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Journal of Resources and Ecology >

2025 , Vol. 16 >Issue 4: 1116 - 1130

DOI: https://doi.org/10.5814/j.issn.1674-764x.2025.04.016

Special Column: Ecotourism and Rural Revitalization

Evaluation of Cultural and Tourism Industry Integration and Its Driving Mechanism in the Northeast Border Regions of China

  • ZUO Li ,
  • BAI Qiuyi ,
  • ZHAO Ao , *
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  • College of Economics and Management, Dalian Minzu University, Dalian, Liaoning 116600, China
*ZHAO Ao, E-mail:

ZUO Li, E-mail:

Received date: 2024-10-10

  Accepted date: 2025-02-10

  Online published: 2025-08-05

Supported by

The Liaoning Federation of Social Sciences(2023lsllhwtkt-08)

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Abstract

The integration and coordinated development of culture and tourism industry is essential for realizing high-quality development in China’s northeast border regions. To assess this integration and driving mechanism, an evaluation index system has been established to quantify the coupling and coordination degree of these sectors at the provincial level from 2013 to 2022. Meanwhile, ordinary least squares (OLS) regression analysis can identify driving factors and its mechanism. The findings indicate that, despite fluctuation and uneven development, the integration of cultural and tourism industry has generally demonstrated a gradual upward trend, remaining predominantly in preliminary-stage development. The degree of coupling and coordination is influenced by four primary factors: economic development level, transportation infrastructure quality, industrial structure optimization and advancement in the digital economy. The outbreak of the global public health crisis has temporarily weakened the impacts of economic development, transportation infrastructure, and the digital economy on the integration process. However, as economic recovery continues to unfold, these factors have been anticipated to exert a sustained and significant influence on facilitating further integration and coordinated development within the cultural and tourism industry thereby accelerating high-quality development in the Northeast border regions. Based on these conclusions, this study proposes measures that focus on enhancing the integration and development of culture and tourism industry from three perspectives, namely integrated model innovation, transportation network enhancement, and industrial structure optimization.

Key words： northeast border regions; integrated development of culture and tourism industry; coupling coordination degree; influencing factors

Cite this article

ZUO Li , BAI Qiuyi , ZHAO Ao . Evaluation of Cultural and Tourism Industry Integration and Its Driving Mechanism in the Northeast Border Regions of China[J]. Journal of Resources and Ecology, 2025 , 16(4) : 1116 -1130 . DOI: 10.5814/j.issn.1674-764x.2025.04.016

1 Introduction

The establishment of China’s Ministry of Culture and Tourism in 2018 marked a significant turning point in emphasis, support and guidance on the integrated development of these two industries, meanwhile opening a new chapter in their integration and coordinated development. While culture has been regarded as the bloodline of the Chinese Nation, it remains underdeveloped as an industry compared to tourism, in terms of industrial structure and economic impact. Conversely, the tourism industry exhibits strong driving effects and multiplier impacts on economic growth. Initially characterized by a homogeneous supply, the tourism industry has evolved to meet increasingly personalized consumer demands. This shift necessitates that the industry incorporate new elements into its offerings, thereby facilitating supply-side structural reform that enhances consumer satisfaction. Consequently, both the output value and contribution to employment growth have consistently surpassed them of cultural industry. Despite their distinct characteristics, cultural and tourism sectors share inherent overlaps in production factors, consumer markets, and technological advancements, enabling mutual reinforcement (Heo and Lee, 2019). In recent years, national authorities have placed considerable importance on their integration development through a series of policies. In 2021, the 14th Five-Year Plan (2021-2025) for National Economic and Social Development of the People’s Republic of China and the Visionary Objectives for 2035” focused on the integration by advocating “developing tourism industry with culture” while simultaneously highlighting culture through tourism initiatives in order to create a distinctive cultural tourism experience. Similarly, the “14th Five-Year Plan for Cultural and Tourism Development” prioritized the integration of cultural and tourism industry as a foundational principle. Furthermore, the Chinese government proposed the necessity for a more profound implementation of regional coordinated development strategies, thereby promoting an integrated advancement of the cultural and tourism industry at broader, deeper, and higher levels. These initiatives reflect China’s determination to achieve coordinated and integrated progress in the cultural and tourism industry.
Since the advent of a new era in China’s socialist development, the Northeast border regions have embraced unprecedented historical opportunities through its consolidated role as a national security barrier and the deepening of border opening-up policies. Additionally, various policy advantages, such as poverty alleviation, border prosperity initiatives and rural revitalization have played a significant role. Simultaneously, the inherent compatibility and strong correlation between culture and tourism have further provided alternative pathways for economic and industrial advancement within the region’s unique resource endowment. The resource characteristics of the northeast border regions, coupled with the high degree of alignment between its tourism sectors and cultural assets, suggest that integration of culture and tourism represents an optimal strategy for developing ecological and cultural resources (Wen, 2019). Transforming the Northeast border regions into a leisure destination that harmonizes culture with tourism not only facilitates the dynamic inheritance of local culture, but also promotes industrial upgrading in cities along development corridors. Furthermore, such integration plays a crucial role in addressing public demands for improved living standards, while reinforcing cultural confidence and amplifying public participation in cultural inheritance.
In recent years, research on the integration of cultural and tourism industry has primarily focused on analyzing the driving mechanism or influencing factors that affect the level of integration through qualitative methods or multi-year panel data, yet overlook the spatial heterogeneity of influencing factors. Consequently, research in this area remains insufficient. Furthermore, while comprehensive analyses at the national level are relatively thorough, there is a notable lack of in-depth studies concerning the development of cultural and tourism integration, specifically within the Northeast border regions. This gap presents considerable opportunities for further research. In contrast to existing literature, this study not only synthesizes current theoretical frameworks regarding cultural and tourism industry integration but also concentrates on the unique geographical context of the Northeast border regions—investigating its distinct challenges and opportunities related to the integration. Additionally, this study has conducted pre-pandemic and post-pandemic comparative analysis using the global public health crisis as a natural experiment, which provides direct evidence regarding how such crises have influenced cultural and tourism industries integration within the northeast border regions. By examining industrial convergence levels and their spatiotemporal evolution patterns, this research has provided actionable insights for achieving high-quality coordinated development between cultural preservation and tourism industrialization in China’s border regions.

2 Literature review

2.1 The connotation of the integration of cultural and tourism industry

The integration of cultural and tourism industry represents a significant trend in contemporary development, influenced by various economic and social factors, also with an inherent coupling relationship (Li, 2019). Lu and Shi (2021) categorized recent perspectives on the relationship between culture and tourism into several theories, including “soul carrier theory,” “poetry and distance theory,” “resource market theory” and “charm and vitality theory.” Above of them collectively emphasize that the integration of cultural and tourism industry should be viewed as a profound synergy rather than merely a superficial combination. The deep integration and development of cultural and tourism industry signifies a process characterized by industrial superposition and reorganization, transcending traditional boundaries within both sectors. This convergence encompasses multiple dimensions such as resources, products, markets, and services (Ma and Zhang, 2020). Artal-Tur et al. (2018) identified that cultural resources can serve as core attractions for tourism. Meanwhile, the tourist experiences driven by culture resources can further be categorized into various forms, including heritage tourism, performance tourism, film tourism, food tourism, and creative tourism (Richards, 2018). By combining cultural elements with tourism demand, distinctive cultural tourism products could be developed to meet diverse preferences of travelers (Hao, 2022). Additionally, integrating cultural elements into tourism services enhanced quality and depth—enabling tourists to experience local cultural charm throughout their service interactions (Gao, 2023).

2.2 The integration mechanism of cultural and tourism industry

Scholars have employed a variety of evaluation methods to thoroughly assess the mechanisms underlying cultural tourism integration. Commonly utilized approaches include the input-output analysis theory, entropy method, coupling coordination degree model, and exploratory spatial data analysis. These methodologies are frequently applied to measure the extent of integration and coordinated development between cultural and tourism sectors. Wang (2020) assessed the comprehensive development of cultural and tourism industry within the Grand Canal Cultural Belt using the entropy method, subsequently measuring the integration level through the coupling coordination degree model. The findings indicate that provinces along the Grand Canal Cultural Belt generally exhibit a low level of cultural and tourism integration. Qin and Cong (2020) constructed an evaluation system of integration based on input-output theory to evaluate integration in Jiangsu’s section of the Grand Canal Cultural Belt. As research progresses, some scholars have begun integrating assessments of cultural and tourism integration levels with GIS spatial analysis. For example, Xiao and Yu (2021) conducted global and local spatial autocorrelation analyses to investigate the potential spatial agglomeration phenomena related to the integration of cultural tourism. They further employed geographic detector models to identify factors influencing China’s green cultural-tourism integration, enriching methodological approaches in this field.

2.3 The integration pathways of cultural and tourism industry

Representative viewpoints within the academic community regarding the integration pathways of cultural and tourism industries primarily encompass forms such as resource, technological, and functional integration. Technological integration refers to the phenomenon whereby the process of tourism informatization accelerates, allowing the convergence of cultural and tourism sectors through technological means, thereby giving rise to new forms of tourism (Ma et al., 2010). Han et al. (2020) proposed mechanisms and marketing strategies for screen tourism in the Internet era; Zhang et al. (2020) highlighted that how films, performance, and cultural industries can leverage tourism to amplify “multiplier effects”, exemplified by Hollywood and other global film hubs.
Resource integration manifests itself in various ways, including the mutual incorporation of cultural resources, such as cultural heritage, religious culture, rural folk culture, and exhibition or festival culture. It may also refer to the reciprocal blending of human resources and financial elements between cultural and tourism sectors. Furthermore, a study by Wu et al. (2020) on rural tourism development in Zhangjiajie demonstrated that enhancing cultural heritage preservation through the creation of distinctive cultural tourism products can substantially strengthen the resilience of rural communities to sector-specific vulnerabilities.
Functional integration refers to a scenario in which the primary social functions and roles inherent in the cultural industry are also encompassed within the diverse functions of the tourism industry, thereby providing a basis for integration. For instance, from an educational standpoint, Wei and Zhu (2020) positioned educational tourism as an organic blend of learning and travel. Shan (2019) explored integrating public libraries into tourism by embedding them in scenic areas, enhancing both cultural dissemination and tourist experiences.

2.4 Literature summary

Overall, scholars have extensively explored cultural and tourism integration. The scope of this research encompasses the definition of integration connotations, elucidation of integration mechanisms, and classification of integration pathways. It is generally acknowledged that the integration is a deep, synergistic process driven by socioeconomic trends. The research spans provincial and regional levels, employing methodologies like the entropy method, coupling coordination degree method, and exploratory spatial data analysis (ESDA). The literature has identified three primary paths for integration: resource, technology, and functional integration. However, it is evident that existing studies predominantly analyze the driving mechanisms or influencing factors affecting the level of cultural-tourism integration through qualitative methods or multi-year panel data. As regional disparities in cultural and tourism industry integration continue to grow, only a limited number of scholars have recognized that identical factors may exert varying impacts across different regions; consequently, relevant research in this domain remains insufficient. Furthermore, while national-level studies are relatively comprehensive, investigations into the integration development of culture and tourism industry within the Northeast border regions lack depth and present ample opportunities for further exploration. In contrast to previous works, this study not only synthesizes existing theoretical frameworks regarding cultural and tourism industry integration, but also concentrates on the specific geographical context of Northeast border regions in order to make further assessment of challenges and opportunities associated with cultural and tourism integration in this region. Furthermore, this study represents the first attempt to utilize the global public health crisis as a measurement standard for comparing results of its occurrence. It visually illustrates the impact of this crisis on the integration of culture and tourism industry in the northeast border regions.
Building upon existing theoretical and empirical research, this study focuses on the three northeastern provinces of China as its research subjects. It analyzes the differences between these two industries and examines their respective social and economic developments from 2013 to 2022. By evaluating the developmental levels of both the cultural and tourism sectors independently, we employ a coupling coordination degree model to assess their integration level. Furthermore, we conduct an in-depth analysis of the influencing factors within this coupling relationship, thereby providing theoretical and policy insights for coordinated development and cultural-tourism synergy in Northeast China.

3 Research design

3.1 Research objectives and data sources

The northeast border regions include Liaoning Province, Jilin Province, Heilongjiang Province, and five eastern municipalities of the Inner Mongolia Autonomous Region. This study selects Liaoning, Jilin, and Heilongjiang provinces as the analytical subjects for the integration of cultural and tourism industry, thereby excluding the five eastern municipalities of Inner Mongolia. This decision is grounded in several key factors.
(1) Representativeness: Liaoning, Jilin, and Heilongjiang provinces are regarded as the core regions of Northeast China and are highly representative in terms of economic development, cultural heritage, and tourism resources.
(2) Data consistency: The selected provinces share a unified source of provincial data and consistent statistical framework that facilitates reliable horizontal and vertical comparisons. This consistency ensured both availability and uniformity of the research data throughout the study period.
(3) Resource constraints: Given the limitations regarding research resources, time constraints, and depth requirements, focusing on these three provinces allows for a more thorough analysis. Expanding the scope to include five eastern municipalities of the Inner Mongolia Autonomous Region would exceed the capacity of this study while potentially compromising its quality and depth.
(4) Administrative boundaries: While geographically adjacent to Northeast China, the five eastern municipalities of the Inner Mongolia Autonomous Region differ in administrative affiliation and socio-economic characteristics. This study prioritizes provincial-level analysis to maintain precision and relevance.
The three northeastern provinces of China have historical significance as the birthplace of ethnic minorities, including the Khitan, Xianbei, Jurchen, and Korean, and the cradle of regimes such as Goguryeo, Liao, Jin, Later Jin, and Qing. A rich array of cultural resources characterized by distinct regional and ethnic traits remains preserved in local communities. Simultaneously, the tourism industry in the northeast border regions exhibits significant potential owing to its abundant and high-quality tourism resources. By 2022, the region boasted 1242 star-rated tourist attractions, 475 star-rated hotels, 92 national nature reserves, 35 cities recognized as “Excellent Tourism Cities of China”, 126 key cultural heritage sites, and 19 renowned scenic sites. In recent years, the tourism industry in northeast China has witnessed significant growth, propelled by increasing disposable income among both urban and rural residents. This economic expansion provides a robust foundation for further development of tourism industry. The vigorous advancement of tourism aligns with the national “14th Five-Year Plan” and prepares for the “15th Five-Year Plan”, serving as a strategic tool to optimize industrial structures, promote regional coordination, and revitalize Northeast China.
In this study, panel data sourced from the Liaoning Statistical Yearbook, the Jilin Statistical Yearbook, the Heilongjiang Statistical Yearbook, the China Urban Statistical Yearbook, and the Peking University Digital Inclusive Finance Index are employed. These data cover the three northeastern provinces over the period from 2013 to 2022 and are used to assess the coupling development level of the culture and tourism industries in border regions.

3.2 Research methodologies

3.2.1 Coupling coordination analysis method

Coupling refers to the phenomenon in which two or more subsystems, under specific environmental conditions, exhibit mutual influence and interaction due to similarities or correlations among certain internal elements within each system. Coupling coordination analysis method facilitates the development of an evaluation framework designed to assess the synergistic effects between culture and tourism. Based on this concept, the “Cultural-Tourism Symbiosis” synergistic effect evaluation mechanism is established to refine the assessment and promote overall development.

3.2.2 Ordinary Least Squares (OLS) regression analysis

Ordinary least squares (OLS) regression analysis is a statistical technique employed to estimate the unknown parameters in linear regression models by minimizing the sum of squared residuals. It aims to identify the best-fitting line that minimizes the total squared vertical distance (residuals) between the observed data points and the line. In the present study, OLS regression can facilitate an exploration of the relationships between the degree of integration within the cultural and tourism industry and various economic indicators, enabling a quantitative assessment of how these indicators influence levels of integration. OLS regression enables the quantification of explanatory variables’ effects on dependent variables and evaluates the R2 value.

4 Model construction

4.1 Establishment of indicator system

The precise selection of input and output indicators is crucial for ensuring a reliable measurement result (Li and Xu, 2020). To ensure the validity of the results, this study disaggregates the cultural industry from the tourism industry based on their characteristics and integration mechanisms, thereby constructing an indicator assessment system from an “input-output” perspective to enhance analytical utility. Adhering to the principle of data accessibility and building upon existing research findings, 15 indicators were selected to establish two evaluation index system for cultural and tourism industry development in the northeast border regions (Table 1, Table 2). The indicators employed in this research were obtained from authoritative and credible sources, ensuring consistency in statistical standards. Through meticulous verification and cross-referencing, both the authenticity and accuracy of the data could be rigorously confirmed.
Table 1 Indicator system of cultural industry
Industry Level 1 indicators Level 2 indicators Units
Cultural industry Output indicators Business income of cultural wholesale and retail trade enterprises above norm Ten thousand yuan
Business income of cultural service enterprises above scale Ten thousand yuan
Input indicators Public library holdings Ten thousand copies
Number of libraries Items
Number of museums Items
Number of performing arts venues Items
Number of performing arts organizations Items
Table 2 Indicator system of tourism industry
Industry Level 1 indicators Level 2 indicators Units
Tourism industry Output indicators Tourism revenue Hundred million yuan
Turnover of accommodation industry above norm Hundred million yuan
Turnover of catering industry above norm Hundred million yuan
Input indicators Number of star-rated hotels Items
Total number of travel agencies Items
Total number of tourist attractions Items
Number of legal entities in the accommodation industry above norm Items
Number of legal entities in the catering industry above norm Items

4.2 Coupling coordination analysis of cultural and tourism industry

4.2.1 Data processing

Within the constructed evaluation index system, each indicator has distinct meaning. Therefore, to ensure the comparability of each index in light of the characteristics of the data, this study standardizes the index data with varying dimensions by means of StataMP 18.0 software. Given that all indicators are positive, the range standardization method is adopted for dimensionless processing. Xij (i=1, 2; j=1, 2, …, 10) represents the j-th indicator of the i-th subsystem, where i=1 denotes the cultural industry development subsystem; i=2 represents the tourism industry development subsystem.
To maintain both integrity and reliability within our data, thereby ensuring scientific rigor and accuracy throughout this study, standardized values equal to 0 are reassigned a minimal positive offset (0.01) to avoid computational distortions (Yin et al., 2022). Since all indicators are inherently positive, we utilized the following formula for standardization:
Yij=xij−min{xj}max{xj}−min{xj}+0.01
where xij represents the j-th indicator of the i-th subsystem, i=1 corresponds to the subsystem representing the development level of the culture industry, and i=2 corresponds to the subsystem representing the development level of the tourism industry; Yij signifies the j-th indicator of the i-th subsystem after undergoing extreme difference standardization; xj represents a set of all observed values of the j-th feature.

4.2.2 Determination of indicator weights using entropy weight method

The “entropy method” is grounded in actual and objective data, thereby eliminating the influence of human interference and subjective factors (Liu et al., 2020). This approach renders calculation results scientifically meaningful. In physics, “entropy” describes the disorder state during system construction. This evaluation method employs this physical quantity to determine the weight of a system. The weighted processing through entropy method is closely related to the information dimension contained in original data, enabling more accurate and reliable information in the indicators. This paper employs StataMP 18.0 software to estimate the weights of cultural and tourism industry indicators in the three northeastern provinces of China (Table 3). By integrating data from these sectors, we measure the comprehensive evaluation indices that reflect the development level of cultural industry (subsequently denoted as U1, U2, U3) and the tourism industry (subsequently denoted as u1, u2, u3) for each province through weighted aggregation, where U∈[0,1], u∈[0,1]. A value of U or u approaching one signifies a higher level of development. The comprehensive development level of cultural and tourism industry in China’s northeastern border regions from 2013 to 2022 are shown in Table 4.
Table 3 Weights of various indicators
Industry Level 2 indicators Liaoning (%) Jilin (%) Heilongjiang (%)
Cultural
industry		 Business income of cultural wholesale and retail trade enterprises above norm 6.15 7.17 9.96
Business income of cultural service enterprises above scale 12.23 3.57 15.16
Public library holdings 10.39 7.14 13.22
Number of libraries 40.82 56.63 15.22
Number of museums 10.76 8.28 17.86
Number of performing arts venues 10.73 7.32 15.25
Number of performing arts organizations 8.90 9.86 13.29
Tourism
industry		 Tourism revenue 7.44 16.78 9.83
Turnover of accommodation industry above the limit 11.17 6.51 9.24
Turnover of catering industry above the limit 12.85 13.60 15.92
Number of star-rated hotels 13.37 15.88 9.04
Total number of travel agencies 13.11 6.79 21.75
Total number of tourist attractions 11.36 5.48 4.84
Number of legal entities in the accommodation industry above the limit 10.34 18.03 11.73
Number of legal entities in the catering industry above the limit 20.32 16.88 17.62
Table 4 Comprehensive evaluation index
Year Liaoning Jilin Heilongjiang
Cultural industry (U1) Tourism industry (u1) Cultural industry (U2) Tourism industry (u2) Cultural industry (U3) Tourism industry (u3)
2013 0.107 0.732 0.008 0.353 0.167 0.614
2014 0.151 0.653 0.079 0.297 0.172 0.513
2015 0.306 0.517 0.069 0.522 0.180 0.486
2016 0.768 0.367 0.123 0.707 0.369 0.479
2017 0.791 0.398 0.229 0.661 0.647 0.480
2018 0.808 0.461 0.302 0.489 0.724 0.448
2019 0.857 0.488 0.269 0.463 0.834 0.480
2020 0.381 0.323 0.303 0.255 0.625 0.328
2021 0.444 0.274 0.385 0.522 0.634 0.291
2022 0.429 0.401 0.974 0.534 0.759 0.611

4.2.3 Integration level of cultural and tourism industries

Generally, coupling typically refers to the mutual influence generated through interactions between two or more systems. The degree of coupling reflects the extent of this interaction and the mutual influence among the involved systems or factors. In the context of the cultural and tourism industry, there exists an interactive developmental relationship,namely an interdependent and mutually constrained coupling entity. By conceptualizing the culture and tourism industry as two interconnected systems, it becomes possible to quantitatively assess their synergistic development by measuring both the degree of coupling and coordination. Meanwhile, we can reveal the integration level of the cultural and tourism industry. This study employs StataMP 18.0 to assess the integration level of cultural and tourism industry. Given that n=2, the formula is as follows:
C=U1U2U1+U222=2U1U2U1+U2
where C indicates the coupling degree of cultural and tourism industry, and U1 and U2 denote the respective comprehensive evaluation indices of both the cultural and tourism industry. However, this model fails to reflect the independent development levels of both industries. Specifically, even if both industries exhibit low development level, the model might still yield a high coupling degree, which does not convey the same implications as the high degree of coordination observed when development levels are elevated (Yu, 2022). Therefore, a coupled coordination degree model was developed to objectively represent the coordinated development level of the cultural and tourism industry.
D=CT
T=αU1+βU2
In this context, C represents the coupling degree, reflecting the strength of interrelationship between cultural and tourism systems. Its value ranges between [0,1], though it fails to indicate the overall development levels of the two systems. T represents the coordination index, while D is the coupling coordination degree, which indicates the extent of coordinated development between two systems. The parameters α and β are undertemined weights that correspond to the relative importance of the cultural and tourism industry, respectively.This study assumes equal importance between the two sectors; thus, both α and β are assigned a value of 0.5 (Zheng et al., 2022).
To illustrate the coupling coordination degree and level, a uniform function distribution method is adopted to classify coupling levels. This classification is based on established standards for dividing coupling coordination degrees (Table 5), allowing us to assess different degrees of imbalance or coordination within our research subjects. This approach partially addresses the limitations inherent in using only coupling degrees, which fail to reflect comprehensive developmental levels across both systems.
Table 5 Criteria for classifying the degree of coupling coordination
Serial number Coherence Level of coordination Coordination phase
1 [0, 0.1] Extreme disorder Budding stage
2 (0.1, 0.2] Severe disorder
3 (0.2, 0.3] Moderate disorder
4 (0.3, 0.4] Mild disorder Initial stage
5 (0.4, 0.5] Borderline disorder
6 (0.5, 0.6] Forced coordination
7 (0.6, 0.7] Initial coordination Stable stage
8 (0.7, 0.8] Intermediate
coordination
9 (0.8, 0.9] Good coordination Mature stage
10 (0.9, 1] Premium coordination
Using the coupling coordination degree model, this study measures the levels of coupling coordination of cultural and tourism industry in China’s northeast border regions from 2013 to 2022. Table 6 presents the mean values of coupling coordination degree for Liaoning, Jilin, and Heilongjiang provinces, with coordination levels and degrees classified according to Table 5. The analysis reveals the following:
Table 6 Levels and grades of coupling coordination
Province Year Comprehensive evaluation
index of cultural industry (U)		 Comprehensive evaluation index
of tourism industry (u)		 D-value of coupling
coordination		 Level of
coordination
Liaoning 2013 0.107 0.732 0.315 4
2014 0.151 0.653 0.485 5
2015 0.306 0.517 0.615 7
2016 0.768 0.367 0.653 7
2017 0.791 0.398 0.706 8
2018 0.808 0.461 0.784 8
2019 0.857 0.488 0.825 9
2020 0.381 0.323 0.453 5
2021 0.444 0.274 0.259 3
2022 0.429 0.401 0.589 6
Jilin 2013 0.008 0.353 0.217 3
2014 0.079 0.297 0.301 4
2015 0.069 0.522 0.455 5
2016 0.123 0.707 0.595 6
2017 0.229 0.661 0.676 7
2018 0.302 0.489 0.632 7
2019 0.269 0.463 0.597 6
2020 0.303 0.255 0.236 3
2021 0.385 0.522 0.693 7
2022 0.974 0.534 0.883 9
Heilongjiang 2013 0.167 0.614 0.315 4
2014 0.172 0.513 0.325 4
2015 0.180 0.486 0.362 4
2016 0.369 0.479 0.649 7
2017 0.647 0.480 0.803 9
2018 0.724 0.448 0.797 8
2019 0.834 0.480 0.872 9
2020 0.625 0.328 0.539 6
2021 0.634 0.291 0.289 3
2022 0.759 0.611 0.964 10
From a comprehensive perspective, the coupling coordination levels of the culture and tourism industry in Liaoning, Jilin, and Heilongjiang provinces display fluctuations. However, a slight upward trend was evident in the overall trajectory. Despite this progress, most years indicate that coupling coordination levels predominantly remain within the budding or initial stages. Specifically, between 2013 and 2022, a significant number of provinces were classified into these early stages. This finding underscores that further enhancement is necessary to achieve higher coordination level. Additionally, the trajectory of coupling coordination development aligned with China’s economic growth patterns, suggesting that provincial economic conditions influence the integration of cultural and tourism industry.
At the provincial level, both Liaoning and Jilin provinces experienced mild disorder in 2013 and 2020, while Heilongjiang Province displayed mild disorder only in 2013. These results indicate low levels of integration between the cultural and tourism industry during these years. However, over time, all three provinces have shown improvements. In particular, Heilongjiang Province reached a state of “premium coordination” by 2022, demonstrating a significant improvement in the integration of its cultural and tourism industry. Nevertheless, the majority of the provinces remained in the “Forced coordination” or “Mild disorder” stages due to uneven development in cultural resources, tourism infrastructure, or policy implementation.
The impact of the global public health crisis on the integration of the cultural and tourism industry is significant and cannot be overlooked. Between 2020 and 2022, travel restrictions and other sanitary and epidemic prevention measures led to a sharp decline in tourism, directly affecting the cultural and tourism sectors, which heavily depend on tourism revenue. In 2020, the coupling coordination degree (D-value) decreased to 0.453 in Liaoning Province, 0.236 in Jilin Province, and 0.539 in Heilongjiang Province, reflecting a decline in development across all three provinces. This demonstrates the negative impact of the global public health crisis on the integration of these industries. However, despite facing severe challenges due to the global public health crisis, these provinces have made efforts to enhance coupling coordination degree within these sectors. For instance, Heilongjiang’s D-value reached 0.964 in 2022, indicating a substantial increase in the integration of its cultural and tourism industry. This improvement could be attributed to increased provincial support for cultural and tourism initiatives during the global public health crisis period, which included promoting online cultural services as well as innovating and marketing cultural tourism products.
Although the global public health crisis has caused short-term impacts on the cultural and tourism industry, it may also present opportunities for transformation, upgrading, and innovative development in the long term. Currently, the levels of coupling coordination development in Liaoning, Jilin, and Heilongjiang provinces exhibit positive upward trends. Therefore, future development efforts should prioritize enhancing integration across all dimensions, particularly concerning the development and utilization of tourism and cultural resources. More profound and comprehensive strategies are needed to promote high-quality growth in these industries. Key measures include strengthening policy support, optimizing the industrial structure, improving service quality, and leveraging technological advancements to effectively address potential future challenges while achieving sustainable development in the cultural and tourism sectors.

5 Influencing factors of integration level of cultural and tourism industry

Analyzing the factors that influence the integration of cultural and tourism industry is crucial for uncovering the driving mechanisms behind this integration, identifying the reasons for spatial disparities in their levels of integration, and proposing targeted strategies for their coordinated development. Based on previous research reviews and regional characteristics, this study focuses on four dimensions to examine the factors affecting cultural-tourism integration in Northeast China’s border regions: economic development level, transportation infrastructure, industrial development status, and digitalization level.

5.1 Variables and data

This study utilizes data from three northeastern provinces of China, covering the period from 2013 to 2022. Relevant data were obtained from the China Statistical Yearbook and provincial statistical yearbooks each year as well as Digital Financial Inclusion Index compiled by Peking University. To investigate the factors influencing the coupling coordination of cultural and tourism industry, four independent variables are selected: economic development, transportation infrastructure, industrial structure optimization, and digital economy development (Shi and Zhan, 2021).
The coupling coordination level of the cultural and tourism industry in these three northeastern provinces from 2013 to 2022 was designated as the dependent variable. To investigate the factors affecting cultural and tourism integration, we identified four independent variables (Table 7). This study employed regional GDP as a measure of economic development, highway mileage to represent transportation infrastructure, proportion of tertiary industry to reflect industrial development status, and the degree of digitalization to indicate advancements in digital economy development.
Table 7 Influencing factors of coupling coordination degree
Sequence Influencing factors Independent variable Units Expected impact
1 Level of economic development X1: RGDP Hundred million yuan +
2 Level of transportation development X2: Road mileage km -
3 Industrial development status X3: Share of tertiary sector % +
4 digital economy development X4: Degree of digitization - +

5.1.1 Descriptive statistics

Descriptive statistics encompass a range of methods and techniques employed to summarize and present the characteristics of a dataset. By simplifying vast amounts of data, this article utilizes StataMP software (Version 18.0) to perform descriptive statistical analysis on the variables. The results are presented in Table 8.
Table 8 Descriptive statistics of variables
Variable name Sample size Maximum Minimum Average Standard deviation Median
Y 30 0.964 0.217 0.563 0.218 0.596
X1 30 28826.1 9427.89 15797.162 5747.882 13005.16
X2 30 168958.033 94191 130710.942 27034.311 122839.524
X3 30 57.1 35.5 48.432 5.866 50.3
X4 30 424.745 224.97 351.961 61.545 379.042
The average value of the integration degree is 0.563, with a standard deviation of 0.218. This indicates that, while there are fluctuations in the integration degree, the overall development demonstrates an upper-medium level.. Notably, the maximum value approaching 1 suggests that, in certain years, the integration of these two industries reached a state of premium coordination. Conversely, the relatively low minimum value signifies that integration has faced challenges or development imbalances in specific periods. Regarding regional GDP, the higher maximum and mean values reflect stronger economic performance in particular years or regions. However, a larger standard deviation coupled with lower minimum values indicates uneven economic development, implying that some areas have encountered slower growth rates. The significant difference between the maximum and minimum values for road mileage underscores the disparities in transportation infrastructure development among the three provinces. While greater highway mileage may enhance intra-regional tourism flows and cultural exchanges, it also highlights the potential imbalances in infrastructure development across different regions. The standard deviation associated with the share of tertiary industry (5.866) indicates varying levels of industrial structure optimization among regions. Additionally, the minimum digitization degree recorded at 224.97, with a standard deviation of 61.545, indicates that certain areas still face substantial challenges in the digitization process.

5.1.2 ADF test

ADF tests are employed to determine whether a time series exhibits a unit root. The presence of a unit root signifies that the time series is non-stationary. The presence of a unit root indicates a non-stationary time series, which generally cannot be subjected to subsequent analyses. This study utilizes data from various factors influencing cultural and tourism integration in three northeastern provinces from 2013 to 2022, by means of Stata 18.0 software to conduct the ADF test. The results, presented in Table 9, demonstrate that all variables exhibited significance at the level.
Table 9 ADF test table
Variable name Differential order t P AIC Critical value Conclusion
1% 5% 10%
Y 0 ‒3.938 0.002*** 1.416 ‒3.679 ‒2.968 ‒2.623 Stable
1 ‒4.104 0.001*** 4.079 ‒3.724 ‒2.986 ‒2.633
2 ‒4.486 <0.001*** 12.086 ‒3.77 ‒3.005 ‒2.643
X1 0 ‒1.694 0.434 349.787 ‒3.738 ‒2.992 ‒2.636 Stable
1 ‒5.409 <0.001*** 294.268 ‒3.689 ‒2.972 ‒2.625
2 ‒1.725 0.418 285.643 ‒3.859 ‒3.042 ‒2.661
X2 0 ‒0.845 0.806 441.977 ‒3.679 ‒2.968 ‒2.623 Stable
1 ‒14.662 <0.001*** 361.533 ‒3.833 ‒3.031 ‒2.656
2 ‒3.102 0.026** 343.826 ‒3.859 ‒3.042 ‒2.661
X3 0 ‒3.06 0.030** 121.469 ‒3.689 ‒2.972 ‒2.625 Stable
1 ‒3.281 0.016** 107.384 ‒3.738 ‒2.992 ‒2.636
2 ‒1.809 0.376 104.986 ‒3.859 ‒3.042 ‒2.661
X4 0 ‒0.901 0.788 165.723 ‒3.809 ‒3.022 ‒2.651 Stable
1 ‒1.834 0.364 151.61 ‒3.833 ‒3.031 ‒2.656
2 ‒4.206 0.001*** 147.466 ‒3.859 ‒3.042 ‒2.661

Note: ***, **, and * represent 1%, 5%, and 10% significance levels, respectively.

5.1.3 Optimal model selection

To determine the most appropriate form of the panel data model, the Hausman test, F-test, and Breusch-Pagan test were performed using Stata 18.0 to analyze the panel data of three northeastern provinces from 2013 to 2022. The results are summarized in Table 10.
Table 10 Optimal model test results
Type of tests Statistic P-value
F-test 0.257 0.776
Breusch-Pagan test 1.592 0.902
Hausman test 0.510 0.973
(1) Hausman test
The Hausman test can be employed to compare the fixed-effects model with the random effects model. P-value less than 0.05 indicates the superiority of the fixed-effects model; otherwise, the random-effects model is preferred. As shown in Table 10, this study yielded a significance P-value of 0.973, which was not statistically significant. Therefore, the null hypothesis cannot be rejected, and the random-effects model is selected.
(2) F-test
The F-test compares the fixed-effects model with the mixed-effects model. P-value less than 0.05 favors the fixed effects model; conversely, the mixed-effects model is chosen. As shown in Table 10, the significance P-value is 0.776, which is not statistically significant, leading to a failure to reject the null hypothesis; therefore, the mixed-effects model was selected.
(3) Breusch-Pagan test
The Breusch-Pagan test can be conducted to compare the random effects model and the mixed-effectsl model. P-value less than 0.05 would indicate that the random effects model is better; otherwise, themixed-effects model would be pre-ferred. According to the Breusch-Pagan test, the significance P-value is 0.902, which is not significant, and the null hypothesis could not be rejected; thus, the mixed-effects model is selected.

5.2 Overall regression analysis

OLS regression analysis is a statistical method for estimating unknown parameters in linear regression models by minimizing the sum of squared residuals. This methodology seeks to identify the optimal straight line that minimizes the total squared vertical distances (residuals) between all the observed data points and the fitted line. In this study, OLS regression analysis was conducted using StataMP 18.0 software to investigate the relationship between the integration degree of cultural and tourism industry, various economic indicators, as well as to assess both the intensity and direction of these indicators’ influence on the integration degree.
Given the profound impact of the global public health crisis on economic and social activities all over the world, it is essential to conduct a separate analysis of the data from data from pre- and post-crisis periods. This approach allows for a more accurate assessment of changes in the integration effects of cultural-tourism industry. The results of OLS regression analysis are displayed in Table 11. For the period spanning from 2013 to 2019, the regression results indicate a goodness-of-fit statistic (R²) of 0.698, with all four explanatory variables, including X1 (economic development level), X2 (transportation infrastructure development), X3 (industrial structure optimization), and X4 (digital economy development) at the 0.05 significance level. This finding suggests that prior to the global public health crisis, these factors significantly influenced the cultural and tourism integration levels within the northeast border regions. In contrast, the regression results for 2013-2022 reveal that only X3 (industrial structure optimization) remained statistically significant at 005 level, meanwhile X1, X2, and X4 were no longer statistically significant. Furthermore, there was a notable decline in goodness-of-fit from 0.698 to 0.288, indicating that the factors influencing cultural and tourism industry integration were notably altered as a result of impacts stemming from the global public health crisis.
Table 11 Results of regression analysis
Variables Y
2013‒2019 2013‒2022
X1 0.000** ‒0.000
(8.02) (‒1.19)
X2 ‒0.000* ‒0.000
(‒3.37) (‒0.52)
X3 0.022** 0.017*
(4.61) (3.81)
X4 0.001* 0.001
(3.04) (1.31)
Constant ‒0.599* ‒0.372
(‒4.02) (‒2.67)
Observations 21 30
R2 0.698 0.288

Note: Robust t-statistics are in the parentheses. ** P<0.01, * P<0.05.

5.2.1 Economic development level

With the enhancement of economic strength and the optimization of industrial structures, the income levels of residents in the northeast border regions have risen, resulting in a discernible trend toward consumption upgrading. This phenomenon directly stimulates both intra- and cross-regional demand for cultural tourism. A higher level of economic development provides significant financial support for the construction and renovation of cultural and tourism infrastructure, thereby accelerating the implementation of projects such as high-quality hotels, theme parks, and cultural experience centers. Leveraging its rich historical and cultural heritage, such as ice-snow culture and industrial heritage, along with policy support and technological advancements driven by economic growth, the region has systematically explored and integrated various cultural resources into tourism product design and promotion. This approach has facilitated the deep integration of cultural and tourism industry. Moreover, economic development has promoted talent aggregation and enhanced innovation capabilities, enabling emerging industries, such as cultural creativity and digital entertainment, to combine with the traditional cultural and tourism sectors, and creating new forms and products. The increasing level of economic development in the northeastern border regions has strongly supported the integration of culture and tourism, enhancing both physical infrastructure and soft power, thereby promoting the competitiveness and sustainability of the cultural-tourism sector.

5.2.2 Transportation accessibility

The level of transportation accessibility exerts a significant yet complex impact on the integration of the cultural and tourism industry in the northeast border regions. While improved transportation infrastructure can boost overall tourism flows, the uneven distribution of resources and historical developmental disparities within the region often result in improved transportation, concentrating tourist activity at well-known attractions, such as the Forbidden City in Shenyang, the Ice-Snow World in Harbin, and Changchun puppet Palace Museum. Consequently, relatively remote regions rich in cultural and natural heritage remain underdeveloped, hindering balanced regional development. Moreover, convenient transportation has accelerated urbanization processes that complicate efforts to preserve traditional villages and local folk culture. The rapid expansion of urban areas can erode tourism resources characterized by rural attributes, adversely affecting tourism reliant on cultural and natural heritage. Additionally, surges in tourist flows may overwhelm ecologically sensitive areas, depleting resources and causing environmental degradation, which poses long-term challenges for sustainable integration. Therefore, it is imperative that transportation planning within the northeast border regions takes these factors into account. Appropriate policy interventions should be implemented to ensure that infrastructure development fosters balanced growth while mitigating potential adverse effects.

5.2.3 Industrial structure optimization and upgrading

The optimization and upgrading of industrial structure in the northeast border regions have generated a positive and far-reaching impact on the integration of the cultural and tourism industry. The modernization of key sectors, such as manufacturing and agriculture, has optimized the economic structure, releasing more consumption demand and leisure opportunities. This transformation provides a stable market foundation for the development of culture and tourism. For instance, old industrial bases have been transformed into multifunctional parks combining historical education and tourism, attracting visitors to engage with industrial heritage. Furthermore, the growth of modern service industries, particularly high-end service industry, has provided essential support for integration by improving service management practices and expertise. For instance, Shenhe District in Shenyang, recognized as a national demonstration area for cultural and tourism integration, exemplifies how advanced services drive resource consolidation and innovation. The expansion of cultural industries themselves, particularly film, television, and digital innovation, has further enriched cultural and tourism products, creating new opportunities for integration. Therefore, industrial structure optimization has not only created favorable external conditions, but also driven innovation and quality enhancement within the culture and tourism sectors, contributing to regional economic transformation.

5.2.4 Digital economy development level

The advancement of the digital economy is fundamentally reshaping the integration patterns of cultural and tourism industry in the northeastern border regions. The widespread adoption of new-generation information technologies, including artificial intelligence, big data, cloud computing, and blockchain, has propelled the sector into a digitalized, networked, and intelligent era. Digital technology has enhanced both the discovery and presentation of cultural and tourism resources. The utilization of VR/AR technology and high-definition imaging facilitates the vivid virtual reproduction of traditional heritage and natural landscapes. Consequently, it enables remote immersive experiences, thereby increasing the appeal of cultural tourism projects. Furthermore, the digital economy has fostered the development of smart tourism service systems. Now, integrated platforms for navigation, booking, and payments optimize service efficiency and industry-digital infrastructure synergy in Northeast China, which streamline the tourism industry chain and improve service quality, thereby further promoting the convergence of cultural and tourism services. Additionally, network marketing and personalized recommendations have facilitated targeted promotion of cultural and tourism products. Big data analytics enable targeted promotion and personalized product design, catering to diverse consumer preferences. Moreover, digital technology has enabled cross-sector integration by merging digital creativity with ice-snow tourism, agritourism, and industrial heritage to create unique products that blend technological and cultural heritage. This synergy has significantly enhanced industrial collaboration within the region, as well as value creation processes. Therefore, the growth of the digital economy has injected momentum into overcoming traditional bottlenecks, creating new growth avenues and steering the industry toward high-quality development.

5.2.5 Results of pre- and post-global public health crisis comparisons

Excluding data from the period affected by the global public health crisis, factors like economic development level, transportation infrastructure, industrial structure optimization, and digital economy development, showed a significant impact on the integration of cultural and tourism industry. However, after adding three years of post-crisis data, only industrial structure optimization remained statistically significant, while the other three factors lost their relevance. This result highlights the importance of robust industrial structures, resilient supply chains, and the ability to withstand external shocks to ensure sustainable integration between the cultural and tourism industry during major public health crises. The level of economic development was severely affected by the global public health crisis, particularly by disrupting the tourism and related service sectors. Tourism-dependent economies suffered severe short-term disruptions, decoupling regional economic performance from industry integration.. Similarly, although transportation infrastructure plays a critical role in attracting tourists under normal circumstances, travel restrictions and health concerns during this crisis nullified transportation's role in driving tourist flows. The lack of significance of digital economic development suggests that digital innovations did not fully offset the negative impact of the global public health crisis on traditional offline travel activities, which have exposed gaps in rapid response capabilities. Finally, the shift in consumer behavior prompted by the global public health crisis towards more localized and online leisure activities has adversely affected the traditional cultural and tourism industry. This transformation diminished the influence of factors that were previously significant drivers of integration prior to the onset of the global public health crisis.

6 Suggestions

6.1 Improve the integration and explore various models

6.1.1 Heritage culture + tourism model

Northeast border regions are endowed with a rich historical heritage, encompassing relics from the Liao and Jin dynasties as well as evidence of border governance during the Ming and Qing dynasties. This region also holds the memories of the “Northeast Anti-Japanese United Army” and commemorates the victories achieved during the “Liberation War”, honoring the sacrifices made by countless heroes in their pursuit of national independence and prosperity. Since the founding of the People’s Republic of China, the region has undergone profound transformations in terms of both economic development and social progress.
Its cultural identity is shaped by a blend of diverse historical layers, ethnic culture, the modernity of industrial cities, and the cross-border charm of the Sino-Russian-Korean border, forming a distinctive “multicultural fusion, spanning ancient and modern eras”.
To leverage this heritage, the region should prioritize balanced preservation and innovative utilization of cultural relics. It is essential to integrate cultural heritage into tourism. The region is advised to utilize modern information technologies such as Internet platforms, virtual reality (VR), and 5G communications to create digital museums, historical site exhibitions, and theme parks dedicated to red culture, particularly those highlighting anti-Japanese resistance sites. Immersive tourism experiences should be developed to enable visitors to engage with history and to appreciate its significance. This would not only promote the northeast as a vibrant land, but also showcase its profound historical and cultural heritage.

6.1.2 Red culture + tourism model

As a key battlefield during China’s Anti-Japanese War, the Northeast possesses abundant red tourism resources, including the Northeast Martyrs’ Memorial Museum and Site of the Fourth Field Army Headquarters. These sites serve as critical platforms for patriotic education and historical remembrance. To further promote red culture, these valuable resources should be systematically integrated into both educational and attractive tourism routes, such as the Heroic Path of the Northeast Anti-Japanese United Army and Liberation War Memorial Tour. Such routes not only permit tourists to trace the footsteps of history but also immerse them in the valor and dedication of revolutionaries (Xu and Li, 2021).
Local authorities or cultural organizations could construct dedicated memorial halls for heroic figures like Yang Jingyu and Zhao Shangzhi, employing multimedia technologies (e.g., holographic projections) to recreate historical scenes and narrate heroic tales. Organizing red culture festivals, historical seminars, and other events centered on these figures can enrich the cultural tapestry of tourist routes while attracting visitors to engage in commemorations and preserve the red spirit. Such initiatives would not only enhance the overall tourism experience but also vividly convey the essence of red culture, thereby inspiring patriotism and a heightened sense of social responsibility among visitors.

6.1.3 Ethnic culture + tourism model

The northeastern border regions hold great potential for developing cultural tourism projects, which focused on ethnic minorities such as the Manchu, Korean, Mongolian, Daur, and Oroqen. By highlighting their unique lifestyle, artistic forms of expression, historical legacies, and cultural experience zones, strong local characteristics can be established. Hosting colorful ethnic festivals, folklore performances, and traditional handicraft exhibitions would allow tourists to observe, appreciate, participate and immerse themselves in the charm of ethnic culture.
Cultural elements can be combined with natural landscapes (e.g., Changbai Mountain, Jingpo Lake) to highlight both ecological and cultural allure. Additionally, promoting ethnic-themed homestays and restaurants to immerse visitors in local traditions is an effective approach to promote the sustainable development of the cultural and tourism industry in the northeastern border regions.

6.2 Improve the transportation network and balance the development of cultural and tourism resources

6.2.1 Optimize the transportation network in remote areas to enhance regional accessibility

In the northeast border regions, transportation networks and infrastructure require more rational planning and development to ensure comprehensive utilization of cultural and tourism resources. Prioritizing the development of transportation facilities in remote regions is crucial for improving accessibility and enabling the full exploration and utilization of the rich cultural and natural heritage of these regions. This provides tourists with diverse and unique travel experience. Priority should be given to building roads connecting key cultural sites, nature reserves, and regions with unique ethnic characteristics. Simultaneously, enhancing the existing public transportation system by introducing additional bus routes and special tourist buses can further facilitate access to these areas. Additionally, innovative approaches like developing self-drive tourism campsites and cycling trails can further encourage exploration of lesser-known yet equally captivating destinations, thereby relieving pressure on popular sites and ensuring a more balanced distribution of cultural and tourism activities.

6.2.2 Protect traditional villages and advocate for sustainable tourism

While constructing transportation networks and upgrading infrastructure, it is imperative to strengthen the protection of traditional villages and ethnic cultural heritage to prevent the erosion of rural tourism resources due to rapid urbanization. Sustainable development principles must guide transportation projects to minimize damage to natural environments and cultural legacies. Establishing protected zones and implementing strict conservation regulations can help to preserve the original architectural and cultural integrity of traditional villages. Furthermore, organizing folk culture festivals and establishing handicraft workshop zones will enable tourists to engage directly with cultural preservation efforts. This approach enhances tourists’ appreciation for and respect for traditional culture and contributes to the sustainable development of cultural tourism by preserving heritage sites and rural traditions for future generations.

6.3 Optimize industrial structure and promote diversified integration and development

6.3.1 Promote industrial upgrading and develop cultural and tourism complexes

The northeast border regions should focus on continuously optimizing their industrial structure and deepening the integration of the culture and tourism sectors with other industries, including modern agriculture, high-quality services, film and television sectors. Transforming old industrial sites into cultural and tourism complexes effectively preserves and revitalizes industrial heritage while offering tourists unique cultural experience. For example, the Tiexi District in Shenyang, Liaoning Province, formerly an industrial hub, has evolved into a comprehensive industrial park that integrates historical and cultural education, tourism, and sightseeing, thereby attracting many visitors. These transformations not only breathe new life into former industrial areas but also serve as new economic growth drivers for the regions. Additionally, leveraging abundant agricultural resources, rural tourism could be developed in the region. For example, establishing an agricultural tourism park in Jilin Province centered on specialty products such as ginseng and corn, integrates agricultural science education with leisure activities, meanwhile attracting urban tourists to experience rural life.

6.3.2 Innovate cultural industry and enrich tourism products

The growth and expansion of cultural industry should receive strong support, especially in fostering the development of the film and television sector and leveraging cultural and technological innovations to diversify the content and formats of cultural tourism products. The Northeast can emulate the success of Hengdian World Studios by building professional film bases to attract production crews, thereby boosting local tourism. Recreating historical scenes through digital technology and developing cultural derivatives, such as VR experience centers and interactive theaters, can expand the scope of the cultural and tourism sectors while promoting their integration. For instance, Heilongjiang Province can capitalize on its ice-snow culture by combining digital creativity to launch tech-driven tourism formats like VR ice-snow simulations and immersive digital exhibitions. Such innovations would appeal to younger tourists and enhance the competitiveness of the Northeast cultural-tourism sectors.

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