Study on the Influencing Factors and Policy Simulation of Low Carbon Business Behavior of Metallurgy Enterprises in Jiangxi Province

As a province with rich resources, Jiangxi Province has many metallurgical enterprises. However, most of Jiangxi metallurgical enterprises' production pattern are not environmentally friendly. The government urgently needs to formulate policies to reduce pollution. This paper uses the method of system dynamics to simulate and analyze the policies affecting metallurgical enterprises, and gives some policy recommendations. Introduction At this stage, the production mode of metallurgical enterprises in Jiangxi is not environmentally friendly. The government urgently needs to formulate policies to reduce pollution. To formulate policies to reduce pollution in metallurgical enterprises, we must first understand the factors that affect low-carbon operations of enterprises. Scholars believe that there are many factors that affect the low-carbon operation of enterprises. From an energy point of view, Michael Grubb, Lucy Butler, Paul Twomey (2006) through analysis of the UK power industry, found that new energy is an important factor affecting the UK power industry, if new energy such as wind and solar energy with enough reserves, the UK power industry can get rid of its dependence on coal and improve the environment. From a social perspective, Eva Heiskanen, Mikael Johnson, and Simon Robinson (2009) believe that individuals will have a huge impact on low-carbon society, including personal preferences, lifestyles, and consumption habits. From a government perspective, Montalvo (2008) believes that there is a direct link between low carbon and government action. He advocates that the government's direct mandatory constraints can reduce the company's carbon emissions. From the perspective of the company itself, Ashford Zwetsloot (2000) believes that technology is the key to low-carbon operations. He believes that low-carbon environmental technologies should be developed to reduce energy consumption. Alexander (2007) agrees with this view. SD Model Before modeling, the author conducted a questionnaire survey on four of the five listed metallurgical enterprises in Jiangxi Province. Through questionnaires and other means, the author obtained some data needed for modeling. In order to reduce the design difficulty and understanding difficulty of the system, this study uses the positive feedback loop to design, and uses Vensim software to draw the causal relationship diagram of the low-carbon business behavior of metallurgy enterprises in Jiangxi Province. The author will add various influencing factors to facilitate subsequent policy simulation, and show the complete flow chart of the influencing factors of lowcarbon business behavior of metallurgy enterprises in Jiangxi Province. According to the author's test, the difference between the simulated value and the actual value is between ±0.05%, indicating that the model is feasible and effective. Due to the limited space, the list of parameters of the SD International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019) Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Intelligent Systems Research, volume 168