results using the LCI to a dataset where infinite or negative values were all assumed to reflect very large populations and replaced the estimate with an arbitrary large value of 9,999 (for reference in the LCI dataset only 51 estimates, or 0.9%, had an or > 9999). Using this 9999 dataset, we found that the main conclusions from the analyses remained the same as when using the LCI dataset, with the exception of the HFI analysis (see discussion in supplementary material; Table S3, Table S4 Fig. S4, S5). We also note that point estimates with an upper confidence interval of infinity (n = 1358) were larger on average (mean = 1380.82, compared to 689.44 and 571.64, for estimates with no CIs or with an upper boundary, respectively). Nevertheless, we chose to retain point estimates with an upper confidence interval of infinity because accounting for them in the analyses did not alter the main conclusions of our study and would have significantly decreased our sample size (Fig. S7, Table S5). We also retained estimates from populations that were reintroduced or translocated from a wild source (n = 309), whereas those from captive sources were excluded during article screening (see above). In exploratory analyses, the removal of these data did not influence our results, and many of these populations are relevant to real-world conservation efforts, as reintroductions and translocations are used to re-establish or support small, at-risk populations. We removed estimates based on duplication of markers (keeping estimates generated from SNPs when studies used both SNPs and microsatellites), and duplication of software (keeping estimates from NeEstimator v2 when studies used it alongside LDNe). Spatial and temporal replication were addressed with two separate datasets (see Table S6 for more information): the full dataset included spatially and temporally replicated samples, while these two types of replication were removed from the non-replicated dataset. Finally, for all populations included in our final datasets, we manually extracted their protection status according to the IUCN Red List of Threatened Species. Taxa were categorized as “Threatened” (Vulnerable, Endangered, Critically Endangered), “Nonthreatened” (Least Concern, Near Threatened), or “N/A” (Data Deficient, Not Evaluated). Mapping and Human Footprint Index (HFI) All populations were mapped in QGIS using the coordinates extracted from articles. The maps were created using a World Behrmann equal area projection. For the summary maps, estimates were grouped into grid cells with an area of 250,000 km2 (roughly 500 km x 500 km, but the dimensions of each cell vary due to distortions from the projection). Within each cell, we generated the count and median of Ne. We used the Global Human Footprint dataset (WCS & CIESIN, 2005) to generate a value of human influence (HFI) for each population at its geographic coordinates. The footprint ranges from zero (no human influence) to 100 (maximum human influence). Values were available in 1 km x 1 km grid cell size and were projected over the point estimates to assign a value of human footprint to each population. The human footprint values were extracted from the map into a spreadsheet to be used for statistical analyses. Not all geographic coordinates had a human footprint value associated with them (i.e., in the oceans and other large bodies of water), therefore marine fishes were not included in our HFI analysis. Overall, 3610 Ne estimates in our final dataset had an associated footprint value.

Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - This set of files includes downscaled projections of decadal means of monthly total precipitation (in millimeters, no unit conversion necessary) for each month of decades 2020-2029, 2050-2059, and 2060-2069 at 2x2 kilometer spatial resolution. Each file represents a mean monthly total in a given decade.

The spatial extent is clipped to a Seward REA boundary bounding box.

Overview:

Most of SNAP#8217;s climate projections come in multiple versions. There are 5 climate models, one 5 model average, 3 climate scenarios, 12 months, and 100 years. This amounts to 21,600 files per variable. Some datasets are derived products such as monthly decadal averages or specific seasonal averages, among others. This specific dataset is one subset of those.

Each set of files originates from one of five top ranked global circulation models or is calculated as a 5 Model Average. These models are referred to by the acronyms: cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, or 5modelavg.

For a description of the model selection process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174

Each set of files also represents one projected emission scenario referred to as: sresb1, sresa2, or sresa1b.

Emmission scenarios in brief:

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change.

These files are bias corrected and downscaled via the delta method using PRISM (http:prism.oregonstate.edu) 1961-1990 2km data as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. Please see http:www.snap.uaf.eduabout for a description of the downscaling process.

File naming scheme:

[variable]_[metric]_[units]_[format]_[assessmentReport] [groupModel][scenario]_[timeFrame].[fileFormat]

[variable] pr, tas, logs, dot, dof, veg, age, dem etc

[metric] mean, total, decadal mean monthly mean, etc

[units] mm, C, in, km

[format] optional, if layer is formatted for special use

[assessmentReport] ar4, ar5

[groupModel] cccma_cgcm31, mpi_echam5, gfdl_cm21, ukmo_hadcm3, miroc3_2_medres, 5modelavg, cru_ts30

[scenario] sresb1, sresa2, sresa1b

[timeFrame] yyyy or mm_yyyy or yyyy_yyyy or mm_yyyy_mm_yyyy

[fileFormat] txt, png, pdf, bmp, tif

examples:

tas_mean_C_ar4_cccma_cgcm3_1_sresb1_05_2034.tif

this file represents mean May, 2034 temperature from the 4th Assessment Report on Climate Change from the CCCMA modeling group, using their CGCM3.1 model, under the B1 climate scenario.

pr_total_mm_ar4_5modelAvg_sresa1b_09_2077.tif

this file represents total September, 2077 precipitation from the 4th Assessment Report on Climate Change from the 5 Model Average, under the A1B climate scenario.

tas = near-surface air temperature

pr = precipitation including both liquid and solid phases

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute < 1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

Methods For each mammal species i with available life-history information, we searched SCOPUS for studies (published before 2018) where the title, abstract, or keywords contained the following search terms:

Scientific species namei AND (demograph* OR population OR life-history OR "life history" OR model) AND (climat* OR precipitation OR rain* OR temperature OR weather) AND (surv* OR reprod* OR recruit* OR brood OR breed* OR mass OR weight OR size OR grow* OR offspring OR litter OR lambda OR birth OR mortality OR body OR hatch* OR fledg* OR productiv* OR age OR inherit* OR sex OR nest* OR fecund* OR progression OR pregnan* OR newborn OR longevity).

We used the R package taxize (Chamberlain and Szöcs 2013) to resolve discrepancies in scientific names or taxonomic identifiers and, where applicable, searched SCOPUS using all scientific names associated with a species in the Integrated Taxonomic Information System (ITIS; http://www.itis.gov).

We did not extract information on demographic-rate-climate relationships if:

A study reported on single age or stage-specific demographic rates (e.g., Albon et al. 2002; Rézoiki et al. 2016)
A study used an experimental design to link demographic rates to climate variation (e.g., Cain et al. 2008)
A study considered the effects of climate only indirectly or qualitatively. In most cases, this occurred when demographic rates differed between seasons (e.g., dry vs. wet season) but were not linked explicitly to climatic factors (e.g., varying precipitation amount between seasons) driving these differences (e.g., de Silva et al. 2013; Gaillard et al. 2013).

We included several studies of the same population as different studies assessed different climatic variables or demographic rates or spanned different years (e.g., for Rangifer tarandus platyrhynchus, Albon et al. 2017; Douhard et al. 2016).

We note that we can miss a potentially relevant study if our search terms were not mentioned in the title, abstract, or keywords. To our knowledge, this occurred only once, for Mastomys natalensis (we included the relevant study [Leirs et al. 1997] into our review after we were made aware that it assesses climate-demography relationships in the main text).

Lastly, we checked for potential database bias by running the search terms for a subset of nine species in Web of Science. The subset included three species with > three climate-demography studies published and available in SCOPUS (Rangifer tarandus, Cervus elaphus, Myocastor coypus); three species with only one climate-demography study obtained from SCOPUS (Oryx gazella, Macropus rufus, Rhabdomys pumilio); and another three species where SCOPUS did not return any published study (Calcochloris obtusirostris, Cynomops greenhalli, Suncus remyi). Species in the three subcategories were randomly chosen. Web of Science did not return additional studies for the three species where SCOPUS also failed to return a potentially suitable study. For the remaining six species, the total number of studies returned by Web of Science differed, but the same studies used for this review were returned, and we could not find any additional studies that adhered to our extraction criteria.

Description of key collected data

From all studies quantitatively assessing climate-demography relationships, we extracted the following information:

Geographic location - The center of the study area was always used. If coordinates were not provided in a study, we assigned coordinates based on the study descriptions of field sites and data collection.
Terrestrial biome - The study population was assigned to one of 14 terrestrial biomes (Olson et al. 2001) corresponding to the center of the study area. As this review is focused on general climatic patterns affecting demographic rates, specific microhabitat conditions described for any study population were not considered.
Climatic driver - Drivers linked to demographic rates were grouped as either local/regional precipitation & temperature values or derived indices (e.g., ENSO, NAO). The temporal extent (e.g., monthly, seasonal, annual, etc.) and aggregation type (e.g., minimum, maximum, mean, etc.) of drivers was also noted.
Demographic rate modeled - To facilitate comparisons, we grouped the demographic rates into either survival, reproductive success (i.e., whether or not reproduction occurre, reproductive output (i.e., number or rate of offspring production), growth (including stage transitions), or condition that determines development (i.e., mass or size). 
Stage or sex modeled - We retrieved information on responses of demographic rates to climate for each age class, stage, or sex modeled in a given study.
Driver effect - We grouped effects of drivers as positive (i.e., increased demographic rates), negative (i.e., reduced demographic rate), no effect, or context-dependent (e.g., positive effects at low population densities and now effect at high densities). We initially also considered nonlinear effects (e.g., positive effects at intermediate values and negative at extremes of a driver), but only 4 studies explicitly tested for nonlinear effects, by modelling squared or cubic climatic drivers in combination with driver interactions. We therefore considered nonlinear demographic effects as context dependent.  
Driver interactions - We noted any density dependence modeled and any non-climatic covariates included (as additive or interactive effects) in the demographic-rate models assessing climatic effects.
Future projections of climatic driver - In studies that indicated projections of drivers under climate change, we noted whether drivers were projected to increase, decrease, or show context-dependent trends. For studies that provided no information on climatic projections, we quantified projections as described in Detailed description of climate-change projections below (see also climate_change_analyses_mammal_review.R).

A 21st century projection under SRES A1B scenario initialized in the year of the 20C_2. (EH5-T63L31_OM-GR1.5L40_A1B_2) Model: ECHM5 The atmospheric GCM ECHM5 has a spectral drnamical core where vorticity, divergence, temperature and surface pressure are represented in the horizontal by a truncated series of harmonics. A semi-Lanrangian scheme is used for the transport of water components (water vapour, cloud liquid water and cloud ice) SRES A1B seems to be the most plausible scenario, describes a future world rapid economic growth, a global population that peaks in mid-century and declines thereafter, and increase cultural and social interactions. The technological emphasis of this scenario is on a balance across all energy sources, not relying too heavily on any particular energy source. No. of Views : 168

One projection under SRES A1B scenario for the 21st century. MRI MODEL: The AGCM component of MRI-CGCM2.3.2 is based on a version of the operational weather forecasting model of the Japan Meteorological Agency (JMA). It has a spectral dynamical core where vorticity, divergence, temperature, specific humidity and surface pressure represented in the horizontal by a truncated series of spherical harmonics. SCENARIO A1B: SRES A1B seems to be the most plausible scenario, describes a future world rapid economic growth, a global population that peaks in mid-century and declines thereafter, and increase cultural and social interactions. The technological emphasis of this scenario is on a balance across all energy sources, not relying too heavily on any particular energy source. Data-data ini adalah hasil kajian NAHRIM pada tahun 2014 berdasarkan The Forth Assessment Report (AR4) of the United Nations Intergovernmental Panel on Climate Change (IPCC). Penafian: Maklumat ini perlu disemak dengan teliti sebelum menggunakannya. Institut Penyelidikan Hidraulik Kebangsaan Malaysia (NAHRIM) tidak bertanggungjawab terhadap sebarang isu yang timbul dari atau berkaitan kerana menggunakan maklumat yang telah disediakan ini.

Grid-based building morphological parameters with global coverage at 30-arc second spatial resolution are currently available in GeoTIFF format. Provided datasets contains three-building morphological parameters (the mean building height Have, plan area density PAD and frontal area density FAD) and two-aerodynamic parameters (aerodynamic roughness length z0 and zero-place displacement d) and sky-view factor (svf).The building morphological datasets were estimated from the global databases such as population, nighttime light, impervious surface area and gross domestic products. Two aerodynamic parameters and sky-view factors are calculated using the empirical equations discussed by Kanda et al. (2013) and Kanda et al. (2005), respectively.1. Raster files: (parameter name)_2013.tifFormat: GeoTIFFProjection: WGS 1984 World Mercator projectionSpatial resolution: 30-arc secondData list: Have_2013.tif, PAD_2013.tif, FAD_2013.tif, d_2013.tif, z0_2013.tif, svf_2013.tif2. Building Original DataFormat: Microsoft Excel WorkbookOriginal_building_data.xlsx contains observed building morphological parameters calculated from three- and two-dimensional building databases, and global databases (impervious surface area ISA and population density adjusted by nighttime light PopdenVIIRS) at each grid code.Validation_analysis.xlsx contains building morphological parameters calculated from three-dimensional building database (observed) and parameters estimated from global databases (predicted) at one-km spatial resolution in Berlin, Singapore and Osaka.Additional_validation_UScities.xlsx contains building morphological parameters at one-km resolution by NUDAPT database (observed) and estimated from global databases (predicted) for 42 US cities. We used this data in the Supplementary Discussion. Megacities_statistic.xlsx contains GDPcity, the maximum, minimum, mean value and standard deviation of each predicted building morphological parameters at 37 megacities. 3. Source CodeProgramming language 1: Python site package in ArcGIS v10.3.1Calculate_parameters.py contains code for calculating observed building morphological parameters from grid-based two- and three-dimensional building database input. We recommend using this script after using the Split By Attributing Tools to convert a fishnet building footprint map into multiple grids.Modifying_population_by_nightlight.py contains code for adjusted population density by nighttime light at each grid.Programming language 2: Python v2.7Converting_grids.py contains code for converting grid-based population density adjusted by nighttime light into a global map. This source code is used after running Modifying_population_by_nightlight.py.

VITAL SIGNS INDICATOR Vulnerability to Sea Level Rise (EN11)

FULL MEASURE NAME Share of population living in zones at risk from various sea level rise forecast scenarios

LAST UPDATED July 2017

DESCRIPTION Vulnerability to sea level rise refers to the share of the historical and current Bay Area population located in areas at risk from forecasted sea level rise over the coming decades. Given that there are varying forecasts for the heightened high tides (i.e., mean highest high water mark), projected sea level impacts are presented for six scenarios ranging from a one foot rise to six feet. A neighborhood is considered vulnerable to sea level rise when at least 10 percent of its land area is forecasted to be inundated by peak high tides in the coming years. The dataset includes at-risk population and population share data for the region, counties, and neighborhoods.

DATA SOURCE National Oceanic and Atmospheric Administration 2017 Sea Level Rise Maps https://coast.noaa.gov/slrdata/

U.S. Census Bureau 2015 American Community Survey http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Projected areas of inundation were developed by BCDC and NOAA at one-foot intervals ranging from one foot to four feet of sea level rise. Regional and local sea level rise analysis is based on data from BCDC’s ART (Adapting to Rising Tides) Bay Area Sea Level Rise and Mapping Project. This data reflects the most up-to-date and detailed sea level rise mapping for the Bay Area. Sea level rise analysis for metro areas is based on national sea level rise mapping from NOAA, which is best for metro-to-metro comparison. To determine the impacts on historical and current populations, inundation areas were overlaid on a U.S. Census shapefile of 2010 Census tracts using Census Bureau population data.

Because census tracts can extend beyond the coastline, the baseline scenario of zero feet was used to determine existing sea level coverage of census tracts. Sea level rise refers to the change from this level. The area of the tract was determined by measuring the component of the tract area not currently under water. This area, rather than the total tract area, was used as the denominator to determine the percentage of the census tract that is inundated under future sea level rise projection scenarios. When at least 10 percent of tract land area is inundated with a given sea level, its residents are considered to be affected by sea level rise.

For the purpose of this analysis, SLR scenarios were assumed not to reflect periodic inundation due to extreme weather events, which may lead to an even greater share of residents affected on a less frequent basis. Prior to the impacts from sea level rise, neighborhoods will experience temporary flooding from extreme weather events which can create significant damage to homes and neighborhoods. It should be noted that by directly reviewing maps and tools through the ART (Adapting to Rising Tides) program, regular inundation sea level rise and temporary flooding from extreme weather events are both available. More information on this approach is available here: http://www.adaptingtorisingtides.org/project/regional-sea-level-rise-mapping-and-shoreline-analysis/

Sea level rise analysis for metro areas reflects local, as opposed to global, sea level rise. Recent data has shown sea level is rising faster in the southeast region of the United States. Regional differences in the rate of sea level rise. More information and data related to the rate of sea level rise for different coastal regions is available here: https://oceanservice.noaa.gov/facts/sealevel-global-local.html

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

BackgroundThe global population aging trend has intensified concerns regarding pancreatic cancer (PC), a leading cause of cancer-related mortality with a 5-year survival rate of 13%. This study evaluates the global burden, temporal trends, and socioeconomic disparities of PC among individuals aged ≥55 years using the 2021 Global Burden of Disease (GBD) data.MethodsAge-standardized incidence, prevalence, mortality, and disability-adjusted life years (DALYs) were analyzed across 204 countries. Joinpoint regression identified temporal trends (1990–2021), while Bayesian Age-Period-Cohort (BAPC) modeling projected future burden. Socioeconomic disparities were assessed via the Socio-demographic Index (SDI), and risk factor contributions were quantified using decomposition analysis.ResultsIn 2021, Finland, Germany, and Japan exhibited the highest age-standardized PC prevalence (ASPR: 64.42–66.17 per 100,000 population), contrasting sharply with Mozambique (ASPR: 2.85 per 100,000 population). Mortality peaked in Greenland (age-standardized death rate, ASDR: 81.85 per 100,000 population) and Monaco (ASDR: 71.75 per 100,000 population). Males showed elevated burden across incidence, prevalence, and mortality (peak age: 70–74 years), with global trends persistently rising (average annual percentage change, AAPC >0, 1990–2021). China experienced a transient mortality decline (AAPC = −0.93, 2011–2015), linked to healthcare reforms. High SDI regions (e.g., Japan) faced amplified burdens driven by aging and metabolic risks, while smoking (15.4–28.5% of deaths and years lived with disability, YLDs) and hyperglycemia (37.8% of YLDs in the U.S.) dominated modifiable risks. Projections diverge significantly: China’s age-standardized incidence rate (ASIR) burden is projected to increase from 27.96 (95% uncertainty interval, UI: 25.76, 30.16) in 2022 to 36.94 (UI: 0, 79.46) by 2045. In contrast, the global ASIR is expected to decline from 31.07 (UI: 30.06, 32.08) to 27.11 (UI: 8.73, 45.57).ConclusionPersistent socioeconomic and gender disparities underscore the need for targeted interventions, including tobacco control, glycemic management, and lifestyle modifications. Prioritizing aging populations in high-SDI regions and addressing underreported risks in low-SDI areas are critical for mitigating the growing PC burden.

This dataset was developed by KTH-dESA and describes settlement patterns relating to electrification in Madagascar. Using the Open Source Spatial Electrification Tool three attributes have been assigned to the settlements retrieved from the Madagascar High Resolution Settlement Layer developed by Facebook Connectivity Lab and CIESIN [1]. The three attributes are as follows: Urban or rural status. The urban cutoff level, i.e. the minimum population density per square kilometer, has been calculated so that the urban population matches the official statistics of 35 % in 2015 [2]. The urban cutoff level was calculated to be 683 people/km2, meaning that all settlements above this value are considered urban. The number of households in the settlements by 2030. Based on the urban or rural status the future population for the settlements have been estimated by applying a population growth rate to match future population projections according to [3] and [4]. The number of households 2030 have then been calculated using the epected urban and rural household sizes by 2030 of 3.7 and 4.4 people per household respectively [5]. Modeled household electrification status in 2015 (1 if the household in the cell are considered electrified by the national grid, 2 if electrified by mini-grids and 0 if non-electrified). The algorithm in OnSSET determines which household are likely to be electrified in 2015 to match the current electrification rate of 15% [6], based on meeting certain conditions for night-time light (NTL), population density and distance to the grid and roads. For Madagascar the settlements were calculated to be electrified by the national grid (RI Antananarico, RI Toamasina and RI Fianarantsoa) if they a) where within 5 km from the grid and had a minimum population density of 2287 people/km2 or minimum NTL of 60 or b) within 10 km from the grid and had a minimum population density of 10000 people/km2 or by mini-grids if they c) had a population density above 3882 people/km2 and minimum NTL of 5 or maximum 20 kilometers to major roads. [1] Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University (2016). High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe https://energydata.info/dataset/madagascar-high-resolution-settlement-layer-2015 [2] United Nations - Economic Commission for Africa. The Demographic Profile of African Countries. (2016). [3] United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2014 Revision. (2014). [4] Unicef - division of data, research and policy. Generation 2030 | Africa. (2014). [5] Mentis, D. et al. Lighting the World: the first application of an open source, spatial electrification tool (OnSSET) on Sub-Saharan Africa. Environmental Research Letters. Vol. 12, nr 8. (2017). [6] USAID. Power Africa in Madagascar | Power Africa | U.S. Agency for International Development. Available at: https://www.usaid.gov/powerafrica/madagascar. (2017).

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

As of April 2024, Bahrain was the country with the highest Instagram audience reach with 95.6 percent. Kazakhstan also had a high Instagram audience penetration rate, with 90.8 percent of the population using the social network. In the United Arab Emirates, Turkey, and Brunei, the photo-sharing platform was used by more than 85 percent of each country's population.

The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).