Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

This statistic shows the median age of the population in Australia from 1950 to 2100. The median age of a population is an index that divides the population into two equal groups: half of the population is older than the median age and the other half younger. In 2020, the median age of Australia's population was 36.9 years.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Au Sable charter township by race. It includes the distribution of the Non-Hispanic population of Au Sable charter township across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Au Sable charter township across relevant racial categories.

Key observations

Of the Non-Hispanic population in Au Sable charter township, the largest racial group is White alone with a population of 2,100 (95.98% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Au Sable charter township
• Population: The population of the racial category (for Non-Hispanic) in the Au Sable charter township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Au Sable charter township total Non-Hispanic population. 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 Au Sable charter township Population by Race & Ethnicity. You can refer the same here

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Climate Risk Datasets: NARCliM 1.0 Climate risk datasets derived from Global Climate Models (GCMs) typically have a coarse spatial resolution (around 100-200km grid cells), whereas water models require data with much finer spatial resolution. The NSW and Australian Regional Climate Modelling (NARCliM) project addresses this by using dynamical downscaling on a subset of available Coupled Model Intercomparison Project (CMIP) Phase 3 GCMs (Evans et al., 2014). In the first phase of this project (NARCliM 1.0), four CMIP3 GCMs were remodelled onto finer resolution grids using three different regional climate models (RCMs). This process produced a suite of 12 modelled results for three distinct 20-year climate periods: 1990-2009, 2020-2039, and 2060-2079.

NARCliM 1.0 utilized the SRES A2 emissions scenario, which describes a highly heterogeneous world characterized by self-reliant regions with well-preserved local identities and a continuously increasing global population (projected to reach 15 billion by 2100). Economic development in this scenario is regionally focused, and per capita economic growth and technological change are more fragmented and slower compared to other emissions scenarios. Notably, potent greenhouse gases such as hydrofluorocarbons and land-use emissions increase rapidly in the latter half of the century, resulting in the highest overall emissions among the SRES scenarios. This scenario is projected to lead to an approximate 2.2-degree Celsius increase in average global temperature by 2079 (relative to the 1980-1999 baseline).

Average Monthly Scaling Factors from NARCliM 1.0 To inform the Regional Water modelling, future climate projections were combined with 10,000 years of long-term paleo-stochastic climate data. This approach aimed to stress-test our water systems under long-term climate variability and projected changes. For this reason, we selected the Global Climate Model (GCM) and Regional Climate Model (RCM) combination that projected the lowest rainfall compared to the present day.

We calculated monthly rainfall ratios by comparing projected rainfall data (2060-79) to current rainfall data (1990-2009) over 20-year periods at each climate station used in the water models. This calculation utilized bias-corrected NARCliM datasets, and the resulting ratios were then multiplied by the stochastic dataset to factor it. Similarly, potential evapotranspiration results from the same NARCliM scenario were used to factor the stochastically generated potential evapotranspiration results.

The resulting rainfall and evapotranspiration data, representing the dry future climate scenario used in the regional water strategy modelling, was derived from the CSIRO’s Mk 3.0 global climate model, downscaled by three RCMs under NARCliM 1.0. This allowed us to understand how proposed options in the draft strategies would perform under the most severe drought conditions in a future characterized by a slow global response to curbing carbon emissions. It is important to note that this is not the only future climate scenario being considered.

We apply this method of generating stochastic data from historic and paleoclimate datasets, and then factoring it using NARCliM 1.0 outputs, to NSW’s inland regions and north coast. For the south-coast region, our analysis is based on changes to East Coast Low seasonal frequency, informed by NARCliM 1.0. These changes were initially applied using available East Coast Low synoptic data. We then generated stochastic data from this altered dataset (DPIE, 2023).

Note: If you would like to ask a question, make any suggestions, or tell us how you are using this dataset, please visit the NSW Water Hub which has an online forum you can join.