Evaluation and Optimization Strategies for Provincial Culture and Tourism Integration from the Perspective of Landscape Narrative: A Case Study of Anhui Province, China

Abstract

Landscape narrative theory, which focuses on the interaction between space, culture, and human experience, provides a practical and interdisciplinary framework for guiding the integration of culture and tourism. By incorporating storytelling elements into tourism planning, it helps transform static cultural assets into engaging visitor experiences. This approach is particularly relevant in provincial contexts where cultural resources are unevenly distributed. Taking Anhui Province, China, as a case study, this research builds a five-dimensional evaluation system covering culture–tourism economy, cultural resources, tourism resources, transportation accessibility, and policy support. Using spatial analytical methods such as Moran’s I and the Spatial Autoregressive (SAR) model, the study identifies clear spatial clustering patterns and influential factors. The SAR model results show that transportation accessibility (coefficient = 0.685, p < 0.01) and policy support (coefficient = 0.736, p < 0.01) significantly promote integration. In contrast, cultural resources (coefficient = −0.352, p < 0.01) and tourism resources (p ≈ 0.11) have limited or no significant direct economic impact. Based on these findings, this paper proposes targeted strategies such as building regional narrative networks, enhancing infrastructure and policy coordination, and fostering collaborative development. The key contribution of this study lies in applying landscape narrative theory at the provincial level to construct a “Theory–Indicators–Method–Strategy” framework, offering new perspectives for promoting high-quality regional culture–tourism integration.

1. Introduction

In recent years, the cultural and tourism industries have emerged as significant drivers of economic growth in China, receiving strong policy support and strategic attention at the national level. Since the formal institutional merger of culture and tourism departments in 2018, the integration of these two sectors has evolved from administrative coordination to more substantial, quality-oriented synergy. This development has also received attention in Anhui Province, which has actively implemented culture–tourism integration strategies aligned with national directives [1,2].

Against this backdrop, landscape narrative theory has increasingly gained recognition as a valuable framework for guiding the high-quality integration of culture and tourism. This theory highlights the embedding of narrative logic into spatial environments, allowing for the activation of cultural symbols, the construction of thematic experiences, and the creation of engaging cultural atmospheres [3,4]. Through narrative design, tourism products become more experiential and emotionally resonant. Landscape narrative has proven particularly effective in cases involving ancient village revitalization [5], intangible cultural heritage transmission [6,7], and cultural district regeneration [8,9]. However, recent critiques have noted that excessive reliance on symbolic design or storytelling techniques may lead to superficial experiences or fragmented implementation, especially when not rooted in local identity or coordinated across scales.

At a larger spatial scale, landscape narrative offers solutions to issues such as fragmented resource allocation and homogenized tourism offerings, providing a holistic and place-based strategy for culture–tourism synergy. While existing scholarship on landscape narrative has yielded fruitful insights, it has predominantly focused on localized settings—such as heritage sites, rural settlements, and community landscapes [10,11,12,13]. This micro-scale bias limits the theory’s applicability to broader spatial governance contexts. However, research at the provincial level remains limited. As meso-scale administrative units, provinces present a unique opportunity for investigating the macro-regional adaptation of narrative theory due to their diverse resource endowments, complex industrial bases, and prominent roles in regional coordination and policy implementation [14,15].

Anhui Province stands out as a representative and empirically valuable case for such an investigation. A map of Anhui Province is provided (Figure 1) to help readers unfamiliar with the region understand its geographical context. As a major cultural and tourism hub in the Yangtze River Delta, Anhui possesses a rich array of tangible and intangible cultural resources, ranging from the iconic Yellow Mountains to the Grand Canal and Huizhou traditional culture [16]. In recent years, the province has actively pursued integration strategies, such as constructing narrative-based cultural corridors, developing immersive storytelling technologies [16,17], and launching cross-regional cultural branding campaigns [18,19]. Nevertheless, challenges persist, including unbalanced development across regions, disconnection between cultural narratives and tourism infrastructure, and the lack of standardized frameworks for evaluating integration effectiveness [20,21,22]. These problems underscore the need for a more critical and scalable application of narrative theory in culture–tourism integration planning.

To address these gaps, this study adopts landscape narrative theory as its analytical foundation and selects Anhui Province as a representative case. It aims to:

• Clearly define an analytical framework and indicator system grounded in narrative logic for evaluating regional culture–tourism integration;
• Apply spatial analytical methods—including data standardization, spatial weight matrices, and the Moran’s I and Spatial Autoregressive (SAR) models—to investigate the spatial differentiation and driving mechanisms of integration;
• Propose optimization strategies grounded in narrative logic to promote more coherent, emotionally engaging, and spatially coordinated regional culture–tourism development.

In doing so, the study seeks to contribute both theoretical advancement to landscape narrative research at the provincial scale and practical policy insights for enhancing regional culture–tourism competitiveness in China.

2. Materials and Methods

2.1. Theoretical Framework and Evaluation System Construction

To guide the evaluation of regional culture–tourism integration, this study constructs a theoretical and analytical framework grounded in landscape narrative theory, offering a novel interdisciplinary lens to coordinate spatial design, cultural expression, and immersive experience. This framework enables the transformation of physical spaces into narrative spaces by integrating fragmented cultural symbols into coherent storylines, thereby enhancing empathetic engagement through immersive scenarios [23].

At the spatial level, the framework establishes multi-scalar narrative networks. At the micro scale, technologies such as augmented reality (AR) and holography are employed to embed dynamic narrative elements and evoke sensory immersion [24]. At the meso-scale, collective memory mapping—using geotagged social media data—supports the construction of “narrative corridors” that connect landscape nodes through thematic trails and cultural routes [25]. At the macro-scale, integrating regional cultural IPs to form cross-regional narrative clusters has proven effective in revitalizing rural culture–tourism and strengthening place identity by aligning localized cultural assets with broader tourism development strategies [26]. However, compared with community-level practices, the implementation of narrative logic at the provincial scale remains less examined and requires further clarification through empirical frameworks.

At the cultural level, cultural landscape gene theory emphasizes the identification, reconstruction, and preservation of key cultural elements to improve spatial coherence and sustainability [27]. Cultural genes follow a three-stage process—identification, value grading, and translational adaptation—transforming abstract cultural elements into narrative scenes and ultimately into consumption-oriented experiences. Moreover, personalized narrative paths, generated by algorithms based on tourist profiles, enhance spatial interaction and emotional immersion [28,29,30].

At the experiential level, the framework integrates temporal, spatial, emotional, and behavioral dimensions. Temporally, it reconstructs historical contexts to evoke a sense of presence [31]; spatially, it enhances embodiment through culturally meaningful spaces; emotionally, it activates “touchpoints” via affective cues; and behaviorally, it fosters interactive loops through participatory installations that reinforce deep narrative memory [32,33].

In practice, the theoretical logic of landscape narrative has been increasingly applied to urban and rural spatial design in China, especially in the construction of themed blocks in characteristic towns. Recent studies have proposed a threefold technical pathway for narrative-driven planning: (1) excavating narrative clues and constructing core themes; (2) spatially arranging narrative texts and integrating plot segments; and (3) shaping narrative places and expressing cultural information through symbolic design. For instance, the planning of the Qihong Town in Huizhou embeds local tea culture narratives into spatial layouts and signage systems, creating walkable storytelling scenes and enhancing immersive visitor experiences. This case exemplifies how literary narrative methods can be translated into planning strategies, spatial design, and visitor routing systems. Such localized experiments provide actionable insights for embedding landscape narrative in regional culture–tourism integration and demonstrate the practical viability of narrative theory beyond conceptual abstraction [34].

Based on this logic, the analytical framework constructed in this study comprises five core dimensions:

• Cultural Resources (C)—narrative origin;
• Tourism Resources (R)—narrative carrier;
• Transportation Accessibility (T)—narrative conduit;
• Policy Support (P)—narrative assurance;
• Culture Tourism Economy (E)—narrative valuation.

This forms a closed-loop narrative mechanism of “cultural expression → narrative carrier → path connectivity → policy assurance → value transformation”. Within this system:

Culture and tourism resources constitute the content production axis; transportation and policy form the environmental support axis; and economic performance provides a feedback hub.

This model offers both a theoretical roadmap and an empirical foundation for integrated development and lays the groundwork for quantitative evaluation of narrative-driven culture–tourism systems in future research (see Figure 2).

To operationalize this framework, a multi-dimensional evaluation index system is developed. Equal weights are initially assigned to all indicators to ensure neutrality; this is later refined through expert consultation and comparative analysis with the existing literature [35]. The final weighting accounts for indicator importance and interdependence.

The indicator system includes five dimensions—Culture–Tourism Economy (E), Cultural Resources (C), Tourism Resources (R), Transportation Accessibility (T), and Policy Support (P)—and provides a comprehensive assessment structure that spans supply, demand, connectivity, and institutional provision (see

Table 1. Evaluation index system based on landscape narrative dimensions in Anhui province.

First Level Indicators	Second Level Indicators	Unit
Culture–Tourism Economy (E)	Domestic tourism per capita spending E1	CNY/person
	Total income of tourism economyE2	billion CNY
	Gross overnight tourism revenue E3	billion CNY
	Number of legal entities of culture and related industries under regulation E4	count
	Operating income of legal entities of culture and related industries on a regular basis E5	million CNY
	GDP per capita E6	CNY/person
Cultural Resources (C)	Number of cultural centers, museums and libraries C1	count
	Number of exhibitions held C2	count
	National Key cultural relics protection unit C3	count
	Number of State-level intangible cultural heritage C4	count
Tourism Resources (R)	Number of A-level scenic spots R1	count
	Number of 5A-level scenic spots R2	count
	Number of 4A-level scenic spots R3	count
	Number of 5-star hotels R4	count
	Number of 4-star hotels R5	count
	Number of travel agencies R6	count
Transportation Accessibility (T)	Number of high-speed rail stations T1	count
	Number of airports T2	count
	Number of highway lines T3	count
Policy Support (P)	Provincial business, culture–tourism integration development cluster P1	count
	Number of key cultural and tourism projects at municipal level P2	count

).

The Culture–Tourism Economy (E) dimension captures post-integration economic output, using indicators such as per capita tourism expenditure, total tourism revenue, overnight tourism revenue, number and income of cultural legal entities, and per capita GDP.

The Cultural Resources (C) dimension measures foundational expressive capacity via the number of museums, exhibitions, key heritage units, and intangible heritage items.

The Tourism Resources (R) dimension includes the number of A-level scenic spots, 4A/5A-rated attractions, star-rated hotels, and travel agencies, indicating the spatial and participatory capacity for narrative conversion.

The Transportation Accessibility (T) dimension evaluates spatial connectivity through the distribution of high-speed rail stations, airports, and expressway entrances.

The Policy Support (P) dimension assesses structural backing through the number of provincial-level clusters and municipal projects.

2.2. Data Sources and Preprocessing

To comprehensively evaluate the development patterns and spatial disparities of culture–tourism integration in Anhui Province, this study adopts a five-dimensional evaluation framework grounded in landscape narrative theory. Relevant statistical data are systematically collected and preprocessed to support quantitative analysis.

The data sources are diverse and authoritative, encompassing both national and provincial levels. Specifically, data were obtained from the following official publications: the Statistical Overview of Culture and Related Industries in Anhui Province, the Anhui Provincial Statistical Yearbook, the Anhui Culture Yearbook, the China Culture and Related Industries Statistical Yearbook, and the China Statistical Yearbook. In addition, municipal statistical yearbooks from various prefecture-level cities in Anhui were consulted. Supplementary data were gathered from local government websites and official portals of culture and tourism bureaus to ensure completeness and real-time accuracy.

To maintain temporal consistency and relevance, all data were aligned to a unified cutoff date of December 2024. For indicators with unpublished 2024 values, the most recent available data (2021–2023) were adopted and adjusted using linear extrapolation based on the average annual growth rates from 2018 to 2020 [36], ensuring the representativeness and reliability of the estimates.

To ensure both conceptual validity and practical applicability, the index system was constructed by integrating both subjective expert judgment and objective statistical analysis. While some indicators inevitably involve subjective classification, these were grounded in established theoretical constructs from landscape narrative theory and regional development literature. Compared with traditional frameworks such as DPSIR or entropy weighting methods, the CRTPE framework provides a more integrative approach that embeds narrative, experiential, and symbolic dimensions essential for evaluating culture–tourism fusion.

To enhance transparency and replicability, the indicator design process was benchmarked against conventional index construction practices, ensuring methodological clarity. Furthermore, future iterations may incorporate participatory co-construction methods, such as Delphi surveys or multi-stakeholder workshops, to further reduce subjectivity and improve weighting robustness.

In the initial phase of indicator system construction, an equal-weight principle was applied to minimize subjectivity. In the second stage, the weighting scheme was refined through a structured process involving three key steps: (1) literature benchmarking [37]; (2) contextual calibration based on Anhui’s regional development characteristics; and (3) expert scoring.

Five experts were selected based on their academic background and practical experience in culture–tourism planning, regional economics, and spatial evaluation. Each expert had participated in national or provincial-level projects, ensuring professional credibility. They were asked to independently assign integer scores to each secondary indicator, with a total of 20 points distributed under each first-level indicator. The average normalized scores across all five experts were then used to derive the final weights.

To further enhance the objectivity of the scheme, inter-indicator correlations were analyzed and factored into the final adjustment, ensuring that the integrated system reflects both empirical relevance and internal consistency. This approach improves the scientific rigor and practical applicability of the index system.

The standardized results for each of the five dimensions—Culture–Tourism Economy (E), Cultural Resources (C), Tourism Resources (R), Transportation Accessibility (T), and Policy Support (P)—are presented in

Table 2. Coefficients of first-level indicators of culture–tourism development in cities in Anhui Province.

First Level Indicators	Culture–Tourism Economy (E)	Cultural Resources (C)	Tourism Resources (R)	Transportation Accessibility (T)	Policy Support (P)
Hefei	84.864	73.867	60.240	93.523	77.132
Huaibei	27.365	36.294	31.587	29.529	24.178
Bozhou	39.232	55.177	39.268	35.760	41.930
Suzhou	40.766	42.129	44.587	34.682	29.168
Bengbu	30.302	44.889	45.582	38.992	36.857
Fuyang	59.438	42.877	52.833	37.793	45.840
Huainan	39.110	32.358	47.258	31.342	36.055
Chuzhou	53.391	38.522	45.014	48.991	41.129
Lu’an	42.953	40.635	38.093	40.924	70.870
Ma’anshan	34.219	39.319	48.362	44.462	42.020
Wuhu	69.140	56.634	47.773	72.941	49.989
Xuancheng	56.325	50.236	60.090	46.212	66.798
Tongling	29.282	36.294	33.933	37.982	28.183
Chizhou	46.346	46.365	42.475	59.932	49.214
Anqing	74.473	49.038	46.654	57.787	69.003
Huangshan	57.375	86.122	88.311	63.362	77.480

,

Table 3. Coefficients of secondary indicators of cultural and tourism development in cities of Anhui Province.

First-Level Indicators	Culture–Tourism Economy (E)
Secondary Indicators	Per capita Spending on Domestic Tourism E1	Total Income from Tourism Economy E2	Gross Income from Overnight Tourism E3	Number of Legalized Units of Cultural and Related Industries on a Regular Basis E4	Operating Income of Legal entities in Culture and Related Industries on a Regular Basis E5	GDP per Capita E6
Hefei	89.62	95.53	95.60	95.36	96.96	85.89
Huaibei	23.35	28.12	28.72	26.17	36.88	43.74
Bozhou	39.16	36.62	36.54	33.40	38.29	24.67
Suzhou	30.12	33.87	34.20	49.84	43.03	23.69
Bengbu	28.47	42.57	41.15	44.62	39.37	38.97
Fuyang	31.83	36.22	36.50	66.53	43.87	22.22
Huainan	25.74	32.47	32.78	31.79	37.02	30.16
Chuzhou	50.75	38.98	40.23	75.66	52.06	63.29
Lu’an	43.45	43.14	43.56	38.64	40.17	26.98
Ma’anshan	37.82	40.91	40.00	42.60	43.36	80.29
Wuhu	84.47	69.93	67.43	58.87	72.55	84.41
Xuancheng	50.63	43.27	43.53	50.30	42.94	50.77
Tongling	40.43	32.41	32.46	34.21	38.33	64.24
Chizhou	74.71	65.21	65.03	31.60	37.12	55.51
Anqing	63.54	62.30	59.84	57.54	43.39	43.28
Huangshan	69.39	69.32	72.56	35.04	55.14	51.57

,

Table 4. Coefficients of secondary indicators of cultural and tourism development in cities of Anhui Province.

First-Level Indicators	Cultural Resources(C)
Secondary Indicators	Number of Cultural Centers, Museums and Libraries C1	Number of Exhibitions Organized C2	State-Level Key Cultural Relics Protection Units C3	Number of State-level Intangible Cultural Heritage C4
Hefei	59.01	83.22	47.47	46.44
Huaibei	36.53	20.24	38.65	30.05
Bozhou	35.95	38.66	43.00	42.11
Suzhou	37.13	46.23	36.54	63.64
Bengbu	39.53	60.48	45.23	37.90
Fuyang	40.76	77.28	28.64	67.60
Huainan	45.75	53.43	43.00	46.44
Chuzhou	50.20	51.77	38.65	33.86
Lu’an	45.75	20.24	38.65	46.44
Ma’anshan	44.49	73.15	38.65	33.86
Wuhu	50.84	46.23	54.22	37.90
Xuancheng	47.02	80.24	67.04	50.82
Tongling	47.65	20.24	34.47	26.49
Chizhou	36.53	43.49	51.97	42.11
Anqing	43.86	22.48	60.81	67.60
Huangshan	97.40	62.58	96.82	96.06

,

Table 5. Coefficients of secondary indicators of cultural and tourism development in cities of Anhui Province.

First-Level Indicators	Tourism Resources (R)
Secondary Indicators	Number of A-Class Scenic Spots R1	Number of 5A-Level Scenic Spots R2	Number of 4A-Grade Scenic Spots R3	Number of 5-Star Hotels R4	Number of 4-Star Hotels R5	Number of Travel Agencies R6
Hefei	77.22	57.25	84.55	95.72	89.34	96.88
Huaibei	16.17	29.40	21.51	41.06	21.11	34.08
Bozhou	55.50	29.40	36.68	41.06	38.76	35.46
Suzhou	23.52	29.40	31.10	41.06	29.15	35.99
Bengbu	37.46	29.40	28.49	68.89	33.79	42.33
Fuyang	48.85	57.25	33.84	41.06	29.15	41.96
Huainan	40.63	29.40	33.84	32.15	49.33	40.66
Chuzhou	55.50	29.40	33.84	32.15	43.98	43.45
Lu’an	72.19	81.15	82.85	41.06	49.33	43.45
Ma’anshan	34.39	57.25	33.84	50.60	33.79	43.07
Wuhu	40.63	57.25	42.65	68.89	69.72	51.02
Xuancheng	76.03	57.25	76.87	50.60	59.96	48.93
Tongling	20.12	29.40	36.68	32.15	29.15	35.64
Chizhou	42.25	57.25	61.12	32.15	49.33	44.20
Anqing	86.07	57.25	76.87	32.15	69.72	49.88
Huangshan	70.84	93.26	72.14	68.89	87.11	74.00

and

Table 6. Coefficients of secondary indicators of cultural and tourism development in cities of Anhui Province.

First-Level Indicators	Transportation Accessibility (T)			Policy Support (P)
Secondary Indicators	Number of High-Speed Rail Stations T1	Number of Airports T2	Number of High-Speed Lines Access T3	Provincial Business, Culture and Tourism Integration Development Cluster P1	Number of Key Cultural and Tourism Projects at Municipal Level P2
Hefei	92.15	77.73	82.29	95.13	41.97
Huaibei	31.28	32.08	17.42	34.88	38.42
Bozhou	40.59	32.08	44.57	63.98	41.97
Suzhou	40.59	32.08	49.68	34.88	53.00
Bengbu	23.27	32.08	37.90	44.40	45.62
Fuyang	60.62	77.73	39.57	54.36	25.65
Huainan	50.63	32.08	30.77	34.88	28.58
Chuzhou	40.59	32.08	91.76	26.43	56.67
Lu’an	40.59	32.08	56.97	44.40	34.98
Ma’anshan	40.59	32.08	27.86	44.40	31.69
Wuhu	77.62	77.73	49.24	63.98	45.62
Xuancheng	69.80	32.08	62.60	34.88	73.27
Tongling	31.28	32.08	23.81	34.88	38.42
Chizhou	23.27	77.73	45.73	44.40	49.31
Anqing	77.62	77.73	67.02	34.88	70.28
Huangshan	40.59	77.73	59.39	79.80	95.61

, respectively. These results serve as the data foundation for the spatial autocorrelation and regression analyses that follow.

2.3. Spatial Analysis Methods

To systematically examine the spatial characteristics and underlying mechanisms of cultural and tourism integration in Anhui Province, this study adopts a comprehensive spatial analytical framework composed of three key methodological components: spatial weight matrix construction, spatial autocorrelation analysis, and spatial econometric modeling.

2.3.1. Construction of the Spatial Weight Matrix

To effectively analyze the spatial characteristics of cultural and tourism development across Anhui Province, this study constructs a spatial weight matrix to reveal spatial dependence and inter-city relationships. This approach helps to understand how cities influence each other during the development of cultural and tourism integration and how spatial location shapes the distribution of key elements such as the culture–tourism economy, cultural resources, and tourism resources.

The spatial weight matrix is constructed based on the geographic coordinates (latitude and longitude) of the administrative centers of each city in Anhui Province. A threshold distance of 150 km is adopted to determine spatial adjacency: cities within this distance are treated as neighbors (weight = 1), while those beyond are not (weight = 0).

This 150 km threshold is selected for the following reasons. First, it corresponds approximately to a two-hour intercity travel time, as indicated in Anhui’s regional transport planning documents [38,39]. Second, it reflects the average geographic distance between prefecture-level cities within the province, ensuring the spatial weights are grounded in realistic interaction scenarios. Third, we conducted robustness checks using alternative thresholds of 200 km and 300 km. While higher thresholds increased the number of statistically significant Moran’s I values, they also introduced spillover effects beyond the provincial boundary, thus reducing the interpretive precision for intra-provincial spatial patterns. Therefore, 150 km provides a practical and statistically balanced cutoff.

The Formula (1) for the spatial weight is defined as follows:

$$W_{ij}=\left\{\begin{array}{l}1,(d_{ij}<150)\\ 0,(d_{ij}>150ori=j)\end{array}\right.$$

(1)

In the formula, $W_{ij}$ denotes the spatial weight between cities in Anhui Province; $d_{ij}$ is the square of the Euclidean distance measured based on the latitude and longitude coordinates of cities and city administrative centers. The binary structure of the weight matrix helps emphasize first-order spatial relationships and supports clear interpretation of spatial effects in the subsequent autocorrelation and regression analyses. To ensure comparability among cities with varying numbers of neighbors, the matrix is row-normalized.

2.3.2. Moran’s I Analysis

In examining the spatial characteristics of the integration of culture and tourism in Anhui Province, determining whether significant spatial clustering exists among regions is crucial for revealing its spatial distribution patterns. To systematically investigate the spatial organization of cultural and tourism integration at the provincial level and its tendency toward agglomeration or dispersion, this study employs Global Mo-ran’s I as the primary tool for spatial autocorrelation analysis. As a classical spatial statistical index, Global Moran’s I effectively measures the overall degree of spatial association among observed variables, thereby identifying whether a given phenomenon exhibits spatial concentration, dispersion, or randomness. The determination of the Global Moran’s I value and its statistical significance provides both theoretical grounding and empirical support for further analysis of local spatial structures and the interpretation of spatial heterogeneity. The calculation formula is presented in Equation (2).

$$I={\displaystyle \frac{n}{\sum _{i=1}^n \sum _{j=1}^n w_{ij}}}\cdot {\displaystyle \frac{\sum _{i=1}^n \sum _{j=1}^n w_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{\sum _{i=1}^n {\left(x_i-\overline{x}\right)}^2}}$$

(2)

In the formula, n is the total number of cities, $x_i$ and$x_j$ are the level of cultural and tourism integration of a city and its neighboring cities, $w_{ij}$ is the spatial weight matrix, $\overline{x}$ is the average value of the level of cultural and tourism integration of all the cities, and the value of Global Moran’s I ranges from −1 to 1, with a negative value indicating that the spatial units are negatively correlated, and vice versa is positively correlated. After calculating the Moran’s I value, it is necessary to further evaluate its statistical significance through hypothesis testing. Specifically, it is necessary to test whether there is a significant difference between the actual observed value and its expected value under the assumption of random distribution, which is usually measured by the standardized Z-value and combined with the corresponding p-value to determine the statistical significance. Under the established significance level, if the p-value is sufficiently small, the original hypothesis can be rejected, i.e., there is no spatial autocorrelation, thus indicating that there is a significant spatial dependence or clustering of the level of cultural and tourism integration.

To further investigate local spatial heterogeneity, this study applies Local Moran’s I to examine the spatial clustering patterns of cultural and tourism integration and to reveal spatial dependencies among different regions. The calculation formula is pro-vided in Equation (3), with the notation consistent with that used in Equation (2). A significance test (p-value calculation) is conducted following the computation of the Local Moran’s I index, enabling a more detailed identification of spatial dependence patterns.

$$I_i={\displaystyle \frac{(x_i-\overline{x})\sum _{j=1}^n w_{ij}(x_j-\overline{x})}{\frac{l}{n}\sum _{i=1}^n {\left(x_i-\overline{x}\right)}^2}}$$

(3)

2.3.3. Spatial Econometric Modeling

To gain deeper insights into the spatial differentiation characteristics and driving mechanisms of culture–tourism integration in Anhui Province, this study adopts spatial econometric methods to conduct an empirical analysis of interregional interactions and key influencing factors. In terms of model selection, considering the potential spatial dependence and spillover effects associated with regional cultural and tourism economic development, both the Spatial Lag Model (SAR) and the Spatial Durbin Model (SDM) are constructed and estimated using the Maximum Likelihood Estimation (MLE) method.

In these models, the level of cultural and tourism economic development is treated as the dependent variable, while tourism resources (R), transportation accessibility (T), policy support (P), and cultural resources (C) are used as explanatory variables.

To compare the models’ goodness of fit, a Likelihood Ratio Test (LRT) is conducted. The resulting p-value of 0.5932 exceeds the commonly accepted significance threshold of 0.05, indicating that the SDM does not significantly outperform the SAR model, even after incorporating spatial lag terms for the explanatory variables. Therefore, in accordance with the principle of parsimony and robustness, the SAR model is ultimately selected as the primary econometric framework. This enables a clearer depiction of the spatial dependence structure and influence pathways underlying the integrated development of culture and tourism in the region.

The SAR model (4) is expressed as follows:

$$E_i=\rho \sum _j w_{ij}{E}_j+{\beta }_0+{\beta }_1{R}_i+{\beta }_2{T}_i+{\beta }_3{P}_i+{\beta }_4{C}_i+{ϵ}_i$$

(4)

In the formula, $E_i$ represents the level of culture and tourism economy of the first $i$ region, $\sum _j w_{ij}{E}_j$ represents the level of culture and tourism economy of the spatial neighboring regions, and the spatial dependence between regions is reflected by the spatial weight matrix $w_{ij}$; $\rho$ is the coefficient of the spatial lag term, which reflects the extent to which the level of culture and tourism economy of a region is affected by the level of culture and tourism economy of the neighboring regions; $R_i$, $T_i$, $P_i$ and $C_i$ represent the variables of tourism resources, transportation accessibility, policy support, and cultural resources, respectively; and ${ϵ}_i$ is the error term.

3. Results

3.1. Spatial Autocorrelation Analysis

To identify the spatial characteristics and regional disparities of culture–tourism integration in Anhui Province, a spatial weight matrix was constructed using a 150-kilometer threshold. Global Moran’s I and Local Moran’s I methods were employed to assess the spatial autocorrelation of five primary indicators.

As shown in

Table 7. Global Moran’s I Index of Cultural and Tourism Development Indicators in Anhui Province.

First-Level Index	Z	P	Global Moran’s I
Culture–Tourism Economy (E)	2.359	0.009	0.207
Cultural Resources (C)	0.009	0.504	0.068
Tourism Resources (R)	1.913	0.028	0.177
Transportation Accessibility (T)	0.266	0.605	0.100
Policy Support (P)	0.087	0.465	0.057

, the Culture–Tourism Economy (E) and Tourism Resources (R) indicators exhibit significant positive spatial autocorrelation across the province. Specifically, the Moran’s I for Culture–Tourism Economy is 0.207 (Z = 2.359, p = 0.009), indicating strong spatial clustering. The Moran’s I for Tourism Resources is 0.177 (p = 0.028), reflecting a certain degree of regional concentration. In contrast, Cultural Resources (C), Transportation Accessibility (T), and Policy Support (P) do not pass the significance test, suggesting a relatively dispersed spatial distribution and the absence of stable spatial dependence structures.

To further explore the local spatial characteristics of culture–tourism integration development, Local Moran’s I was used for LISA cluster analysis to identify “High-High” and “Low-Low” clustering patterns. As illustrated in Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7, cities such as Huangshan, and Anqing form typical “High-High” clusters, indicating strong radiative influence on surrounding areas. In contrast, cities such as Suzhou, Huaibei fall into “Low-Low” clusters or exhibit a dispersed pattern, highlighting their relatively lagging status in the integration process.

3.2. SAR Model Results and Interpretation

For the spatial econometric analysis, the Spatial Lag Model (SAR) was selected as the primary analytical framework. As shown in

Table 8. SAR model regression results and variable effects.

Variables	Regression Coefficient Estimate	Significance Level	Effect of Variable	Variables
Spatial lag Rho	0.61226	p < 0.001	Positive (+)	Neighboring regions’ cultural and tourism economic development has a significant positive spillover effect on the region, with strong spatial dependence.
Tourism Resources (R)	−0.224856	p ≈ 0.11	Negative (−)	Local tourism resources are abundant but the utilization efficiency may not be sufficient, and have not been effectively transformed into economic advantages.
Transportation Accessibility (T)	0.685010	p < 0.001	Positive (+)	Improved transportation conditions can effectively promote the development of cultural and tourism integration, which is one of the main driving factors.
Policy Support (P)	0.735999	p < 0.001	Positive (+)	Policy support has a significant positive effect on the economic development of culture and tourism.
Cultural Resources (C)	−0.352146	p < 0.01	Negative (−)	Although there are many cultural resources, there is the problem of precipitation or low conversion rate, which inhibits the development of culture–tourism economy in the short term.

, the spatial lag term Rho = 0.61226 is statistically significant at the 1% level, indicating strong spatial spillover effects and high interdependence among cities in terms of culture–tourism economic development.

Key findings for individual variables include:

• Transportation Accessibility (T) and Policy Support (P) show significant positive effects, with regression coefficients of 0.685 and 0.736, respectively (p < 0.01). These results affirm their critical role as drivers of high-quality integration. Improved transport infrastructure and coordinated policy incentives directly support mobility, investment, and the clustering of tourism functions, thereby enhancing overall integration outcomes.
• Cultural Resources (C) exhibit a significant negative correlation (coefficient = –0.352, p < 0.01), suggesting that while the region is rich in cultural heritage and institutions, many assets remain “locked” in static formats and are not effectively transformed into economically productive experiences. This phenomenon, often described as “resource sedimentation”, indicates a structural deficiency in creative activation, industrial integration, and the development of narrative-based or digital products.
  This result is consistent with studies in other inland regions like Henan and Gansu, where a similar paradox of cultural richness coexisting with weak performance is observed, highlighting the lack of enabling mechanisms to convert symbolic capital into marketable value.
• Tourism Resources (R), although conventionally seen as core elements, are not statistically significant in the model (p ≈ 0.11). This may be attributed to oversupply and the homogeneity of scenic attractions, reducing their marginal contribution to integrated outcomes. Many sites remain spatially fragmented and thematically disconnected, which limits their ability to deliver compelling and immersive tourism experiences.
  This finding underscores the need to move from a model of quantity accumulation toward one of qualitative transformation through narrative integration, interactive content, and collaborative service design.
• These results suggest that the success of culture–tourism integration in Anhui Province depends not solely on the presence of resources, but more importantly on their narrative potential, spatial connectivity, and alignment with supportive infrastructure and institutional frameworks. The SAR model reveals the spatial logic behind uneven integration outcomes and offers insight into how latent value can be unlocked through systemic design and coordination.

3.3. Existing Issues

Based on the above findings, three prominent challenges persist in Anhui Province’s pursuit of culture–tourism integration:

• Uneven Regional Development and Significant Spatial Disparities
  Spatial clustering results indicate that cities such as Huangshan form “High-High” clusters, while cities like Huaibei, Suzhou, fall into “Low-Low” zones. Taking Huaibei as an example, it has fewer than 20 A-level scenic spots—only one third of Hefei’s total—and has launched only four provincial-level culture–tourism projects since 2020. This reflects insufficient investment in culture–tourism infrastructure and underutilized development potential. Tongling and Suzhou also lack immersive tourism products and narrative-driven integration models, with both cities recording low per capita tourist spending and short visitor durations. Overall, the absence of inter-regional coordination mechanisms has hindered the radiative impact of core cities.
• Transportation and Policy as Core Drivers; Limited Resource Transformation Capacity
  While transportation accessibility and policy support play a significant role in promoting culture–tourism integration, regions rich in cultural and tourism resources often lack effective mechanisms for content activation and experiential chain development, resulting in low resource transformation efficiency. This pattern is not unique to Anhui Province but is also widely observed in transitional regions such as Henan and Gansu. Relevant studies highlight the persistent disconnect between “resources–scenes–experiences” as a key obstacle to improving the spatial effectiveness of integration. For instance, research on traditional village spatial structures in Henan Province reveals that, although cultural resources are abundant, the absence of narrative logic and thematic expression mechanisms leads to short tourist stays and low consumption willingness, creating a contradiction between resource richness and underwhelming performance [40]. Similarly, an empirical analysis from Gansu Province demonstrates that due to homogeneous tourism offerings and insufficient service infrastructure, the cultural–tourism integration has not translated into regional economic momentum. Although spatial clustering exists, radiative influence remains weak [41]. These findings suggest that the absence of resource transformation mechanisms is a structural challenge not only in Anhui but also in other regions. Therefore, embedding narrative-driven product planning and immersive design strategies is essential to bridge the gap between cultural resources and economic value creation.
• Lack of Resource Expression and Narrative-Driven Integration Pathways
  Despite having rich cultural and tourism resources, many regions in Anhui lack systematic expression mechanisms and spatial narrative frameworks. This results in symbolic fragmentation, disconnected branding efforts, and the underutilization of existing assets. In comparison, European regions such as Tuscany, Italy, have effectively adopted place-based narrative strategies to repackage scattered cultural heritage into cohesive tourism corridors and experiential routes. For instance, the “Seeing Stories” project in Florence illustrates how narrative planning can integrate tangible and intangible heritage into immersive storytelling experiences, enhancing both spatial coherence and tourist engagement [42]. Such international practices suggest that Anhui could significantly improve its integration effectiveness by embedding landscape storytelling principles into spatial planning, product design, and regional promotion.

In summary, culture–tourism integration in Anhui Province is transitioning from a phase of resource accumulation to one of value transformation. To promote high-quality development, it is essential to incorporate landscape narrative as a mediating mechanism, enhancing the expressiveness of cultural resources, optimizing the spatial experience structure, and establishing regional coordination and institutional support systems to drive integration to a higher level.

4. Discussion: Optimization Strategy for Regional Culture–Tourism Integration Development Based on Landscape Narrative

Drawing on the theoretical framework and empirical research findings, this paper proposes the following strategies to promote the application of landscape narratives in the culture–tourism integration of Anhui Province. These strategies aim to synergize regional cultural heritage preservation and industrial upgrading.

4.1. Constructing a Province-Wide Narrative Community: Reshaping the Overall Structure of Regional Cultural Expression

In response to the spatial heterogeneity inherent in Anhui’s culture–tourism integration, it is imperative to establish a hierarchically structured narrative framework spanning the entire provincial territory. Such a framework would strengthen the coherence of Anhui’s cultural identity while enhancing the internal consistency and communicative efficacy of its cultural narratives.

At the provincial level, the focus should be on identifying and articulating overarching thematic narratives capable of unifying diverse cultural resources under a shared symbolic framework. Initiatives such as the “Xin’an Cultural Corridor” and the “Legacy of Huizhou Merchants” could serve as strategic narrative backbones, sup-porting the development of a culturally resonant and nationally recognizable tourism brand. These macro-narratives would not only address current challenges—such as the fragmentation of cultural imagery and the lack of iconic identity, but also offer an integrative platform for provincial-level narrative cohesion.

At the meso (sub-regional)-level, the narrative structure can be further differentiated based on spatial and cultural specificity. Drawing upon the geographical logic of the Yangtze–Huaihe Ecological and Cultural Corridor and the multi-nuclear cultural origin model, three core narrative zones may be delineated: (1) the Classical Heritage of Southern Anhui, (2) the Revolutionary Memory of the Wanjiang Region, and (3) the Folk Traditions of Northern Anhui. These narrative zones are designed to systematically recover regional cultural genealogies and place-based identities, thereby constructing a cross-regional narrative network characterized by spatial continuity and semiotic alignment.

Moreover, acknowledging the differentiated cultural endowments across regions, each narrative zone should develop localized sub-narrative themes that reflect the uniqueness of its cultural landscape. This approach ensures that while each locality contributes to a unified provincial narrative, it simultaneously retains its distinctive voice and identity.

At the micro-level, local governments, counties, and cultural heritage sites are encouraged to mobilize their specific historical and cultural resources in crafting site-based, experiential storytelling. By integrating localized legends, memories, and spatial symbols, these micro-narratives can enrich visitors’ sense of immersion and deepen cultural identification.

Through this three-tiered narrative architecture—spanning provincial, sub-regional, and local scales—an “integrated yet differentiated” model of cultural expression can be achieved. A unified narrative core is complemented by diverse regional chapters, forming a layered, interlocking storytelling system. This multiscale approach not only links spatially dispersed resources through coherent cultural logics but also facilitates cross-regional synergy and coordinated development in the broader context of culture–tourism integration.

4.2. Activating the Narrative Potential of Cultural Resources: Advancing the Chain Transfor-Mation from Resource to Scene to Experience

To address the limited utilization of cultural and tourism resources, it is essential to activate the latent narrative potential embedded in cultural assets by transforming static heritage into dynamic narrative scenes and immersive visitor experiences. This transformation follows a staged “resource–scene–experience” chain, which can be operationalized through a three-step implementation strategy.

First, extract narrative themes from cultural resources. For each resource—be it a historic village, an intangible craft, or a vernacular practice—its embedded stories, values, and cultural meanings should be systematically unearthed and distilled into coherent narrative themes.

Second, construct narrative scenes. Based on the extracted themes, spatial storytelling should be embedded into tourist environments through scenography design. This may involve placing symbolic props and immersive installations within scenic areas, or reconstructing historical atmospheres in cultural districts, thereby allowing visitors to enter and inhabit the narrative world physically and affectively.

Third, develop interactive experiences. By designing participatory activities or products, visitors are brought into the narrative process, assuming the roles of story participants rather than passive observers. Techniques such as role-playing, interactive performances, or hands-on DIY workshops allow visitors to “learn through doing and know through play.”

The city of Taizhou provides a compelling example. There, intangible cultural heritage has been effectively integrated into local festivals: visitors participate in boat races and enjoy live folk performances, gaining a direct and embodied understanding of water–town traditions [43]. In addition, interactive workshops such as puppet theatre and scriptwriting sessions allow tourists to manipulate puppets and compose local legends themselves. These are complemented by creative cultural products including lacquerware and straw weaving, establishing a diversified cultural economy that combines tourism, retail, and participatory making. This model has not only prolonged visitor stays but also extended the tourism consumption chain.

Empirical cases such as these demonstrate that activating the narrative potential of cultural resources can effectively bridge the transformation from resource to scene and ultimately to experience. This approach enriches the supply of culture–tourism products while significantly enhancing visitor engagement and satisfaction. Theoretically, it expands the application of landscape narrative theory within the domain of resource development; practically, the chain-transformation model facilitates the conversion of cultural value into economic value, promoting the efficient and sustainable utilization of cultural resources as integrated culture–tourism assets.

4.3. Leveraging Transportation and Policy as Foundational Carriers to Support Cross-Regional, Narrative-Driven Culture–Tourism Development

To address issues such as weak regional collaboration and uneven levels of policy support, it is imperative to treat transportation infrastructure and policy coordination as foundational carriers for narrative-driven, cross-regional culture–tourism development. These two pillars provide the material and institutional basis for enabling spatial and narrative linkages across administrative boundaries. Furthermore, improvements in digital expression and intelligent feedback mechanisms are needed to enhance the perceptual efficiency of narrative scenes.

From the infrastructure perspective, efforts should be made to upgrade existing road networks connecting key tourist destinations, develop scenic byways and self-driving routes, and improve signage and service facilities along these corridors. Cross-regional tourist mobility can be facilitated by introducing dedicated culture–tourism buses and railway lines that connect key narrative routes, thereby reducing barriers to interregional travel. Additionally, solutions to the “last-mile” challenge, such as scenic shuttle buses and shared bicycles, should be deployed to improve accessibility between major transportation hubs and individual attractions. These foundational investments will provide robust support for the spatial realization of cross-regional narrative itineraries, allowing visitors to literally traverse the storyline in physical space.

Beyond functional efficiency, the experiential design of transportation routes de-serves systematic consideration. Cultural landscape installations, narrative rest stops, and interactive storytelling elements can be embedded along the way, transforming the journey into an integral part of the narrative experience rather than a mere transit process.

On the institutional level, policy coordination constitutes the organizational backbone of cross-regional narrative development. Given the disparities in resources, funding, and administrative capacity across regions, provincial governments should assume a leading role in establishing interregional cooperation frameworks and supportive policies. Financially, targeted subsidies and funding incentives can be allocated to narrative tourism projects involving underdeveloped areas, helping to balance unequal development baselines. At the same time, preferential policies should be introduced to encourage private sector participation in the construction and operation of cross-regional cultural routes, for example, through tax reductions, land-use support, and infrastructure co-investment schemes.

To address administrative fragmentation and jurisdictional barriers, innovative governance mechanisms such as “enclave cooperation” or delegated management models may be adopted. These approaches can help ensure consistency in ticketing systems, service standards, and marketing strategies along the narrative corridor. Once the basic transportation and policy frameworks are in place, participating regions should be encouraged to align their infrastructure and hospitality services with shared narrative themes. This would help form an integrated model of “connectivity and convergence”, where transport connectivity facilitates tourist flows, and policy integration enables the seamless circulation of cultural resources.

Through reinforced transportation links and policy support, Anhui can develop a series of high-quality, narrative-driven tourism routes that not only accommodate but capitalize on regional differences, transforming spatial heterogeneity into narrative diversity. In doing so, the province can promote the sharing of resources, the co-movement of tourists, and the balanced, high-quality development of its culture–tourism sector.

4.4. Enhancing Digital Expression and Intelligent Feedback Mechanisms to Improve the Per-Ceptual Efficiency of Narrative Environments

Against the backdrop of rapid digital transformation, the integration of culture and tourism increasingly requires digital technologies to enrich the expressive power of narrative environments and intelligent systems to enhance visitors’ perceptual engagement. At present, many cultural attractions still rely primarily on traditional means of narrative delivery, such as on-site interpreters and static signage, which often suffer from limited informational capacity and lack of interactivity. As a result, visitors’ understanding of the deeper cultural connotations remains superficial and fragmented.

To address this gap, it is crucial to actively incorporate digital media and intelligent technologies to transform static cultural content into dynamic and interactive experiences. For instance, augmented reality (AR)- and virtual reality (VR)-based navigation systems can be deployed, allowing visitors to scan physical sites using mobile devices or AR glasses and receive real-time overlays of historical imagery, animated figures, or audio narratives. Such applications create temporally layered, immersive storytelling experiences. Similarly, technologies such as holographic projection and immersive theatre can be employed in museums or heritage sites to reconstruct key historical scenes, enabling visitors to virtually “relive” the past.

These forms of digital expression significantly enhance the immediacy and emotional resonance of narrative spaces, thereby increasing the efficiency with which information is absorbed and interpreted by visitors. Furthermore, the integration of intelligent feedback mechanisms allows for the dynamic alignment of narrative content with user preferences, forming a responsive and adaptive storytelling ecosystem.

Specifically, through big data analytics and artificial intelligence, tourist behavior and feedback within a site can be systematically monitored and analyzed, ranging from movement paths, dwell times, and voice inputs to social media interactions. On the one hand, such data enable the identification of common patterns and pain points. For example, if a particular exhibit receives consistently low engagement, it may indicate narrative inadequacy, prompting targeted enhancements such as enriched storytelling elements or the addition of interactive components.

On the other hand, intelligent systems can deliver real-time, personalized narrative content based on individual visitor behavior. For instance, if a user displays heightened interest in Huizhou architecture, the system could automatically recommend related legends or provide deeper interpretive routes. Conversely, content areas showing limited interest could have their delivery frequency reduced. This “perception–feedback–adjustment” closed-loop mechanism ensures a high degree of alignment between narrative supply and visitor interest, thereby continuously improving the quality of the visitor experience.

Ultimately, the convergence of digital expression and intelligent feedback represents a paradigm shift from static, one-way cultural communication toward dynamic, interactive storytelling. This shift significantly enhances both the perceptual efficiency and participatory appeal of narrative environments, fostering deeper cultural engagement and a more personalized tourism experience.

4.5. Establishing a Triadic Co-Creation Mechanism Among Residents, Tourists, and Platforms to Foster Place-Based Identity and Cultural Co-Creation

To ensure the sustainable development of culture–tourism and the revitalization of local culture, it is essential to establish a triadic collaborative mechanism that actively engages residents, tourists, and platforms. Such a model emphasizes shared participation in cultural content creation, thereby strengthening place-based identity and promoting the co-creation and sharing of cultural values.

Traditionally, culture–tourism development has been predominantly top-down, led by government agencies or private enterprises, with local residents and tourists assuming relatively passive roles. However, recent studies have highlighted the importance of resident-driven value co-creation in the context of intangible cultural heritage (ICH) tourism. Given that ICH relies on living transmission, local residents are uniquely positioned to serve as its primary stewards. When tourists and residents engage in interactive and collaborative cultural experiences, the result is not only more meaningful and enriched tourism products but also the dynamic renewal of cultural heritage itself.

In the context of Anhui’s culture–tourism integration, local residents should be actively engaged and redefined from passive “observers” to active “narrators” and “co-creators” of cultural narratives. This transformation can be facilitated by encouraging residents to participate as cultural volunteers or part-time storytellers who share local legends and demonstrate traditional crafts, thereby enhancing the authenticity and emotional depth of visitor experiences. In parallel, community-based organizations can be supported in designing participatory cultural events, such as harvest festivals, temple fairs, and village parades, that invite tourist involvement and embed visitors in the rhythms of everyday community life.

In this participatory process, tourists are no longer simply consumers but are transformed into co-creators of cultural experiences. For example, they may help choreograph an ICH performance, collaborate with local artisans to produce handmade souvenirs, or contribute ideas to cultural exhibitions. These interactions foster emotional bonds between residents and tourists: visitors develop a stronger sense of identification with local culture, while residents gain renewed pride and appreciation for their heritage, resulting in mutual enhancement of place attachment and cultural recognition.

Within this triadic model, platforms function as both facilitators and infrastructure providers. These platforms may take the form of physical institutions such as culture– tourism coordination centers or village-based tourism co-operatives, as well as digital platforms that enable real-time interaction. For instance, some homestay hosts have successfully used online platforms to invite guests to participate in community events, thus deepening integration between tourists and local life.

By institutionalizing co-creation platforms that link residents and tourists, Anhui can foster the emergence of culturally vibrant and socially inclusive tourism products, grounded in local heritage yet shaped through creative transformation. Residents gain tangible benefits and symbolic recognition through participation, while tourists enjoy meaningful and memorable experiences. Government bodies and businesses, as platform stakeholders, also benefit from enhanced destination reputation and long-term sustainability. In this way, a virtuous cycle of mutual benefit, cultural revitalization, and co-development can be established, advancing a resilient and community-anchored model of culture–tourism.

5. Conclusions

In the context of high-quality development and cultural self-confidence, landscape narrative theory has emerged as a vital conceptual bridge connecting cultural resources with tourism experiences. It offers a renewed theoretical framework and practical anchor for advancing the integrated development of culture and tourism. Anchored in an analysis of the spatial heterogeneity and underlying mechanisms of culture–tourism integration in Anhui Province, this study constructs a comprehensive evaluation system encompassing five key dimensions: culture–tourism economy, cultural resources, tourism resources, transportation accessibility, and policy support. Employing spatial econometric models, the study identifies transportation infrastructure and policy coordination as critical drivers of integration, while insufficient resource transformation and regional development imbalances remain major constraints.

Based on these findings, the study proposes a set of strategic pathways: constructing a province-wide narrative network, activating the narrative potential of cultural resources, strengthening cross-regional collaboration, enhancing digital narrative capabilities, and fostering co-creation between residents and tourists. These strategies aim to reshape cultural expression through narrative logic, optimize the spatial structure of development, and enhance the experiential value of tourism, thereby promoting Anhui’s culture–tourism integration in a more collaborative, innovative, and immersive direction.

The significance of this study lies in its pioneering introduction of landscape narrative theory into the provincial-level evaluation framework of culture–tourism integration. By proposing a narrative-driven model of spatial analysis and strategic optimization, the study not only contributes new theoretical insights to the literature on culture–tourism synergy, but also offers actionable guidance for regional policy and planning practices.

However, this research has certain limitations. First, due to constraints in data availability, some variables related to narrative practices could not be directly quantified and were represented through proxy indicators. Second, although spatial regression models reveal important associations, they cannot fully capture the causal mechanisms underlying narrative-based transformations. Third, although the weighting process draws on expert evaluations and regional development benchmarks, the construction of narrative-related indicators inevitably involves some degree of subjectivity. Future studies may enhance methodological transparency by introducing more formalized techniques such as the Delphi method or AHP-based multi-criteria decision-making models.

Fourth, the study currently lacks primary qualitative data, such as interviews with local stakeholders, tourists, or community planners, which limits its ability to empirically validate how narrative strategies are perceived or experienced in practice.

Future research should incorporate mixed-method approaches, combining spatial modeling with fieldwork-based qualitative data, including semi-structured interviews, participatory observations, and user experience studies. These additions will enhance the explanatory power of narrative theory, provide stronger empirical grounding, and improve the policy applicability of research outcomes in real-world planning and development contexts.