A tale of two population crises in recent Chinese history

被引:61
作者
Lee, Harry F. [1 ,2 ]
Zhang, David D. [1 ,2 ,3 ]
机构
[1] Univ Hong Kong, Dept Geog, Hong Kong, Hong Kong, Peoples R China
[2] Univ Hong Kong, Int Ctr China Dev Studies, Hong Kong, Hong Kong, Peoples R China
[3] Guizhou Normal Univ, Sch Geog & Environm Sci, Guizhou 550001, Peoples R China
关键词
CLIMATIC-CHANGE; DYNASTIC CYCLES; GROWTH DYNAMICS; EASTERN CHINA; TEMPERATURE; CULTIVATION; MORTALITY; ASIA; WAR;
D O I
10.1007/s10584-012-0490-9
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The fall of the Ming dynasty in the first half of the 17th century and the Taiping Rebellion from 1851-1864 were two of the most chaotic periods in Chinese history, and each was accompanied by large-scale population collapses. The 'Kang-Qian Golden Age' (also known as 'High Qing'), during which population size expanded rapidly, falls in between the two. Scholars remain divided in their opinions concerning the above alternation of population growth and decline as to whether variations in population size or climate change should be identified as the root cause. In either case, the synergistic impact of population growth and climate change upon population growth dynamics is overlooked. In the present study, we utilized high-resolution empirical data, qualitative survey, statistical comparison and time-series analysis to investigate how the two factors worked synergistically to drive population cycles in 1600-1899. To facilitate our research, we posited a set of simplified pathways for population growth in historical agrarian China. Our results confirm that the interrelation between population growth, climate change and population crises in recent Chinese history basically followed our posited pathways. The recurrences of population crises were largely determined by the combination of population growth and climate change. Our results challenge classic Malthusian/post-Malthusian interpretations and historians' views of historical Chinese population cycles.
引用
收藏
页码:285 / 308
页数:24
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