Probabilistic population forecasting: Short to very long-term

TLDR

Population forecasts guide government and private planning up to about 2100, traditionally deterministic, but probabilistic forecasts—introduced by the UN in 2015 using Bayesian methods—are now preferred to assess accuracy, risk, and long‑term impacts such as carbon emissions projected to 2300. The paper aims to extend the UN Bayesian probabilistic forecasting method to very‑long‑range horizons by integrating expert review and elicitation. They extend the UN Bayesian framework by incorporating expert review and elicitation to generate forecasts beyond 2100. The extended forecasts predict continued growth through the 21st century, stabilization in the 22nd, and a decline in the 23rd.

Abstract

Population forecasts are used by governments and the private sector for planning, with horizons up to about three generations (around 2100) for different purposes. The traditional methods are deterministic using scenarios, but probabilistic forecasts are desired to get an idea of accuracy, assess changes, and make decisions involving risks. In a significant breakthrough, since 2015, the United Nations has issued probabilistic population forecasts for all countries using a Bayesian methodology that we review here. Assessment of the social cost of carbon relies on long-term forecasts of carbon emissions, which in turn depend on even longer-range population and economic forecasts, to 2300. We extend the UN method to very-long range population forecasts by combining the statistical approach with expert review and elicitation. While the world population is projected to grow for the rest of this century, it will likely stabilize in the 22nd century and decline in the 23rd century.