Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
Lancet. 2020 Oct 17;396(10258):1285-1306. doi: 10.1016/S0140-6736(20)30677-2. Epub 2020 Jul 14.
Understanding potential patterns in future population levels is crucial for anticipating and planning for changing age structures, resource and health-care needs, and environmental and economic landscapes. Future fertility patterns are a key input to estimation of future population size, but they are surrounded by substantial uncertainty and diverging methodologies of estimation and forecasting, leading to important differences in global population projections. Changing population size and age structure might have profound economic, social, and geopolitical impacts in many countries. In this study, we developed novel methods for forecasting mortality, fertility, migration, and population. We also assessed potential economic and geopolitical effects of future demographic shifts.
We modelled future population in reference and alternative scenarios as a function of fertility, migration, and mortality rates. We developed statistical models for completed cohort fertility at age 50 years (CCF50). Completed cohort fertility is much more stable over time than the period measure of the total fertility rate (TFR). We modelled CCF50 as a time-series random walk function of educational attainment and contraceptive met need. Age-specific fertility rates were modelled as a function of CCF50 and covariates. We modelled age-specific mortality to 2100 using underlying mortality, a risk factor scalar, and an autoregressive integrated moving average (ARIMA) model. Net migration was modelled as a function of the Socio-demographic Index, crude population growth rate, and deaths from war and natural disasters; and use of an ARIMA model. The model framework was used to develop a reference scenario and alternative scenarios based on the pace of change in educational attainment and contraceptive met need. We estimated the size of gross domestic product for each country and territory in the reference scenario. Forecast uncertainty intervals (UIs) incorporated uncertainty propagated from past data inputs, model estimation, and forecast data distributions.
The global TFR in the reference scenario was forecasted to be 1·66 (95% UI 1·33-2·08) in 2100. 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. The reference projections for the five largest countries in 2100 were India (1·09 billion [0·72-1·71], Nigeria (791 million [594-1056]), China (732 million [456-1499]), the USA (336 million [248-456]), and Pakistan (248 million [151-427]). 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 <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. Our alternative scenarios suggest that meeting the Sustainable Development Goals targets for education and contraceptive met need would result in a global population of 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.
Our findings suggest that continued trends in female educational attainment and access to contraception will hasten declines in fertility and slow population growth. A sustained TFR lower than the replacement level in many countries, including China and India, would have economic, social, environmental, and geopolitical consequences. Policy options to adapt to continued low fertility, while sustaining and enhancing female reproductive health, will be crucial in the years to come.
Bill & Melinda Gates Foundation.
了解未来人口水平的潜在模式对于预测和规划不断变化的年龄结构、资源和医疗保健需求以及环境和经济格局至关重要。未来的生育模式是估计未来人口规模的关键投入,但它们周围存在大量不确定性,以及估计和预测方法的差异,这导致全球人口预测存在重要差异。人口数量和年龄结构的变化可能会对许多国家的经济、社会和地缘政治产生深远影响。在这项研究中,我们开发了新的方法来预测死亡率、生育率、移民和人口。我们还评估了未来人口变化的潜在经济和地缘政治影响。
我们将参考和替代情景中的未来人口作为生育率、移民和死亡率的函数进行建模。我们为 50 岁时的完成队列生育率(CCF50)开发了统计模型。完成队列生育率比总生育率(TFR)的时期衡量更稳定。我们将 CCF50 建模为受教育程度和避孕需求满足的时间序列随机游走函数。年龄特定生育率作为 CCF50 和协变量的函数进行建模。我们使用基本死亡率、风险因素标度和自回归综合移动平均(ARIMA)模型来预测 2100 年的特定年龄死亡率。净移民作为社会人口指数、人口增长率和战争与自然灾害死亡人数的函数进行建模;并使用 ARIMA 模型。该模型框架用于根据教育程度和避孕需求满足的变化速度,在参考情景和替代情景中进行开发。我们估计了参考情景中每个国家和地区的国内生产总值。预测不确定性区间(UI)纳入了从过去数据输入、模型估计和预测数据分布中传播的不确定性。
参考情景中的全球 TFR 预计在 2100 年将达到 1.66(95%UI 1.33-2.08)。在参考情景中,全球人口预计将在 2064 年达到峰值,为 97.3 亿(88.4-109),并在 2100 年下降至 87.9 亿(68.3-118)。参考情景中 2100 年五个最大国家的预测结果分别为:印度(10.9 亿[0.72-1.71])、尼日利亚(7910 万[594-1056])、中国(7320 万[456-1499])、美国(3360 万[248-456])和巴基斯坦(2480 万[151-427])。研究结果还表明,世界上许多地区的年龄结构正在发生变化,预计到 2100 年全球将有 23.77 亿(19.11-28.7)岁以上的人,1.70 亿(1.11-28.1)岁以下的人。到 2050 年,预计有 151 个国家的总和生育率(TFR)低于更替水平(TFR<2.1),预计到 2100 年,将有 183 个国家的 TFR 低于更替水平。参考情景中的 23 个国家,包括日本、泰国和西班牙,预计到 2100 年人口将下降 50%以上;中国的人口预计将下降 48.0%(-6.1 至 68.4)。中国预计将在 2035 年成为最大的经济体,但在参考情景中,美国预计将在 2098 年再次成为最大的经济体。我们的替代情景表明,实现教育和避孕需求的可持续发展目标将导致全球人口在 2100 年达到 62.9 亿(48.2-87.3),如果假设这些驱动因素的 99%变化率,那么全球人口将达到 68.8 亿(52.7-95.1)。
我们的研究结果表明,女性受教育程度和获得避孕措施的持续趋势将加速生育率下降并减缓人口增长。包括中国和印度在内的许多国家持续出现低于更替水平的总和生育率,将对经济、社会、环境和地缘政治产生影响。在未来几年,适应持续低生育率、同时维持和增强女性生殖健康的政策选择将至关重要。
比尔及梅琳达·盖茨基金会。