Intermodal transportation, as an efficient form of transport organization, is a key technology and means for the logistics industry to balance transport costs and efficiency. How to deeply analyze the key factors constraining the level of development of multimodal transportation in China, building multimodal transportation development evaluation index system and evaluation model, evaluating comprehensively the multimodal transportation development level in China, and putting forward targeted countermeasures is of great practical significance. Based on the hesitant fuzzy multiattribute decision-making method, this paper analyzes the key factors influencing the level of multimodal transportation development from the perspective of sea-rail intermodal transport and further uses the interval number discretization and likelihood deviation maximization multiattribute decision-making method to construct a mixed-data index evaluation model, which is combined with data from 11 provinces (cities) in China. The study shows that there is a big gap among different provinces (cities) in China concerning the level of multimodal transportation development. Besides, the volume of intermodal container transportation and railway mileage are the core factors affecting the level of multimodal transportation development, and the results of the evaluation model can objectively reflect the level of multimodal transportation development and problems in each province (city). The research results further enrich the theoretical system of intermodal transportation development and provide a reference for the relevant management departments in formulating intermodal transportation plans and policies by putting forward pertinent suggestions in the field of management. In the future, the research direction should include the ecological environment, economic benefits, and other indicators into the evaluation system, expand the research scope of the modes of combined transportation such as public rail transport and public air transport, and improve the applicable scope of the evaluation model and the evaluation capability of specific scenarios.

In order to systematically solve the problem of formulating agricultural green development strategies based on the four dimensions of policy green, industry green, science and technology green, and awareness green, this paper uses the method of combining the Gini coefficient with hesitation fuzzy multi-attribute decision-making to carry out research. The research shows that: (1) The three most critical factors affecting the green development of agriculture are “consumers’ awareness of purchasing green agricultural products”, “investment in the construction of rural ecological civilization” and “the number of farmers’ scientific and technological training”, and the corresponding index weights are 0.12, 0.1 and 0.1, respectively. (2) There are differences in policy, industry, science and technology, and consciousness in the green development of agriculture. The green development of policy shows an obvious upward trend. The industrial green development shows a relatively gentle upward trend. The green development of science and technology showed rapid growth from 2011 to 2017, and the growth slowed down in the following two years and showed a downward trend in 2020. The awareness of green development shows a relatively stable upward trend. (3) The green development of agriculture is jointly driven by the relevant interests of the supply side, the demand side, and the regulator, and the demand side’s requirements for high-quality agricultural products become a stronger driving force for the green development of agriculture. The change in the consciousness and thinking of agricultural product producers on the supply side has become the basis for the green development of agriculture, and the implementation of government policies has become an important guarantee for the green development of agriculture. The innovation of this paper is to build an evaluation index system including government, consumers, farmers’ awareness of multiple subjects, government policies at all levels, industrial production status, and agricultural production technology. At the same time, we build an evaluation model that can comprehensively analyze quantitative and qualitative indicators. In view of the need for a holistic analysis of the agricultural system in the study of agricultural green development, future studies will include air pollution emissions and agricultural transport services in the evaluation model to improve the scientific and universal model.