美国华盛顿大学Christopher J L Murray团队对2017-2100年195个国家和地区的人口生育率、死亡率、迁移和人口状况进行了分析。2020年7月14日，该成果发表在《柳叶刀》杂志上。
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形势至关重要。未来生育模式是估计未来人口规模的一个关键投入，但它们被大量的不确定性和不同的估计和预测方法所影响，导致全球人口预测存在重大差异。在许多国家，不断变化的人口规模和年龄结构可能会对经济、社会和地缘政治产生深远影响。在这项研究中，研究组开发了一种新型方法，来预测死亡率、生育力、迁移和人口，还评估了未来人口变化的潜在经济和地缘政治影响。
研究组在参考情景和替代情景中模拟了未来人口作为生育率、迁移率和死亡率的函数。他们针对50岁时的完整队列生育率开发了统计模型（CCF50），与同期的总生育率（TFR）相比，在一段时间内要稳定得多。研究组将CCF50建模为受教育程度和满足避孕需求的时间序列随机游走函数，将特定年龄的生育率建模为CCF50和协变量的函数。
研究组使用基本死亡率、风险因子标量和自回归综合移动平均（ARIMA）模型对2100年的特定年龄死亡率进行了建模。净迁移是根据社会人口指数、原始人口增长率以及战争和自然灾害造成的死亡进行建模的；并使用ARIMA模型。该模型框架用于根据教育程度和避孕要求的变化速度制定参考情景和替代情景。研究组还在参考方案中估算了每个国家和地区的国内生产总值。
到2100年，参考方案中的全球TFR预测为1.66。在参考情景中，预计全球人口将在2064年达到顶峰，为97.3亿，并在2100年下降至87.9亿。2100年对五个人口最多国家的参考预测为：印度10.9亿、尼日利亚7.91亿、中国7.32亿、美国3.36亿、巴基斯坦2.48亿。研究结果还表明，全球许多地区的年龄结构正在发生变化，预计2100年全球将有23.7亿人年龄超过65岁，17.0亿人小于20岁。
到2050年，预计有151个国家的TFR低于更替水平（TFR <2.1）；到2100年，预计有183个国家的TFR低于更替水平。在参考情景中，从2017年到2100年，包括日本、泰国和西班牙在内的23个国家预计人口减少幅度超过50％；预计中国人口将减少48.0%。预计到2035年中国将成为最大经济体，但在参考情景中，预计美国将在2098年再次成为最大经济体。研究组的替代方案表明，实现可持续发展目标中的教育和避孕需求目标将导致2100年全球人口达到62.9亿，假设这些驱动因素的变化率为99%，则全球人口将达到68.8亿。
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和人口增长缓慢。如果TFR持续低于更替水平，包括中国和印度在内的许多国家，将产生一系列经济、社会、环境和地缘政治后果。适应持续低生育率，同时维持和加强女性生殖健康的政策选择，在今后几年将至关重要。
附：英文原文
Title: Fertility, mortality, migration, and population scenarios for 195 countries and territories from 2017 to 2100: a forecasting analysis for the Global Burden of Disease Study
Author: Stein Emil Vollset, Emily Goren, Chun-Wei Yuan, Jackie Cao, Amanda E Smith, Thomas Hsiao, Catherine Bisignano, Gulrez S Azhar, Emma Castro, Julian Chalek, Andrew J Dolgert, Tahvi Frank, Kai Fukutaki, Simon I Hay, Rafael Lozano, Ali H Mokdad, Vishnu Nandakumar, Maxwell Pierce, Martin Pletcher, Toshana Robalik, Krista M Steuben, Han Yong Wunrow, Bianca S Zlavog, Christopher J L Murray
Issue&Volume: 2020-07-14
Abstract: Background
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.
Methods
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.
Findings
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.
Interpretation
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.
DOI: 10.1016/S0140-6736(20)30677-2
Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30677-2/fulltext
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