Research on low‐carbon evaluation of clean energy use in rural residential buildings based on Analytic Hierarchy Process method

As an important part of the construction industry, rural residential buildings are characterized by low energy utilization, unreasonable structure, and low consumption level, so it is particularly important to study the low carbon transformation and evaluation system. In this study, 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 low‐carbonization of rural residential buildings was determined by Analytic Hierarchy Process (AHP), and the system was used in a rural residential building low‐carbonization evaluation study. The results show that “Energy utilization,” “Envelope structure,” and “Economic factors” have obvious influence on the low carbonization of buildings, with the weights of 36.4%, 24.5%, and 19.5%, respectively. Among the secondary indicators, the top four weightings are “clean energy utilization rate” (0.152), “external wall insulation system” (0.090), “window performance” (0.088), and “electricity consumption” (0.084), which are the most critical factors influencing the low carbonization level of clean energy utilization in rural residential buildings. Finally, based on the constructed low‐carbonization evaluation system, we propose targeted solution strategies to provide a theoretical basis for establishing an effective assessment system for the low‐carbonization of clean energy in rural residential buildings.


INTRODUCTION
2][3] At the 75th General Assembly of the United Nations (UNGA) video conference in 2020, General Secretary Xi Jinping proposed that China had decided to strive to reach peak CO 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. 4,5ccording 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. 6On 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. 7,8Therefore, it is of great significance to study the evaluation of low carbonization of rural residential buildings for rural revitalization and energy conservation. 9cholars 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 utilization 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.Ma et al. 12 explores the changes in carbon emissions from residential building operations and the main drivers through the Generalized Divisia Index Method (GDIM).The study shows that the pace of decarbonization of residential building operations is slowing down globally, with 209.43 billion tons of CO 2 decarbonized in 30 countries from 2000 to 2019, with a decarbonization efficiency of 3.4%.Xiang et al. 13 used the decomposing structural decomposition (DSD) method to assess the progress of decarbonization of commercial building operations in 16 countries over the past two decades and to inform the search for best practice pathways to decarbonize commercial building operations.Zhang 14 studied how the development of clean heating in rural areas can be optimized, using Transient System Simulation Program (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.Zhang et al. 15 showed that increasing the proportion of clean power generation, improving building-integrated power generation, increasing building electrification and reducing end-use energy intensity are effective decarbonization strategies for residential building operations.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 utilization in rural residential buildings rarely uses multiple indicators to measure the low-carbon nature of energy utilization, and the low-carbon evaluation of clean energy utilization in rural residential buildings is insufficient.Therefore, three questions are raised regarding the decarbonization of rural residential buildings in China: • What are the impact indicators affecting rural residential buildings?
• How to evaluate the degree of clean energy decarbonization of rural residential buildings?
• How can we use this evaluation system to guide the low-carbonization of existing and future rural residential buildings?
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 Analytic Hierarchy Process (AHP), 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.

DETERMINATION OF EVALUATION INDEXES
Evaluating the low carbonization of clean energy utilization 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, 16,17 the evaluation system was constructed from six aspects, including Energy utilization, Envelope structure, Layout design, Building materials, Behavioral habits, and Economic factors, and the preliminary screening index was conducted.
In order to improve the scientificity and representativeness of the low-carbon evaluation system for clean energy utilization 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.
where X ij denotes the rating of the jth index by the ith respondent and Q j denotes the arithmetic mean of the jth index.
where S j denotes the standard deviation of the expert's score for the jth indicator.
where N j denotes the coefficient of variation of the expert's score on the jth 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.

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. 18,19The steps to construct the hierarchical model are as follows.
First, a pairwise comparison judgment matrix is constructed.Second, the matrix weights are calculated by normalizing the matrix corresponding to the maximum eigenvalue  max .
Then, 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.
Further, the weights of primary index and secondary index are calculated respectively.Additionally, the low-carbon index (LCI) is calculated.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 utilization, which is called the "LCI", and the detailed calculation formula is as follows.
Where Q j denotes the evaluation score of secondary indicators, W j denotes the weight of secondary indicators, Q i denotes the sum of the scores of primary indicators, W i denotes the weight of primary indicators, and LCI denotes the low-carbon index.

Questionnaire distribution 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 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. Finally, 95 valid scores were recovered and the questionnaire data were averaged to obtain the AHP judgment matrix list (Table 2).

AHP analysis
The weights of the secondary indicators were calculated by Equations ( 2)-( 4) above and integrated to form Table 3.The weights of "Energy utilization", "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) "window performance" (0.088), "electricity consumption" (0.084) and "clean energy consumption" (0.080), accounting for more than half of the total weight, which is an important influence on the clean energy utilization of rural residential buildings.The factors of clean energy utilization 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 utilization," "Envelope," and "economic factors" are important influencing factors in the assessment of clean and low carbon use of rural residential buildings. 20fter 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.

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 shownnotes the weight of primary indicators, in Table 5.
The comprehensive evaluation results of the LCI of the six rural residential buildings are shown in Figure 1.According to the LCI situation in Figure 1, it can be seen that there are two high carbon types and one medium carbon types in the unrenovated buildings (M1-M3), and two low carbon types and one medium carbon type and in the retrofitted buildings (H1-H3), which are better than the unrenovated buildings.In addition, the average LCI of the unrenovated buildings is 2.53, and the average LCI of the renovated buildings is 4.28.The LCI of the renovated buildings is significantly higher than that of the unrenovated ones, and the low-carbon level is higher, indicating that the renovation can effectively improve the low-carbon level of residential buildings.As shown in Figure 2A, by comparing the scores of the six primary indicators of each household, we can find that the scores of the indicators are M2, M1, and M3 in descending order, mainly because M2 has a greater advantage in "Enclosures structure" and "Energy utilization."As can be seen from Figure 2B, H2 has the highest score, followed by H3 and H1, respectively, although H2 does not have obvious advantages in "Economic factors" and "Behavioral habits," but it is outstanding in "Energy utilization."Although H2 does not have a clear advantage in "Economic factors" and "Behavioral habits," it has a strong performance in "Energy utilization," and according to the previous section, "Energy utilization" has a higher weighting factor.
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 Figure 3A, 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 utilization, 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 utilization" and "Envelope," which have a significant impact on improving LCI. 21M3 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 utilization 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. 22The energy utilizationd 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 utilization," 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 23 ; 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.

CONCLUSION
This paper takes the assessment of clean energy low-carbonization in rural residential buildings as the research direction, constructs a conceptual framework for low-carbonization assessment, evaluates the level of clean energy low-carbonization in rural residential buildings by AHP method, further selects a rural residential building as the research case of the evaluation index system, and applies the index system constructed in the previous paper to assess its low-carbonization.The key findings are summarized as follows.

Key findings
• This study evaluates the factors influencing the decarbonization of rural residential buildings by constructing a hierarchical model.The results show that among the six primary indicators, "Energy utilization," "envelope structure" and "economic factors" have larger weight values of 0.364, 0.245, and 0.195, respectively.The weight values of "layout design," "building materials," and "behavior" are not so high, 0.041, 0.058, and 0.102, respectively.The top four indicators are "clean energy utilization" (0.152), "exterior wall insulation system" (0.090), "window performance" (0.088) and "electricity consumption" (0.084), accounting for more than half of the total weight, are important factors influencing the clean energy utilization of rural residential buildings.
• From the case study, the average LCI of unmodified buildings is 2.53, and the average LCI of modified buildings is 4.28.The main reason for the higher level of low-carbonization of clean energy utilization in modified rural residential buildings is the increased use of renewable energy sources such as electricity, biomass, and solar energy through the modification of both energy utilization and building envelope structure.Then from the key influencing factors, "clean energy utilization rate," "proportion of electricity consumption," "exterior wall insulation system," and "window performance" are the key influencing factors for the low carbonization level of clean energy utilization in a rural residential building.

Upcoming works
Driven by the background of "carbon peaking and carbon neutrality", there are several other issues that deserve future research in order to further accelerate the low-carbon transformation of rural residential buildings in China.Firstly, the development of a low-carbon evaluation system for rural residential buildings should be continued to provide a basis for determining the decarbonization path of rural residential buildings in China.Then, in terms of methodology, this study has constructed a low-carbon evaluation system for rural residential buildings through index optimization and AHP method, but considering the cost and difficulty of obtaining index data, individual indexes are not retained, such as "heat transfer performance" in "envelope structure".For example, "heat transfer performance" in "envelope structure" also has a certain influence on the evaluation of low carbonization.Finally, in the case study, this study only analyzes the current situation of the research subjects, but because of the wide distribution of rural areas in China and the great differences in rural conditions in different regions, the systematic evaluation should be discussed in a differentiated manner, so as to improve the evaluation of low carbonization of rural residential buildings with better guidance.

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I G U R E 1 Low-carbon index and average of each farm household.
Evaluation index factors of clean energy decarbonization of residential buildings.
TA B L E 1 List of AHP judgment matrix.
TA B L E 2 Weights of the primary and secondary indexes.Classification of evaluation levels.
TA B L E 3 Evaluation results of the primary and secondary indexes.
TA B L E 5