Fig. 1 Conceptual framework for assessment
(2) In order to improve the scientificity and representativeness of the
low-carbon evaluation system for clean energy use in rural residential
buildings, the variation coefficient method was used to optimize the
indicators in the indicator database.
The arithmetic mean, standard deviation and coefficient of variation of
the index scores were calculated by Equations 1, 2 and 3, respectively.
\begin{equation}
Q_{j}=\frac{1}{n}\sum_{i=1}^{n}X_{\text{ij}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (1)\nonumber \\
\end{equation}where Xij denotes the rating of the j-th index by the
i-th respondent and Qj denotes the arithmetic mean of
the j-th index.
\begin{equation}
S_{j}=\sqrt{\frac{\sum_{i=1}^{n}{(X_{\text{ij}}-Q_{j})}^{2}}{n-1}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (2)\nonumber \\
\end{equation}where Sj denotes the standard deviation of the expert’s
score for the j-th indicator.
\begin{equation}
N_{j}=\frac{S_{j}}{Q_{j}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (3)\nonumber \\
\end{equation}where Nj denotes the coefficient of variation of the
expert’s score on the j-th indicator
As shown in Table 1, the 20 factors that have a significant impact on
the evaluation of clean energy decarbonization of rural residential
buildings were finally identified.
Table 1 Evaluation index factors of clean energy decarbonization of
residential buildings