Introduction

Research background

Intangible Cultural Heritage (ICH), as a crucial carrier of Chinese traditional culture, not only preserves the contemporary value of ICH resources but also enhances national cultural identity and confidence [1]. Furthermore, ICH serves as an important cultural resource for the high-quality development of the tourism industry. Together, they constitute the engine for the integration of ICH protection and cultural tourism industry development. Therefore, the coordinated symbiotic development of ICH and tourism is of significant importance for achieving high-quality development in the Jiang-Zhe-Hu and promoting the dynamic inheritance of cultural heritage. However, currently, a significant portion of ICH resources have yet to enter the tourism market, posing an urgent issue on how to protect, activate, and promote ICH within tourism activities [2]. As one of the crucial s for China’s economic and cultural development, the Jiang-Zhe-Hu area’s formation mechanisms and spatial distribution characteristics of ICH are closely tied to factors such as culture and historical traditions. A comprehensive understanding of the spatial distribution characteristics and driving factors of ICH helps better grasp its essence and features, analyze its correlation with tourism resources, and determine its potential application value and development space in the tourism market. This knowledge provides a theoretical basis for subsequent activation efforts, thereby promoting a virtuous interaction and development between ICH and the tourism market.

The necessity of this study is reflected in several aspects: (1) Lack of Theoretical Foundation: Current literature on the coordinated development of ICH and the tourism industry is relatively limited, lacking a systematic theoretical framework and empirical research support. This study aims to fill this gap by analyzing the integration mechanisms of ICH and the tourism industry, providing new perspectives for theoretical development. (2) Urgency of Practical Demand: With the rapid development of the tourism industry, effectively integrating ICH resources to enhance tourism experiences and cultural value has become a pressing practical issue. This necessitates an in-depth exploration of the application of ICH in the tourism market to achieve the protection and revitalization of cultural heritage. (3) Need for Policy Support: Given the emphasis placed by national and local governments on cultural heritage protection and tourism industry development, researching the coordinated development of ICH and tourism will provide a scientific basis for policy formulation and promote the implementation and optimization of relevant policies.

Therefore, this study will focus on the following research questions: (1) What are the spatial differentiation mechanisms of ICH in the Jiang-Zhe-Hu ? (2) How is ICH utilized in the Jiang-Zhe-Hu ? Is there a lack of integration between ICH and the tourism industry? (3) How can we activate the application of ICH in the tourism market while preserving its authenticity and cultural roots?

The data collection for this study is divided into two main parts: Dataset 1 and Dataset 2, both of which are associated with the “Measurement Indicator System for the Coupling Coordination Development of Intangible Cultural Heritage and Tourism in the Jiangsu-Zhejiang-Shanghai Region.” All primary data in Dataset 1 are sourced from 2022, including POI data and statistical information from yearbooks, which serve as the baseline for this dataset. Dataset 2, on the other hand, is based on the issuance dates of relevant policies, with the cutoff year set as 2022.

Literature review

Intangible cultural heritage (ICH)

The protection and utilization of ICH are important theoretical and practical propositions in cultural development. Foreign scholars initially explored foundational issues concerning ICH, such as its characteristics, classification, assessment, and policies [3, 4]; Subsequently, research delved into issues related to the balance between protection and utilization of ICH, cultural commodification, community involvement, laws related to ICH protection, and issues of sustainable development [5, 6]; Over time, research on the authenticity of ICH experiences, quality of these experiences, cultural identity, and tourism consumption has increased [7, 8].

In China, ICH research has mainly concentrated on several areas. First, scholars emphasize the importance of legal frameworks and local community participation in ICH protection and transmission [9]. Second, they explore the role of ICH in shaping cultural identity and constructing social identity [10]. Additionally, research has examined the relationship between ICH and economic development, particularly the integration of cultural tourism and the assessment of its economic benefits [11]. Moreover, there is a growing focus on the application of digital technologies in ICH preservation and dissemination, exploring how innovative methods can revitalize traditional culture [12]. Finally, Chinese scholars have analyzed the international communication and cooperation mechanisms for Chinese ICH in the context of globalization [13].

Coordinated development of ICH and the tourism industry

In terms of research themes, domestic scholars have primarily focused on the integration of ICH and the tourism industry from two perspectives. On one hand, studies have explored ICH tourism development models, the tourism development value of ICH, and the bidirectional effects between ICH and tourism. On the other hand, scholars have examined the role of ICH -tourism coupling and coordination in rural cultural revitalization, rural poverty alleviation, and analyzed the coupling logic and spatial distribution patterns of ICH and tourism. Studies have also assessed the spatial distribution characteristics and driving factors of ICH in Southwest China.

Common research methods for studying the coordinated development of ICH and the tourism industry include coupling coordination models, multiple linear regression, system dynamics models, and case studies. For instance, Yang [14] used the Coupling Coordination Degree (CCD) model to reveal the relationship between the preservation of cultural landscapes (PSCL) and socio-economic levels (SEL) in 43 villages in Leishan County, Guizhou Province. Yao [15] conducted a survey of 424 tourists on Meizhou Island, using structural equation modeling to test the relationships between perceived value, place attachment, and revisit intentions. Xu [16] employed a system dynamics model to simulate the dynamic relationship between ICH protection and tourism development, emphasizing the role of policy interventions in achieving coordinated development. Esfehani [17] analyzed the interaction between ICH and tourism in a region of the UK, using in-depth interviews and participant observation to reveal how local culture attracts tourists through festivals, while also pointing out the negative impact of over-commercialization on cultural authenticity. In summary, current research methods include quantitative analysis, case studies, and system modeling, providing theoretical and empirical support for understanding the coordinated development of ICH and the tourism industry.

Application of explainable machine learning in heritage geography

Explainable Machine Learning (XML) has garnered increasing attention in heritage geography, particularly in the protection and management of cultural heritage. As the volume of data grows and model complexity increases, employing interpretable models allows researchers and decision-makers to better understand the results, thereby making more informed decisions. XML aims to enhance the transparency of machine learning models, enabling users to comprehend the decision-making process. Liu [18] posits that methods such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) can provide valuable explanations for complex models. These methods are particularly crucial in heritage geography, as they help stakeholders understand the factors influencing cultural heritage.

Common research models and methods include the following categories: 1. Risk Assessment Models - Random Forest: Rodriguez, J. M. P. employed a random forest model to analyze the impact of climate change on cultural heritage. Using the LIME method, the researchers identified key environmental variables influencing risk assessments, such as temperature fluctuations and precipitation. The advantage of this approach lies in its ability to handle high-dimensional data and provide variable importance scores, enabling decision-makers to formulate protection strategies targeting specific threats [19]. 2. Driver Analysis - XGBoost (Extreme Gradient Boosting): Xu, T. applied machine learning to comprehensively analyze glacier melting and its significant influencing factors in the Tibet Autonomous Region (TAR). The XGBoost algorithm was used to explore the nonlinear relationships between glacier melting and these features. Shapley values were employed to enhance model transparency by quantifying the influence of each feature on the melting process [20]. This method not only improved predictive accuracy but also increased the model’s interpretability, providing data support for urban planning. 3. Public Participation and Decision Support - Linear Regression and Visualization Tools: St ICH, B. studied how explainable machine learning can enhance public participation in heritage value assessment [21]. The application of explainable machine learning in heritage geography offers new perspectives and tools for the protection and management of cultural heritage. By enhancing model interpretability, researchers and decision-makers can better understand and apply machine learning outcomes, promoting the advancement of heritage geography.

In summary, existing research primarily focuses on the analysis of ICH from a single-dimensional perspective, lacking a comprehensive theoretical framework that integrates the mechanisms of spatial differentiation and tourism development. Many studies have failed to effectively combine explainable machine learning with coupling coordination models, resulting in an incomplete understanding of ICH spatial differentiation. Additionally, current research is often limited to specific cultural heritage or tourism development case studies, without providing systematic insights into the Jiang-Zhe-Hu region. This gap hinders the ability to reflect the unique cultural context and development needs of this area. Therefore, this study focuses on the 25 cities in the Jiang-Zhe-Hu, adopting a meso-level perspective to analyze the spatial distribution patterns, formation mechanisms, and the coupling coordination between ICH resources and the tourism industry. By integrating geospatial analysis techniques, machine learning algorithms, and the coupling coordination degree model, this research aims to provide valuable insights for the sustainable protection, innovative development, and region-specific tourism growth of ICH resources in the Jiang-Zhe-Hu.

Methods and data sources

Study area

In the report “Strategies and Policies for al Coordinated Development” by the Development Research Center of the State Council, it proposes the division of eight major economic zones. Among them, the Eastern Coastal Economic Zone includes Shanghai, Jiangsu, and Zhejiang provinces. The Jiang-Zhe-Hu, consisting of Jiangsu, Zhejiang, and Shanghai, has surpassed a trillion yuan GDP, with per capita GDP nearing the levels seen in developed countries. The choice of studying the Jiang-Zhe-Hu urban agglomeration is driven by its central role in the integrated development of the Yangtze River Delta. Not only does this boast abundant ICH, but it also possesses a mature mechanism for al coordinated development. Investigating the integrated development of ICH and tourism in this area not only helps uncover the intrinsic mechanisms linking ICH preservation and tourism development but also offers valuable insights and models for other s to emulate. From a geographical perspective, the Jiang-Zhe-Hu is situated in the Yangtze River Delta, serving as a vital economic and cultural hub in eastern China. This area is characterized by a dense network of rivers and highly developed infrastructure, facilitating extensive economic activities and cultural exchanges. The climate here is primarily subtropical monsoon, featuring distinct seasons, which support rich biodiversity and diverse agricultural practices. The ’s historical and cultural heritage is intricately linked to its economic development, making it an ideal case study for examining the interactive relationship between cultural preservation and tourism growth (Fig. 1).

Fig. 1
Fig. 1
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Study area

Research methods

Kernel density

Kernel density analysis calculates the density of features within their surrounding neighborhoods, reflecting the distance decay pattern and facilitating the revelation of spatial distribution characteristics of point data. Using ArcToolbox tools for this analysis, a kernel density map can be generated to reveal the spatial distribution features of ICH in the Jiang-Zhe-Hu [22].

Coupling coordination degree model

The coupling coordination degree is an indicator used to measure the level of coordinated development between different systems, widely employed to assess the coordinated development of systems such as tourism economy, ecological conservation, and urbanization [23]. This study utilizes the coupling coordination degree to measure the coordinated development between ICH resources and the tourism industry. The calculation involves three steps using entropy weight method, comprehensive evaluation, and coupling degree calculation formula. The formula for Coupling Coordination Degree (D) is:

$$\begin{aligned} \ D = \sqrt{C \times T} \ \end{aligned}$$
(1)

\(D\) represents the coupling coordination index between the comprehensive development system of ICH and the comprehensive development system of the tourism industry. \(D \in (0, 1)\), with a higher value indicating a higher level of coordinated development between ICH resources and the tourism industry. \(C\) denotes the coupling degree between ICH and the tourism industry. \(T\) represents the comprehensive evaluation index of the combined coupling system. In this formula, coefficients \((a)\) and \((b)\) are set to 0.5 each as specified in this study. In this study, these coefficients are set to 0.5 each. Using this formula, the level of coupling coordination development between ICH resources and the tourism industry in the Jiang-Zhe-Hu can be calculated.

Drawing on existing research findings, based on the calculation of the comprehensive evaluation index and the setting of the Coupling Coordination Degree model, in conjunction with the results of coupling coordination and relative development degree calculations, the phase and type classification criteria for the coupled coordination development of ICH resources and the tourism industry in the Jiang-Zhe-Hu are presented in Table 1.

Table 1 Classification of coupling coordination degree
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Gradient boosting decision trees (GBDT)

GBDT, proposed by Professor Friedman from Stanford University in 2001, is an intelligent algorithm widely used for classification, regression, and ranking tasks in recommendation systems [24]. It can identify the driving factors behind the formation of national spatial patterns. The core of GBDT involves iteratively generating the next generation of weak classifiers using base classifiers such as Classification and Regression Trees (CART), ultimately resulting in a globally optimal ensemble model for artificial intelligence. The specific formula is as follows:

$$\begin{aligned} \ F(x) = \sum _{m=1}^{M} \gamma _m h_m(x) \ \end{aligned}$$
(2)

F(x) represents the final ensemble model, which is the weighted sum of all base models. M denotes the total number of models, also corresponding to the number of iterations. ym represents the learning rate of the m-th base model, controlling its contribution to the ensemble. is the m-th base model, typically a decision tree .

XGBoost

XGBoost, proposed by Chen and Guestrin, is an improved method based on the gradient boosting model (GBDT). Compared to methods such as Boosting and Bagging, XGBoost demonstrates superior predictive performance. The improvements of XGBoost primarily focus on optimizing model training efficiency and reducing overfitting [25]. In the specific training process, assuming \(X\) is the set of relevant indicators of driving factors and \(Y\) is the dependent variable, the given training dataset is \((X, Y)\), where \(X_i\) represents an input sample instance, \(P\) denotes the number of independent variables, and \(s\) indicates the number of samples.

$$\begin{aligned} \ {\hat{y}}_i = F(x_i) = \sum _{k=1}^{K}\, f_k(x_i) \ \end{aligned}$$
(3)

\({\hat{y}}_i\) represents the predicted value of sample \(x_i\). \(F(x_i)\) is the final ensemble model, which denotes the weighted sum of all base models \(f_k(x_i)\). \(K\) is the total number of models, also indicating the number of iterations. \(f_k(x_i)\) is the \(k\)-th base model, typically a regression tree.

Random forest (RF) and SHAP (SHapley Additive exPlanations)

Random forest (RF): RF is an ensemble learning method primarily used for classification and regression tasks. Proposed by Leo Breiman in 2001, RF improves model accuracy and stability by combining predictions from multiple decision trees [26]. The main steps include data preparation, bootstrapping, decision tree construction, and model integration. Compared to traditional regression models, RF offers advantages such as high accuracy, resilience to overfitting, handling of high-dimensional data, and provision of feature importance.

$$\begin{aligned} \ {\hat{y}}(x) = \frac{1}{N} \sum _{i=1}^{N} T_i(x) \ \end{aligned}$$
(4)

\({\hat{y}}(x)\) represents the predicted output for input \(x\). \(N\) denotes the number of decision trees in RF. \(T_i(x)\) denotes the predicted output of the \(i\)-th decision tree for input \(x\).

SHAP(SHapley Additive exPlanations)

Although the Random Forest model can accurately predict the dependent variable, compared to linear regression analysis, the XGBoost method is a “black box” model that makes it difficult to explain which factors have a significant impact on the dependent variable. Therefore, to further analyze the driving factors of the dependent variable, this study adopts the SHAP value interpretability method to explain and analyze the independent variables. The SHAP value interpretability method not only overcomes some statistical shortcomings of methods like Feature Permutation and LIME in interpretability, but also ranks the importance of each independent variable, calculating the size and direction of influence of each driving factor [27]. Thus, SHAP value interpretability is suitable for identifying and explaining the driving factors of the dependent variable. Proposed by Lundberg and Lee, the SHAP value interpretability method is rooted in the concept of Shapley values from cooperative game theory, producing a prediction for each sample that reflects the contribution of each feature in that sample, i.e., the SHAP value. SHAP values simultaneously indicate both the magnitude and direction of each driving factor’s impact on the explained variable.

Neural network model (NN model)

The NN model is a computational model inspired by the biological nervous system, used to simulate how the human brain processes information. It consists of numerous artificial neurons (or nodes) interconnected through connections (or weights), forming a complex network structure. The NN model is particularly suited for handling complex nonlinear relationships and large-scale data, making it widely applied in fields such as image recognition, natural language processing, predictive analytics, and others [28].

Construction of indicator models and data sources

This study constructed two indicator models: Model 1, designed to investigate the influence mechanisms of ICH driving factors, and Model 2, developed to examine the coupling and coordinated development level between ICH resources and the tourism industry across 25 cities in Jiangsu, Zhejiang, and Shanghai.

In Model 1, the kernel density of ICH was designated as the dependent variable. Eight driving factors were selected as independent variables, encompassing both natural and social aspects. These factors include topography, hydrological conditions, ecological level, economy, population density, cultural foundation, and transportation conditions. The specific indicators and data sources are presented in Table 2.

Table 2 Factors influencing the spatial distribution of ICH in the Jiang-Zhe-Hut
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Model 2 comprises a total of 15 indicators across two systems. The indicator system for the tourism development system (U1) consists of two primary indicators: industry scale (A1) and industry efficiency (A2). The secondary indicators include A1-1, A1-2, A2-1, A2-2, A2-3, and A2-4. The ICH development system (U2) utilizes resource advantage(B1) and tangible carriers(B2) as measurement indicators. Its secondary indicators encompass B1-1, B1-2, B1-3, B1-4, B1-5, B2-1, B2-2, B2-3, and B2-4. The data sources for each indicator are presented in Table 3. Following the construction of the indicator system, this study initially conducted dimensionless processing on the raw data to address the issue of disparate units among various indicators. Prior to proceeding with subsequent data analysis, to ensure the specificity and accuracy of the research results, the entropy weight method was employed to assign weights to the corresponding data of each indicator. This approach was adopted to avoid the subjectivity inherent in manually setting weights, thereby ensuring scientific rigor and randomness in the calculation process.

Table 3 Measurement indicator system for the coupling and coordinated development of ICH and tourism industry in the Jiang-Zhe-Hu
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Result and discussion

Spatial distribution characteristics of ICH in the Jiang-Zhe-Hu

From a geographical distribution perspective, the ICH in the Jiang-Zhe-Hu exhibits a distinct pattern of al concentration, characterized by a “3 main cores, 2 sary cores” distribution. The “3 main cores” refer to three high-density core areas formed in the surrounding s of Shanghai, northern Hangzhou in Zhejiang Province, and southern Suzhou in Jiangsu Province. The “2 sary cores” indicate two secondary high-density core areas formed in Yangzhou and Nanjing in central and western Jiangsu Province, respectively (Fig. 2).

Fig. 2
Fig. 2
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Jiang-Zhe-Hu ICH spatial distribution kernel density map

Based on the kernel density distribution maps from each province, Nanjing, Suzhou, and Yangzhou are the primary high-density areas for ICH distribution in Jiangsu Province [29]. Wuxi, Xuzhou, Nantong, and Changzhou also exhibit relatively high-density areas. In northern Jiangsu, cities such as Lianyungang, Huai’an, and Suqian show relatively sparse ICH resources. In Zhejiang Province, Hangzhou and Shaoxing are high-density areas for ICH distribution, with Ningbo also being a high-density area. Jinhua and Wenzhou have slightly lower ICH point densities but are still relatively concentrated. Southern Zhejiang has fewer ICH resources, and cultural resources are relatively scattered. In Shanghai, the central urban area represents an extremely high-density area for ICH distribution. Cultural heritage sites are densely concentrated, especially in districts such as Huangpu, Jing’an, and Xuhui. Peripheral areas like Pudong New Area and Minhang District exhibit slightly lower ICH point densities but are still relatively concentrated (Fig. 3).

Fig. 3
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Kernel density map of ICH spatial distribution in 25 cities

Overall, the distribution of ICH in the Jiang-Zhe-Hu shows a clustered and uneven pattern, reflecting the influence of both natural and human geographical environments on the layout of ICH. In all three areas, ICH points are predominantly concentrated in economically developed cities with rich historical and cultural backgrounds. These cities include Nanjing, Yangzhou, and Suzhou in Jiangsu Province, Hangzhou in Zhejiang Province, and the central urban area of Shanghai. This distribution pattern indicates a significant correlation between the density of ICH distribution and the level of economic development [30]. Internal to each province, there are significant al variations in the distribution of ICH. In Shanghai’s central urban area, northern Jiangsu, and southern Zhejiang, ICH exhibits a more dispersed distribution. Conversely, in southern Jiangsu and northern Zhejiang, ICH tends to be more concentrated. The reasons for these patterns are as follows:(1) The Jiang-Zhe-Hu is characterized by dense rivers and lakes, which have a significant impact on the aggregation of ICH (ICH). Along the Yangtze River, Grand Canal, and the vicinity of Taihu Lake and Hongze Lake, there are high-density core areas of ICH projects in the.(2) The Jiang-Zhe-Hu boasts a profound al cultural heritage, with each province and city featuring distinctive cultural elements. Examples include the Jianghuai culture, Wu-Yue culture, and the Haipai culture. Among these, the most influential cultural schools originated from Suzhou, Wuxi in the Taihu, Jiaxing, Hangzhou, and other Ningzhen areas associated with the Wu-Yue culture. The Haipai culture evolved from the Wu-Yue culture, integrating the spirit of the times. The people of Wu-Yue are adept at pursuing a refined aesthetic psychology and possess unique creative abilities, which are major reasons for the concentration of ICH [31].

Analysis of the mechanisms of ICH formation in the Jiang-Zhe-Hu

Analysis of driving factors

  1. (1)

    Accuracy of Predictions Analysis

    To evaluate the superiority and reliability of the RF model in feature importance analysis, this study introduces three comparative models: GBDT, XGBoost, and Multilayer Perceptron (MLP).The comprehensive fitting performance of the RF model was validated using a training set and test set split in an 8:2 ratio. Prior to training the model, missing values were handled using mean imputation and interpolation methods. To eliminate the effects of differing magnitudes, data of varying scales were standardized. The aforementioned preprocessing steps enhance the predictive performance and generalization ability of the model. By ensuring that the machine learning model can fully utilize data features during training, these steps improve prediction accuracy. The results indicate that the RF model exhibits the lowest MAE (9.33) and the highest \(\text {R}^{2}\) (0.671) among all models, demonstrating the best performance in terms of mean absolute error and variance explanation capability (Fig. 4). This suggests that the RF model has superior goodness-of-fit, strong stability, and robustness, making its results the most reliable (Figs. 5, 6, 7; Table 4)

    Fig. 4
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    Comparison of actual and predicted values for the model

    Fig. 5
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    Non-linear relationship between each feature and the dependent variable

    Fig. 6
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    Training model fit effect

    Fig. 7
    Fig. 7
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    ROC curve and AUC area of the model fit

    Table 4 Comparison of basic parameters of machine learning models
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  2. (2)

    Analysis of Indicator Importance

    The results of the RF model show that the importance scores and rankings of influencing factors are as follows: population: 0.512> Religious sites: 0.199> GDP: 0.084> Elevation: 0.070> Traditional village: 0.051> NDVI: 0.045> Road mileage: 0.028> river: 0.012. Population, number of religious sites, and GDP have significant influences on the distribution of ICH in the Jiang-Zhe-Hu. In contrast, geographical factors such as elevation, traditional village presence, NDVI, road mileage, and river have relatively minor impacts, with river exerting the least influence. This ranking further highlights that in the Jiang-Zhe-Hu, the transmission of ICH is more influenced by cultural factors rather than geographical environment [32] (Fig.  8).

    Fig. 8
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    Ranking of driving factor importance

Analysis of driving mechanisms

To accurately uncover the spatial distribution of ICH in the Jiang-Zhe-Hu and its nonlinear interaction with driving factors, this study integrates SHAP analysis to examine the correlation between driving factors and ICH density. The Fig. 9 displays the SHAP values illustrating the impact of each feature variable in the RF model. SHAP values are used to explain the contributions of model outputs. The X-axis represents SHAP values, where larger values indicate greater influence of the feature on the model output. The Y-axis represents the feature variables. Colors denote the magnitude of feature values, with blue indicating lower values and red indicating higher values. Each point represents a data point for a sample, and its position reflects the SHAP value of that feature for that sample [33]. As shown in the figure 9: Number of religious places, population, GDP, and number of traditional villages have a positive impact on ICH density. NDVI, elevation, road mileage network length, and river have relatively minor effects, with NDVI exhibiting a negative impact (Figs. 10, 11).

Fig. 9
Fig. 9
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Visualization of SHAP values

Fig. 10
Fig. 10
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SHAP dependence plot

Fig. 11
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The distribution map of ICH and driving factors in Jiang-Zhe-Hu

  1. (1)

    Natural Environment

    Elevation: Elevation height is closely related to human habitation and cultural heritage transmission [34]. Figure  9 demonstrates a relatively balanced distribution of positive and negative values for Elevation, without significant bias, indicating a minimal impact of elevation on the density of ICH in the Jiang-Zhe-Hu. This finding is consistent with previous research. For example, Meng [35] in a study on Shandong Province, discovered that the spatial distribution of ICH in the region is minimally affected by topography. The flourishing of local folk arts and traditional crafts is primarily attributed to the abundance of water resources and convenient transportation, indicating that cultural activities are more frequent in low-altitude areas, where the protection and transmission of ICH resources are relatively easier. Jiang-Zhe-Hu generally has low elevations. Jiangsu Province is characterized by vast plains and numerous lakes, with flat terrain; Zhejiang Province is dominated by mountainous and hilly areas with picturesque natural landscapes; and Shanghai is located on the low-lying alluvial plain of the Yangtze River Delta. However, despite being a low-altitude region, the spatial distribution of ICH in Jiangsu, Zhejiang, and Shanghai is less influenced by elevation. Instead, factors such as socioeconomic development, urbanization, and cultural diversity play a more significant role in shaping the spatial clustering of ICH in this area.

    River: In early human societies, populations often congregated along riverbanks due to the essential provisions of water, transportation, and resources that rivers provided, which significantly influenced societal development. Consequently, the formation and transmission of ICH are closely linked to rivers. Huang, Y. pointed out that the canal has played a crucial role in the evolution of Suzhou’s cultural heritage. In contrast, factors such as population, elevation, water transportation, and cultural arts have had relatively insignificant impacts, emphasizing the importance of rivers in cultural transmission [36]. Figure 9 reveals a slightly greater number of positive values than negative values for rivers, with some high SHAP (Shapley Additive Explanations) red points indicating a positive correlation trend. This aligns with the earlier statement that “the Yangtze River, the Beijing-Hangzhou Grand Canal, and the vicinity of Taihu and Hongze Lakes are high-density core areas of ICH projects.” The Yangtze River basin and the Beijing-Hangzhou Grand Canal have historically served as major arteries for economic and cultural exchange in China. These s are rich in historical heritage and traditional cultures, including ancient canal culture and water village culture, making them important hubs for the preservation of intangible heritage. Furthermore, rivers in these s facilitate convenient water transportation, enabling rapid dissemination and exchange of goods, people, and cultural information among different areas. This accessibility not only fosters economic development but also promotes the integration and exchange of diverse cultural forms across s, such as the Wuxia culture and Three Gorges culture in the upper reaches of the Yangtze River, and the water village culture in the lower reaches, along with the canal culture of the Beijing-Hangzhou Grand Canal.

    Ecological Factor: The relatively balanced distribution of positive and negative SHAP values for NDVI suggests that NDVI has a limited impact on model outputs, which is associated with the high level of urbanization in the Jiang-Zhe-Hu of China. Jiang-Zhe-Hu, as one of China’s economically developed areas, experiences rapid urbanization and significant changes in land use, leading to increased urban heat island effects and intensified human activities that disturb the environment. These factors directly or indirectly diminish the explanatory power of NDVI in explaining the spatial distribution of ICH. Similarly, Gabriele, M. used remote sensing technology and GIS (Geographic Information Systems) to analyze vegetation changes in areas with different levels of urbanization. The study evaluated how urban-induced land use changes and environmental disturbances affect the utility of NDVI in explaining the spatial distribution of ICH [37]. The results indicate that in the context of high urbanization, the impact of ecological factors on the spatial distribution of ICH resources is relatively limited, with socio-economic and cultural factors playing a more significant role. This finding aligns with the results of the present study, demonstrating that in highly urbanized environments, ecological factors have a comparatively minor influence on the spatial distribution of ICH resources.

  2. (2)

    Socio-Cultural Factors

    Economic Factors: The level of economic development significantly influences the spatial distribution of ICH in the Jiang-Zhe-Hu. Figure 9 illustrates a predominance of positive values for GDP, accompanied by some high SHAP values indicated by red points, suggesting a moderate positive correlation. Jiang-Zhe-Hu benefits from favorable conditions and environments in terms of resource allocation, cultural market demand, education for heritage preservation, and cultural innovation. These factors contribute to the promotion, preservation, and dissemination of local ICH. Moreover, s with higher economic development levels exhibit a broader and deeper demand for culture and the arts. ICH activities, being integral to local culture, satisfy the needs of residents and tourists for cultural experiences. Therefore, ICH activities in these areas are more attractive and possess greater market potential. Taking Nanjing, the capital of Jiangsu Province, as an example, the city not only boasts a r ICH historical and cultural heritage but also hosts numerous museums, art galleries, and cultural events. This has attracted a large number of art enthusiasts and tourists, making it an important venue for the display and exchange of modern art, and fulfilling the public’s quest for contemporary artistic exploration [38]. Moreover, as cultural consumption in Nanjing continues to grow, many residents engage in various cultural activities, such as theater, concerts, and traditional opera performances. This heightened cultural demand not only fosters the flourishing of local artistic creation but also promotes the preservation and development of intangible cultural heritage, such as Peking opera.

    Population Factor: Population has a significant positive impact on the density of ICH. This implies that s with higher population densities can greatly facilitate the preservation and dissemination of ICH. Areas with higher populations tend to exhibit greater market demand and cultural consumption potential, along with more cultural facilities and venues such as museums, theaters, and exhibition centers. These venues provide platforms for the display, performance, and exchange of ICH activities, enabling them to reach a broader audience and be more widely appreciated. As a result, population density and associated cultural infrastructure collectively promote the preservation, innovation, and development of ICH. Wang, J. argues that the Yangtze River Delta region, with its long history and cultural diversity, has attracted a large immigrant population from across the country and even the globe. This diversity provides a r ICH cultural foundation for the transmission and innovation of ICH [32]. Population mobility not only facilitates the exchange of diverse cultures but also promotes the reconfiguration and innovation of traditional culture within contemporary contexts. For instance, Shanghai’s opera culture and Suzhou’s embroidery techniques have incorporated foreign cultural elements, becoming significant components of the modern cultural industry and thereby advancing the innovative development of ICH content.

    Cultural Factors: The model results indicate that the distribution of SHAP values for traditional villages is balanced between positive and negative values, showing no significant positive or negative correlations. This suggests that current ICH projects in traditional villages have not been effectively integrated into local ICH industry development plans and economic activities. There is a lack of systematic measures for protection, inheritance, and utilization, resulting in a weak integration with the broader ICH industry. Traditional villages serve as crucial bearers of ICH, preserving rich traditional cultures, customs, and handicrafts. Therefore, they should ideally serve as important bases for the transmission of ICH activities. In the future, governments at all levels should leverage the unique cultural value and attractiveness of traditional villages to develop cultural tourism. This approach not only promotes the inheritance of ICH but also drives local economic development, thereby enhancing the economic benefits and societal impact of ICH activities and increasing ICH density. Additionally, the Number of Religious Sites significantly influences the model outputs, with areas having more religious sites showing a notable positive impact on ICH density. Religious sites reflect the local religious and cultural atmosphere and traditions, which are closely associated with ICH. Religious sites reflect the local religious cultural atmosphere and traditions, and religious culture is often closely linked with ICH. Religious ceremonies are typically significant components of ICH. Many local traditional festivals and ceremonial activities are driven by religious beliefs. For instance, Christian Christmas, Buddhist Ullambana Festival, and Islamic Eid al-Fitr are all important manifestations of local culture. These ceremonies not only reflect religious beliefs but also carry r ICH cultural content and community memories [39].

    Transportation: The SHAP values predominantly cluster near zero (Fig. 9), indicating that Road Mileage has a minimal impact on the spatial distribution of ICH. The Jiang-Zhe-Hu boasts a rich historical background and profound cultural heritage. Many ICH projects have become deeply integrated into the local social structure and cultural traditions, possessing strong self-protection and inheritance capabilities. These ICH projects hold significant importance in the daily lives of local residents and are less influenced by modern transportation infrastructure. Furthermore, in recent years, local governments in the Jiang-Zhe-Hu have implemented effective measures to protect ICH projects in urban planning and infrastructure development. For instance, during the construction and expansion of road networks, efforts are made to avoid areas where ICH projects are located or implement corresponding protective measures. These policies and measures effectively mitigate the potential negative impact of increased road network density on ICH projects. As a result, despite the development of modern transportation infrastructure, the cultural significance and integrity of ICH projects in the Jiang-Zhe-Hu remain relatively preserved and unaffected. However, there are also cases where the spatial distribution of ICH is significantly influenced by transportation. Oakes (2016) highlights that certain ethnic cultural heritage projects in southwestern China, such as the Miao embroidery and song and dance traditions, have been notably affected by the rapid expansion of highways and rail networks. This development has imposed a substantial impact on the spatial distribution of ICH [40]. The growth in transportation infrastructure has brought in large numbers of outside populations and tourists, causing traditional cultural forms in these remote areas to gradually lose their original living foundations and community support. As population mobility increases, the transmission of traditional crafts has shifted towards commercialization, leading to a dilution of their cultural essence. This phenomenon is primarily due to the reliance of these ICH projects on closed or semi-closed community environments. The rapid expansion of transportation infrastructure disrupts this closed nature, leading to an excessive integration of cultural forms with modern economic activities, thereby weakening the purity of cultural transmission. In contrast, the Yangtze River Delta region benefits from protective policies and cultural facility support, wh ICH mitigates the impact of transportation infrastructure on ICH projects.

    In conclusion, the transmission of ICH in the Jiang-Zhe-Hu is predominantly influenced by cultural factors. This is closely tied to the ’s longstanding historical and cultural traditions, rich social customs and lifestyles, population mobility and cultural exchanges, as well as government cultural policies. As one of China’s most economically developed s, Jiang-Zhe-Hu attracts significant population influx and cultural exchanges. Population mobility and cultural exchanges foster interactions and integration among different s, ethnicities, and cultures, resulting in a diverse array of ICH. Moreover, the government has dedicated substantial resources and efforts to protect and promote ICH through various cultural activities and policy support, thereby facilitating its transmission and development. Therefore, in comparison, geographical environmental factors have a lesser impact on the transmission of ICH.

Analysis of coupling coordination between ICH resources and tourism industry in the Jiang-Zhe-Hu

Comprehensive evaluation of ICH resources and the tourism industry

Based on the coupling coordination model, the level of coordination between ICH resources and the tourism industry development in the Jiang-Zhe-Hu is assessed. Overall, in 2022, the coordination development between ICH industry and tourism industry in Jiang-Zhe-Hu is at a moderate level, with varying degrees of imbalance still present. Further efforts are needed to strengthen policy guidance and resource integration to promote deep integration between the two sectors. Shanghai shows the best performance, achieving a good coordination level. Many cities exhibit moderate imbalance, while some show slight imbalance or barely coordinated efforts. In terms of ICH resources: Zhejiang Province generally surpasses Jiangsu Province, with Lishui having the highest evaluation value and the most abundant resources, while Zhoushan ranks the lowest among all cities with only 0.028 evaluation value. Shanghai also possesses relatively abundant ICH resources, ranking second after Lishui. In Jiangsu Province, southern cities generally have richer ICH resources, with Suzhou, Yangzhou, and Nanjing receiving the highest ICH comprehensive evaluations, and Lianyungang, Suqian, and Huai’an having the lowest ICH evaluation values. Regarding the development level of the tourism industry: In 2022, the overall development level of the tourism industry in Jiang-Zhe-Hu ’s prefecture-level cities is not high, largely impacted by the COVID-19 outbreak at the end of 2019. Although China started lifting comprehensive control measures in March 2022, the severe impact of the 3-year-long pandemic on the tourism industry has hindered rapid recovery. Moreover, due to the developed nature of the tourism industry in Jiang-Zhe-Hu, the impact of the pandemic has been more pronounced, resulting in generally lower development levels across prefecture-level cities. Shanghai performs the best in this regard, with the highest comprehensive evaluation score for its tourism industry. Southern Jiangsu Province and northern Zhejiang Province also show relatively good comprehensive evaluation scores for tourism industry development, such as Suzhou, Nanjing, and Hangzhou, with scores ranging from 0.023 to 0.036. Cities like Yancheng, Lianyungang, Zhenjiang, and Lishui have moderate to lower-middle-level tourism evaluation scores, indicating room for improvement in their tourism industries. Cities including Zhoushan, Huai’an, Taizhou, Quzhou, and Suqian have the lowest evaluation scores for tourism development, reflecting lagging tourism industry development in these areas (Fig. 12).

Fig. 12
Fig. 12
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Scores of ICH resources, tourism industry, and coupling coordination degree in Jiang-Zhe-Hu

The coupling degree (C) reflects the degree of mutual dependence and influence between the tourism industry and ICH. A higher value indicates a closer interaction between the two. A high coupling degree signifies that the two systems can cooperate effectively and promote development together. Table 5 shows that high coupling degree cities include Changzhou (0.987) and Zhoushan (0.993). These two cities exhibit very high coupling degrees, indicating a strong relationship between the tourism industry and ICH resources, with a solid foundation for resource development and utilization. Cities with moderate coupling degrees include Suzhou (0.985) and Wuxi (0.984). Although these cities show high coupling degrees, reflecting good interaction between the two, attention is still needed regarding coordination in practical applications. Cities with low coupling degrees include Suqian (0.641) and Lishui (0.427). These cities have noticeably lower coupling degrees, indicating a weaker connection between the tourism industry and ICH resources, and there is an urgent need to enhance their integration. Overall, cities with high coupling degrees (such as Changzhou and Zhoushan) are better able to utilize their ICH resources to promote tourism development, whereas cities with low coupling degrees face challenges in optimizing resource utilization.

Based on the above analysis, it is evident that the relationship between the tourism industry and ICH resources in Shanghai is relatively coordinated, with balanced development. The tourism market development level is prominent, and although ICH resources are not as abundant as in Zhejiang, they still maintain a good level. In Jiangsu Province, cities in the southern generally exhibit higher levels of tourism industry development, ICH resources, and coupling coordination compared to those in the northern. Most cities in northern Jiangsu show moderate to slight imbalance in coupling coordination, indicating a need for further strengthening and optimization of the coupling relationship between the tourism industry and ICH resources. Within Zhejiang Province, the overall evaluation of tourism industry and ICH resources is relatively high. However, the coupling coordination level in most cities still falls within moderate to slight imbalance, suggesting room for improvement in the coupling relationship between the tourism industry and ICH resources.

Table 5 Comprehensive evaluation and coupling coordination degree of ICH resources and tourism industry
Full size table

Analysis of coupling coordination between ICH resources and the tourism industry

Based on the classification of coupling coordination in various cities of the Jiang-Zhe-Hu, it is evident that Shanghai has the highest level of coordination, reaching a good coordination level. This indicates that Shanghai’s tourism industry and ICH resources have achieved a high level of development, with a close coordination between the two. In the future, efforts can continue to strengthen the integration of the tourism industry and ICH resources, optimize development structures, and enhance the core competitiveness of the tourism industry. Suzhou and Hangzhou show a state of barely coordinated coupling. While their tourism industries are relatively mature, further enhancement is needed in coordinating with ICH resources. It is necessary to optimize the tourism industry structure, strengthen the protection and development of ICH resources, improve the quality and characteristics of the tourism industry, and deepen the integration between tourism and ICH resources. Nanjing, Jinhua, Ningbo, and Wenzhou are on the brink of imbalance in coupling coordination. Nanjing’s tourism industry is developed, but insufficient development and utilization of ICH resources have resulted in an imbalance between tourism and ICH resource development. Despite high coupling, the lack of effective policy support and adequate funding prevents ICH resources from fully leveraging their potential in the tourism industry. Therefore, increased investment in ICH projects and the formulation of policies conducive to the integration of ICH and tourism industries are recommended to promote ICH protection and tourism resource development [41]. Cities like Jinhua, Ningbo, and Wenzhou possess abundant ICH resources but relatively weaker tourism industries. There is inadequate integration between ICH resources and the tourism industry, resulting in insufficient synergistic effects. Future efforts should focus on integrating ICH resources with tourism projects, exploring the essence of ICH, developing locally distinctive tourism routes and products, and enhancing the competitiveness of ICH in the tourism market.

In Changzhou, Wuxi, Yangzhou, Jiaxing, Shaoxing, Taizhou, and Lishui, the coupling coordination is mildly mismatched. Changzhou and Jiaxing have relatively weak foundations in both tourism industry and ICH resources, leading to lagging development in both sectors. Although Wuxi and Yangzhou have relatively well-developed tourism industries, underutilization of ICH resources results in low synergistic effects between tourism and ICH resources. Shaoxing, Taizhou, and Lishui possess abundant ICH resources; however, slow development in the tourism industry prevents full utilization of these ICH resources. Lianyungang, Nantong, Xuzhou, Yancheng, Zhenjiang, Huzhou, and Quzhou are in a state of moderate mismatch in coupling coordination. These cities have weak foundations in both tourism industry and ICH resources, leading to insufficient deep integration and interaction between tourism and ICH resources. The lack of effective policy support and adequate financial investment hinders ICH resources from fully leveraging their potential in the tourism industry [42].

Huai’an, Suqian, Taizhou, and Zhoushan are in a state of severe mismatch in coupling coordination. These cities generally suffer from weak foundations in both the tourism industry and ICH resources, as well as inadequate integration and deep fusion of resources. To promote the coordinated development of ICH and the tourism industry, measures such as enhancing tourism infrastructure and service quality, increasing the development and utilization of ICH resources, implementing supportive policies, and strengthening resource integration are needed to enhance synergies between the two sectors.

Based on the above analysis, none of the cities in the Jiang-Zhe-Hu have achieved an optimal level of coordination between ICH and the tourism industry, indicating that full integration and coordination have not yet been realized. Shanghai performs the best, leading in both ICH resource protection and tourism industry development. Efficient resource integration and policy support have enabled Shanghai to achieve a good level of coordination. Hangzhou and Suzhou have rich ICH resources and relatively well-developed tourism industries, but there is still untapped potential that needs further enhancement in coordination. Nanjing, Wenzhou, Jinhua, and Ningbo face integration issues between ICH and tourism resources, resulting in underutilization of resources and lower coordination levels. Wuxi, Taizhou, Jiaxing, Lishui, Shaoxing, Yangzhou, and Changzhou exhibit significant gaps in coordination between their tourism industries and ICH resources, requiring strengthened efforts in resource integration and promotion. Cities like Huzhou, Xuzhou, Nantong, Quzhou, Yancheng, and Lianyungang show lower coordination levels, necessitating more policy support and development strategies in the future. Cities such as Zhenjiang, Huai’an, Zhoushan, Taizhou, and Suqian face severe mismatches between ICH resources and tourism industry development, requiring fundamental improvements in resource utilization and development strategies to achieve mutual economic and cultural benefits (Fig. 13).

Fig. 13
Fig. 13
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Comprehensive evaluation and coupling coordination degree of ICH resources and tourism industry

Suggestions

Strengthen the integration of regional cultural and tourism resources

The Jiang-Zhe-Hu, characterized by neighboring and culturally similar areas, should actively apply the successful experiences from the Lijiang Cultural Ecology Protection Zone in Yunnan. This involves breaking administrative boundaries and establishing cross-regional cooperation mechanisms to enhance the protection of ICH and achieve deep integration with the tourism industry [43]. Given the shared cultural background in the Jiang-Zhe-Hu, characterized by cultural similarities, it is essential to strengthen regional cooperation. This can be achieved by effectively utilizing similar cultural backgrounds among cities, such as integrating Suzhou’s garden culture, Yangzhou’s garden culture, and Shanghai’s garden culture. By leveraging Suzhou’s leading role and establishing cultural ecological protection zones, regional coordination and mutual benefits can be promoted. Additionally, integrating theme park cultures in Hangzhou, Shanghai, and Suzhou, as well as other cultural aspects like mountainous and water cultures, automotive culture, canal culture, and celebrity culture, can enhance regional cultural tourism resource collaboration. Furthermore, for complementary regional tourism resources such as Suzhou’s garden culture, Shanghai’s urban culture, and Jiaxing’s Jiangnan culture, a connected approach should be adopted to exploit regional synergies and form a complementary cultural network. For shared cultural resources across multiple regions, such as ICH projects, the Grand Canal, and Taihu Lake, it is advisable to jointly organize large-scale events. This will drive cultural development in relatively underdeveloped areas and promote coordinated regional development.

Transform government functions and innovate institutional reforms

In recent years, the management system for cultural and tourism resources in China has remained fragmented and siloed, with unclear debt, authority, and benefit relationships between the government and enterprises. Excessive government involvement in enterprise management, along with bureaucratization and lack of enterprise autonomy, has led to relatively low efficiency in the development of the cultural industry [44]. To promote the coordinated development of regional cultural and tourism resources, institutional reforms should focus on the following aspects: Firstly, transforming government functions is essential for the institutional reform of regional cultural and tourism resource coordination. Some administrative tasks should be delegated to cultural institutions to enhance their autonomy and streamline government functions. Additionally, there should be a clear separation between government and enterprises, with the establishment of specialized cultural management companies to detach governmental roles from cultural industries. Secondly, regional cultural and tourism resource departments should establish modern enterprise management systems for cultural tourism resources. This would involve adapting enterprises to market rules, creating a favorable external environment for businesses, and developing legal and regulatory frameworks for coordinated regional cultural and tourism resource development. Enterprises should also adapt to internal changes by transforming management practices and philosophies, implementing standardized management, and developing modern management systems. Thirdly, improving the investment and financing system is crucial. The current system is overly dependent on national funding, wh ICH is limited and restricts the growth of the cultural industry. Encouraging market entry for cultural industries and attracting private investors is necessary. The government should leverage fiscal and tax policies to promote investment in the cultural and tourism resource industry. Finally, reforming the management system for cultural and tourism resources is essential. The existing system involves multiple departments with overlapping responsibilities, leading to management difficulties. Integrating cultural departments is a necessary step to address these challenges and progressively transition to a more cohesive cultural system.

Fully explore the cultural connotations of ICH and promote innovation in cultural tourism products

Insufficient exploration of cultural connotations is a major reason for the underdevelopment of cultural tourism resources, manifesting in both the depth of exploration and the breadth of integration. Firstly, there should be a deeper exploration of the garden culture, water town culture, Su-style lifestyle culture, and craft art culture in the Jiang-Zhe-Hu, integrating these with traditional tourism resources for innovation. For example, classical garden resources can be connected to form a cohesive cultural tourism product cluster; water town culture can be used to develop tourism models for ancient towns and villages; and craft art culture can serve as the basis for developing cultural and creative tourism projects. Such deep integration of cultural connotations with tourism resources can drive a transformation of tourism products from being singular and outdated to being diversified and customized. Secondly, to broaden the integration of cultural connotations, a full industrial chain approach should be employed. Integration focused on individual aspects of cultural tourism value is insufficient for creating a comprehensive, immersive cultural tourism experience. Enhancing the value of cultural tourism products and establishing a systematic cultural tourism industry ecosystem requires linking different segments and enterprises within the cultural industry through cultural connotations. For instance, integrating the dispersed lakes, ancient towns, and cities of Jiangnan water towns through a cohesive cultural narrative can unify spatially scattered tourism resources under a cultural concept. Similarly, exploring local folklore and constructing a full industry chain of cultural tourism integration-from folklore activity exploration, artistic processing, and cultural performances to art events-can effectively connect cultural resource exploration with consumer experiences. Furthermore, new media platforms play a crucial role in innovating cultural tourism products. Utilizing next-generation information technologies to redevelop traditional cultural connotations and innovate tourism activity modes can significantly enhance the visitor experience. For example, historical origins or production processes of garden culture, historical artifacts, and craft art can be digitally modeled, and VR technology can bridge the time and space gaps between visitors and abstract cultural connotations, thus increasing visitor engagement [45]. Additionally, upgrading traditional performance stages with smart technology, coordinating elements such as sound, light, electricity, and mechanics, can recreate historical festivals and integrate historical cultural resources with tourism activities, allowing visitors to participate in the creation of audio and video cultural tourism products and interact with them.

Conclusion

This study investigates the spatial distribution of ICH in the Jiang-Zhe-Hu and its integration with the tourism industry, leading to the following conclusions:

  1. (1)

    ICH resources in Jiang-Zhe-Hu exhibit an aggregated distribution pattern characterized by “3 primary cores and 2 sary cores”. This includes core cities such as Shanghai, Hangzhou, and Suzhou, which act as high-density primary core areas spreading outward, and secondary core areas centered around cities like Yangzhou and Nanjing.

  2. (2)

    In the Jiang-Zhe-Hu, human factors exert a greater influence on the distribution of ICH, whereas geographic environmental factors have a lesser impact. Specifically, population, number of religious places, and GDP exhibit significant positive effects on the distribution of ICH in the Jiang-Zhe-Hu. Conversely, NDVI (Normalized Difference Vegetation Index) and road mileage have relatively minor effects on ICH distribution, with NDVI showing a negative influence.

  3. (3)

    Overall in 2022, the coordination level between ICH resources and the tourism industry in Jiang-Zhe-Hu was moderate. Shanghai showed the best performance, while other areas displayed varying degrees of coordination imbalance. In terms of ICH resources, Zhejiang Province surpassed Jiangsu Province overall, with Lishui having the highest evaluation and most abundant resources. Regarding tourism industry development, although Shanghai led in comprehensive evaluation, cities like Suzhou, Nanjing, Hangzhou, Wuxi, and Changzhou also achieved relatively high levels.

  4. (4)

    According to the coupling coordination model, Shanghai demonstrated the best coordination level between ICH resources and the tourism industry, reaching a good coordination level. In Jiangsu Province, the integration of ICH resources with the tourism industry was relatively better in southern areas (such as Nanjing and Suzhou), yet there remains an imbalance in northern Jiangsu. Zhejiang Province generally exhibited a higher degree of integration between ICH resources and the tourism industry compared to Jiangsu, but there is still room for improvement in the depth of integration, necessitating enhanced policy support and resource integration.

Although this study extensively explored the spatial differentiation mechanisms of ICH and its integration with tourism development using interpretable machine learning and coupling coordination models, there are several limitations related to data depth and breadth, model applicability, and interdisciplinary research. To further refine and develop the content and methodology of this study, future research could consider the following improvements: (1) Increase the depth and breadth of data related to ICH resources and tourism industry, particularly by integrating Geographic Information Systems (GIS) and remote sensing technologies to obtain more precise spatial data. (2) Continue to explore and optimize interpretable machine learning models and their applications in understanding the spatial differentiation of ICH and tourism integration. Introduce emerging machine learning algorithms and methods such as deep learning and ensemble learning to improve predictive accuracy and explanatory power. (3) Focus on Micro-Level Research : Future studies should particularly emphasize micro-level research, exploring smaller research areas such as specific communities or villages’ ICH. This includes in-depth investigations into the behaviors of individual cultural practitioners, tourists’ cultural experiences, and local community engagement with and identification with ICH. Qualitative research methods, such as interviews and case studies, should be employed to understand how these micro-level factors impact the protection and integration of ICH with tourism. Additionally, integrating methods such as social network analysis can shed light on the interactions between individuals and communities, thereby enr ICH ing our understanding of the relationship between ICH and the tourism industry.