3.1. Stationary Test
To prevent the pseudo-regression phenomenon, testing the stationarity of each data variable is essential. In order to ensure the robustness and comprehensiveness of the empirical analysis results, this paper uses the LLC, IPS, Fisher ADF, and Fisher PP methods in EViews 9.0 to identify their stationarity.
Table 2 and
Table 3 below show that the level values of financial efficiency, technological progress, and relative energy price have passed the assessment of the four test methods. But the IPS and ADF test results for the level value of industrial structure are unstable, and the ADF and PP test results for green finance are unstable. Additionally, the four test results for the development of the clean energy industry, financial scale, and financial expenditure level exhibit instability. Furthermore, the stationarity of the first-order difference of each variable significantly rejects the original hypothesis, indicating that each variable follows a first-order single-integer sequence. Consequently, this paper establishes a PVAR model based on the first-order difference of all variables. Moreover, the unit root test results for each respective variable by region are listed in
Table 4. Due to space constraints, only the results of LLC and IPS test methods are listed, and the first-order difference of all variables in each region rejects the original hypothesis.
Since several variables used in this paper are first-order single-integration variables, the cointegration test method is used to analyze long-term and stable cointegration relationships between variables. This paper also applies the Johansen cointegration test to observe the cointegration relationships among eight variables. The results are shown in
Table 5.
We can observe that there are at least four cointegration vectors in the entire nation and in the eastern, central, and western regions. This suggests the possibility of establishing a long-term and stable cointegration relationship between the variables. Therefore, the PVAR model can be established, enabling subsequent analysis.
3.2. Estimation Results of PVAR Model
The optional lag order of the model needs to be determined before estimating the parameters of the PVAR model. This decision is mainly based on information criteria, which help find the optimal lag order for the variable data. Considering the values of the AIC, BIC, and HQIC, this paper finally determines the best lag order of each model specifically, order 4 and establishes the PVAR (4) model.
Table 6 shows the national and regional GMM estimation results of the panel VAR model. It is found that the GMM results of variable dCE are not entirely significant, but some conclusions can be drawn as follows:
- (1)
The relationship between financial scale and clean energy industry development
In the development equation of the clean energy industry, the coefficient estimates in the national model show that the lag of financial scale in the third period has an unfavorable impact on the clean energy industry (the coefficient is −2.2503), significant at the 10% level. In contrast, during the second and fourth periods, we observe positive coefficients: 2.8019 and 2.8017, respectively. These are significant at the levels of 5% and 1%. This indicates a positive relationship between China’s clean energy industry development and the lag of the financial development scale in phases II and IV at the present stage. In the early stages, the expansion of the financial scale contributes positively to the growth of the clean energy industry. In the eastern region, the dynamic response of clean energy to the lag in financial scale during the second and third periods is weak while the response to the lag of the financial scale in the first and fourth periods is significantly positive at the levels of 10% and 5% (coefficients are 2.2143 and 2.1810). In the central region, the response of clean energy industry development to financial scale is negative in the first and third periods and positive in the second period, and the impact effect is significantly positive at the 5% level in the fourth period (coefficient is 3.8453). In the western region, the correlation between clean energy and financial scale is positive in the lag phases I and IV (the coefficients are 4.9715 and 4.1834) and is significant at the levels of 5% and 10%, respectively. In the third phase, the correlation becomes significantly negative at the level of 10%. Therefore, the eastern, central, and western regions show a more balanced impact compared to the national level. Notably, the development of the clean energy industry is positively affected by the lag of the financial scale in the fourth phase. This indicates that an early focus on financial scale can stimulate the development of this industry.
Regarding the influence of financial efficiency on clean energy industry development in the eastern and central regions, the role of financial efficiency in the lag phases I to IV is not significant. This indicating that early financial efficiency has little influence on clean energy industry development in these regions. In the western region, however, the lag of financial efficiency in the second stage is significant at the level of 5% (the coefficient is −144.1532). Therefore, it appears that early financial efficiency plays a limited impact in shaping the clean energy industry. This observation could be attributed to the priority placed on addressing capital scarcity rather than focusing primarily on fund utilization efficiency.
Examining the impact of green finance on the development of the clean energy industry at the national level, green finance shows significant positive coefficients at the levels of 10%, 10%, and 1% in the first, second, and third lag periods, respectively (coefficients are 6.6051, 6.3226, and 12.5604). This indicates that green finance plays a crucial role in promoting the development of the clean energy industry, with its effect gradually increasing over time. It signifies that financial institutions, such as banks, investing funds into the clean energy industry through loans play an essential role in promoting clean energy development. The three-phase lag coefficient of green finance in the eastern region is 9.6776, which is significant at the level of 5%. Green finance in the central region indicates a similar financial efficiency and has no remarkable impact on clean energy. In the western region, the lag of green finance in the first, second, and third phases is significant at the levels of 1%, 10%, and 10%, respectively (the coefficients are 19.7367, 17.0915, and 18.0902). This suggests that early green finance in the western region also promotes the development of clean energy, with the most significant impact being observed during the first phase.
- (2)
The correlation between industrial structure and clean energy industry development
The regression coefficients for the first and third stages of industrial structure lag are highly negative at the national level. In contrast, the regression coefficient for the fourth stage lag is significantly positive at the level of 5% (the coefficient is 0.2951). In the eastern region, the direction of impact of the first three lag periods remains negative while the effect of the fourth lag period is positive but not significant. In the central region, the lag-phase-I coefficient is negative and significant at the level of 10% whereas the lag-phase-IV coefficient is 0.5368, significantly positive at the 5% level. In the western region, lag phase I exhibits significance at the 10% level (coefficient is −1.0060), and lag phase II is also significant at the 10% level (coefficient is 1.1438). These findings indicate that the development of clean energy in different regions is affected by the lag values of the industrial structures, with varying directions and magnitudes of impact mainly due to the distinct industrial distribution across these regions.
- (3)
The relationship between technological progress and clean energy industry development
The impact of technological improvement on clean energy industry development is multifaceted. At the national level, the lag of technological progress in phases I and IV is significantly positive at the level of 10% (coefficients are 10.5479 and 6.8792). This indicates that the improvement of early technological progress contribute to the development of the clean energy industry, likely by promoting research and development (R&D) in clean energy technologies. In the eastern region, the change direction varies, it is negative, positive, positive, and negative, with the lag in the second period being significant at the level of 10% (the coefficient is 9.9946). In the central region, the lag in technological progress in the fourth period is significantly positive at the level of 10% (the coefficient is 56.8265), indicating that early technological progress in the central region evidently plays a significant role in promoting clean energy industry development. In the western region, although the change directions of technological progress are positive, negative, positive, and positive, none of these are significant. This regional heterogeneity highlights that the impact of technological improvement on clean energy development varies across different parts of the country. In the central region, the impact of early technological progress on the clean energy industry appears more pronounced.
- (4)
The relationship between fiscal expenditure and clean energy industry development
The lag coefficient of national fiscal expenditure in the second phase is 0.2364, which is significant at the level of 5%. The lag coefficient of the fourth period in the eastern region is 0.5875, which is also significant at the level of 5%. But the impact of fiscal expenditure in the central region is weak. In the western region, the fiscal expenditure lag in the first period is significant at the level of 1% (coefficient is 0.3098). This indicates that the increase in early fiscal expenditure at the national level (whole country) and in the eastern and western regions can promote the level of improvement in the clean energy industry. With the increased fiscal expenditure, some funds will flow to the clean energy industry, providing policy and financial guarantees for industrial growth and promoting clean energy development.
- (5)
The relationship between the relative price of energy and clean energy industry development
In the development equation of the clean energy industry at the national level, the lag coefficient for relative energy price in phases I and II is positive. The lag coefficient in phase II is significant at the level of 5% (the coefficient is 0.0562). The lag coefficient in phases III and IV is negative and not significant, indicating that early-stage energy prices have a certain role in promoting clean energy industry development. When energy prices increase in the previous stage, the clean energy industry tends to increase R&D and energy utilization to promote further development. For the eastern region, the coefficients for the first four periods are positive, with the coefficient of the second period being 0.1121, and significant at the level of 10%. In the central region, the impact of energy prices on clean energy industry development is not significant. In contrast, the western region shows that the lag of energy prices in the third and fourth periods is significant at the level of 10%, with coefficients of −0.0668 and −0.0688, respectively. This regional variation highlights that the influence of energy prices on clean energy development varies across regions, with a more pronounced impact observed in the eastern region.
Table 6.
GMM estimation results.
Table 6.
GMM estimation results.
Explanatory Variable | Whole Country | Eastern Region | Central Region | Western Region |
---|
L.h_dCE | −0.2156 ** | −0.1088 | −0.4082 ** | −0.1144 |
L.h_dFIR | −1.0407 | 2.2143 * | −2.1605 | 4.9715 ** |
L.h_dFE | −2.4855 | 7.1959 | −33.7673 | 32.4142 |
L.h_dGCL | 6.6051 * | 4.0802 | −1.3670 | 19.7367 ** |
L.h_dIS | −0.7542 *** | −0.3497 | −0.7792 * | −1.0060 * |
L.h_dTI | 10.5479 * | −3.3286 | 57.9755 | 83.9756 |
L.h_dPFE | −0.1222 | 0.2295 | −0.0449 | 0.3098 *** |
L.h_dPCE | 0.0045 | 0.0541 | −0.0284 | −0.0048 |
L2.h_dCE | −0.1194 | −0.0928 | −0.0422 | −0.1652 * |
L2.h_dFIR | 2.8019 ** | 1.1112 | 3.3673 | 3.3943 |
L2.h_dFE | −36.3244 | −23.1487 | 62.0592 | −144.1532 ** |
L2.h_dGCL | 6.3226 * | 4.1794 | −0.9055 | 17.0915 * |
L2.h_dIS | 0.1068 | −0.0127 | −0.1121 | 1.1438 * |
L2.h_dTI | 0.4980 | 9.9946 * | −43.5916 | −98.9376 |
L2.h_dPFE | 0.2364 ** | −0.0887 | −0.2090 | −0.0453 |
L2.h_dPCE | 0.0562 * | 0.1121 ** | 0.0330 | 0.0300 |
L3.h_dCE | −0.0455 | 0.1216 | 0.0975 | −0.2675 *** |
L3.h_dFIR | −2.2503 * | −0.0300 | −0.7967 | −5.2357 * |
L3.h_dFE | −41.0763 | −53.3339 | −41.4995 | −46.1671 |
L3.h_dGCL | 12.5604 *** | 9.6776 ** | 0.1399 | 18.0902 * |
L3.h_dIS | −0.3267 * | −0.0415 | −0.4600 | −0.7337 |
L3.h_dTI | 1.8403 | 1.6199 | −26.3017 | 66.6797 |
L3.h_dPFE | −0.1277 | −0.3871 | −0.2141 | −0.1747 |
L3.h_dPCE | −0.0079 | 0.0271 | −0.0024 | −0.0668 * |
L4.h_dCE | −0.0174 | 0.1847 ** | −0.0240 | −0.0806 |
L4.h_dFIR | 2.8017 *** | 2.1810 ** | 3.8453 ** | 4.1834 * |
L4.h_dFE | 18.0852 | 28.4388 | 32.2997 | −32.0841 |
L4.h_dGCL | −2.7015 | −4.2336 | 2.5200 | 10.9307 |
L4.h_dIS | 0.2951 ** | 0.3193 | 0.5368 ** | 0.5820 |
L4.h_dTI | 6.8792 * | −2.3467 | 56.8265 * | 5.2269 |
L4.h_dPFE | 0.1037 | 0.5875 ** | 0.2692 | 0.1085 |
L4.h_dPCE | −0.0153 | 0.0128 | 0.0202 | −0.0688 * |