Sustainable Land Use in Tourism and Industrialization: Competition, Conservation, and Coordinated Development

Abstract

The coordinated development of tourism and industrialization is essential for achieving sustainable and inclusive growth in the tourism sector, as well as for ensuring long-term regional economic sustainability. This study is motivated by the observation that land is a key factor influencing the coordination between tourism and industrialization, yet the specific role of land use remains underexplored. Therefore, the objective of this paper is to investigate the nonlinear relationship and underlying mechanisms through which tourism development impacts industrialization, with a particular focus on land transfers. To achieve this, the study employs an empirical approach using multi-source data—including data on China’s A-level scenic areas and land transfers—combined with an econometric method. The results indicate a U-shaped relationship between both the quantity and quality of tourism resources and the growth of industrial enterprises, as well as an inverted U-shaped relationship between the concentration of tourism resources and industrial development. The research finds that tourism development influences industrialization through two primary land-related mechanisms: the factor competition effect and the resource conservation effect. This study also investigates the potential for synergistic development between the tourism and industrial sectors, providing valuable insights for the sustainable economic advancement of land-based tourism and industrialization.

1. Introduction

Tourism and industry are both pivotal drivers of national economic development [1]. Industrialization is characterized by the expansion of industrial production factors, a rising share of industrial output within the overall economy, and the increasing role of the industrial sector in propelling economic growth. A robust industrial base, particularly in manufacturing, not only accelerates modernization but also enables low-income economies to transition into middle- and high-income status. Concurrently, with the advancement of industrialization, tourism has emerged as a key sector in creating jobs [2] and has developed into a major consumer-oriented service industry [3]. However, excessive dependence on tourism may generate negative consequences, such as rising living costs, increased vulnerability of the industrial system to external shocks, and the formation of resource dependency traps—all of which may impede the sustainability of regional economic development [4].

For most economies, the industrial sector remains a critical engine of economic growth. However, in many regions experiencing rapid tourism development, the industrial share of the economy has declined, accompanied by a reduction in industrial employment [5]. This decline in industrial added value and job opportunities signals broader structural shifts, often interpreted as a trend toward deindustrialization [6]. In addition, both tourism and industrial development are heavily dependent on land resources. For example, the construction of tourism facilities, such as scenic spots and theme parks, and industrial facilities, such as production factories and warehouses, all require large areas of land. Within a specific region, competition for land resources not only affects the development of these sectors but also influences the sustainable development of the entire regional economy. The competition and transfer of local resources, including land, serve as a key mechanism for the interaction between the tourism sector and the industrial sector [7].

In this context, an important question arises: Does the rapid expansion of tourism lead to deindustrialization by crowding out the industrial sector, or can it generate positive spillovers that ultimately reinforce industrial development? Does the impact of tourism on industrialization follow a direct linear pattern, or does it exhibit nonlinear characteristics? Addressing this question has become increasingly relevant for understanding the interplay between tourism growth and industrialization. What role does land play in the interaction between tourism and industrialization? Existing studies have yet to conduct a detailed exploration of this relationship from the perspective of land resource utilization. This gap limits our understanding of the interaction between tourism and industrialization at a more granular, element-based level. Therefore, the impact of tourism on industrialization should be thoroughly studied from the micro perspective of land resources.

The large tourism and manufacturing industries in China provide excellent examples for studying the relationship between tourism and industrialization [8]. According to data from the Ministry of Culture and Tourism, China’s tourism sector contributed CNY 5.8 trillion to the GDP in 2024, accounting for 4.3% of the total GDP, highlighting its positive impact on economic growth. Meanwhile, China’s industrial added value in 2024 was CNY 49.2 trillion, accounting for 36.5% of GDP. China has long maintained the world’s largest industrial scale [9]. However, deindustrialization may lead to a range of adverse consequences [10,11]. Therefore, in the process of industrial structural change, the relationship between tourism and industrialization is worth exploring.

Based on the above analysis, the objective of this study is to examine the impact of tourism development on China’s industrialization from the perspective of land resource transactions, and to explore both the underlying mechanisms and the heterogeneity of this impact. To achieve this, the study takes China as a case example, utilizing multi-source micro-level data such as A-level scenic spot records and land transfer data. Econometric methods, including negative binomial regression, are employed to conduct the empirical analysis.

The marginal contribution of this paper lies in deepening the understanding of the nonlinear relationship between tourism development and industrialization, and in expanding the theoretical mechanisms through which tourism development influences industrialization from the perspective of land use. It also offers micro-level insights into the pathways through which tourism affects industrialization. The research value of this study lies in providing a developmental path for industrial integration and offering practical implications for the coordinated allocation of land resources to support the sustainable and inclusive development of the tourism industry.

2. Literature Review

Existing research on the relationship between tourism and industrialization presents divergent views. Some scholars argue that tourism development leads to deindustrialization, where short-term market prosperity is followed by a decline in long-term economic growth. The Dutch Disease theory provides a key theoretical foundation for explaining this phenomenon. The prosperity of non-tradable sectors may reduce capital accumulation and market competitiveness in tradable sectors, thereby contributing to industrial decline [12]. Similarly, excessive growth in Macao’s tourism-driven gaming industry has suppressed manufacturing development [13]. This dynamic is exemplified by Guilin, a tourist city in China, where tourism development—absent external interventions—has crowded out industrial investment and shifted labor from the industrial sector to tourism, resulting in industrial decline [14]. A recent study also examined tourism-led Dutch Disease in selected South Asian countries, revealing that increased tourism activity reduces manufacturing competitiveness in the long run, confirming the persistence of Dutch Disease effects in the region [15].

Furthermore, some scholars argue that tourism development may hinder industrialization by negatively impacting the green environment. Most existing studies focus on the carbon emissions associated with tourism growth. For instance, Zhu et al. (2024) employ dynamic panel data models to test the impact of tourism on the local green environment and confirm the mediating effects of urbanization, industrialization, and international trade, all of which are affected by the crowding-out effects of tourism [16].

However, from the perspective of the vertical correlation theory in new economic geography, tourism development does not necessarily crowd out the growth of other economic sectors and may even promote re-industrialization and pro-industrialization [17,18]. On the one hand, some scholars have found that rapid tourism growth does not lead to long-term deindustrialization [19]. Even in cities known for their industrial foundations, such as those in Northwestern Italy, tourism can drive significant economic and social transformations by fostering socio-economic opportunities, enhancing community well-being, meeting leisure demands, and supporting social inclusion [20].

On the other hand, tourism can stimulate manufacturing development through its multiplier effects [21]. For example, Li et al. (2024) apply a non-survey method to estimate inter-regional input–output tables, using backward and forward linkages to assess tourism’s role in the economy, and structural path analysis to trace the inter-sectoral and inter-regional flows of tourism effects. Their findings indicate that the manufacturing sector benefits most from tourism, followed by trade, construction, agriculture, transportation, and electricity and gas sectors [22]. Additionally, several scholars argue that the impact of tourism on industrial development is nonlinear [23]. Tourism affects industrialization through mechanisms such as resource transfer and income enhancement, with the overall impact depending on the balance between these two forces [7]. An inverted U-shaped relationship between tourism development and manufacturing has been identified in China’s western regions, suggesting that the tourism–industrialization nexus varies across spatial contexts [24].

While these studies have advanced the theoretical understanding of tourism’s impact on the industrial sector, several limitations remain. First, there is insufficient analysis of the influencing processes and underlying mechanisms. The existing literature shows that the relationship between tourism and the industrial sector is complex and inconsistent. Most studies focus on traditional perspectives, such as resource transfer and income effects, but lack in-depth empirical analysis of the underlying processes. Second, the research scale is insufficiently micro-level, and sample selection needs refinement. Most studies are conducted at the national, provincial, or city levels, emphasizing the macro-level impacts while neglecting micro-level mechanisms. However, tourism development is inherently a local, micro-level activity that influences macroeconomic outcomes through micro-level actors. Therefore, theoretical exploration and empirical testing at a more micro-level are necessary.

In light of this, this paper introduces the perspective of land factor transfer to explore in detail the role of land resource flow in the influence of tourism on industrialization. This approach not only helps to expand the factor flow mechanism of this proposition but also conducts empirical analysis at the micro level, thereby enriching the empirical evidence of the tourism sector’s role in industrialization.

3. Materials and Methods

3.1. Research Hypotheses

3.1.1. Tourism and Industrialization Under a Nonlinear Relationship

Tourism development and industrialization are two critical drivers of regional economic growth, and their interaction is shaped by various factors, including land resource scarcity, shifts in market demand, and the direction of technological innovation. These factors collectively create a complex relationship between tourism and industrialization, characterized not only by competition but also by potential synergies, ultimately influencing the structure and development trajectory of regional economies. This complexity suggests that the impact of tourism development on industrialization is not a straightforward linear relationship, but rather a nonlinear one—an idea that forms the basis of this hypothesis. It addresses the gap identified in the literature review regarding the insufficient exploration of the impact process of tourism development on industrialization.

On the one hand, tourism development can significantly promote industrialization. First, the demand effect plays a key role. Tourism creates market demand for various industrial sectors, boosting the performance of related businesses. For example, tourism in areas like food, accommodation, transportation, sightseeing, shopping, and entertainment drives manufacturing growth in these industries. Activities such as mountain, waterfront, and winter tourism create demand for cable cars, yachts, and ski equipment, while the tourism market for souvenirs and handicrafts supports small-scale industries [25]. Second, the income-increasing effect of tourism promotes industrial development. Spending by both tourists and tourism workers creates a local market, raising regional income and increasing demand for industrial goods, which helps the growth of manufacturing industries [17]. Third, there is a technological innovation effect. As tourism often requires high environmental standards, it encourages industries to adopt greener technologies and practices. In a low-carbon economy, this push for eco-friendly production helps reduce pollution and supports sustainable industrial development [26]. These positive effects suggest that tourism development can enhance industrialization when the level of tourism development is moderate.

On the other hand, excessive tourism development can inhibit industrialization through resource transfer effects and structural imbalance. High-quality tourism development requires superior environmental conditions, which can be compromised by the environmental degradation and air pollution caused by extensive industrialization [27]. In regions where tourism is the dominant industry, efforts to promote green, low-carbon, and circular economies often result in the phasing out of high-pollution, high-energy-consumption, and high-emission industries. The elimination or relocation of such polluting industries reduces the relative share of industrial activities, slowing industrial growth and hindering industrialization. In economies overly reliant on tourism, the sector’s prosperity may lead to currency appreciation, weakening the international competitiveness of the industrial sector and further suppressing industrialization [28]. These negative effects suggest that tourism development can constrain industrialization when the level of tourism development exceeds a certain threshold.

Given the dual nature of tourism’s impact on industrialization—exhibiting both promoting and inhibiting effects—it is reasonable to hypothesize that the overall relationship between tourism development and industrialization is nonlinear. Specifically, this influence may follow a U-shaped pattern, wherein the positive effects of tourism diminish or even reverse once tourism development surpasses a certain threshold. Therefore, the following hypothesis is proposed:

Hypothesis 1:

The impact of tourism development on industrialization exhibits a nonlinear relationship.

3.1.2. Tourism, Factor Competition, and Industrialization

The prosperity of tourism triggers a reallocation of key production factors—land, labor, and capital—thereby reshaping the trajectory of industrialization and transforming the regional economic landscape. Among these factors, land assumes a particularly central role due to its finite availability, immobility, and spatial specificity. As a critical input in the tourism development [29], land becomes the primary domain of competition between tourism and industry. This competition is most evident in three dimensions: the redistribution of land resources, the escalation of the land-use costs, and the long-term transformation of the urban spatial structures.

First, the development of tourism tends to crowd out industrial land. Tourism projects often take precedence in occupying land with superior natural conditions and prime geographical locations—such as waterfronts, historic urban districts, and scenic mountainous areas. These areas are also highly suitable for industrial development, leading to a “crowding-out effect” whereby industrial expansion faces increasing land constraints. Moreover, under urban development strategies and local government planning, tourism tends to be assigned higher political priority. As a result, it receives greater access to land quotas, fiscal resources, and investment support [30], further marginalizing industrial development and weakening the land foundation necessary for a diversified industrial structure.

Second, the development of tourism drives up the cost of industrial land. Tourism activities significantly enhance the commercial value of land, particularly in areas adjacent to popular attractions [31]. As tourism resources are developed, land prices escalate, increasing the financial burden on industrial enterprises. This leads to compressed profit margins, diminished market competitiveness, and restricted opportunities for expansion. The rapid growth of tourism infrastructure and the broader tourist economy further accelerates land appreciation, making it increasingly difficult for cost-sensitive industries—especially manufacturing—to afford land in strategic locations. Consequently, many such enterprises are forced to either scale down operations or relocate, thereby undermining the momentum of local industrialization.

Third, tourism infrastructure exerts a long-term locking effect on the spatial configuration of land use. Once significant portions of land are allocated for the development of scenic areas, hotel clusters, and tourism-oriented commercial complexes, the urban spatial structure tends to become rigidly oriented toward tourism functions [32]. This service-oriented spatial fixation constrains the expansion potential of industrial parks and undermines the sustainability of industrial clustering. As a result, the cumulative effect of such land use patterns reduces the flexibility of urban planning and limits the adaptive capacity of industrial development. These mechanisms often overlap, making tourism—under certain conditions—a potential impediment to industrialization, particularly in regions where land resources are limited and development policies are skewed toward a single sector. Therefore, the following hypothesis is proposed:

Hypothesis 2:

Tourism development influences industrialization through factor competition effects.

3.1.3. Tourism, Resource Protection, and Industrialization

Tourism is highly dependent on natural and cultural resources, with land being a central issue in the socio-ecological challenges associated with tourism development and resource protection [33]. The growth of tourism often prompts local governments to strengthen the protection of tourism resources, leading to the implementation of strict land-use regulations [34]. This resource protection effect not only lays the foundation for sustainable regional economic development but also influences the process of industrialization.

First, strict resource protection policies are often implemented to support tourism development. The protection of tourism resources typically relies on specific land spaces, such as natural scenic areas, historical and cultural heritage sites, and ecologically sensitive zones. These areas are frequently designated as restricted or prohibited development zones. Through mechanisms such as land-use controls and the establishment of ecological red lines, the expansion and spatial layout of industrial land are effectively constrained, resulting in an exclusionary effect on industrial development. In some cases, industrial projects requiring large-scale land acquisition become unfeasible, thereby slowing the pace of industrialization [35].

Second, in pursuit of aligning resource protection with tourism development, local governments often prioritize the allocation of high-quality land quotas to projects such as eco-tourism, cultural and creative industries, healthcare, and leisure. This objectively requires regions to limit high-pollution, high-energy-consuming industrial sectors, promote the green and low-carbon transformation of traditional industries [36], and improve land-use efficiency. The preferential allocation further compresses the land availability and approval capacity for industrial projects. Such an institutional bias in land use exacerbates the constraints on industrial development within the broader context of environmental conservation. Simultaneously, the contraction of industrial land—driven by resource protection policies—leads to higher acquisition and operational costs, thereby diminishing the feasibility of industrial expansion. This impact is particularly severe for traditional industries characterized by high levels of pollution and energy consumption, which face increasingly stringent barriers to access and scale in a resource-constrained, tourism-prioritized environment. Therefore, the following hypothesis is proposed:

Hypothesis 3:

Tourism development influences industrialization through resource protection effects.

Figure 1 provides an analytical framework depicting the impact of tourism development on industrialization from the perspective of land use based on the above hypothetical analyses. The relationship between tourism development and industrialization is nonlinear, as tourism exerts both positive and negative effects on industrial growth. The interplay between these effects—and their varying significance at different stages of development—gives rise to this nonlinear relationship (Hypothesis 1).

At the micro level, production factors form the basis of the interaction between tourism and industrialization. Taking land use as an example, tourism development affects industrialization primarily through two mechanisms: the factor competition effect and the resource protection effect. The factor competition effect refers to tourism’s demand for production factors—such as land—driving up costs and hindering the expansion of industrial enterprises (Hypothesis 2). The resource conservation effect refers to the implementation of restrictions on industrial development to preserve tourism resources, such as limiting land use quotas for industrial purposes and reducing the availability of land for industrial projects (Hypothesis 3).

3.2. Econometric Model

The methodological framework of this study is grounded in the core theoretical concepts of new economic geography [17,21] and systematically examines the nonlinear interaction mechanisms between tourism development and industrialization. The theoretical foundation draws on the analytical paradigm proposed by Zeng and Zhu (2011) regarding the dynamic relationship between tourism agglomeration and industry [17], incorporating the spatial competition effects of production factors into the analytical framework. Additionally, building on Faber and Gaubert’s (2019) theory of tourism-related economic externalities [21], this study specifically investigates the nonlinear impact of tourism development on industrialization.

To test the nonlinear relationship between tourism development and industrialization proposed by Hypothesis 1, this study adopts the classical approach of introducing quadratic terms into the linear model [37], allowing for an intuitive identification of turning points in the effect of tourism on the industrialization process. The following nonlinear econometric model is adopted:

$$ln{indus}_i={\alpha }_0+{\alpha }_1{lntour}_i+{\alpha }_2{\left(ln{tour}_i\right)}^2+{\alpha }_{3}ln{Z}_i+{\epsilon }_i$$

(1)

In the model, ${indus}_i$ is the explained variable of this model, representing the level of industrialization, which is measured by the number of industrial enterprises above the designated size; ${tour}_i$ is the core explanatory variable of this paper, denoting the level of tourism development, which includes three dimensions: the quantity of tourism resources, the quality of tourism resources, and the spatial structure of tourism resources; the quadratic term ${\left(ln{tour}_i\right)}^2$ is used to test the existence of the nonlinear effect in the tourism–industrialization nexus; $Z_i$ is a series of control variables; ${\alpha }_0$ is the intercept term of the model; ${\alpha }_1$ is the marginal elasticity coefficient of tourism development on industrialization; ${\alpha }_2$ is the coefficient of the nonlinear effect of tourism development; ${\alpha }_3$ is the comprehensive impact coefficient of the control variable group; ${\epsilon }_i$ represents the random disturbance term.

1.

Dependent Variable

The dependent variable in the benchmark regression is the number of industrial enterprises above the designated size (indus). Unlike traditional studies that use macro-level indicators such as industrial output or industrial added value to measure industrial development, this study adopts a micro-level perspective to examine the impact of tourism development on industrial growth and to explore the underlying mechanisms through which tourism influences industrial enterprises. On the one hand, analyzing the relationship from a micro perspective helps overcome the limitations of conventional macro indicators by directly capturing changes in industrial organization and market dynamics. On the other hand, this approach is better suited for testing the crowding-out effects of tourism development on industrial sectors [38]. Accordingly, this study uses the number of industrial enterprises above the designated size as the core dependent variable.

2.

Independent variable

This study selects three indicators to measure the level of tourism development (Tour) at the county level: the amount of tourism resources (tournum), the quality of tourism resources (tourrich), and the spatial structure of tourism resources (tourss).

First, the amount of tourism resources is measured by the number of A-level scenic spots. A-level scenic spots in China are part of an official certification and rating system used to evaluate the quality of tourist attractions based on multiple criteria, including transportation accessibility, sightseeing facilities, safety, and sanitation, in accordance with the national standard Classification and Evaluation of Quality Grades for Tourist Attractions (GB/T 17775-2003) [39]. The ratings range from A to AAAAA, with higher levels indicating superior overall quality. This study uses the number of A-level scenic spots to represent the quantity of tourism resources for two main reasons. First, the evaluation process is based on strict national standards, ensuring both authority and comparability across regions. Second, the number of A-level scenic spots provides an objective reflection of the foundational scale of tourism resources at the county level and serves as a key leading indicator for forecasting regional tourism development [40].

Second, the richness of tourism resources aims to measure the quality of tourism resources. It is assigned values proportionally based on the annual visitor requirements for different levels of scenic spots, as stipulated in the Division and Evaluation of Quality Grades of Tourist Attractions (GB/T 17775-2003) published by the Ministry of Culture and Tourism. The annual visitor requirements for A to AAAAA level scenic spots are more than 30,000, 100,000, 300,000, 500,000, and 600,000, respectively. These levels are then assigned values of 1, 3.3, 10, 16.67, and 20, respectively. The data for all A-level scenic spots are aggregated to the county level. The indicator of tourism resource richness innovatively combines scenic spot grades with visitor reception capacity using a weighted approach. This method captures substantial differences in service quality, facility standards, and market appeal among scenic spots of varying grades. Meanwhile, it avoids the information loss commonly associated with traditional categorical variables based solely on grades, thereby providing a more accurate representation of the quality gradient of tourism resources.

Third, the spatial structure of tourism resources is measured by spatial agglomeration using the longitude and latitude information of A-level scenic spots. Specifically, the coefficient of variation (cv) for the longitude (cv_longitude) and latitude (cv_latitude) of A-level scenic spots in a county is calculated. These two coefficients of variation are then used to construct the spatial agglomeration indicator for A-level scenic spots, defined as lncv = −ln (cv_longitude × cv_latitude). The reason for using the coefficient of variation instead of the standard deviation in the formula is that the coefficient of variation not only reflects the degree of spatial distribution dispersion of A-level scenic spots but also eliminates the measurement scale and dimension effects caused by the vast territory and large differences in longitude and latitude in China. A smaller product of the coefficients of variation for longitude and latitude indicates a higher degree of agglomeration of tourism resources in the county.

3.

Control Variable

Control variables are a series of explanatory variables that affect industrialization. There are six control variables, based on previous research [21,24].

Population size (pop), measured by the number of permanent residents, reflects both market scale and human capital concentration. A larger population not only indicates a broader market base but also suggests a stronger pool of human capital, both of which can facilitate industrial agglomeration and support the long-term growth of industrial enterprises.

Economic development level (pergdp), measured by per capita GDP. Per capita GDP reflects the level of economic development and serves as a comprehensive indicator of capital accumulation, market demand, and industrial base, thereby exerting a direct influence on the industrialization process [41].

Industrial structure (indstruc), measured by the proportion of the secondary and tertiary industries. The proportion of the secondary industry directly characterizes the degree of industrialization, while the tertiary industry affects industrial efficiency through the spillover effect of producer services [42]. An increase in the proportion of the tertiary industry suggests a higher supply of productive services in the county, which may also positively influence the development of industrial enterprises.

Government intervention (gov), measured by the ratio of general public budget expenditure and revenue to GDP, is an important factor in this study. Industrialization is a necessary stage for late-developing countries and an important goal of government economic policy. Governments can influence the trajectory of industrialization through the implementation of industrial policies and investments in infrastructure [43]. Considering the degree of government intervention helps control the externalities brought about by government policies.

Financial development level (loan), measured by the proportion of loans outstanding by financial institutions to GDP at the end of the year. Credit supply eases corporate financing constraints and affects the survival rate of industrial enterprises entering the market [44]. The development of county finance may influence the entry decisions and growth of industrial enterprises in the region.

Terrain features (terrain), measured by terrain relief, shape the geomorphological pattern that lays the foundation for regional development. Terrain factors are an important consideration in the location choices of manufacturing industries. The new economic geography emphasizes that terrain affects the location choice of industries through transportation costs and agglomeration economies [45]. This study uses terrain relief as a proxy for terrain factors.

To further investigate the factor competition effect of tourism on industrialization proposed by Hypothesis 2, this paper presents a new econometric model based on the transfer of land plots. Drawing on the classic analytical methods for factor allocation effects in land economics [46], this model tests the impact of tourism development on industrial land use by controlling for land conveyance characteristics and incorporating multiple fixed effects.

$$ln{indusland}_j={\beta }_0+{\beta }_1{lntour}_j+{\beta }_{2}ln{Z}_j+{\gamma }_j^m+{\epsilon }_j$$

(2)

In this model, the dependent variable $ln{indusland}_j$ represents the level of industrialization development, measured by the price per unit area of industrial land nearest to A-level scenic spots. The core explanatory variable, ${tour}_i$, denotes the level of tourism development and is represented by the distance from the nearest industrial land to A-level scenic spots. The control variables $ln{Z}_j$ include plot ratio, industrial land use duration, and terrain relief. ${\beta }_0$ is the intercept term; ${\beta }_1$ is the coefficient of the core explanatory variable; ${\beta }_2$ represents the combined effect of the control variables. In addition, the model controls for different fixed effects (${\gamma }_j^m$), including county-level effects, land grade effects, and land source effects.

To investigate the resource conservation effect of tourism on industrialization proposed by Hypothesis 3, this paper presents a new econometric model based on the transfer of land plots. This model integrates the theory of resource conservation [47] and the land development competition hypothesis [48], enabling an effective examination of the impact of tourism resource conservation on the development of surrounding industrial land.

$$ln{indusland}_j={\theta }_0+{\theta }_1{lntourexp}_j+{\theta }_{2}ln{Z}_j+{\gamma }_j^m+{\epsilon }_j$$

(3)

In this model, the dependent variable $ln{indusland}_j$ represents the development level of industrialization based on the status of industrial land, measured by the number of industrial land transfers within 5 km, 10 km, 15 km, and 20 km of National Scenic and Historic Areas. The core independent variable ${lntourexp}_j$ represents tourism-related variables, measured by the maintenance expenditure of National Scenic and Historic Areas. The control variables $ln{Z}_j$ include plot ratio, industrial land use duration, and terrain relief. ${\theta }_0$ is the intercept term of the model; ${\theta }_1$ is the coefficient of the core explanatory variable; and ${\theta }_2$ is the combined coefficient of the control variables. The model also controls for fixed effects, including individual fixed effects and time fixed effects.

3.3. Data Source

The basic research scale of this study is the county, and the economic and social development data at the county level serve as the foundational data. The county data are sourced from the County Statistical Yearbook and include information on industrial enterprises above the designated size in the county, as well as other economic and social data. In addition, this study also includes three types of manually curated data: tourist attraction data, land transfer data, and terrain data.

The tourist attraction data are sourced from the A-level scenic spot data published by the tourism administrative department. To construct the tourism variables required for this study, the administrative codes and address information of each A-level scenic spot were matched with the Baidu Maps API (Geocoding API) backend database to geocode each A-level scenic spot and obtain its precise geographic coordinates (longitude and latitude) as the original indicators.

Land transfer data are obtained from the land use rights transfer transaction results published on the China Land Market Network (www.landchina.com (accessed on 10 May 2025)). Each land transfer record includes detailed information such as administrative district, project name, project location, area, transaction price, land use, supply method, land use duration, plot ratio, and industry classification. Market-oriented industrial land-related data were filtered from the land use and supply method category for analysis. The geographical coordinates of each industrial land plot were extracted from their project locations. Subsequently, all A-level scenic spot data were stored in a MySQL database. Using the Go programming language to query data from the database and applying a distance calculation formula, the study identified the distance from each A-level scenic spot to the nearest industrial land plot and the number of industrial land transfers within 5 km, 10 km, 15 km, and 20 km of the A-level scenic spots.

The DEM data are sourced from the United States Geological Survey’s global elevation data, with a resolution of 90 m, and were used to calculate the terrain relief of counties across the country using ArcGIS 10.7 software. The terrain relief data for each county were matched with the industrial land data based on county administrative codes.

Specifically, county-level data were processed for missing values, corrected for outliers, and standardized. Scenic spot data were verified for geographic coordinates and subjected to spatial deduplication to ensure accurate positioning. Industrial land data were standardized in terms of usage and unified in coordinate systems. For spatial analysis, precise projection methods were employed to construct buffer zones, and terrain data were professionally processed to generate relief indicators. Finally, multi-source data were accurately matched using administrative division codes. All datasets meet the quality standards required for spatial analysis, providing a reliable foundation for the subsequent research.

After decades of development following the reform and opening up, the number of land-dependent tourist attractions in China has remained largely stable, with little change. Hence, the data, collected up to 31 December 2019, underwent a step-by-step cleaning and matching process. Afterward, a total of 11,970 records of A-level scenic spots and 37,087 records of industrial land transfers were obtained for all counties in the country.

3.4. Descriptive Statistics

This paper conducts descriptive statistics for benchmark regression variables, including the number of observations, mean, standard deviation, minimum, and maximum for each variable. The statistical descriptions of all variables are presented in logarithmic form.

Table 1. Descriptive statistics of benchmark regression variables.

Variable	N	Mean	Standard Deviation	Minimum	Maximum
lnindus	1854	4.004	1.338	0	8.109
lntournum	1896	1.206	0.774	0	3.611
lntourrich	1794	3.516	0.872	0	5.658
lntourss	1551	13.061	1.658	0.288	21.351
lnpop	1896	3.615	0.909	−1.702	5.515
lnpergdp	1896	10.563	0.639	8.830	15.288
lnindstruc	1896	−0.215	0.158	−1.328	0
lngov	1896	−1.109	0.592	−5.320	1.312
lnloan	1880	−0.309	0.510	−3.352	1.611
lnterrain	1896	−0.833	1.749	−9.036	1.871

lists the descriptive statistics for the variables included in this paper. The study first conducts a multicollinearity test using the variance inflation factor (VIF) for all variables. The VIF values for each variable are less than 10, indicating no multicollinearity issues.

4. Results

4.1. Nonlinear Effect

The study conducts an overall estimation of the model, examining the impact of tourism development on county-level industrialization. The dependent variable in the baseline regression is the number of industrial enterprises, while the core independent variables are the number of tourism resources, the quality of tourism resources, and the spatial structure of tourism resources. Given that the number of industrial enterprises in each county is a count variable, this benchmark regression employs a negative binomial model for model estimation.

As shown in

Table 2. Baseline regression results.

	(1)	(2)	(3)
lntournum	−0.067
	(−1.31)
lntournum2	0.040 **
	(2.11)
lntourrich		−0.262 ***
		(−3.18)
lntourrich2		0.043 ***
		(3.50)
lntourss			0.063
			(1.55)
lntourss2			−0.003 **
			(−1.99)
lnpop	0.938 ***	0.903 ***	0.954 ***
	(26.73)	(26.77)	(25.37)
lnpergdp	0.791 ***	0.777 ***	0.771 ***
	(17.37)	(17.53)	(16.14)
lnindstruc	1.152 ***	1.168 ***	1.283 ***
	(10.51)	(10.60)	(10.82)
lngov	−0.247 ***	−0.259 ***	−0.244 ***
	(−3.51)	(−3.63)	(−3.13)
lnloan	0.104 ***	0.104 ***	0.102 **
	(2.83)	(2.76)	(2.48)
lnterrain	−0.125 ***	−0.125 ***	−0.122 ***
	(−13.86)	(−13.53)	(−11.90)
Constant	−7.918 ***	−7.271 ***	−8.025 ***
	(−14.98)	(−13.85)	(−12.48)
N	1839	1758	1513
R2	0.818	0.812	0.818

Notes: The t statistics are shown in parentheses; ** p < 0.05, *** p < 0.01.

, the coefficients for the number of tourism resources and their quadratic term are negative and positive, respectively. The quadratic term is significant at the 5% level, indicating that the impact of the number of tourism resources on industrial development is nonlinear. Meanwhile, the coefficients for the quality of tourism resources and their quadratic term are both significantly different from zero at the 1% level. The former is negative, while the latter is positive, suggesting that the impact of the richness of tourism resources on industrial development is also U-shaped, similar to the number of tourism resources. Therefore, Hypothesis 1 is confirmed.

In the early stages of tourism development, when the number and richness of tourism resources are relatively low, the growth of the tourism economy may lead to the reallocation of labor and capital from the industrial sector to the tourism sector. This reallocation can increase industrial costs and the prices of tradable goods [49], thereby reducing industrial investment. The resource reallocation effect and price effect of tourism development can, therefore, inhibit industrial development.

However, when tourism continues to flourish, it can provide substantial market opportunities and demand for industry, indirectly enhancing the economic benefits of industrial enterprises through induced effects. For example, mountain, lakeside, and winter sports tourism activities generate consumer demand for cable cars, yachts, and ski equipment manufacturing, respectively. The development of these tourism-related manufacturing industries can, in turn, promote industrial growth. Moreover, when tourism in a county is thriving, the associated consumption effect can create a large local market, increase local income levels, and subsequently boost the demand for industrial products, driving the optimization and development of upstream manufacturing enterprises. In addition, tourism development enhances regional visibility and improves the business environment, thereby lowering publicity and operational costs for attracting investment and promoting the growth of local manufacturing industries. The rapid development of tourism can, therefore, lead to demand effect, income effect, and brand effect that promote industrial development.

Whether it is the number of tourism resources or the richness of tourism resources, all these factors suggest that the impact of tourism development on industry is nonlinear (Figure 2). The two U-shaped curves indicate that when tourism resources are relatively scarce, it is difficult to generate scale effects or promote industrial enterprise growth through industrial development. However, once regional tourism resources reach a certain level of development, i.e., turning point (TP), they can gradually attract more tourists and related industries to the area, thereby fostering industrial agglomeration and generating greater market demand and development opportunities for industrial enterprises.

Furthermore, the coefficients for the spatial structure of tourism resources and their quadratic term are positive and negative, respectively. This, combined with the curve in Figure 2, indicates that the spatial agglomeration of tourism resources has an inverted U-shaped impact on industrial development. When tourism resources are more spatially concentrated, the negative impact on the development of industrial enterprises becomes more pronounced. Spatial agglomeration of tourism resources typically leads to higher land prices in the area, which, in turn, increases the land costs for industrial enterprises entering the market. It also results in greater congestion in infrastructure and public services, both of which may hinder the development of industrial enterprises.

The above results indicate that whether tourism development promotes or hampers industrialization depends on multiple parameters. When the cost of industrial tradable goods is sufficiently low and the demand for tourism products is sufficiently high, the income and demand effects brought about by tourism development will outweigh the resource reallocation and price effects, thereby avoiding the resource curse trap. Conversely, if these conditions are not met, tourism development may crowd out industrial development. The study will further analyze the micro transmission mechanisms of the impact of tourism development on industry in the mechanism test section.

Regarding the control variables, the population size, the economic development level, the industrial structure, and the financial situation all have significant positive impacts on industrialization. However, the degree of government intervention and terrain factors have negative impacts on industrial development. Although tourism resources differ from natural resources such as coal and oil, they can be regarded as a type of natural resource [19]. Governments are more inclined to extract rents from natural resource endowments [50], which leads to rent-seeking behavior that hampers the entry of industrial enterprises and curbs industrial development. Terrain factors are an important consideration for manufacturing enterprises when making location decisions. Industrial enterprises generally prefer areas with flatter terrain when selecting sites for development.

4.2. Mechanism Analysis

To verify the micro-mechanism of tourism affecting industrial development, this paper further introduces the perspective of land transfer for empirical analysis. Specifically, it analyzes in detail the factor competition effect and resource conservation effect of tourism on industrialization.

4.2.1. Factor Competition Effect

Model (4) in

Table 3. Tourism distance and industrial land price.

	(4)	(5)	(6)	(7)	(8)	(9)
lntourdist	−0.013 ***	−0.000	−0.013	−0.012 *	−0.024 ***	0.133
	(−3.34)	(−0.49)	(−0.81)	(−1.91)	(−2.81)	(0.52)
Control variable	Yes	Yes	Yes	Yes	Yes	Yes
County effect	Yes	Yes	Yes	Yes	Yes	Yes
Land grade effect	Yes	Yes	Yes	Yes	Yes	Yes
Land source effect	Yes	Yes	Yes	Yes	Yes	Yes
N	10807	92	1893	5283	3315	224
R2	0.893	0.999	0.935	0.907	0.926	0.997

Notes: The t statistics are shown in parentheses; * p < 0.1, *** p < 0.01.

shows that the coefficient for tourism distance is significantly negative at the 1% level, indicating that the price per unit area of industrial land closer to A-level scenic spots is higher. The development of tourism attracts related industries such as food, accommodation, transportation, sightseeing, shopping, and entertainment to cluster around the A-level scenic spots. The entry of a large number of commercial and service industries competes with manufacturing enterprises for limited land. Moreover, the rapid expansion of tourism continuously improves the level of infrastructure and public services in the area, leading to rising land prices [51]. Manufacturing enterprises seeking to obtain land in this area will face higher land costs, which, in turn, increase the entry costs for industrial enterprises and inhibit industrial development in the region. Therefore, tourism development will crowd out industrial development by driving up the price of surrounding industrial land.

This paper further categorizes A-level scenic spots according to their grades and analyzes the differences in the impact of tourism on industrial development. Models (5) to (9) in Table 3 present the regression results for A scenic spots to AAAAA scenic spots, respectively. The results of models (5) and (6) show that the development of A and AA scenic spots has limited impact on the transfer of industrial land. This may be because the grade of these scenic spots is not high, indicating that the core competitiveness of the tourism industry in the area is weak, and the development of the tourism industry does not significantly affect industrialization.

For AAA and AAAA scenic spots, the results of models (7) and (8) show that the closer to these scenic spots, the higher the price of industrial land, which is consistent with the results of the full sample. In terms of the size and the significance level of the estimated parameter coefficients, the higher the grade of the scenic spot, the greater the negative impact on the price of industrial land transfers nearest to the A-level scenic spot. This indicates that the development of AAA and AAAA scenic spots will, to some extent, crowd out the land and production space required by other industries. Tourism promotes economies of scale and scope through the industrial correlation effect, which may lead to a situation where tourism dominates, resulting in a resource curse effect. This, in turn, reduces the transfer of industrial land and inhibits the development of regional industrialization.

However, the estimated coefficient of AAAAA scenic spots becomes positive but is not statistically significant. AAAAA scenic spots may negatively affect industrial development due to factor competition, but they can also positively drive a substantial increase in local market demand, promoting the demand for industrial products and the development of manufacturing. This potential boosting effect causes the coefficient to shift from negative to positive, although not significantly.

The study further selects the quality of tourism resources (tourrich) as the explanatory variable and the number of industrial land transfers within 5 km, 10 km, 15 km, and 20 km of A-level scenic spots as the dependent variables.

Table 4. Tourism resource richness and industrial enterprise development.

	(10)	(11)	(12)	(13)
lntourrich	−0.001	−0.001	−0.002 **	−0.002 ***
	(−0.30)	(−1.27)	(−2.28)	(−3.40)
Control variable	Yes	Yes	Yes	Yes
County effect	Yes	Yes	Yes	Yes
Land grade effect	Yes	Yes	Yes	Yes
Land source effect	Yes	Yes	Yes	Yes
N	4799	7193	8516	9316

Notes: The t statistics are shown in parentheses; ** p < 0.05, *** p < 0.01.

shows that the richness of tourism resources negatively impacts the number of industrial land transfers, indicating that the richer the tourism resources, the fewer the number of industrial land transfers in the surrounding areas. This further illustrates the resource curse issue associated with tourism resources [52]. Abundant tourism resources may crowd out industrial development by reducing the number of industrial land transfers. In contrast, the impact on the number of industrial land transfers within 15 km and 20 km is significant at least at the 5% significance level, and it is greater than the impact on the number of industrial land transfers within 5 km and 10 km.

4.2.2. Resource Conservation Effect

Table 5. Resource maintenance expenditure and industrial land transfer.

	(14)	(15)	(16)	(17)
lntourexp	−0.022	−0.044 *	−0.047 **	−0.043 **
	(−0.55)	(−1.72)	(−2.25)	(−2.02)
Control variable	Yes	Yes	Yes	Yes
Individual effect	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes
N	527	980	1238	1385
R2	0.521	0.686	0.753	0.793

Notes: The t statistics are shown in parentheses; * p < 0.1, ** p < 0.05.

shows that the higher the maintenance expenditure of National Scenic and Historic Areas, the fewer the number of industrial land transfers. Examining the coefficients from the number of industrial land transfers within 5 km to those within 20 km, the significance level increases, with the maintenance expenditure of National Scenic and Historic Areas having the greatest impact on the number of industrial land transfers within 15 km.

Compared to the A-level scenic spots identified by tourism authorities, the National Scenic and Historic Areas designated by China’s Ministry of Housing and Urban–Rural Development place greater emphasis on the protection of scenic spots [53], which greatly restricts land development in their surrounding areas. According to the overall planning of National Scenic and Historic Areas issued by the Ministry of Housing and Urban–Rural Development, a graded protection system must be implemented within National Scenic and Historic Areas, with protection zones designated as first, second, and third-level protection areas. Different levels of strict management and protection are enforced within these zones, such as prohibiting any development and construction activities in the first-level protection zone. This may result in varying land development and utilization efficiencies within different ranges of National Scenic and Historic Areas.

The establishment of National Scenic and Historic Areas requires localities to invest more funds in protection [54], thereby suppressing local industrial development to some extent. Furthermore, when tourism is booming or the grade of tourist attractions is high, a certain amount of expenditure is allocated for maintenance to protect the natural environment and landscape of the scenic spot. This makes it difficult to maximize the development and utilization of land resources within the vicinity of the scenic spot, reducing the number of industrial land transfers and hindering industrial development in the area.

4.3. Further Analysis

From the literature review and the regression results in this study, it is evident that there is, to some extent, a dilemma between ensuring the prosperity of the tourism industry and promoting industrial development. However, high-quality economic development requires coordination among sectors, which also involves the coordinated development of tourism and industrialization [1]. Given the prominent role of tourism in the transition from an industrial economy to a service economy, the coordinated development of tourism and industrialization is particularly crucial for regional economic development.

In this study, industries are classified into two categories: non-technology-intensive industries and technology-intensive industries. This classification is determined by the degree of technological innovation and capital intensity involved in production processes. Technology-intensive industries are characterized by their heavy reliance on advanced technologies, research and development (R&D), and substantial capital investment. Examples include communication equipment manufacturing, computer and electronic equipment manufacturing, pharmaceutical manufacturing, transportation equipment manufacturing, instruments, meters, and cultural and office machinery manufacturing, chemical fiber manufacturing, metal products manufacturing, general equipment manufacturing, special equipment manufacturing, electrical machinery and equipment manufacturing, chemical raw materials and chemical products manufacturing, automobile manufacturing, railway transportation equipment manufacturing, as well as ship and floating structure manufacturing. In contrast, non-technology-intensive industries generally require lower levels of technological innovation and capital input, encompassing all other industries not listed above.

Based on Equation (2), the dependent variable is defined as the unit price of the nearest industrial land parcel to an A-level scenic spot, while the explanatory variable represents the distance from that industrial land to the nearest A-level scenic spot (tourdist).

As shown in

Table 6. Tourism development and land transfer for technology-intensive industries.

	(18)	(19)
lntourdist	−0.009	−0.012 ***
	(−1.49)	(−2.98)
Control variable	Yes	Yes
County effect	Yes	Yes
Land grade effect	Yes	Yes
Land source effect	Yes	Yes
N	1994	8813
R2	0.949	0.905

Notes: The t statistics are shown in parentheses; *** p < 0.01.

, for both technology-intensive and non-technology-intensive industries, the closer the industrial land is to A-level scenic spots, the higher the price of land transfer. However, the coefficient for non-technology-intensive industries is not only higher than that for technology-intensive industries but also significant at the 1% confidence level. This indicates that when tourism develops rapidly, the price of industrial land transfer for non-technology-intensive industries is higher, and the crowding-out effect of tourism development on them is greater than on technology-intensive industries. The coefficient for technology-intensive industries is negative but not statistically significant, which suggests that, with the development of tourism and the continuous technological innovation of industrial enterprises, it may be possible to achieve coordinated development between tourism and industry.

The empirical results from the literature and the practical development of industries have clearly demonstrated the significant limitations of positioning the tourism industry as the sole pillar of regional economic development. Moreover, the over-prosperity of the tourism industry may crowd out many other local industries, particularly suppressing the development of the manufacturing sector, thereby generating a phenomenon akin to Dutch Disease, which is detrimental to the long-term development of the overall economy.

There exists a relationship between tourism and industry that is both complementary and substitutive. The coordinated development of the two sectors not only fosters their sustainable evolution but also contributes to the high-quality development of the entire national economy [1]. This study finds that tourism may develop in coordination with technology-intensive industrial enterprises, suggesting a viable development path for both tourism economies and industrialized economies. According to the study, these regions can adjust their industrial structure and layout while developing tourism, focusing on phasing out low-value-added, low-tech industries, and concurrently fostering technology-intensive industries. This approach promotes a shift in the mode of economic growth and drives industrial upgrading, allowing the complementary functions of tourism and industry to be fully realized.

5. Discussion

This paper systematically analyzes the impact of tourism development on industrialization from the perspective of industrial land transfer using micro-level data. It reveals the mechanisms through which tourism influences industrialization, particularly through land factor competition and resource conservation effects. The theoretical contributions of this paper are as follows:

First, from the theoretical perspective of industrial land transfer, this paper analyzes the impact of tourism development on industrialization. By examining the price and quantity of industrial land transfers, it reveals how tourism development influences the development of industrial enterprises through land factor competition and resource conservation, thereby enriching the empirical research on the relationship between tourism and industrialization.

Second, this paper verifies the nonlinear relationship between tourism development and industrialization. It argues that tourism and industrialization are not inherently contradictory and can achieve coordinated development through technological innovation and industrial structure optimization. This finding enriches the theory of coordinated industrial development and provides theoretical support for the high-quality development of regional economies.

Third, this paper incorporates scenic spot and land transfer data to conduct micro-level empirical analysis, uncovering the underlying mechanisms linking tourism and industrialization. This approach not only extends existing research findings but also enhances the methodological foundation for studies on the relationship between tourism and industrialization.

The coordinated development of tourism and industrialization from a land-use perspective is essential. To achieve this, further discussion can be conducted from the perspectives of land zoning, pricing, and rights.

Land Zoning: Spatial zoning is a fundamental tool for mitigating conflicts between tourism and industrial land use. By instituting clear divisions of land use, areas can be designated to prioritize either tourism development or the retention of industrial functions. This helps prevent spatial overlap and resource competition, reducing mutual exclusivity between land uses and avoiding the tourism squeezing out industry. It also promotes a spatial pattern characterized by the division between tourism and industry, fostering functional complementarity within the region.

Land Pricing: Differential pricing can serve as an effective economic instrument to manage land competition. Land transfer prices should be adjusted based on land use type, resource endowment, and locational attributes. For example, higher prices for industrial land in high-value tourism zones can help limit the entry of non-core industries, while lower prices for industrial land in peripheral areas can attract environmentally friendly manufacturing clusters.

Land Development Rights: Transferable development rights (TDRs) offer a flexible mechanism for allocating land development rights and are highly effective in managing conflicts between different land uses. TDRs allow for a cross-regional balance of land-use functions without increasing overall development intensity. They help guide industries to relocate to areas with greater resource capacity and lower land costs, thereby reducing the risk of industrial congestion in core tourism zones.

6. Conclusions

Based on a comprehensive review of the impact of tourism development on industrialization, this paper constructs an econometric model using data on A-level scenic spots, National Scenic and Historic Areas, and industrial land transfers. The study empirically examines the impact of tourism development on industrialization at the county level and tests the mechanisms through which tourism development affects industrialization at the micro level of land transfer, thereby enriching the theoretical understanding of the impact of tourism development on industrialization. The conclusions are as follows:

First, the study finds a U-shaped relationship between the quantity and quality of tourism resources and the development of industrial enterprises, while the agglomeration of tourism resources exhibits an inverted U-shaped relationship with the development of industrial enterprises.

Second, this study examines the factor competition effect and resource conservation effect of tourism development on industrialization. By analyzing the impact of tourism development on the price and quantity of industrial land, the study demonstrates how tourism can inhibit the industrialization process by increasing the price of industrial land and reducing the number of industrial land transfers. Specifically, the greater the richness of tourism resources, the fewer the industrial land transfers in surrounding areas, and the more pronounced the crowding-out effect on industrial development. Moreover, for AAA and AAAA scenic spots, the higher the grade of the scenic spot, the more significant the negative impact on the price of industrial land closest to the scenic spot. Based on these findings, the study concludes that tourism development can hinder industrialization by competing for the production factors and protecting the tourism resources.

Third, the study further explores the coordinated development of the tourism sector and the industrial sector, with a particular focus on the differences between technology-intensive and non-technology-intensive industries. The findings indicate that the crowding-out effect of tourism development on non-technology-intensive industries is more pronounced, while its impact on technology-intensive industries is limited. As tourism develops and technological innovation in industrialization progresses, it may become possible to achieve a more coordinated development between tourism and industrialization.

6.1. Policy Implications

This study also offers valuable policy guidance and practical insights for industrial development, providing a scientific basis for achieving balanced tourism development and fostering coordinated growth between the tourism economy and the manufacturing economy.

First, the pricing of industrial land should be carefully regulated. The rapid expansion of tourism typically results in increased industrial land prices, which in turn reduces the availability of land for industrial purposes, discourages the entry of industrial enterprises, and ultimately constrains industrial development. However, competition among counties for land use serves as a significant driver of regional economic growth, and the relocation or withdrawal of manufacturing enterprises may hinder overall economic progress. Consequently, local governments must implement measures to prevent the excessive escalation of land costs, thereby mitigating the risk of industrial displacement due to soaring land and housing prices. Planners should fully understand the land use requirements and future development trends of various industries, including tourism and manufacturing industries, and use spatial zoning to prevent conflicts in land demand, thereby supporting the sustainable development of the regional economy.

Second, it is essential to effectively manage the relationship between resource protection and development. The core competitiveness of the tourism industry is derived from its tourism resources, many of which are natural landscapes. To ensure the long-term appeal of these resources, local governments and tourist administrations must implement measures to protect them from degradation, including allocating funds for the regular maintenance of scenic areas. In this context, the development of certain industries with high energy consumption, pollution, and emissions may need to be restricted or even phased out. Industrial enterprises should focus on upgrading their processes and adopting green technologies in line with their resource endowments and comparative advantages, thereby enhancing their compatibility with the evolving demands of tourism development.

Third, it is essential to prioritize technological innovation, optimize the industrial structure, and promote coordinated development between tourism and industry. Promoting scientific and technological innovation is key to achieving high-quality, coordinated development between the two sectors. On the one hand, local government should encourage the tourism and manufacturing industries to improve total factor productivity through technological innovation and empowerment. This is especially important for regions where tourism is a pillar industry, as the low employment threshold in the tourism sector may limit regional human capital accumulation [55], which could affect long-term regional economic growth. On the other hand, local government should fully leverage scientific and technological innovation to drive industrial development. They should encourage the manufacturing sector to pursue product innovation, technological advancement, and business model transformation, thereby enhancing industrial competitiveness and promoting sustainable regional development.

6.2. Limitations

Due to limitations in the research samples and the construction of key indicators, this paper only examines the impact of county-level tourism development on industry in 2019 and does not explore the long-term effects of tourism on industry, which could be investigated in the future. Additionally, since both tourism development and industrialization are significantly influenced by government policies and environmental pollution, and this paper does not construct corresponding indicators for empirical analysis, future research could consider additional scenarios for in-depth analysis.