The cooperative and conflictual interactions between the United States, Russia, and China: A quantitative analysis of event data

doi:10.1007/s11442-020-1808-6

Abstract

Abstract:

The United States, Russia and China are militarily and economically among the most powerful countries in the post-Cold War period, and the interactions between the three powers heavily influence the international system. However, different conclusions about this question are generally made by researchers through qualitative analysis, and it is necessary to objectively and quantitatively investigate their interactions. Monthly-aggregated event data from the Global Data on Events, Location and Tone (GDELT) to measure cooperative and conflictual interactions between the three powers, and the complementary cumulative distribution function (CCDF) and the vector autoregression (VAR) method are utilized to investigate their interactions in two periods: January, 1991 to September, 2001, and October, 2001 to December, 2016. The results of frequencies and strengths analysis showed that: the frequencies and strengths of USA-China interactions slightly exceeded those of USA-Russia interactions and became the dominant interactions in the second period. Although that cooperation prevailed in the three dyads in two periods, the conflictual interactions between the USA and Russia tended to be more intense in the second period, mainly related to the strategic contradiction between the USA and Russia, especially in Georgia, Ukraine and Syria. The results of CCDF indicated that similar probabilities in the cooperative behaviors between the three dyads, but the differences in the probabilities of conflictual behaviors in the USA-Russia dyad showed complicated characteristic, and those between Russia and China indicated that Russia had been consistently giving China a hard time in both periods when dealing with conflict. The USA was always an essential factor in affecting the interactions between Russia and China in both periods, but China’s behavior only played a limited role in influencing the interactions between the USA-Russia dyad. Our study provides quantitative insight into the direct cooperative and conflictual interactions between the three dyads since the end of the Cold War and helps to understand their interactions better.

Key words: USA-Russia-China, cooperation and conflict, interactions, GDELT, complementary cumulative distribution function (CCDF), vector autoregression model (VAR)

YUAN Lihua, SONG Changqing, CHENG Changxiu, SHEN Shi, CHEN Xiaoqiang, WANG Yuanhui. The cooperative and conflictual interactions between the United States, Russia, and China: A quantitative analysis of event data[J].Journal of Geographical Sciences, 2020, 30(10): 1702-1720.

Figures/Tables 9

Table 1

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Table 2

Table 3

Table 4

References 57

1	Abb P, Strüver G , 2015. Regional linkages and global policy alignment: The case of China-Southeast Asia relations. In: German Institute of Global and Area Studies. GIGA Working Papers, No.268. Germany: Hamburg.
2	Azar E E , 1980. The Conflict and Peace Data Bank (COPDAB) Project. Journal of Conflict Resolution, 24(1):143-152.
3	Bodas-Sagi D J, Labeaga J M , 2016. Using GDELT data to evaluate the confidence on the Spanish government energy policy. International Journal of Interactive Multimedia and Artificial Intelligence, 3(6):38-43.
4	Chen Xiaoqiang, Yuan Lihua, Shen Shi et al., 2019. The analysis of geo-relationships among China and its neighboring countries. Acta Geographica Sinica, 74(8):1534-1547. (in Chinese)
5	Chi Zhipei, Hou Na , 2019. Quantitative research on big data and bilateral relations: A case study of GDELT and Sino-US relations. Quarterly Journal of International Politics, 4(2):67-88. (in Chinese)
6	Cukier K, Mayer-Schoenberger V , 2013. The rise of big data: How it’s changing the way we think about the world. Foreign Affairs, 92(3):28-41.
7	Dickey D A, Fuller W A , 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical Association, 74:427-431.
8	Dickey D A, Fuller W A , 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4):1057-1072.
9	Dixon W J , 1986. Reciprocity in United States-Soviet relations: Multiple symmetry or issue linkage? American Journal of Political Science, 30(2):421-445.
10	Du Debin, Duan Dezhong, Liu Chengliang et al., 2016. Twenty-five years of progress in geopolitics research: Efforts from China's geographers. Journal of Geographical Sciences, 26(8):1223-1242.
11	Elshendy M, Fronzetti C , 2017. Big data analysis of economic news: Hints to forecast macroeconomic indicators. International Journal of Engineering Business Management, 9:1-12.
12	Freeman J R, Williams J T, Lin T , 1989. Vector autoregression and the study of politics. American Journal of Political Science, 33(4):842-877.
13	Gao J B, Hu J , 2014. Financial crisis, Omori’s law, and negative entropy flow. International Review of Financial Analysis, 33:79-86.
14	Gao J B, Leetaru K H, Hu J et al., 2013. Massive media event data analysis to assess world-wide political conflict and instability. In: Greenberg A M, Kennedy W G, Bos N D, 2013. Social Computing, Behavioral-Cultural Modeling and Prediction. Heidelberg: Springer, 284-292.
15	Ge Quansheng, Jiang Dong, Lu Feng et al., 2018. Spatio-temporal simulation of the geopolitical environment system. Journal of Geographical Sciences, 28(7):871-880.
16	Gladkyy O , 2003. American foreign policy and US relations with Russia and China after 11 September. World Affairs, 166(1):3-23.
17	Goldstein J S , 1991. Reciprocity in superpower relations: An empirical analysis. International Studies Quarterly, 35(2):195-209.
18	Goldstein J S , 1992. A conflict-cooperation scale for WEIS events data. Journal of Conflict Resolution, 36(2):369-385.
19	Goldstein J S, Freeman J R , 1991. U.S.-Soviet-Chinese relations: Routine, reciprocity, or rational expectations?. American Political Science Review, 85(1):17-35.
20	Gonzalo D C S, Garcíaherrero A, Ortiz A et al., 2015. An empirical assessment of social unrest dynamics and state response in Eurasian countries. Social Science Electronic Publishing, 3(3):1-29.
21	Guo Zhenyuan , 2002. China-US-Russia relationship since September 11. Peace and Development, ( 3):1-5. (in Chinese)
22	Kalyvas S N, Balcells L , 2010. International system and technologies of rebellion: How the end of the cold war shaped internal conflict. American Political Science Review, 104(3):415-429.
23	Kang H, Reuveny R , 2000. An exploration of multi-country political conflict/cooperation interdependence. International Interactions, 26(2):129-152.
24	Kang H, Reuveny R , 2001. Exploring multi-country dynamic relations between trade and conflict. Defence & Peace Economics, 12(3):175-196.
25	Kornberg J F , 1996. Chinese foreign policy: Theory and practice (review). China Review International, 3(1):251-254.
26	Lazer D, Pentland A, Adamic L et al., 2014. Computational social science. Science, 323(1):721-723.
27	Leetaru K, Schrodt P A , 2013. GDELT: Global data on events, location, and tone, 1979-2012. In: The International Studies Association. Annual Conference of the International Studies Association. San Diego, California, March 29, 2013.
28	Levin N, Ali S, Crandall D , 2018. Utilizing remote sensing and big data to quantify conflict intensity: The Arab Spring as a case study. Applied Geography, 94:1-17.
29	Li P W , 2015. The role of USA in Cross-Strait relations: A time series analysis approach. Soochow Journal Political Science. 33(3):207-270. (in Chinese)
30	Li Qingsi , 2017. The Sino-US-Russian relations in dynamic balance. Frontiers, ( 19):6-14. (in Chinese)
31	Li Saojun , 2002. Conflict-cooperation model: Quantitative analysis of Sino-US relations. World Economics and Politics, ( 4):43-49. (in Chinese)
32	Li Xing, Kong Rui , 2010. Russian factors on Sino-US relationship. Russian, East European & Central Asian Studies, ( 5):60-68. (in Chinese)
33	Liu Fenghua , 2017. Triangular relationship among China, Russia and the US after the Ukraine crisis: Adjustment and trend. International Economic Review, ( 4):103-115. (in Chinese)
34	Liu Yi, Wang Yun, Yang Yu et al., 2019. Regional integration and interaction of the Guangdong-Hong Kong-Macao Greater Bay Area. Acta Geographica Sinica, 74(12):2455-2466. (in Chinese)
35	Liu Yungang, Wang Fenglong, An Ning , 2018. The duality of political geography in China: Integration and challenges. Geopolitics, doi: 10.1080/14650045.2018.1465042.
36	Lu M , 2001. Vector Autoregression (VAR): An approach to dynamic analysis of geographic processes. Geografiska Annaler. Series B, Human Geography, 83(2):67-78.
37	Ma Menglan, Fang Peng, Gao Jianbo et al., 2017. Does ideology affect the tone of international news coverage? In: 2017 International Conference on Behavioral, Economic, Socio-Cultural Computing (BESC). Poland: Krakow, 16-18 October, 2017.
38	Ma Mingqing, Yuan Wu, Ge Quansheng et al., 2019. Big data analysis of social development situation in regions along the Belt and Road. Progress in Geography, 38(7):1009-1020. (in Chinese)
39	McGinnis M D, Williams M G T , 1989. Change and stability in superpower rivalry. The American Political Science Review, 83(4):1101-1123.
40	Moore W H , 1995. Action-reaction or rational expectations? Reciprocity and the domestic-international conflict nexus during the "Rhodesia Problem". Journal of Conflict Resolution, 39(1):129-167.
41	Pang Xun, Liu Ziye , 2019. China-U.S. in Massive Machine-coded event data: Influence of reciprocity, policy inertia, and a third power. World Economics and Politics, ( 5):53-79. (in Chinese)
42	Qi Haixia , 2012. An analysis of the factors affecting China’s relations with the other major powers. Chinese Journal of European Studies, ( 5):61-78. (in Chinese)
43	Rajmaira S, Ward R M D , 1990. Evolving foreign policy norms: Reciprocity in the superpower triad. International Studies Quarterly, 34(4):457-475.
44	Shen Shi, Song Changqing, Cheng Changxiu et al., 2020. GDELT: Big event data for sensing global social dynamics. World Regional Studies, 29(1):71-76. (in Chinese)
45	Shen Shi, Yuan Lihua, Ye Sijing et al., 2019. The fluctuation and background analysis of geopolitical relations between China and USA during the last 40 years. Scientia Geographica Sinica, 39(7):1063-1071. (in Chinese)
46	Sims C , 1980. A comparison of interwar and postwar business cycles: Monetarism reconsidered. American Economic Review, 70:250-257.
47	Song Changqing, Ge Yuejing, Liu Yungang et al., 2018. Under taking research on Belt and Road Initiative from the geo-relation perspective. Geographical Research, 37(1):3-19. (in Chinese)
48	Theocharous A L, Anastasios Z, Neophytos L et al., 2018. Tourism, instability and regional interdependency: Evidence from the Eastern-Mediterranean. Defence and Peace Economics, 31(3):245-268.
49	Ward M D , 1982. Cooperation and conflict in foreign policy behavior: Reaction and memory. International Studies Quarterly, 26(1):87-126.
50	Ward M D, Rajmaira S , 1992. Reciprocity and norms in U.S.-Soviet foreign policy. Journal of Conflict Resolution, 36(2):342-368.
51	Weidmann N B , 2016. A closer look at reporting bias in conflict event data. American Journal of Political Science, 60(1):206-218.
52	Yan Xuetong , 2010. The instability of China-US relations. The Chinese Journal of International Politics, 3(3):263-292.
53	Yan Xuetong, Qi Haixia , 2012. Football game rather than boxing match: China-US intensifying rivalry does not amount to Cold War. The Chinese Journal of International Politics, 5(2):105-127.
54	Yonamine J E , 2013. A nuanced study of political conflict using the Global Datasets of Events Location and Tone (GDELT) dataset. University Park, Pennsylvania: Doctoral Dissertation of Pennsylvania State University, University Park, 1-183.
55	Yuan Yihong, Liu Yu, Wei Guixing , 2017. Exploring inter-country connection in mass media: A case study of China. Computers Environment & Urban Systems, 62:86-96.
56	Zhang Chuchu, Xiao Caowei, Liu Helin , 2019. Spatial big data analysis of political risks along the Belt and Road. Sustainability, 11, doi: 10.3390/su11082216.
57	Zhao Huasheng , 2018. Research on the New Triangular Relationship between China, Russia and the U.S. Russian, Central Asian & East European Studies, ( 6):5-29. (in Chinese)

Dyads	Direction	Variables	Descriptions of variables
USA-CHN dyad	USA-CHN	UC_p, UC_f	Scales of cooperative/conflictual behavior from the USA toward China
USA-CHN dyad	CHN-USA	CU_p, CU_f	Scales of cooperative/conflictual behavior from China toward the USA
USA-RUS dyad	USA-RUS	UR_p, UR_f	Scales of cooperative/conflictual behavior from the USA toward Russia
USA-RUS dyad	RUS-USA	RU_p, RU_f	Scales of cooperative/conflictual behavior from Russia toward the USA
RUS-CHN dyad	RUS-CHN	RC_p, RC_f	Scales of cooperative/conflictual behavior from Russia toward China
RUS-CHN dyad	RUS-CHN	CR_p, CR_f	Scales of cooperative/conflictual behavior from China toward Russia

Independent variables	1992.01-2001.09 period				2001.10-2016.12 period
	Dependent variable				Dependent variable
	UC_p	CU_p	UC_f	CU_f	UC_p	CU_p	UC_f	CU_f
UC_p(-1)	-	0.038***	0.091**	0.159*	-	0.767	0.681	0.615
CU_p(-1)	0.649	-	0.090**	0.389	0.572	-	0.306	0.234
UR_p(-1)	0.296	0.460	0.161*	0.543	0.052**	0.033***	0.054**	0.571
RU_p(-1)	0.264	0.383	0.208	0.849	0.156*	0.146*	0.224	0.633
RC_p(-1)	0.007***	0.019***	0.328	0.024***	0.444	0.342	0.503	0.619
CR_p(-1)	0.007***	0.018***	0.382	0.018***	0.432	0.331	0.319	0.646
UC_f(-1)	0.866	0.754	-	0.149*	0.027***	0.031*	-	0.010***
CU_f(-1)	0.295	0.537	0.042***	-	0.760	0.698	0.269	-
UR_f(-1)	0.143*	0.210	0.146*	0.012***	0.833	0.836	0.083**	0.078**
RU_f(-1)	0.872	0.873	0.933	0.867	0.856	0.979	0.101*	0.550
RC_f(-1)	0.223	0.485	0.357	0.880	0.127*	0.055**	0.283	0.671
CR_f(-1)	0.473	0.561	0.496	0.669	0.557	0.253	0.829	0.731
R²	0.726	0.713	0.558	0.570	0.892	0.897	0.912	0.914
Adjusted R²	0.694	0.680	0.506	0.519	0.884	0.890	0.905	0.908
F-statistic	22.741	21.320	10.826	11.356	115.926	122.708	145.113	149.057

Independent variables	1992.01-2001.09 period				2001.10-2016.12 period
	Dependent variable				Dependent variable
	UR_p	RU_p	UR_f	RU_f	UR_p	RU_p	UR_f	RU_f
UC_p(-1)	0.759	0.642	0.431	0.190*	0.097	0.060**	0.678	0.720
CU_p(-1)	0.842	0.719	0.568	0.273	0.055**	0.032***	0.642	0.840
UR_p(-1)	-	0.579	0.304	0.123*	-	0.827	0.762	0.900
RU_p(-1)	0.112*	-	0.163*	0.113*	0.856	-	0.805	0.766
RC_p(-1)	0.518	0.388	0.504	0.753	0.930	0.862	0.887	0.916
CR_p(-1)	0.250	0.166*	0.338	0.535	0.944	0.878	0.927	0.938
UC_f(-1)	0.832	0.770	0.656	0.919	0.057**	0.049***	0.308	0.208
CU_f(-1)	0.651	0.387	0.796	0.878	0.937	0.720	0.460	0.644
UR_f(-1)	0.391	0.626	-	0.332	0.123*	0.159*	-	0.003***
RU_f(-1)	0.863	0.559	0.511	-	0.430	0.287	0.787	-
RC_f(-1)	0.969	0.958	0.723	0.585	0.416	0.470	0.346	0.675
CR_f(-1)	0.978	0.941	0.542	0.605	0.604	0.428	0.886	0.535
R²	0.553	0.540	0.597	0.604	0.869	0.869	0.880	0.849
Adjusted R²	0.501	0.486	0.550	0.558	0.860	0.860	0.872	0.839
F-statistic	10.628	10.075	12.707	13.093	93.769	93.501	103.572	79.374

Independent variables	1992.01-2001.09 period				2001.10-2016.12 period
	Dependent variable				Dependent variable
	RC_p	CR_p	RC_f	CR_f	RC_p	CR_p	RC_f	CR_f
UC_p(-1)	0.919	0.795	0.950	0.553	0.445	0.446	0.613	0.339
CU_p(-1)	0.673	0.807	0.626	0.728	0.236	0.263	0.856	0.538
UR_p(-1)	0.627	0.502	0.598	0.091	0.011***	0.011***	0.312	0.172*
RU_p(-1)	0.732	0.681	0.914	0.044***	0.007***	0.009***	0.277	0.163*
RC_p(-1)	-	0.601	0.911	0.119*	-	0.845	0.745	0.799
CR_p(-1)	0.480	-	0.971	0.415	0.696	-	0.851	0.603
UC_f(-1)	0.006***	0.001***	0.973	0.608	0.514	0.661	0.581	0.777
CU_f(-1)	0.004***	0.001***	0.614	0.602	0.303	0.398	0.359	0.195*
UR_f(-1)	0.965	0.453	0.131*	0.048***	0.062**	0.153*	0.028***	0.195*
RU_f(-1)	0.745	0.670	0.187*	0.905	0.566	0.833	0.181*	0.360
RC_f(-1)	0.000***	0.000***	-	0.887	0.272	0.214	-	0.198*
CR_f(-1)	0.001***	0.000***	0.054**	-	0.184*	0.202	0.917	-
R²	0.539	0.561	0.444	0.454	0.692	0.684	0.682	0.695
Adj. R²	0.485	0.509	0.379	0.391	0.670	0.662	0.659	0.673
F-statistic	10.024	10.947	6.845	7.145	31.616	30.524	30.180	32.109

[1]	Jinxi SONG, Dandong CHENG, Junlong ZHANG, Yongqiang ZHANG, Yongqing LONG, Yan ZHANG, Weibo SHEN. Estimating spatial pattern of hyporheic water exchange in slack water pool [J]. Journal of Geographical Sciences, 2019, 29(3): 377-388.
[2]	Xue DAI, Rongrong WAN, Guishan YANG. Non-stationary water-level fluctuation in China’s Poyang Lake and its interactions with Yangtze River [J]. Journal of Geographical Sciences, 2015, 25(3): 274-288.
[3]	SONG Yang, YAN Ping, LIU Lianyou. A review of the research on complex erosion by wind and water [J]. Journal of Geographical Sciences, 2006, 16(2): 231-241.
[4]	ZHANG Qiang, JIANG Tong, SHI Yafeng, Lorenz KING, LIU Chunling, Martin METZLER. Paleo-environmental changes in the Yangtze Delta during past 8000 years [J]. Journal of Geographical Sciences, 2004, 14(1): 105-112.
[5]	WANG Hui-xiao, LIU Chang-ming, YANG Zhi-feng. Assessment and utilization of soil water resources [J]. Journal of Geographical Sciences, 2001, 11(1): 87-91.