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