An evaluation of the economic, social, and ecological risks of China-Mongolia-Russia high-speed railway construction and policy suggestions

doi:10.1007/s11442-018-1512-y

Abstract

Abstract:

The construction of China-Mongolia-Russia high-speed railways is a strategic move to promote transportation infrastructure inter-connectivity between these countries, which will accelerate the implementation of the China-Mongolia-Russia Economic Corridor. However, well-planned China-Mongolia-Russia high-speed railways demand accurately identifying construction risks, scientifically evaluating risk levels, and mapping the spatial distribution of these risks. Therefore, this study established the integrated risk evaluation model (IREM) to scientifically evaluate the economic, social, and ecological risks of China-Mongolia-Russia high-speed railway construction and determine their magnitude and spatial distribution pattern. Based on this analysis, we propose designs for the east and west China-Mongolia-Russia high-speed railways and policy suggestions to mitigate construction risks. Suggestions include developing innovative cooperation of the "high-speed railway for resources and market", strengthening communication and technology dissemination, and applying innovative engineering techniques and setting buffers; establishing collaborative prevention and control systems to mitigate the three major ecological risks in the China, Mongolia, and Russia trans-border areas; and promoting economic integration by improving strategic coordination. In summary, this study provides scientific support for designing the China-Mongolia-Russia high-speed railways minimizing construction risks.

Key words: China-Mongolia-Russia high-speed railway construction, risk evaluation, designs of high-speed railways, policy suggestions, IREM

Suocheng DONG, Yang YANG, Fujia LI, Hao CHENG, Jingnan LI, Alexey BILGAEV, Zehong LI, Yu LI. An evaluation of the economic, social, and ecological risks of China-Mongolia-Russia high-speed railway construction and policy suggestions[J].Journal of Geographical Sciences, 2018, 28(7): 900-918.

Figures/Tables 7

Figure 1

Figure 2

Table 1

Table 2

Table 3

Figure 3

Figure 4

References 38

[1]	Chen C L, 2012. Reshaping Chinese space-economy through high-speed trains: Opportunities and challenges.Journal of Transport Geography, 22: 312-316. doi: 10.1016/j.jtrangeo.2012.01.028
[2]	Chen Y T, Chen G H, Li M J, 2004. Classification and research advancement of comprehensive evaluation methods.Journal of Management Science in China, 7(2): 69-79. (in Chinese)
[3]	China Economic Herald (CEH), 2015. Construction of west line of China-Mongolia-Russia economic corridor is of great significance. , 2015-09-25.
[4]	China National Bureau of Statistics, 2016. 2016 China Statistical Yearbook. Beijing: China Statistics Press. (in Chinese)
[5]	Ding J X, Jin F J, Wang J E ,et al. 2013. Competition game of high-speed rail and civil aviation and its spatial effect: A case study of Beijing-Shanghai high-speed rail.Economic Geography, 33(5): 104-110. (in Chinese)
[6]	Dong S C, Li Z H, Li F J ,et al. 2017. The green development mode of the “B&R”. China Economic Times, 05-11(A05). (in Chinese)
[7]	European Commission (EC), 1992. The Treaty on the European Union. Luxembourg, Office for Official Publications of the European Communities.
[8]	European Commission (EC), 1994. White Paper on Growth, Competitiveness and Employment. Luxembourg: Office for Official Publications of the European Communities.
[9]	Givoni, M, 2005. Aircraft and high speed train substitution: The case for airline and railway integration [D]. London: University College London.
[10]	Givoni M, 2006. Development and impact of the modern high-speed train: A review.Transport Reviews, 26(5): 593-611. doi: 10.1080/01441640600589319
[11]	He G Z, Zhang L, Lu Y L, 2009. Environmental impact assessment and environmental audit in large-scale public infrastructure construction: The case of the Qinghai-Tibet railway.Environmental Management, 44: 579-589. doi: 10.1007/s00267-009-9341-5 pmid: 19636608
[12]	Jiang B, Chu N C, Xiu C L et al.Xiu C L , 2016. Comprehensive evaluation and comparative analysis of accessibility in the four vertical and four horizontal HSR networks in China.Acta Geographica Sinica, 71(4): 591-604. (in Chinese) doi: 10.11821/dlxb201604005
[13]	Jiang H B, Xu J G, Qi Y, 2010. The influence of Beijing-Shanghai high-speed railways on land accessibility of regional center cities.Acta Geographica Sinica, 65(10): 1287-1298. (in Chinese)
[14]	Mongolia National Registration and Statistics Office, 2017. 2015 Mongolian Statistical Yearbook..
[15]	Mu C L, Lu L, Huang J F ,et al. .2015. Research on Yangtze River Delta tourist traffic pattern and linkage under high-speed rail network.Economic Geography, 35(12): 193-202. (in Chinese)
[16]	National Development and Reform Commission (NDRC). Plan for Construction of China-Mongolia-Russia Economic Corridor. , 2016-09-14.
[17]	OECD, 2009. Competitive interaction between airports, airlines and high-speed rail. .
[18]	Okada H, 1994. Features and economic and social effects of the Shinkanse.Japan Railway & Transport Review, SI, 9-16.
[19]	Ou J, Song D, Zhou N Y, 2014. World Bank Report of China high-speed railway: Construction cost analysis. (in Chinese)
[20]	Ozturk Z, Ozturk T, 2010. Ecological factor of the land transport: On Istanbul.Teknik Dergi, 21(1): 4979-4985.
[21]	Peng Z L, Zhang Q, Yang S L, 2015. Overview of comprehensive evaluation theory and methodology.Chinese Journal of Management Science, 23(11): 245-256. (in Chinese)
[22]	Popp J N, Boyle S P, 2017. Railway ecology: Underrepresented in science?,Basic and Applied Ecology, 19: 84-93. doi: 10.1016/j.baae.2016.11.006
[23]	Preston J, 2012. High speed rail in Britain: About time or a waste of time?,Journal of Transport Geography, 22: 308-311. doi: 10.1016/j.jtrangeo.2012.03.005
[24]	Ryder A, 2012. High speed rail.Journal of Transport Geography, 22: 303-305. doi: 10.1016/j.jtrangeo.2012.03.004
[25]	Shaw S L, Fang Z X, Lu S W et al.Lu S W , 2014. Impacts of high speed rail on railroad network accessibility in China.Journal of Transport Geography, 40: 112-122. doi: 10.1016/j.jtrangeo.2014.03.010
[26]	Song S B, Cai H J, Xu L F, 2003. Indicators system for region sustainable water resources utilization and its assessing methods.Advances in Water Sciences, 14(5): 647-652. (in Chinese)
[27]	Statistical Department of Irkutsk Oblast, 2016. 2015 Irkutsk Oblast Statistical Yearbook. (in Russian)
[28]	Statistical Department of Republic of Buryatia, 2016. 2015 Republic of Buryatia Statistical Yearbook. . . (in Russian)
[29]	Statistical Department of Zabaykalsky Krai, 2016. 2015 Zabaykalsky Krai Statistical Yearbook. . (in Russian)
[30]	UN Comtrade Database..
[31]	Wang D G, 2014. The influence of Beijing-Shanghai high-speed railway on tourist flow and time-space distribution.Tourism Tribune, 29(1): 75-82. (in Chinese) doi: 10.3969/j.issn.1002-5006.2014.01.008
[32]	Wang D G, Chen T, Lu L et al.Lu L , 2015a. Mechanism and HSR effect of spatial structure of regional tourist flow: Case study of Beijing-Shanghai HSR in China.Acta Geographica Sinica, 70(2): 214-233. (in Chinese) doi: 10.11821/dlxb201502004
[33]	Wang D G, Chen Y, Chen T et al.Chen T , 2015b. Optimizing tourist spatial structure for large scale regional metropolitan circles under the Beijing-Shanghai high-speed rail.Resource Science, 37(3): 581-592. (in Chinese)
[34]	World Bank Open Data, 2017.
[35]	Xinhua News Agency (XNA), 2017. Nearly 80% of Mongolia’s land are under desertification in different degrees. , 2017-06-17.
[36]	Xu C L, Xun Y A, 2011. Regional influence and significance of the high-speed rail era.Resources and Environment in the Yangtze Basin, 20(6): 650-654. (in Chinese) doi: 10.1007/s12182-011-0124-2
[37]	Yu T, Chen Z, Zhu P Y, 2015. Characteristics and mechanism of high speed rail-driven suburbanization in China: A case study of Beijing-Shanghai high-speed rail.Scientica Geographica Sinica, 37(3): 581-592. (in Chinese) doi: 10.1007/s11783-011-0280-z
[38]	Zhong Y X, Huang J, Wen Y Z, 2015. Impact of high-speed railway on spatial patterns of Chinese cities’ accessibility.Scientica Geographica Sinica, 35(4): 387-395. (in Chinese) doi: 10.1083/jcb.201208150

Object	Standard	Sub-standard	Index	Weight	Type
Comprehensive economic- social- ecological risk	Economic risk	Economic development	GDP per capita	0.0883	Negative indices
		Economic development	Growth rate of GDP	0.0394
		Passenger capacity	Population density	0.0743
		Passenger capacity	Growth rate of population	0.0216
		Transportation infrastructure	Railway network density	0.0282
		Attractive power	Tourism resources	0.0120
			Energy resources	0.0120
			Mineral resources	0.0120
			Education resources	0.0120
	Social risk	Government and residential support	Government and residential support for high-speed railway construction	0.1015
	Social risk	Social stability	Unemployment rate	0.0985	Positive indices
	Ecological risk	Disaster	Area of forest fire per 10,000 km²	0.1048
		Disaster	Number of geologic hazards	0.1497
		Ecological destruction	Forest coverage rate	0.0934
			Number of natural reserves	0.0751
			Biodiversity	0.0770

Research units	EN	ED	PC	TI	AP	S	SGP	ST	EC	D	EDT	CESE
Mongolia	0.2237	0.1002	0.0743	0.0282	0.0211	0.1662	0.0677	0.0985	0.3203	0.2545	0.0658	0.7102
Republic of Buryatia	0.2498	0.1158	0.0879	0.0273	0.0187	0.1422	0.1015	0.0407	0.2191	0.0733	0.1459	0.6111
Zabaykalsky Krai	0.2672	0.1255	0.0944	0.0266	0.0207	0.1586	0.1015	0.0571	0.1765	0.0507	0.1259	0.6024
Irkutsk Oblast	0.2122	0.0744	0.0920	0.0274	0.0183	0.1184	0.0677	0.0507	0.1660	0.0213	0.1447	0.4966
Heilongjiang	0.2224	0.0753	0.0913	0.0236	0.0323	0.0500	0.0000	0.0500	0.1700	0.0000	0.1700	0.4425
Inner Mongolia	0.1738	0.0367	0.0882	0.0249	0.0241	0.0371	0.0000	0.0371	0.1390	0.0171	0.1219	0.3499
Hebei	0.1907	0.0712	0.0644	0.0152	0.0399	0.0355	0.0000	0.0355	0.0746	0.0171	0.0576	0.3009
Beijing	0.0467	0.0023	0.0097	0.0000	0.0347	0.0000	0.0000	0.0000	0.1144	0.0748	0.0396	0.1611

Risk category	Economic risk	Social risk	Ecological risk	Comprehensive risk
Low risk	[0.0467, 0.1202]	[0, 0.0554]	[0.0746, 0.1565]	[0.1611, 0.3442]
Medium risk	(0.1202, 0.1937]	(0.0554, 0.1108]	(0.1565, 0.2384]	(0.3442, 0.5272)
High risk	(0.1937, 0.2672]	(0.1108, 0.1662]	(0.2384, 0.3203]	(0.5272, 0.7102]