Analysis of Distribution Characteristics and Driving Factors of Forestry Enterprises in China Using Geospatial Technology and Models

Abstract

Forestry enterprises play a pivotal role in economic development, ecological civilization construction, and sustainable development. This study employs GIS-based spatial analysis to examine the distribution patterns and interrelationships of forestry enterprises, investigating their key determinants and spatial heterogeneity. The findings provide valuable insights for policymakers aiming to optimize industrial structures and enhance national ecological security. This research develops a comprehensive evaluation index system to assess the factors influencing forestry industry development in China. Nine factors are considered: human resources, economic development, industrial structure, technological support, trade development, financial environment, natural conditions, urbanization, and transportation. Using panel data from 367 cities in 2020, the Multiscale Geographically Weighted Regression (MGWR) method quantifies the influence of these factors and their spatial variations. The results show the following. (1) Forestry enterprises in China exhibit persistent spatial clustering. The eastern regions have a notably higher concentration than the western regions, and new enterprises are increasingly concentrated in a few hotspot cities in the east. (2) The spatial center of forestry enterprises has steadily moved southeast. Initially, the distribution was balanced in the eastern regions, but it has become highly concentrated in the southeastern coastal areas. (3) Regarding spatial autocorrelation, regions within the northwest cold spot cluster have been disappearing entirely. The northeast and southwest hotspot clusters have shrunk significantly, while the southeast hotspot cluster has remained large. (4) Permanent population size and green land area are the most strongly positively correlated with forestry enterprise distribution. Patent authorizations, orchard area, and forest land area also show positive effects. In contrast, road density and total import/export volume are negatively correlated with the number of forestry enterprises. This aligns with the structure of China’s forestry industry, which relies more on natural resources and market demand than on economic development level or financial environment. (5) The factors influencing forestry enterprise distribution show significant spatial variation, driven by regional factors such as resources, economy, and population. These factors ultimately determine the spatiotemporal distribution of forestry enterprises. This study provides data-driven insights to optimize the distribution of forestry industries and formulate more effective ecological protection policies.

1. Introduction

In the 18th century, the term “sustainability” was introduced to forestry literature by the work “Sylvicultura oeconomica” [1]. Since then, forest management in North America and Europe has undergone a significant evolution, shifting from an initial focus on timber production to a more contemporary approach centered on multi-functional forest management (MFM), which prioritizes ecological conservation and social welfare [2,3]. In the 21st century, the United Nations successively introduced initiatives such as the Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs), and sustainable forest management (SFM) emerged as a pivotal concern underpinning societal sustainability [4]. Several countries, including Canada [5], Sweden [6], and Germany [7], have implemented numerous innovative management models and policies to promote SFM, aiming to achieve the sustainable use of resources and ecological balance. Currently, the Chinese Government is promoting a comprehensive green transformation of economic and social development, which implies a harmonious coexistence between humanity and nature, epitomizing green and sustainable growth principles. Forestry, as a green industry, holds multifaceted value in ecological, economic, and social dimensions, occupying a profoundly significant position within the national economic and social sustainable development strategy [8]. Furthermore, forestry represents the largest green economic sector, making significant contributions to job creation, poverty reduction, and regional economic balance. Additionally, it plays a pivotal role in enhancing air quality, providing green recreational spaces, and improving overall public well-being. Since the establishment of the People’s Republic of China, its value has increased rapidly. From 2.39 × 10⁹ Chinese yuan in 1949, forestry output surged to 7.55 × 10¹² Chinese yuan in 2020 [9], significantly contributing to environmental benefits, the accumulation of ecological assets, and the facilitation of green economic growth. Forestry, with forests as its primary operational focus, encompasses the principal terrestrial ecosystem and serves as the largest carbon reservoir on land. Forests play a pivotal role in providing strategic support for global climate change mitigation efforts [10,11,12]. Consequently, forestry development is a fundamental concern for economic and social sustainable development. The advancement of forestry yields substantial economic benefits and engenders significant ecological, environmental, and social dividends [13].

The forestry industry is the backbone of forestry economic development, with diverse industrial types and a complex system, as well as a lengthy industrial chain encompassing primary, secondary, and tertiary sectors [14]. Forestry enterprises, on the other hand, are businesses primarily focused on forest resources, the environment, and their products. The operational activities of forestry enterprises are poised to generate positive impacts across economic, ecological, and social domains [15]. On one hand, forestry enterprises can provide products that meet societal needs and contribute to their own healthy, sustainable development. On the other hand, they play a crucial role in promoting economic development and ecological environment conservation. Given the crucial role of forestry enterprises in shaping both their own interests and the broader socio-economic and ecological balance, understanding their spatial distribution patterns and the factors driving these changes is critical for ensuring sustainable development.

Current research on forestry enterprises mainly examines business operations, management, social impacts, and industrial clustering. Factors such as debt, liquidity, leverage, return on assets, firm size, and GDP are also considered to be closely related to the capital structure of forestry enterprises [16,17,18]. Furthermore, others have indicated that forestry policies are crucial factors influencing the development of the forestry industry, directly impacting site selection and operational conditions of forestry enterprises [19,20,21]. Regarding the social values of forestry enterprises, scholars have examined carbon sequestration projects [22], ecological construction [23], social responsibility [24], and other aspects. They argue that forestry enterprises differ significantly from conventional enterprises, particularly in terms of green and sustainable development, whereby forestry enterprises undertake distinctive social responsibilities.

In addition, there is a growing body of empirical studies on forestry industry clusters. These studies primarily utilize Porter’s Diamond Model theory to examine the development strategy of forestry industry clusters [25]. For example, cheap labor, low energy costs, and a favorable geographic location with well-developed transportation infrastructure are key competitive advantages of the forest industry of the Republic of North Macedonia [26]. Different from this, forest timber resources are considered the competitive advantage of forestry clusters in the northeastern region of Minnesota in the United States [27]. With the maturation of GIS spatial analysis methods, scholars have begun to explore the distribution of the forestry industry from a spatial perspective. Studies have confirmed that the distribution of forestry industry clusters exhibits distinct spatial locational characteristics [28,29]. The selection of industrial locations is influenced by a series of complex factors. Through analysis of factors such as labor, transportation conditions, and raw materials, potential forestry industry clusters can be identified.

In China, the Statistical Yearbook is an annual publication compiled by government statistical departments, featuring a comprehensive set of statistical data systematically and continuously track the development of various economic and social aspects. Scholars extensively explore various statistical indicators related to the forestry industry within the Statistical Yearbook data, using them as proxies for assessing the level of forestry industry development. Researchers employed spatial econometric models to confirm that forestry industry clustering promotes economic growth in the forestry sector [30]. By describing the spatial and temporal distribution characteristics and evolutionary process of forestry industry agglomeration and analyzing the reasons behind the spatial distribution pattern, a strategy for the optimal development of China’s forestry industry can be proposed [31,32]. Utilizing spatial data analysis as an entry point allows for a more precise comparison of forestry industry development before and after the implementation of forestry policies or systems, thereby providing a more effective evaluation of the policy’s impact.

Currently, the study of enterprise spatial patterns is a prominent topic in economics, geography, and management [33]. Many scholars have utilized GIS spatial analysis techniques to explore the spatio-temporal evolution trends of enterprises, investigate the mechanisms influencing enterprise distribution, and propose schemes for optimizing enterprise layout and resource allocation [34,35]. For industries such as agriculture and forestry, there is relatively less research on enterprise spatial patterns. However, some scholars have studied leading agricultural enterprises. For instance, scholars have examined the spatial patterns of agricultural enterprises across various spatial scales and ranges using GIS spatial analysis methods grounded in location theory. They have analyzed the spatial distribution characteristics of agricultural enterprises and investigated influencing factors using Geo Detector, multiple linear regression, and spatial econometric models, among other analytical methods [36,37].

The above studies show that current research on enterprise layout tends to use location theory to explore the spatial patterns of secondary and tertiary industries such as manufacturing, science and technology enterprises, and service industries [38,39]. However, there is an obvious lack of breadth and depth in the research on the layout of primary industries, such as agriculture and forestry. The selection of influencing factors is questionable, such as the appropriateness of using the total length of regional highways as an influencing factor for agricultural enterprises in research. For instance, in the case of China’s provincial-level administrative regions, the total length of highways obviously does not accurately reflect the transportation advantages and disadvantages of the region due to the significant differences in the area of each region. In-depth spatial analysis is scarce, and there is a notable gap in utilizing geographic location big data to explore the spatial distribution patterns of forestry enterprises. This limitation may stem from the challenges in obtaining data related to enterprises in the primary industry such as forestry. Moreover, research on the spatial pattern of the forestry industry has primarily relied on panel data or the use of relevant consumption data of forest products to substitute for the level of the forestry industry in the region. This approach allows for the analysis of forestry industry agglomeration from a macro perspective, such as the provincial area [40,41]. However, using consumption data as an indicator, which is based on aggregated industry-level data, neglects more granular micro-level information, particularly the location data of individual enterprises, thus failing to capture the spatial dynamics and trends at the enterprise level.

Consequently, this study centers on China as the focal point and constructs a spatial repository of forestry enterprises utilizing data sourced from the Qixinbao commercial data survey platform. It examines the spatio-temporal evolution attributes of forestry enterprises at a granular level, using individual geographic coordinates (latitude and longitude) as spatial units, and employing methodologies including standard deviation ellipse (SDE), and kernel density analysis (KDA). Moreover, it delves into the spatial autocorrelation of forestry enterprise counts across various cities through hotspot analysis, and offers a comprehensive depiction of the spatial and temporal evolutionary trajectory of Chinese forestry enterprises on both a macroscopic and microscopic scale. The study aims to provide a thorough depiction of the spatial and temporal developmental trends of Chinese forestry enterprises, with the objective of elucidating their spatial and temporal evolutionary attributes. Lastly, it amalgamates information from the statistical yearbook, land area statistics, and supplementary survey data, utilizing methodologies such as Ordinary Least Square (OLS), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) to investigate the spatial variability of factors impacting forestry enterprise counts across distinct urban centers.

2. Materials and Methods

2.1. Data Sources

This paper focuses on forestry-related enterprises in mainland China, encompassing 367 cities across 31 provinces. Data from Hong Kong, Macao, and Taiwan were excluded due to data availability constraints. The enterprise sample data were primarily sourced from the Qixinbao enterprise information query platform (www.qixin.com/, accessed on 30 October 2022), providing information such as enterprise name, registration status, registration time, registered capital, address, and enterprise type. Initially, we cleaned the original forestry enterprise data, excluding enterprises with “withdrawn”, “canceled”, or “revoked” status. Subsequently, we utilized the geocoding service of the Amap open platform (https://lbs.amap.com/, accessed on 1 December 2022), converting textual enterprise addresses into geographic coordinates (Figure 1), thereby establishing a spatial database of forestry enterprises. This study obtained data from over 310,000 forestry enterprises registered before 31 December 2020, spanning five time periods, 2000, 2005, 2010, 2015, and 2020, to examine their temporal and spatial dynamics.

The spatial layout process of forestry enterprises, like that of other enterprises, is influenced by various factors, including human resource status, transportation accessibility, urban planning, financial environment, and natural environment, among others, which are important considerations for enterprise decision-makers in site selection [42,43,44]. This paper, taking into account data accessibility, comprehensively considers nine aspects, namely human resources, economic development level, industrial structure development status, scientific and technological support, trade development status, financial environment, natural environment, urban construction, and transportation accessibility, along with 25 specific indexes, as influencing factors. The influencing factors on China’s forestry enterprise spatial pattern are summarized as follows (

Table 1. Factors influencing the number of forestry enterprises distributed and definitions.

Variable Name	Variable Definition	Variable Symbol	Unit
Human Resources	Average wage of employees in non-private urban units	AWE_NPU	Yuan
	Number of employees covered by basic urban old-age insurance	NEC_BUOI	Persons
	Permanent resident population	PRP	Persons
Economic Development Level	Gross Domestic Product (GDP)	GDP	100 million Yuan
	Per capita GDP	PCGDP	Yuan
	Total retail sales of social consumer goods	TRS_SCG	10 thousand Yuan
Industrial Structure Development	Average registered capital of enterprises	ARC_E	Yuan
	Proportion of the primary industry in the regional GDP	PPI_RGDP	%
	Proportion of the secondary industry in the regional GDP	PSI_RGDP	%
	Proportion of the tertiary industry in the regional GDP	PTI_RGDP	%
Technological Support	Number of patents granted	NPG	Items
Trade Development	Total import and export volume	TIEV	CNY 10 thousand
	Number of industrial enterprises above designated size	NIE_ADS	Units
	Volume of highway freight transportation	VHFT	10 thousand tons
Financial Environment	Year-end balance of RMB loans by financial institutions	YEB_RMB_FI	CNY 10 thousand
	Year-end balance of RMB deposits by financial institutions	YEB_RMB_DI	CNY 10 thousand
Natural Conditions	Orchard area	OA	Hectares
	Forest area	FA	Hectares
	Grassland area	GLA	Hectares
Urban Construction	Built-up area of urban districts	BA_UD	Square kilometers
	Green area	GRA	Hectares
	Green coverage rate of built-up areas	GCR_BA	%
	Urban industrial and mining land area	UIMLA	Hectares
	Urban warehouse land area	UWLA	Hectares
Transportation Convenience	Density of highways within the territory	DHWT	Kilometers per square kilometer

), based on research findings from related primary industry enterprises such as agriculture and forestry [45,46]. The average value of enterprise registered capital is derived from the “registered capital” attribute data of forestry enterprises in 2020. Data on forest land area, orchard land area, grassland area, urban industrial and mining land area, and urban warehousing land area were obtained from the China National Land Survey Results Sharing and Application Service Platform (https://gtdc.mnr.gov.cn/, accessed on 1 June 2023) for the year 2020. The data on road density within the country represent the ratio of total road length to jurisdictional area, sourced from the Statistical Yearbook. The remaining data primarily stemmed from the “Statistical Yearbook of China’s Cities 2021” published by the National Bureau of Statistics, supplemented by information from the “Statistical Yearbook of China’s Counties” (County and City Volume)-2021 and the statistical yearbook of the respective host city, where necessary. In analyzing the influencing factors, this study focuses on prefecture-level cities. However, due to the unique administrative divisions in China, municipalities, autonomous regions, leagues, and counties directly under provincial control are also considered as cities, resulting in a total of 367 cities.

2.2. Research Methods

In this study, spatial analysis methods such as standard deviation ellipse, kernel density analysis, hotspot analysis, and exploratory regression analysis were conducted using ArcGIS 10.2 software [47], while OLS, GWR, and MGWR analyses were performed using MGWR 2.2 [48].

2.2.1. Standard Deviation Ellipse

The SDE and centroid are effective indicators of the dispersion degree in the distribution of forestry enterprises [49]. The size of the ellipse reflects the concentration level of the overall spatial patterns, while its long axis indicates the dominant direction of distribution. The formula for calculating the SDE is as follows:

$${SDE}_x=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2}{n}}}$$

(1)

$${SDE}_y=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(y_i-\overline{y}\right)}^2}{n}}}$$

(2)

In the equation, $x_i$ and $y_i$ represent the spatial coordinates of each element; ($\overline{x}$,$\overline{y}$) denotes the arithmetic mean center of the elements; k represents the total number of elements; ${SDE}_x$ and ${SDE}_y$ represent the geographic coordinates of the centroid of the SDE for forestry enterprises.

2.2.2. Kernel Density Analysis

KDA is utilized to calculate the density of features within their surrounding neighborhoods, providing an intuitive depiction of the clustering-dispersion degree of forestry enterprises [50]. The computational formula is given as follows:

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

(3)

In the equation, f(x) represents the kernel density estimate; n is the number of forestry enterprises; h denotes the bandwidth, where h > 0; k signifies the kernel function; and (x − $x_i$) represents the Euclidean distance from the estimation point x to the sample point $x_i$. A higher value of f(x) indicates a greater degree of enterprise clustering, while a lower value signifies the opposite.

2.2.3. Hotspot Analysis

Hotspot analysis, a form of local spatial autocorrelation analysis, identifies clustering patterns within local regions, effectively delineating areas of high-value (hotspots) and low-value (cold spots) aggregation in spatial terms [51]. The computational formula for hotspot analysis is given as follows:

$$G_i^{*}={\displaystyle \frac{{\sum }_{j=1}^n{w}_{ij}\left(x_j-\overline{x}\right)}{S\sqrt{\frac{\left[n{\sum }_{j=1}^n{w}_{ij}^2-{\left({\sum }_{j=1}^n{w}_{ij}\right)}^2\right]}{n-1}}}}$$

(4)

In the equation, $G_i^{*}$ represents the local Moran’s I index for region i; n denotes the total number of regions; $w_{ij}$ stands for the spatial weight matrix; S denotes the standard deviation; $x_j$ represents the number of forestry enterprises in region j; and $\overline{x}$ signifies the mean of $x_i$ and $x_j$.

2.2.4. Exploratory Regression Analysis

When a study involves numerous explanatory variables, all of which are significant factors influencing the dependent variable, it can be challenging to find an OLS model that meets all criteria. Exploratory regression simplifies this process by traversing through all combinations of explanatory variables to identify the optimal combination that meets the criteria [52]. Although similar to stepwise regression, exploratory regression not only identifies combinations with higher R-squared values but also utilizes p-values and Variance Inflation Factors (VIF) as screening criteria. Given the varying units and substantial differences in numerical values among indicators [53], it is necessary to normalize the variables to mitigate the influence of heteroscedasticity in the computation results. Subsequently, the exploratory regression tool in ArcGIS 10.2 was used to screen the combinations of indicators based on the following criteria:

• A substantial adjusted R-squared (R² > 0.40) value must be achieved for the combination of indicators.
• Each individual indicator must be significant at a level of 10% (p < 0.1).
• VIF values must be significantly lower than 10.0.
• Indicator combinations with the smallest Akaike Information Criterion (AIC) values must be selected.

2.2.5. Ordinary Least Squares Regression (OLS) and Geographically Weighted Regression (GWR) Model

Multivariate linear regression analysis comprises two main types: global linear regression analysis and local linear regression analysis. Among global linear regression methods, the most traditional model is the OLS model [54]:

$$y={\beta }_0+{\sum }_{k=1}^n{\beta }_k{x}_k+\epsilon$$

(5)

In the equation, y represents the observed dependent variable; ${\beta }_0$ is the estimated intercept; $x_k$ denotes the value of the kth independent variable; ${\beta }_k$ represents the estimated coefficient for the kth independent variable; n signifies the number of independent variables; and $\epsilon$ stands for the error term.

The OLS model provides global estimates for the sample and variables without considering the influence of spatial autocorrelation. The GWR model is an extension of the linear regression model, capable of fitting samples with spatial autocorrelation well and reflecting their spatial characteristics [55]. The setup of the GWR model is as follows:

$$y_i={\beta }_0(u_i,v_i)+{\sum }_k{\beta }_k(u_i,v_i)x_{ik}+{\epsilon }_i$$

(6)

In the equation, $y_i$ represents the n × 1 dimensional dependent variable, $x_{ik}$ represents the n × k dimensional explanatory variable matrix, ${\beta }_k(u_i,v_i)$ denotes the regression coefficient of factor k at regression point i, where k represents the number of independent variables, $(u_i,v_i)$ denotes the latitude and longitude coordinates of observation point i, and ${\epsilon }_i$ represents the random error term of the distribution.

2.2.6. Multiscale Geographically Weighted Regression (MGWR) Model

The MGWR model belongs to the local regression model, which optimizes the GWR model [56]. MGWR allows each variable to have its own specific bandwidth; that is, each independent variable can be regressed under its optimal bandwidth [57]. In this study, the MGWR model is used to examine the extent of influence of each factor on the number of forestry enterprises, along with their spatial distribution and spatial heterogeneity. This approach provides a deeper understanding of how various factors affect the distribution and growth of these enterprises. The formula of the MGWR model is as follows:

$$Y_i={\sum }_{j=1}^k{\beta }_{b{w}_j}\left(u_i,v_i\right)X_{ij}+{\epsilon }_i$$

(7)

In the equation, $Y_i$ represents the number of forestry enterprises in a specific city, $X_{ij}$ represents the factors affecting the distribution of the number of forestry enterprises, ${\beta }_{b{w}_j}$ denotes the regression coefficient of the jth influencing factor, where the subscript $b{w}_j$ indicates the bandwidth to which the regression coefficient of the jth influencing factor applies, and ($u_i,v_i$) represents the coordinates of the city center. The larger the absolute value of the regression coefficient, the stronger the effect on the distribution of forestry enterprises.

3. Results

3.1. Analysis of the Evolution of Spatial Patterns of Forestry Enterprises in China

3.1.1. Standard Deviation Ellipse of Spatial Distribution of Forestry Enterprises in China

To visually illustrate the locational characteristics and distribution trends of spatial clustering among Chinese forestry enterprises, we used SDEs at different time points to investigate the directional differences and central changes in their spatial distribution. The SDE effectively illustrates the density and directional shifts of Chinese forestry enterprises, providing a better depiction of the spatial dynamic clustering process (Figure 2). From 2000 to 2020, the overall spatial distribution of forestry enterprises exhibited a distinct “southwest-northeast” directional pattern, with SDEs primarily covering the southwestern, southeastern, central, northern, and eastern regions of China, excluding most areas in the western and northeastern parts of the country. This pattern showed significant regularity over time. Examining the trajectory of centroid migration, the trend was quite evident. In 2000 and 2005, the centers were located in Nanyang City, Henan Province. In 2010 and 2015, the centers were in Xiangyang City, Hubei Province, while in 2020, they shifted to Suizhou City, Hubei Province. The migration path was clearly from northwest to southeast. From 2000 to 2005, the centroids of the SDEs for forestry enterprises remained in Henan Province, indicating a relatively slow migration speed. During this period, forestry enterprises had already shown a trend of expanding towards the southeast, albeit at a gentle pace. From 2005 to 2010, the centroids of the standard deviation ellipses shifted from Henan Province to Hubei Province. Between 2010 and 2015, the centroids of the SDEs showed the fastest migration speed and the greatest displacement distance. During this period, the growth rate of forestry enterprise distribution in the southeastern direction significantly exceeded that in the northwest region, with a concentration primarily in the east-southeast direction. From 2015 to 2020, the centroids of the SDEs for forestry enterprises continued to shift slightly southeastward, remaining within the boundaries of Hubei Province. Examining the distribution of the SDEs from 2000 to 2020, the expansion of the distribution range was consistently southeastward, with the expansion amplitude gradually increasing. This indicates that the southeastern direction has been the primary area of new additions for forestry enterprises in China during the past two decades. Conversely, there has been a gradual contraction in the southwest and northeast directions, although the extent varies.

The parameters of the SDE more effectively represent the specific spatial distribution of forestry enterprises (

Table 2. The SDE parameters of forestry enterprises from 2000 to 2020.

Time	CenterX	CenterY	XstdDist	YStdDist	Rotation	Area	Ellipticity
2000	112.122	32.683	887.952	1355.919	33.026	378.220	0.345
2005	112.320	32.548	902.834	1338.982	31.575	379.757	0.326
2010	112.564	32.229	920.730	1304.338	28.946	377.265	0.294
2015	112.706	31.629	942.800	1273.811	24.823	377.268	0.260
2020	113.082	31.500	1005.330	1213.388	20.628	383.208	0.171

). Regarding the centroid of the ellipse, from 2000 to 2020, the geographic coordinates of the center of Chinese forestry enterprises shifted mainly within the range of 31.50° N to 32.68° N and 112.12° E to 113.08° E. The smallest centroid migration distance occurred from 2000 to 2005, whereas the largest migration distance was from 2010 to 2015. In terms of the rotation angle, the azimuth angle gradually decreased from 33.03 to 20.63, indicating a diminishing trend. By 2020, the “southwest to northeast” pattern of forestry enterprise distribution showed signs of weakening, with a notable increase in the southeast direction. However, the overall spatial distribution direction predominantly maintained the “southwest to northeast” pattern. In terms of the major and minor axes, from 2000 to 2020, the major axis of the ellipse decreased annually from 1355.919 to 1213.388, while the minor axis increased annually from 887.952 to 1005.330. Forestry enterprises showed a shrinking trend in the northeast to southwest direction and an expanding trend in the northwest to southeast direction. The most significant difference between the major and minor axes occurred in 2000, indicating strong centripetal force and clear directionality in the spatial distribution of forestry enterprises. The disparity between the major and minor axes gradually decreased from 2000 to 2020, with the ellipticity decreasing continuously and the directional trend becoming less pronounced. In terms of the ellipse’s area, from 2000 to 2015, the standard deviation ellipse’s area fluctuated slightly, while from 2015 to 2020, the area notably increased. During this period, the distribution range of forestry enterprises expanded, primarily concentrated in the “northwest to southeast” direction, with a significant increase in the number of forestry enterprises, particularly in southeast China.

3.1.2. Changes in Spatial Distribution Density of Forestry Enterprises in China

The spatial distribution of forestry enterprises in China was analyzed using the kernel density estimation method, and the results were categorized into five classes using the natural breaks method (Figure 3). At each time point, the distribution density of forestry enterprises in China has exhibited significant spatial clustering, consistently displaying a “multiple clusters, multiple centers” characteristic. The evolution rate from 2000 to 2010 was relatively moderate, with little variation in the distribution of central clusters across regions except for the northeast. From 2010 to 2020, the evolution rate accelerated rapidly, and the spatial distribution density in the southeastern coastal areas consistently remained high. Meanwhile, the spatial distribution density in the northeast exhibited a decreasing trend, whereas forestry enterprises in the western regions consistently maintained a relatively low density. From an overall perspective, from 2000 to 2020, the number of forestry enterprises continued to increase, transitioning from a clustered to a patchy, and then to a point-like distribution. The hotspots became more concentrated, with the kernel density values steadily increasing. The maximum value rose from 0.023 to 0.784, an approximately 34.087-fold increase. The spatial distribution density of forestry enterprises has shifted from a relatively balanced distribution in 2000 to a highly concentrated distribution in 2020, characterized by clustered aggregations centered around Guangzhou, Nanning, and Xiamen. This indicates that the spatial distribution of Chinese forestry enterprises consistently maintains a clustering feature, with the clustering trend becoming increasingly evident.

In 2000, forestry enterprises in China exhibited a widespread distribution, which was characterized by multiple high-density graded block clusters. The largest area of high-density kernel values was observed in the cluster spanning Chengdu to Chongqing. The hotspots were concentrated and interconnected, radiating outward to form multiple concentric zones of different kernel density levels. The spatial distribution pattern of forestry enterprises was not solely constrained by administrative boundaries but rather shaped more by local conditions and natural extensions. In the northwest region, areas with low to moderate kernel density predominated, with only a few moderate-density zones observed in the northern part of Xinjiang and the southeastern part of Gansu. The northwest region, relatively deficient in forestry economic resources, had fewer forestry enterprises, which made it difficult to form large-scale aggregation areas spatially. By 2005, the areas of high and moderately high kernel density levels in various provinces gradually decreased. High-density zones were only observed as single-core agglomeration areas in eight cities, including Chengdu, Chongqing, Kunming, Guangzhou, Xiamen, Sanming, Beijing, and Shenyang. These high-density zones were no longer contiguous, and moderately high-density areas were mainly concentrated in adjacent inter-provincial areas and single-core agglomeration areas within provinces, indicating a noticeable trend of differentiation. In 2010, the areas with high and moderately high kernel density levels for forestry enterprises in China decreased further, accompanied by a noticeable reduction in the number of agglomeration centers. Two new agglomeration areas emerged in Nanning and Fuzhou, characterized by rapid growth in the number of forestry enterprises, surpassing those in other regions nationwide and thereby forming new high-density agglomeration zones. Conversely, Kunming, Chongqing, and Shenyang were downgraded to moderately high-density areas, indicating relatively slower growth rates in forestry enterprise numbers than surrounding regions. While these areas still exhibited higher forestry enterprise densities than their surroundings, they no longer constituted high-density agglomeration zones at the national scale. Overall, most provinces across the country demonstrated a clear “single-core” effect in the spatial distribution of forestry enterprises. For instance, in provinces such as Hunan, Hubei, Jiangxi, and Anhui, there was typically only one core area with moderately high density, signifying a significant agglomeration effect for forestry enterprises, where single-core agglomeration formed in areas with advantageous forestry development within the provinces. In 2015, the number of agglomeration centers for forestry enterprises in China continued to decline, accompanied by a significant reduction in the area of high and moderately high kernel density zones. Among these, only Nanning, Guangzhou, and Xiamen, along with their surrounding areas, remained high-density zones. Among them, Nanning’s agglomeration area boasted the largest area, indicating the highest concentration of forestry enterprises in the region. This underscores Nanning’s strong emphasis on high-quality forestry development, effectively leveraging its ecological resources to gain a competitive edge in industrial development. In 2020, due to the absolute growth in the number of forestry enterprises, areas with low and moderately low kernel density levels expanded to cover most parts of the country. Although the number of forestry enterprises in these regions also increased, the absolute growth was relatively small. High-density zones were only concentrated in the vicinity of Nanning, Guangzhou, and Xiamen, where the absolute number of forestry enterprises was high, forming high-density agglomeration areas centered around these three cities. Overall, the spatial distribution pattern of forestry enterprises in the country showed significant differentiation, with lower density in the northern regions compared to the south and lower density in the western regions compared to the east. Geographically, the area of agglomeration zones in inland regions notably decreased, with high-value agglomeration centers mainly located in the southeastern coastal areas. This suggests a rapid expansion of forestry enterprise distribution patterns in coastal areas, particularly in the southeast.

3.1.3. Analysis of Spatial Distribution Hotspots of Chinese Forestry Enterprises

Using hotspot analysis tools to identify cold and hotspot regions within the study area, the spatial distribution of forestry enterprise quantities from 2000 to 2020 was assessed (Figure 4). In 2000, forestry enterprises formed hotspot clusters primarily in the southwestern provinces such as Yunnan, Sichuan, Guizhou, and Chongqing; in the southeastern provinces such as Jiangxi, Fujian, and Guangxi; in the northeastern provinces such as Heilongjiang and Jilin; and in northwestern provinces, mainly in Xinjiang, Tibet, and Qinghai. In 2005, the distribution of cold and hotspots in forestry enterprises was similar to that of the year 2000, with minimal changes in the spatial distribution range. From 2010 onwards, the spatial distribution range of cold and hotspots in forestry enterprises was noticeably reduced, with a significant increase in the number of non-significant regions. In 2015, hotspot clusters were observed in regions surrounding Guangxi, Guangdong, Heilongjiang, and Chongqing, with the cold spot areas limited to Tibet. By 2020, cold spot regions had completely disappeared, and the prominence of the northeastern hotspot cluster had diminished. Instead, the southeastern hotspot cluster, particularly in Fujian, Guangdong, Guangxi, and Jiangxi, became more pronounced. The southwestern hotspot cluster, centered around Chongqing, exhibited a significant decrease in range compared to 2000. Over the period from 2000 to 2020, there was a notable differentiation in the spatial distribution of forestry enterprise quantities, with an expansion of non-significant regions, complete disappearance of cold spot clusters, and a substantial reduction in the spatial extent of hotspot clusters, resulting in a patchy and belt-shaped distribution pattern.

3.2. Analysis of Factors Influencing the Distribution of the Number of Forestry Enterprises by City

3.2.1. Exploratory Regression Analysis Result

Through the exploratory regression analysis method, the optimal combination of independent variables was explored. The exploratory regression method demonstrates significant advantages in establishing the optimal linear regression model. By analyzing the results of exploratory regression, relevant variables were selected to construct the optimal variable combination for the OLS model. Based on the aforementioned selection criteria, the optimal combination of influencing factors was identified, consisting of seven factors: (1) DHWT, (2) TIEV, (3) NPG, (4) PRP, (5) GRA, (6) OA, and (7) FA.

3.2.2. Comparison of OLS, GWR, and MGWR Regression Models

Building upon the results of the exploratory regression analysis, the OLS model was first employed to determine the impact of the optimal combination of influencing factors on the distribution of forestry enterprises in China. The reasonableness of the selected indicators was subsequently assessed, as shown in

Table 3. OLS model estimation results.

Variable	Coefficient	Standard Error	T-Test	p-Value	VIF
Constant term	−0.000	0.038	−0.000	1.000	–
DHWT	−0.081	0.043	−1.875	0.061	1.277
TIEV	−0.671	0.089	−7.528	0.000	5.490
NPG	0.510	0.105	4.833	0.000	7.672
PRP	0.304	0.068	4.500	0.000	3.145
GRA	0.421	0.072	5.810	0.000	3.624
OA	0.115	0.041	2.798	0.005	1.159
FA	0.145	0.042	3.468	0.001	1.210

. The regression results reveal a coefficient of determination (R²) of 0.480 and an adjusted R² of 0.470, and an Akaike Information Criterion (AICc) of 820.108. Additionally, the VIF values for all variables were below 10, indicating low multicollinearity and a good overall fit of the model. Specifically, the NPG, PRP, GRA, OA and FA exhibit positive effects on the distribution of forestry enterprises. Conversely, DHWT and TIEV have negative effects. The collinearity among the seven indicators is relatively low, indicating the rationality of the selected indicators.

The previous analysis of the spatial agglomeration and distribution direction of forestry enterprises in China shows that the number of forestry enterprises within the urban area has significant spatial autocorrelation. If spatial autocorrelation exists, the OLS model estimates are biased, and the GWR or MGWR models are more applicable. Meanwhile, the Koenker (BP) statistic of the OLS model is 0.442 × 10⁻³, which indicates that the spatial heterogeneity of the influencing factors is not taken into account in the model. Therefore, the GWR and MGWR models are utilized to deal with spatial heterogeneity and heteroscedasticity, enabling an in-depth exploration of the factors influencing the distribution of forestry enterprises at the prefecture and municipal levels. The comparison results of the three models are as follows (

Table 4. Comparison of OLS, GWR, and MGWR regression model results.

Explanatory Variable	Model	Sample Size	Sum of Squares of the Residuals	AICc	R2	Adjust R2
Number of forestry enterprises	OLS	367	190.889	820.108	0.480	0.470
Number of forestry enterprises	GWR	367	65.999	662.081	0.820	0.760
Number of forestry enterprises	MGWR	367	64.804	561.178	0.823	0.787

): R² = 0.820, adjusted R² = 0.760, AICc = 662.081 for the GWR model; R² = 0.823, adjusted R² = 0.787, AICc = 561.178 for the MGWR model. The R² and adjusted R² of the GWR and MGWR models are significantly larger than those of the OLS model, and the difference in AICc is much greater than 3. This indicates that the MGWR and GWR models are significantly better than the OLS model, and that considering the spatial heterogeneity of the influencing factors is necessary for explaining the effects of the variables on the distribution of forestry enterprises. Moreover, the local spatial variability characteristics of the influencing factors can be measured.

According to the statistics of the mean, standard deviation, minimum, median, and maximum of the coefficients of the GWR model in

Table 5. GWR model estimation results.

Variable	Bandwidth	Mean	Standard Deviation	Minimum	Median	Maximum
Constant term	60.000	0.139	0.323	−0.422	0.096	1.313
DHWT	60.000	−0.146	0.276	−1.390	−0.063	0.247
TIEV	60.000	−0.231	1.248	−2.481	−0.423	4.960
NPG	60.000	0.151	0.747	−1.897	0.247	1.976
PRP	60.000	0.544	0.468	−0.163	0.408	2.445
GRA	60.000	0.410	0.529	−0.882	0.250	2.136
OA	60.000	0.031	0.090	−0.198	0.036	0.324
FA	60.000	0.260	0.272	−0.477	0.203	1.324

, the coefficients have obvious fluctuations, indicating that there is a significant difference in the effect of the influencing factors on the distribution of forestry enterprises in different cities and municipalities. The regression coefficients of the seven factors show positive and negative fluctuations in different cities and municipalities, indicating that there is a positive and negative correlation between the influencing factors and the number of forestry enterprises. At the same time, the larger the absolute value of the regression coefficient of a factor in a city, the greater the influence of the factor on the number of forestry enterprises in that city.

The spatial heterogeneity of different impact factors is an essential consideration in geography. The bandwidths of the GWR models are all 60, which can only reflect the average of the spatial scales. In contrast, the MGWR model can identify the spatial scales of the impact factors, and the smaller the bandwidths, the more obvious the spatial heterogeneity characteristic of the impact factors.

Table 6. MGWR model coefficients.

Variable	Bandwidth	Mean	Standard Deviation	Minimum	Median	Maximum
Constant term	43.000	0.048	0.253	−0.390	−0.009	1.173
DHWT	50.000	−0.165	0.219	−1.319	−0.090	0.172
TIEV	166.000	−0.217	0.180	−0.585	−0.140	−0.012
NPG	366.000	0.105	0.003	0.099	0.105	0.112
PRP	46.000	0.410	0.311	0.046	0.334	2.172
GRA	82.000	0.498	0.338	0.019	0.505	1.119
OA	366.000	0.048	0.001	0.046	0.048	0.050
FA	310.000	0.159	0.047	0.080	0.157	0.246

shows that the bandwidths of each influencing factor on the number of forestry enterprises are 50 (DHWT), 166 (TIEV), 366 (NPG), 46 (PRP), 82 (GRA), 366 (OA), and 310 (FA). This indicates that the spatial heterogeneity of PRP is the most significant, while the spatial effect scales of GRA and OA are the largest and their spatial heterogeneity is the smallest. Compared to GWR, MGWR has different bandwidths for different influencing factors, reflecting the variability of spatial scales of influencing factors, resulting in better outcomes. The coefficients of MGWR show that TIEV has a negative effect on the number of forestry enterprises in all regions, DHWT has a negative effect on the number of forestry enterprises in localized areas, and the remaining influencing factors have a positive effect.

3.2.3. Spatial Distribution of Coefficients in the MGWR Regression Model

The analysis of the distribution quantity of forestry enterprises reveals the presence of scale effects in their spatial distribution across China. Therefore, by integrating the spatial distribution of regression coefficients for each variable, we can further elucidate the spatial heterogeneity of factors influencing the distribution quantity of forestry enterprises. Using the natural breaks method, this study categorizes the influence intensity of each factor into five levels and visualizes them, as shown in Figure 5.

The regression coefficient for road density exhibits a negative effect in most parts of China, which contradicts the expected outcome. Regions such as Fujian, Guangxi, Jilin, Heilongjiang, and the southern part of Zhejiang are known for their abundant forestry resources. However, the relatively small regression coefficient for road density indicates a stronger negative correlation between road density and the quantity of forestry enterprises in these areas. These regions are predominantly mountainous and have low road densities. Despite this, the rich forestry resources serve as crucial advantages for guiding the development of the forestry industry, resulting in a higher quantity of forestry enterprises. Conversely, in most parts of China, higher road density often corresponds to higher levels of urbanization instead of abundant forestry resources. This situation is unfavorable for the spatial layout and development of the primary forestry industry. Consequently, there are fewer forestry enterprises in these areas, as indicated by the negative regression coefficient. In the southeastern part of Tibet, as well as in the central-northern parts of Guangdong and adjacent areas, the regression coefficients for road density exhibit a positive effect. In Tibet, particularly in its southeastern region, the transportation infrastructure is relatively underdeveloped. This area is rich in forestry resources, and the forestry industry is highly concentrated here. Given the importance of transportation in the site selection for forestry enterprises, road density positively influences the forestry industry in this region. In Guangdong Province, where the forestry industry is dominated by the secondary sector, with a structure of 15.30:63.10:21.60 (primary–secondary–tertiary), the central-northern region serves as the core area for forestry industry development, especially in the Pearl River Delta region. In these areas, forestry resources are relatively scarce, but high road densities indicate higher levels of economic development. Therefore, areas with higher road densities tend to have more forestry enterprises (Figure 5a).

The regression coefficients for the total import and export values are significant and consistently negative. This indicates a negative correlation between the total import and export values and the number of forestry enterprises across different regions of China. Essentially, regions with higher total import and export values tend to have fewer forestry enterprises. Regions with high total import and export values are typically more economically developed and tend to focus more on high value-added industries, such as manufacturing and services, rather than traditional forestry. Analyzing the spatial distribution of the regression coefficients, the areas where the negative correlation between total import and export values and the number of forestry enterprises is strongest include southern regions such as Guangdong, Guangxi, Hainan, and Hunan, which are more developed in foreign trade. Areas with a median negative correlation are primarily located in regions where both forestry resources and foreign trade are relatively underdeveloped, such as Xinjiang, Tibet, Qinghai, Gansu, and Sichuan. The areas with the weakest negative correlation are situated in the border regions of Henan, Hubei, Shaanxi, and Shanxi provinces (Figure 5b).

The regression coefficients for resident population size are significant and consistently positive. This indicates a positive correlation between resident population size and the number of forestry enterprises across different regions of China. China’s forestry industry system is developing well, with the proportions of primary, secondary, and tertiary industries in forestry being 32:45:23 in 2020, primarily dominated by the primary and secondary industries. These industries are characterized by extensive operations, being large in scale but not strong, with a relatively small proportion of the tertiary industry. They mainly rely on labor-intensive processes, leading to a significant demand for labor. Analyzing the spatial distribution of the regression coefficients reveals notable spatial heterogeneity, with significant differences in the degree of positive correlation across regions. High-value areas are primarily located in Guangxi, which is China’s largest production base for timber, wood-based panels, forest chemical products, and integrated forestry-paper industries. The demand for labor is higher in Guangxi due to its substantial forestry industry. The second-highest value areas include regions such as Fujian, Hainan, Heilongjiang, and Jilin, which are abundant in forestry resources. Fujian Province has ranked first in forest coverage rate in China for consecutive years, and the six major forestry groups in China are all located in the northeastern region (Figure 5c).

The regression coefficients for the number of patent authorizations show a positive effect in all regions of China. This indicates that regions with higher numbers of patent authorizations tend to have more forestry enterprises. However, the overall deviation of the coefficients is not significant, suggesting a relatively consistent level of influence. Analyzing the spatial distribution of the regression coefficients for patent authorizations, there is an overall trend of decreasing influence from north to south. This suggests that the relative impact of patent authorizations on the number of forestry enterprises in China gradually decreases from north to south. High-value areas are primarily concentrated in Guangdong, Guangxi, Yunnan, and Hainan. In contrast, low-value areas are primarily found in regions such as Xinjiang, Gansu, Inner Mongolia, Ningxia, and some parts of Heilongjiang. Patents are essential products of technological innovation. An increase in the number of patent authorizations reflects the innovation vitality and strength within the forestry sector. This facilitates technological and industrial upgrades in the forestry industry, driving it towards modernization, intelligence, and green development. This, in turn, enhances the overall industrial level and competitiveness of the forestry industry (Figure 5d).

The regression coefficients for green space area range from 0.019 to 1.119, indicating a positive correlation between green space area and the distribution of forestry enterprises across different regions of China. This implies that regions with a larger amount of green space tend to have more forestry enterprises. Green spaces primarily consist of vegetation and serve various purposes, such as ecological improvement, environmental protection, providing recreational areas for residents, and beautifying urban areas. Forestry enterprises play a crucial role in landscaping and greening efforts by providing professional expertise and experience in maintaining green spaces. They assist in planting, managing, and protecting vegetation in green spaces, ensuring the healthy upkeep and stable operation of green ecosystems. Analyzing the spatial distribution of regression coefficients for green space area, high-value regions are primarily concentrated in Hainan, Guangdong, Guangxi, Hunan, Jiangxi, and Fujian, while low-value regions are found in Anhui, Jiangsu, Zhejiang, Shanghai, Liaoning, Jilin, and Heilongjiang. In recent years, there has been an increased emphasis on ecological conservation and green space development across various regions. This has promoted the involvement and development of forestry enterprises in landscaping and green initiatives, leading to a positive correlation between green space area and the number of forestry enterprises (Figure 5e).

The regression coefficients for orchard area are relatively close and all positively correlated with the number of forestry enterprises. Orchards are primarily fruit-producing areas, and China is the world’s largest producer of fruit. The fruit industry is a significant sector within China’s forestry industry, with the country leading in terms of total fruit production, cultivation area, and variety of fruit resources. Therefore, regions with larger orchard areas tend to have more forestry enterprises. Analyzing the spatial distribution of regression coefficients for orchard area, most parts of Xinjiang fall into the high-value zone. Compared to other regions, Xinjiang’s fragile ecological environment may not be conducive to the development of timber production and other forestry sectors. However, owing to its unique climatic conditions and resource advantages, Xinjiang’s forestry and fruit industries have been thriving, with the fruit industry as a dominant sector. Therefore, forestry development in Xinjiang is primarily driven by the distinctive growth of the fruit industry. In other regions, the forestry industry comprises a more diverse range of sectors, and the orchard industry occupies a larger land area, which is relatively scattered. Additionally, the orchard industry is characterized by small-scale and individual enterprises. These factors contribute to a lower degree of correlation between orchard area and the number of forestry enterprises. While there is a positive correlation, the strength of this relationship is comparatively weaker than that of other factors (Figure 5f).

The impact of forest land area on the number of forestry enterprises indicates a positive correlation across all regions. Forest land area is a crucial indicator of forestry resource abundance. The unique nature of the forestry industry means both the primary and secondary sectors heavily rely on forest resources. Therefore, regions with larger forest land areas tend to have more forestry enterprises. Analyzing the variation in regression coefficients, regions with abundant forest resources such as Fujian, Zhejiang, Anhui, and Jiangxi show a stronger impact. The forestry industry in these regions differs significantly from other areas. Besides commercial forestry and fruit production, activities such as forest health care, agroforestry, ecotourism, and forestry engineering also play crucial roles. Although Jiangsu and Shanghai may not have abundant forest resources, their advantages in technological expertise, industrial structure, market demand, and innovation environment may lead to higher productivity per unit of forest land area. Conversely, the impact is weaker in regions with relatively scarce forest resources such as Xinjiang, Tibet, Qinghai, and Gansu (Figure 5g).

4. Discussion

In previous research, the analysis of spatial patterns and driving factors of enterprises has reached considerable maturity [34,35]. However, investigations into the evolution of spatial patterns within primary industries such as forestry remain relatively nascent, with limited depth and breadth of exploration and a correspondingly weak research foundation [36,37]. This paper, amalgamating methodologies from spatial pattern studies in other industries, embarks on an inaugural examination of the spatiotemporal dynamics of Chinese forestry enterprises. Employing techniques such as kernel density estimation, standard deviation ellipses, and hotspot analysis within GIS spatial analysis frameworks, we intricately outline the spatial distribution trends and autocorrelation of forestry enterprises at a fine scale. Taking into consideration the unique characteristics and diversities within the forestry industry, we identified 25 specific indicators across nine dimensions, including human resources, economic development levels, industrial structure dynamics, technological support, trade patterns, financial environments, natural conditions, urban infrastructure, and transportation accessibility, as influencing factors. Using exploratory regression analysis, we systematically compared all possible combinations of these factors and identified the optimal combination, comprising: (1) DHWT, (2) TIEV, (3) NPG, (4) PRP, (5) GRA, (6) OA, and (7) FA. Subsequently, we conducted a comparative assessment of the precision of OLS, GWR, and MGWR models. Additionally, we visualized the results of the MGWR model to explore the spatial heterogeneity issues concerning the factors affecting the spatial distribution of forestry enterprises in China, elucidating variations in the direction and magnitude of these factors’ impacts across different regions.

This study investigates the evolution of spatial agglomeration of forestry enterprises in China from the perspectives of global spatial autocorrelation, local spatial autocorrelation, and spatial clustering distribution. Drawing upon the analytical frameworks proposed by previous studies, we employed various GIS-based spatial analysis methods to address these issues [45,46]. The research findings confirm that forestry enterprises in China exhibit a high degree of spatial agglomeration in the southeastern coastal regions, including Guangdong, Guangxi, and Fujian, forming hotspots of high agglomeration. Conversely, the three northeastern provinces have entirely withdrawn from the high agglomeration hotspot zone. The findings of this study are largely consistent with earlier literature on the spatial agglomeration trends of forestry enterprises in China [31]. As forest resource over-exploitation and ecological degradation intensify in the Northeast, forestry enterprises face stricter regulations and mounting ecological restoration pressures. This has driven the relocation of new enterprises to areas with a better balance of ecological protection and economic development. The shift highlights the close link between ecological health and enterprise growth, as businesses adapt their strategies or locations to comply with stricter environmental standards.

For example, the spatial distribution of China’s forestry industry is not balanced, with key provinces such as Guangdong, Shandong, Fujian, Zhejiang and Guangxi consistently forming the core region [58]. High agglomeration zones of China’s forest products manufacturing industry have shifted away from provinces such as Heilongjiang, Jilin, Liaoning, Hebei, and Tianjin [32]. Additionally, scholars have explored the spatial agglomeration of China’s forestry industry, covering topics such as forestry-industry integration [31], forestry manufacturing [32], and the forestry cultural industry [59], providing valuable groundwork for this study. As anticipated, the spatial distribution pattern of forestry enterprises in China indicates a strong correlation between their spatial distribution and the distribution of forestry natural resources.

Over the past two decades, the spatial pattern of forestry enterprises in China has undergone significant regional shifts, coinciding with changes in the distribution of cold and hotspots of spatial agglomeration. The disappearance of the cold spot agglomeration in the northwest, along with notable reductions in the spatial extent of the hotspot agglomerations in the northeast and southwest, marks a large-scale reconfiguration of the spatial landscape of China’s forestry enterprises. Furthermore, the complete disappearance of high-intensity hotspot agglomerations and the substantial expansion of hotspot agglomerations in the southeastern coastal provinces are noteworthy trends. Previous research has provided limited insight into the underlying reasons for these changes in the spatial distribution of forestry enterprise agglomeration zones in China. Primarily, the arid and water-deficient conditions in the northwest region render its ecological environment fragile, with relatively limited forest and grassland resources. However, despite these ecological challenges, the northwest region boasts a long-standing history of deciduous fruit tree cultivation, making it a significant production base for such trees in China. The substantial economic potential inherent in the forestry and fruit industries has garnered significant attention from the government, fostering rapid growth in this sector. Furthermore, the Chinese government has implemented a series of stringent forest protection policies to comprehensively safeguard forest resources. Initiatives such as the Natural Forest Conservation Project launched in 1998 and the new ban on logging in natural forests implemented in 2015 have been pivotal in restricting the exploitation and logging of natural forests. These policies have resulted in a sharp decline in timber production in the northeastern region, as natural forests constitute one of the primary sources of timber [60]. The current policies have effectively curbed excessive deforestation, but they have also impacted the timber industry in certain regions. To achieve a balance between ecological protection and economic development, it is recommended that the government incorporate sustainable timber production and alternative resource utilization in future policy designs, tailoring policies to the specific characteristics of different regions. Additionally, it is suggested to strengthen economic compensation mechanisms at the local level to mitigate the negative impacts of reduced timber production on livelihoods and the economy.

As China’s economy rapidly advances and urbanization accelerates, the demand for timber and other forest products continues to grow. However, the exploitation of natural forest resources is constrained, leading to an imbalance between supply and demand. This discrepancy has compelled forestry enterprises in the northeastern region to either halt production or relocate to regions with richer resources. Forest resources are the foundation for the development of the forestry industry. In China, domestic timber production primarily relies on plantation forests. Due to the favorable climatic conditions in the southern regions, which facilitate high timber yields, these areas serve as the primary bases for fast-growing and high-yield forests. The abundant timber resources provide forestry enterprises with ample raw material supply, supporting their production activities. This scenario enhances production efficiency, reduces costs, and endows forestry enterprises in the southern regions with a competitive edge in the market. Moreover, as the world’s largest producer, trader, and consumer of forest products, China’s dependence on timber imports has exceeded 50%. The southeastern coastal regions benefit from geographical advantages, low transportation costs, and convenient export channels, providing forestry enterprises with a competitive edge in timber imports and wood product exports. This facilitates the importation of timber from overseas and enables international trade. Consequently, coastal areas have developed distinct advantages in forestry industry aggregation. With a high level of marketization and favorable commercial environments, the southern regions are more conducive to attracting industrial clusters, thereby promoting economic growth in the forestry sector and enhancing international competitiveness.

The regional disparities in the factors influencing the spatial distribution of forestry enterprises are markedly pronounced, exhibiting evident spatial heterogeneity, each with unique underlying causes. The predominantly negative regression coefficients for road density indicate a negative correlation between road density and the number of forestry enterprises in most regions. This contradicts the results observed in the spatial distribution patterns of other industries, where road density is positively correlated [61]. However, forestry is highly dependent on resources. Thus, higher road densities signify greater levels of urbanization, indicating lower levels of forestry resource abundance, which is unfavorable for the layout and development of primary forestry industries, resulting in fewer forestry enterprises. However, it is worth noting that Guangdong leads the nation in forestry industry output value, boasting a well-developed modern forestry industry system. Forestry enterprises in Guangzhou and its surrounding areas primarily focus on the timber product trade, with a heavy reliance on transportation networks to facilitate efficient operation of the industry chain and market integration. Consequently, an increase in road density favors the development and operation of forestry enterprises, stimulating the prosperity of the forestry industry. Therefore, road density exhibits a positive correlation with the number of forestry enterprises in most regions. Conversely, there is a negative correlation between the total volume of goods imports and exports and the number of forestry enterprises, indicating that China’s more economically developed regions are undergoing transformation and upgrading towards technology-intensive and capital-intensive industries, instead of relying on natural resource-based industries like traditional forestry.

The resident population is a key factor for industrial agglomeration advantages [62]. The forestry industry in China operates in an extensive manner, characterized by large-scale operations but lacking in robustness, and the demand for labor is substantial. The increase in the number of patents granted reflects the innovative vitality and strength of a region, facilitating the enhancement of forestry’s technological support capabilities and reinforcing technological reforms and innovations, and promoting the optimal spatial distribution. Forestry enterprises are not only key participants in green space construction but also play a crucial role in improving the ecological environment, enhancing the social functions of green spaces, and promoting their long-term sustainability. China, being the world’s largest fruit producer, considers horticulture a significant sector within its forestry industry. Particularly in regions like Xinjiang, characterized by distinctive horticultural features and limited forest resources, the impact of orchard area on the number of forestry enterprises is more pronounced. Similarly, the forest area factor shows a positive correlation with the distribution of forestry enterprises. Given the relatively small scale of China’s forestry enterprises overall, with the majority being small and medium-sized enterprises heavily reliant on traditional production factors, regions with larger forest areas tend to have more forestry enterprises. These factors collectively suggest that the distribution of forestry enterprises needs to strike a balance between labor costs, regional market capacity, transportation convenience, level of technological development, economic development, and resource advantages.

China’s rich and diverse forestry resources have significant implications for the spatial evolution characteristics and influencing factors of forestry enterprises. The empirical investigation into the spatiotemporal evolution patterns of Chinese forestry enterprises and the analysis of influencing factors provide valuable insights for understanding trends in forestry enterprise development and optimizing spatial layout. Furthermore, they offer crucial references for forestry industry development and policy-making. The following policy recommendations are proposed. Firstly, it is necessary to promote the optimization of resource allocation efficiency in China’s forestry industry and advance the development of economies of scale. The spatial distribution of hotspots for Chinese forestry enterprises indicates that the aggregation of the forestry industry not only effectively drives local forestry economic growth but also positively influences the development of forestry industries in neighboring provinces through economic spillover effects. Therefore, continuous efforts should be made to optimize the industrial structure of the forestry sector, transition towards scale and intensification, establish forestry industry demonstration zones with diversified industries, expand the scale of forestry industry aggregation, and promote the coordinated development of forestry industries in both local and neighboring regions. Furthermore, the forestry industry system encompasses a wide range, long industrial chains, and diverse product categories, with different regions positioned at different points along the forestry industry chain. The Southwest and Southeast regions boast abundant forestry resources and should strengthen the solid foundation of the primary and secondary industries, enhance forestry investment and management, and elevate the technological level of forestry products. The Northeast region should focus on resource integration and vigorously develop the scale advantage of the understory economy. The Northwest region should continue to optimize the structural layout of the fruit industry, form fruit industry clusters, and promote the high-quality development of the fruit sector. Secondly, efforts should be made to promote industrial structural adjustments, leverage the advantages of characteristic industries, facilitate the deep integration of industries, and expand the effective supply of high-quality products. To refine the primary industry, in regions like the Northwest, where the fruit industry is well-developed, it is essential to make effective use of forestry and human resources. Focusing on characteristic fruit industries such as apples, walnuts, and kiwifruits, efforts should be made to promote product refinement and deep processing. To strengthen the secondary industry, in areas like the Southeast, priority should be given to the development of wood and bamboo processing industries, wood and bamboo furniture industries, and the forest chemical industry. This will drive the clustering and high-quality development of advanced manufacturing, establish modern forestry industry platforms, promote the aggregation of forestry industry advantages, strengthen industrial clustering development, and promote the integration of industrial chains, balancing resources and market advantages. To expand the tertiary industry, we should fully tap into the carbon sink, tourism, health, and cultural values within China’s forestry industry, fostering new formats and industries, leveraging capital advantages, and creating a series of distinctive service brands. In conclusion, it is necessary to enhance government guidance and increase investment in technology. The forestry industry has made significant contributions to socioeconomic development, regional poverty alleviation, and the pursuit of environmental aesthetics in China. It is advisable for the government to introduce forestry industry policies aimed at supporting structural adjustments and fostering development. Additionally, the government can incentivize greater capital investment in the forestry industry and bolster endeavors such as the cultivation of superior species, the development of new forestry materials, the exploration of biomass energy in forestry, and the manufacturing of intelligent forestry equipment through research and development initiatives. By breaking ground in pivotal areas with promising prospects for industrialization, the government can elevate the technological standards of the industry, enhance innovation capabilities, and steer industrial development towards a direction characterized by higher quality and sustainability. Furthermore, the development of the forestry sector is intrinsically linked to the availability of forest resources. Therefore, in addition to optimizing resource allocation, promoting economies of scale, and fostering diversified development, greater emphasis must be placed on the protection of forest resources. It is essential to firmly enforce the ban on logging in natural forests to ensure the long-term sustainability of forest ecosystems.

However, this study’s limitations include insufficient exploration of the attribute information value of forestry enterprises. For instance, this study did not categorize enterprises as state-owned or private for analysis, which could have provided a more nuanced understanding. Nonetheless, these limitations do not undermine the generality of the conclusions, as they still offer valuable insights for future research in similar domains and can inform local governments in China in formulating forestry industry development policies. Moving forward, subsequent studies should consider categorizing and analyzing forestry enterprises based on their types and scales, thereby yielding more detailed results in the analysis of influencing factors. This study overlooks critical factors such as regulatory frameworks, forest law enforcement, and land ownership and usage rights, which significantly influence the spatial distribution and evolution of forestry enterprises. The strength of regulatory frameworks and law enforcement affects the operational environment, shaping geographic distribution and clustering patterns. Forest law enforcement is essential for sustainable development and may alter regional spatial patterns. Clarity in land ownership and usage rights is crucial for the development potential of forestry enterprises, with land usage rights issues potentially hindering expansion in certain areas. Future research should incorporate these factors to examine how regulatory environments, law enforcement, and land tenure influence forestry enterprises’ spatial distribution and evolution. The MGWR model has limitations in handling qualitative data. Future research could integrate machine learning techniques, such as decision trees and random forests, to analyze mixed qualitative and quantitative data and uncover their nonlinear effects on spatial distribution. This approach would enhance spatial analysis and provide actionable insights for policymakers, improving policy effectiveness. Additionally, future studies should consider environmental factors such as contributions to ecological protection, carbon sequestration, and resource efficiency to better understand forestry enterprises’ role in green development. Exploring the optimization of the balance between ecological protection and economic development is also crucial for sustainable spatial planning.

5. Conclusions

The present study focuses on Chinese forestry enterprises, analyzing their spatial patterns and exploring the factors influencing their spatial distribution. The primary findings are as follows.

From the perspective of the overall spatial pattern of forestry enterprises, forestry enterprises exhibit uneven spatial distribution across regions. The range of high-density clusters of forestry enterprises has narrowed, and the number of clustering centers has significantly decreased. Overall, from 2000 to 2020, the evolution of forestry spatial patterns has been rapid, with a significant reduction in clustering centers and intensified spatial clustering in the southeastern region, indicating that the intensity of spatial clustering of forestry enterprises is continuously increasing.

From the perspective of local correlation of forestry enterprises, the distribution range of hot and cold spots remained consistent from 2000 to 2010. The hotspots of forestry enterprises were located in three major clustering areas, the southwest, southeast, and northeast, while the cold spot was in the northwest, indicating significant spatial clustering of forestry enterprises. From 2010 to 2020, significant changes occurred in the distribution of hot and cold spots of forestry enterprises. The hotspot clustering area in the northeast essentially disappeared, while the hotspot clustering area in the southwest was significantly weakened, and the hotspot clustering area in the southeast showed a strengthening trend.

The results of the OLS, GWR, and MGWR analyses were compared, indicating that the MGWR model, with an R² of 0.823, adjusted R² of 0.787, and AICc of 561.178, performed better, providing a more comprehensive explanation of the effects of variables on the distribution of forestry enterprises. The evaluation of factors influencing the number of forestry enterprises in China revealed that five indicators—NPG, PRP, GRA, OA, and FA—had a positive impact on the distribution of forestry enterprises, while the TIEV had a negative impact. Additionally, the DHWT primarily exerted a negative influence on the distribution of forestry enterprises, though some areas showed a positive effect.

The contributions of this study are evident in several aspects. Firstly, it establishes a spatial database of forestry enterprises based on commercial survey data, addressing the issue of low precision in spatial analysis associated with panel data. This database, replete with extensive high-precision spatial data on forestry enterprises, provides essential data support for the analysis of the spatial evolution of forestry enterprise patterns at both fine and large spatial scales. Secondly, it enriches and supplements previous research on the spatial agglomeration of forestry enterprises. This study bridges the gap between macro-level research based on panel data and micro-level analysis relying on enterprise operational information in the forestry industry. Leveraging high-precision data of forestry enterprises and GIS spatial analysis methods, it delves into the spatial agglomeration evolution characteristics of forestry enterprises from both the national and regional levels. Furthermore, it conducts a time-series analysis to address the controversies arising from previous studies that substituted forestry sales data for indicators of forestry industry development. This study provides a more authoritative description of the clustering features of China’s forestry industry. Finally, by selecting 25 specific indicators across nine aspects to empirically investigate the factors influencing the distribution of forestry enterprises in various cities of China, this study employs exploratory regression analysis to identify the optimal variable combinations. It compares the accuracy of OLS, GWR, and MGWR models and uses the MGWR regression results to analyze the spatial heterogeneity of influencing factors, thereby offering insights into the differential impact levels of various factors. Thus, this study offers a new perspective on exploring the spatial clustering of forestry enterprises in China, contributing to a more comprehensive understanding of the distribution of forestry enterprises in different regions and the limiting factors influencing them.