“Made in China” has spread all over the world, and China has the status of “world factory”. However, the weak ability of independent innovation has affected the sustainable development of China’s manufacturing industry. The industrial revolution with digital and intelligent manufacturing as the core is coming. In the future, our development is not fast, and the key is how to be sustainable and healthy. The development of zero carbon vehicles such as intelligent transportation and electric vehicles is one of the highlands of technological competition in the transportation field, and it is also the core measure to achieve carbon peak and carbon neutralization. Identifying important firms in the automobile industry has always been a topical issue. This study used the transaction data of listed companies in China’s automobile manufacturing industry to build a complex network based on the quantitative data of enterprise development status using network modeling and the multi-attribute decision evaluation method. The relationship between the network structure of the financial market and the multi-index sustainability evaluation of enterprises were also studied. By extracting the financial information of environmental protection investment from the social responsibility reports of listed companies and analyzing the current status of target investment in the automobile manufacturing industry, the research shows that the current environmental protection investment in the automobile manufacturing industry is not strong and the government needs to increase supervision. The finding reveals three dynamic relationships for practical impact. The empirical result verifies that our method is effective and reliable. This approach can effectively overcome the effect of subjective factors on evaluation and provide sustainable evaluation strategy suggestions for investors in the automobile manufacturing industry.
CITATION STYLE
Liang, H., & Yao, H. (2022). Construction of China’s automobile financial market network and its sustainability evaluation. Frontiers in Environmental Science, 10. https://doi.org/10.3389/fenvs.2022.907311
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