Following the “going out” strategy, Chinese oil and gas companies have been widely involved in investing and operating business abroad to mitigate the increasing energy imbalance between supply and demand. Overseas oil investment, characterized by high risks and high returns, plays a significant role in ensuring energy security and strengthening global competitiveness in China. However, compared with overseas upstream projects, the downstream oil refining investment is still in the preliminary development stage, with limited experience for references, which further increases the risk surrounding such multibillion-dollar ventures. Hence, it is significant to assess the investment risks so as to help investors be fully aware of them and then make optimal investment decisions. To this end, this paper successfully identifies the main risk factors, including the local investment environment risk, technical risk, organization management risk, health, safety and environment (HSE) and social responsibility risk, and economic risk. Then, a qualitative-quantitative comprehensive risk evaluation method, combining the fuzzy mathematics and the grey system theory, is proposed and applied to analyze the investment risks of one Chinese overseas oil refining project as a case study. The assessment results are basically in accordance with the practical conditions, which validate the reliability and reasonability of the proposed risk assessment model in regard to the overseas oil refining project. The findings of this research provide the theoretical foundation and practical methodology of the risk analysis for future investment in oil refining areas.

As the shale gas revolution expands globally, the future potential and economic profits of overseas shale gas reserves have attracted the interest of Chinese investors. Overseas shale gas development is becoming an investment hotspot for Chinese oil companies. However, this multibillion-dollar venture is surrounded by a complex and uncertain environment. Therefore, this paper carries out an integrated and publicly available model for assessing risk in overseas shale gas investments. The purpose of this model is to address the index weight calculation and risk ranking and provide investor with risk information. In view of this, the comprehensive weights are obtained based on an analytic hierarchy process (AHP) and entropy weight methods; and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is performed to rank target countries. First, the paper identities five categories of risks with full consideration of the economic risk, political risk, geological risk, technological risk, and internal managements risk. Based on the risk identification, the assessment index system is established and valued. Secondly, China is taken as an example nation to use this model to prove the effectiveness of the proposed model and help the investor make wise decisions. According to the results, low-risk countries, such as Canada, Argentina, United States, and Algeria can be considered to be future key targets of shale gas investment abroad, while investors should be more cautious of high-risk countries such as South Africa and Brazil. Finally, policy recommendations are proposed to optimize the overseas shale gas investments from both the government and investor perspectives.

As one of the basic industries supporting the national economy development and energy demand, the refining industry is expected to provide combustion energy, reduce pollution emission, and improve utilization efficiency. With more stringent requirement for environmental protection, refining enterprises have to insist on the sustainable development to achieve industrial optimization. Evaluation of the sustainability of enterprises can help them understand their situation more objectively and guide them to establish modes for sustainable development. In this study, the evaluation system is firstly built from perspectives of economic, ecological, and social sustainability, including six second-grade indexes and seventeen third-grade indexes, which can accurately reflect the entire sustainability contents of refining enterprises. Then, a DEA-based model is constructed, which selects seven input indexes (e.g., the asset–liability ratio and comprehensive energy consumption per unit of output) and nine output indexes (e.g., return on assets, asset turnover, and science and technology investment strength). The DEA-based model can not only objectively evaluate the sustainability level, but also find out the restriction factors for further optimization. Third, to demonstrate the validity of the model, 15 enterprises are selected for case studies, among which only four are identified as having strong sustainability. For the other 11 enterprises, projection analyses are implemented, and the DMU values of three enterprises characterized by low efficiency are adjusted to find out the restriction factors, which reflect the model’s efficiency and its potentially wide application in the future. Finally, specific suggestions are proposed for the enhancement of sustainability of refining enterprises.