World population stabilization unlikely this century.

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.

Bayesian probabilistic population projections for all countries.

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.

Probabilistic projections of the total fertility rate for all countries.

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.

Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China.

This paper analyzes characteristics, major driving forces and alternative management measures of land-use change in Kunshan, Jiangsu province, China. The study used remote sensing (RS) maps and socio-economic data. Based on RS-derived maps, two change matrices were constructed for detecting land-use change between 1987 and 1994, and between 1994 and 2000 through pixel-to-pixel comparisons. The outcomes indicated that paddy fields, dryland, and forested land moderately decreased by 8.2%, 29% and 2.6% from 1987 to 1994, and by 4.1%, 7.6% and 8% from 1994 to 2000, respectively. In contrast, the following increased greatly from 1987 to 1994: artificial ponds by 48%, urban settlements by 87.6%, rural settlements by 41.1%, and construction land by 511.8%. From 1994 to 2000, these land covers increased by 3.6%, 28.1%, 23.4% and 47.1%, respectively. For the whole area, fragmentation of land cover was very significant. In addition, socio-economic data were used to analyze major driving forces triggering land-use change through bivariate analysis. The results indicated that industrialization, urbanization, population growth, and China's economic reform measures are four major driving forces contributing to land-use change in Kunshan. Finally, we introduced some possible management measures such as urban growth boundary (UGB) and incentive-based policies. We pointed out that, given the rapidity of the observed changes, it is critical that additional studies be undertaken to evaluate these suggested policies, focusing on what their effects might be in this region, and how these might be implemented.