Mechanism analysis of influencing factors on financing efficiency of strategic emerging industries under the "dual carbon" background: evidence from China.

In order to deal with severe problems such as environmental pollution and climate change, the Chinese government has proposed the goal of carbon neutrality in 2030 and carbon peak in 2060. Strategic emerging industries have become key areas of high-quality growth of green economy. In order to solve the practical problems of insufficient funds and financing constraints, this paper empirically measures the financing efficiency of strategic emerging industries. Based on the Super Slack-Based Measure model, this paper selects the data analysis of listed companies in Beijing, Tianjin and Hebei from 2011 to 2020. At the same time, this paper systematically combs the index system that affects financing efficiency based on grounded theory. Based on the binary relation and structural level of adjacent matrix and reachable matrix, the explanatory analysis is carried out. On this basis, a systematic GMM model is established to explore the significance of different factors influencing financing efficiency. The research shows that the strategic emerging industry is still in the initial stage, the financing efficiency is not high and the financing output is insufficient. The factors affecting financing efficiency can be divided into 6 dimensions, 20 indicators in total and 5 multipole hierarchical levels. Credit financing, equity financing, financing constraints, technological innovation and government support are the important factors affecting financing efficiency.

Research on capital allocation efficiencies with four-dimensional factor capitals from China's intelligent manufacturing enterprises.

Compared with traditional manufacturing enterprises, intelligent manufacturing enterprises pay more attention to the investment of knowledge capital and technological capital. Taking 258 intelligent manufacturing listed companies in China from 2015 to 2020 as research samples, the paper selects the material capital, human capital, knowledge capital and technological capital of enterprises as the input variables of Cobb-Douglas production function. Considering that enterprises are often affected by spatial correlation, stochastic frontier panel model, spatial lag stochastic frontier panel model and dynamic spatial lag stochastic frontier panel model are constructed to measure capital allocation efficiencies of enterprises. The results show that all the factor capitals in the three models have a significant positive impact on enterprises' performance, and the dual lag effect of time and space is significant. Moreover, it is more reasonable to use the dynamic spatial lag stochastic frontier panel model to estimate the parameters and measure capital allocation efficiencies. The development of intelligent manufacturing industry has significant space-time spillover effect among provinces. About 52.98% of intelligent manufacturing enterprises have high capital allocation efficiencies, but 12.04% still need to further optimize capital allocation. The gap between the actual performance of the sample enterprises and efficiency frontier is mainly due to technical ineffectiveness. From a regional perspective, the top ten enterprises with high capital allocation efficiencies are all in the eastern region, but the average of capital allocation efficiency is the highest in the western region, followed by the eastern and central regions.

Evaluation of financing efficiency of strategic emerging industries in the context of green development: evidence from China.

Strategic emerging industries are key areas to transform the traditional industrial model of high pollution, high energy consumption, and high emissions. This paper focuses on the measurement of financing efficiency of strategic emerging industries. On the one hand, in order to overcome the interference of external environment and statistical error existing in the traditional single data envelope model, the SSBM-BOOT five-stage model is proposed. On the other hand, Malmquist index method and Luenberger productivity method are combined to evaluate dynamic efficiency, which select Beijing-Tianjin-Hebei listed companies' data. The results show that (1) external environmental factors play a significant role in financing efficiency. The empirical results of the SSBM-BOOT five-stage model show that environmental factors "raise" the efficiency value of the whole Beijing-Tianjin-Hebei region. (2) Based on the revised data and the overall and decomposition results of SBM-ML index, it can be seen that the regional industry is still in the stage of scale expansion, and the financing efficiency depends on technological innovation that needs to be improved. Finally, the paper puts forward the concrete strategies of creating industrial development environment, promoting technological innovation, and establishing green investment and financing mechanism.