Ecology, Culture, and Tourism Integration Efficiency, Spatial Evolution, and Influencing Factors in China

Abstract

To explore the integration efficiency of ecology, culture and tourism in China, this study uses a Super-Efficiency SBM model with undesirable outputs to measure integration efficiency, employs kernel density estimation (KDE) to analyze dynamic spatial distribution characteristics, applies the standard deviational ellipse (SDE) to examine the migration trend of the spatial agglomeration center of gravity, and uses Tobit regression to identify spatiotemporal influencing factors. The findings show that: the national integration efficiency presents a trend that first decreases and then increases, with North and South China having relatively high integration efficiency. The national integration efficiency has gone through three stages: narrowing differences, coexistence of slow efficiency, and gradient effects, and increasing efficiency with weakened multipolarization. The degree of spatial agglomeration has gradually increased, and the center of gravity has shifted eastward as a whole. The internal gaps in East and South China have expanded, while the internal balance in North China has improved; the internal differences in other regions have narrowed. The influencing factors of integration efficiency have shifted from traditional economy-led to innovation and institutional collaboration. Economic development level and market openness have a positive impact on the overall integration efficiency, while transportation conditions show a restraining effect.

1. Introduction

Guided by the new development philosophy and the deep integration of culture and tourism, the concept of ecological civilization further advances the sustainable development of the cultural and tourism sectors [1]. In line with the dominant trend of interactive and integrated industrial development, the integration of ecological, cultural, and tourism industries has become a core focus in contemporary society. Through the reconstruction and integration of industrial chains, as well as the multi-dimensional convergence of technology, operations, and markets [2], it has achieved mutual benefits between ecological protection and the growth of cultural and tourism industries [3]. Guided by policies such as “building national cultural parks” and “prioritizing ecological protection”, China has leveraged local cultural and natural landscapes to activate economic growth points. However, industrial expansion has brought about a series of environmental and resource allocation issues [4,5]. Against this backdrop, evaluating the integration benefits of ecology, culture, and tourism in China to explore paths for maximizing benefits at minimum resource and environmental costs is not only crucial for expanding the domestic cultural tourism market and promoting regional resource sharing, but also provides references for global eco-tourism innovation and cultural heritage protection.

Currently, existing studies can be categorized into two main directions. The first direction focuses on theoretical exploration, examining the mechanisms, logical pathways, and promotion strategies for the integration of multi-industries including ecology, culture, and tourism [6,7,8], with a primary focus on binary relationships [9,10,11]. However, over time, it has shifted to a more comprehensive ternary perspective [7,12,13]. Early studies on the ecology-tourism relationship emphasized natural resource conservation [14,15], but have expanded to include not only economic benefits [16,17] but also the maintenance of ecological sustainability [6,18] and cultural heritage [19]. Yet, the cultural sustainability of ecotourism fails to be fully developed without deeper cultural analysis [20]. In the culture-tourism context, many scholars see culture as a primary content provider for tourism, while tourism serves as a major income source for culture [10,21]. Yet, as culture becomes increasingly localized and practical, tourism must consider ecological factors to explore new markets. Scholars argue that cultural practices of nations, regions, and ethnic groups are crucial for promoting ecological civilization [22,23], but the development of cultural tourism may also exert pressure on the ecological environment. Thus, empirical research on the integration of the ecological, cultural, and tourism industries from a three-dimensional perspective is rare, the narrow scope of binary frameworks is insufficient to address the diverse cultural, environmental, and socioeconomic contexts of ecotourism. Studies failing to integrate cultural diversity also raise concerns about their applicability across different regions [24].

The second direction involves the construction of an indicator system for evaluating the integration of ecology, culture and tourism, along with methodologies for measurement and assessment [25]. Studies on Chinese regions mainly adopt a binary perspective and the coupling coordination model to evaluate the integration development levels at different scales, such as the Two Rivers Basin and urban agglomerations [26,27]. Some researchers have combined the coupling coordination degree with Moran’s I index to explore the spillover effects of urban industrial development [28,29,30], while others have applied the spatial Durbin model (SDM) to analyze regional tourism development disparities [31]. Additionally, kernel density estimation (KDE) has been used to analyze dynamic trends [16]. However, focusing solely on system coordination does not offer clear guidance for improving inefficient systems. Furthermore, the coupling coordination method, based on static indicators, suffers from subjectivity in weighting, lacks dynamism, and struggles to address undesirable outputs. As a result, some scholars are shifting toward exploring ecological efficiency or the integration of cultural tourism, using methods like the three-stage DEA efficiency model [32] and the Super-SBM model [33,34,35].

Studies on tourism-related integration efficiency and regional disparities show diversified characteristics, with the DEA model being the mainstream method for efficiency measurement. Cracolici et al. (2008) combining Stochastic Frontier Analysis (SFA), found that efficiency differences in tourism destinations primarily stem from input–output imbalances, misjudgments by management organizations, and production process heterogeneity [36]. Sellers and Casado (2018) and Sánchez (2023) revealed that the efficiency of the tourism hotel industry is associated with environmental factors, regional tourism models, market characteristics, and quality investments [37], and confirmed oversupply in natural scenic areas prior to the pandemic [38]. For Gössling et al. (2005) and Lacko et al. (2024), differences in tourism ecological efficiency mainly derive from tourism culture, industrial pollution, and EU policies [39,40]. Figueroa et al. (2018) have found that tourism efficiency is determined by cultural endowments, cultural activities, and natural resources [41], and the study of Pardo Martínez and Cotte Poveda (2024) further shows that tourism efficiency indices in Central and South America showing distinct trends closely related to the utilization of economic, natural, and social resources [42]. Beyond the DEA model, Éber et al. (2018) used hyperlink network analysis to show that policymakers should fully leverage the attractiveness of natural and cultural resources to enhance tourism system efficiency [43]. Additionally, scholars from countries including the Xu and Chi (2017) [44], Assaf and Agbola (2011) [45], Ohe and Peypoch (2016) [46] have also made important contributions to the improvement of tourism efficiency and regional tourism performance evaluation.

Overall, research on the integration of ecology, culture, and tourism faces three key challenges: first, research perspectives are confined to binary concepts such as ecotourism or cultural tourism, lacking systematic integrated analysis of the three; second, quantitative research is insufficient, and existing methods struggle to reveal the driving mechanisms of integration efficiency; third, data coverage is limited, with most studies focusing on specific regions and lacking comprehensive assessment of national overall trends and regional interconnections.

Accordingly, this study raises three research questions: (1) whether the integration efficiency of ecology, culture and tourism shows stage-specific and regional differences; (2) what are the evolutionary characteristics and distribution patterns of integration efficiency in temporal and spatial dimensions; (3) which factors influence the development of integration efficiency, and whether these factors vary over time and across regions. By addressing these questions, this paper aims to provide an efficiency perspective for evaluating the deep integration of the three sectors. Through analyzing the spatiotemporal differences, evolution and influencing factors of integration efficiency, it intends to offer stakeholders insights into resource allocation, ecological-cultural heritage protection and tourism economic benefit transformation, while providing references for promoting regional sustainable tourism.

2. Theoretical Analysis

This study, based on symbiosis theory and industrial integration theory, explores the interaction mechanisms between ecological culture and tourism. Symbiosis theory emphasizes the mutual interaction, co-evolution, and resource sharing between different systems. In the context of ecological cultural tourism, the relationship between ecology, culture, and tourism can be viewed as a mutually beneficial symbiotic relationship. Ecological cultural tourism, which integrates the protection and utilization of local natural and cultural resources, aligns with the “all for one” tourism development model and involves the multiple interactions of ecotourism, cultural-tourism integration, and ecological civilization. These three elements are interdependent and interact to form a cohesive symbiotic system.

The relationship between ecology and tourism needs to shift from parasitic or asymmetric symbiosis to mutually beneficial, symmetric symbiosis (Figure 1). Ecology provides essential resources for tourism, while tourism both exerts pressure on and supports ecological protection. It can damage the environment but also offer funding and management support for its conservation. The ecological environment provides rich ecological services for the tourism industry, including ecotourism products [47], recreational spaces [48], and aesthetic value [49], all of which tourism activities rely on varying degrees. Conversely, the development of tourism can also exert negative impacts and potential risks on the ecological environment, such as the destruction of natural habitats, depletion of water sources, and landscape degradation [50]. The growth of the tourism industry and economy has resulted in undesirable outputs, such as industrial waste and large amounts of household garbage. However, as sustainable development progresses, there is potential for effective improvements in areas such as waste utilization, forest coverage, and urban greening. Therefore, implementing effective ecosystem management and sustainable tourism development strategies to ensure the rational use of resources and the maintenance of ecological balance is of paramount importance. Through this dynamic balance of interactions, tourism can not only stimulate economic growth but also promote community-oriented environmental protection activities [51], providing essential support and funding for ecological conservation [52].

In the relationship between ecology and culture, a healthy ecological environment provides the material foundation and spatial framework for cultural inheritance and development. In turn, culture, by emphasizing harmony with nature, offers value guidance and behavioral norms for ecological development. Ecology and culture can form a mutually beneficial symbiotic relationship, where a favorable ecological environment shapes human lifestyles, social structures, and values, influencing the characteristics and development paths of different cultures. With the growing awareness of ecological civilization, eco-culture has gradually become a significant driver of cultural consumption. The rise of emerging cultural consumption models, such as ecotourism and green consumption, reflects a renewed recognition of the value of the ecological environment and a pursuit of sustainable lifestyles. This consumption model not only promotes the spread of eco-culture but also drives the green transformation of related industries, further strengthening the mutually beneficial symbiosis between ecology and culture. Culture also affects how stakeholders interpret and manage ecological information [53]. Some cultures, by advocating for harmony with nature, support sustainable resource use [11], aligning their values with ecological civilization, and fostering environmental protection and improvement.

In the relationship between culture and tourism, culture provides rich meaning and attraction, serving as an important spiritual resource, while tourism offers a platform for the dissemination and preservation of culture. Cultural and heritage resources are not only key symbols of identity and cultural confidence but have also become essential drivers of tourism economic development. There is increasing emphasis on how to wisely utilize and pass down these cultural assets while ensuring their protection [12]. Culture enhances the attractiveness of tourism destinations by offering local spiritual products, while the development of culture-related businesses improves tourism service quality [19]. By providing educational experiences, encouraging creativity, and influencing the development of tourists’ attitudes and values, tourism elevates perceived value and enhances the travel experience, through offerings like museums and artistic performance [54]. While tourism enriches cultural resources, it also plays a crucial role in spreading culture, preserving, and innovating cultural heritage, and fostering related cultural industries, such as creative cultural products, film production, and performing arts, thereby enhancing the local cultural influence and reinforcing cultural confidence.

3. Materials and Methods

3.1. Procedure

The data analysis in this paper comprises three main steps (Figure 2). First, by constructing an index system for the integrated development of ecology, culture, and tourism industries, a Super-SBM model incorporating undesirable outputs is adopted to measure the average development trend of national integration efficiency in China and the annual integration efficiency values of various provinces.

Then, the spatial evolution trends of integration efficiency are examined. Kernel density estimation is used to calculate the spatial dynamic distribution of integration efficiency for exploring changes in regional disparities, while the standard deviational ellipse method is applied to analyze the changing trends in the spatial distribution pattern of integration efficiency and the migration of agglomeration centers.

Finally, based on the construction of the indicator system for influencing factors, Tobit regression analysis is conducted from both temporal and spatial perspectives to explore the main factors affecting integration efficiency.

3.2. Participants

Due to limitations in data availability, this study does not include data from Hong Kong, Macao, and Taiwan of China. It takes 31 provinces in the Chinese mainland as the research objects, with the study period from 2012 to 2021, as illustrated in Figure 3. Based on the relevant literature [55], the study areas include northern, northeastern, eastern, southern, southwestern, northwestern, and central China.

3.3. Instruments

3.3.1. Evaluation of the Integration Efficiency

1.

Indicator System

Drawing on existing research [27,30,56] and the framework of interactive impacts in ecological cultural tourism, an integrated development index for ecology, culture, and tourism was developed from an input–output perspective. The evaluation index system consists of three subsystems and a total of 30 indicators, as outlined in

Table 1. Evaluation index system for the integrated development of ecology, culture, and tourism.

System Level	Criterion Level	Indicator Level	Property
E Ecological Environment System	E1 Input Factors	E11 Forest Coverage Rate	+
		E12 Green Coverage Rate of Built-up Areas	+
		E13 Rate of Non-Hazardous Treatment of Municipal Solid Waste	+
		E14 Investment in Urban Environmental Infrastructure	+
		E15 Investment in Environmental Pollution Control	+
	E2 Undesirable Outputs	E21 Total Industrial Wastewater Discharge	−
		E22 Total Sulfur Dioxide Emissions	−
		E23 Total Nitrogen Oxides Emissions	−
		E24 Average PM2.5 Concentration	−
		E25 Amount of Municipal Solid Waste Collected	−
C Cultural Industry System	C1 Input Factors	C11 Number of Employees in Major Cultural Institutions	+
		C12 Number of Cultural Enterprises Above Designated Size	+
		C13 Number of Performances by Performing Arts Groups	+
		C14 Number of Museums and Cultural Institutions	+
		C15 Cultural Expenditure	+
	C2 Expected Outputs	C21 Operating Income of Cultural Enterprises Above Designated Size	+
		C22 Value Added of Culture and Related Industries	+
		C23 Audience Attendance for Performing Arts Groups	+
		C24 Number of Visitors Received by Museums	+
		C25 Actual Revenue from Radio and Television	+
T Tourism Industry System	T1 Input Factors	T11 Number of Travel Agencies	+
		T12 Number of Star-Rated Hotels	+
		T13 Number of A-Level Scenic Areas	+
		T14 Passenger Turnover of Highways	+
		T15 Number of Employees in Major Tourism Organizations	+
	T2 Expected Outputs	T21 Domestic Tourism Revenue	+
		T22 Foreign Exchange Revenue from Tourism	+
		T23 Number of Domestic Tourists Received by Travel Agencies	+
		T24 Operating Income of Star-Rated Hotels	+
		T25 Ticket Revenue from A-Level Scenic Areas	+

. Among them, “+” indicates indicators that play a positive role in improving integration efficiency, and “−” indicates negative effects. In the ecological environment system, investment in urban environmental infrastructure, which is commonly used in standalone assessments of urban economic–environmental sustainability [57,58], included to quantify its foundational role in supporting cultural-tourism development and environmental carrying capacity. For the cultural industry system, we repurposed actual revenue from radio and television, a metric that has traditionally been peripheral in prior research [59,60], to measure brand communication efficacy in cross-sector integration. Innovatively, passenger turnover of highways was introduced into the tourism subsystem to capture the spatio-temporal linkages between tourist mobility patterns and eco-cultural resource distribution, thus, traffic-related indicators are used as a new indirect variable for resource transformation [61,62].

2.

Super-Efficiency SBM² model with undesirable outputs

In the calculation and analysis of the integration efficiency of ecological, cultural, and tourism sectors, the Super-Efficiency SBM model effectively mitigates scale effects by incorporating slack variables. This model can handle diversified indicators, assist in identifying efficiency frontiers, and quantify the efficiency differences among decision-making units, thereby enabling more efficient resource allocation [63]. Additionally, considering the negative impact of environmental pollution within the ecological environment, this study introduces an SBM model with undesirable outputs to account for these adverse effects. Each DMU has m types of inputs, q₁ expected outputs, and q₂ undesirable outputs. The input matrix vector is denoted as $X\in R^{m\times n}$, the expected output matrix vector as $Y^a\in R^{q_1\times n}$ and the undesirable output matrix as $Y^b\in R^{q_2\times n}$. The corresponding slack variables are represented as $P^{-}\in {\mathrm{R}}^{\mathrm{m}}$, $P^a\in R^{q_1}$, and $P^b\in R^{q_2}$. The SBM model that incorporates undesirable outputs can be expressed as follows:

$$\begin{gathered} \mathrm{m}\mathrm{i}\mathrm{n}\mathsf{\rho }={\displaystyle \frac{1-\frac{1}{\mathrm{m}}\sum _{\mathrm{i}=1}^{\mathrm{m}}\frac{{\mathrm{p}}_{\mathrm{i}}^{-}}{{\mathrm{x}}_{\mathrm{i}\mathrm{k}}}}{1+\frac{1}{{\mathrm{q}}_1+{\mathrm{q}}_2}(\sum _{\mathrm{i}=1}^{{\mathrm{q}}_1}\frac{{\mathrm{p}}_{\mathrm{i}}^{\mathrm{a}}}{{\mathrm{y}}_{\mathrm{i}\mathrm{k}}^{\mathrm{a}}}+\sum _{\mathrm{i}=1}^{{\mathrm{q}}_2}\frac{{\mathrm{p}}_{\mathrm{i}}^{\mathrm{b}}}{{\mathrm{y}}_{\mathrm{i}\mathrm{k}}^{\mathrm{b}}})}} \\ \mathrm{s}.\mathrm{t}.\left\{\begin{array}{c}{\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}+{\mathrm{p}}_{\mathrm{i}}^{-}={\mathrm{x}}_{\mathrm{i}\mathrm{k}}\mathrm{i}=1,2,\cdots ,\mathrm{m};\\ {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{y}}_{\mathrm{u}\mathrm{j}}^{\mathrm{a}}-{\mathrm{p}}_{\mathrm{u}}^{\mathrm{a}}={\mathrm{y}}_{\mathrm{u}\mathrm{k}}^{\mathrm{a}}\mathrm{u}=1,2,\cdots ,{\mathrm{q}}_1;\\ {\sum }_{\mathrm{j}=1}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{y}}_{\mathrm{v}\mathrm{j}}^{\mathrm{b}}+{\mathrm{p}}_{\mathrm{v}}^{\mathrm{b}}={\mathrm{y}}_{\mathrm{v}\mathrm{k}}^{\mathrm{b}}\mathrm{v}=1,2,\cdots ,{\mathrm{q}}_2;\\ {\mathsf{\lambda }}_{\mathrm{j}}>0,{\mathrm{p}}_{\mathrm{i}}^{-}\ge 0,{\mathrm{p}}_{\mathrm{u}}^{\mathrm{a}}\ge 0,{\mathrm{p}}_{\mathrm{v}}^{\mathrm{b}}\ge 0j=1,2,\cdots ,n\end{array}\right. \end{gathered}$$

(1)

where $x_{ik}$ represents the $i$-th input variable of the $k$-th DMU. In this case, the value of $\rho$ ranges from [0,1], and a DMU is considered SBM efficient if and only if $\rho$ = 1, which implies that $p_i^{-}$ = 0, $p_u^a$ = 0, and $p_v^b$ = 0. However, when multiple DMUs achieve SBM efficiency, the efficiency values of all the DMUs being equal to 1 make it challenging to differentiate and compare them. To address this limitation, Tone (2001) proposed the Super-Efficiency SBM model, which overcomes the issue of being unable to compute efficiency values for all DMUs [63]. In this study, data from 31 provinces are first introduced into the Super-SBM model with undesirable outputs to determine that the integration efficiency of each DMU is indeed effective. The Super-SBM model incorporating undesirable outputs is subsequently applied, as formulated below:

$$\begin{gathered} \mathrm{m}\mathrm{i}\mathrm{n}\mathsf{\rho }={\displaystyle \frac{\frac{1}{\mathrm{m}}\sum _{\mathrm{i}=1}^{\mathrm{m}}\frac{\overline{\mathrm{x}}}{{\mathrm{x}}_{\mathrm{i}\mathrm{k}}}}{\frac{1}{{\mathrm{q}}_1+{\mathrm{q}}_2}(\sum _{\mathrm{i}=1}^{{\mathrm{q}}_1}\frac{\overline{{\mathrm{y}}^{\mathrm{a}}}}{{\mathrm{y}}_{\mathrm{i}\mathrm{k}}^{\mathrm{a}}}+\sum _{\mathrm{i}=1}^{{\mathrm{q}}_2}\frac{\overline{{\mathrm{y}}^{\mathrm{b}}}}{{\mathrm{y}}_{\mathrm{i}\mathrm{k}}^{\mathrm{b}}})}} \\ \mathrm{s}.\mathrm{t}.\left\{\begin{array}{c}\overline{\mathrm{x}}\ge {\sum }_{\mathrm{j}=1,\ne \mathrm{k}}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{x}}_{\mathrm{i}\mathrm{j}}\mathrm{i}=1,2,\cdots ,\mathrm{m};\\ \overline{{\mathrm{y}}^{\mathrm{a}}}\le {\sum }_{\mathrm{j}=1,\ne \mathrm{k}}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{y}}_{\mathrm{u}\mathrm{j}}^{\mathrm{a}}\mathrm{u}=1,2,\cdots ,{\mathrm{q}}_1;\\ \overline{{\mathrm{y}}^{\mathrm{b}}}\ge {\sum }_{\mathrm{j}=1,\ne \mathrm{k}}^{\mathrm{n}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathrm{y}}_{\mathrm{u}\mathrm{j}}^{\mathrm{b}}\mathrm{v}=1,2,\cdots ,{\mathrm{q}}_2;\\ {\mathsf{\lambda }}_{\mathrm{j}}>0,\overline{\mathrm{x}}\ge {\mathrm{x}}_{\mathrm{i}\mathrm{k}},\overline{{\mathrm{y}}^{\mathrm{a}}}\ge {\mathrm{y}}_{\mathrm{i}\mathrm{k}}^{\mathrm{a}},\overline{{\mathrm{y}}^{\mathrm{b}}}\ge {\mathrm{y}}_{\mathrm{i}\mathrm{k}}^{\mathrm{b}}\mathrm{j}=1,2,\cdots ,\mathrm{n}\end{array}\right. \end{gathered}$$

(2)

3.3.2. Measuring the Spatial Evolution Trend of Integration Efficiency

1.

Kernel density estimation

This study uses kernel density estimation to measure the spatial clustering intensity and diffusion trends of eco-cultural tourism integration efficiency, revealing its spatial heterogeneity and multi-scale distribution characteristics [64], thereby analyzing the spatial relationship and dynamic complexity between efficiency values and geographic factors. Kernel density estimation (KDE) is a nonparametric method used to estimate the probability density function of a random variable, which can reflect the distribution of sample data and its uneven dynamic trend characteristics [65]. The relevant formula is as follows:

$$K\left(x\right)={\displaystyle \frac{1}{\sqrt{2\pi }}}exp(-{\displaystyle \frac{x^2}{2}})$$

(3)

$$f\left(x\right)={\displaystyle \frac{1}{nh}}{\sum }_{i=1}^{n}K\left({\displaystyle \frac{x-x_i}{h}}\right)$$

(4)

where x represents the input integration efficiency value, n represents the sample size and bandwidth, $K\left(x\right)$ represents the kernel function value, and $f\left(x\right)$ represents the estimated probability density function.

2.

Standard Deviation Ellipse Analysis

Standard deviation ellipse (SDE) analysis includes parameters such as the centroid, semimajor and semiminor axes, and orientation angle, which can be used to analyse the directional distribution and degree of concentration or dispersion of spatial data. Standard deviation ellipse analysis quantifies the spatial aggregation intensity and dynamic expansion trends of ecological-cultural tourism integration efficiency by examining the coverage and directional shift of standard deviation ellipses. This method reveals the spatial restructuring characteristics of the evolution from a unipolar to a multipolar aggregation core area, with the centroid migration trajectory identifying the spatial polarization or equilibrium of efficiency distribution. The formulas for calculating each parameter are as follows:

Centroid:

$$\begin{gathered} {\mathrm{x}}_0={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}{\mathrm{x}}_{\mathrm{i}}/{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}} \\ {\mathrm{y}}_0={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}{\mathrm{y}}_{\mathrm{i}}/{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}} \end{gathered}$$

(5)

Semimajor and semiminor axes:

$$\begin{gathered} {\mu }_x=\sqrt{{\sum }_{i=1}^n{(w_i{\overline{x}}_i\cos \theta -w_i\overline{y_i}\sin \theta )}^2/{\sum }_{i=1}^n{w_i}^2} \\ {\mu }_y=\sqrt{{\sum }_{i=1}^n{(w_i{\overline{x}}_i\sin \theta -w_i\overline{y_i}\cos \theta )}^2/{\sum }_{i=1}^n{w_i}^2} \end{gathered}$$

(6)

Azimuth:

$$\tan \mathsf{\theta }={\displaystyle \frac{\left[\left({\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}^2{\overline{\mathrm{x}}}_{\mathrm{i}}^2-{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}^2{\overline{\mathrm{y}}}_{\mathrm{i}}^2\right)+\left(\sqrt{{\left({\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}^2{\overline{\mathrm{x}}}_{\mathrm{i}}^2-{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}^2{\overline{\mathrm{y}}}_{\mathrm{i}}^2\right)}^2+4{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}^2{\overline{\mathrm{x}}}_{\mathrm{i}}^2{\overline{\mathrm{y}}}_{\mathrm{i}}^2}\right)\right]}{\left(2{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{w}}_{\mathrm{i}}^2{\overline{\mathrm{x}}}_{\mathrm{i}}^2{\overline{\mathrm{y}}}_{\mathrm{i}}^2\right)}}$$

(7)

where ${\overline{x}}_i=x_i-x_0$, ${\overline{y}}_i=y_i-y_0$, and $w_i$ represents the weight, with the integration efficiency of ecology, culture and tourism serving as the weight in this study.

3.3.3. Measuring Influencing Factors of Integration Efficiency

1.

Indicator System

This study, based on the research of relevant scholars [66,67,68], selects the following influencing factors, with related indicators outlined in

Table 2. Selection of influencing factors of integration efficiency.

Independent Variable	Abbreviation	Index
Economic development	ED	GDP per capita
Consumption potentiality	CP	Per capita disposable income of all residents
Density of population	PD	Population per unit of land area
Industrial structure	TR	Proportion of the added value of the tertiary industry in regional GDP
Technological innovation	RD	Ratio of internal R&D expenditure to regional GDP
Traffic condition	TC	Proportion of total mileage of highway and railway operation in regional area
Governmental regulation	GR	Proportion of investment in environmental pollution control in regional GDP
Market openness degree	MO	Proportion of total goods import and export trade in regional GDP

. Notably, the governmental regulation indicator was innovatively introduced from the perspective of balancing ecological protection and tourism development benefits, using the proportion of investment in environmental pollution control in regional GDP to reflect the supportive intensity of government ecological input for the sustainable development of cultural tourism. Regarding market openness, although rarely incorporated in existing studies on integration efficiency of eco-cultural tourism or cultural tourism, a higher degree of market openness from the perspective of trade liberalization and value conversion of eco-cultural tourism resources can attract more international investments and tourists, thereby creating additional opportunities and resources for enhancing integration efficiency.

2.

Tobit regression

Tobit regression, also known as truncated regression, is an improved regression model that can be used to analyse limited dependent variables and truncated dependent variables, effectively overcoming the errors associated with traditional linear regression (Tobin, 1958) [69]. Based on the integration efficiency of decision-making units (DMUs) obtained from the SBM model evaluation, this study uses integration efficiency as the dependent variable and introduces other influencing factors as independent variables. The regression coefficients are then used to determine the direction and strength of the impact on the integration efficiency. The formula is as follows:

$${\mathrm{Y}}_{\mathrm{i}\mathrm{t}}=\left\{\begin{array}{l}{\mathrm{Y}}^{\mathrm{*}}={\mathsf{\alpha }}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_{\mathrm{i}\mathrm{t}}^{\mathrm{T}}{\mathrm{X}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\epsilon }}_{\mathrm{i}\mathrm{t}},{\mathrm{Y}}_{\mathrm{i}\mathrm{t}}^{\mathrm{*}}>0\\ 0,{\mathrm{Y}}_{\mathrm{i}\mathrm{t}}^{\mathrm{*}}\le 0\end{array}\right.$$

(8)

where $X_{it}$ represents the independent variable; $Y_{it}$ is the truncated dependent variable; ${\alpha }_{it}$ is the intercept term; ${\beta }_{it}^T$ is the vector of regression parameters; and ${\epsilon }_{it}$ is the disturbance term, where $\epsilon ~N(0,{\mathsf{\delta }}^2)$. Since the integration efficiency values are truncated discrete distribution data and all positive, the truncation point can be set to 0. To compare the impact of different variables on the integration efficiency values, standardized coefficients are employed.

3.4. Data Sources

The data sources for this paper are as follows: ecological environment data were sourced from the “China Statistical Yearbook (2012–2022)” and the “China Environmental Statistical Yearbook (2012–2022),” with PM2.5 data obtained from the Global Surface PM2.5 Concentration Estimates by the Atmospheric Composition Analysis Group at the University of Washington. Cultural industry data were taken from the “China Cultural and Related Industries Statistical Yearbook (2012–2022)” and the “China Cultural Relics and Tourism Statistical Yearbook (2019–2022).” Tourism industry data came from the “China Tourism Statistical Yearbook (2012–2018).”

For missing data on some indicators, provincial and municipal yearbooks and government reports were consulted, followed by interpolation for fitting. The primary software used included ArcGIS 10.8, Stata 17, and MaxDea 8.0. The study used a ten-year interval with three-year gaps, focusing on key time nodes in 2012, 2015, 2018, and 2021.

4. Results

4.1. Integration Efficiency of Ecology, Culture and Tourism

4.1.1. The National and Regional Development Trends of the Integration Efficiency

The integration efficiency of “ecology-culture-tourism” was calculated via MaxDea 8.0 software, as shown in

Table 3. Efficiency value of ecology, culture, and tourism integration in each province and city from 2012−2021.

Area	Province	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
North	Beijing	1.07	1.05	1.02	1.06	1.11	1.08	1.12	1.14	1.09	1.49
	Tianjin	1.20	1.05	1.03	1.03	1.18	1.06	1.07	1.03	1.09	1.15
	Hebei	1.03	1.03	1.01	1.05	1.03	1.04	1.06	1.09	1.00	1.08
	Shanxi	1.10	1.00	0.61	1.00	1.00	1.04	1.04	1.05	1.00	1.09
	Neimenggu (Inner Mongolia)	1.02	1.01	0.61	0.53	0.49	0.61	1.02	1.05	0.27	1.14
Northeast	Liaoning	1.04	1.03	0.63	1.01	1.00	1.01	1.02	1.07	0.17	1.01
	Jilin	1.07	1.06	1.02	0.62	0.57	1.02	1.01	1.04	0.36	1.04
	Heilongjiang	1.08	1.11	1.04	1.01	1.01	1.02	1.00	1.04	1.09	1.12
East	Shanghai	1.12	1.01	1.03	1.00	1.09	1.14	1.07	1.17	1.07	1.33
	Jiangsu	1.09	1.04	1.02	1.01	1.03	1.07	1.04	1.03	1.01	1.11
	Zhejiang	1.00	1.03	1.03	1.02	1.01	1.03	1.06	1.12	1.01	1.09
	Anhui	1.06	1.00	1.01	0.70	1.04	1.03	1.05	1.06	0.29	1.03
	Fujian	1.03	1.04	1.03	1.01	1.02	1.04	1.02	1.03	0.48	1.08
	Jiangxi	1.07	1.02	1.01	1.02	1.02	1.03	1.04	1.03	0.11	1.07
	Shandong	1.05	1.04	1.02	1.01	1.02	1.03	1.02	1.09	1.04	1.06
Central	Henan	1.03	1.02	1.00	0.44	1.01	0.48	1.00	1.01	0.07	1.02
	Hubei	1.06	0.54	1.01	1.02	1.02	1.07	1.03	1.07	1.04	1.06
	Hunan	1.02	0.52	0.66	0.57	0.59	0.68	1.02	1.00	1.00	1.02
South	Guangdong	1.10	1.07	1.02	1.06	1.05	1.02	1.05	1.26	1.02	1.15
	Guangxi	0.56	0.40	0.59	0.64	0.66	1.01	1.03	1.05	0.27	1.05
	Hainan	1.10	1.06	1.15	1.06	1.09	1.08	1.10	1.12	1.10	1.19
Southwest	Chongqing	1.03	1.01	1.00	0.64	0.72	1.00	1.03	1.07	0.27	1.05
	Sichuan	1.08	1.10	1.02	1.05	1.04	1.03	1.03	1.16	0.18	1.13
	Guizhou	1.08	1.03	1.01	1.03	1.05	1.05	1.04	1.27	0.15	1.05
	Yunnan	1.05	1.13	1.01	1.02	1.02	1.02	1.06	1.15	1.02	1.05
	Xizang (Tibet)	1.89	1.30	1.07	1.12	1.13	1.16	1.12	2.07	1.24	1.28
Northwest	Shannxi	1.08	1.04	1.01	1.03	1.03	1.05	1.06	1.04	0.09	1.03
	Gansu	1.15	1.05	1.01	1.01	1.02	1.04	1.03	1.09	1.04	1.29
	Qinghai	1.22	1.09	1.06	1.04	1.11	1.11	1.06	1.05	1.03	1.12
	Ningxia	1.22	1.07	0.45	1.01	1.01	1.04	1.02	1.43	1.01	1.11
	Xinjiang	1.06	1.04	0.63	1.01	1.04	1.07	1.17	1.10	1.04	1.04

. Given that the integration efficiency among different provinces is generally considered to exhibit variable returns to scale, this study employs the undesirable Super-SBM model under nonradial, nonoriented, and variable returns to scale (VRS) conditions for analysis.

From the perspective of average integration efficiency across China, integration efficiency exhibits a decline-then-rise trend, with a significant drop in 2020 due to COVID-19 (Figure 4a). Before 2015, despite the cultural industry’s 11.7% annual growth, slowing global economic development and the tourism sector’s mere 1.98% growth led to a gradual efficiency decline from 1.09 to 0.93, indicating an ineffective integration phase. Since 2015, excluding the pandemic-induced fluctuation in 2020, accelerated development in ecology, culture, and tourism has driven efficiency upward. The 2018 merger of cultural and tourism authorities, coupled with advancing ecological civilization institutions, peaked efficiency at 1.13 in 2019.

From the seven major regions (Figure 4b), the efficiency evolution of the seven major regions from 2012 to 2021 converged with the national trend but showed significant regional disparities due to differences in economic foundations, industrial development support, and ecological environment construction. From 2012 to 2015, efficiency in all regions declined: Southwest and Northwest China exceeded the national average in 2012, while East, North, Northeast, and Central China were in an effective integration state, and South China’s integration efficiency was below 1. In 2015, the whole country entered a low-efficiency stage. In 2018, the national average efficiency was 1.045, with Northeast and Central China slightly lagging and other regions balanced. In 2021, North and South China led in integration efficiency, and the gap between Northeast, Central China and other regions became apparent.

4.1.2. Integration Efficiency Development of Different Regions and Provinces

To further analyze the regional differences in integration efficiency, we examine the provincial-level performance (Table 3). North and East China exhibit strong agglomeration and radiation effects, while Northeast China shows notable deficiency in core-city radiation. In 2021, Beijing ranked first nationally with an integration efficiency of 1.49, driving Tianjin and Hebei’s efficiency gains via the Beijing-Tianjin-Hebei Coordination Strategy. East China, bolstered by robust economic clout, cultural resources, and policy support, saw Shanghai rank second nationally, with Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, and Shandong all exceeding 1.03 in 2021. In Northeast China, Liaoning, Jilin, and Heilongjiang recorded efficiencies of 1.01, 1.04, and 1.12, respectively, constrained by seasonal tourism demand and insufficient cultural exploration.

South, Southwest, Central, and Northwest China feature core-city dominance but limited influence, requiring strengthened regional collaboration. Guangdong and Hainan achieved efficiencies of 1.15 and 1.19 in 2021 in South China. In Southwest China, Tibet, Sichuan, and Guizhou performed well, with Chongqing influencing peripheral areas, though Yunnan and Guangxi showed volatile agglomeration effects. Central China saw Hubei and Henan with weaker integration efficiency than Hunan. In Northwest China, Qinghai and Ningxia improved, but Gansu lagged significantly, while Xinjiang’s efficiency was constrained by geography and economic development.

4.2. Integration Efficiency Spatial Evolution Trends

4.2.1. Dynamic Analysis of the Spatial Distribution

Based on kernel density curve analysis, the spatial distribution dynamics of ecology, culture and tourism integration efficiency in China can be divided into three stages (Figure 5). From 2012 to 2015, integration efficiency improved with narrowed regional disparities, though polarization emerged due to early policy support and concentrated market demand [70]. During 2015–2018, polarization evolved into multi-peak distribution, as uneven resource allocation and market saturation differences slowed efficiency growth and widened regional gaps [71]. From 2018 to 2021, the pandemic caused a significant peak decline, but integration efficiency recovered with reduced regional disparities after 2020, driven by rebounding market demand.

Seven regions show significant spatial disparities in integration efficiency: North China sees decreasing peak height and narrowing width under multi-peak patterns, slowing efficiency growth; Central China features annual peak height increases and width narrowing, shrinking internal gaps; Northwest China transitions from gradient effects to single-peak distribution with reduced differences; Northeast China shifts from dual to single peaks, alleviating polarization; Southwest China maintains a dual-peak structure with the main peak rising/narrowing and secondary peak declining, signaling efficiency improvement and weakened stratification; East and South China witness efficiency growth but widened internal gaps, with East China evolving from single to multi-peaks and intensifying polarization, and South China showing flattened left tails and expanded regional disparities. The causes are as follows: North and Northeast China demonstrate stability and resource integration under transition policies, East China’s provincial gaps widen due to resource agglomeration while Central China narrows internal differences via regional collaboration, excessive resource concentration in the Pearl River Delta exacerbates imbalances in South China, and Southwest China bridges gaps through eco-economic synergy with characteristic industries whereas Northwest China leverages Belt and Road-initiated border advantages for regional convergence.

4.2.2. Analysis of the Spatial Center of Gravity Migration and Evolution

In terms of the migration of the geometric center of spatial distribution, the spatial agglomeration degree of ecological-cultural tourism integration efficiency continues to increase. The spatial form has transitioned from extensive diffusion to intensive development, and the spatial distribution pattern shows an eastward shift across the country, with the center of gravity located within Henan Province. The changes in the migration of the agglomeration center are illustrated in Figure 6, with relevant parameters presented in

Table 4. Chinese global fusion efficiency standard deviation ellipse parameters.

Year	Longitude of Center of Gravity (°E)	Barycentric Dimension (°N)	Major Semi Axis (km)	Minor Semiaxis (km)	Azimuth (°)	Ellipse Area (m sq km)
2012	110°49′00″	34°16′00″	1273.75	1074.95	72.27	4,301,300,813.46
2015	111°5′35″	34°2′56″	1237.48	1099.77	69.63	4,275,281,618.97
2018	111°16′15″	34°6′17″	1216.67	1095.73	63.29	4,187,951,066.68
2021	111°23′19″	34°10′52″	1217.43	1085.90	59.87	4,152,996,969.48

.

Regarding the axes and orientation angle of the standard deviation ellipse, the orientation angle rotated counterclockwise by 12.4° along the main axis, with the long semi-axis shortened and the short semi-axis stably fluctuating between 1074.95 km and 1095.73 km. This indicates weakened north-south spatial agglomeration and enhanced east-west agglomeration of eco-cultural tourism integration efficiency, which is closely linked to the construction of regional transportation corridors and cross-regional tourism passageways. The full utilization of ecological resources and improvement of transportation networks have significantly enhanced tourism mobility and resource utilization efficiency in the east-west direction [72,73].

Regarding the area of the standard deviation ellipse, the area in 2021 has noticeably reduced by 3.45% compared to 2012, indicating a decrease in spatial dispersion and a reduction in the spatial extent of the integration efficiency center, which is now located within Henan Province. This suggests that the integration of ecological protection and regional development in the Yellow River Basin has further promoted resource agglomeration [70], while the radiating effect of East China has strengthened [74]. The gap in ecological cultural tourism resources in the radiated areas has narrowed, accelerating the integration of ecological cultural tourism.

4.3. Analysis of the Factors Influencing the Efficiency of the Integration Efficiency

4.3.1. Evolution of the Influencing Factors Across Different Periods

From 2012 to 2021, the driving mechanism of China’s integration efficiency underwent complex evolution, characterized by weakened traditional economic drivers, rising innovation elements, deepened institutional coordination, and intertwined structural contradictions. The COVID-19 pandemic accelerated the differentiation and restructuring of system vulnerabilities and resilience (

Table 5. Tobit regression analysis of the driving factors behind the integration efficiency of ecology, culture, and tourism in China from 2012 to 2021.

Variable	Regression Coefficient
	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
ED	0.358 *	0.103	0.235	0.001	0.024	−0.024	0.015	0.057	0.012	0.006
CP	−0.767 ***	−0.258	−0.302 *	−0.188	−0.214	−0.264 **	−0.041 *	−0.268 *	0.097	−0.026
PD	0.315 **	0.048	0.009	0.284 *	0.040	0.398 ***	0.040 *	0.299 **	0.111	0.151 **
TR	−0.200	−0.032	0.037	−0.313 *	0.022	−0.518 ***	−0.055 *	−0.357 *	−0.105	−0.18 *
RD	0.019	−0.028	0.062	0.077	0.067	0.059	−0.003	0.010	−0.039	0.062 **
TC	0.029	−0.005	−0.061	0.021	−0.024	0.124 **	0.005	0.008	−0.103	−0.030
GR	−0.024	0.045	−0.114 ***	−0.066	0.025	0.012	0.032 ***	0.085 **	0.019	0.018
MO	0.090 *	0.117 **	0.019	0.096	0.124	0.192 ***	0.047 ***	0.192 *	0.073	0.012

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

).

In 2012, economic agglomeration conflicted sharply with ecological overload, with economic development and population density peaking while consumption suppression remained notable. Consistent with China’s situation then, Constant and Taylor (2020) found ecological carrying capacity imbalance in South Africa’s rural-urban nexus during economic agglomeration, indicating the ubiquity of ecological pressure amid economic concentration [53]. By 2015, the system entered a transition phase, seeing weakened traditional economic drivers, a sharp drop in per capita GDP contribution, disconnection between tertiary industry and ecological processes, and ineffective technological innovation inputs, and similar issues are prevalent among BRICS nations and emerging economies [75]. In 2018, institutional innovation made breakthroughs as government environmental governance shifted to proactive prevention, and market openness synergized with transportation infrastructure growth. However, the expanded negative elasticity of the tertiary industry revealed institutional barriers, and innovation input–output conversion faced time lags. By 2021, the system entered an innovation-driven restructuring phase, with R&D investment surging 21.7-fold to become the dominant driver. The impact of population density dropped by 52.1%, signaling the fade of agglomeration dividends. The pandemic caused market openness to plummet by 93.8% and suppressed transportation infrastructure, highlighting profound external risks. System stability under innovation dominance now faces new challenges. This corresponds to Zhang (2023), who found that increased population density during urbanization exerts a 28% negative impact on the ecological efficiency of tourist destinations [76].

4.3.2. Analysis of Regional Differences in the Influencing Factors

Across all of China, economic development and market openness exhibit positive impacts on integration efficiency, while transportation conditions show significant negative effects (

Table 6. Tobit regression analysis results of driving factors of ecology, culture, tourism integration efficiency in seven regions of China.

Variable	Regression Coefficient
	Nationwide	North	Northeast	East	Central	South	Southwest	Northwest
ED	0.435 *	3.249 ***	9.83 **	0.102	0.943	6.414 *	1.331	0.977
CP	−0.264	−3.149 ***	−2.413	−0.084	−0.527	−3.284	−0.624	−0.769
PD	0.070	−2.526 ***	49.878 ***	0.331 **	−6.353	−5.748	3.876	−4.283
TR	0.084	−1.005 ***	1.009 **	−0.449	−0.129	2.249 *	0.100	0.259
RD	−0.086	−0.064	−6.228 ***	−6.587	−2.185	−1.118	−1.162	−2.715 *
TC	−0.244 ***	2.866 ***	−14.496 ***	−0.304 **	1.281	0.800	−1.106	2.551
GR	−0.045	0.042	0.023	6.058	−0.334	−0.455	0.001	−0.311
MO	0.199 *	−0.272	−0.071	−0.273	8.867	0.318	0.733	−1.651 *

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

). With the improvement of China’s economic development level and market openness, trade and cultural exchanges have promoted the rapid growth of inbound tourism, laying a foundation for attracting foreign capital and advanced international ecological tourism management technologies. The development of transportation conditions has not only increased opportunities for peripheral areas to attract tourists and narrowed urban development gaps, but also reduced tourism benefits and undermined integration efficiency due to low accommodation costs in marginal areas and the lack of effective integration with cultural tourism destinations and environmental protection management measures. Wang (2021) further evidenced that improved transportation conditions may, in some cases, cause excessive resource concentration, thereby suppressing tourism efficiency in peripheral areas [77].

Significant regional disparities exist in influencing factors across seven regions. North China’s economic-transportation support for transformation is constrained by traditional heavy industry path dependence, causing consumption leakage and ecological pressure. Northeast China gains short-term ice-snow economy benefits, but infrastructure overexpansion and culturally-inadaptable innovation impose long-term limits. East China’s population density boosts integration, while saturated transport leads to hotspot overcrowding and ecological overload. South China’s economic–industrial upgrading drives high-end format and diversifies integration models via modern service transformation. Northwest China’s misaligned innovation and low openness exacerbate ecological risks, with cultural homogenization and profit outflows from commercialized scenic areas suppressing efficiency. Southwest China’s fragmented resources, inadequate transport, and cultural preservation-development conflicts complicate drivers. Central China’s low factor efficiency and “heavy investment-light operation” model hinder traditional drivers, with both regions showing integration’s multi-dimensional equilibrium.

5. Discussion

To align with the trends of sustainable development and integration efficiency enhancement, governments should optimize monitoring and regulatory policies and implement location-specific resource allocation strategies: prioritizing the demonstration effects of high-efficiency regions, cultivating eco-culture-tourism creative industry clusters, and promoting industrial chain collaboration between low-efficiency and high-efficiency regions to achieve factor complementarity and experience transmission. Meanwhile, smart tourism infrastructure should be preferentially deployed in resource-rich areas to unleash resource potential, driving dynamic and balanced improvement of regional integration efficiency. These recommendations respond to the regulatory gaps in African and Asian tourist destinations highlighted by Mutana and Mukwada [78] in their research on tourism sustainability in South Africa.

In addressing spatial disparities in integration efficiency, factor allocation should be optimized during periods of significant gradient effects, guiding capital and technology flow to low-efficiency regions through policies. Ecological, cultural, and tourism resource sharing platforms should be established based on regional characteristics. Building upon Qiu et al.’s [79] proposals for environmental fiscal incentives, this study further suggests using tax incentives to encourage high-efficiency cities in eco-culture-tourism integration to transfer management models to disadvantaged regions, enhancing the quality and efficiency of existing resource utilization. Additionally, focusing on integration efficiency agglomeration hubs, this study proposes leveraging their locational advantages to construct cross-regional factor circulation networks via digital technologies within ecological carrying capacity thresholds, shortening the spatiotemporal distance between core scenic areas and tourist markets to improve visitor experience and promote the transformation of eco-cultural resources into experiential products. This aligns with the core factors of government response, technology innovation, local belongingness, and consumer-employee confidence identified by Sharma et al. [80] for building resilience frameworks in global tourism post-COVID-19.

Confronting the structural contradictions in factors influencing regional integration efficiency, resource-dependent heavy industrial cities need to activate service economies through innovative consumption scenarios to enhance tourism circularity, while some regions should strengthen the adaptability of innovative technologies to local cultures—consistent with the sustainable tourism concepts for EU countries proposed by Alonso-Almeida et al. [81] and Vu & Hartley [82]. In light of Guo et al.’s [83] expectations regarding human capital allocation on tourism ecological efficiency, this study suggests directing tourism-related human capital investment toward emerging fields such as sustainable tourism education [84] and digital virtual tourism [85], leveraging emerging technologies to achieve value matching and collaborative creation between cultural tourism and ecological resources.

6. Conclusions and Limitations

6.1. Conclusions

Unlike existing studies that focus on the degree of integration of ecology, culture, and tourism, which primarily use coupling coordination models, this study innovatively examines the actual value brought by the deep interactions between ecology and cultural tourism through the lens of interaction quality and resource cooperation integration benefits. The main conclusions are as follows:

(1)

The integration efficiency of ecology, culture, and tourism in China and its seven major regions exhibits a trend of decline followed by an increase, but a sharp decline occurred in 2020 due to the COVID-19 pandemic. This finding stands in sharp contrast to the conclusion of continuous improvement in regional integration degree of Lu et al. [25], revealing a divergence in development trajectories between integration efficiency and degree, efficiency may experience periodic setbacks during the integration process due to abrupt shocks or systemic adjustments. In terms of regional disparities, North and South China show significantly higher integration efficiency, followed by East, Southwest, and Northwest China, while Northeast and Central China exhibit relative gaps compared to other regions. This is similar to the causes of regional efficiency disparities in the EU [40], Chile [41], etc., which are closely related to national institutional design, industrial structure, and regional natural resources.

(2)

Regarding the spatial dynamic distribution of China’s eco-cultural-tourism integration efficiency: During 2012–2015, despite an overall imbalanced state, inter-regional disparities narrowed. From 2015 to 2018, integration efficiency growth slowed across regions, with a notable gradient effect. Between 2018 and 2021, efficiency showed a gradual upward trend, accompanied by reduced polarization. This aligns with Cheng et al.’s [86] findings on tourism ecological efficiency in the Hanjiang River Basin but differs from Liu et al. [87], who observed sustained growth in tourism efficiency in the Beijing-Tianjin-Hebei region pre-COVID-19, enabling comparisons between national and regional micro-level efficiencies. Significant variations exist in efficiency evolution across the seven regions: North China saw slowing efficiency growth but improved internal balance; Northeast, Central, Southwest, and Northwest China experienced efficiency gains with narrowed internal gaps; East and South China showed overall efficiency improvements but widened internal disparities. This is consistent with Deng et al. [88], Gan et al. [34], and Zhang et al. [89] on single-region eco-tourism efficiency, though this study further reveals dynamic changes in intra-regional differences from a 3D integration efficiency perspective. In terms of spatial agglomeration, national integration efficiency concentration gradually increased, with the gravity center shifting eastward, primarily located in Henan Province. This partially aligns with Guo et al. [83], who noted a southwestward shift of the agglomeration center before 2014, while this study supplements the eastward migration trend post-2015.

(3)

In examining the influencing factors of integration efficiency in ecology, culture, and tourism across China, from 2012 to 2021, the driving mechanism of ecology, culture, and tourism integration efficiency in China shifted from being led by traditional economic forces to a dual emphasis on innovation and institutional coordination. The pandemic accelerated the differentiation and restructuring of system vulnerabilities and resilience factors. This provides further evidence from the perspective of integration efficiency for the studies by Chen et al. [90], Zhao et al. [91], and Luo et al. [92] on how emerging digital technologies, green environmental policies, and traditional industrial innovation promote eco-tourism. At the national level, economic development and market openness exert positive effects, while transportation conditions pose constraints. The influencing factors of integration efficiency in the seven major regions exhibit significant regional characteristics: economic development, human capital agglomeration, and industrial transformation have positive impacts on integration efficiency, whereas resource-based dependency, increased R&D investment exacerbating ecological risks due to the lack of cultural adaptability in technological innovation, excessive infrastructure expansion, and insufficient market investment impose negative effects on regional efficiency. This offers new perspectives and richer causal analyses for Zhang et al.’s [93] research on regional disparities in integration efficiency.

6.2. Limitations

For model optimization, integrating network DEA and dynamic DEA to develop a dynamic Super-Efficiency SBM model can incorporate the internal operational structure of the system. This allows calculating the relative efficiency of decision-making units by considering the operational processes of internal subsystems rather than solely focusing on input–output efficiency, thereby enabling in-depth analysis of the operational efficiency and dynamic changes of subsystem in ecological-cultural-tourism integration. In indicator design, given that the Super-Efficiency SBM mitigates efficiency bias from scale differences via slack variables, this study primarily uses total indicators to reflect the actual burdens and contributions of each province. For future research, to better address scale effects, per capita indicators will be included to more accurately reflect the relative efficiency and resource utilization levels across regions. Incorporating metrics such as inherent ecological resource stocks, economic losses from environmental governance, and economic input costs of cultural-tourism operations would improve the construction of undesirable output indicators.