Haibo Kuang

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.

The research on the competitiveness of container multimodal transport port hub, comprehensively considering the software and hardware strength of port hub, is of great significance to analyze the specific source competitiveness of port hub, the optimal allocation of multimodal transport capacity resources and port operation management planning. Based on this, according to the principle of fuzzy set and membership function in fuzzy theory, this paper adopts the modeling method of multi-attribute decision-making to deeply analyze the key influencing factors of port hub competitiveness, extract the core indicators from the four aspects of business capacity, transport capacity resources, infrastructure and service quality, and then construct the fuzzy multi-attribute decision-making evaluation model of port hub competitiveness, and take the container transportation in Northeast China as an example. The research shows that the key influencing factors of multimodal transport port hub competitiveness have changed from traditional infrastructure factors to flexible influencing factors such as transportation service quality, transportation capacity and resource integration ability, transportation business efficiency, and port enterprises are in the critical period of industry transformation and upgrading. Strengthening inter industry integration, building industrial ecological alliance and building a multi-mode transportation integrated service chain with port as the core play an obvious role in promoting the formation of the core competitiveness of port hub. Among the ports along the coast of Liaoning, Yingkou Port has the strongest competitiveness against the supply of corn containers in Northeast China, and the competitiveness of other ports from strong to weak is Jinzhou port, Beiliang port, Panjin Port, Huludao port, Dandong port, Dalian port and Suizhong port. The research results enrich the theoretical system of multimodal transport port hub competitiveness research, and provide reference suggestions for port enterprises, transportation enterprises, government departments and other relevant subjects in strategy formulation, policy implementation, layout planning and so on.

The research on the competitiveness of container multimodal transport port hub, comprehensively considering the software and hardware strength of port hub, is of great significance to analyze the competitiveness of specific goods sources of port hub. Based on this, this paper adopts a multi-attribute decision-making modeling method to construct a port hub competitiveness evaluation model from four aspects: business capabilities, capacity resources, infrastructure, and service quality, and conducts empirical research on container transportation in Northeast China as an example. Studies have shown that the key factors affecting the competitiveness of multimodal port hubs have changed from traditional infrastructure factors to flexible factors such as transportation service quality, transportation capacity resource integration capabilities, and transportation business efficiency. Strengthening inter-industry integration will promote the core competitiveness of port hubs. Formation has obvious effects. The research results have enriched the theoretical system of multimodal transport port hub competitiveness research, and provided reference suggestions for port companies, government authorities and other relevant entities in strategic formulation and policy planning.