Forecast for China Population to Halve by 2100

There is a new study of world population from 2018 to 2100 which projects global population peaking in 2064 at 9.73 billion and US population peaking at 360 million and falling back to 330 million. The researchers used new models to forecast fertility, all-cause mortality, migration, and population. The researchers expect far more success in getting a wealthier, more educated and contraceptive using African and World populations.

They are expecting the developed countries and newly developed countries to all have families with 0, 1 or 2 children. They are expecting China to lose nearly 100 million people every decade or 10 million people every year from 2030 to 2100. Nextbigfuture notes that upper and lower bound of the projections are insanely wide. They have China having virtually no children and falling to 456 million people in 2100 or having triple with nearly 1.5 billion in 2100.

The reference projections for the five largest countries in 2100 were
India (1·09 billion [the forecasted range for India is 0·72 billion to 1·71 billion],
Nigeria (791 million [594–1056]),
China (732 million [456–1499]),
the USA (336 million [248–456]), and
Pakistan (248 million [151–427]).

Nextbigfuture notes that there is no way that China would permit its population to drop from 1.4 billion today to half or less in 2100.

The researchers expect Africa will see more educated people and higher contraception usage. This was what the UN population forecasters expected around 2000 but Africa did not adopt contraception at the expected rates. The UN ended up changing the projection and expected world population to not peak by 2100 and for the population to go to 11 billion. 10.9 billion is still the high end of the 2100 world population forecast range for the Lancet study.

This forecast has such extremes that it will be easy to see if it is on track with population levels by 2050 and if there are effective policy responses by countries with populations that are already shrinking. Will Japan, Russia and other countries effectively act to stop or slow population decline?

They forecast a cratering of the global fertility rate to 1·66 by 2100. This is almost half a child per family below replacement levels. Nextbigfuture notes that it would take a massive increase in longevity to offset massively low fertility just to stabilize future population levels.

In the reference scenario, the global population was projected to peak in 2064 at 9·73 billion (8·84–10·9) people and decline to 8·79 billion (6·83–11·8) in 2100.

Findings also suggest a shifting age structure in many parts of the world, with 2·37 billion (1·91–2·87) individuals older than 65 years and 1·70 billion (1·11–2·81) individuals younger than 20 years, forecasted globally in 2100.

By 2050, 151 countries were forecasted to have a TFR lower than the replacement level (TFR less than 2·1), and 183 were forecasted to have a TFR lower than replacement by 2100. 23 countries in the reference scenario, including Japan, Thailand, and Spain, were forecasted to have population declines greater than 50% from 2017 to 2100; China’s population was forecasted to decline by 48·0% (−6·1 to 68·4). China was forecasted to become the largest economy by 2035 but in the reference scenario, the USA was forecasted to once again become the largest economy in 2098.

The authors have a scenario where everyone in the developed world reaches all of the UN Sustainable Development Goals targets for education and contraceptives. The global population would crater to 6·29 billion (4·82–8·73) in 2100 and a population of 6·88 billion (5·27–9·51) when assuming 99th percentile rates of change in these drivers.

Technical limitations and challenges

The model used multiple covariates, including many risk factors as drivers of mortality rates in the future and educational attainment and contraceptive met need as drivers of fertility. This approach is in sharp contrast to UNPD models, which are non-causal time-series models that do not include any covariates. Modelers disagree on whether the use of covariates beyond time is a strength or a limitation. The use of time alone as the driver has the advantage that time is easily forecasted, but has the strong limitation that, because time is not causal per se, these models assume that the correlation between time and true causal determinants remains the same in the past as in the future. By contrast, we explicitly built into each component of our population model the associations between drivers and the outcome, such as tobacco and lung cancer, and CCF50 and educational attainment. Explicit modeling of these associations also means that we can develop policy-relevant scenarios: what will happen in a particular place if the government invests in schools and increases educational attainment? The limitation of this approach is that each of these independent drivers must be forecasted into the future for the reference scenario. The issue is the trade-off between the benefits of modeling. The explicit driver compared with the challenges of forecasting the independent drivers into the future.

SOURCES – Lancet
Written By Brian Wang,