Input Data and Programs for SDG Population Scenarios and Sensitivity Analysis of UN Probabilistic Projections

Achieving the Sustainable Development Goals (SDGs) set by the UN in 2015 for the period up to 2030 would lead to a global population of between 8.2 and 8.7 billion by 2100, according to a new study from the International Institute for Applied Systems Analysis (IIASA) and the Asian Demographic Research Institute (ADRI) at Shanghai University.

According to the study that uses data from the Wittgenstein Centre for Demography and Global Human Capital (IIASA, VID/ÖAW, University of Vienna) and the UN population division, achieving the SDGs would lead to population growth below even the lower bound of recent UN probabilistic population projections.

The SDGs include 17 goals with 169 different targets, aimed at fighting poverty, reducing inequality, and addressing climate change, while leaving nobody behind. They include goals such as quality primary and secondary education for all children, gender equality, and reduced child mortality, which all have direct and indirect impacts on population growth.

The study builds on a set of scenarios, published in 2014, by age, sex and level of education for all countries in the world (Data are available from the Wittgenstein Centre Data Explorer, Version 1.2). It is the first to assess how successful implementation of the SDGs would affect population growth. Assuming that for the period 2015-2030 the goals will serve as a turbo boost for development, it finds that achieving the SDGs would lead to global population peaking by 2060, and declining to between 8.2 and 8.7 billion by 2100.

The IIASA/ADRI projections assess three different scenarios for the implementation of the SDGs, focusing on the goals that could impact fertility and mortality rates and thus population growth.

Codes and Data

SDG Population Projection

Input data, projection codes (written in R) and results for three SDG population scenarios until 2100.

Sensitivity Analysis of UN Probabilistic Population Projections

Codes (written in R) analyzing the sensitivity of UN WPP2015 projections to different fertility forecasts of UN.