Solar PV installed capacity and solar PV generation are the most basic indicators of solar PV power efficiency. Therefore, we selected solar PV installed capacity, the cumulative number of solar PV patents, gross capital formation, and labor as input variables and solar PV generation as the output variable.
System efficiency is obtained by determining the system area and calculating the solar radiation amount. The panel efficiency under temperature effect, the PV panel efficiency and instant production amount are calculated in Equations (7), (8), and (9) [18, 19].
In particular, gross capital formation (% of GDP), labor, solar PV installed capacity, the cumulative number of solar PV patents, solar PV generation, the proportion of the urban population in the total population, GDP per capita, and carbon dioxide emissions were the variables used in the evaluation.
External environmental variables and statistical noise were non-contributing in terms of solar PV power efficiency scores, leading to an underestimation of solar PV power efficiency scores in stage 1. The reasons for this underestimation are less related to the low level of technology than to the relatively poor external environment.
The SFA model removed the effects of the external environmental variables and statistical noise on solar PV power efficiency. The results are summarized below. The first-stage results indicate that the solar PV power efficiency of the 26 countries considered fluctuated upward and then downward between 2000 and 2020.
As shown in Fig. 4, solar PV power efficiency also grew slowly in the initial stage. However, the center of solar PV generation in China is different from the center of massive consumption of solar PV generation, meaning there is no transmission line to connect the western region with the eastern region effectively .
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