© W. Zhang, Published by EDP Sciences, 2025

1 Introduction

The assessment of industrial heritage reutilization value (AIHRV) aims to systematically analyze and quantify the value of industrial heritage (VIH) in various aspects, including culture, economy, environment and society [1–4], and to promote its effective protection and rational use. Firstly, it can excavate and pass on historical and cultural values [5], enabling the public to have a deeper understanding and sense of identity of industrial heritage and its backstory, and enhancing local identity. Secondly, from the economic point of view [6], the assessment can identify the potential of heritage reutilization, attract investment, create employment opportunities and promote local economic development. Environmentally, the reutilization of industrial heritage (RIH) can help save resources and reduce waste, promote sustainable development, and reduce the burden of new development projects on the environment. At the same time, it can also bring social benefits to the community by creating cultural spaces and activity venues [7], enhancing the quality of life of residents and community cohesion. Finally, a comprehensive assessment provides a scientific basis for the government and enterprises to make decisions, making them more targeted and feasible in planning and implementing projects. Therefore, the AIHRV is not only related to the protection and utilization of the heritage itself, but also the key to realizing the triple benefits of economy, society and environment.

The current state of research on the AIHRV has gradually gained attention from academia and society in recent years [8,9]. With the acceleration of global urbanization, many industrial heritages are facing abandonment or demolition. In order to protect these assets of historical and cultural values, scholars have conducted a large number of studies to explore the effectiveness and feasibility of their reutilization. Reference [10] discussed the evolution of the cultural heritage protection system on values, policies and practices and emphasized the tendency for industrial heritage to be redefined and managed. Reference [11] analyzed the application of sustainable assessment of built heritage for local industrial heritage, and the sustainability as the base is used to choose some indices. Reference [12] proposed an approach for assessing the value of industrial heritage using a Dempster–Shafer theory and stated that industrial heritage has a relationship with political, economic, cultural, social, scientific, technological, and architectural fields. In [13], heritage valuation and spatial compatibility are discussed using the analytic hierarchy process (AHP) for solving the multicriteria problem. Reference [14] used an improved entropy TOPSIS method for evaluating the adaptive reutilization potentiality of industrial heritage and computed the reutilization potentiality values of each hierarchical evaluation index. Reference [15] utilized a review to discuss the frameworks, methodologies, and assessment methods about the adaptive architectural heritage reutilization. For understanding the industrial heritage protection design of iterative reconstruction, reference [16] analyzed industrial heritage protection design through the GA optimization algorithm and iterative reconstruction. For explaining the importance and application value of big data on the industrial heritage planning and decision-making, reference [17] discussed the concept and application of industrial heritage planning and decision-making by big data theory. In [18], Greek industrial buildings are reused for special interest/alternative tourism. In [19], Egyptian National Museum is analyzed as an object to discuss heritage buildings reutilization assessment problem. Reference [20] introduced societal impacts of the restoration and renovation project of industrial heritage. Reference [21] took Romania as example to analyze reutilization models for technical and industrial heritage. The above analysis and discussion revolve around the reutilization of industrial heritage, but there are relatively few studies on the AIHRV, therefore, this paper proposes the reutilization value assessment method for industrial heritage based on the hierarchical analysis method.

The main contributions in this paper are concluded as follows:

• This paper proposed an indicator system for AIHRV, which can fully take into account 8 first-level indices and 24 second-level indices.

• This paper proposes the method of AIHRV based on an analytic hierarchy process, establishes the hierarchical structure model of AIHRV, constructs the judgment matrix of AIHRV indices, calculates the maximum eigenvalue (ME) of the judgment matrix by the square root method, and tests the consistency of the hierarchical single ranking and the hierarchical total ranking.

• Considering the subjective scoring method and the objective weight assessment method, the AIHRV level is formulated, and the correctness and validity of the proposed method are verified through case studies.

2 Index system for AIHRV

This paper proposes an index system for AIHRV, which is used to assess the various VIHs in the process of reutilization, including historical and cultural value, socio-economic value, environmental value, etc. The index system is intended to help decision makers better understand and assess the reutilization potential of industrial heritage, in order to promote the protection and reutilization of industrial heritage, to facilitate the inheritance of cultural values, and to maintain historical memory.

2.1 First-level indices for assessing the VIH

The AIHRV is a process of comprehensively considering various factors such as historical value, cultural value, social value, artistic value, economic value, environmental value, etc. By comprehensively analyzing these factors, the overall value of industrial heritage reutilization can be more accurately determined. This paper proposes the first-level indicators of AIHRV and their connotations as shown in Table 1.

2.2 Second-level indices for assessing the VIH

This paper proposes 24 comprehensive value evaluation indices for industrial heritage reuse based on the structure of eight different types of first-level indices at the decision-making guideline level, constituting a comprehensive value evaluation indices system of industrial heritage, and the system is oriented to the protection and utilization of industrial heritage, which takes into account the requirements of protection as well as the needs of utilization. 24 reutilization value evaluation indices and their definitions for industrial heritage are shown in Table 2.

3 Value assessment approach of industrial heritage reutilization based on the hierarchical analysis

3.1 Grade standard for assessing the VIH

The AIHRV needs to give the assessment scheme, and this paper constructs the AIHRV grade standard, as shown in Table 3, which sets five AIHRV grades, namely: poor (S < 1.5), poor (1.5 ≤ S < 2.5), average (2.5 ≤ S < 3.5), good (3.5 ≤ S < 4.5) and very good (S ≥ 4.5), with full 5 points, and their judgments correspond to no value, low value, medium value, valuable and very valuable.

By combining the grade standard for AIHRV and the expert scoring values of the second-level indices, the actual grade S(k) of AIHRV can be obtained, and the calculation formula is as follows:

$$S\left(k\right)={\displaystyle \sum _{i=1}^{N_{FI}}}{\displaystyle \sum _{j=1}^{N_{SI}}}{\omega }_{ij}\left(k\right)\times S_{ij}\left(k\right)$$(1)

where N_FI and N_SI are numbers for first-level indicators and second-level indicators.

3.2 Establishing a hierarchical model for assessing the VIH

The hierarchical structure of AIHRV proposed in this paper consists of a target layer, a middle layer (i.e., the first-level indices layer and a second-level indices layer) and a strategy layer, as shown in Figure 1. In this hierarchy, the target layer is “the AIHRV”; the intermediate level consists of 8 categories of first-level indices (i.e., B₁, B₂, …, B₇, B₈), 24 second-level indices (i.e., C₁₁, C₁₂, C₁₃, …, C₈₁, C₈₂, C₈₃) belonging to the first-level indices, and the strategy layer consists of D₁, D₂, D₃, D₄ and D₅.

Fig. 1 Hierarchical model for assessing the reutilization value.

3.3 Constructing index judgment matrix of for assessing the VIH

Constructing the index judgment matrix of industrial heritage value assessment is the information basis of hierarchical analysis method. Using the judgment matrix and the ranking method, the ranking of the importance of each strategy can be obtained. To reduce the difficulties of comparing factors with different properties and to improve accuracy, this paper utilizes a 9-level scale [22,23] used in the analytic hierarchy process for comparison of factors. The evaluation scale is shown in Table 4.

Through the two-by-two comparison of the industrial heritage value assessment indices of each layer forms its judgment matrix, which is shown in Table 5. In Table 5, S and L are the upper-level criteria and the lower-level criteria to which they belong (e.g., S is the first-level index B₁, and L is the second-level index C₁ to which belongs B₁), and a_ij is the result of the comparison of the importance of index i with that of index j. The value of a_ij is obtained according to the 9 importance levels and their values listed in the table, and its expression is as follows:

$$a_{ij}=\frac{1}{a_{ji}}.$$(2)

3.4 Computing the maximum eigenvalue of the judgment matrix

In order to carry out the consistency test for judgment matrix, it needs to calculate the ME of the judgment matrix. So, this paper uses the square root method to calculate the ME of the judgment matrix. Let the judgment matrix be M = (a_ij)_n×n, the specific calculation steps of the square root method for calculating the eigenvectors of the judgment matrix are as follows:

S1) Compute the product of the elements of each row in matrix:

$$m_i={\displaystyle \prod _{j=1}^n}{a}_{ij}\text{,}\begin{array}{c}\hfill \mathrm{}\hfill \end{array}\text{1,2,}\cdots \text{,3}$$(3)

S2) Calculate the nth root of m_i:

$${{\displaystyle \overline{w}}}_i=\sqrt[n]{m_i}\text{,}\begin{array}{c}\hfill \mathrm{}\hfill \end{array}\text{1,2,}\cdots \text{,3}$$(4)

S3) Normalize the vector ${\displaystyle \overline{w}}=\left({{\displaystyle \overline{w}}}_1,{{\displaystyle \overline{w}}}_2,\cdots {{\displaystyle \overline{w}}}_n\right)$:

$$\frac{1}{w_i}=\frac{1}{{{\displaystyle \overline{w}}}_i}\times {\displaystyle \sum _{i=1}^n}{{\displaystyle \overline{w}}}_k\text{,}\begin{array}{c}\hfill \mathrm{}\hfill \end{array}\text{1,2,}\cdots \text{,3}$$(5)

S4) Calculate the ME:

$${\lambda }_{\max }=\frac{1}{n}{\displaystyle \sum _{i=1}^n}\left(\frac{1}{w_i}\times {\displaystyle \sum _{j=1}^n}{a}_{ij}{w}_j\right).$$(6)

3.5 Consistency test for hierarchical single ordering and hierarchical total ordering

In order to verify whether the judgment matrix satisfies the consistency, this paper utilizes the consistency index [24] to test the judgment matrix. For a single level, the consistency index (CI) of the judgment matrix is defined as follows:

$$C{I}_i=\frac{{\lambda }_{\max }-n}{n-1}$$(7)

where is λ_max the ME; n is the order of the judgment matrix.

In order to overcome the influence of different scales, this paper adopts consistence ratio (CR) to characterize the consistency evaluation index, in which the consistency test is considered to be satisfied (i.e., judgment matrix A has consistency) if CR < 0.1. CR_i of the ith judgment matrix is calculated as follows:

$$C{R}_i=\frac{C{I}_i}{R{I}_i}=\frac{{\lambda }_{maxi}-n_i}{\left(n_i-1\right)\times R{I}_i}$$(8)

where RI is the random consistency index (RCI), and its value is shown in Table 6.

In order to evaluate the consistency of the hierarchical total ordering results, similar to the hierarchical single ordering, a consistency test is also required, then the CI, RI and CR_Totall of the hierarchical total ordering results can be calculated by the following formula:

$$CI={\displaystyle \sum _i^m}{w}_i\times C{I}_i$$(9)

$$RI={\displaystyle \sum _i^m}{w}_i\times R{I}_i$$(10)

$$C{R}_{Totall}=\frac{CI}{RI}=\frac{{\displaystyle \sum _i^m}{w}_i\times C{I}_i}{{\displaystyle \sum _i^m}{w}_i\times R{I}_i}$$(11)

where w_i is the factor weight of the first layer (i.e., first-level layer).

3.6 Flowchart for assessing RIH

In order to assess the reutilization value of industrial heritage using hierarchical analysis, the flowchart of this assessment process is given in Figure 2. Firstly, judgment matrices of the target level on the first-level indices (i.e., historical value, cultural value, social value, artistic value, economic value, environmental value and sustainable value) and the judgment matrices of the second-level indices (e.g., the judgment matrices of C₁₁, C₁₂ and C₁₃) are constructed. Secondly, the ME of each judgment matrix is calculated and the consistency of the judgment matrices is checked. Subsequently, the reutilization value of industrial heritage is assessed based on the scores of the secondary indices and their weights.

Fig. 2 Flowchart for assessing the reutilization value of industrial heritage.

4 Case studies

4.1 Constructing judgment matrices of first-level and second-level indices

This paper selects an industrial heritage to analyze the application of the proposed method. A renovation centered on a “culture+” strategy was carried out for this industrial heritage, preserving industrial relics such as watchtower, dome workshop, and red-brick factories without large-scale demolition, and forming the current industrial heritage reutilization.

Based on the nine-level scale method, we constructed the judgment matrix of first-level indices (shown in Tab. 7) and eight judgment matrices of second-level indices (shown in Tabs. 8–15) for the assessment of reutilization value of industrial heritage, and calculated the weights of each factor of the first-level indices and each factor of the second-level indices according to the hierarchical analysis method.

4.2 Calculating the ME of judgment matrix

In this paper, the ME of the judgment matrix is calculated according to the square root method, and the ME of the judgment matrix of the first-level indices and the judgment matrix of the second-level indices is shown in Figure 3, in which the ME of the judgment matrix A is 8.576, and the MEs of the judgment matrices of the eight second-level indices are 3.086, 3.004, 3.044, 3.004, 3.094, 3.086, 3.094 and 3.018.

Fig. 3 The MEs of judgment matrix for first-level and second-level indices.

4.3 Consistency test of judgement matrix

In order to verify whether the judgment matrix meets the consistency, this paper uses the consistency index to test the judgment matrix, then the hierarchical single sorting index CI and CR values of the first-level and second-level indices are shown in Figure 4. In Figure 4, the CR value of judgment matrix A is 0.05834, and the CR values of the eight second-level indices judgment matrices are 0.06315, 0.00318, 0.03799, 0.00318, 0.08105, 0.07394, 0.08105, and 0.00793, respectively. It can be seen that the hierarchical single-ordered consistency test CR values are all less than 0.1. Therefore, the judgment matrix of first-level indices and the judgment matrix of second-level indices constructed in this paper meet the consistency test requirements.

On the basis of the above calculation process, the indices and their weight coefficients of the AIHRV have been obtained, as shown in Table 16. It can be seen from Table 16 that: 1) the maximum and minimum weight coefficients for the first-level indices are 0.2171 (the sustainable value) and 0.0393 (the artistic value), and it indicates that the sustainable value has a higher care; 2) the maximum and minimum weight coefficients for the second-level indices are 0.11717 and 0.00327.

Fig. 4 Hierarchical single ordering index CR value.

4.4 Reutilization value assessment results of industrial heritage

According to the scoring method of experts, the individual scores of 24 second-level indices (C_ij, i = 1, 2, 3, ..., 8; j=1, 2, 3) of AIHRV are determined, as shown in Figure 5. Then, according to the AIHRV level criteria and the respective weight coefficients of the second-level indices, the score of each second-level index is multiplied with its corresponding weight coefficient, and then the weighted scores are summed up to get the weighted score of the industrial heritage. The score of the case selected in this paper is 4.3186, and according to the five AIHRV grades set in this paper (i.e., poor (S < 1.5), poor (1.5 ≤ S < 2.5), average (2.5 ≤ S < 3.5), good (3.5 ≤ S < 4.5), and very good (S ≥ 4.5)), it can be seen that the reutilization value of this industrial heritage is assessed as D₄ (i.e., valuable).

Fig. 5 Scores for 24 second-level indices.

5 Conclusions

The reutilization value assessment of industrial heritage has a great significance to the protection and sustainable development of industrial heritage. Therefore, this paper comprehensively considers 8 first-level indices (i.e., historical value, cultural value, social value, artistic value, scientific and technological value, economic value, environmental value, and sustainable value) and 24 secondary indicators (e.g., historical era, cultural symbolism, and public participation, etc.), and this paper proposes an assessment of industrial heritage reutilization value based on the hierarchical analysis method by comprehensively considering eight indicators. Firstly, this paper puts forward the index system of AIHRV and establishes the hierarchical standard of AIHRV. Secondly, it puts forward the method of AIHRV based on hierarchical analysis, including: establishing the hierarchical structure model of AIHRV, constructing the judgment matrix of industrial heritage value assessment indices, calculating the ME of the judgment matrix by square root method, consistency test of the hierarchical single ranking and the hierarchical total ranking, and so on. Thirdly, combining with the criteria of AIHRV level, the subjective scoring method and objective weight assessment method are considered to solve the reutilization value assessment level of the industrial heritage. Finally, the validity of the proposed method is verified through the case study, and the following conclusions are obtained:

• The AIHRV method based on the hierarchical analysis method proposed in this paper can effectively take into account the differentiated impacts of various factors;

• According to the results of the data taken from the case study in this paper, the weight coefficient of the sustainable value is the highest among the first-level indices (0.2171), and the combined weight coefficient of the green environmental protection is the highest among the second-level indices (0.11834), which are more worthy of attention. These factors are more worthy of attention;

• After comprehensive subjective and objective assessment, the reutilization value of industrial heritage obtained from the data taken in the case of this paper is assessed as D4, which indicates that the development of this industrial heritage is good.

Although the proposed method can assess the reutilization value of industrial building heritage, it also has application limitation (like judgment bias or inconsistency). Thus, a research work aiming at these aspects will be implemented in the future work.

Funding

This work is supported by Anhui Provincial Research Project for Higher Education Institutions (No. 2024AH040324).

Conflicts of interest

The author declares no conflict of interests.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Author contribution statement

Wenjun Zhang designed research, performed research, analyzed data, wrote and revised the paper.

References

All Tables

All Figures

	Fig. 1 Hierarchical model for assessing the reutilization value.
In the text

	Fig. 2 Flowchart for assessing the reutilization value of industrial heritage.
In the text

	Fig. 3 The MEs of judgment matrix for first-level and second-level indices.
In the text

	Fig. 4 Hierarchical single ordering index CR value.
In the text

	Fig. 5 Scores for 24 second-level indices.
In the text