Grey Clustering Analysis Method for Overseas Energy Project Investment Risk Decision

In 2013, China proposed the “Belt & Road Initiative” which aims to invest the “Belt & Road” countries so as to help them develop their infrastructure and economy. China consumes the largest part of fossil energy of the whole world, so it is China’s priority to consider its energy supplying security. Therefore, it becomes urgent for China to invest the “Belt & Road” countries’ energy facilities. There comes a question: how to evaluate the overseas energy investment risk? To answer this question, this paper proposes a deep learning method to assess such risk of the 50 “Belt & Road” countries. Specifically, this paper first proposes an indicator system in which 6 main factors are separated into 36 sub-factors. This paper makes use of hierarchical convolution neural networks (CNN) to encode the historical statistics. The hierarchical structure could help CNN handle the long historical statistics more effectively and efficiently. Afterward, this paper leverages the self-attention layer to calculate the weights of each sub-factor. It could be observed that the resource potential is the most important indicator, while “years of China’s diplomatic relations” is the most important sub-indicator. Finally, we use a conditional random field (CRF) layer and softmax layer to compute the assessment scores of each country. Based on the experimental results, this paper suggests Russia, United Arab Emirates (UAE), Malaysia, Saudi Arabia, Pakistan, Indonesia, and Kazakhstan to be China’s most reliable choices for energy investment.

show abstract

“…Ref. [23] points out the risk of energy demands that may have a huge influence on the cooperation of the energy project. Ref.…”

Section: Related Work 21 Energy Investment Risk Assessmentmentioning

confidence: 99%

Energy Investment Risk Assessment for Nations along China’s Belt & Road Initiative: A Deep Learning Method

Liang

Jiang

2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…This study will focus on the Chonta micro watershed and the Milos Creek. The issues in this area are mainly the high socioenvironmental conflict between civil society and the mining company, as there is non-compliance with environmental commitments [10]. The population denounced the environmental impact on the components water, soil and air.…”

Section: Case Studymentioning

confidence: 99%

Applying the grey clustering method to assess water quality in a watershed in Cusco, Peru

Delgado¹,

Llanos²,

Puma³

et al. 2020

Proceedings of the 18th LACCEI International Multi-Conference for Engineering, Education, and Technology: Engineering, Integrat

View full text Add to dashboard Cite

Water quality assessment is a current issue of increasing concern in many countries around the world for reasons such as population health, national economic development and the environmental quality of ecosystems. At this juncture, the Grey Clustering method is used to assess water quality at discharge points, from the beginning to the end of the environmental monitoring process in the area of influence of the Anabi mining unit in the Chonta and Milos micro-watershed. The parameters evaluated were pH, dissolved oxygen, total suspended solids (TSS), iron and manganese. The results obtained through the Grey Clustering methodology showed a monitoring point with contamination from a treated water discharge. On the other hand, in order to obtain greater efficiency in the evaluation of water quality, national standard DS 004-2017-Minam (Water Quality Standards) and international standards were used through the PRATI index. Through the results obtained it was observed that (by means of the Prati index) there is a better classification of the water quality in each point, therefore this research becomes an important tool for future studies to consider the Prati index for greater reliability of results.

show abstract

“…This study will focus on the Chonta micro watershed and the Milos Creek. The issues in this area are mainly the high socio-environmental conflict between civil society and the mining company, as there is non-compliance with environmental commitments [10]. The population denounced the environmental impact on the components water, soil and air.…”

Section: Case Studymentioning

confidence: 99%

Water Quality Assessment in a Watershed in Cusco, Peru using the Grey Clustering Method

Delgado,

M.,

2019

IJEAT

View full text Add to dashboard Cite

Water quality assessment is a current issue of increasing concern in many countries around the world for reasons such as population health, national economic development and the environmental quality of ecosystems. At this juncture, the Grey Clustering method is used to assess water quality at discharge points, from the beginning to the end of the environmental monitoring process in the area of influence of the Anabi mining unit in the Chonta and Milos micro-watershed. The parameters evaluated were pH, dissolved oxygen, total suspended solids (TSS), iron and manganese. The results obtained through the Grey Clustering methodology showed a monitoring point with contamination from a treated water discharge. On the other hand, in order to obtain greater efficiency in the evaluation of water quality, national standard DS 004-2017-Minam (Water Quality Standards) and international standards were used through the PRATI index. Through the results obtained it was observed that (by means of the Prati index ) there is a better classification of the water quality in each point, therefore this research becomes an important tool for future studies to consider the Prati index for greater reliability of results.

show abstract

Grey Clustering Analysis Method for Overseas Energy Project Investment Risk Decision

Cited by 33 publications

References 2 publications

Energy Investment Risk Assessment for Nations along China’s Belt & Road Initiative: A Deep Learning Method

Energy Investment Risk Assessment for Nations along China’s Belt & Road Initiative: A Deep Learning Method

Applying the grey clustering method to assess water quality in a watershed in Cusco, Peru

Water Quality Assessment in a Watershed in Cusco, Peru using the Grey Clustering Method

Contact Info

Product

Resources

About