Energy Investment Risk Research Articles

Risk assessment of China's overseas energy investments considering the response ability to major risk events: A case study of COVID-19

This paper constructs a new risk evaluation index system for overseas energy investment, while considering the country's response ability to major risk events using COVID-19 as experience. The entropy weight method and the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method are used to conduct overall and sub-dimensional risk assessment for 52 countries. The results show that: (1) among the seven risk dimensions considered, the response ability to major risk events dimension has the highest weight, accounting for 16.47 %. This indicates that Chinese energy corporations cannot ignore the risk posed by major risk events, such as COVID-19, in their overseas investments. (2) Among the 52 countries investigated, investment risk is significantly lower in developed countries than in developing countries, mainly because of the better performance of the former across the four dimensions of economics foundation, governing ability, social development, and investment environment. (3) The COVID-19 pandemic has impacted the landscape of China's overseas energy investment risk. Due to the different performance of each country in other dimensions, the risk rankings of the majority of developing countries decreased when considering their COVID-19 response, while that of most developed countries increased.

Research Advances in Energy Investment Risk Management—A Bibliometric Perspective

To clarify the evolution of energy investment risk management research, we used the Web of Science database, bibliometric, knowledge mapping, and inductive methods to comb through a total of 2150 relevant literature from 2010 to 2021. Firstly, we analyze the time series distribution, core author groups, important journals and source distribution of relevant papers in the field of energy investment risk, and describe the current research status and hot spots of energy investment risk. Secondly, CiteSpace and VOSviewer software are used to cluster the keywords in the field of energy investment risk. The study found four shortcomings in energy investment risk research: firstly, the construction of indicators lacks basis; secondly, the evaluation results lack persuasive power; thirdly, the indicator system lacks pertinence, and fourthly, the evaluation methods and models are not perfect. The directions for future in-depth research include the construction of multivariate evaluation index systems, evaluation methods and models for different industries in a targeted manner.

Assessment of safety management system on energy investment risk using house of quality based on hybrid stochastic interval-valued intuitionistic fuzzy decision-making approach

The purpose of the study is to construct a novel hybrid stochastic interval-valued intuitionistic fuzzy (IVIF) decision approach to the house of quality (HoQ)-based analysis of safety management system with the respect to the energy investment risks. For this purpose, two step analyses of IVIF decision making models by using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are applied for weighting and ranking criteria of energy investment risks and safety management system, respectively. Additionally, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology is also taken into consideration to make a comparative evaluation. The novelty of study is to propose a new hybrid decision making model based on IVIF sets and to analyse the probability and non-probability degrees of the extreme values with the stochastic data comparatively for the robustness check. The results show that global economic uncertainty is the most important criterion while the changes in the policy and regulatory at the country level has relatively the weakest importance among the energy investment risk. Additionally, the competitive environment is defined as the most prominent factor of safety management system on the energy investment risk when the HoQ-based fuzzy matrix is applied. However, the analysis demonstrates that hybrid decision making approaches based on interval-valued fuzzy sets and stochastic values provide the coherent results for weighting the criteria of energy investment risks and ranking the factors of the safety management system with HoQ. Based on these results, it is understood that uncertainties in the global economy increase investors' future worries. When global economic uncertainties are high, the market must be effectively monitored, and the necessary actions should be taken immediately. For this purpose, the necessary training should be given to the personnel currently working in these companies. In this framework, staff-based training programs should be prepared according to the job descriptions of the employees. Additionally, regarding the new recruitment personnel process, qualified employees should be selected based on the job descriptions.

Energy Investment Risk Assessment for Nations Via Seq2seq Model

China’s “Belt & Road Initiative” has been proposed for several years, which has stimulated the economic and financial development of the countries alongside the “Belt & Road”. For a world’s leading energy consuming country, China tries to secure the energy supply from the resource-rich countries via oversea energy investment. In this paper, we propose a sequence to sequence (seq2seq) model to evaluate the energy investment risk of 50 countries alongside the “Belt & Road Initiative”. Specifically, we first build an indicator system mainly containing six factors. Then we adopt Bi-long-short term memory (Bi-LSTM) as encoder to process the historical statistics. Afterward, we use self-attention mechanism to assign the weights on the six factors of the indicator system. Finally we use a hierarchical convolution neural network decoder to generate the assessment results. Our findings indicate that resource potential and Chinese factor are the most important indicators. And through our thorough investigation, we find that Russia, Kazakhstan, Pakistan, United Arab Emirates, Saudi Arabia, Malaysia and Indonesia are the most recommended target countries for China’s oversea energy investment.

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

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.

Evaluation and Forecasting of Wind Energy Investment Risk along the Belt and Road Based on a Novel Hybrid Intelligent Model

The timely and effective investment risk assessment and forecasting are of great significance to ensure the investment safety and sustainable development of wind energy along the Belt and Road. In order to obtain the scientific and real-time forecasting result, this paper constructs a novel hybrid intelligent model based on improved cloud model combined with GRA-TOPSIS and MBA-WLSSVM. Firstly, the factors influencing investment risk of wind energy along the Belt and Road are identified from three dimensions: endogenous risk, exogenous risk and process risk. Through the fuzzy threshold method, the final input index system is selected. Secondly, the risk evaluation method based on improved cloud model and GRA-TOPSIS is proposed. Thirdly, a modern intelligent model based on MBA-WLSSVM is designed. In modified bat algorithm (MBA), tent chaotic map is utilized to improve the basic bat algorithm, while weighted least squares support vector machine (WLSSVM) adopts wavelet kernel function to replace the traditional radial basis function to complete the model improvement. Finally, an example is given to verify the scientificity and accuracy of the model, which is helpful for investors to make fast and effective investment risk forecasting of wind energy along the Belt and Road. The example analysis proves that the proposed model can provide reference and basis for investment corpus to formulate the investment strategy in wind energy along the Belt and Road.

Renewable energy investment risk assessment in belt and road initiative countries under uncertainty conditions

Access to and investment in renewable energy are considered as essential factors in sustainable development. Recently, renewable energy sources and assessing their investment risk have attracted much attention, especially for companies willing to invest in the Belt and Road Initiative countries. As the main objective of the present research, investment risk factors of renewable energies in these countries are identified and categorized into five groups as economic, technical, environmental, social, and political using TODIM (Portuguese acronym for Interactive Multi-Criteria Decision Making) method. Then, the identified factors are weighted by the fuzzy-analytic network process (F-ANP) and different renewable energy sources are ranked by COPRAS, MABAC, and GRA methods under uncertainty conditions. Finally, the sensitivity analysis of investment decision making was carried out with the emphasis being placed on the tradability aspect of renewable energy projects. The results show that wind energy has a lower investment risk besides the higher economic justification, followed by hydropower. The results of this research are useful for policymakers, investors, and companies working in the field of renewable energies to assist them in the assessment of their investment projects and the expansion of their international markets.

Assessing the renewable energy investment risk factors for sustainable development in Turkey

In the wake of Paris agreement, global economies have been putting their full efforts in mitigation and adaption to limit the global temperature within 2 °C higher than the pre-industrial level. Redirection of investment flows to renewable energy technologies (RETs) is paramount to achieve the goals set in global climate agreement. The Turkish government is determined to attract investment in RETs for cleaner energy production and sustainable socio-economic development in Turkey. However, it is very challenging to substitute environmentally harmful traditional energy systems with renewable energy production and supply. Investing in and developing renewable energy projects (REPs) are posed to various inherent risks. Assessment and analysis of risk factors of renewable energy investments (REIs) are indispensable for a practical and profound policy guideline for mitigation. This study provides a Multi-Criteria Decision Methodology (MCDM) based three-stage decision framework to assess and examine the risk factors of REIs in Turkey. The first stage is the identification of risk factors of REI using the Delphi method. The second stage is the assessment of identified risk factors of REI by using the Analytical Hierarchy Process (AHP). The third stage is the evaluation and prioritization of strategies to overcome risk factors of REI projects by using Fuzzy Weighted Aggregated Sum Product Assessment (FWASPAS). The Delphi method unveiled 6 major risk factors with 23 sub-risk factors. Results of AHP analysis unfolded economic & business risk as a major risk factor. The FWASPAS analysis concluded a comprehensive and explicit explanation of the RET choices to be the most suitable of six strategies to deal with the investment risk factors for sustainable development in Turkey.

Renewable energy investment risk: An investigation of changes over time and the underlying drivers

Building an energy system compatible with the Paris Agreement requires large-scale investment in renewable energy technologies (RET). Designing effective energy policies, therefore, requires an understanding of the dynamics of RET investment risk. This study draws on RET project data and 40 interviews with investors in Germany, Italy and the United Kingdom. We identify the five most relevant RET investment risk types (curtailment, policy, price, resource and technology), show their relative importance over time and use a network analysis of interview transcripts to identify the drivers behind the observed changes. We show that risk premiums and investment risk have declined for solar photovoltaics and onshore wind technologies in all three countries. Increasing technology reliability at a lower cost, data availability, better assessment tools and credible and stable policies were crucial elements of this declining investment risk. While policy and technology risks have become relatively less important over time, curtailment and price risks are becoming relatively more important. From these insights, we derive recommendations for policymakers aiming to accelerate the transition towards a Paris-compatible energy system.

Renewable energy investment risk assessment for nations along China’s Belt & Road Initiative: An ANP-cloud model method

With the active promotion of the “Belt & Road initiative”, renewable energy investment shows the market prospect of explosive growth in various countries. However, the overseas renewable energy investment risks faced by Chinese companies are synthesized, variegated and long-term, requiring comprehensive assessments and joint responses. To evaluate the risks in renewable energy investment for nations along “Belt & Road initiative”, this paper establishes an ANP-cloud framework in consideration of the randomness of information. Firstly, 32 risk factors covering technical, political, economic, resource, social/environmental risks and Chinese factors are identified and 54 nations are studied and classified into six groups. Then, the analytic network process (ANP) method is employed to determine four sets of weights based on grouping, so that mutual influence among factors can be taken into account. Furthermore, the overall risks are calculated through the cloud model according to their membership degree. The results indicate that political risks, economic risks and resource risks occupy the main determinant position during the overseas renewable energy investment while Chinese factors have certain effects on investment behaviors. Finally, recommendations are given for investors and decision-makers to help choose a more appropriate nation to invest in, strengthen risk prevention and control as well.

Investment risk evaluation for new energy resources: An integrated decision support model based on regret theory and ELECTRE III

As conventional energy resources are limited and polluting, new energy resources, being renewable and environmentally friendly, have been receiving increasing attention in recent years. However, no study on new energy investment, which acts a significant role in promoting the development and use of new energy resources, has been conducted. To cover this gap, an applicable decision support model is established by integrating Z-numbers, regret theory and elimination and choice translating reality III (ELECTRE III) to address new energy investment risk evaluation problems. In this way, Z-numbers are used to describe the decision-making information involved in the problems, a suggested method is combined with regret theory to determine the utility, rejoice and regret values of Z-information, and ELECTRE III is introduced to handle multiple criteria evaluation comprehensively. To elucidate and validate the application of the established model, a case study for new energy investment in Qingshuitang Industrial Zone is conducted and in-depth results analysis and discussion are implemented. The study shows that solar energy is the best investment project and environment is the most important investment factor. Moreover, the results demonstrate that the established model can effectively support new energy investment decision-making and it performs better than other existing methods.

Energy investment risk assessment for nations along China’s Belt & Road Initiative

Overseas energy investment as an effective way of securing energy supply is being favored by the world’s leading energy consuming countries. However, energy investment has the potential high risk on multiple forms, including political and regulatory risk, currency, liquidity and refinancing risk as well as resource risk and so on. To effectively evaluate overseas energy investment risk, this study proposed a new indicator system from six dimensions. Furthermore, a fuzzy integrated evaluation model based on the entropy weight was constructed to rate the energy investment risk for 50 nations along China’s “Belt & Road initiative”. The findings indicate that resource potential and Chinese factors have become the main determinant of energy investment risk, while environmental constraints and political risk should also be considered for investing decisions. In conclusion, Saudi Arabia, United Arab Emirates, Pakistan, Kazakhstan, and Russia are the most ideal choices for China’s energy investment balancing resource potential and investment environment.

Wind farm investment risks under uncertain CDM benefit in China

China has set an ambitious target to increase its wind power capacity by 35 GW from 2007 to 2020. The country’s hunger for clean power provides great opportunities for wind energy investors. However, risks from China’s uncertain electricity market regulation and an uncertain energy policy framework, mainly due to uncertain Clean Development Mechanism (CDM) benefits, prevent foreign investors from investing in China’s wind energy. The objectives of this paper are to: (1) quantify wind energy investment risk premiums in an uncertain international energy policy context and (2) evaluate the impact of uncertain CDM benefits on the net present values of wind power projects. With four scenarios, this study simulates possible prices of certified emissions reductions (CERs) from wind power projects. Project net present values (NPVs) have been calculated. The project risk premiums are drawn from different and uncertain CER prices. Our key findings show that uncertain CDM benefits will significantly affect the project NPVs. This paper concludes that the Chinese government needs revising its tariff incentives, most likely by introducing fixed feed-in tariffs (FITs), and re-examining its CDM-granting policy and its wind project tax rates, to facilitate wind power development and enable China to achieve its wind energy target.