Do oil price shocks drive unemployment? Evidence from Russia and Canada

A new multi-criteria decision making approach based on wins in league to avoid rank reversal: A case study on prioritizing environmental deterioration strategies in arid urban areas

The Effect of Interfacial Tension on Co2 Oil-Based Foam Stability Under Different Temperatures and Pressures

Efforts to control climate change with the aim of achieving carbon neutrality by 2050 have had the most significant impact on businesses operating in the energy sector, which produce large amounts of greenhouse gas (GHG) emissions. In light of such policies, oil and gas companies have set goals aimed at reducing GHG emissions and achieving carbon neutrality, but the issue remains open as to how such activities and progress towards these goals can be evaluated. This study attempts to assess the activities and progress of oil and gas companies towards carbon neutrality, with a focus on quantitative evaluation of goal achievement. First, an algorithm was developed for selecting global oil and gas companies for the analysis that reported their activities in 2022. Using this algorithm, a list of companies was compiled and their goals with regard to carbon neutrality were analyzed. Second, an assessment of how information is presented in corporate reports and which activities aimed at achieving carbon neutrality are reflected there was performed using the proposed checklist. Third, a method for evaluating the progress of oil and gas companies towards intermediate goals in the area of carbon neutrality was developed and tested. The method is based on assessing and comparing trends for oil and gas companies aiming to achieve intermediate goals in reducing carbon intensity. As a result, companies were classified into three categories: (1) those showing carbon neutrality achievement rates exceeding the expected average annual rates, (2) those with fixed carbon neutrality achievement rates below the expected average annual rates, and (3) those demonstrating no movement towards intermediate goals or a negative trend. The main methods used in this study included content analysis, checklist development, decomposition, critical and comparative analysis, and simple statistical methods.

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Decarbonization Measures: A Real Effect or Just a Declaration? An Assessment of Oil and Gas Companies’ Progress towards Carbon Neutrality

Earth’s natural systems provide essential resources such as water, food, energy, and materials that we all are dependent upon [...]

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Sustainable Development of Energy, Water and Environment Systems (SDEWES)

Technology, technology, technology: An integrated assessment of deep decarbonization pathways for the Canadian oil sands

Recognizing that the evaluation of the overseas petroleum investment environment is affected by many uncertain factors and that there are problems with current evaluation methods, this paper proposes a mathematical evaluation model of an overseas oil resources investment environment, based on a combination of the weighting and uncertainty measure theory. Combining international investment environment theory with the characteristics of the petroleum industry, this paper establishes an evaluation index system for the overseas petroleum investment environment and the linear uncertainty measure function of each index. Using the subjective weight obtained using an analytic hierarchy process together with the objective weight obtained using the entropy weight method, the optimal weight of each evaluation index was obtained using minimum relative information entropy. A multi-index evaluation matrix of the top 12 oil-producing countries in Africa was calculated. Finally, the credible degree recognition criterion was used to judge the order and level of the oil investment environment. This model provides an effective method for the evaluation of the overseas petroleum investment environment. The results show that Nigeria and Angola have the best investment climate, followed by Algeria, Egypt, and Libya. In general, Africa is an important strategic partner of China and is rich in oil resources. Although Africa’s oil industry is fraught with complex challenges and headwinds, challenges also present opportunities.

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An Evaluation of the Petroleum Investment Environment in African Oil-Producing Countries Based on Combination Weighting and Uncertainty Measure Theory


 The prediction of possibility and risk classification of collapse is an important issue in the process of highway construction in mountain area. Based on the principle of rough set and support vector machine, a landslide hazard prediction model was established. First of all, according to field investigation, an evaluation index system and a sample set of evaluation index data were established, the rough set decision table was constructed by preprocessing the original data based on the function classification of standard evaluation index, and then, the influence indexes of the collapse activity were reduced by rough set theory, and the main 9 indexes affecting the collapse activity as the key discriminant factors of support vector machine model, namely slope shape of slope, aspect of slope, slope of slope, height of slope, exposed structural face, stratum lithology, relationship between weakness face and free face, vegetation cover rate and weathering degree of rock were extracted. Then, taking the data of 13 post earthquake collapses provided by the author etc in Yingxiu-Wolong highway of Hanchuan County as training samples, the optimal model parameters were analyzed and calculated. When the penalty parameter C is 8 and the kernel parameter g is 0.5, the correct rate of cross-validation is 100%, and the model is optimal. At last, 4 other landslide data were tested, the discriminant results of the test sample data were compared with the results obtained by uncertainty measure and distance discriminant analysis. The results show that the discriminant results of the test sample data by RS-SVM were consistent with the results obtained by uncertainty measure and distance discriminant analysis, the accurate rate is 100%. The collapse hazard analysis model based on rough set and support vector machine can reduce the computation while ensuring the accuracy of evaluation, and better solve the small sample and nonlinear problems, can provide certain a good idea for collapse hazard evaluation in the future.

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Rui Xing

Papers

Technology, technology, technology: An integrated assessment of deep decarbonization pathways for the Canadian oil sands

An Evaluation of the Petroleum Investment Environment in African Oil-Producing Countries Based on Combination Weighting and Uncertainty Measure Theory

Evaluation of Highway Collapse Hazard Based on Rough Set and Support Vector Machine