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As the global environmental crisis evolves, China’s traditional industries have serious problems of high energy consumption, high emission and low energy efficiency. Developing green industries has become an important direction of China’s industrial transformation and upgrading. This paper selects scientific innovation industry as a typical representative of green industry and studies its financing environment assessment. In order to solve the financing dilemma faced by the science and technology innovation industry, this paper puts forward the evaluation method of the financing environment of science and technology innovation industry based on Bayesian network from the Angle of industry particularity. In this paper, Netica software is used to construct a Bayesian network model of the financing environment of the science and technology innovation industry, and the financing environment of the science and technology innovation industry in 2016-2020 is inferred according to the annual probability distribution. Then, the sensitivity analysis is carried out, and the hierarchical policy simulation is used to simulate the conditional probability of the initial node and the intermediate node respectively, so as to determine the impact of each node on the financing environment, and finally obtain the optimization path. The research results can be of great value for improving the financing environment of scientific innovation industry and promoting the development of green industry. [ABSTRACT FROM AUTHOR]

Oil tankers carry large quantities of liquefied chemical cargoes that are flammable, explosive and/or toxic. Hence, a collision with a tanker that causes an oil spill poses a severe threat to the marine environment and human life. In order to quantify and analyze the risk factors of ship collision oil spill, this paper adopts a combination of knowledge element (KE) and dynamic Bayesian networks (DBN) to conduct an emergency scenario study based on the “scenario-response” model. Firstly, the key elements of “accident scenario state, human factors, emergency measures, and emergency goals” are selected to represent the accident. Then, the mechanism of accident evolution is analyzed according to the case, and DBN is used to build a scenario model of oil spills from tanker collisions. Finally, to verify the importance of human factors and the scientificity of emergency measures, the oil spill accident due to the collision between the two vessels known as MT “SANCHI” and MV “CF CRYSTAL” is used as an example for analysis. The accident model deduction results are in line with reality, and the research results help relevant decision makers to understand the deduction process of oil spills from tanker collisions, which is of great significance to enhance the safety of oil tanker shipping and marine environmental protection. [ABSTRACT FROM AUTHOR]