Introduction

In the context of global post-industrial transformation, industrial heritage has shifted from being viewed as a byproduct of industrial decline to becoming a crucial element of urban cultural identity and sustainable development1,2. As highlighted by the International Committee for the Conservation of Industrial Heritage (TICCIH), industrial heritage not only holds technological and historical significance but also plays a central role in facilitating urban ecological restoration, economic revitalization, and the preservation of collective memory1,3,4. Consequently, the protection and adaptive reuse of industrial heritage have become pivotal concerns in global urban development strategies5. For developing countries with rich industrial histories, such as China, former industrial cities face distinct challenges. Many of these cities continue to house large-scale industrial heritage from diverse historical periods. However, the traditional approach that prioritizes the physical and material preservation of heritage is no longer sufficient to address the complex crises of environmental degradation, value conflicts, and the fragmentation of collective memory.

Shenyang, often referred to as the “eldest son of Chinese industry,” is a quintessential example of China’s old industrial cities, playing a pivotal role in the establishment of the national industrial system in the 20th century6. The city currently boasts 64 key industrial heritage spaces, categorized into three primary types: national industrial heritage, colonial industrial heritage, and industrial heritage from the period of national economic recovery (1949–1957). Despite its immense historical and cultural significance, the governance of Shenyang’s industrial heritage remains deeply rooted in the traditional paradigm previously discussed. Existing local practices either focus on quantifying the intrinsic value of heritage or on optimizing one-dimensional preservation technologies, while neglecting the coordinated management of industrial historical spaces as complex systems7,8,9. This gap has resulted in governance failures. For instance, ecological restoration efforts around heritage sites fail to meet community needs, and the commercialization of these sites clashes with public perceptions of cultural value, highlighting the urgent need to establish a comprehensive theoretical framework to guide evidence-based practices.

In recent years, academic focus on industrial heritage has shifted from a sole emphasis on physical preservation to a more multidimensional approach to collaborative governance10,11,12. However, significant gaps persist in this body of research. First, most studies either focus on assessing the intrinsic value of heritage or on optimizing conservation techniques, with limited exploration of the interactive mechanisms within complex industrial historical spatial systems13,14. Second, research on the coupling mechanisms of industrial historical spaces remains confined to one-dimensional analysis, lacking a comprehensive theoretical framework for multidimensional coupling15,16. Finally, studies on coordination obstacles tend to concentrate on single factors, such as policy implementation biases and funding shortages, without addressing the cascading effects of multiple interacting factors17,18.

To address this gap, this study introduces the “Environment-Value-Memory” coupling framework as an innovative approach to constructing a coordinated evaluation system for complex industrial historical spatial systems. Specifically, using 64 representative industrial historical spaces in Shenyang as case studies, this research employs a combination of GIS spatial analysis, semi-structured interviews, and fieldwork, integrating both quantitative and qualitative methods to systematically examine the interactive mechanisms among the three systems: environment, value, and memory.

Every city is the product of a dynamic and gradual evolutionary process, where space and time are closely intertwined objective elements. The logical foundations of spatial organization often reflect the historical characteristics of a space, collectively constituting the concept of “historical space”19,20,21.“Industrial historical space” extends the meaning and scope of “historical space” within a specific context. It can be defined as a spatial complex with historical significance and contemporary relevance, shaped by industrial production, distribution, and settlement since the Industrial Revolution. On the one hand, it retains the essential connotations of historical space, while on the other hand, it differs from the concept of industrial heritage in that it emphasizes a larger spatial dimension and places greater importance on the intangible aspects of space22,23.

In terms of spatial scope, “industrial historical space” generally includes the physical spaces occupied by buildings, structures, and infrastructure historically used for industrial production, forming a relatively independent area in geographic space. From the perspective of spatial scale, “industrial historical space” can be categorized into three levels: the block, district, and city levels. Industrial historical spaces at different scales not only vary in size but also exhibit distinct characteristics in terms of historical transformation, spatial form, structural features, and the shaping of urban identity and character. This study takes 64 block-level industrial historical spaces in Shenyang as a case study to explore their coordination and the factors that obstruct such coordination under the “Environment-Value-Memory” coupling framework.

The elements within the environment, value, and memory systems of industrial historical spaces interact in ways that both promote and constrain one another, collectively forming a coupled system within these spaces. The environment system, consisting of the natural and built environment subsystems, serves as the foundation for the preservation and revitalization of industrial historical spaces and is also a key indicator for evaluating their sustainable development24,25,26. The value system, which includes the historical, scientific, artistic, and social value subsystems, is not only essential for the significance of industrial historical spaces but also plays a vital role in evoking the memories of indigenous communities27. The memory system, which encompasses both material and intangible memory subsystems, significantly influences people’s perceptions and interpretations of the environment and plays a crucial role in promoting the preservation of industrial historical spaces28. The coordinated and integrated development of the environment, value, and memory systems collectively constructs the full image of these spaces (Fig. 1).

Fig. 1: Schematic diagram of the coupling coordination mechanism model for ‘Environment-Value-Memory’ in industrial historical space.
Fig. 1: Schematic diagram of the coupling coordination mechanism model for ‘Environment-Value-Memory’ in industrial historical space.
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The model illustrates the interactive mechanism among the Environment, Value, and Memory systems within industrial historical spaces. The diagram depicts the Environment system as the foundation, the Value system as the carrier, and the Memory system as the driving force. Arrows indicate the promoting and inhibiting factors influencing the degree of coordination, as well as the spillover effects and coupling interactions between highly coordinated and lowly coordinated spaces.

As a major industrial city in China, Shenyang is rich in industrial historical spaces. However, due to significant variations in the quality of the built environment, spatial types, and construction periods, the degree of coordination and coupling between the environment, values, and memory within these spaces varies across different locations. Industrial historical spaces characterized by a high degree of coordination among the environment, value, and memory systems can, to some extent, promote the optimization of environments in less coordinated industrial historical spaces and strengthen heritage memory through the proximity and clustering effects of geospatial networks29,30. Industrial historical spaces with highly coordinated environments, values, and memories exert a certain spillover signs on neighboring spaces, with the intensity of this effect closely linked to distance31. Therefore, industrial historical spaces, based on the coupling of environment, values, and memory, form complex, dynamic, and nonlinear systems, where the degree of coordination and coupling is influenced by both facilitating and inhibiting factors.

This study introduces the “Environment-Value-Memory” coupling perspective and, adhering to principles of representativeness and hierarchical structure, constructs a coordinated evaluation system for industrial historical spaces, comprising 23 tertiary indicators across environment, value, and memory subsystems. By integrating GIS spatial analysis and semi-structured interviews, the study dissects the interactive mechanisms where the “environment serves as the foundation, value as the carrier, and memory as the driving force” within these three systems.

Using 64 neighborhood-level industrial heritage spaces in Shenyang as case studies, a coupling degree model quantifies the integration level of the “Environment-Value-Memory” system. Combined with an obstacle degree diagnosis model, it diagnoses obstacles at both the indicator and subsystem levels, revealing the intrinsic logic of their synergistic development and identifying key constraints. Based on these findings, this research provides theoretical support for addressing complex crises—such as environmental degradation and value conflicts—in industrial historical spaces within legacy industrial cities, while also outlining practical pathways for multidimensional collaborative governance.

Methods

Overview of the study area

Shenyang, a prominent old industrial city in China, is home to a wealth of industrial historical spaces. These spaces not only bear witness to Shenyang’s own industrial achievements but also to the broader history of Chinese industry, forming an integral part of the region’s urban cultural landscape and historical memory32,33. Since 2018, both the Liaoning Provincial and Shenyang Municipal governments have prioritized the preservation, development, and utilization of these industrial historical spaces. This effort has resulted in the issuance of several policy documents, including the “Interim Measures for the Management of Provincial-level Industrial Heritage in Liaoning Province” and the “Shenyang Industrial Heritage Protection Plan,” aimed at ensuring the effective preservation and revitalization of Shenyang’s industrial heritage. To further enhance the preservation of these spaces, a province-wide survey has been conducted by a research team since May 2022. This survey details the location, current status, ownership, and industry of these historic industrial spaces. As of February 2025, 64 such spaces had been identified in Shenyang, underscoring the richness and significance of the city’s industrial historical spaces. However, surveys reveal that Shenyang’s industrial historical spaces continue to face challenges, including environmental degradation, conflicting values, and fragmented memories. Therefore, using Shenyang as a case study, this research aims to explore the synergistic relationships among the environment, value, and memory of industrial historical spaces, as well as the factors that hinder their integration and coordination.

To ensure the significance and effectiveness of the selected spaces in improving the environment, enhancing value, and strengthening the memory of industrial historical spaces, the sample selection process took into account factors such as the comprehensive value of these spaces, the characteristics of the natural and built environments, their impact on urban and social development, and their preservation status. The final selection of 64 block-level industrial historical spaces selected in this study is derived from a combination of existing official surveys and the team’s former survey. Firstly, we conducted an initial screening of all provincial-level industrial heritage sites listed by the Liaoning Provincial Department of Industry and Information Technology. Industrial heritage sites included in this list typically possess significant historical, social, and technological value. Secondly, we identified sites of substantial historical value through the list of historic buildings published by the Liaoning Provincial Department of Housing and Urban-Rural Development. Finally, to ensure the accuracy and representativeness of our sample, our research team conducted on-site verification and supplementary investigations between September 2024 and May 2025. We excluded spaces that had been completely demolished, altered beyond recognition, or lost their industrial historical attributes. Concurrently, we supplemented the list with eight unrecorded locations through local archives and interviews with elderly residents. Drawing on existing research34,35, the 64 industrial historical spaces were categorized into four groups: 27 spaces in the industrial production category, 4 in the industrial transportation category, 6 in the industrial society category, and 27 in the industrial culture category (Fig. 2). Classification takes precedence over the primary functions undertaken by the space throughout its historical development and its current functional positioning. Concurrently, consideration is given to the core values of industrial heritage spaces and the interaction between environment and memory. Detailed classification criteria are presented in Table 1.

Fig. 2: Distribution of industrial historical spaces in Shenyang’s central urban area.
Fig. 2: Distribution of industrial historical spaces in Shenyang’s central urban area.
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The map displays the spatial locations of the 64 identified industrial historical spaces. Different markers indicate the four classifications: industrial production category, industrial transportation category, industrial society category, and industrial culture category.

Table 1 Classification criteria for industrial historical spaces
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Data source and processing

The data used in this study include environment, value, and memory data related to industrial historical spaces (Table 2). Seven sets of spatial environmental data for industrial heritage sites were primarily sourced from open platforms such as AutoNavi Maps and OpenStreetMap. The value data for these sites consist of 11 items, primarily gathered through semi-structured interviews and on-site inspections. Additionally, 5 items of memory data related to industrial historical spaces were obtained through semi-structured interviews and questionnaires. Primary data regarding value and memory were collected between November 2024 and May 2025. The study utilized a purposive sampling method to distribute 1322 questionnaires to former workers, local residents, and visitors, resulting in 1211 valid responses (91.6% response rate). Semi-structured interviews were conducted with 86 stakeholders, each lasting between 20 and 35 min. Due to the diverse data collection methods and the multivariate nature of the data, data processing plays a crucial role in this study. First, the collected data undergoes thorough cleaning and preprocessing to ensure accuracy and consistency, including addressing missing values, outliers, and duplicates36. Second, during preprocessing, appropriate transformations and formatting are applied based on data type and structure, including standardization or normalization, to mitigate the influence of different dimensions on data analysis. Finally, the processed data is input into a coupled coordination model for computational analysis. The results are used to categorize coordination levels and analyze spatial distribution characteristics. Lastly, an obstacle degree diagnosis model is employed to identify coordination obstacles (Fig. 3).

Fig. 3: Flow chart of the study.
Fig. 3: Flow chart of the study.
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The flowchart outlines the three-step methodological framework employed in this research. Step 1 details the data sources and research methods, including GIS analysis and surveys. Step 2 illustrates the construction of the coupling coordination model, incorporating the Environment, Value, and Memory systems. Step 3 describes the analysis of coupling degrees and the diagnosis of obstacle factors at both the system and indicator levels.

Table 2 Data sources and components
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Model construction

The coupling model effectively captures the interdependencies arising from the exchange of material, energy, or information between systems37,38,39. To examine the interdependencies and repulsions among the three systems—environment, value, and memory—within industrial historical spaces, this study uses the model’s composite system coupling (C) as its characteristic value. This value ranges from 0 to 1, with values closer to 1 indicating stronger coupling (Table 3). The coupling model formula is:

$$C={3}^{3}\sqrt{\frac{{f}_{\left(x\right)}\times {f}_{\left(y\right)}\times {f}_{\left(z\right)}}{{\left({f}_{\left(x\right)}+{f}_{\left(y\right)}+{f}_{\left(z\right)}\right)}^{3}}}$$
(1)
$${f}_{(x)}=\mathop{\sum }\limits_{i=1}^{7}{a}_{i}\times {x}_{i}$$
(2)
$${f}_{(y)}=\mathop{\sum }\limits_{i=1}^{11}{b}_{i}\times {y}_{i}$$
(3)
$${f}_{(z)}=\mathop{\sum }\limits_{i=1}^{5}{c}_{i}\times {z}_{i}$$
(4)

Where, \(C\) represents the coupling degree of the composite system, \({f}_{(x)}\), \({f}_{(y)}\),and \({f}_{(z)}\) represent the comprehensive evaluation functions of the environment system, the value system, and the memory system, respectively. \({a}_{i}\), \({b}_{i}\) and \({c}_{i}\) are the corresponding indicator weights, while \({{x}}_{i}\) \({y}_{i}\) and \({z}_{i}\) are the standardized indicators in the corresponding systems.

Table 3 Coupling degree classification criteria
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Simply relying on the coupling degree model to assess the coordinated development status between subsystems may lead to conclusions that do not align with the actual situation40. For example, when both subsystems are at either a low or high level, they may both exhibit high coupling characteristics. The coupling coordination degree model is proposed to more accurately reflect the actual level of coordinated development between the two entities, denoted as \(D\). This value ranges from 0 to 1, with higher values indicating more advanced development of the subsystems (Table 4). The model formula is:

$$D=\sqrt{C\times T}$$
(5)
$$T=a{f}_{(x)}+{{bf}}_{(y)}+{{cf}}_{(z)}$$
(6)

Where, \(T\) represents the coordinated development index among the three subsystems, reflecting the overall contribution of the three systems to the degree of coordination; \(a\), \(b\), \(c\) are undetermined coefficients representing the subsystem contribution coefficients. Since the degree of mutual promotion and relative importance of the three systems are assumed to be equal, in the actual calculation, \(a\), \(b\), and \(c\) are each assigned a value of 0.33.

Table 4 Criteria and types of coordinated development
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The protection of industrial historical spaces should not only focus on evaluating coupling coordination but also identify the key obstacles that hinder the improvement of the coordinated development level of these spaces, to formulate and adjust targeted development strategies and measures. Building on the evaluation of coupling coordination, this study introduces an obstacle diagnosis model to diagnose and analyze the obstacles affecting the coordination level of various industrial historical spaces in Shenyang. The obstacle diagnosis model relies on factor contributions and deviations to calculate the degree of obstacles at the subsystem and indicator levels, thereby identifying the main restrictive factors41,42. The model formula is:

$${F}_{j}={W}_{j}{W}_{j}^{* }$$
(7)
$$I=1-{X}_{{ij}}^{* }$$
(8)
$${K}_{j}=\frac{{F}_{j}\times {I}_{j}}{\sum {F}_{j}\times {I}_{j}}$$
(9)

Where, \({F}_{j}\) represents the factor contribution of the \(j\) evaluation indicator, \({W}_{j}\) is the indicator weight under the criterion layer, \({W}_{j}^{* }\) is the weight of the criterion layer to which the indicator belongs, \({I}_{j}\) is the deviation, \({X}_{{ij}}^{* }\) is the normalized positive value of each indicator, and \({K}_{j}\) is the obstacle degree of the \(j\) indicator.

Construction of the indicator system

Industrial historical spaces are characterized by complex structures, diverse hierarchies, and the interplay of subsystems. A fully coordinated industrial historical space should comprehensively reflect environmental suitability, diverse values, and rich memories. Therefore, based on the “Environment-Value-Memory” coupling and coordination mechanism, and adhering to the principles of representativeness, hierarchy, and accessibility in indicator selection, this study constructs a composite system evaluation index for the three subsystems of environment, value, and memory, comprising 23 third-level indicators (Table 5).

Table 5 Evaluation index system of “Environment-Value-Memory” coupling system
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The combined value assignment method integrates the advantages of both subjective and objective weighting methods, providing a more comprehensive reflection of the actual situation of the evaluation object43. In this study, employing the combined value assignment method helps to better balance the influencing factors across different systems, thus improving the objectivity and accuracy of the evaluation. First, the indicator judgment matrix is constructed using Analytic Hierarchy Process (AHP) software, and experts are consulted to determine the relative importance of each indicator. The expert panel was established with 10 interdisciplinary specialists, covering the core research fields related to industrial historical spaces, comprising 3 experts from the field of Architecture and Urban Planning, 3 experts specializing in industrial heritage conservation, 2 experts from environmental science, and 2 experts in Sociology. Next, the weight coefficients and consistency tests are calculated based on the judgment matrix values. Subsequently, the entropy method is applied to normalize the data after non-negative processing, and the normalized results are used to calculate the entropy values of the indicators. Finally, the weights obtained from the two methods are combined. The detailed calculation process is outlined in Steps 1–6 below.

(1)Data preprocessing: Due to differences in the dimension and magnitude of the 23 tertiary indicators, a non-negative transformation was first performed to eliminate the influence of negative values on entropy calculation. For positive indicators and negative indicators, the following transformations were applied:

$${\text{For}}\,{\text{positive}}\,{\text{indicators}}\!:\,{x}_{{ij}}^{{\prime} }=\frac{{x}_{{ij}}-\min ({x}_{j})}{\max ({x}_{j})-\min ({x}_{j})}$$
(10)
$${\text{For}}\,{\text{negative}}\,{\text{indicators}}\!:\,{x}_{{ij}}^{{\prime} }=\frac{\max ({x}_{j})-{x}_{{ij}}}{\max ({x}_{j})-\min ({x}_{j})}$$
(11)

Where, \({x}_{{ij}}\) is the original value of the \(i\) sample under the \(j\) indicator; \(\max ({x}_{j})\) and \(\min \,({x}_{j})\) are the maximum and minimum values of the \(j\) indicator, respectively; \({x}_{{ij}}^{{\prime} }\) is the non-negative transformed value of \({x}_{{ij}}\).

(2)Standardization of transformed data: To ensure the comparability of data across indicators, the non-negative transformed values were normalized to the range [0, 1]. The standardization formula is:

$${p}_{{ij}}=\frac{{x}_{{ij}}^{{\prime} }}{{\sum }_{i=1}^{n}\,{x}_{{ij}}^{{\prime} }}$$
(12)

Where, \({p}_{{ij}}\) is the standardized value of the \(i\) sample under the \(j\) indicator; \(n\) is the total number of samples. \({\sum }_{i=1}^{n}\,{x}_{{ij}}^{{\prime} }\) is the sum of non-negative transformed values of the \(j\) indicator across all samples.

(3)Calculation of indicator entropy values: The entropy value \({e}_{j}\) of the \(j\) indicator reflects the degree of information redundancy: lower entropy indicates more effective information and greater indicator discrimination. The formula is:

$${e}_{j}=\frac{1}{{\text{ln}}\,n}\mathop{\sum }\limits_{i=1}^{n}{p}_{{ij}}\,{\text{ln}}\,{p}_{{ij}}$$
(13)

Where, \({e}_{j}\in [0,1]\);when \({p}_{{ij}}=0,{\text{ln}}\,{p}_{{ij}}\) is defined as 0.

(4)Calculation of indicator difference coefficients: The difference coefficient \({g}_{j}\) measures the degree of variation in the \(j\) indicator: higher difference coefficients indicate greater contribution of the indicator to the evaluation. The formula is:

$${g}_{j}=1-{e}_{j}$$
(14)

(5)Calculation of objective weights: The entropy weight \({w}_{j}^{e}\) of the \(j\) indicator is determined by normalizing the difference coefficients. The formula is:

$${w}_{j}^{e}=\frac{{g}_{j}}{{\sum }_{j=1}^{m}{g}_{j}}$$
(15)

Where, \(m=23\) is the total number of indicators; \({\sum }_{j=1}^{m}{g}_{j}\) is the sum of difference coefficients of all indicators.

(6)Combination of subjective and objective weights: The subjective weigh \({w}_{j}^{s}\) and objective weigh \({w}_{j}^{e}\) were combined to generate the final comprehensive weight \({w}_{j}\) of each indicator. The combination formula is:

$${w}_{j}=\frac{{w}_{j}^{s}\times {w}_{j}^{e}}{{{\sum }_{j=1}^{m}(w}_{j}^{s}\times {w}_{j}^{e})}$$
(16)

Where, \({w}_{j}^{s}\times {w}_{j}^{e}\) is the product of subjective and objective weights of the \(j\) indicator; \({\sum }_{j=1}^{m}{(w}_{j}^{s}\times {w}_{j}^{e})\) is the sum of products of all indicators.

In summary, by coupling the environment, value, and memory systems of industrial historical spaces, a coordinated coupling factor system for industrial historical spaces is developed, considering the natural environment, built environment, historical value, and material memory, as shown in Fig. 4.

Fig. 4: “Environment-Value-Memory” coupling coordination factor system.
Fig. 4: “Environment-Value-Memory” coupling coordination factor system.
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The diagram represents the hierarchical structure of the evaluation index system. The central core represents the coupling system, surrounded by the three main systems (Environment, Value, Memory). The outer nodes represent the subsystem layers (e.g., Natural Environment, Historical Value) and the specific tertiary indicators (E11 to M23), showing the comprehensive framework used for quantitative evaluation.

Ethical statements and informed consent

This study was conducted in strict accordance with the ethical principles of the Declaration of Helsinki. The research protocol, including the semi-structured interviews and questionnaires, was reviewed and approved by the Ethics Committee of Shenyang Jianzhu University (Approval No.: 2024SJZU-A022). Additionally, informed consent was obtained from all participants involved in the study. Before completing the questionnaire or participating in the interview, researchers explained the study’s purpose, rights, and responsibilities to the participants. Participants were explicitly informed of the anonymity of their responses and their right to withdraw at any time.

Results

Analysis of the coordination characteristics of “Environment-Value-Memory” coupling

The coupling and coordination of Shenyang’s industrial historical spaces exhibit notable variations across different categories. Industrial culture and industrial production perform relatively well overall, while industrial transportation and industrial society require considerable optimization (Fig. 5). Based on the “Environment-Value-Memory” coupling framework, the coordination of Shenyang’s industrial historical spaces is analyzed from multiple dimensions using four indicators: coupling coordination degree, coupling degree, coordination index, and coordination degree. In the value-memory dimension, industrial culture and industrial production historical spaces perform the best, indicating that these two categories significantly outperform others in coordinating value exploration and memory transmission. Industrial production, however, has the lowest coupling coordination, highlighting its inadequacy in integrating social functions with historical memory. In the environment-value dimension, industrial production and industrial transportation display a polarized relationship. The former benefits from the synergy between environmental governance and value reuse in production spaces, while the latter suffers from weaker coordination due to the fragmentation of transportation facilities from the historical environment. In the environment-memory dimension, the industrial society and industrial production categories perform better, suggesting that social activities and production practices are more likely to strengthen collective memory through environmental carriers, while the industrial transportation category exhibits the lowest coupling coordination, potentially due to the physical damage caused by transportation facilities to memory carriers. This phenomenon is deeply rooted in Shenyang’s industrial transportation development history and urban policy evolution. As a key hub of the Chinese Northeast Railway since the early 20th century, Shenyang’s industrial transportation infrastructure (e.g., railway branches, freight yards) was initially constructed to serve colonial-era resource extraction and post-1949 heavy industry layout. However, under the ‘East-Move, West-Build’ urban renewal strategy and subsequent ‘Shenyang Transportation Infrastructure Renovation Plan,’ many old railway corridors and freight terminals were either demolished for residential or commercial development or isolated from the urban fabric due to the expansion of modern expressways. This historical evolution and policy-driven transformation led to the fragmentation of industrial transportation spaces, weakening their connection with historical memory carriers (e.g., old factories, worker communities) and resulting in systemic coordination dilemmas.

Fig. 5: Coupling and coordination results of different types of industrial historical spaces in Shenyang.
Fig. 5: Coupling and coordination results of different types of industrial historical spaces in Shenyang.
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The bar charts illustrate the performance of the four categories of industrial historical spaces (a is the industrial production category, b is the industrial culture category, c is the industrial society category, and d is the industrial transportation category). The bars represent the numerical values for the Coupling Coordination Degree, Coupling Degree, Coordination Index, and Degree of Coordination across different dimensions (Environment-Value, Environment-Memory, and Value-Memory).

The industrial culture category achieved the highest coordination level in the “Value-Memory” dimension, demonstrating its effectiveness in integrating value and memory through cultural symbols. However, the coordination levels for the “Environment-Value” and “Environment-Memory” dimensions in both the industrial culture and industrial transportation categories were relatively low, indicating that these two types of industrial historical spaces face vulnerabilities in specific coupling scenarios. The industrial production category achieved high coordination levels in both the “environment-value” and “Value-Memory” dimensions, reflecting the potential for synergy between the production space, value exploration, and environmental governance components of Shenyang’s industrial historical spaces. Furthermore, correlation analysis between coupling and coordination indices revealed that some coupled systems exhibited high coupling but low coordination, while others exhibited low coupling but high coordination. For example, the industrial transportation category demonstrated high coupling in the “Value-Memory” dimension but a low coordination index, indicating a strong interdependence between heritage value and memory, yet a lack of effective coordination pathways. The industrial culture category, on the other hand, exhibited low coupling in the “environment-memory” dimension but similar coordination indices, suggesting that cultural interventions could potentially compensate for the inherent interdependence between these systems.

From a spatial perspective, the intersection of Huanggu, Shenhe, and Heping districts, which served as an early subsidiary of the Chinese Eastern Railway, formed the initial prototype of Shenyang’s industrial space. The overlapping of industrial buildings from different periods—such as the railway subsidiary factories during the Fengtianyi era, the machinery factories during the Manchukuo era, and the electronics industry in the early years of the People’s Republic of China—has resulted in a high level of industrial historical spatial coordination. Following the industrial shift under the “East-Move, West-Build” strategy, the vacated spaces in the central Tiexi District were systematically redeveloped, exemplified by the development of the China Industrial Museum complex. As a result, the level of industrial historical spatial coordination in the central Tiexi District is relatively high, notably surpassing that of the surrounding areas. Meanwhile, the western part of Dadong District, home to the former Fengtian Arsenal, retains “memory anchors” (such as the Dongmao Warehouse) and a complete memory network, despite the constraints of military confidentiality, leading to a high level of industrial historical spatial coordination (Fig. 6).

Fig. 6: Spatial distribution characteristics of the coupled coordination of Shenyang’s industrial history spaces.
Fig. 6: Spatial distribution characteristics of the coupled coordination of Shenyang’s industrial history spaces.
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The map employs a grid-based visualization to show the spatial variation in coordination levels across the city. The color gradient represents the degree of coordination, ranging from extreme disorder to excellent coordination. The map highlights specific clusters, such as the Central Tiexi District and Western Dadong District, and illustrates the spatial spillover effects radiating from high-coordination cores.

Analysis of subsystem obstacles impeding the coupling of Environment-Value-Memory

From the perspective of the “Environment-Value-Memory” coupling, a quantitative analysis of the system-level obstacle factors of Shenyang’s industrial historical spaces revealed shortcomings in system-level coordination. The results indicate significant differences in obstacle factors across different categories of industrial historical spaces, with the conflict between the environment and value systems being particularly prominent, while the obstacle factors in the memory system are relatively low (Fig. 7). Specifically, the industrial transportation and industrial production categories exhibit the highest obstacle levels in the built environment dimension (E2), with values of 0.236 and 0.249, respectively. This not only suggests a conflict between the layout of industrial transportation facilities and the ecological carrying capacity, but also reflects the ongoing pressure from production activities on the surrounding environment. From a macro policy perspective, Shenyang’s long-term focus on optimizing modern transportation efficiency (e.g., the construction of ring roads and high-speed rail hubs) has led to insufficient consideration of heritage protection in industrial transportation spaces. For example, the ‘Liaoning Provincial Comprehensive Transportation Network Plan (2021–2035)’ prioritizes intercity connectivity but lacks specific provisions for the conservation of historical transportation infrastructure. Additionally, the colonial-era railway subsidiary lines and post-1950s freight terminals, once the backbone of Shenyang’s industrial logistics, now face structural conflicts between their historical layout and contemporary ecological and living demands— a common systemic challenge for old industrial cities transitioning from production-oriented to livable-oriented development. In contrast, industrial historical spaces in the industrial society category demonstrate lower obstacle levels in both the natural environment (E1) and the built environment (E2), indicating relatively good environmental coordination. This may be linked to Shenyang’s recent planning policies focused on the renewal and renovation of old industrial communities.

Fig. 7: Results of subsystem obstacle factor analysis.
Fig. 7: Results of subsystem obstacle factor analysis.
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The bar chart compares the obstacle degree values across the three systems (Environment, Value, Memory) for the four categories of industrial historical spaces. The x-axis represents the subsystems (E1 is Natural environment, E2 is Built environment, V1 is Historical value, V2 is Technological value, V3 is Artistic value, V4 is Social value, M1 is Material memory, M2 is Immaterial memory). The y-axis indicates the obstacle degree, with higher bars representing greater constraints on coordinated development.

Within the heritage value system, the obstacle level for historical value is notably higher than for scientific, technological, artistic, and social values. Specifically, the industrial society category (0.3100) and the industrial culture category (0.3191) exhibit the highest obstacle levels in the historical value (V1) dimension. This suggests that, in the context of industrial historical spaces, Shenyang’s industrial heritage faces a systematic contradiction between historical value excavation and its practical reuse, underscoring the urgency of preserving the continuity of these spaces. Notably, the obstacle level for the industrial memory system is generally below 0.075, suggesting that existing management measures, such as the construction of the Tiexi Foundry Museum and the Workers’ Village Living Museum, have been effective in preserving industrial memory. However, the obstacle level for intangible memory (M2) within the industrial society heritage category is significantly higher than for other categories, likely due to the fragmentation of community memory carriers. This raises concerns regarding the sustainability of memory transmission.

Analysis of the indicator layer’s obstacle degree

To further diagnose the obstacles to coordination within Shenyang’s industrial historical spaces, this study also examined obstacle factors at the indicator level (Fig. 8). The results revealed significant variations in core obstacle factors across systems. Within the environment system, the spatial connectivity of industrial transportation (E24) exhibited the highest obstacle level (0.1033), likely due to poor integration between railway corridors, factory roads, and the urban road network. For example, the pedestrian system at the former locomotive depot site of Shenyang Railway Station has been disrupted by surrounding land development, weakening accessibility to the industrial historical space. Greenery coverage (E11) and noise levels (E13) were the primary factors hindering the coordinated development of industrial society and production-related industrial historical spaces. These findings suggest a clear conflict between the ecological and environmental elements of Shenyang’s industrial historical spaces and industrial production activities.

Fig. 8: Results of obstacle factor analysis at the indicator layer.
Fig. 8: Results of obstacle factor analysis at the indicator layer.
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The chart displays the obstacle degree for specific tertiary indicators across the different industrial heritage categories. The x-axis lists the indicator codes (e.g., E11, E24, V14, M12), and the y-axis represents the calculated obstacle degree. The height of the bars reflects the extent to which each specific indicator hinders the overall coupling coordination of the industrial historical spaces.

In the heritage value system, the three most significant obstacle indicators are the association between historical events and figures (V14), the value of style and decorative arts (V32), and the impact of industrial technological progress (V21). These indicators also exhibit distinct characteristics across different types of industrial historical spaces. For instance, the association between historical events and figures (V14) shows an obstacle of 0.1944 in industrial culture-type historical spaces, while the value of style and decorative arts (V32) presents an obstacle of 0.0862 in industrial production-type historical spaces. Meanwhile, the impact of industrial technological progress (V21) demonstrates a high obstacle level in industrial society-type historical spaces. These findings suggest that Shenyang’s industrial historical spaces may suffer from a lack of historical context, structural biases in value perception, and strategic lag in heritage revitalization.

In the memory system, memory accuracy (M12), memory emotion (M22), and memory continuity (M23) are the primary obstacle factors for industrial society-type, industrial culture-type, and industrial production-type historical spaces, respectively. These obstacle factors collectively reflect the lack of memory accuracy and discontinuity in emotional carriers within the industrial historical space system.

Discussion

This study innovatively adopts an “Environment-Value-Memory” coupling perspective, focusing on Shenyang, a typical old industrial city in China, to explore the coordination mechanisms and obstacles of 64 block-level industrial historical spaces. The study yields three key findings. First, heterogeneity in the level of coupling coordination across both spatial and typological dimensions is a prominent observation. Spatially, industrial historical spaces in Shenyang’s central urban area exhibit higher levels of “Environment-Value-Memory” coupling coordination. This is due to their proximity to urban public services, stronger policy support for ecological restoration, and easier access to resources for value activation. In contrast, peripheral areas face the dual constraints of environmental degradation and weak memory preservation, resulting in lower coordination levels. This spatial pattern supports the spillover effect hypothesis within the “Environment-Value-Memory” coupling mechanism: highly coordinated industrial historical spaces (such as national industrial heritage sites) can drive the optimization of surrounding areas through knowledge sharing and resource aggregation, although the effect diminishes with distance.

Secondly, from a typological perspective, industrial production spaces exhibit the most significant coordination contradictions. These spaces often carry environmental legacy issues. While possessing high historical and technological value, their memory preservation is hindered by their disconnection from contemporary social life. In contrast, industrial cultural spaces tend to achieve better “Environment-Value-Memory” coordination. This is because their built environments are relatively well-preserved, their cultural values are more readily recognized by the public, and memory activation directly links environmental quality with perceived value. Industrial transportation and industrial social spaces tend to fall in between; their coordination is constrained by their smaller scale and limited functionality.

Finally, the obstacle degree diagnosis model is expected to identify the core constraints of different subsystems. For the environmental subsystem, the built environment (E1) is the primary obstacle, reflecting the long-standing environmental legacy of Shenyang’s heavy industrial development. For the value subsystem, the historical value (V1) and artistic value (V3) are key bottlenecks, indicating that traditional conservation paradigms still fall short in fully exploring the value of industrial heritage. For the memory subsystem, the material memory (M1) presents a relatively high obstacle, though still lower than the obstacle levels of all dimensions in the other two systems. This suggests that existing management measures have had some success in preserving industrial memory.

Unlike traditional heritage conservation frameworks that often focus on dualistic relationships, the “Environment-Value-Memory” framework offers a triadic perspective that addresses the specific challenges of industrial historical spaces. Traditional conservation paradigms typically prioritize the “Structure-Environment” coupling, focusing on material authenticity and the remediation of the physical fabric (e.g., repairing facades, ecological restoration). While this ensures the physical survival of the site, it often treats the site as a static “exhibit,” detaching it from dynamic social processes. This framework incorporates “Value” and “Memory” as active variables, arguing that a well-preserved environment is meaningless without the continuous interpretation of its value and the active transmission of memory. In the context of urban regeneration, many existing studies employ an “Environment-Economy” coupling mechanism, assessing how heritage reuse drives land value and commercial profit. While effective for economic revitalization, this approach often leads to gentrification or the Disneyfication of industrial sites, where the physical shell remains, but the “soul” is lost. The E-V-M framework introduces “Memory” as a distinct regulatory system. It posits that sustainable development requires not just economic return but also the preservation of the emotional bond between the site and its community. Finally, most existing multi-criteria evaluations treat “memory” or “social impact” merely as a sub-indicator within a broader “Social Value” category. A key innovation of this study is elevating the Memory System to an equal footing with the Environment and Value systems. The results of this study confirm that a space can possess both high physical quality and high historical value, yet still fail to coordinate effectively due to memory fragmentation. This validates the necessity of the E-V-M framework in diagnosing “invisible” governance failures that traditional models might overlook.

This study makes three key theoretical contributions to industrial heritage research and urban spatial governance. First, it enriches the theory of multi-system coupling in industrial historical spaces. By clarifying the connotations of the “Environment-Value-Memory” subsystems and elucidating their interactive mechanisms, this research expands the theoretical boundaries of industrial heritage studies from material space to integrated socio-ecological-value space. This represents a significant extension of Cherry’s research linking “cultural value” with “urban economic regeneration” 44. The developed coupled coordination evaluation index system not only provides a reusable theoretical tool for quantifying the coordinated development level of industrial historical spaces but also serves as an important supplement to Pratt’s theoretical framework on cultural value and social interaction45. Second, the study advances a paradigm shift in industrial heritage governance theory. Traditional governance theories emphasize top-down physical preservation, whereas this study proposes that effective governance must rely on the synergistic enhancement of the “Environment-Value-Memory” system. This systemic coordination paradigm offers a new theoretical perspective for addressing the complex challenges faced by old industrial cities. Third, the research deepens the theoretical understanding of industrial heritage’s spatial spillover signs. The findings confirm that industrial historical spaces with high “Environment-Value-Memory” coordination generate positive spillover signs on surrounding areas, with distance attenuating this impact. From the perspective of system coupling, high-coordination spaces achieve a balanced interaction among environment, value, and memory, forming a “synergistic energy field.” This energy field radiates outward, driving the unbalanced subsystems of surrounding spaces to adjust. For example, high-coordination sites’ well-preserved built environment (E2) sets a benchmark for neighboring spaces’ environmental renovation, while their activated collective memory (M2) inspires similar memory transmission practices. Second, this spillover sign is consistent with the “core-periphery theory” 46. High-coordination spaces act as “core areas” with strong resource allocation capabilities, and surrounding industrial historical spaces serve as “periphery areas” that absorb spillover benefits. The distance-decay characteristic arises from the increasing cost of spatial interaction (e.g., information transmission, resource flow) with expanding distance, which weakens the synergy between the core and periphery.

The conclusions of this study provide practical guidance for the governance of industrial historical spaces in Shenyang and other old industrial cities. First, targeted governance should be implemented for different types of industrial historical spaces. For example, old railway lines in transportation and social industrial historical spaces can be transformed into industrial memory trails, linking surrounding residential areas, and integrating transportation functions, memory inheritance, and community services. Second, it is recommended to promote the clustered development of industrial historical spaces based on spatial spillover signs. For instance, industrial historical spaces with high coordination can be identified as core nodes, and clusters of industrial historical spaces can be developed along urban transportation corridors to amplify spillover signs and facilitate the sharing of surrounding spatial resources. Finally, the “Environment-Value-Memory” coordination indicators proposed in this study should be integrated as key considerations when local governments update existing heritage protection policies.

This study conducted a comprehensive coordination assessment of 64 representative industrial historical spaces in Shenyang, a typical legacy industrial city. The research integrated GIS spatial analysis with quantitative coupling and obstacle diagnosis models to evaluate the systemic synergy within the “Environment-Value-Memory” framework. The interactive mechanisms among physical environments, heritage values, and collective memories were selected as key analytical elements to explore the promoting and inhibiting factors within the conservation process. Although the study followed a rigorous methodological framework and logical progression, ensuring the reliability and validity of the findings, the conclusions offer both theoretical contributions to industrial heritage governance and practical pathways for adaptive reuse. Nevertheless, the following limitations remain: First, the spatial scope is limited to Shenyang, which may affect the generalizability of the conclusions. Industrial historical spaces in other old industrial cities may exhibit different “Environment-Value-Memory” coupling characteristics due to variations in their industrial histories, resource endowments, and urban development paths. Future research could expand the scope to include cross-regional comparative analyses. Second, the research data lacks long-term time series observations. This study used questionnaire data to analyze the static state of “Environment-Value-Memory” coupling coordination, which does not capture dynamic changes. Future research could incorporate long-term tracking data to explore the evolutionary trends and driving factors of “Environment-Value-Memory” coupling. Additionally, the obstacle model overlooks the interaction effects between factors. The current model assumes that obstacle factors act independently, but in reality, social identity and belonging, and emotional memory, may interact. Future studies could employ more advanced models, such as partial least squares structural equation modeling (PLS-SEM), to investigate the interactive mechanisms of obstacle factors. Finally, it is important to note that the “spatial spillover signs” found in this study remain a conceptual inference based on spatial distribution patterns and theoretical deduction. The current coupling coordination model focuses on the interaction within the subsystems of a single spatial unit. We did not employ spatial econometric methods to statistically verify the causal relationship or quantify the intensity of influence between adjacent spaces. Therefore, the current evidence does not yet form a complete empirical chain for spillover signs. Future research should introduce spatial weight matrices to quantitatively test the validity of the mechanisms proposed in this paper.

In response to the limitations of traditional industrial heritage governance, this study constructs an “Environment-Value-Memory” coupling framework for industrial historical spaces. Focusing on 64 block-level industrial historical spaces in Shenyang, this study integrates GIS spatial analysis, semi-structured interviews, and three quantitative models—coupling degree, coordination degree, and obstacle degree—to explore the “Environment-Value-Memory” coupling mechanism and identify key obstacle factors.

The main conclusions are as follows: (i) The “Environment-Value-Memory” coupling coordination level of Shenyang’s industrial historical spaces exhibits significant spatial and typological heterogeneity. The central urban area and industrial culture-type spaces demonstrate a higher degree of coordination, while peripheral areas and industrial production-type spaces experience more prominent coordination contradictions; (ii) There are differences in the core obstacle factors across different subsystems: the environment subsystem is characterized by the built environment, the value subsystem by historical value, and the memory subsystem by material memory; (iii) Highly coordinated industrial historical spaces generate positive spillover effects on surrounding areas, which can be leveraged to promote regional cluster development.

This study enriches the theory of multi-system coupling in industrial historical spaces, fosters a paradigm shift in industrial heritage governance from material preservation to systemic coordination, and contributes to the theory of industrial historical space spillover. This research framework is not only applicable to the old industrial bases of Northeast China but also offers valuable insights for the preservation of industrial historical spaces in other developing countries, such as India and Brazil.