Big Data-Based Assessment of Political Risk along the Belt and Road

Abstract

Political risk assessment has become increasingly important in recent years, especially with the launch of the Belt and Road Initiative (BRI) and with Covid-19 still ravaging the world. This study aims to assess systematically the political risk of BRI countries during the period from 2013 to 2019 based on three big data sets, the Global Database of Events, Language, and Tone (GDELT), China Global Investment Tracker (CGIT), and Armed Conflict Location & Event Data Project (ACLED). It is found that to properly quantify the political risks for BRI countries, the type of events, “Material Conflict”, and a variable characterizing the degree of cooperation/conflicts of the events, the Goldstein Scale, are of critical importance. Based on the chosen type of events and variable, we design a normalized variable to assess political risk of any country in any year so that comparison among different countries can be meaningly made. By decomposing political risk into two components, domestic and international, and examining the spatiotemporal evolution of political risk along the Belt and Road, we find that the sum of the number of BRI countries with the extremely high level and the high level of domestic, international, and (overall) political risk all reached the peak in 2015, and decreased thereafter, and that often the level of domestic political risk along the Belt and Road was higher than the international political risk. It is also found that a strong positive correlation exists between political risk and China’s total investments and construction contracts along the Belt and Road during this period. The implications of this positive correlation are discussed. The analysis presented here may help to promote the sustainable development of BRI, and be extended to examine the risks associated with foreign investments other than BRI projects.

Internet of Things-Enabled Tourism Economic Data Analysis and Supply Chain Modeling

“…The data platform is widely used as an important data analysis tool. Moreover, it is scientific and reliable to take the Baidu index as a statistical analysis tool in the era of big data (Sun et al, 2021;Yaacob et al, 2021). Therefore, the search indexes on the Baidu index platform are selected here as the OPA data.…”

Investment Suitability Assessment Based on B&R Symbiotic System Theory: Location Choice of China’s OFDI

“…Sun et al found a strong positive correlation between political risk and China's total investments. They confirmed that it would be necessary for China to invest in B&R countries with low or even negligible political risks [15]. Duan et al have put forward a fuzzy integrated evaluation model for energy investment risk based on entropy weight, conducting an integrated assessment of the energy investment risk of B&R countries.…”

“…With the continuous advancement of the BRI, academic circles have conducted extensive discussions on issues related to investment between China and B&R countries [3,6,8], and the location choice has been a hot issue for numerous scholars [4,[9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. At present, the research on the location choice of China's OFDI in B&R countries can be roughly divided into two aspects.…”

“…The second aspect is the research on China's OFDI location choice in B&R countries with risk assessment. As China's OFDI spans across countries and regions featuring a diverse range of religions, cultures, economies, and legal systems, this diversity will more likely bear high and unexpected investment risks for Chinese enterprises, such as political, economic, environmental, financial, and social risks [12,[14][15][16]. Some scholars have studied and assessed the risk of China's OFDI in detail for various risk categories and industries.…”

Big Data COVID-19 Systematic Literature Review: Pandemic Crisis

“…As a result of its geographic structure, this research was able to quickly absorb and integrate massive geographic data, including the WHO. [71,79,81,82,85,87]…”