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\begin{document}

\title{Research on the low carbonization of clean energy use in rural
residential buildings in China under the background of ``carbon peaking
and carbon neutrality''}



\author[1]{yao wang}%
\author[2]{qiankun Hong}%
\affil[1]{Hangzhou Hengzheng Engineering Cost Office}%
\affil[2]{Zhejiang Tongji Vocational College of Science And Technology}%


\vspace{-1em}



  
  \date{June 16, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
As an important part of the construction industry, rural residential
buildings are characterized by low energy utilization, unreasonable
structures and low consumption levels, and it is particularly important
to study their low-carbon transformation and evaluation system. In view
of the many low-carbon transformation needs of rural residential
buildings, the existing research results were analyzed in depth, and the
coefficient of variation method was used to identify the important
factors affecting the low-carbon transformation of rural residential
buildings, and the evaluation system of rural residential buildings'
low-carbon transformation was determined by Analytic Hierarchy Process
(APH), and the system was used in a rural residential building
low-carbon evaluation study. The results show that the influence of
``energy use'', ``envelope structure'' and ``economic factors'' on the
decarbonization of buildings is obvious, with the weights of 36.4\%,
24.5\% and 19.5\% respectively. Among the secondary indicators, ``clean
energy utilization'', ``electricity consumption'', ``external wall
insulation system'' and ``window performance'' are the most important
factors in reducing carbon emissions in rural areas. The most critical
influencing factors for the low carbonization level of clean energy in
rural residential buildings are ``window performance''. Finally, based
on the constructed low carbonization evaluation system, we propose a
targeted solution strategy to provide a theoretical basis for the
establishment of an effective low carbonization evaluation system for
clean energy in rural residential buildings.%
\end{abstract}%



\sloppy


\textbf{1 Introduction}

With the rapid economic development in recent years, China's total
carbon emissions have also jumped to the top of the world, increasing
from 1.500 billion tons in 1980 to 11.470 billion tons in 2021, showing
a large increase and high emissions {[}1-3{]}. At the 75th UNGA video
conference in 2020, General Secretary Xi Jinping proposed that China had
decided to strive to reach peak CO\textsubscript{2} emissions by 2030
and to achieve the goal of carbon neutrality by 2060. This marks a
gradual transition in China's energy consumption structure from being
dominated by traditional energy sources to a two-wheel drive and
synergistic development of traditional and new energy sources {[}4,
5{]}.

According to statistics, China's total energy consumption in the
construction industry in 2019 is 2.233 billion tons of standard coal,
and the total carbon emissions in the construction process is 4.997
billion tons of carbon dioxide, accounting for about half of the total
carbon emissions in the country {[}6{]}. On the other hand, rural
residential buildings, as an important part of China's building sector,
account for about 25\% of the total building energy consumption in the
country; however, due to the low energy utilization rate, unreasonable
structure and low consumption level of rural residential buildings, the
energy consumption of rural residents has been increasing year by year
{[}7, 8{]}. Therefore, it is of great significance to study the
evaluation of low carbonization of rural residential buildings for rural
revitalization and energy conservation {[}9{]}.

Scholars at home and abroad have conducted research on how to achieve a
low-carbon transition in rural residential buildings, with research
focusing on both rural energy use and energy evaluation. Filippo
Padovani et al {[}10{]} examined the technical characteristics of
electrification for sustainable heating in remote rural areas in the
Midwest of the United States, showing that if the goal of using all
renewable energy is achieved, building carbon emissions will be reduced.
Dominguez Cristina et al {[}11{]} analyzed the pathways to clean energy
transition in rural Kenya and showed that women play a key role in the
energy transition as decision makers, with female-headed households
preferring to switch to cleaner fuels at an early stage. Zhang {[}12{]}
studied how the development of clean heating in rural areas can be
optimized, using TRNSYS software to simulate energy consumption in
combination with a study of 500 typical farm households to simulate the
current status of heating in farm houses in Shandong, the current status
of maintenance structure, heating energy structure and other major
influencing factors, and to design five clean heating options for
economic and environmental benefit analysis in rural areas of Shandong.
Yuan et al {[}13{]} carried out a questionnaire survey on the
construction of farm houses for farmers in the rural areas of Chang'an
District, Xi'an, and categorized and analyzed them from a low-carbon
perspective. The survey results show that scholars at home and abroad
are fully aware of the importance of low-carbon technologies in rural
housing. It can be seen that scholars at home and abroad have fully
affirmed the importance of low-carbon rural residential buildings,
analyzed the problems of low-carbon development of rural residential
buildings and proposed corresponding solutions. However, there is still
a lack of research on the topic of low-carbon transformation of rural
residential buildings, which does not match the scale of the total floor
area of rural residential buildings in China. Moreover, the assessment
of clean energy use in rural residential buildings rarely uses multiple
indicators to measure the low-carbon nature of energy use, and the
low-carbon evaluation of clean energy use in rural residential buildings
is insufficient.

Based on the existing research results, this paper identifies the
important factors affecting the low-carbon transition of rural
residential buildings from the perspective of sustainable development of
building low-carbonization, determines the low-carbon evaluation system
of rural residential buildings by APH, and applies the system to a rural
residential building low-carbon evaluation study, and then proposes
targeted low-carbon strategies to provide a theoretical basis for the
establishment of an effective rural residential building clean energy
low-carbon evaluation system.

\textbf{2 Theoretical basis and determination of evaluation indexes}

\textbf{2.1 Theoretical basis}

\textbf{Decarbonization theory:} decarbonization generally refers to the
use of more efficient energy use and social functioning to promote
sustainable urban development through technological advances and changes
in social systems in the process of socioeconomic development {[}14{]}.
In this paper, the main study is the decarbonization of rural
residential buildings.

\textbf{Sustainable development theory:} is the replacement of used
resources with resources of equal or greater value to maintain the
world's productivity without degrading or endangering natural
ecosystems. Sustainable development links concerns about the carrying
capacity of natural systems to the social, political and economic
challenges facing human society. Its development has evolved from
academic research to international attention to a broad consensus among
major countries around the globe. Sustainable development is a way of
organizing society that can endure in the long term and implies taking
into account the importance of both the present and the future, such as
the conservation of the environment and natural resources, social and
economic equity {[}15, 16{]}.

\textbf{2.2 Determination of evaluation indicators}

(1) Evaluating the low carbonization of clean energy use in rural
residential buildings is a systematic project involving complicated
influencing factors, and in order to determine the influencing factors
more comprehensively and systematically, the evaluation conceptual
framework constructed through a large number of analyses of the current
research of Chinese scholars in related low carbonization evaluation
{[}17, 18{]}, from six aspects such as energy use, envelope structure,
layout design, building materials, behavioral habits, and economic
factors The evaluation system was constructed to initially screen
indicators (Fig. 1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

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 X\textsubscript{ij} denotes the rating of the j-th index by the
i-th respondent and Q\textsubscript{j} 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 S\textsubscript{j} 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 N\textsubscript{j} 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\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
Assessment factors for clean energy decarbonization of rural residential
buildings & Tier 1 Indicators & Secondary indicators\tabularnewline
\midrule
\endhead
& Energy Use & Clean Energy Utilization\tabularnewline
& & Coal use\tabularnewline
& & Share of electricity consumption\tabularnewline
& Enclosures & Window to wall ratio\tabularnewline
& & Window Performance\tabularnewline
& & External wall insulation system\tabularnewline
& Layout design & Building orientation arrangement\tabularnewline
& & Geographical Distribution\tabularnewline
& & Living space per capita\tabularnewline
& Construction Materials & Utilization rate of new building
materials\tabularnewline
& & Building material reuse\tabularnewline
& & Building material saving\tabularnewline
& Behavioral habits & Energy-saving appliance usage rate\tabularnewline
& & Low Carbon Behavior Awareness\tabularnewline
& & Water conservation\tabularnewline
& & Domestic waste recycling treatment\tabularnewline
& Economic Factors & Annual household income per capita\tabularnewline
& & Clean Energy Consumption\tabularnewline
& & Clean Energy Prices\tabularnewline
& & Building renovation subsidy satisfaction\tabularnewline
\bottomrule
\end{longtable}

\textbf{3 Build the hierarchical model}

\textbf{3.1 Model Methodology}

Analytic Hierarchy Process (AHP) decomposes the elements related to the
decision object into goal level, indicator level and other levels, etc.
It is an easy way to make decisions for some more complex and ambiguous
problems, and the analysis process is more suitable for multi-level
interleaved goal systems, and the goal values are difficult to describe
the decision problem quantitatively {[}19, 20{]}. The steps to construct
the hierarchical model are as follows.

(1) Construct a pairwise comparison judgment matrix.

(2) The matrix weights are calculated by normalizing the matrix
corresponding to the maximum eigenvalue \selectlanguage{greek}λ\selectlanguage{english}\textsubscript{max}.

\begin{equation}
\lambda_{\max}=\frac{1}{n}\sum_{i=1}^{n}\frac{BW_{n}}{W_{i}}\text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }\ (4)\nonumber \\
 \end{equation}

(3) The eigenvectors corresponding to the maximum eigenvalues are used
as the weight vectors to compare the influence of each factor on a
factor in the upper level. The consistency index is calculated as.

\begin{equation}
CI=\frac{\lambda-n}{n-1}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (5)\nonumber \\
 \end{equation}

(4) Calculate the weights of the primary and secondary indicators
respectively

(5) Calculate the Low Carbon Index (LCI). The scores of the second-level
indicators are multiplied by the weights of the second-level indicators
and added together to obtain the scores of the corresponding first-level
indicators, and then the scores of the first-level indicators are
weighted and added together to obtain the total score of the low-carbon
evaluation of clean energy use, which is called the ''Low Carbon
Index'', and the detailed calculation formula is as follows.

\begin{equation}
Q_{i}=\sum_{i=1}^{n}{W_{j}Q_{j}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (6)\nonumber \\
 \end{equation}\begin{equation}
LCI=\sum_{i=1}^{6}{W_{i}Q_{i}}\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (7)\nonumber \\
 \end{equation}

Where Qj denotes the evaluation score of secondary indicators, Wj
denotes the weight of secondary indicators, Qi denotes the sum of the
scores of primary indicators, Wi denotes the weight of primary
indicators, and LCI denotes the low carbon index.

(6) Based on the above calculation results, the low-carbon performance
of rural residential buildings is judged, and for high-carbon
residential buildings, this study hopes to achieve low carbonization
through retrofitting.

\textbf{3.2 Questionnaire design and data collection}

Based on the in-depth analysis and survey, a total of 110 questionnaires
were distributed to government departments, research institutions and
universities in the fields of energy planning and construction,
respectively, and 95 questionnaires were collected. Experts were invited
to score each index in Table 1 as 9, 7, 5, 3 and 1, and add the adjacent
median of 2, 4, 6 and 8. The data of the 95 questionnaires screened were
averaged to obtain the AHP judgment matrix list.

Table 2 List of AHP judgment matrix\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Judgment Matrix & Energy Use & Enclosures & Layout design & Construction
Materials & Behavioral habits & Economic Factors\tabularnewline
\midrule
\endhead
Energy Use & 1 & 3 & 4 & 6 & 5 & 3\tabularnewline
Enclosures & 1/3 & 1 & 3 & 4 & 4 & 2\tabularnewline
Layout design & 1/4 & 1/3 & 1 & 1 & 1/3 & 1/4\tabularnewline
Construction Materials & 1/6 & 1/4 & 1 & 1 & 1 & 1/3\tabularnewline
Behavioral habits & 1/5 & 1/4 & 3 & 2 & 1 & 1/3\tabularnewline
Economic Factors & 1/3 & 1/3 & 4 & 3 & 2 & 1\tabularnewline
\bottomrule
\end{longtable}

\textbf{3.3 AHP analysis}

The weights of the secondary indicators were calculated by (2), (3) and
(4) above and integrated to form Table 3. The weights of ''energy use'',
''envelope'' and ''economic factors'' are larger, 0.364, 0.245 and 0.195
respectively, while the weights of ''layout design'', ''building
materials'' and ''behavior'' are less. Among the secondary indicators,
the top five ranked by weight are ''clean energy utilization'' (0.152),
''external wall insulation system'' (0.090) and ''energy efficiency''
(0.152). ''(0.090), ''window performance'' (0.088), and ''electricity
consumption'' (0.084), accounting for more than half of the total
weight, which is an important influence on the clean energy use of rural
residential buildings. The factors of clean energy use in rural
residential buildings are important factors. Most of the indicators with
lower weights are ''behavior'', ''building materials'' and ''layout
design'', mainly because these influencing factors are influenced by the
geographical environment and the original status of the building. For
example, the ''appropriateness of site selection'' in the secondary
index is determined at the beginning of building construction, so it is
difficult to change it again during renovation, but it affects the
energy source and its distribution during the operation and use of the
building, which has a significant impact on the LCI. However, it affects
the energy resources and their distribution during the operation phase
of the building, and has a significant impact on LCI. From the above
analysis, it is clear that ''energy use'', ''envelope'' and ''economic
factors'' are important influencing factors in the assessment of clean
and low carbon use of rural residential buildings {[}21{]}.

Table 3 I and II index weights\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Tier 1 Indicators & Weighting of primary indicators Wi & Secondary
indicators & Weighting of primary indicators Wj & Combination weights
Wi*Wj\tabularnewline
\midrule
\endhead
Energy use (A) & 0.364 & Clean Energy Utilization (A1) & 0.417 &
0.152\tabularnewline
& & Coal use (A2) & 0.087 & 0.032\tabularnewline
& & Electricity consumption share (A3) & 0.231 & 0.084\tabularnewline
Envelope (B) & 0.245 & Window-to-wall ratio (B1) & 0.179 &
0.044\tabularnewline
& & Window performance (B2) & 0.359 & 0.088\tabularnewline
& & External wall insulation system (B3) & 0.366 & 0.090\tabularnewline
Layout design (C) & 0.041 & Building orientation arrangement (C1) &
0.282 & 0.012\tabularnewline
& & Geographical distribution (C2) & 0.072 & 0.003\tabularnewline
& & Living space per capita (C3) & 0.192 & 0.008\tabularnewline
Construction materials (D) & 0.058 & New building materials utilization
rate (D1) & 0.302 & 0.018\tabularnewline
& & Building material reuse rate (D2) & 0.258 & 0.015\tabularnewline
& & Building material saving (D3) & 0.332 & 0.019\tabularnewline
Behavioral habits (E) & 0.102 & Energy saving appliance usage rate (E1)
& 0.177 & 0.018\tabularnewline
& & Low carbon behavior awareness (E2) & 0.228 & 0.023\tabularnewline
& & Water conservation (E3) & 0.158 & 0.016\tabularnewline
& & Domestic waste recycling treatment (E4) & 0.199 &
0.020\tabularnewline
Economic factors (F) & 0.195 & Annual per capita household income (F1) &
0.383 & 0.075\tabularnewline
& & Clean Energy Consumption (F2) & 0.412 & 0.080\tabularnewline
& & Clean Energy Prices (F3) & 0.113 & 0.022\tabularnewline
& & Building renovation subsidy satisfaction (F4) & 0.093 &
0.018\tabularnewline
\bottomrule
\end{longtable}

After the calculation, the LCI evaluation level division is further
derived as shown in Table 4. the LCI is less than 3, indicating the
urgent need for the low carbon evaluation index system to identify its
own shortcomings and prioritize the optimization of the indexes with
higher weight values in order to improve its low carbon performance
under limited conditions. Meanwhile, due to the poor condition of
existing rural residential buildings, their renovation work is
relatively large. For buildings with LCI in {[}3, 4), the low-carbon
evaluation situation is medium, and the buildings can be retrofitted
according to different criteria, giving priority to indicators with good
low-carbon performance. buildings with LCI in the range of {[}4, 5{]}
can temporarily not be retrofitted because of good conditions in all
aspects. buildings with LCI of high or medium carbon type, this paper
hopes that through retrofitting, the LCI can reach a score of 4 or
higher to achieve their low-carbon Transformation.

Table 4 Classification of evaluation levels\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Grade & Low carbon type & Medium carbon type & High carbon
type\tabularnewline
\midrule
\endhead
Low Carbon Index LCI & \(LCI\in\left[4,\ 5\ \right]\) & \(LCI\in[3,\ 4)\) &
\(LCI\in[0,\ 3)\)\tabularnewline
\bottomrule
\end{longtable}

\textbf{4 Low Carbon Assessment Case Studies}

In order to apply the constructed evaluation system to the assessment of
the low carbonization of rural residential buildings, this study
selected village A in Zhejiang Province as the research object, and
investigated a total of 227 rural households in the village by means of
visits and surveys to obtain the basic overview of the research object
and the required content of evaluation indexes in the village, and
conducted a detailed survey of 6 rural residential buildings (3 each of
unrenovated and renovated), and the unrenovated and renovated The
numbers of unrenovated and renovated were recorded as M1-M3 and H1-H3,
respectively, for analysis. The specific assessment results are shown in
Table 5.

Table 5 Indicator evaluation results\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
Tier 1 Indicators & Secondary indicators & M1 & M2 & M3 & H1 & H2 & H3 &
Weights\tabularnewline
\midrule
\endhead
Energy Use & Clean Energy Utilization & 3 & 4 & 1 & 4 & 5 & 5 &
0.152\tabularnewline
& Coal use & 3 & 3 & 1 & 2 & 5 & 4 & 0.032\tabularnewline
& Share of electricity consumption & 2 & 3 & 2 & 1 & 3 & 4 &
0.084\tabularnewline
Enclosures & Window to wall ratio & 3 & 3 & 2 & 5 & 5 & 5 &
0.044\tabularnewline
& Window Performance & 1 & 2 & 1 & 3 & 4 & 5 & 0.088\tabularnewline
& External wall insulation system & 2 & 3 & 1 & 3 & 5 & 3 &
0.090\tabularnewline
Layout design & Building orientation arrangement & 5 & 5 & 5 & 5 & 5 & 5
& 0.012\tabularnewline
& Geographical Distribution & 4 & 4 & 4 & 4 & 4 & 4 &
0.003\tabularnewline
& Living space per capita & 2 & 3 & 1 & 3 & 3 & 3 & 0.020\tabularnewline
Construction Materials & Utilization rate of new building materials & 1
& 1 & 1 & 1 & 1 & 1 & 0.018\tabularnewline
& Building material reuse & 2 & 3 & 1 & 3 & 4 & 4 & 0.015\tabularnewline
& Building material saving & 4 & 3 & 1 & 5 & 5 & 5 &
0.019\tabularnewline
Behavioral habits & Energy-saving appliance usage rate & 5 & 4 & 4 & 4 &
5 & 4 & 0.018\tabularnewline
& Low Carbon Behavior Awareness & 5 & 3 & 3 & 3 & 3 & 3 &
0.023\tabularnewline
& Water conservation & 4 & 5 & 1 & 4 & 5 & 5 & 0.016\tabularnewline
& Domestic waste recycling treatment & 4 & 4 & 1 & 3 & 5 & 4 &
0.020\tabularnewline
Economic Factors & Annual household income per capita & 5 & 5 & 5 & 5 &
5 & 5 & 0.075\tabularnewline
& Clean Energy Consumption & 3 & 4 & 1 & 4 & 4 & 3 &
0.080\tabularnewline
& Clean Energy Prices & 1 & 3 & 3 & 3 & 4 & 3 & 0.022\tabularnewline
& Building renovation subsidy satisfaction & 5 & 3 & 1 & 3 & 3 & 5 &
0.018\tabularnewline
\bottomrule
\end{longtable}

According to the formula, the comprehensive evaluation results of LCI of
6 rural residential buildings are shown in Table 6. According to the LCI
in Table 6, it can be seen that there are 2 high-carbon and 1
medium-carbon buildings in the unrenovated buildings (M1-M3) and 2
low-carbon and 1 medium-carbon buildings in the renovated buildings
(H1-H3), which are better than the unrenovated buildings.

Table 6 Integrated assessment results\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Number & M1 & M2 & M3 & H1 & H2 & H3\tabularnewline
\midrule
\endhead
LCI & 2.68 & 3.24 & 1.66 & 3.81 & 4.73 & 4.29\tabularnewline
Grade & High carbon type & Medium carbon type & High carbon type &
Medium carbon type & Low carbon type & Low carbon type\tabularnewline
\bottomrule
\end{longtable}

As shown in Fig 2, the average low-carbon index of the unrenovated
buildings is 2.53, and the average low-carbon index of the renovated
buildings is 4.28. The low-carbon index of the renovated buildings is
significantly higher than that of the unrenovated ones, and the level of
low-carbonization is higher, indicating that renovation can effectively
improve the low-carbonization level of residential buildings.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Fig. 2 Low carbon index and average of each farm household

As shown in Fig 3a, by comparing the scores of the six primary
indicators of each farmer, it can be found that the area formed by the
area connected by the dots of each indicator score is significantly
higher in M1 than in M2 and M3, and the area size is M2 \textgreater{}
M1 \textgreater{} M3, mainly because M2 has a greater advantage in
''envelope structure'' and ''energy use''. M2 \textgreater{} M1
\textgreater{} M3, mainly because M2 has greater advantages in
''envelope'' and ''energy use''. As can be seen from Fig 3b, the area of
H2 formed by the connection of the dots of each index score is
significantly higher than that of H1 and H3, and the size of the area is
H2 \textgreater{} H3 \textgreater{} H1, although the advantage of H2 in
''economic factors'' and ''behavioral habits'' is not obvious. However,
H2 has an outstanding performance in ''energy use'', and according to
the previous section, ''energy use'' has a higher weighting factor.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Fig. 3 Comparative analysis of primary indicators (a) M1, M2, M3, (b)
H1, H2, H3

In order to further evaluate the level of low carbonization of each
household, this study analyzed the secondary indicators of each
household, as shown in Fig 4a, a total of eight secondary indicators in
M2 have a weighted evaluation score greater than M1 and M3, which all
contribute to the assessment of low carbonization. Checking the research
situation of M2, we found that the windows used in the building are
aluminum windows, which have very good heat insulation effect and play a
good role in reducing heat loss, and the exterior wall construction is
made of Sanchi wall, which is better than both M1 and M3, so the
building has higher heat insulation effect and plays an important role
in LCI. In the case of energy use, M2 uses more biomass, mainly tree
branches, and biomass has an important role in low carbon, and these two
indicators also show the importance of ''energy use'' and ''envelope'',
which have a significant impact on improving LCI {[}22{]}. M3 is a
''high-carbon'' case with the lowest evaluation score, and the analysis
of its score can help to improve the level of low carbonization in the
future. Generally, because M3 did not carry out envelope renovation and
energy renovation, the energy used is mainly bulk coal and cellular
coal, which is used a lot and causes relatively serious pollution. For
M3, the indicators with good weighted scores are ''per capita annual
household income'' and ''window-to-wall ratio'', which shows that M3 has
a good per capita annual income level and has the economic ability to
use clean energy. The ''window-to-wall ratio'' also lays a certain
foundation for future envelope renovation and does not require too many
changes in windows and doors.

Through the study of the renovated H1-H3, it was found that before the
renovation as M1-M3, the walls of the buildings did not have good
insulation materials, and they could only rely on burning large amounts
of coal for heating and as a source of energy consumption such as
domestic hot water and cooking in winter. However, in recent years,
through the transformation of the envelope structure and the change of
energy utilization, the insulation layer has been added to the exterior
walls of residential buildings, and the insulation material is mainly of
the polystyrene board type, which has a good thermal insulation effect
and guarantees an effective improvement of the indoor thermal
environment {[}23{]}. The energy used is also cleaner and low-carbon,
and the reduction in the amount of coal combustion has improved the
indoor air quality and changed the original ''dirty, messy, and poor''
situation. In terms of ''energy use'', biomass energy resources are
abundant in the study village, and the renovated buildings have
increased the use of new biomass stoves and solid-formed biomass
pellets, and in recent years have increased the use of solar
photovoltaic power generation, which can supplement household
electricity and reduce the use of coal compared with no energy
renovation. In terms of ''envelope'', the addition of wall insulation
systems has improved indoor temperatures, and windows have been replaced
with aluminum and plastic windows from the original wooden windows or
old-fashioned steel windows. The ''layout design'' aspect, as an
indicator with limited changes during renovation, was also selected with
different regional conditions in mind, and although it did not change
much in the case study, it will play its importance in a broader range
of uses in the future {[}24{]}; the research site selected for this
paper was Zhejiang Province, so ''site rationality'', ''building
orientation arrangement'', and ''geographic location distribution'' have
not changed, and ''living area per capita '' had some changes before and
after the renovation, but this paper only focuses on residential
buildings, and the renovation did not have an impact.\selectlanguage{english}
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Fig. 4 Comparative analysis of secondary indicators (a) M1, M2, M3; (b)
H1, H2, H3

\textbf{5 Conclusion}

This paper takes the assessment of the low carbonization of clean energy
use in rural residential buildings as the research direction, constructs
a conceptual framework for low carbonization assessment and an
evaluation index system, selects a rural residential building as a
research case of the evaluation index system, and applies the index
system constructed in the previous paper to assess its low
carbonization. The main findings are as follows.

(1) From the perspective of the assessment indicators for the low
carbonization of clean energy use in rural residential buildings, the
indicator system involves a number of aspects, which have a great deal
to do with envelope structure and economic factors in addition to energy
utilization, and also have an influence on layout design, building
materials and behavioral habits. The weighting of the primary indicators
is set at 36.4\% for energy utilization, 24.5\% for envelope structure,
4.1\% for layout design, 5.8\% for building materials, 10.2\% for
behavioral habits and 19.5\% for economic factors.

(2) From the case studies, the main reason for the higher level of
decarbonization of clean energy use in retrofitted rural residential
buildings is the increased use of renewable energy sources such as
electricity, biomass and solar energy through both energy utilization
and building envelope modifications. In terms of key influencing
factors, ''clean energy utilization'', ''share of electricity
consumption'', ''external wall insulation system'' and ''window
performance '' are key influencing factors for the level of
decarbonization of clean energy use in a rural residential building.

\textbf{Acknowledgements}

This work was supported by the Foundation of Zhejiang Educational
Committee (NO. Y202147779), the Fundamental Research Funds for the
Colleges (NO. FRF21QN001) and the Natural Science Foundation of Zhejiang
Province (NO. LZJWY22E090005).

\textbf{Conflict of Interest}

The authors declare no potential conflict of interest.

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