Abstract
The association between climate change and human crises in history was conceived a century ago. Yet, it remained controversial and even questioned in academia for some time because it was usually substantiated with selective historical cases and verified by qualitative methods. The causal mechanism of the climate-crisis connection was rarely explored. Breakthrough did not occur until high resolution paleo-climate reconstructions become available in recent years. Based on high resolution paleo-climate reconstructions and fine-grained historical socio-economic datasets, we adopted a pioneering approach to examining quantitatively the climate-crisis relationship in pre-industrial societies. Our research findings demonstrated scientifically the association between climate change and various human crises (i.e., population checks, population collapses, and socio-political chaos) in China, Europe, and other countries/regions in the Northern Hemisphere in the pre-industrial era. Furthermore, we worked out a set of causal linkages showing how climate change is eventually translated into human crises. Those linkages were validated by statistical methods. Our results concluded that deteriorating climate, which led to reduced land carrying capacity, was the ultimate cause of human crises in pre-industrial societies. This challenges the Malthusian explanation for human misery. Our studies start a new page in climate change research. With the increasing availability and precision of paleo-climate reconstructions, the application of more sophisticated methods in the climate-crisis research is going to be facilitated. Hopefully, the role played by climate change in human history can be fully unveiled in the near future.
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Notes
- 1.
Huntington (1907) suggests that in historical China, the Mongol conquests in the thirteenth century and Manchu conquests in the seventeenth century were primarily triggered by climate change.
- 2.
Deteriorating climate refers to climatic cooling or warming. Cooling shortens the crop growing season and reduces farmland area (Galloway 1986); warming shortens the duration between sowing and harvesting and increases evapotranspiration (Lobell and Field 2007). Both are detrimental to agricultural productivity, especially to a primarily agricultural economy characterized by a low level of technology and high dependence on climate. Together with the side effects of climate change, such as shifts in rainfall pattern, the carrying capacity of the agro-economy shrinks significantly in a deteriorating climate.
- 3.
Briffa and Osborn (2002) chose five representative climate series of the last millennium in the Northern Hemisphere (Jones et al. 1998; Mann et al. 1999; Briffa 2000; Crowley and Lowery 2000 and Esper et al. 2002) to discuss differences between the records of various independent paleo-temperature studies. The five recalibrated temperature anomaly series were arithmetically averaged to give the Northern Hemispheric temperature anomaly series (in °C, from the AD 1961–1990 mean) (Fig. 14.1a). Although there are China-wide temperature reconstructions with a millennium year length of record (Wang et al. 2001; Yang et al. 2002), they did not reach the annual scale that is required by our research. Given that the major long-term cooling events revealed by the temperature reconstructions of China and the Northern Hemisphere were basically synchronous in the past millennium, Briffa and Osborn’s (2002) ‘averaged’ Northern Hemisphere temperature anomaly series was chosen as the standard paleo-temperature record to quantitatively delineate the cold and warm phases. The boundaries of the warm and cold phases were delineated at the mean temperature point between minimum and maximum values of two contiguous phases on Briffa and Osborn’s (2002) averaged series. A cold or warm phase would be determined if the average temperature change had an amplitude >0.14 °C, in order to obtain an equal aggregate duration of cold and warm periods. Based on the averaged series, six major cycles of warm and cold phases were identified between AD 1000 and 1911. The cold phases spanned AD 1110–1152, 1194–1302, 1334–1359, 1448–1487, 1583–1717, and 1806–1911, while the warm phases spanned AD 1000–1109, 1153–1193, 1303–1333, 1360–1447, 1488–1582, and 1718–1805 (Fig. 14.1a).
- 4.
- 5.
According to the physical regionalization of China (Zhao 1986), China is divided into three macro regions, namely: (1) North China – with continental humid, semi-humid, semi-arid, and arid temperate climate influenced by both the monsoons and the westerlies. Its average annual temperature ranges from very low to 14 ºC. Major agriculture products are spring wheat (northern part) and winter wheat (southern part). Economic activities are mainly pastoral because of the relatively low average annual precipitation of 50–750 mm, with >100 frost days per year; (2) Central China – with a climate dominated by the monsoons, with annual temperature ranging from 14 to 18 ºC, and 10–80 frost days per annum. The region has served as China’s major rice producing area; and (3) South China – with south sub-tropical and tropical climates and average annual temperature ranging from 19 to 22 ºC. The long growing season permits double- or triple-cropping in a year. Frost days are <10 per annum.
- 6.
Our temperature data come from Mann and Jones’ (2003) Northern Hemisphere annual temperature trajectory, supplemented by the Southern Hemisphere and global temperature trajectories.
- 7.
Our war data was elicited from Brecke’s (1999) Conflict Catalog, which is the most inclusive global war dataset so far, documenting a total of 2,912 wars fought in AD 1400–1900. The wars include all recorded violent conflicts that meet Richardson’s magnitude ≥1.5 criterion (≥32 deaths). The geographical locations of wars in the dataset are divided by their natural locations.
- 8.
Apart from Brecke’s Conflict Catalog, we also included another two global war datasets for comparison: Wright’s (1942) dataset, which documents all hostilities involving members of the family of nations, whether international, civil, colonial, or imperial, which were recognized as states of war in the legal sense or which involved over 50,000 troops; and Luard’s (1986) dataset, which documents those encounters that involved at least one sovereign state and involved substantial, organized fighting over a significant period (also known as principle wars).
- 9.
The calculation of the war frequencies and the ratio of wars was based on Brecke’s (1999) Conflict Catalog.
- 10.
Data were smoothed by the 40-year Butterworth low-pass filter to remove fluctuations on time-scales <40Â year prior to statistical analysis.
- 11.
- 12.
The centennial climate variability in the Northern Hemisphere was elicited by arithmetically averaging the 12 most recent and authoritative paleo-temperature reconstructions chosen by the Intergovernmental Panel on Climate Change (in °C, from the AD 1961–1990 mean) (Jansen et al. 2007), then smoothed by the 100-year Butterworth low-pass filter to remove fluctuations on time-scales <100 years (Fig. 14.3a).
- 13.
The independent variables are time and temperature anomalies in the Northern Hemisphere. Time (t) presumably represents technology and/or capital accumulation. An attempt is made to eliminate the trend from the population, using parabolic (t and t2), squared (t2) and cubic (t3) terms (see Galloway 1986). The regressions were corrected for autoregressive disturbances using the Prais-Winsten estimation method. Results show that the various detrending procedures did not affect the significance of temperature.
- 14.
The term ‘population collapse’ refers to negative population growth.
- 15.
The calculation of population losses was based on Jiang (1993).
- 16.
See footnote no. 3 for the dates of cold and warm phases in China.
- 17.
See footnote no. 6 for the details of Northern Hemisphere temperature change. The calculation of population growth rate was based on McEvedy and Jones (1978). Prior to statistical analysis, all data were smoothed by the 40-year Butterworth low-pass filter to remove fluctuations on time-scales <40Â years.
- 18.
The identification of population collapses was based on McEvedy and Jones (1978).
- 19.
See footnote no. 12 for centennial climate variability in the Northern Hemisphere from AD 800 to 1900. Based on the temperature series, we established warming and cooling thresholds according to the averaged temperature anomalies of the starting century of the Medieval Warm Period and Little Ice Age, respectively. A period in which the temperature anomaly was > −0.3 °C was classified as a warm phase; a period in which the temperature anomaly was < −0.42 °C was classified as a cold phase. It should be noted that the average AD 1961–1990 temperature anomaly is 0 °C, which is c. 0.3 °C higher than that of the Medieval Warm Period. In line with these criteria, we identified a warm phase in the Medieval Warm Period (AD 954–1114) and four cold phases in the Little Ice Age [C1 (AD 1236–1359), C2 (AD 1459–1510), C3 (AD 1554–1741), C4 (AD 1804–66)]. These represent periods of climate deterioration. The remaining years were classified as mild phases (Fig. 14.4a).
- 20.
The delineation of major climatic zones in the Northern Hemisphere is made according to the modified Köppen classification system (see London Times 2007). Four climate zones are delineated as follows: (1) tropical humid – rainy climate with no winter, coolest month above 18 °C; (2) warmer humid – rainy climate with mild winter, coolest month above 0 °C, but below 18 °C, warmest month above 10 °C; (3) cooler humid – rainy climate with severe winter, coldest month below 0 °C, warmest month above 10 °C; and 4) dry – dry climate; limits are defined by formulae based on rainfall effectiveness.
- 21.
See footnote no. 13 for the specifications of the regression models.
- 22.
See footnote no. 3 for the dates of the cold phases in China.
- 23.
The times of dynastic changes were based on official records published by government bodies and historians. The dynasties included those that ruled most parts of China, and those established by remote tribes that once occupied an area equivalent to over ten provinces of the current Chinese territory.
- 24.
The temperature anomaly series in Europe was derived from two authoritative temperature reconstructions at the annual scale. This first one is Luterbacher et al.’s (2004) temperature reconstruction for European land areas (25°W to 40°E and 35°N to 70°N) spanning AD 1500–2003. The second temperature reconstruction is associated with Osborn and Briffa’s (2006) temperature dataset spanning AD 800–1995, which contains 14 regional temperature-related proxy records. However, only those regional temperature series nested within Europe were combined to show the temperature change in Europe over time. It was done by normalizing each of the above series and then taking their arithmetical average. The above two temperature reconstructions were derived from different proxies and reconstructed by different methods. In order to combine the two reconstructions together, each of them was normalized to homogenize the original variability of all series. It should be noted that this transformation cannot preserve the numerical values of temperature variation, but will provide the relative amplitude of temperature change. Then, the two normalized series were arithmetically averaged and then smoothed by 100-year Butterworth low-pass filter to generate the Europe temperature composite.
- 25.
According to Lyon et al. (1969) and Roberts (1996), the delineation of golden and darks ages in Europe over the last millennium is as follows: Golden ages – High Middle Ages (eleventh to thirteenth centuries), Renaissance (fifteenth to mid-sixteenth centuries), and Enlightenment (mid-seventeenth to late eighteenth centuries). Dark ages – Crisis of Late Middle Ages (fourteenth century), General Crisis of the Seventeenth Century (mid-sixteenth to mid-seventeenth centuries), and the Age of Revolution (late eighteenth to mid-nineteenth centuries).
- 26.
Given that we addressed whether climate change is a credible cause for large-scale societal crisis from the macro-historic perspective, macro-historic and aggregate features are privileged over micro-historic and individual ones; general trends are preferred to particular moments or events; and broad distinctions or geographical uniformities take precedence over localized analyses.
- 27.
Please refer to the SI Appendix, Materials and Methods I–XI of Zhang et al.’s (2011a) study for the specification and data source of each variable.
- 28.
Based on the Northern Hemisphere (Fig. 14.6a, red line, cf. footnote no. 12) and European (Fig. 14.6a, black line, cf. footnote no. 24) temperature anomaly series, we divided our study period into Mild Phase 1 (AD 1500–1559; average temperature = 0.43σ), Cold Phase (AD 1560–1660; average temperature = −0.59σ), and Mild Phase 2 (AD 1661–1800; average temperature = 0.24σ). The Cold Phase coincided with the General Crisis of the Seventeenth Century. In Mild Phase 2, there was brief cooling in AD 1700 and 1750.
- 29.
If one factor (A) causes another factor (B), then changes of A happen first, followed by changes of B, that is the cause precedes the effect. Under this circumstance, Granger Causality Analysis could be used to test whether A which precedes B could better help predict B by adding A. Then, we consider A is the Granger cause of B (i.e., the causal relationship between A and B). Granger Causality Analysis has been used widely in business, economics, sociology, psychology, politics, biology, and medicine. It also is regarded as an effective method to verify causal relationships in the social sciences. Before Granger Causality Analysis, an Augmented Dickey–Fuller test was adopted to check the stationarity of data. Any nonstationary data were subjected to first- or second-level differencing. Then regressions were run (by controlling the number of lags) to identify the causal relation.
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Acknowledgements
This research was supported by the Hui Oi-Chow Trust Fund (201302172003 and 201205172003), HKU Seed Funding Programme for Basic Research (201109159014), and Research Grants Council of The Government of the Hong Kong Special Administrative Region of the People’s Republic of China (HKU745113H, HKU758712H, HKU7055/08H, and HKU705-HSS-12).
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Lee, H.F., Zhang, D.D. (2015). Quantitative Analysis of Climate Change and Human Crises in History. In: Kwan, MP., Richardson, D., Wang, D., Zhou, C. (eds) Space-Time Integration in Geography and GIScience. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9205-9_14
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