The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 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 lives 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 few years 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.

About this dataset

Context

The dataset tabulates the Industrial township 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 Industrial township 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 2023, the population of Industrial township was 808, a 0.12% decrease year-by-year from 2022. Previously, in 2022, Industrial township population was 809, an increase of 0.12% compared to a population of 808 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Industrial township increased by 184. In this period, the peak population was 812 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Industrial township is shown in this column.
• Year on Year Change: This column displays the change in Industrial township 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 Industrial township Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Industry 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 Industry 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 2023, the population of Industry was 390, a 0.26% decrease year-by-year from 2022. Previously, in 2022, Industry population was 391, an increase of 0.26% compared to a population of 390 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Industry decreased by 175. In this period, the peak population was 565 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Industry is shown in this column.
• Year on Year Change: This column displays the change in Industry 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 Industry Population by Year. You can refer the same here

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

In 2023, it is estimated that the BRICS countries have a combined population of 3.25 billion people, which is over 40 percent of the world population. The majority of these people live in either China or India, which have a population of more than 1.4 billion people each, while the other three countries have a combined population of just under 420 million. Comparisons Although the BRICS countries are considered the five foremost emerging economies, they are all at various stages of the demographic transition and have different levels of population development. For all of modern history, China has had the world's largest population, but rapidly dropping fertility and birth rates in recent decades mean that its population growth has slowed. In contrast, India's population growth remains much higher, and it is expected to overtake China in the next few years to become the world's most populous country. The fastest growing population in the BRICS bloc, however, is that of South Africa, which is at the earliest stage of demographic development. Russia, is the only BRICS country whose population is currently in decline, and it has been experiencing a consistent natural decline for most of the past three decades. Growing populations = growing opportunities Between 2000 and 2026, the populations of the BRICS countries is expected to grow by 625 million people, and the majority of this will be in India and China. As the economies of these two countries grow, so too do living standards and disposable income; this has resulted in the world's two most populous countries emerging as two of the most profitable markets in the world. China, sometimes called the "world's factory" has seen a rapid growth in its middle class, increased potential of its low-tier market, and its manufacturing sector is now transitioning to the production of more technologically advanced and high-end goods to meet its domestic demand.

In 2025, there are six countries, all in Sub-Saharan Africa, where the average woman of childbearing age can expect to have between 5-6 children throughout their lifetime. In fact, of the 20 countries in the world with the highest fertility rates, Afghanistan and Yemen are the only countries not found in Sub-Saharan Africa. High fertility rates in Africa With a fertility rate of almost six children per woman, Chad is the country with the highest fertility rate in the world. Population growth in Chad is among the highest in the world. Lack of healthcare access, as well as food instability, political instability, and climate change, are all exacerbating conditions that keep Chad's infant mortality rates high, which is generally the driver behind high fertility rates. This situation is common across much of the continent, and, although there has been considerable progress in recent decades, development in Sub-Saharan Africa is not moving as quickly as it did in other regions. Demographic transition While these countries have the highest fertility rates in the world, their rates are all on a generally downward trajectory due to a phenomenon known as the demographic transition. The third stage (of five) of this transition sees birth rates drop in response to decreased infant and child mortality, as families no longer feel the need to compensate for lost children. Eventually, fertility rates fall below replacement level (approximately 2.1 children per woman), which eventually leads to natural population decline once life expectancy plateaus. In some of the most developed countries today, low fertility rates are creating severe econoic and societal challenges as workforces are shrinking while aging populations are placin a greater burden on both public and personal resources.

The lack of a recent summarizing description of population density in Germany that contains detailed information of pre-industrial times motivated the author of this study to undertake an analysis of population history of Northern Germany between 1740 and 1840. The goal of the study is to analyze the development of population regarding different aspects of population history and historical demographics. The author tries to connect geographic data with family data and then he relates it with economic, political and cultural development. The main part of the study ‘population dynamics’ gives an overview over demographic developments in a century characterized by demographic changes. Insights in the general changes in population size, the phases of Northern German population development and in relevant components for increases in population (e.g. decrease in mortality) are given. Finally the population determinants are developed, first in a concrete regional historic context of some areas (Marsch, nordwestliches Binnenland, Münsterland, Ostwestfalen, Ostelbien) and then more general external factors are included in the analysis. The generative structure of pre-industrial population, the industrial development, seasonal work and colonization are covered. There is an extra chapter on the development of urban population which includes the factors: urbanization, decrease in mortality, first signs of birth controls and migration. These regional considerations are opposed to an investigation of the general framework of demographical changes. In this context also grain prices and prevention from smallpox are taken into account.

Systematic of the data:

Sub-regions: 1. Holstein 2. The Hanseatic cities 3. Mecklenburg and Wester Pomerania
4. Prussia’s middle provinces
5. Core area of Lower Saxony 6. Weser-Ems-Area 7. Westphalia

Topics: 1. Births (excl. still births) 2. Deaths (incl. still births) 3. Still births 4. Marriages 5. Illegitimate births
6. Infant and child mortality 7. Population status

Mortality tables: A. Holstein (Propsteien) 1775/98, 1801/05 B. East Friesland 1775/98, 1835/39 C. County of Mark und märkische Kreise 1775/98, 1820/34 D. Kurmark 1775/98, 1835/39

Register of data tables: - Probability of death decennially in the German Reich 1881/90 - Handed down census results from Braunschweig-Lüneburg - Advances is historical tables of Westphalia
- Migration balances of Prussian government districts 1816-1840 - Population and households in Hamburg 1764-1824 - Population in Northern Germany and Germany - Approximated values for net migration 1751-1840 - Age specific decline in mortality 1775/98-1835/39 - Decline in child mortality - Fertility and marriage behavior by family reconstruction - Proportion of singles by department s and arrodissements 1811 - Average age at birth ca. 1740-ca.1840 - Regression analysis on deaths (excl. children) – marriages - Regional differences in population increases - Population density and mortality 1780-1799 - Population balances of Marschgebiete und der Fehmarn Island - Population balances of North Western Germany (without Küstenmarsch) - Budget structures of the parish Vreden 1749 - Population balances of areas with high industry densities - Budget structures of County of Mark 1798 - Budget structures in Minden-Ravensburg and Tecklenburg 1798 - Natality, mortality and cottage industry in Ravensberg 1788-1798 - North Western German areas with low birth rates
- Colonists resident in Prussia 1740-1786 - Social structure of rural population 1750 – 1790/98 - Social structure of rural population in Halberstädter - Urban population (legal definition of city) - Mortality due to tuberculosis in rural and urban areas - Average mortality rates in large cities
- Infant mortality and decline in mortality in Berlin S - Rural and urban migration balances 1741/1778-1840 - Birth rates - Cumulative elasticity of population movement - Average marriage rates in Hannover in comparison - Mortality due to smallpox - Share of infant and child mortality due to smallpox -Magnitude of the decrease in child mortality - Reduction of infant mortality - Regional differences in the decline in infant mortality

The data can be requested via order form or by personal request via email or telephone. PDF-form and contact data: http://www.gesis.org/dienstleistungen/daten/daten-historische-sozialf/querschnittsdaten/

Today, globally, women of childbearing age have an average of approximately 2.2 children over the course of their lifetime. In pre-industrial times, most women could expect to have somewhere between five and ten live births throughout their lifetime; however, the demographic transition then sees fertility rates fall significantly. Looking ahead, it is believed that the global fertility rate will fall below replacement level in the 2050s, which will eventually lead to population decline when life expectancy plateaus. Recent decades Between the 1950s and 1970s, the global fertility rate was roughly five children per woman - this was partly due to the post-WWII baby boom in many countries, on top of already-high rates in less-developed countries. The drop around 1960 can be attributed to China's "Great Leap Forward", where famine and disease in the world's most populous country saw the global fertility rate drop by roughly 0.5 children per woman. Between the 1970s and today, fertility rates fell consistently, although the rate of decline noticeably slowed as the baby boomer generation then began having their own children. Replacement level fertility Replacement level fertility, i.e. the number of children born per woman that a population needs for long-term stability, is approximately 2.1 children per woman. Populations may continue to grow naturally despite below-replacement level fertility, due to reduced mortality and increased life expectancy, however, these will plateau with time and then population decline will occur. It is believed that the global fertility rate will drop below replacement level in the mid-2050s, although improvements in healthcare and living standards will see population growth continue into the 2080s when the global population will then start falling.

Throughout most of human history, global population growth was very low; between 10,000BCE and 1700CE, the average annual increase was just 0.04 percent. Therefore, it took several thousand years for the global population to reach one billion people, doing so in 1803. However, this period marked the beginning of a global phenomenon known as the demographic transition, from which point population growth skyrocketed. With the introduction of modern medicines (especially vaccination), as well as improvements in water sanitation, food supply, and infrastructure, child mortality fell drastically and life expectancy increased, causing the population to grow. This process is linked to economic and technological development, and did not take place concurrently across the globe; it mostly began in Europe and other industrialized regions in the 19thcentury, before spreading across Asia and Latin America in the 20th century. As the most populous societies in the world are found in Asia, the demographic transition in this region coincided with the fastest period of global population growth. Today, Sub-Saharan Africa is the region at the earliest stage of this transition. As population growth slows across the other continents, with the populations of the Americas, Asia, and Europe expected to be in decline by the 2070s, Africa's population is expected to grow by three billion people by the end of the 21st century.

Data collection from official statistics and church registers

About this dataset

Context

The dataset tabulates the Industry town 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 Industry town 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 2023, the population of Industry town was 801, a 0.50% increase year-by-year from 2022. Previously, in 2022, Industry town population was 797, an increase of 0.63% compared to a population of 792 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Industry town increased by 16. In this period, the peak population was 928 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Industry town is shown in this column.
• Year on Year Change: This column displays the change in Industry town 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 Industry town Population by Year. You can refer the same here

Taking 2005 as the base year, the future population scenario was predicted by adopting the logistic model of population. This model not only effectively describes the pattern of changes in population and biomass but is also widely applied in the field of economics. The urbanization rate was predicted using the urbanization logistic model. Based on the observed horizontal pattern of urbanization, a predictive model was established by determining the parameters in the parametric equation by applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data represent the non-agricultural population. The logistic model was used to predict the future gross domestic product of each county (or city), and then the economic development level of each county (or city) in each period (in terms of GDP per capita). The corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changes in China and the research area lagged behind the growth in GDP, so the changes were adjusted according to the need for future industrial structure scenarios in the research area.

According to the most recent population forecasts for Switzerland (Bundesamt für Statistik 2015), the share of old-age dependants (older than 65 years) relative to the working age population (20-64) is going to increase from 29.1% in 2015 to 48.1% in 2045. In the same time span, total population is expected to grow from 8.3 million to 10.2 million while the potential workforce is growing from 4.8 million to 5.3 million. As a result, potential labour supply per capita is decreasing and at the same time the share of old-age dependants as well as the average age of the population are increasing rapidly. Among other problems, this is going to lead to significant distortions on labour markets; such as labour shortages or shifts in the structure of labour demand due to shifts in final goods demand. Furthermore, the current political climate in Switzerland tends towards restricting immigration. Since the Swiss economy already relies heavily on foreign workers, a restriction of immigration might aggravate the predicted labour supply shortages even further. The goal of this research project is to evaluate the consequences of population ageing for the Swiss labour market. A special focus lies on the labour demand side, specifically on medium and long term sectoral and occupational shifts caused by a decrease in (skilled) labour supply and a change in consumer demand structure due to the demographic change. Moreover, the general equilibrium effects of different policy reforms will be evaluated and compared. To achieve this goal we construct a dynamic overlapping generations (OLG) computable general equilibrium (CGE) model of Switzerland and calibrate it with current Swiss data. Models of this type are the conventional approach to evaluating inter- and intra-generational effects of population ageing. However, only few studies focus on the labour market and even fewer emphasise the demand side. The evidence is particularly scarce for Switzerland, where only a handful of general equilibrium analyses relating to population ageing have been conducted. In order to facilitate estimating realistic parameters of the model as well as calibrating the model to expected short and medium term industry-specific developments we conduct a customised firm level survey, which, on its own, already constitutes a significant contribution to the relevant literature. The finalised model does not only allow us to predict transitional and long-term effects of the demographic change on the economy and the industry structure. It also provides us with the ability to evaluate and compare different reform proposals, such as an increase in the retirement age, reforms of the pension and healthcare systems and different immigration scenarios. As such, we will be able to give recommendations for optimal policy choice and provide valuable inputs to the political debate.

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The food packaging robotics market share is expected to increase by USD 1.03 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 10.19%.

This food packaging robotics market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers food packaging robotics market segmentations by application (primary packaging and secondary packaging) and geography (APAC, Europe, North America, MEA, and South America). The food packaging robotics market report also offers information on several market vendors, including ABB Ltd., DENSO Corp., FANUC Corp., MIDEA GROUP, Mitsubishi Electric Corp., OMRON Corp., Seiko Epson Corp., Syntegon Technology GmbH, Teradyne Inc., and Yaskawa Electric Corp. among others.

What will the Food Packaging Robotics Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Food Packaging Robotics Market Size for the Forecast Period and Other Important Statistics

Food Packaging Robotics Market: Key Drivers, Trends, and Challenges

Based on our research output, there has been a neutral impact on the market growth during and post COVID-19 era. The assurance of safety and quality is notably driving the food packaging robotics market growth, although factors such as lack of skilled workforce may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the food packaging robotics industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Food Packaging Robotics Market Driver

One of the key factors driving the food packaging robotics market growth is the assurance of safety and quality. Developments in robotic technology focus on safety parameters. Packaging robots are installed on the assembly line and work in collaboration with industrial workers. These robots can work in multiple areas and directions, due to which they are equipped with multiple safeguards. For instance, e-stop buttons, light curtains, guard switches, safety controllers, and stop buttons are used to provide safety at work. These components monitor the robots and allow them to work independently. The food industry is adopting packaging robots that help industrial workers maintain stringent hygiene and safety standards. Packaging robots are equipped with safety controllers that allow them to monitor the movement of arms. This keeps the movements in control, ensuring the safety of the manual workers in the vicinity. This will further drive the food packaging robotics market during the forecast period.

Key Food Packaging Robotics Market Trend

Flexibility of robots in undertaking multiple tasks is the major trend influencing the food packaging robotics market growth. Robotic technology is becoming highly advanced because of the growing need for automation in the food industry. The incorporation of advanced sensors and vision technology with differential arm movements allows packaging robots to work in different applications. Packaging robots are flexible as they have variable axis movements. In addition, the variable arm size and reachability enable them to work with products of different sizes, weights, and shapes. The incorporation of vision technology in packaging robots enables them to determine the position and speed of the moving objects on the conveyor belt with great precision. Packaging robots can adjust the movement of the arm and payload capacity according to the products on the conveyor belt. These robots can be deployed in picking, palletizing, and packaging operations and allow food manufacturers to execute multiple applications with minimal human intervention.

Key Food Packaging Robotics Market Challenge

Lack of skilled workforce is one of the key challenges hindering food packaging robotics market growth. Human workers need to have knowledge about the working of robots, along with periodic servicing requirements to ensure smooth operations. The lack of technical expertise often deters end-users from adopting robots, as they consider the downtime involved in maintenance tasks as a considerable loss. This poses a challenge, as many end-users hesitate to learn and adopt new technologies for operations. The continuous and repetitive nature of packaging operations involves the integration of data for better decision-making. However, the demographic transition witnessed in countries such as China and Japan due to the growing number of older people is resulting in the non-availability of skilled technicians with knowledge in operating the robots. This factor has largely contributed to the slow adoption of food packaging robots. As a result, the global food packaging robotics market faces

A global phenomenon, known as the demographic transition, has seen life expectancy from birth increase rapidly over the past two centuries. In pre-industrial societies, the average life expectancy was around 24 years, and it is believed that this was the case throughout most of history, and in all regions. The demographic transition then began in the industrial societies of Europe, North America, and the West Pacific around the turn of the 19th century, and life expectancy rose accordingly. Latin America was the next region to follow, before Africa and most Asian populations saw their life expectancy rise throughout the 20th century.

The study on the future of work was conducted by Kantar Public on behalf of the Press and Information Office of the Federal Government. During the survey period from 13 to 22 June 2023, German-speaking people aged 16 to 67 in Germany, excluding pensioners, were surveyed in online interviews (CAWI) on the following topics: current life and work situation, future expectations, the use of AI and the digitalization of the world of work as well as attitudes towards demographic change and the shortage of skilled workers. The respondents were selected using a quota sample from an online access panel. Future: general life satisfaction; satisfaction with selected aspects of life (working conditions, education, qualifications, health situation, professional remuneration, family situation, financial situation); expectations for the future: rather confident vs. rather worried about the private and professional future; rather confident vs. rather worried about the professional future of younger people or the next generation; rather confident vs. rather worried about the future of Germany; confidence vs. concern regarding the competitiveness of the German economy in various areas (digitalization and automation of the working world, climate protection goals of industry, effects of the Ukraine war on the German economy, access to important raw materials such as rare earths or metals, reliable supply of energy, number of qualified specialists, general price development, development of wages and salaries, development of pensions); probability of various future scenarios for Germany in 2030 (Germany is once again the world export champion, unemployment is at an all-time low - full employment prevails in Germany, the energy transition has already created hundreds of thousands of new jobs in German industry, Germany has emerged the strongest in the EU from the crises of the last 15 years, the price crisis has led to the fact The price crisis has meant that politics and business have successfully set the course for the future, citizens can deal with all official matters digitally from home, German industry is much faster than expected in terms of climate targets and is already almost climate-neutral, Germany is the most popular country of immigration for foreign university graduates, the nursing shortage in Germany has been overcome thanks to the immigration of skilled workers). 2. Importance of work: importance of different areas of life (ranking); work to earn money vs. as a vocation; importance of different work characteristics (e.g. job security, adequate income, development prospects and career opportunities, etc.). 3. Professional situation: satisfaction with various aspects of work (job security, pay/income, development/career opportunities, interesting work, sufficient contact with other people, compatibility of family/private life and work. Work climate/ working atmosphere, further training opportunities, social recognition, meaningful and useful work); job satisfaction; expected development of working conditions in own professional field; recognition for own work from the company/ employer, from colleagues, from other people from the work context, from the personal private environment, from society in general and from politics; unemployed people were asked: currently looking for a new job; assessment of chances of finding a new job; pupils, students and trainees were asked: assessment of future career opportunities; reasons for assessing career opportunities as poor (open). 4. AI: use of artificial intelligence (AI) in the world of work rather as an opportunity or rather as a danger; expected effects of AI on working conditions in their own professional field (improvement, deterioration, no effects); opportunities and dangers of digitization, AI and automation based on comparisons (all in all, digitization leads to a greater burden on the environment, as computers, tablets, smartphones and data centers are major power guzzlers vs. All in all, digitalization protects the environment through less mobility and more efficient management, artificial intelligence and digitalization help to reduce the workload and relieve employees of repetitive and monotonous tasks vs. artificial intelligence and digitalization overburden many employees through further work intensification. Stress and burnouts will increasingly be the result, artificial intelligence and digitalization will primarily lead to job losses vs. artificial intelligence and digitalization will create more new, future-proof jobs than old ones will be lost, our economy will benefit greatly from global networking through speed and efficiency gains vs. our economy is threatened by global networking by becoming more susceptible to cyberattacks and hacker attacks, digitalization will lead to new, more flexible working time models and a better work-life balance vs. digitalization will lead to a blurring of boundaries between work and leisure time and thus, above all, to more self-exploitation by employees). 5. Home office: local focus of own work currently, before the corona pandemic and during the corona pandemic (exclusively/ predominantly in the company or from home, at changing work locations (company, at home, mobile from on the road); Agreement with various statements on the topic of working from home (wherever possible, employers should give their employees the opportunity to work from home, working from home leads to a loss of cohesion in the company, working from home enables a better work-life balance, digital communication makes coordination processes more complicated, home office makes an important contribution to climate protection due to fewer journeys to work, home office leads to a mixture of work and leisure time and thus to a greater workload, home office leads to greater job satisfaction and thus to higher productivity, since many professions cannot be carried out in the home office, it would be fairer if everyone had to work outside the home); attitude towards a general 4-day working week (A four-day week for everyone would increase the shortage of skilled workers vs. a four-day week for everyone would increase motivation and therefore productivity). 6. Demographic change: knowledge of the meaning of the term demographic change; expected impact of demographic change on the future of Germany; opinion on the future in Germany based on alternative future scenarios (in the future, poverty in old age will increase noticeably vs. the future generation of pensioners will be wealthier than ever before, in the future, politics and elections will be increasingly determined by older people vs. the influence of the younger generation on politics will become much more important, our social security systems will continue to ensure intergenerational fairness and equalization in the future vs. the distribution conflicts between the younger and older generations will increase noticeably, future generations will have to work longer due to the shortage of skilled workers vs. people will have to work less in the future due to digitalization and automation and will be able to retire earlier). 7. Shortage of skilled workers: shortage of skilled workers in own company; additional personal burden due to shortage of skilled workers; company is doing enough to counteract the shortage of skilled workers; use of artificial intelligence (AI) in the company could compensate for the shortage of skilled workers; evaluation of various measures taken by the federal government to combat the shortage of skilled workers (improvement of training and further education opportunities, increasing the participation of women in the labor market (e.g. by expanding childcare services, more flexible working hours, offers for older skilled workers to stay in work longer, facilitating the immigration of foreign skilled workers); evaluation of the work of the federal government to combat the shortage of skilled workers; attractiveness (reputation in society) of various professions with a shortage of skilled workers (e.g. social pedagogues/educators); evaluation of the work of the federal government to combat the shortage of skilled workers. B. social pedagogue, nursery school teacher, etc.); job recommendation for younger people; own activity in one of the professions mentioned with a shortage of skilled workers. Demography: sex; age; age in age groups; employment; federal state; region west/east; school education; vocational training; self-placement social class; employment status; occupation differentiated workers, employees, civil servants; industry; household size; number of children under 18 in the household; net household income (grouped); location size; party sympathy; migration background (respondent, one parent or both parents). Additionally coded were: consecutive interview number; school education head group (low, medium, high); weighting factor.

The recent experiments performed at the Hadley Centre have used the new Unified Model (Cullen, 1993). These experiments represent a large step forward in the way climate change is modelled by GCMs and raises new possibilities for scenario construction. This experiment has overcome some of the major difficulties that were associated with the previous generations of equilibrium (circa IPCC 1990) and cold-start transient (circa IPCC 1992) climate change experiments. HadCM2 has a spatial resolution of 2.5 degrees x 3.75 degrees (latitude by longitude) and the representation produces a grid box resolution of 96 x 73 grid cells. This produces a surface spatial resolution of about 417km x 278 km reducing to 295 x 278km at 45 degrees North and South (comparable to a spectral resolution of T42). The equilibrium climate sensitivity (DT2x) of HadCM2, that is the global-mean temperature response to a doubling of effective CO2 concentration, is approximately 2.5 degrees C, although, this quantity varies with the time-scale considered. This is somewhat lower than most other GCMs (IPCC, 1992). In order to undertake a 'warm-start' experiment it is necessary to perturb the model with a forcing from an early historical era, when the radiative forcing was relatively small compared to the present. The Hadley Centre started their experiments performed with HadCM2 with forcing from the middle industrial era, about 1860 Mitchell et al., 1995 and Johns et al., 1995. The greenhouse gas only integrations, HadCM2GG, used the combined forcing of all the greenhouse gases as an equivalent CO2 concentration. A further series of integrations, HadCM2GS, used the combined equivalent CO2 concentration plus the negative forcing from sulphate aerosols. The HadCM2GG integrations simulated the change in forcing of the climate system by greenhouse gases since the early industrial period (taken by HadCM2 to be 1860). The addition of the negative forcing effects of sulphate aerosols represents the direct radiative forcing due to anthropogenic sulphate aerosols by means of an increase in clear-sky surface albedo proportional to the local sulphate loading (refer to Mitchell et al., 1995 for details of this method). The indirect effects of aerosols were not simulated. The modelled control climate shows a negligible long term trend in surface air temperature over the first 400 years. The trend is about +0.04 degrees C per century, which is comparable to other such experiments. HadCM2CON represents an improvement over previous generations of GCMs that have been used at the Hadley Centre (Johns et al., 1995 and Airey et al., 1995). The experiments performed have simulated the observed climate system using estimated forcing perturbations since 1860. Johns et al., (1995) and Mitchell et al., (1995) have established that HadCM2's sensitivity is consistent with the real climate system. The agreement between the observed global-mean temperature record and that produced in these experiments is better for HadCM2GS than for HadCM2GG. This implies that HadCM2Gs has captured the observed signal of global-mean temperature changes better than HadCM2GG for the recent 100-year record. The climate sensitivity of HadCM2 is about 2.5 degrees C The central elements of the B1 future are a high level of environmental and social consciousness combined with a globally coherent approach to sustainable development. A strong welfare net prevents social exclusion on the basis of poverty. However, counter-currents may develop and in some places people may not conform to the main social and environmental intentions of the mainstream in this scenario family. Particular effort is devoted to increasing resource efficiency. Comprehensive incentive systems, combined with advances in international institutions, permit the rapid diffusion of cleaner technology. R and D to this end is also enhanced together with education and capacity building for clean and equitable development. Organizational measures are adopted to reduce material wastage, maximizing reuse and recycling. The combination of technical and organizational change yields high levels of material and energy saving as well as reductions in pollution. Labor productivity also improves as a byproduct of these efforts. Variants considered within the B1 family of scenarios include different rates of GDP growth and dematerialization (e.g., energy intensity declines). The demographic transition to low mortality and fertility occurs at the same rate as in A1 but for slightly different reasons, motivated partly by social and environmental concerns. Global population reaches nine billion by 2050 and declines to about seven billion by 2100. This is a world with high levels of economic activity and significant and deliberate progress toward international and national income equality. Global income per capita in 2050 ... Visit https://dataone.org/datasets/doi%3A10.5063%2FAA%2Fdpennington.258.1 for complete metadata about this dataset.

Throughout the Common Era, Japan's population saw relatively steady growth between each century. Failed invasions and distance from Asia's mainland meant that Japan was unaffected by many pandemics, primarily bubonic plague, therefore its development was not drastically impeded in the same way as areas such as China or Europe. Additionally, religious practices meant that hygiene was prioritized much more in Japan than in other regions, and dietary customs saw lower rates of meat consumption and regular boiling of water in meals or tea; both of these factors contributed to lower rates of infection for many parasitic or water-borne diseases. Fewer international conflicts and domestic stability also saw lower mortality in this regard, and Japan was an considered an outlier by Asian standards, as some shifting trends associated with the demographic transition (such as lower child mortality and fertility) began taking place in the 17th century; much earlier time than anywhere else in the world. Yet the most significant changes came in the 20th century, as Japan's advanced healthcare and sanitation systems saw drastic reductions in mortality. Challenges Japan's isolation meant that, when pandemics did arrive, the population had less protection and viruses could have higher mortality rates; smallpox has been cited as the deadliest of these pandemics, although increased international contact in the late 19th century brought new viruses, and population growth slowed. Earlier isolation also meant that crop failure or food shortages could leave large sections of the population vulnerable, and, as mentioned, the Japanese diet contained relatively little meat, therefore there was a higher reliance on crops and vegetables. It is believed that the shortage of arable land and the acidity of the soil due to volcanic activity meant that agriculture was more challenging in Japan than on the Asian mainland. For most of history, paddy fields were the most efficient source of food production in Japan, but the challenging nature of this form of agriculture and changes in employment trends gradually led to an increased reliance in imported crops. Post-Sakoku Japan Distance from the Asian mainland was not the only reason for Japan's isolation; from 1603 to 1853, under the Tokugawa shogunate, international trade was restricted, migration abroad was forbidden, and most foreign interaction was centered around Nagasaki. American neo-imperialism then forced Japan to open trade with the west, and Japan became an imperial power by the early-1900s. Japanese expansion began with a series of military victories against China and Russia at the turn of the century, and the annexation of Taiwan, Korea, and Manchuria by the 1930s, before things escalated further during its invasion of China and the Second World War. Despite its involvement in so many wars, the majority of conflicts involving Japan were overseas, therefore civilian casualties were much lower than those suffered by other Asian countries during this time. After Japan's defeat in 1945, its imperial ambitions were abandoned, it developed strong economic ties with the West, and had the fastest economic growth of any industrial country in the post-WWII period. Today, Japan is one of the most demographically advanced countries in the world, with the highest life expectancy in most years. However, its population has been in a steady decline for over a decade, and low fertility and an over-aged society are considered some of the biggest challenges to Japanese society today.

In the year 1500, the share of Western Europe's population living in urban areas was just six percent, but this rose to 31 percent by the end of the 19th century. Despite this drastic change, development was quite slow between 1500 and 1800, and it was not until the industrial revolution when there was a spike in urbanization. As Britain was the first region to undergo the industrial revolution, from around the 1760s until the 1840s, these areas were the most urbanized in Europe by 1890. The Low Countries Prior to the 19th century, Belgium and the Netherlands had been the most urbanized regions due to the legacy of their proto-industrial areas in the medieval period, and then the growth of their port cities during the Netherlands' empirical expansion (Belgium was a part of the Netherlands until the 1830s). Belgium was also quick to industrialize in the 1800s, and saw faster development than its larger, more economically powerful neighbors, France and Germany. Least-urban areas Ireland was the only Western European region with virtually no urbanization in the 16th and 17th century, but the industrial growth of Belfast and Dublin (then major port cities of the British Empire) saw this change by the late-1800s. The region of Scandinavia was the least-urbanized area in Western Europe by 1890, but it saw rapid economic growth in Europe during the first half of the following century.

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.