An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors
Abstract
:1. Introduction
2. Efficiency Measurement of the PV Industry in China
2.1. Selection of Samples and Indicators
2.2. Analysis of DEA Efficiency Measurement Results
3. Malmquist Index Calculation
4. Efficiency Measurement of the PV Power Generation Industry in China
4.1. Selection of Samples and Indicators
4.2. Analysis of DEA Efficiency Measurement Results
5. Analysis of Factors Affecting the Efficiency of the PV Industry
5.1. Analysis of Factors Affecting the Efficiency of PV Installations
- The asset turnover rate passed the significance test in both models and has a positive correlation with industrial efficiency, indicating that the abundant capital has a significant role in promoting the efficiency of the PV device industry. The higher the abundance of enterprise funds, the more likely the enterprise is to invest money in production equipment upgrading, technology upgrading, production costs reduction, and efficiency level improvement. During the “12th Five-Year Plan” period, the PV device manufacturing industry in China gradually turned to refined development, in which the production cost of polycrystalline silicon links dropped to less than $10/kg, the conversion rate of PV cells and modules reached over 15%, and the localization rate of PV equipment reached 70% or more; the technology upgrade of the PV device industry was significantly accelerated [70]. Therefore, for PV installation enterprises, it is necessary to improve the use of inefficient and idle assets, speed up the recovery of sales funds, and thus improve the overall technical efficiency of the industry.
- The proportion of technicians was found to have a positive relationship with industrial efficiency. In Model 2, a 0.1% level of significance test was passed, indicating that the improvement of R&D capability can promote the development of the device industry. An increase of 1 unit for technicians can increase the overall technical efficiency of the industry by approximately 40%. Compared with accelerating capital turnover, focusing on changes in the structure of employees, the efficiency of the industry can be greatly improved.
- The per capita output value passed the significance test in both model 1 and model 2, showing a positive correlation with industrial efficiency, indicating that the increase in per capita output has a positive impact on the efficiency of the PV industry. Therefore, at the same input level, the higher the labor efficiency level, the higher the overall technical efficiency of the industry.
- The increasing in the ratio of sales cost to total cost had a positive impact on the efficiency of the PV industry, but it did not pass the significance test in both models. At this point, the increase in corporate sales expenses is related to the increase in corporate marketing investment, which has improved the efficiency of the industry. But this effect is not significant. The reason is that China’s installation products are mainly targeted at overseas markets, while overseas political, economic, and trade tariffs, and other policy risks are relatively high. On the other hand, China’s PV installed market is increasing during the 12th Five-Year Plan period. The increased market demand for PV products result in a large number of inefficient enterprises entering the market, exacerbating competition and lowering the profitability level of enterprises, which is not conducive to the sustainable development of the PV device industry.
- The empirical analysis showed that the production scale in model 1 had a negative impact on industrial efficiency, but the impact was not significant. In model 2, after the introduction of the secondary item of scale, the production scale of the primary item had a positive impact on the industrial efficiency, while the secondary item had a negative correlation with the industrial scale, which indicates that the relationship between production scale and industrial efficiency an open-down “U-shaped” relation. It shows that in the initial stage of industrial development, with the expansion of scale, the RTS increases, and the industrial efficiency shows an increasing trend. However, when the scale of the industry reaches a critical point, as the scale of the industry continues to expand, the RTS begins to decline. At the same time, the operating costs of enterprises increase, management becomes ineffective, and productivity turns idle, leading to an inefficiency in the scale of the industry.
- The government subsidy did not pass the significance test in both models, indicating that the incentive effect of the government subsidy on the development of the equipment industry is not good, and the government subsidy needs further improvement. China’s PV subsidies are mainly financial subsidies, which are one-off subsidies. With a lack of post-regulation mechanisms, it is difficult to achieve effective subsidies, and the effect on sustainable development of industries is not obvious. Model 1 without the government subsidy squared item has a negative subsidy coefficient, while the model with squared items has a positive subsidy coefficient and a negative square factor coefficient. We think that because government subsidies may have an inverted U-shaped effect, the government subsidies at this time have not yet reached the inflection point and are still on the left side of the inverted U-shape.
5.2. Analysis of Factors Affecting the Efficiency of the PV Power Generation Industry
6. Conclusions and Suggestion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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No. | Enterprise | Main Products |
---|---|---|
1 | Aikang Technology | PV accessories products, PV brackets |
2 | Zhongli Group | Components, cells |
3 | Daquan New Energy | Polysilicon, silicon wafer |
4 | Foster | Solar battery backplane |
5 | Hairun PV | Silicon wafer, cell assembly |
6 | Jinjing Technology | PV glass |
7 | Jing Yuntong | Silicon wafer, PV equipment |
8 | Jinko Energy | PV equipment |
9 | Jingsheng Electromechanical | Polysilicon |
10 | Jinggong Technology | PV equipment |
11 | Kstar | Optical inverter, etc. |
12 | Lin Yang Energy | PV modules, cell sheets |
13 | Longji shares | Polysilicon, cell sheets, components |
14 | CSG Holding | Solar glass, components, battery |
15 | TBEA | Inverter |
16 | Tianlong Photoelectric | Polycrystalline silicon PV equipment |
17 | GCL System Integration Technology | Polysilicon, PV modules |
18 | Sunshine power | Optical inverter |
19 | Yicheng New Energy | Polysilicon, silicon wafer |
20 | Yinxing Energy | PV power generation equipment |
21 | Yingli Green Energy | Polysilicon, silicon wafer, battery assembly |
22 | Yuhui Sunshine | Silicon wafer, battery assembly |
23 | Zhengtai Electric | PV equipment |
24 | Tianjin Zhong Huan semiconductor | Polysilicon, silicon wafer |
25 | Kehua Hengsheng | Transformer |
Enterprise | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Average |
---|---|---|---|---|---|---|---|---|
CMC Magnetics | 0.794 | 0.766 | 0.715 | 0.85 | 0.832 | 0.772 | 0.793 | 0.789 |
Jing Yuntong | 0.969 | 0.732 | 0.526 | 0.765 | 0.875 | 1.000 | 0.918 | 0.826 |
Jingsheng Electromechanical | 1 | 1 | 0.822 | 1 | 0.971 | 1.000 | 0.986 | 0.968 |
Longji shares | 0.885 | 0.709 | 0.669 | 0.861 | 0.884 | 0.893 | 0.849 | 0.821 |
Daquan New Energy | 1 | 0.525 | 0.59 | 1 | 1 | 0.948 | 1.000 | 0.866 |
Tianlong Photoelectric | 0.786 | 0.161 | 0.357 | 0.348 | 0.219 | 0.816 | 1.000 | 0.527 |
Yicheng New Energy | 0.834 | 0.774 | 0.687 | 0.836 | 0.815 | 0.849 | 0.690 | 0.784 |
Jinko Energy | 0.969 | 0.761 | 1 | 1 | 0.987 | 0.837 | 0.800 | 0.908 |
Zhengtai Electric | 1 | 1 | 1 | 1 | 0.971 | 0.963 | 0.955 | 0.984 |
Hairun PV | 0.903 | 0.688 | 0.658 | 0.669 | 0.769 | 0.789 | 0.660 | 0.734 |
Jinjing Technology | 1 | 0.85 | 0.782 | 0.861 | 0.842 | 0.839 | 0.829 | 0.858 |
Jinggong Technology | 1 | 0.563 | 0.548 | 0.799 | 0.825 | 0.917 | 0.913 | 0.795 |
Sunshine power | 0.826 | 0.707 | 0.912 | 1 | 1 | 1.000 | 1.000 | 0.921 |
Kstar | 0.809 | 0.843 | 0.779 | 0.908 | 0.942 | 1.000 | 1.000 | 0.897 |
Yinxing Energy | 0.906 | 1 | 1 | 1 | 0.885 | 0.960 | 0.905 | 0.951 |
Yingli Green Energy | 0.82 | 0.775 | 0.78 | 0.796 | 0.742 | 0.734 | 0.663 | 0.759 |
Yuhui Sunshine | 0.694 | 0.685 | 0.761 | 0.826 | 0.823 | 0.760 | 0.815 | 0.766 |
Foster | 1 | 1 | 1 | 1 | 1 | 1.000 | 0.999 | 1.000 |
Lin Yang Energy | 0.833 | 0.913 | 0.886 | 1 | 1 | 0.889 | 0.905 | 0.918 |
GCL integration | 0.819 | 0.456 | 0.33 | 1 | 1 | 1.000 | 1.000 | 0.801 |
Aikang Technology | 0.803 | 0.774 | 0.902 | 0.979 | 0.937 | 0.808 | 0.830 | 0.862 |
CSG | 0.997 | 0.908 | 0.918 | 0.914 | 0.86 | 0.841 | 0.773 | 0.887 |
Zhongli Group | 1 | 1 | 1 | 0.909 | 0.88 | 0.833 | 0.902 | 0.932 |
Kehua Hengsheng | 0.796 | 0.79 | 0.735 | 0.881 | 0.886 | 0.975 | 0.892 | 0.851 |
TBEA | 0.915 | 0.764 | 0.83 | 0.875 | 0.861 | 0.853 | 0.898 | 0.857 |
Annual Average | 0.889 | 0.729 | 0.739 | 0.867 | 0.847 | 0.891 | 0.879 | - |
Enterprise | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Average |
---|---|---|---|---|---|---|---|---|
Tianjin Zhong Huan Semiconductor | 0.798 | 0.770 | 0.792 | 0.891 | 0.851 | 0.801 | 0.853 | 0.822 |
Jing Yuntong | 0.969 | 0.771 | 0.573 | 0.766 | 0.912 | 1.000 | 0.920 | 0.844 |
Jingsheng Electromechanical | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Longji shares | 0.886 | 0.720 | 0.703 | 0.882 | 0.900 | 1.000 | 1.000 | 0.870 |
Daquan New Energy | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.978 | 1.000 | 0.997 |
Tianlong Photoelectric | 1.000 | 0.564 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.938 |
Yicheng New Energy | 0.853 | 1.000 | 0.717 | 0.841 | 0.840 | 0.861 | 0.732 | 0.835 |
Jinko Energy | 1.000 | 0.763 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.966 |
Zhengtai Electric | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Hairun PV | 1.000 | 0.735 | 0.762 | 0.718 | 0.804 | 0.840 | 0.666 | 0.789 |
Jinjing Technology | 1.000 | 0.856 | 0.807 | 0.868 | 0.844 | 0.905 | 0.883 | 0.881 |
Jinggong Technology | 1.000 | 0.665 | 0.816 | 0.862 | 1.000 | 0.975 | 0.933 | 0.893 |
Sunshine power | 0.990 | 0.739 | 0.912 | 1.000 | 1.000 | 1.000 | 1.000 | 0.949 |
Kstar | 1.000 | 1.000 | 0.895 | 0.929 | 0.949 | 1.000 | 1.000 | 0.968 |
Yinxing Energy | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.919 | 0.988 |
Yingli Green Energy | 0.909 | 1.000 | 1.000 | 0.841 | 0.774 | 0.808 | 0.741 | 0.868 |
Yuhui Sunshine | 0.707 | 0.721 | 0.781 | 0.832 | 0.825 | 0.792 | 1.000 | 0.808 |
Foster | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Lin Yang Energy | 0.846 | 0.921 | 0.909 | 1.000 | 1.000 | 0.961 | 0.996 | 0.948 |
GCL System Integration Technology | 0.834 | 0.495 | 0.800 | 1.000 | 1.000 | 1.000 | 1.000 | 0.876 |
Aikang Technology | 0.863 | 0.820 | 1.000 | 0.985 | 0.962 | 0.833 | 0.831 | 0.899 |
CSG Holding | 1.000 | 0.940 | 0.993 | 0.927 | 0.887 | 0.947 | 0.891 | 0.941 |
Zhongli Group | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.942 | 1.000 | 0.992 |
Kehua Hengsheng | 0.962 | 0.856 | 0.860 | 0.886 | 0.891 | 1.000 | 0.957 | 0.916 |
TBEA | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Annual Average | 0.941 | 0.839 | 0.884 | 0.925 | 0.934 | 0.946 | 0.933 | - |
Enterprise | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2017 RTS |
---|---|---|---|---|---|---|---|---|
CMC Magnetics | 0.995 | 0.995 | 0.903 | 0.954 | 0.978 | 0.964 | 0.929 | drs |
Jing Yuntong | 1.000 | 0.949 | 0.918 | 0.998 | 0.959 | 1.000 | 0.998 | irs |
Jingsheng Electromechanical | 1.000 | 1.000 | 0.822 | 1.000 | 0.971 | 1.000 | 0.986 | drs |
Longji shares | 0.999 | 0.985 | 0.953 | 0.977 | 0.982 | 0.893 | 0.849 | drs |
Daquan New Energy | 1.000 | 0.525 | 0.590 | 1.000 | 1.000 | 0.969 | 1.000 | - |
Tianlong Photoelectric | 0.786 | 0.285 | 0.357 | 0.348 | 0.219 | 0.816 | 1.000 | - |
Yicheng New Energy | 0.978 | 0.774 | 0.958 | 0.994 | 0.971 | 0.986 | 0.943 | drs |
Jinko Energy | 0.969 | 0.997 | 1.000 | 1.000 | 0.987 | 0.837 | 0.800 | drs |
Zhengtai Electric | 1.000 | 1.000 | 1.000 | 1.000 | 0.971 | 0.963 | 0.955 | drs |
Hairun PV | 0.903 | 0.935 | 0.863 | 0.931 | 0.956 | 0.939 | 0.992 | drs |
Jinjing Technology | 1.000 | 0.993 | 0.968 | 0.993 | 0.998 | 0.927 | 0.938 | drs |
Jinggong Technology | 1.000 | 0.847 | 0.671 | 0.926 | 0.825 | 0.941 | 0.979 | irs |
Sunshine power | 0.834 | 0.956 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | - |
Kstar | 0.809 | 0.843 | 0.870 | 0.977 | 0.992 | 1.000 | 1.000 | - |
Yinxing Energy | 0.906 | 1.000 | 1.000 | 1.000 | 0.885 | 0.960 | 0.985 | irs |
Yingli Green Energy | 0.902 | 0.775 | 0.780 | 0.947 | 0.959 | 0.908 | 0.894 | drs |
Yuhui Sunshine | 0.982 | 0.950 | 0.973 | 0.994 | 0.997 | 0.960 | 0.815 | irs |
Foster | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.999 | irs |
Lin Yang Energy | 0.984 | 0.991 | 0.975 | 1.000 | 1.000 | 0.925 | 0.909 | drs |
GCL System Integration Technology | 0.982 | 0.920 | 0.413 | 1.000 | 1.000 | 1.000 | 1.000 | - |
Aikang Technology | 0.930 | 0.944 | 0.902 | 0.994 | 0.974 | 0.970 | 0.998 | drs |
CSG Holding | 0.997 | 0.966 | 0.925 | 0.986 | 0.969 | 0.888 | 0.867 | drs |
Zhongli Group | 1.000 | 1.000 | 1.000 | 0.909 | 0.880 | 0.884 | 0.902 | drs |
Kehua Hengsheng | 0.827 | 0.923 | 0.854 | 0.994 | 0.994 | 0.975 | 0.933 | drs |
TBEA | 0.915 | 0.764 | 0.830 | 0.875 | 0.861 | 0.853 | 0.898 | drs |
Annual Average | 0.945 | 0.868 | 0.837 | 0.937 | 0.906 | 0.942 | 0.943 |
Year | Overall Technical Efficiency Change (EC) | Technological Change (TC) | Pure Technical Efficiency Change (Pech) | Scale Efficiency Change (Sech) | Total Factor Productivity Change (TFP) |
---|---|---|---|---|---|
2011–2012 | 0.763 | 0.951 | 0.821 | 0.929 | 0.725 |
2012–2013 | 1.076 | 0.952 | 0.993 | 1.084 | 1.025 |
2013–2014 | 1.324 | 0.87 | 1.204 | 1.1 | 1.151 |
2014–2015 | 0.978 | 0.987 | 1.069 | 0.915 | 0.966 |
2015–2016 | 1.101 | 1.334 | 1.025 | 1.074 | 1.469 |
2016–2017 | 0.974 | 1.268 | 0.976 | 0.998 | 1.235 |
Average annual rate of change | 1.022 | 1.047 | 1.008 | 1.014 | 1.07 |
Indicator Name | Unit | ||
---|---|---|---|
Input indicator | X1 | Cumulative installed capacity | Ten thousand kW |
X2 | Number of employees | Ten thousand people | |
X3 | PV power investment | Billion yuan | |
Output indicator | Y | Power generation | Billion kW·h |
Area | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Average Value |
---|---|---|---|---|---|---|---|---|
Hebei | 0.335 | 0.339 | 0.482 | 0.850 | 0.850 | 0.571 | ||
Shanxi | 0.030 | 0.871 | 1.000 | 0.519 | 0.482 | 0.908 | 0.908 | 0.674 |
Inner Mongolia | 0.103 | 0.545 | 0.356 | 0.579 | 0.824 | 1.000 | 1.000 | 0.630 |
Liaoning | 0.063 | 0.487 | 0.631 | 0.619 | 0.813 | 0.813 | 0.571 | |
Shanghai | 0.952 | 0.927 | 1.000 | 0.616 | 0.303 | 0.043 | 0.043 | 0.555 |
Jiangsu | 0.237 | 0.660 | 0.449 | 0.363 | 0.523 | 0.710 | 0.710 | 0.522 |
Zhejiang | 0.535 | 0.341 | 0.350 | 0.332 | 0.444 | 0.444 | 0.408 | |
Anhui | 0.346 | 0.433 | 0.167 | 0.216 | 0.660 | 0.660 | 0.414 | |
Fujian | 1.000 | 0.350 | 0.386 | 0.472 | 0.479 | 0.479 | 0.528 | |
Jiangxi | 0.409 | 0.350 | 0.328 | 0.395 | 0.723 | 0.723 | 0.488 | |
Shandong | 0.762 | 0.702 | 0.620 | 0.416 | 0.362 | 0.603 | 0.603 | 0.581 |
Henan | 0.150 | 0.535 | 0.832 | 0.832 | 0.587 | |||
Hubei | 0.529 | 0.292 | 0.467 | 0.332 | 0.830 | 0.830 | 0.547 | |
Guangdong | 0.083 | 0.230 | 0.111 | 0.122 | 0.393 | 0.591 | 0.591 | 0.303 |
Hainan | 0.182 | 1.000 | 0.404 | 0.819 | 0.560 | 0.844 | 0.844 | 0.665 |
Yunnan | 1.000 | 0.662 | 0.623 | 0.688 | 0.696 | 0.986 | 0.986 | 0.806 |
Tibet | 1.000 | 0.679 | 1.000 | 0.927 | 0.999 | 1.000 | 1.000 | 0.944 |
Shaanxi | 0.940 | 0.932 | 0.193 | 0.339 | 0.952 | 0.952 | 0.718 | |
Gansu | 0.519 | 0.553 | 0.487 | 0.518 | 0.689 | 0.986 | 0.986 | 0.677 |
Qinghai | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.998 | 1.000 |
Ningxia | 0.887 | 1.000 | 0.751 | 1.000 | 0.848 | 0.963 | 0.963 | 0.916 |
Xinjiang | 0.637 | 0.203 | 0.895 | 1.000 | 1.000 | 1.000 | 0.789 | |
Annual average | 0.353 | 0.579 | 0.475 | 0.444 | 0.517 | 0.78 | 0.783 | --- |
Area | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Average Value |
---|---|---|---|---|---|---|---|---|
Hebei | 0.339 | 0.346 | 0.482 | 0.851 | 0.850 | 0.574 | ||
Shanxi | 0.524 | 0.881 | 1.000 | 0.540 | 0.482 | 0.909 | 0.908 | 0.749 |
Inner Mongolia | 0.237 | 0.549 | 0.522 | 0.580 | 0.824 | 1.000 | 1.000 | 0.673 |
Liaoning | 0.443 | 0.487 | 1.000 | 0.973 | 0.857 | 0.813 | 0.762 | |
Shanghai | 1.000 | 1.000 | 1.000 | 1.000 | 0.782 | 1.000 | 1.000 | 0.969 |
Jiangsu | 0.481 | 0.663 | 0.658 | 0.365 | 0.523 | 0.711 | 0.710 | 0.587 |
Zhejiang | 0.536 | 0.342 | 0.368 | 0.332 | 0.444 | 0.444 | 0.411 | |
Anhui | 0.530 | 0.449 | 0.269 | 0.216 | 0.660 | 0.660 | 0.464 | |
Fujian | 1.000 | 0.559 | 1.000 | 1.000 | 1.000 | 0.550 | 0.851 | |
Jiangxi | 0.544 | 0.359 | 0.456 | 0.395 | 0.723 | 0.723 | 0.533 | |
Shandong | 1.000 | 0.715 | 0.650 | 0.447 | 0.362 | 0.603 | 0.603 | 0.626 |
Henan | 0.384 | 0.535 | 0.832 | 0.832 | 0.646 | |||
Hubei | 0.536 | 0.305 | 0.840 | 0.344 | 0.830 | 0.830 | 0.614 | |
Guangdong | 0.875 | 0.412 | 0.163 | 0.178 | 0.393 | 0.591 | 0.591 | 0.458 |
Hainan | 1.000 | 1.000 | 0.944 | 0.899 | 0.702 | 1.000 | 1.000 | 0.935 |
Yunnan | 1.000 | 0.667 | 0.623 | 0.721 | 0.697 | 0.987 | 0.986 | 0.812 |
Tibet | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 |
Shaanxi | 0.952 | 0.952 | 0.306 | 0.339 | 0.952 | 0.952 | 0.742 | |
Gansu | 0.898 | 0.554 | 0.541 | 0.538 | 0.690 | 0.986 | 0.986 | 0.742 |
Qinghai | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.999 | 1.000 |
Ningxia | 1.000 | 1.000 | 0.809 | 1.000 | 0.851 | 0.963 | 0.963 | 0.941 |
Xinjiang | 0.640 | 0.217 | 0.936 | 1.000 | 1.000 | 1.000 | 0.799 | |
Annual average | 0.775 | 0.699 | 0.546 | 0.572 | 0.577 | 0.85 | 0.836 | --- |
Area | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2017 RTS |
---|---|---|---|---|---|---|---|---|
Hebei | 0.991 | 0.980 | 0.998 | 0.9995 | 1.000 | - | ||
Shanxi | 0.051 | 0.988 | 1.000 | 0.960 | 0.996 | 0.9996 | 1.000 | - |
Inner Mongolia | 0.433 | 0.993 | 0.682 | 1.000 | 1.000 | 1.0000 | 1.000 | - |
Liaoning | 0.142 | 1.000 | 0.631 | 0.636 | 0.9479 | 1.000 | - | |
Shanghai | 0.952 | 0.927 | 1.000 | 0.616 | 0.388 | 0.0426 | 0.043 | irs |
Jiangsu | 0.492 | 0.996 | 0.682 | 0.995 | 0.998 | 0.9997 | 1.000 | - |
Zhejiang | 0.999 | 0.994 | 0.950 | 0.996 | 1.0000 | 1.000 | - | |
Anhui | 0.653 | 0.964 | 0.623 | 0.999 | 1.0000 | 1.000 | - | |
Fujian | 1.000 | 0.626 | 0.386 | 0.472 | 0.4792 | 0.872 | irs | |
Jiangxi | 0.751 | 0.974 | 0.720 | 0.996 | 1.0000 | 1.000 | - | |
Shandong | 0.762 | 0.981 | 0.954 | 0.931 | 0.995 | 1.0000 | 1.000 | - |
Henan | 0.390 | 0.999 | 1.0000 | 1.000 | - | |||
Hubei | 0.988 | 0.957 | 0.556 | 0.965 | 0.9999 | 1.000 | irs | |
Guangdong | 0.095 | 0.559 | 0.682 | 0.683 | 0.999 | 1.0000 | 1.000 | - |
Hainan | 0.182 | 1.000 | 0.428 | 0.911 | 0.797 | 0.8438 | 0.844 | irs |
Yunnan | 1.000 | 0.992 | 0.999 | 0.955 | 0.995 | 0.9991 | 0.999 | irs |
Tibet | 1.000 | 0.679 | 1.000 | 0.927 | 0.998 | 1.0000 | 1.000 | - |
Shaanxi | 0.987 | 0.978 | 0.629 | 0.994 | 0.9999 | 1.000 | - | |
Gansu | 0.578 | 0.999 | 0.901 | 0.962 | 0.993 | 0.9995 | 1.000 | - |
Qinghai | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.0000 | 1.000 | dis |
Ningxia | 0.887 | 1.000 | 0.928 | 1.000 | 0.996 | 0.9998 | 1.000 | irs |
Xinjiang | 0.995 | 0.935 | 0.956 | 1.000 | 1.0000 | 1.000 | - | |
average | 0.451 | 0.827 | 0.871 | 0.777 | 0.896 | 0.932 | 0.94 |
Variable Name | Model 1 | Model 2 | ||||
---|---|---|---|---|---|---|
Coefficient Value | Standard Deviation | Z Value | Coefficient Value | Standard Deviation | Z Value | |
Asset turnover | 0.2196576 | 0.0561253 | 3.91 *** | 0.2179421 | 0.0560076 | 3.89 *** |
Technical staff | 0.3962003 | 0.1291575 | 3.07 *** | 0.4080893 | 0.1279905 | 3.19 *** |
Per capita output value | 5.173824 | 1.227784 | 4.21 *** | 5.099016 | 1.224846 | 4.16 *** |
Ratio of sales cost to total cost | 0.0303791 | 0.777543 | 0.36 | 0.0247968 | 0.0774734 | 0.32 |
Production scale | −4.03 | 0.000761 | 0.940843 | 0.000182 | 0.0001558 | 1.17 |
Scale square | 2.01 | 1.43 | −1.40 | |||
government subsidy | −0.0010566 | 0.0363536 | 0.853351 | 0.0069734 | 0.039787 | 0.18 |
Subsidy square | −00025288 | 0.0101583 | −0.25 |
Variable | Coefficient | Standard Deviation | Z Statistic | p Value |
---|---|---|---|---|
ln (GDP) | −0.211334 | 0.1978433 | −1.07 | 0.288 |
Equipment utilization | 1.093636 | 0.0754971 | 14.49 | 0.000 *** |
Solar development utilization | 0.0039358 | 0.0074361 | 0.53 | 0.598 |
Electricity consumption | 0.000016 | 0.0000533 | 0.30 | 0.765 |
ln (power generation scale) | 0.0451229 | 0.0192829 | 2.34 | 0.021 ** |
ln (CO2) | −0.1460661 | 0.0570799 | −2.56 | 0.012 ** |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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Cai, H.; Liang, L.; Tang, J.; Wang, Q.; Wei, L.; Xie, J. An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors. Sustainability 2019, 11, 6693. https://doi.org/10.3390/su11236693
Cai H, Liang L, Tang J, Wang Q, Wei L, Xie J. An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors. Sustainability. 2019; 11(23):6693. https://doi.org/10.3390/su11236693
Chicago/Turabian StyleCai, Hao, Ling Liang, Jing Tang, Qianxian Wang, Lihong Wei, and Jiaping Xie. 2019. "An Empirical Study on the Efficiency and Influencing Factors of the Photovoltaic Industry in China and an Analysis of Its Influencing Factors" Sustainability 11, no. 23: 6693. https://doi.org/10.3390/su11236693