4.1. Spatiotemporal Evolution Analysis
Based on the previous text, the Super-SBM model was used to calculate the energy efficiency of the Yellow River Basin, and the entropy-weighted TOPSIS method was used to calculate the green development level of the Yellow River Basin.
Figure 3 and
Figure 4 were drawn based on the calculation results.
Regarding the temporal evolution of energy efficiency in the Yellow River Basin as a whole (
Figure 3), the mean energy efficiency increased from 0.298 in 2011 to 0.464 in 2020, with an average annual growth rate of 6.2%, showing a steady upward trend. Based on the regional division, from 2011 to 2016, the overall energy efficiency in the Yellow River Basin was in the order of midstream area > downstream area > upstream area, while from 2017 onwards, it was in the order of downstream area > midstream area > upstream area. During the study period, the level of green development in the Yellow River Basin fluctuated between 0.170 and 0.397, with an average annual growth rate of 14.8%, showing a fluctuating upward trend. The average level of green development in the Yellow River Basin increased steadily from 2011 to 2015, declined from 2015 to 2018, and then, rebounded after 2018, showing an overall upward trend. The temporal evolution trends of the average levels of green development in the upstream, midstream, and downstream regions of the Yellow River Basin are generally consistent, and are ranked by region as follows: downstream area > midstream area > upstream area.
The energy efficiency and green development levels of the prefectural cities in the Yellow River Basin in 2011, 2015, and 2020 were visually displayed using ArcGIS (
Figure 4). Overall, the average energy efficiency and green development levels of the prefectural cities in the Yellow River Basin from 2011 to 2020 showed an upward trend and had spatially heterogenous characteristics.
In terms of energy efficiency, our study found that the Yellow River Basin had spatial distribution characteristics of “high in the east and low in the west and central regions”, and showed a clear spatial clustering trend. However, there was an unbalanced development trend in the upper, middle, and lower reaches of the Yellow River Basin. In 2011, the energy and chemical industries accounted for a large proportion in the upper reaches of the Yellow River Basin, and the dependence on fossil fuels was high, resulting in low energy efficiency due to unreasonable energy development and utilization. The middle and lower reaches of the Yellow River Basin had good natural and economic conditions, advanced technologies, and high energy efficiency compared to the upper reaches. After 2015, with the implementation of the “Two Mountains” theory and increased ecological protection efforts, the Yellow River Basin’s ecological environment was improved and its energy structure optimized through the development of clean energy, which improved the overall energy efficiency in the region. This improvement was mainly concentrated in Shandong and Henan provinces in the lower reaches of the Yellow River Basin, where cities such as Jinan, Qingdao, and Zhengzhou had a high level of economic development, as well as large service and advanced manufacturing industries, resulting in a relatively high demand for clean energy and effective improvement in energy efficiency. The upper reaches of the Yellow River Basin were dominated by heavy industries, such as steel and metallurgy, which had high primary energy demand, leading to low energy efficiency. Other regions had moderate-to-low energy efficiency levels, and while they showed improvement in energy efficiency, they also produced high carbon emissions due to the adjustment and upgrading of their industrial structure.
In terms of green development, during the study period, the Yellow River Basin showed a spatial distribution pattern centered on the capital cities, radiating outwards. The green development level gradually spread from high levels at city centers to lower levels around cities such as Jinan, Zhengzhou, Taiyuan, and Xi’an, forming a development trend centered on these cities. In 2011, the overall green development level in the Yellow River Basin cities was relatively low. In 2020, the green development level had significantly improved and showed a spatial clustering trend, with cities such as Guyuan, Xi’an, Zhengzhou, Taiyuan, Jinan, and Qingdao showing higher green development levels, and with some areas radiating from the capital cities to surrounding cities. Due to the presence of high-energy, high-emission industries in the upper reaches of the Yellow River Basin, these areas produced high emissions of industrial pollutants and inflicted serious damage on the ecological environment, hindering regional green development. In recent years, the upper reaches of the Yellow River Basin have developed clean energy and achieved significant results in green development; however, the development of industry still relies on fossil fuels such as coal and oil, the proportion of new energy is relatively small, and the green development levels in these areas are still lower than those in the middle and lower reaches. The cities in the middle and lower reaches of the Yellow River Basin have benefited from a national development plan and innovation-driven development strategies, such as those implemented by the Guanzhong Plain City Group and Shandong Peninsula City Group, and are committed to comprehensive economic, societal, and ecological development; this plays a significant role in promoting the overall energy efficiency and green development levels in the Yellow River Basin.
4.2. Benchmark Regression Analysis
- (1)
Analysis of Regression Results
In this study, Stata 16.0 software was used to process the data. To ensure the scientific validity of the model setup, a Hausman test was conducted on the sample data before regression. The test results (prob > chi2 = 0.0000) rejected the null hypothesis, indicating the selection of a fixed-effect panel measurement model.
There is a significant linear relationship between energy efficiency and green development in the Yellow River Basin, as shown in
Table 5.
Table 5 includes (1) regression without control variables and (2) regression with control variables. The results show that the estimated energy efficiency coefficient is positive, regardless of whether the control variables are included, and that energy efficiency has a significant promoting effect on the transformation of green development in the Yellow River Basin, as seen in the results of the 1% significance test. In order to test whether there is a non-linear relationship between energy efficiency and green development, (3), the second term of energy efficiency was included in the regression. The results show that the estimated coefficient of the second term of energy efficiency failed the significance test, which means that there is no non-linear relationship between energy efficiency and green development. Among all the control variables in the regression model, the estimated coefficients of human capital, urbanization level, and industrial structure upgrading are positive and significant, indicating that they have a promoting effect on green development in the Yellow River Basin. This verifies the important impacts of human capital, urbanization level, and industrial structure upgrading on green development in the Yellow River Basin. These impacts include improving the quality of human capital, accelerating urbanization, improving the traditional industrial structure, developing strategic emerging industries, and accelerating industrial structure upgrading, and play an important role in promoting ecological protection and high-quality development in the Yellow River Basin. The level of foreign direct investment has a positive correlation with green development, but it failed the significance test, which means that the behavior of Yellow River Basin cities in attracting foreign investment has a limited impact on green development.
- (2)
Mediating Effect
Energy efficiency promotes green development in the Yellow River Basin by enhancing technological innovation capabilities. In
Table 6, case (4) reflects the baseline regression results of the impact of energy efficiency on green development in the Yellow River Basin, showing the total effect of energy efficiency improvement on green development. The estimated results show that the total effect coefficient (c) is 0.167, and is significant at the 5% level, indicating that improved energy efficiency can effectively promote low-carbon transformation and green development in the Yellow River Basin. Case (5) represents the regression results of the effects of energy efficiency on technological innovation, with the estimated coefficient (a) being 0.828 and significant, indicating that improvement in energy efficiency can drive green technological innovation by forcing enterprises to increase investment in technological innovation, and that it can promote the popularization and application of green technologies. In case (6), the coefficient (b) of the effect of technological innovation on green development in the Yellow River Basin is 0.029 and significant at the 1% level. This indicates that technological innovation is the intrinsic driving force promoting green development in the Yellow River Basin, helping to adjust the energy consumption structure, promote energy conservation and emission reduction, and improve the ecological environment, thus realizing the comprehensive green transformation of the economy and society in the Yellow River Basin. The estimated coefficient (c’) of energy efficiency is 0.143 and is significant. In summary, technological innovation has a significant indirect effect on energy efficiency, affecting green development in the Yellow River Basin. Based on the model estimation results, and with reference to the coefficient product test method for the mediating effect analysis, it can be concluded that c = ab + c’; technological innovation has a partial mediating effect on energy efficiency, affecting green development in the Yellow River Basin; the mediating effect accounts for 14.4%. This verifies that improvement in energy efficiency can drive enterprises to accelerate green technological innovation. It can promote R&D and the popularization and application of green and low-carbon technologies, thus assisting in transformative green development and achieving high-quality development in the Yellow River Basin.
- (3)
Further Analysis
The recent book on “The Impact of Energy Technology Innovation in China on Energy Conservation and Emission Reduction: Theory and Evidence” points out that technological innovation has both direct and indirect impacts on the improvement of energy efficiency. Therefore, based on Equation (8), an interaction term between energy efficiency and technological innovation was added, as shown in Equation (13), and the regression results can be seen in
Table 7, situation (7). The estimated coefficient is positive and passes the 10% significance test, indicating that technological innovation plays a promoting role in the dynamic process of energy efficiency and green development.
Furthermore, energy efficiency may impact the green development of the Yellow River Basin under different levels of technological innovation. This paper built a panel threshold model based on Equation (8) [
36], as shown in Equation (14).
In this formula, q is the threshold variable, I(·) is a function that takes a value of 0 or 1, and is the specific threshold value.
Based on Equation (8), a panel threshold model [
36] was constructed using technological innovation as the threshold variable and energy efficiency as the core explanatory variable. The results of the test can be seen in situation (8) in
Table 6. Technological innovation passed the single-threshold test (
p = 0.046), and its threshold value is 1.557. The estimated coefficients are positive, indicating that energy efficiency can effectively promote the transformation of green development in the Yellow River Basin, regardless of whether the technological innovation level crosses the threshold value. When the technological innovation level crosses the threshold value, the coefficient increases from 0.052 to 0.156 and passes the 1% significance test, showing that as the technological innovation level continues to improve, the research and promotion of green and low-carbon technologies, new products, etc., and the driving effect of energy efficiency on the transformation of green development in the Yellow River Basin, become significantly stronger, effectively promoting ecological protection and high-quality development in the Yellow River Basin.
4.3. Robustness Test
To ensure the robustness of the above empirical results, the following three methods were used for robustness testing, and the results are shown in
Table 8.
① Endogeneity test: This paper used the system GMM method to solve possible endogeneity problems, and the results are shown in
Table 8 (9). The energy efficiency and technological innovation coefficients did not change, and they passed the significance test. The autocorrelation test and Hansen test were then conducted on the model; the results showed that the model had first-order autocorrelation but no second-order autocorrelation, and the selected instrumental variables were effective.
② Replacing the technological innovation explanatory variable: In addition to using the number of patent grants to measure technological innovation level, patent applications can also be used, so this paper replaced the technological innovation variable with the number of inventions per 10,000 people, and the regression results are shown in
Table 8 (10). The coefficient of the explanatory variable was significantly positive and passed the significance test.
③ Trimming the tails: This paper used winsorization to trim the outliers in the text, and the regression results obtained after processing the data are shown in
Table 8 (11). The signs are consistent with the benchmark regression results.
In conclusion, through the above three methods, it is found that energy efficiency and technological innovation have a positive impact on green development, indicating the reliability and validity of the empirical results in this paper.