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Projections of countries’ future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. We propose a Bayesian method for probabilistic population projections for all countries. The total fertility rate and female and male life expectancies at birth are projected probabilistically using Bayesian hierarchical models estimated via Markov chain Monte Carlo using United Nations population data for all countries. These are then converted to age-specific rates and combined with a cohort component projection model. This yields probabilistic projections of any population quantity of interest. The method is illustrated for five countries of different demographic stages, continents and sizes. The method is validated by an out of sample experiment in which data from 1950–1990 are used for estimation, and applied to predict 1990–2010. The method appears reasonably accurate and well calibrated for this period. The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition and whose fertility has started to recover towards replacement level, mostly in Europe. The results also indicate that the potential support ratio (persons aged 20–64 per person aged 65+) will almost certainly decline dramatically in most countries over the coming decades.

We describe a Bayesian projection model to produce country-specific projections of the total fertility rate (TFR) for all countries. The model decomposes the evolution of TFR into three phases: pre-transition high fertility, the fertility transition, and post-transition low fertility. The model for the fertility decline builds on the United Nations Population Division's current deterministic projection methodology, which assumes that fertility will eventually fall below replacement level. It models the decline in TFR as the sum of two logistic functions that depend on the current TFR level, and a random term. A Bayesian hierarchical model is used to project future TFR based on both the country's TFR history and the pattern of all countries. It is estimated from United Nations estimates of past TFR in all countries using a Markov chain Monte Carlo algorithm. The post-transition low fertility phase is modeled using an autoregressive model, in which long-term TFR projections converge toward and oscillate around replacement level. The method is evaluated using out-of-sample projections for the period since 1980 and the period since 1995, and is found to be well calibrated.

The United Nations (UN) recently released population projections based on data until 2012 and a Bayesian probabilistic methodology. Analysis of these data reveals that, contrary to previous literature, the world population is unlikely to stop growing this century. There is an 80% probability that world population, now 7.2 billion people, will increase to between 9.6 billion and 12.3 billion in 2100. This uncertainty is much smaller than the range from the traditional UN high and low variants. Much of the increase is expected to happen in Africa, in part due to higher fertility rates and a recent slowdown in the pace of fertility decline. Also, the ratio of working-age people to older people is likely to decline substantially in all countries, even those that currently have young populations.

Future world population growth is fuelled by two components: the demographic momentum, which is built into the age composition of current populations, and changes in reproductive behaviour and mortality of generations yet to come. This paper investigates, by major world regions and countries, what we know about population growth, what can be projected with reasonable certainty, and what is pure speculation. The exposition sets a frame for analysing demographic driving forces that are expected to increase human demand and pressures on land and water resources. These have been contrasted with current resource assessments of regional availability and use of land, in particular with estimates of remaining land with cultivation potential. In establishing a balance between availabilty of land resources and projected needs, the paper distinguishes regions with limited land and water resources and high population pressure from areas with abundant resources and low or moderate demographic demand. Overall, it is estimated that two-thirds of the remaining balance of land with rainfed cultivation potential is currently covered by various forest ecosystems and wetlands. The respective percentages by region vary between 23% in Southern Africa to 89% in South-Eastern Asia. For Latin America and Asia the estimated share of the balance of land with cultivation potential under forest and wetland ecosystems is about 70%, in Africa this is about 60%. If these were to be preserved, the remaining balance of land with some potential for rainfed crop cultivation would amount to some 550 million hectares. The regions which will experience the largest difficulties in meeting future demand for land resources and water, or alternatively have to cope with much increased dependency on external supplies, include foremost Western Asia, South-Central Asia, and Northern Africa. A large stress on resources is to be expected also in many countries of Eastern, Western and Southern Africa.

This note analyzes China's provincial diversity from two perspectives. First, the regional gross domestic products of China's 31 mainland provinces are compared with the national GDP of other countries. This demonstrates that China's most advanced provinces and urban areas have per capita GDP levels comparable to those of Sweden and Singapore. On the other hand, China's least developed provinces have a standard of living similar to those of Sudan and Honduras. The second part of the analysis demonstrates that China's economic diversity is not unique. In fact, European countries exhibit almost the same degree of income diversity as do Chinese provinces.

The author analyzes five anthropogenic driving forces of land-use change in China: population growth, urbanization, industrialization, changes in lifestyles and consumption, and shifts in political and economic arrangements and institutions. The intention is to demonstrate the broad range of factors other than biogeophysical conditions that will affect future land-use patterns in China. A first set of statistical data was collected to analyze these demographic and socioeconomic trends. The author also includes new estimates on China's cultivated land area, indicating that it is more seriously underreported in official statistics than previously acknowledged.

The author questions the conventional approach to studying global land-use changes, which is focused on agriculture-related alterations driven by population growth. He argues the need to abandon the oversimplified model of a linear relationship between population growth, increase in food demand, and agricultural expansion and intensification, leading to deforestation and land-cover modification. There are numerous other types of land-cover modification, such as those caused by shifts in lifestyles and food preferences, manmade catastrophes, wars, urban infrastructure expansion, changes in industrial production, fossil resource exploration, and modes of transportation. The author presents FAO data which indicate that a significant proportion of arable land worldwide is cultivated for lifestyle-related products, such as stimulants, sugar, and tobacco. A review of historical trends also shows that changes in land-use patterns were frequently linked to changes in lifestyles.

Methane (CH4) is one of the trace gases in the atmosphere that is considered to play a major role in what is called the \"greenhouse effect.\" There are six major sources of atmospheric methane: emission from anaerobic decomposition in (1) natural wetlands; (2) paddy rice fields; (3) emission from livestock production systems (including intrinsic fermentation and animal waste); (4) biomass burning (including forest fires, charcoal combustion, and firewood burning); (5) anaerobic decomposition of organic waste in landfills; and (6) fossil methane emission during the exploration and transport of fossil fuels. Obviously, human activities play a major role in increasing methane emissions from most of these sources. Especially the worldwide expansion of paddy rice cultivation, livestock production and fossil fuel exploration have increased the methane concentration in the atmosphere. Several data sets help estimate atmospheric methane concentration up to 160,000 years back. Major sources and sinks of present-day methane emission and their relative contribution to the global methane balance demonstrate great uncertainties in the identification and quantification of individual sources and sinks. Most recent methane projections of the Intergovernmental Panel on Climate Change (IPCC) for 2025 and 2100 are discussed and used to estimate the contribution of population growth to future methane emission. Finally the paper discusses options and restrictions of reducing anthropogenic methane emissions to the atmosphere.

The paper reviews seven types of Internet-based technologies and services that may be especially suitable for rural areas. Its main focus is to analyze, which of these applications could promote rural development and prevent further economic and socio-demographic decline in peripheral rural areas. In particular, we will analyze whether these technologies have the potential to create income alternatives for the rural population. The paper also criticizes the current rural development policy of the European Union, which is heavily biased towards the agro-environmental measures, largely ignoring the potential of Internet-based businesses and services for rural job creation.