1 Introduction

With the continuous improvement of China's urbanization level, the construction industry has played a crucial role in enhancing urban infrastructure. However, it has also brought about an increasingly severe issue of energy consumption. Therefore, as a key industry focused on energy conservation and emissions reduction, the construction industry in China urgently needs to transition from being resource-consuming to resource-saving, especially in the context of China's goals of peak carbon emissions and carbon neutrality [1]. Green buildings, as one of the applications of the low-carbon concept in the construction field, have been developing in the Chinese construction industry for over two decades [2]. During this time, relevant design standards, evaluation systems, and other aspects have been established. However, the focus has primarily been on the design stage. Furthermore, due to the long construction and operation periods of building projects, there are still significant research gaps in the comprehensive life cycle evaluation of green buildings. These gaps include insufficient tracking and evaluation throughout the whole life cycle, lack of unified evaluation standards for green buildings, and a lack of evaluation and analysis from the perspective of carbon emissions [3].

This study adopts a whole life cycle evaluation perspective and focuses on relevant indicators for building carbon emissions. It establishes a conceptual model for the low-carbon level of green buildings and conducts a comprehensive evaluation analysis that integrates multiple dimensions. Subsequently, grey relational analysis and the analytic hierarchy process (AHP) are employed to rank and evaluate green building projects. A comparative analysis is conducted with the current prevailing green building evaluation methods, aiming to explore new perspectives and dimensions for the low-carbon evaluation of green buildings. The study also discusses strategies for emphasizing low-carbon considerations in future green building construction processes. The specific research path is illustrated in Fig. 1:

Fig. 1
figure 1

Research pathway

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The innovation of this study is pay more attention on the process. Compared to traditional green building assessments, this study focuses on the low-carbon performance of green buildings from multiple dimensions and over a longer time span. It also proposes the importance of green management methods. At the same time, by emphasizing the process and strengthening the dynamic control of the whole life cycle, the low-carbon performance of green buildings can be effectively improved, which is the significance of this study.

2 Theory and methods

2.1 The whole life cycle theory

Life Cycle Assessment (LCA) is a method for evaluating the environmental burden and resource consumption of a product through quantitative analysis. It covers the whole life cycle of a product, from raw material production to disposal, and is one of the application areas of the Whole Life Cycle Theory [4]. LCA should follow systemic principles and consider the entire process from the beginning of production to final disposal of a product. It should provide specific descriptions of the time frame, factors, and adopt transparent and standardized methods [5, 6]. LCA not only evaluates the resource consumption and environmental impacts associated with a product throughout its life cycle but also enables quantitative analysis of the current product's environmental impacts. LCA research in China has been applied to various industrial and construction products, providing a rich theoretical foundation for the evaluation and research in this field.

Research in the Chinese construction industry on life cycle assessment is primarily conducted from the perspective of building energy and CO2 emissions analysis [7]. Currently, the relevant studies in the field of construction based on the LCA theory can be categorized into two types: the first type focuses on partial research, including building materials, building components, etc. [8]. The second type involves holistic research on buildings [9]. Researchers such as Zheng Yanming and Li Qiming have applied LCA to real estate development management, analyzing the potential environmental impacts of the construction and development process of buildings based on the LCA theory. They have established a management model that facilitates green development in construction projects [10]. Jun W has proposed the strategy of "lifecycle-oriented green building design" based on LCA theory, highlighting the issues to consider when applying LCA theory to the process of architectural design [11]. Gong Zhiqi and Zhang Zhihui have also used LCA theory to study the physical and environmental conditions in the life cycle of cement [12, [13]. The object of life cycle assessment is a product system, which can be a tangible material product, a production process, or even an activity, representing intangible products [14].

In the 1990s, the International Society of Environmental Toxicology and Chemistry (SETAC) held the first international seminar on whole Life Cycle Assessment and proposed the concept of Life Cycle Assessment (LCA) for the first time. Then in 1997, the International Organization for Standardization formulated the principles and framework of life cycle assessment on the basis of summarizing the world's advanced environmental management experience. At the same time, the United States developed the Building for Environmental and Economic Sustainability (BEES) computer evaluation software system, which provides a systematic analysis tool for rational selection of environmentally and economically balanced building products. In the construction area, experts and scholars from different countries have also carried out extensive discussions. For example, Ignacio Zabalza Bribian from Spain conducted a simplified study on the life cycle assessment structure of buildings [15], and Daniel Kellenberger from New Zealand conducts in-depth research on relevant building components during the building life cycle [16], Kanghee Lee in South Korea conducted a life cycle assessment on a construction project [17].

The application of the life cycle theory is diverse, spanning from the industrial sector to the construction industry, providing sample references for the establishment of evaluation systems and selection of indicators [18]. By reviewing the literature, it is found that the relevant research on the life cycle of the construction industry includes the entire process of building construction or the emphasis on green concepts during the design stage. However, there is still a lack of research on the evaluation of green buildings throughout their life cycle. LCA, as a comprehensive and systematic evaluation method, not only allows for quantitative assessment of green buildings but also enables the comparison of different building solutions or construction methods in terms of their relative advantages and disadvantages. It can also analyze the economic aspects of green buildings, providing a more comprehensive evaluation from multiple perspectives [19, 20]. Based on the life cycle theory, this study defines the full life cycle of a building into five stages: pre-construction preparation, design, construction and assembly, operation and maintenance, and demolition [21], as shown in Fig. 2.

Fig. 2
figure 2

Evaluation dimensions based on the life cycle theory

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2.2 Green building evaluation

Since the 1990s, countries around the world have developed various types of green building evaluation systems, making significant contributions to the practice and promotion of green buildings [22,23,24,25]. Examples include the BREEAM evaluation system in the UK, the LEED green building rating system in the US, the CASBEE comprehensive environmental performance evaluation system in Japan, the NABERS building environmental rating system in Australia, EcoProfile in Norway, ESCALE in France [26], among others. One of the representative green building evaluation systems in China is the Green Olympic Building Assessment System, which is based on the standards of Japan's CASBEE (The Japanese comprehensive environmental performance evaluation system). It follows the guiding principles of whole-process monitoring and phased evaluation, and establishes an evaluation index system for decision-making, design, construction, and acceptance and operation management stages. Building on these rich foundations of green building evaluation systems, researchers have conducted studies on building energy consumption analysis and assessment methods based on the LCA theory using different green building evaluation criteria [27,28,29].

Although the concept of green buildings and their related evaluation systems has been relatively recent in China, they serve as the theoretical foundation for the development of green buildings in the country. With continuous improvements in the theoretical system and enriched practical research, China has established corresponding green building evaluation standards in different regions, resulting in regional differences. Currently, there is still a lack of systematic research in the academic community on the basic principles of comprehensive evaluation of low-carbon buildings and the development of low-carbon building evaluation systems from multiple perspectives.To address these gaps, this study relies on the research foundation of LCA in the field of construction to conduct a comprehensive life cycle assessment of green buildings, focusing on the concepts and scope of green buildings.

2.3 Grey relational analysis-hierarchical analysis method

In 1982, Professor Deng Julong proposed the Grey System Theory, which has since been widely applied in various industries [30]. This theory has found extensive applications not only in engineering control and agricultural systems but has also provided important research methods for disciplines such as management and forecasting [31]. As the Grey System Theory has developed to the present day, it has yielded abundant application achievements and formed a relatively complete scientific system, including specific components such as Grey Correlation Analysis, Grey Cluster Evaluation, Grey System Prediction Models, and more [32, 33].

The representation of grey color in the grey system theory is based on different levels of information clarity in control theory. For example, "black" represents completely missing or unknown information, "white" represents fully known information, and "grey" lies in between, indicating incomplete or ambiguous information. Based on this, these three colors correspond to three types of systems: black system, grey system, and white system. In general, the information we deal with is mostly incomplete, so we use mathematical methods to quantize grey data and transform it into white data. This process is known as data whitening [34].

2.3.1 Grey correlation degree

Grey correlation analysis is one of the fundamental components of grey system theory. Its advantage lies in its less stringent requirement for sample size and the absence of the need to analyze in advance whether there are typical distribution patterns in the samples. It is one of the mainstream methods in the fuzzy theory system due to its reasonable computational complexity [35]. Grey correlation analysis measures the degree of similarity between comparative sequences and optimal sequences based on the magnitude of grey correlation coefficients. A higher grey correlation coefficient indicates a closer proximity to the optimal sequence. In this study, based on the theory of the whole life cycle, the specific steps for conducting grey correlation analysis in the low-carbon evaluation of green buildings are as follows:

1) Derive the data column for grey correlation degree. Assuming the evaluation system has m sample items and each experimental plan has n optimization indicators, the matrix representing the evaluation indicators can be expressed as formula 1:

$$ X{ = }\left( {x_{ij} } \right)_{n \times m} = \left( {\begin{array}{*{20}l} {x_{11} } \hfill & {\quad x_{12} } \hfill & {\quad \cdots } \hfill & {\quad x_{1m} } \hfill \\ {x_{21} } \hfill & {\quad x_{22} } \hfill & {\quad \cdots } \hfill & {\quad x_{2m} } \hfill \\ \vdots \hfill & {\quad \vdots } \hfill & {\quad \vdots } \hfill & {\quad \vdots } \hfill \\ {x_{n1} } \hfill & {\quad x_{n2} } \hfill & {\quad \cdots } \hfill & {\quad x_{nm} } \hfill \\ \end{array} } \right)\left( {i = 1,2, \ldots ,n;\;j = 1,2, \ldots ,m} \right) $$
(1)

Select the maximum values of each evaluation indicator to construct the reference sequence for the ideal solution (as formula 2):

$$ X_{0} \left( k \right) = \left[ {X_{0} \left( 1 \right),X_{0} \left( 2 \right), \ldots ,X_{0} \left( n \right)} \right] $$
(2)

The remaining values form the comparative sequence (as formula 3):

$$ X_{i} (k) = \left( {X_{1} ,X_{2} , \ldots ,X_{m} } \right){ = }\left( {\begin{array}{*{20}l} {x_{1} (1)} \hfill & {\quad x_{2} (1)} \hfill & {\quad \cdots } \hfill & {\quad x_{m} (1)} \hfill \\ {x_{1} (2)} \hfill & {\quad x_{2} (2)} \hfill & {\quad \cdots } \hfill & {\quad x_{m} (2)} \hfill \\ \vdots \hfill & {\quad \vdots } \hfill & {\quad \vdots } \hfill & {\quad \vdots } \hfill \\ {x_{1} (n)} \hfill & {\quad x_{2} (n)} \hfill & {\quad \cdots } \hfill & {\quad x_{m} (n)} \hfill \\ \end{array} } \right) $$
(3)

2) Data normalization. To avoid inaccuracies in calculations due to data differences, the original data is first normalized using formula 4 to obtain normalized indicators (as formula 4):

$$ x_{ij} = \left\{ {\begin{array}{*{20}c} {\frac{{x_{ij} - \min_{j} \left\{ {x_{ij} } \right\}}}{{\max_{j} \left\{ {x_{ij} } \right\} - \min_{j} \left\{ {x_{ij} } \right\}}}\left( + \right)} \\ {\frac{{\max_{j} \left\{ {x_{ij} } \right\} - x_{ij} }}{{\max_{j} \left\{ {x_{ij} } \right\} - \min_{j} \left\{ {x_{ij} } \right\}}}\left( - \right)} \\ \end{array} } \right. $$
(4)

3) Calculate the correlation coefficient. The correlation coefficient (\(\zeta_{0i} (k)\)) between the k-th reference sequence and the comparative sequence is calculated using the formula 5:

$$ \zeta_{0i} (k) = \frac{{\Delta_{\min } + \zeta \Delta_{\max } }}{{\Delta_{0i} \left( k \right) + \zeta \Delta_{\max } }} $$
(5)

In the formula, \(\zeta\) represents the resolution coefficient, which is typically chosen between 0.1 and 0.5. A smaller value reflects greater differentiation between correlation coefficients. In this study, a value of \(\zeta { = }0.5\) was selected after referring to relevant literature [36, 37]. \(\Delta_{\min }\) denotes the minimum absolute difference between X0 and Xi, \(\Delta_{\max }\) represents the maximum absolute difference between X0 and Xi, and \(\Delta_{oi} (k)\) denotes the absolute difference between X0 and Xi. \(\Delta_{\min }\), \(\Delta_{\max }\), and \(\Delta_{oi} (k)\) are calculated using formulas 6, 7, and 8, respectively.

$$ \Delta_{\min } = \mathop {\min }\limits_{i} \mathop {\min }\limits_{k} \left| {X_{0} (k) - X_{i} (k)} \right| $$
(6)
$$ \Delta_{\max } = \mathop {\max }\limits_{i} \mathop {\max }\limits_{k} \left| {X_{0} (k) - X_{i} (k)} \right| $$
(7)
$$ \Delta_{0i} \left( k \right) = \left| {X_{0} (k) - X_{i} (k)} \right| $$
(8)

4) Calculate the correlation degree. Each optimization indicator has a different impact on the system. The weighted average of the correlation coefficients is calculated using formula 9.

$$ r_{0i} = \frac{1}{n}\omega_{k} \sum\limits_{i = 1}^{n} {\zeta_{0i} (k)} $$
(9)

In the formula 9, \(\omega_{k}\) represents the weight of the k-th indicator, which is determined using the AHP method.

Grey correlation analysis can analyze the development trend of data based on the correlation among factors with a small number of samples, and it does not require the data to reflect the typical distribution law. Compared with other factor analysis methods with high data requirements, it is more suitable for the research of this paper.

2.3.2 AHP method

The AHP method is a systematic analysis method proposed by American operations researcher T.L. Saaty in the 1970s [38]. It involves establishing a hierarchical structure of multiple levels, combining expert ratings of various indicators, constructing pairwise comparison matrices based on the relative importance of each pair of indicators, and calculating the weights of each level of indicators [39]. The specific steps are as follows:

  1. (1)

    Construct a hierarchical structure model following the format of goal level, criterion level, and alternative level. In this structure, factors at each level are subordinate to factors in the higher level and, at the same time, determine factors in the lower level.

  2. (2)

    Construct judgment matrices. The judgment matrix \(A = (a_{ij} )_{n \times n} (i,j = 1,2, \cdots ,n)\) is constructed based on the degree of importance (as formula 10), using a 1 to 9 scale determined by the experts (Table 1). It is calculated through the expert scoring method.

    $$ A = (a_{ij} )_{n \times n} = \left( {\begin{array}{*{20}c} {a_{11} } & {a_{12} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} \cdots } & {a_{1n} } \\ \begin{gathered} a_{21} \hfill \\ {\kern 1pt} {\kern 1pt} {\kern 1pt} \vdots \hfill \\ \end{gathered} & \begin{gathered} a_{22} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} \cdots \hfill \\ {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} \vdots \hfill \\ \end{gathered} & \begin{gathered} a_{2n} \hfill \\ {\kern 1pt} {\kern 1pt} {\kern 1pt} \vdots \hfill \\ \end{gathered} \\ {a_{n1} } & {a_{n2} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} {\kern 1pt} \cdots } & {a_{nn} } \\ \end{array} } \right) $$
    (10)
    Table 1 1–9 Evaluation scale and its meaning
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  3. (3)

    Calculate the matrix weights. Normalize each column vector of matrix \(A = (a_{ij} )_{n \times n}\) as shown in formula 11:

    $$ \omega_{ij} = \frac{{a_{ij} }}{{\sum\nolimits_{i = 1}^{n} {a_{ij} } }} $$
    (11)

Sum the rows of \(\omega_{ij}\) as shown in formula 12:

$$ \omega_{i} = \sum\limits_{j = 1}^{n} {\omega_{ij} } $$
(12)

Normalize \(\omega_{i}\) by dividing each element by the sum as shown in formula 13:

$$ \omega_{i} = \frac{{\omega_{i} }}{{\sum\nolimits_{i = 1}^{n} {\omega_{i} } }} $$
(13)

Obtain the eigenvector \(\omega = \left[ {\omega_{1} ,\omega_{2} , \cdots ,\omega_{n} } \right]^{{\text{T}}}\). Calculate the maximum eigenvalue \(\lambda_{\max }\) of the judgment matrix using formula 14:

$$ \lambda_{\max } = \sum\limits_{i = 1}^{n} {\frac{{(A\omega )_{i} }}{{n\omega_{i} }}} $$
(14)

In this formula 14, \(\omega\) represents the normalized eigenvector corresponding to the eigenvalue \(\lambda_{\max }\). \(\omega_{i}\) represents the weight values of each element in the order of importance.

4) Consistency check. Perform a consistency check by calculating the corresponding eigenvector for each pairwise judgment matrix and using consistency standards and consistency ratios. The consistency index C.I. is determined by formula 15:

$$ C.I. = \frac{{\lambda_{\max } - n}}{n - 1} $$
(15)

The consistency ratio C.R. is determined by formula 16. A higher value of C.I. indicates poorer consistency in the matrix. If the matrix fails the consistency test, it needs to be adjusted and rechecked. In the formula, R.I. represents the random index, which can be found in Table 2. When the consistency ratio \(C.R. < 0.1\) is satisfied, the matrix passes the consistency test. If it does not satisfy the criterion, it means that the matrix fails the consistency test and needs to be adjusted until it passes. When the judgment matrix is of order less than 3, it is considered to have perfect consistency, so both C.I. and C.R. are 0.

$$ C.R. = \frac{C.I.}{{R.I.}} $$
(16)
Table 2 Average standard values of random consistency indicators
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The characteristic of analytic hierarchy process is that it can model the decision-making process for decision analysts in a systematic way. Qualitative indicators become quantitative indicators after mathematical calculation, and the analysis ideas are clearer and more logical. Secondly, the method does not require much quantitative data in the analysis, but requires the relationship between qualitative factors to be fully clear and specific. This method is especially suitable for the analysis, research and decision-making of complex problems, and can carry out scientific analysis of multi-criteria and multi-objective decision-making problems. Therefore, the AHP method is scientific and applicable to determine the weight in this paper.

3 Construction of a low-carbon evaluation model for green buildings based on the life cycle theory

3.1 Selection of evaluation indicators

3.1.1 Principles for selecting indicators for the life cycle of green buildings

  1. (1)

    Principle of scientific rigor

    Scientific rigor is the primary principle in establishing an indicator system for evaluating green buildings. The evaluation process should be based on scientific methods, taking into account the actual situation and literature review. It is important to ensure the authenticity and reliability of the indicators to guarantee the scientific and reliable nature of the research findings.

  2. (2)

    Principle of data accessibility

    The evaluation work involves a combination of quantitative and qualitative research methods. Considering the accessibility of data, some indicators can be obtained from relevant documents and parameters, while others may require scientific scoring by relevant personnel based on the actual situation. Therefore, when setting indicators, it is important to specify the data sources or scoring criteria to ensure objectivity of the results.

  3. (3)

    3) Principle of life cycle assessment

    The life cycle principle is crucial throughout the indicator selection process. Currently, evaluation systems primarily focus on the design and operational stages of buildings. However, each stage of a building's life cycle affects its level of low-carbon performance. Therefore, when selecting evaluation indicators, it is important to consider the whole life cycle perspective and incorporate all stages into the evaluation and analysis work.

3.1.2 Life cycle low-carbon analysis of green buildings

  1. (1)

    Preparatory stage

    In the pre-project phase, the optimization of resource allocation has an impact on the carbon emissions of the whole life cycle of green buildings. Factors such as land resource planning and the formulation of green management measures can affect the low-carbon performance of green buildings throughout their life cycle. Therefore, in the preparatory stage, optimization should be carried out in these areas, with a focus on efficiency standards and technological emissions reduction [40].

  2. (2)

    Design stage

    Low-carbon design requires the collaboration of multiple disciplines and stakeholders. In the early stages of design, architects should engage in extensive communication with clients and various professionals to ensure effective consensus and goal-setting regarding low-carbon objectives. During the preliminary design process, integration of disciplines such as structure, HVAC (heating, ventilation, and air conditioning), plumbing, electrical, and interior design is essential. Each discipline should engage in comprehensive communication and quantifiable estimation of carbon reduction techniques. Examples include improving the thermal insulation performance of building envelope, enhancing the air tightness of doors and windows, emphasizing natural lighting and color utilization in interior design, applying energy-efficient lighting fixtures and implementing appropriate lighting design, utilizing energy-efficient electrical appliances with smart controls, focusing on passive design strategies such as natural ventilation, exploring innovative technologies like radiant cooling and solar water heating, and actively adopting green building materials that incorporate carbon sequestration and emissions reduction innovations.

  3. (3)

    Construction stage

    According to Tang et al., management practices have a significant impact on carbon emissions during the construction phase. By selecting appropriate management strategies, it is possible to reduce greenhouse gas emissions without increasing project costs or delaying project schedules, as suggested by Tang and colleagues [41]. Qin et al. proposed several aspects of low-carbon construction, including material and equipment selection, construction transportation, on-site arrangement, construction processes, and construction management [42]. Among these aspects, material selection is a critical control point, especially for decorative and finishing construction that involves various categories of building materials. In practice, it is important to select material suppliers that can meet design requirements while prioritizing green building materials and utilizing local or short-distance transportation for materials. This approach maximizes the reduction of direct and indirect carbon dioxide emissions associated with materials, achieving a balance between design effectiveness and low-carbon goals. Additionally, it is essential to minimize material waste during construction and continuously explore and improve construction techniques and processes.

  4. (4)

    Operation and maintenance stage

    Carbon emissions during the operational stage of a building primarily result from direct or indirect emissions generated by various building systems. Compared to the design and construction stages, the operational stage has a longer observation timeframe, making it the most critical phase in the whole life-cycle of a building in terms of carbon emissions [43]. Research has shown that carbon emissions during the operational stage can account for a significant portion of the total emissions. Among these emissions, 40% are related to electricity consumption, such as HVAC (heating, ventilation, and air conditioning) systems, lighting, and elevator systems. Although the design stage provides the foundation for operations and maintenance, optimizing the use of building products and equipment can greatly reduce carbon emissions over an extended period. Therefore, it is important to prioritize operations and maintenance management and personnel structure. Establishing comprehensive management and training systems, implementing hierarchical responsibilities, and promptly and accurately recording operational data are crucial. Subsequently, emphasis should be placed on data analysis and management iteration, enabling low-carbon reduction throughout the whole life-cycle of the building.

  5. (5)

    Demolition stage

    Due to the relatively short duration of green building assessments in China, the corresponding sample observation period has not yet been completed. Therefore, there is limited carbon tracking data available for the demolition stage of buildings [44]. However, current research indicates that carbon emissions during the demolition and cleaning phase primarily stem from the use of large construction vehicles that consume energy during the demolition process. Additionally, emissions are generated during the transportation of debris and building materials after the demolition is completed. Furthermore, the incineration of building waste can also contribute to carbon emissions.

3.1.3 Analysis of green building evaluation dimensions

According to the relevant standards for green buildings in China, the current evaluation of green buildings mainly focuses on five dimensions: resource utilization, environmental protection, comfort and livability, low-carbon economic benefits, and building sustainability. The specific evaluation content within these five dimensions includes:

  1. (1)

    Resource utilization

    Recyclable material usage: Refers to the use of building materials processed from recycled waste that meets construction standards. Increasing the proportion of recyclable materials in material consumption can not only reduce waste and be environmentally friendly but also save building material consumption [45, [46].

    Transportation distance of major building materials: Based on optimizing resource allocation, for large quantities of major materials with a high cost proportion, it is important to select suppliers appropriately to reduce pollution and carbon emissions during transportation and lower transportation costs.

    Reused material turnover Rate: Reused materials can be reused without undergoing complex procedures, as long as they pass simple processing and inspection. The turnover rate of recyclable materials indicates the environmental and carbon reduction performance of the building, as well as the cost-effectiveness in the process, and reflects the value generated during the demolition phase of the building.

    Construction site layout: During the construction phase, the proper layout of the construction site can have a positive impact on the low-carbon emissions of green building projects. It plays an important role throughout the building's lifecycle, reflecting the goals of conserving land resources, rational layout, and reducing losses and waste [47].

    Water-saving measures evaluation: Water-saving measures are analyzed from a lifecycle perspective, including the emphasis on water-saving paths and equipment selection during the design phase, various water-saving measures taken during the construction phase, and water-saving practices during building operation and maintenance.

  2. (2)

    Environmental protection

    Implementation of Green Management System: Developing corresponding green management systems for each phase of the construction life cycle is an important guarantee for the successful implementation of green buildings, providing scientific management solutions for carbon emissions throughout the process [48].

    Dust control: Construction activities can have an impact on the surrounding environment, and dust pollution is a significant contributor to urban air pollution. Therefore, attention should be paid to the treatment of building dust in the evaluation of green buildings.

    Utilization of natural resources: In new construction, renovation, and expansion projects, the underground structures and existing vegetation may be affected by construction activities. Therefore, during the architectural design phase, the utilization of natural resources should be considered to minimize the damage to existing resources.

  3. (3)

    Comfortable and livable environment

    Green space ratio: The factor referring to the current green building evaluation standards of China.

    Utilization of natural ventilation: During the design phase, it is important to consider parameters such as building orientation, layout, and window area to maximize the harmony between the building and nature. By utilizing natural resources effectively, including natural ventilation, it is possible to reduce energy consumption during the building's use phase.

    Daylighting level of buildings: The factor mainly focuses on the utilization of natural light during the design phase and the use of high-efficiency and energy-saving lighting sources during the building's operation.

  4. (4)

    Low-carbon economic benefits

    Carbon emissions reduction rate during operation: Research on the carbon emissions throughout the building's life cycle indicates that operational carbon emissions account for more than 35%. Therefore, reducing carbon emissions during the operational phase is crucial for assessing the overall low-carbon performance of buildings and offers significant potential for emissions reduction.

    Incremental benefits of green technology measures: Including economic benefits, environmental benefits and social benefits..

  5. (5)

    Building sustainability

    Intelligent operation and maintenance of buildings: Common smart operation measures include energy management systems, air quality monitoring systems, water quality monitoring systems, and integration with smart city services.

    Utilization rate of renewable energy: Refers to the proportion of renewable energy such as wind and solar energy instead of traditional energy..

    Building adaptability: The adaptability of buildings during the later maintenance phase should be considered in architectural design. This improves the efficiency of renovation, expansion, or space utilization. Compared to ordinary buildings, adaptable buildings are better equipped to accommodate changes in functional usage [49, [50].

3.2 Construction of evaluation model

3.2.1 Construction of indicator system

Based on the analysis of the above dimensions and in combination with relevant indicators, a comprehensive life cycle green building low-carbon evaluation system is established, as shown in Table 3.

Table 3 Indicator system for the life cycle low-carbon evaluation model of green buildings
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3.2.2 The determination of indicator weights

Based on the constructed evaluation indicator system described above, let's assume that the first-level indicator set is denoted as \(X = \left\{ {X_{i} \left| {i = 1,2, \ldots ,m} \right.} \right\}\), and the weights of indicator Xi are described as \(\omega_{i} = \left\{ {i = 1,2, \ldots ,m} \right\}\). The second-level indicator set is described as \(X_{i} = \left\{ {X_{i1} ,X_{i2} , \cdots ,X_{ij} } \right\}\), and the weights for different indicators are described as \(\omega_{ij}\).

The green building low-carbon evaluation model established in this paper adopts the AHP method through expert surveys to establish a hierarchical weight system for the indicators at the first and second levels. The overall weight distribution for each indicator in the evaluation system is obtained by multiplying the weights of the two levels. The specific calculation results for the weight distribution are shown in Table 4.

Table 4 Weight distribution of first-level and second-level indicators for low-carbon evaluation of construction Projects
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4 Empirical case study

4.1 Project overview

In order to further validate the applicability of the above evaluation indicators in the low-carbon assessment of green buildings, it is necessary to collect project sample data for comparative testing. The samples are selected based on the principles of scientificity and consistency. Scientificity refers to selecting a reasonable sample size and collecting objective and comprehensive data, while consistency refers to maintaining consistency in the time dimension and objective conditions of the samples.

Considering the availability of data, the stability of green building evaluation from the time dimension and the standards for green building evaluation in different regions of China, this paper mainly selects representative commercial building cases to carry out research, and also considers the differences and connections between East China and South China. Four representative commercial building projects in Guangzhou of Guangdong province, Shenzhen of Guangdong province and Hangzhou of Zhejiang province were selected.

In this study, the expert scoring method was used to collect data on the above indicators from four different types of green building projects. Based on the satisfaction level of each evaluation indicator, four levels were set: Excellent, Good, Fair, and Poor. The corresponding scores are presented in Table 5.

Table 5 Evaluation score setting
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Table 6 presents the basic profiles of four typical projects for the green building assessment in this study.

Table 6 Basic information of sample projects for green building assessment
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4.2 Application of green building low-carbon evaluation based on whole lifecycle theory

The green building low-carbon evaluation based on whole life cycle theory includes a total of 16 indicators. Based on the scoring of these indicators for the four projects outlined in the previous section, a 16 × 4 indicator matrix X is established using the grey relational analysis method. The reference sequence is constructed using the maximum values of each evaluation indicator. The original data is normalized to obtain matrix \(X^{\prime}\). According to Formula 4, the correlation coefficient matrix between the reference sequence and the comparison sequence is calculated as \(X^{\prime\prime}\).

$$ X = \left( {\begin{array}{*{20}c} 6 & 4 & 6 & 4 \\ 7 & 7 & 5 & 8 \\ 8 & 5 & 9 & 6 \\ 6 & 9 & 4 & 8 \\ 9 & 9 & 9 & 6 \\ 5 & 4 & 9 & 6 \\ 8 & 4 & 6 & 5 \\ 7 & 6 & 9 & 4 \\ 9 & 8 & 8 & 6 \\ 5 & 9 & 7 & 4 \\ 6 & 7 & 6 & 8 \\ 9 & 4 & 5 & 4 \\ 9 & 5 & 8 & 6 \\ 5 & 7 & 9 & 6 \\ 7 & 7 & 7 & 6 \\ 5 & 8 & 6 & 9 \\ \end{array} } \right)X^{\prime} = \left( {\begin{array}{*{20}c} {0.25} & 0 & {0.4} & 0 \\ {0.5} & {0.6} & {0.2} & {0.8} \\ {0.75} & {0.2} & 1 & {0.4} \\ {0.25} & 1 & 0 & {0.8} \\ 1 & 1 & 1 & {0.4} \\ 0 & 0 & 1 & {0.4} \\ {0.75} & 0 & {0.4} & {0.2} \\ {0.5} & {0.4} & 1 & 0 \\ 1 & {0.8} & {0.8} & {0.4} \\ 0 & 1 & {0.6} & 0 \\ {0.25} & {0.6} & {0.4} & {0.8} \\ 1 & 0 & {0.20} & 0 \\ 1 & {0.2} & {0.8} & {0.4} \\ 0 & {0.6} & 1 & {0.4} \\ {0.5} & {0.6} & {0.6} & {0.4} \\ 0 & {0.8} & {0.4} & 1 \\ \end{array} 1} \right)X^{\prime\prime} = \left( {\begin{array}{*{20}c} {0.25} & {0.14} & {0.24} & {0.37} \\ {0.15} & {0.14} & {0.22} & {0.28} \\ {0.11} & {0.14} & {0.21} & {0.17} \\ {0.08} & {0.14} & {0.24} & {0.13} \\ {0.23} & {0.08} & {0.20} & {0.37} \\ {0.25} & {0.14} & {0.14} & {0.24} \\ {0.11} & {0.08} & {0.24} & {0.14} \\ {0.11} & {0.14} & {0.13} & {0.17} \\ {0.25} & {0.06} & {0.21} & {0.36} \\ {0.15} & {0.06} & {0.24} & {0.20} \\ {0.25} & {0.14} & {0.10} & {0.19} \\ {0.15} & {0.14} & {0.10} & {0.18} \\ {0.25} & {0.05} & {0.24} & {0.37} \\ {0.25} & {0.06} & {0.13} & {0.28} \\ {0.25} & {0.08} & {0.10} & {0.22} \\ {0.25} & {0.14} & {0.08} & {0.19} \\ \end{array} } \right) $$

Finally, combining the weight coefficients of each indicator, the grey correlation degree of each solution is calculated using formula 9, and they are ranked accordingly as shown in Table 7.

Table 7 Grey correlation degree and ranking of each test scheme
Full size table

4.3 Analysis of application effects

In order to test the performance of the proposed model in improving the living environment and enhancing the low-carbon evaluation level of green building design, a comparative experiment was conducted. The evaluation criteria for the experiment were revised based on the national standard "Green Building Evaluation Standard" GB/T 50378–2006, in collaboration with relevant organizations. The evaluation of green buildings was based on factors such as land and outdoor environment conservation, energy and energy utilization, water and water resource utilization, materials and material resource utilization, and indoor environmental quality. Four evaluation levels were used: excellent, good, fair, and poor. Finally, to quantitatively analyze the performance, different evaluation methods were employed, resulting in a comparison of low-carbon evaluations for the four green building projects, as shown in Table 8:

Table 8 Comparison of low-carbon evaluation results for green buildings based on the whole life cycle
Full size table

Analysis from Table 8 shows that the evaluation results obtained through the method proposed in this paper are consistent with the actual results of mainstream green building evaluations. The evaluation rankings of the four case projects using the method in this paper align with the actual green building evaluation scores. On the other hand, when using the methods from Literature I [51] and Literature II [52] for calculation, there are certain differences compared to the actual green building evaluation scores. This indicates that the evaluation results obtained through the method proposed in this paper are accurate and suitable for practical application. From another perspective, this method provides an evaluation of the low-carbon performance of green buildings.

5 Suggestion and conclusion

5.1 Suggestion

  1. (1)

    Focus on the early stage

    The early preparation and design stage of green buildings lay the foundation for the low-carbon management of the building's whole life-cycle. Therefore, it is important to emphasize low-carbon management in the early stage of green building projects. This can be achieved by establishing a scientific and standardized project initiation process and developing a low-carbon indicator system to guide energy-saving and emissions reduction efforts throughout the lifecycle of green buildings.

  2. (2)

    Carbon reduction as the core

    In various existing green building evaluation systems, different evaluation criteria have their own emphasis. However, in the context of China's dual carbon goals, the evaluation of green buildings that are tailored to China's urban development should prioritize the focus on carbon emissions. Carbon reduction should be the core criterion guiding all management measures.

5.2 Conclusion

Through a comprehensive analysis of domestic and international typical low-carbon building evaluation systems, and by systematically studying the key elements within the evaluation system frameworks, this research has developed a comprehensive low-carbon evaluation system for the whole life-cycle of green buildings based on grey relational analysis and AHP. The evaluation system theoretical model is divided into two levels: "system framework" and "evaluation dimensions". At the system framework level, the study discusses the rules and elements of model construction, including indicator sets, weighting systems, rating benchmarks, and evaluation results. At the evaluation dimensions level, it explores the relevant evaluation indicators and processes for multiple stages throughout the lifecycle. The system framework and dimensions complement each other to form a comprehensive and integrated evaluation system.

This paper proposes a modeling method for the low-carbon evaluation of green buildings based on the dimensions of the whole life-cycle and conducts experimental comparisons. The research results show that the proposed evaluation approach can provide a scientific and objective comprehensive assessment of green buildings throughout their lifecycle. It can also suggest specific directions for resource conservation, maximizing land, water, and material savings to achieve environmental protection and pollution mitigation objectives. The evaluation results meet the current international standards for green buildings and provide accurate models and data for the design and evaluation of green buildings.