**2. Review of the Literature on the Potentiality for the Adaptive Reuse of Industrial Heritage**

#### *2.1. Research on Reuse Potentiality*

Regarding the urban dimension of reuse potentiality, Gui Jin et al. conducted fundamental theoretical research on the design of spatial redevelopment and spatial conservation models for regional sustainable development, constructing an evaluation hierarchy based on spatial development and conservation [3]. Ana Martinovi et al. have developed a theoretical framework for the integration of social sustainability factors into urban regeneration processes in post-conflict areas, using a bottom-up approach to surveying and interviewing the social perceptions of all pertinent interests to obtain targeted influences on the socially sustainable heritage regeneration of industrial heritage. The importance and viability of including the social component of sustainability in strategies for regenerating cultural resources in post-conflict situations are emphasized [4]. This can be used as a trustworthy reference for identifying an indicator system for the growth of the urban dimension of reuse potentiality. Regarding the ontological dimension of reuse potentiality, Wang Jianguo and Jiang Nan categorized reuse potentiality into four categories, historical and cultural, industrial transformation, functional renewal, and economic benefits, and used this information to develop a reuse potential evaluation system based on the building base environment, building shape, structural equipment, internal space, and economic technology [5]. Vardopoulos, I., considers sustainable development potentiality, which implies the realization of benefits when adapting, including physical–economic, functional, environmental, political–social, and cultural potential, and adaptive reuse potential assessment, which focuses more on conservation and sustainable development strategies and provides recommendations on whether to engage in adaptive reuse and the priority of adaptive reuse for the target of the assessment [6]. Wijesiri, W.M.M., on the other hand, recently proposed the concept of the Green Adaptive Reuse (GAR) of buildings as an effective strategy to extend the life of facilities and reduce their carbon footprint, contributing to the preservation of an important heritage that determines cultural development [7] by following and extending Craig Langston's evaluation system and employing it to construct a GAR model to determine the potential for reuse of existing resources [8]. Regarding the potentiality for the reuse of industrial building heritage, Craig Langston predicts buildings' service lives based on the potential obsolescence of physical, economic, functional, technical, social, and legal criteria, guides design strategies by assessing the potentiality to enable building retrofitting to maximize adaptive reuse potential, and verifies the size and ranking of the adaptive reuse potential using the adapt STAR model [9]. This method combines the development of the adaptive reuse of old buildings with the objective of reducing the environmental impact of climate change and contributing to greater energy and resource efficiencies. Fan Shengjun summarized the potentiality evaluation system, which included architectural integrity, locational value, historical continuity, future profitability, and environmental friendliness, based on future value characteristics that reflect recycling potential [10].

The existing system of indicators for assessing reuse potentiality is proposed without providing a foundation for determining the indicators. The majority of value evaluation systems are carried forward from previous generations, resulting in insufficient adaptation to the specificity of industrial heritage for adaptive reuse. The interaction of the evaluation elements' opinions has not been considered. The reuse potentiality derived from the evaluation index system has a relatively limited scope of application and only applies to a specific area of a single industrial building, thereby lacking the relevance and generalizability necessary to guide the renovation strategy. In addition, research on reuse potentiality is more focused on heritage ontology, and the evaluation system does not completely account for the impact of adaptive reuse on the urban environment. To assess the adaptive potential of industrial heritage, the scope of the index system must be expanded to include not only individual industrial buildings but also their location, the surrounding area, and urban regeneration development.

#### *2.2. Research on Quantitative Methods for Reuse Potentiality*

The majority of existing studies on the preservation and reuse of industrial heritage adopt a qualitative approach. Due to the overuse of experience-based rules, subjective factors have a significant influence on outcomes and lack support from scientific theory. In recent years, the use of quantitative methods in industrial heritage research has increased due to the development of big data in the field of heritage conservation and the need for precise and scientific research. They are primarily used for assessing the reuse value of industrial heritage, risk analysis, and post-reuse evaluation, but quantitative research methods on reuse potentiality are scarce. The research on reuse potentiality should enhance the accuracy and applicability of predictions as well as build an adaptable potentiality evaluation system that can be utilized at various phases of industrial heritage preservation.

The most common quantitative methods for assessing the potentiality of industrial heritage are listed below. Although Adaptive Reuse Potential (ARP) is effective at estimating the remaining life of buildings and facilitating the analysis and comparison of various reuse objects, the physical life degradation of buildings cannot be precisely calculated, resulting in ambiguous potential evaluation results. The Single Factor Superposition Method simplifies the assessment factors based on quantification and operability principles, and the calculation procedure is more reproducible; however, it is more difficult to obtain assessment parameters and data [11]. Although the data obtained by aggregating and quantifying subjective and objective judgments using the Analytic Hierarchy Process (AHP) and Delphi method are more scientific, subjective factors also influence the variables and corresponding weights [12]. ELimination Et Choix Traduisant la REalité (ELECTRE) has a clearer concept of the superiority of decision options, which can improve decision accuracy, but the method for determining the weights does not take into account the influence of the interaction between the attributes of the indicators on the evaluation results [13]. It is difficult to assess multiple factors at various layers, making the decision outcome uncertain and making it difficult to implement complex decisions regarding reuse potentiality. The VIse Kriterijumski Optimizacioni Racun (VIKOR) ranking process compares group utility values and combined utility values to determine the merits of the evaluation options [14]. Individual high evaluation indicators, when applied to a system of evaluation indicators, can easily trump certain low evaluation indicators, which are also crucial for determining the potentiality of heritage reuse.

To minimize the influence of subjective factors on the evaluation system's results, it is necessary to determine the optimal ranking of the relative magnitudes of the reuse potentialities for multiple options. After combining several multi-attribute decision methods, the Technique for Ordering Preferences by Similarity to the Ideal Solution (TOPSIS) was finally used to determine the relative magnitudes and ranking of the reuse potentialities. The TOPSIS method is a sequential selection technique based on the similarity of ideal targets. The normalized data normalization matrix is used to identify the optimal and inferior targets among multiple targets. The proximity of each evaluation objective to the ideal is determined by separately calculating the distance between each objective and the positive and negative ideal solutions. The objectives are ranked by their magnitudes, and this is used as the basis for evaluating their superiority. The method is suitable for determining the magnitude of the reuse potential by comparing the ranking after calculating the weight of the multi-objective method, which enables a more objective assessment of the reuse potentialities of multiple options and provides decision-makers with targeted guidance. It is unaffected by the order of evaluation options, is suitable for the cross-sectional comparison of multiple evaluation options, is easier when handling fuzzy data, is simpler to calculate, and produces more objective quantitative results. Due to the indicator system's strong reliance on weight, it is easily influenced by the subjective factors of decision-makers, and different weighting schemes appear inconsistent for decision results, so it is more important for the calculation of indicator weights in use. In this paper, we calculate the comprehensive weight of the evaluation index system using the Continuous Ordered Weighted Averaging operator (C-OWA) and the Entropy Weight Method. C-OWA is appropriate for uncertain

multi-attribute decisions for which the attribute weights are known with certainty and the attribute values are given as interval numbers in order to reduce the subjective factors of the evaluator and the extreme values of the evaluation data on the calculation errors of the indicator weights and to take into account the influence of the indicator factors in the order. The method was used to calculate the weight of the graded order of the evaluation indicators for reuse potentiality. The Entropy Weight Method is a quantitative method of objective weighting in which the entropy value is used to determine the dispersion of an indicator and the information entropy is used to calculate the weight of each indicator. The entropy weight is modified according to each indicator so as to obtain a more accurate and scientific weighting of the grading criteria indicators. Finally, the comprehensive weights are obtained by linear weighting. The calculation of the comprehensive weight contributes to the improvement of the scientific nature and accuracy of a multi-objective decision analysis, and the evaluation process is more operable and appropriate for the processing and analysis of quantitative data within a multi-layer potentiality evaluation system. Improved entropy TOPSIS enables an objective evaluation of the evaluation object and circumvents the issue in which the solution closest to the ideal solution is also clear to the negative ideal solution. The objective comprehensive assignment based on the determination of order and criterion weights increases the comparative analysis between evaluation indicators, and the combined use of the two methods can improve the scientific and rational nature of the evaluation results, significantly reducing the subjectivity of the results calculated by inviting experts to score the conventional TOPSIS method.
