Table 3. Descriptive statistics of major variables.

4. Empirical Results and Analysis

4.1. Spatial Correlation Test

This paper uses stata12.0 to test the spatial correlation of green technological innovation efficiency of every province and city in China, and calculates the global Moran index value of green technological innovation of enterprises. The calculation results are shown in Figure 1. In 2006-2016, the Moran values reflected by the curve were all greater than 0.2, and the Moran values showed an overall upward trend except for a slight decline in 2009 and 2011. In the case of 5% significance level, Z value of Zhengtai statistic is more than 2.5, P value is less than 0.05, Moran value has passed the test of significance level, and can be tested by spatial econometric model.

Moran scatter plot divides enterprise innovation into four quadrants. Among them, the first quadrant represents the spatial connection form that High-Innovative provinces are surrounded by other High-Innovative regions, the second quadrant represents the spatial connection form that low-innovative provinces are surrounded by High-Innovative regions, and the third quadrant represents that low-innovative regions are surrounded by low-innovative regions, the fourth quadrant represents the spatial form in which the high-observed areas are surrounded by low-valued areas Due to space constraints, this paper reports scatter plots for 2010 and 2014 (Figure 2 and Figure 3), and summarizes the corresponding results of scatter plots into Table 4. From Table 4, we can see that enterprise innovation in Jiangsu, Zhejiang, Shanghai, Beijing, Tianjin and Hebei show obvious spatial agglomeration effect. The Southern Comprehensive Economic Zone with Guangdong as its core is gradually forming agglomeration, while the central and western regions are divided into regions with low innovation activities.

4.2. Empirical Model Analysis

According to the results of spatial correlation test and Hausman test, the SEM

Figure 1. Moran exponential scatter plot.

Figure 2. Moran index scatter plot (2010).

Figure 3. Moran index scatter plot (2014).

Table 4. Provinces corresponding to the global scatter map for the representative years in 2006-2016.

model is used as the final analysis model. In order to avoid multiple collinearity in the model, and to verify the stability of the impact of regional financial ecological environment on enterprise innovation promotion, the model coefficients and their significant changes were tested by adding control variables step by step. Specific regression results are shown in Table 5.

The coefficient of financial ecological environment in the model is positive, which shows that the current financial ecological environment in China has a positive role in promoting green technological innovation of enterprises. Provinces with good financial ecological environment can promote the efficiency of green technological innovation of local enterprises, and the use of innovative resources of enterprises is more efficient because of their higher resource allocation efficiency and better governance environment. The growth of regional financial eco-environment index is positively correlated with the innovation ability of enterprises, which indicates that regional financial development has an obvious promoting effect on the innovation ability of regional enterprises, and supports the hypothesis of this paper. At the same time, the regression coefficients of GOV, EOC, FD and SYS are significantly positive at the level of 5%. It shows that the regional financial ecological environment is a complex ecosystem, and the sound development of each element can promote the green technological innovation of enterprises.

As far as the results of control variables are concerned, after adding three control variables one by one, the fitting degree of the model is maintained at about 20%, the fitting effect of the model is relatively stable, and the coefficient of core variables has not changed significantly. The coefficients of FDI, PGDP and REG are also positive. The increase of foreign capital introduction ensures that enterprise investment in green technological innovation continues to increase steadily; the increase of PGDP indicates that the improvement of economic development will promote the development of green technological innovation, and the implementation of environmental regulation policy will enhance enterprises’ awareness of environmental protection, thus having a positive impact on green technological innovation.

Table 5. Model estimation results.

4.3. Robustness Test

Although the panel spatial error model can well test the impact of regional financial ecological environment on technological innovation, it is also necessary to further test the robustness of the model.

Replace the spatial weight matrix. In order to further verify the robustness of the estimation results of the benchmark model, using the same model and estimation method, the “economic adjacent” matrix is selected as a new spatial matrix for robustness test. Economic distance is measured by the absolute reciprocal of the difference in PGDP between provinces I and J. Setting the “economic adjacency” weight matrix takes into account the competition between government departments and enterprises in different regions in the development of green technology innovation strategy interaction, that is, they will refer to the behavior decision of other regions with similar economic level in this region. As shown in Table 6, the estimation results have not changed significantly. The significant coefficients of the main explanatory variables are consistent with the benchmark model, which shows that the empirical results are robust.

Table 6. Robustness test results.

Assume variable returns to scale. The explanatory variables selected above are the efficiency of green technology innovation measured by DEA index method, assuming that the scale reward is constant. This assumption can be relaxed in robustness test, assuming that the scale reward is variable, and then estimating using the spatial panel model as well. Regression results are shown as Table 7, these did not change significantly. The significance of the estimated coefficients of the main explanatory variables was consistent with the benchmark model. It shows that the estimation results of the empirical part of this paper are robust.

5. Conclusions and Suggestions

5.1. Conclusions

The green technological innovation ability of Chinese enterprises shows significant spatial autocorrelation in spatial distribution, and shows an upward trend. Enterprise innovation shows obvious local cluster differentiation. Enterprise green technology innovations in Jiangsu, Zhejiang, Shanghai, Beijing, Tianjin and Hebei

Table 7. Robustness test results.

show obvious spatial agglomeration effect, which may be related to the level of economic development in these areas and the high level of investment in regional scientific and technological innovation. The Southern Comprehensive Economic Zone, with Guangdong as its core, is gradually forming a cluster, while the central and western regions are divided into regions with low innovation activities. At present, the country is also vigorously developing economic and industrial innovation in the western regions.

The empirical results show that there is a significant positive correlation between the financial ecological environment index and the efficiency of green technology innovation. That is to say, the improvement of financial ecological environment is conducive to the development of green technology innovation ability of enterprises. In addition, the sensitivity of green technology innovation to the four sub-environments of financial ecological environment is ranked as: government governance, financial development, system and integrity culture, and economic basis. Financial ecological environment will significantly affect the capital cost and demand for green technological innovation. The better the regional financial ecological environment is, the lower the capital cost of green technological innovation will be. On the contrary, areas with poor financial ecological environment will invisibly increase the financing cost of enterprise innovation, and the investment of innovation funds is insufficient. Financial ecological environment is closely related to enterprise innovation income. The better the regional financial ecological environment is, the higher the income of enterprise innovation will be.

5.2. Suggestions

Firstly, strengthen the restraint mechanism of government behavior and strengthen government supervision. The improvement of financial ecological environment depends largely on the government’s governance. First, restrain the government itself by establishing and perfecting its supervision and management institutions, so as to ensure that they do not interfere excessively in the financial market and affect the relative independence of the financial sector. Second, deepen the reform of government system, improve the efficiency of government administration, and give full play to the government’s function of guiding financial resources to promote green technology innovation. Third, strengthen the implementation of environmental policies, such as controlling the amount of tradable emission permits, improving the severity of environmental policies, reducing enterprise pollution emissions, and encouraging enterprises to innovate in green technology.

Secondly, give full play to the financing function of Finance and increase investment in green technology innovation. Broaden financing channels for green technological innovation, and ensure the sustained and stable investment of enterprises in green technological innovation. Establish a special green technology innovation fund to stimulate enthusiasm for green technology innovation.

Thirdly, improve the protection system of green technology intellectual property rights and cultivate social honesty consciousness. Maintain fair competition order in the market, enterprises and consumers jointly strengthen trademark awareness, prevent embezzlement and fraudulent use. Strengthen independent intellectual property rights of enterprises from the perspective of safeguarding green development. What’s more, we should encourage the growth of credit intermediary service system represented by credit evaluation industry, improve the integrity archival management of enterprises and individuals, strengthen moral restraint, and promote honest and trustworthy social values.

Last but not least, improve the quality and efficiency of economic growth and establish a mechanism for industrial linkage development. Under the background of deepening supply-side reform, on the one hand, all regions should start from effective supply, eliminate backward production capacity, optimize the allocation of stock resources, accelerate technological innovation of enterprises, promote industrial transformation and upgrading, expand high-quality incremental supply, and develop in a direction conducive to intensification and greening. On the other hand, we should strengthen the sense of cooperation and innovation among provinces, cities and regions, share advanced technology and superior resources, and strengthen cooperation.

In short, only through the joint efforts of the government, finance, economy, honesty and credit culture system, can we effectively improve the regional financial ecological environment, give full play to the role of regional financial ecological environment in promoting the efficiency of green technological innovation of enterprises, and ultimately promote the systematic and large-scale independent innovation of enterprises in China.


This work was supported by the National Social Science Fund of China [15CJY078].

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.


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