Risk evaluation of energy investment projects along the Belt and Road based on cloud model and evidence theory

Abstract

China's major energy projects along the Belt and Road are characterized by large-scale capital investment, long construction cycles, and complex investment environments, making it more difficult to assess project investment risks. Based on the PESTEL theory, a risk evaluation index system is constructed for energy enterprises' investment projects along the Belt and Road. In view of the uncertainty of energy investment projects, a risk evaluation method combining a cloud model and an evidence theory is proposed. To examine the developed index system and risk evaluation method, an overseas hydropower investment project is chosen and the risk factors are analyzed. The results suggest that the risk level of the enterprise's project is highest, the political and environmental risks are both high, the economic and project risks are both at average level, and the energy risk is relatively low, which verifies the effectiveness of this information fusion method. The method can be used to solve the problem of linguistic ambiguity and uncertainty in multi-attribute decision problems and is applicable to similar investment projects, thus providing a new technical path to assess the investment risks of energy enterprises along the Belt and Road.

Conflict Evidence Fusion Algorithm Based on Cosine Distance and Information Entropy

“…However, the data collected by multiple sensors are not always consistent, and when faced with conflicting data, it is necessary to use D-S evidence theory for data fusion to obtain more accurate data. D-S evidence theory is widely applied in various fields of information fusion, such as decision making [1][2][3][4][5][6], pattern recognition [7][8][9], information fusion [10][11], supplier management [12][13][14], risk assessment [15][16], fault diagnosis [17][18] and so on [19][20].…”