Spatiotemporal Evolution and Coordinated Coupling of Non-Timber Forest-Based Economy and Ecological Carrying Capacity in Changbai Mountain

Abstract

Against the background of ecological civilization construction and the transformation of state-owned forest regions after the logging ban, balancing economic development with ecological protection has become an important issue in China’s forest areas. The development of the non-timber forest-based economy plays a critical role in advancing high-quality, green economic growth in China and contributes significantly to sustainable resource utilization. This study examines data from key state-owned forests and the natural environment in the Changbai Mountain region of Jilin Province from 2013 to 2023. A comprehensive evaluation model and a coupling coordination model, based on the human–land relationship framework, are employed to assess temporal changes in economic growth quality, ecological environment carrying capacity, and their coupling coordination. The quality of non-timber forest-based economic growth exhibited an overall upward trend. Fusong County, Wangqing County, and Dunhua City consistently maintained high levels, while Helong City experienced the largest decline. The spatial distribution followed a “high center, low periphery” pattern, with the 2015 logging ban serving as a key turning point in promoting ecological transformation. The per capita ecological environment carrying capacity improved across the region, with significant increases in Dunhua, Helong, and Antu Counties. A radial decline from the central to peripheral areas was observed, with the highest values in Wangqing and Antu Counties. The coupling coordination degree between economic growth and ecological environment fluctuated between 0.4 and 0.6. In 2023, Wangqing County reached a state of intermediate coordination (index > 0.7), whereas Linjiang remained in a dysfunctional state (index < 0.5). Spatial clustering of coordination weakened over time, as indicated by Moran’s I values of 0.32, 0.21, and 0.09 in 2013, 2018, and 2023, respectively. These findings provide a quantitative foundation for promoting the coordinated development of human–land systems and guiding high-quality regional growth in forest-based economic zones.

1. Introduction

The coupling of human and Earth systems represents a frontier in Earth system science and serves as a critical theoretical foundation for sustainable regional development. The report of the 20th Party Congress elaborated on the five Chinese characteristics of Chinese-style modernization, one of which emphasizes “modernisation in which people and nature live in harmony [1].” This paradigm shift underscores the growing importance of economic growth quality, which must prioritize ecological sustainability alongside economic advancement. Consequently, the focus of development in contemporary China should transition from the quantity to the quality of economic growth, a concept closely tied to the ecological environment’s carrying capacity [2].

The relationship between economic growth quality and ecological sustainability is critical, as economic activities must now align with environmental considerations to support long-term development. Economic growth quality refers to the rational and coordinated allocation of economic investment, industrial structure, and infrastructure planning necessary for sustainable development. It also encompasses improvements in national living standards, the integration of ecological and environmental protection, and the explicit promotion of human well-being through healthy livelihoods, nutritious food, and other essential aspects of quality of life, particularly in the context of a sustained upward trend in overall economic volume over time [3,4,5]. Research on economic growth is essential for promoting the coordinated development of regional human–land systems, with growing attention paid to the spatiotemporal evolution of regional economic growth quality and its driving mechanisms [6,7,8]. Studies in this field emphasize both the conceptual dimensions and evolutionary mechanisms of growth quality, integrating spatiotemporal patterns with broader socio-economic contexts.

However, a notable gap remains in research examining the contribution of the non-timber forest-based economy to economic growth quality, particularly in its role in promoting ecologically sustainable development. According to the Terminology of the Forest Economy (T/CSF 001-2018) [9], the non-timber forest-based economy refers to a composite, eco-friendly economic model based on forests, woodlands, and their integrated ecosystems, developed in strict accordance with the principles of sustainable management. Grounded in the disciplines of ecology, economics, and systems engineering, this economy leverages forest ecological environments and resources to support activities such as forest plantations and breeding, product processing, and tourism [10,11,12]. These activities—including forest management, sustainable breeding, and the responsible use of forest products—not only align with ecological principles but also contribute to regional economic growth while maintaining environmental integrity. By relying on forests and their ecosystems, the non-timber forest-based economy offers a promising approach to reconciling economic development with ecological conservation, fostering a sustainable path forward amid growing environmental constraints [13,14].

In light of China’s new development phase—commonly referred to as the “new normal”—enhancing the quality of growth within the non-timber forest-based economy is essential for achieving high-quality economic development. The National Development and Reform Commission, together with other relevant departments, issued the Opinions on Scientific Utilization of Forest Land Resources to Promote High-Quality Development of Wood-based Food and Oil and Non-timber Forest-Based Economy, which underscores the need to balance economic growth with ecological conservation. Existing research has examined the economic and ecological benefits of the non-timber forest-based economy as a catalyst for the sustainable development of forested regions in China. For example, Li et al. [15], Qiao et al. [16], and Wang et al. [17], respectively, investigated the business models, development cases, and economic inequality effects of integrating the three major sectors of the non-timber forest-based economy, while Yang [18] identified policy, capital, and education as the most significant factors influencing its contribution. Most scholarly efforts have focused on development pathways and the economic environment, with some studies analyzing spatial and temporal distribution patterns and the driving factors of growth quality from a geographical perspective. However, there remains a lack of research that addresses these spatial–temporal patterns and influencing mechanisms from an ecological perspective.

Since the 20th century, rapid population growth, over-exploitation of natural resources, and serious environmental pollution have severely restricted regional socio-economic development. Such development is fundamentally dependent on the support of natural systems, and ecological carrying capacity—an essential component of sustainable development—serves as a key indicator of the sustainability of regional economies. Ecological carrying capacity refers to the quantifiable extent of resources that the Earth can provide for human use, specifically the total biologically productive land and water areas available to support human activities. The Report of the 18th National Congress of the Communist Party of China emphasized the importance of establishing a monitoring and early warning mechanism for resource and environmental carrying capacity and implementing restrictive measures in areas with overloaded water, soil, and environmental conditions [19]. This initiative is critical for promoting ecological civilization and advancing regional coordinated development, mutual support, and cooperation. It involves evaluating the background conditions and evolutionary patterns of regional resources and environments, assessing carrying capacity, and scientifically formulating strategies to enhance it. A substantial body of research has examined ecological carrying capacity in terms of evaluation methods, spatiotemporal evolution, and driving mechanisms. Common approaches include net primary productivity, ecological footprint analysis, ecosystem service-based evaluations, state space methods, energy–ecological footprint models, and system dynamics models. As the field has evolved, ecological carrying capacity has been increasingly integrated into related disciplines such as land systems [20], watershed systems [21], ecologically fragile zones [22], tourism regions [23], and urban systems [24]. However, relatively little research has been conducted on the spatiotemporal dynamics of ecological carrying capacity in forest resource-rich areas, representing a significant gap in the literature.

The exploration of the coupling relationship between economic development and the ecological environment is a critical area in the study of human–land interactions. Researchers have conducted extensive theoretical and empirical research in this field [25,26]. Within the current research framework, most studies have focused on assessing the interactive coupling between economic development and ecological environment using system theory-based paradigms, such as the coupled coordination degree model and the gray correlation model [27,28]. Although academic understanding of the general coupling relationship between economic development and the ecological environment has become increasingly mature, a notable gap remains in the investigation of how the quality of non-timber forest-based economic development interacts with ecological sustainability, particularly in forest resource-rich areas. This research gap is especially significant because the relationship between non-timber forest-based economic growth quality and ecological sustainability is both complex and multi-dimensional, posing challenges for conventional research paradigms that tend to emphasize broader economic–environmental interactions.

The primary significance of this research lies in addressing the need for a more nuanced understanding of how non-timber forest-based economic development can be aligned with ecological capacity and sustainability. The relationship between economic development and the ecological environment is inherently complex and requires a multi-dimensional approach beyond conventional single-paradigm models. In the context of territorial spatial planning, it is particularly important to understand how spatial linkages between economic development and ecological capacity can be effectively managed in regions with distinct ecological characteristics. This study seeks to provide practical guidance for spatial planning that integrates both economic and ecological considerations, particularly in forest resource-rich areas where ecological sustainability is critical.

To address the identified research gaps and advance the understanding of sustainable forest economic development, this study sets out to achieve the following core objectives: (1) to construct a comprehensive, multi-dimensional evaluation index system that accurately and effectively reflects the full connotation of non-timber forest-based economic growth quality, tailored to the characteristics of forest ecosystem-dependent economic models; (2) to systematically decode the dynamic coupling and interactive mechanism between non-timber forest-based economic growth quality and ecological carrying capacity, filling the gap in targeted research on this relationship in forest-rich regions; (3) to identify the spatiotemporal evolution patterns of both non-timber forest-based economic growth quality and regional ecological carrying capacity, as well as the evolutionary characteristics of their coupled coordination state; and (4) to provide a theoretical basis and practical guidance for the coordinated development of human–land systems in forest zones, supporting the formulation of spatial planning and development strategies that harmonize non-timber forest-based economic growth with ecological sustainability.

2. Evaluation Indicator System and Data Sources

2.1. Study Area

Jilin Province is located in northeastern China, bordering Russia’s Primorsky Krai and North Korea to the southeast and adjacent to Heilongjiang, Inner Mongolia, and Liaoning to the north, west, and south, respectively. The province spans from 121°38′ to 131°17′ E and 40°52′ to 46°18′ N. Situated on the eastern side of the Eurasian continent at mid-latitudes, Jilin experiences a temperate continental monsoon climate and is characterized by temperate vegetation composed primarily of mixed coniferous and broad-leaved forests [29]. The terrain generally slopes from southeast to northwest, with low mountains of the Changbai Mountains dominating the east and meadows, lakes, and wetlands scattered across the central and western plains. Due to variations in historical development, topography, and climate, forest resources in Jilin Province are predominantly concentrated in the eastern Changbai Mountain region, where cold temperatures and abundant precipitation create ideal conditions for forest growth [30]. This region represents a typical mid-temperate forest zone, characterized by a high diversity of forest species, significant economic value, and strong forest growth and accumulation [31]. Within the study area, a large proportion of the forests are natural, making the Changbai Mountain region one of the most important forest areas in China [32].

The total area of economic woodland and underwood economy in Jilin Province is approximately 400,000 ha, accounting for 4.2% of the total woodland area [33]. The Changbai Mountain region of Jilin Province has a total area of 2019.97 km², and the main land cover types are woodland, grassland, cropland, wetland, artificial surfaces, and others, respectively [34]. Additionally, the region also has abundant resources, including forest landscapes and tourism resources. The amount of bird protection benefits accounted for 2.99 billion yuan, soil and water conservation 2.972 billion yuan, water conservation 1.668 billion yuan, atmospheric purification 484 million yuan, and farmland protection 129 million yuan [35]. The non-timber forest-based economy in the Changbai Mountain region has unique development characteristics, primarily driven by abundant forest resources and ecological advantages. The region’s diverse forest types provide numerous non-timber forest products, such as mushrooms, ginseng, and pine nuts, which have high market value. Additionally, the Changbai Mountain area boasts rich ecotourism resources, with activities such as forest sightseeing and skiing closely linked to the non-timber forest-based economy, further driving regional economic growth [36,37]. In particular, medicinal plants like ginseng and gastrodia have become key economic pillars. The forests in Changbai Mountain not only provide resources for economic growth but also offer significant ecological services, such as water conservation and carbon storage, promoting a sustainable development model.

However, the scale and comprehensive benefits of the underwood economy industry have always been low. In 2025, the Jilin Forestry and Grassland Bureau formulated and issued the Implementation Plan for Deepening Collective Forest Tenure System Reform in Jilin Province to promote regional economic development and form a professional production model of industrial clusters; thus, the Jilin Province non-timber forest-based economy is in urgent need of quality economic growth to maximize the ecological benefits of the existing woodland resources.

In this study, 9 county-level administrative units in the eastern Changbai Mountain region in Jilin Province were studied, namely Linjiang City, Fusong County, Huadian City, Jiaohe City, Antu County, Hailong City, Dunhua City, Wangqing County, and Hunchun City (Figure 1).

2.2. Regional Economic Growth Quality Evaluation Indicator System

Based on the connotation of economic growth quality and the relevant literature [38,39,40], this study integrates the characteristics of non-timber forest-based economic development, while taking into account data accessibility and the hierarchical structure of the evaluation system, to construct an evaluation index system for the quality of non-timber forest-based economic growth. The system comprises 18 specific indicators grouped into five subsystems: stable economic growth, efficient economic growth, sustainable economic growth, social welfare upgrades, and ecological improvement (Figure 2). The connotation of each subsystem and the selection basis of indicators are as follows:

①

Stable economic growth measures whether the economic growth process in a region follows a steady growth pattern. There are four basic indicators of stable economic growth selected for this study: efficiency of economic returns, degree of industrial concentration, elasticity of economic growth, and economic growth guaranteed inputs. Efficiency of economic returns covers the operational effectiveness of the non-timber forest-based economy; degree of industrial concentration reflects the degree of concentration of non-timber forest-based economy; elasticity of economic growth reflects the ability of non-timber forest-based economy to resist pressure, and the greater the elasticity, the more stable the economic structure; and economic growth guaranteed inputs are used to reflect the impact of exogenous impetus on the quality of economic growth.

②

Efficient economic growth is used to measure the efficient output converted in the process of economic growth. Corresponding to the concept of non-timber forest-based economy, the indicators are constructed with the contribution rate of primary industry, secondary industry, and tertiary industry to reflect the impact of industrial growth on the quality of economic growth.

③

Sustainable economic growth is used to measure the vitality of the non-timber forest-based economy and is characterized by regional scientific and technological innovation capacity, economic growth efficiency, the degree of optimization of industrial structure, and industrial development input indicators. Scientific and technological innovation capacity acts as the core endogenous driver for sustainable economic growth, underpinning efficient forest resource utilization, industrial upgrading, and low-carbon green development of the non-timber forest-based economy. Economic growth efficiency is reflected by labor productivity, which reflects the impact of output efficiency on the quality of economic growth. The degree of industrial structure optimization reflects the degree of optimization of the structure of the three industries and the internal structure and mode of non-timber forest-based economy growth. Industrial development inputs refer to the investment made by the region for the development of the non-timber forest-based economy.

④

Social welfare upgrades encompass both individual welfare upgrades and public welfare level upgrades, and the basic indicators used in this study are: medical security benefits, material standard of living, and infrastructure input.

⑤

Ecological improvements are the indicators of ecological environmental protection and damage in the development of the non-timber forest-based economy. Forest resources are selected to reflect the impact of changes in forest stock on the quality of growth of non-timber forest-based economy. Selection of the area of planted forest reflects the degree of improvement of the forest environment. Investment in ecological construction and conservation in forest areas reflects the strength of ecological protection in the process of non-timber forest-based economy growth. Industrial exhaust emissions measure the ecological costs of economic growth.

The models and indicators used in this study are selected based on their relevance to the key aspects of non-timber forest-based economy and ecological environment dynamics, particularly in the context of Changbai Mountain. The coupling coordination model is chosen because it is well suited to assess the degree of coordination between economic and ecological systems, which is critical for understanding sustainable development [41,42,43]. The coupling coordination model’s applicability in the study area is supported by the fact that this model has been widely used in studies of sustainable development in regions with interdependent economic and ecological systems. It evaluates how these two systems are integrated and balanced, which is a central concern in this study. The specific indicators are selected based on their ability to comprehensively reflect the multifaceted nature of non-timber forest-based economy growth, covering economic, social, and ecological dimensions. The inclusion of these indicators ensures that the evaluation system captures all essential elements of the growth quality of the non-timber forest-based economy.

2.3. Data Sources

The period of 2013–2023 was selected as the research cycle for this study, considering the accessibility and representativeness of the data. Data for the study were mainly obtained from the China Forestry Statistical Yearbook (2014–2024), Jilin Provincial Statistical Yearbook (2014–2024), regional statistical yearbooks, statistical bulletins on national economic and social development, and regional government work reports. Relevant data from the Changbai Mountain Autonomous Management Committee were also referred to. Data relating to the population are based on the local resident population. Data relating to ecological carrying capacity calculations were obtained from the Jilin Provincial Statistical Yearbook, Jilin Provincial Social and Economic Survey Data, China Statistical Yearbook, China Agricultural Yearbook, and Food and Agriculture Organization of the United Nations (FAO) statistical database “http://www.fao.org/waicent/portal/statistics.zh.asp (20 November 2025)”. Land use data were obtained from the Resource and Environment Data Centre of the Chinese Academy of Sciences “http://www.resdc.cn (20 November 2025)”, with a data accuracy of 100 m. Landsat TM/ETM remote sensing images were used as the main data source [44]. This study focuses on the analysis of changes in six primary land categories: arable, forest, grass, construction, water, and unused.

The 30 m resolution DEM data were sourced from the Shuttle Radar Topography Mission (SRTM), a joint project managed by NASA and the US Geological Survey (USGS). These elevation datasets, available through the NASA Earthdata portal “https://dwtkns.com/srtm30m/ (20 November 2025)”, were downloaded in .hgt format via the platform’s grid-based interactive interface. The raw data were decompressed and spatially subsetted using the “Extract by Mask” tool in ArcGIS 10.8 to match the boundaries of the study area. The 2023 LULC dataset was derived from the Chinese National Land Use/Cover Database (CNLUCC v2.0), which is managed by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences “https://www.resdc.cn (20 November 2025)”. This national-scale inventory, created through visual interpretation of Landsat TM/OLI imagery, follows a hierarchical classification scheme comprising 6 primary categories (cultivated land, forest, grassland, waterbody, built-up land, and unused land) and 25 subcategories. The 2023 dataset (30 m spatial resolution, GeoTIFF format) was reclassified into primary categories using the “Reclassify” function in ArcGIS.

3. Methodology

This study examines the spatiotemporal evolution and coordinated coupling of the non-timber forest-based economy and ecological carrying capacity in Jilin Province, with particular attention to the 2015 ecological civilization protection policy banning commercial logging in key state-owned forest areas. The years 2013, 2018, and 2023 were selected as representative time points: 2013 as the pre-policy baseline, 2018 to capture early transformation effects, and 2023 to evaluate long-term development, industrial maturity, and spatial stabilization. To reveal spatial differentiation, the Jenks natural breaks method was used to classify core evaluation indices into low, medium, and high levels, which were visualized with graded color schemes at the county level.

3.1. Entropy Combination Weighting Method

The analytic hierarchy process (AHP) is a systematic method of analysis that combines qualitative and quantitative analysis, proposed by Saaty (1990) [45]. This study constructed an AHP judgment matrix through expert consultation. Five experts from the fields of ecological environment and urban planning were invited to compare the relative importance of evaluation indicators using Saaty’s 1–9 scale, and pairwise comparison matrices were established accordingly. The consistency ratio (CR) of each judgment matrix was calculated to verify its reliability. All CR values were less than 0.10 and ranged from 0.021 to 0.087, indicating that the judgment matrices met the consistency requirements. The entropy method is an objective weighting method that calculates the information entropy of indicators and determines their weight according to their relative changes in the system; indicators with large relative changes have larger weights [46]. The hierarchical analysis method is relatively subjective, while the entropy method is objective. The entropy combination weighting method uses the principle of minimum relative information entropy, combining the hierarchical analysis method with the entropy method, which can better reduce the influence of subjectivity and objectivity. The evaluation index system of each participant factor is not uniform, owing to the scale between coefficients; therefore, the judgment matrix must be standardized in the evaluation study to eliminate the influence of different units and measurements on the indicators.

The normalization method for positive impact indicators is as follows:

$$R_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{X_{ij}-{\mathrm{m}\mathrm{i}\mathrm{n}}_j\left(X_{ij}\right)}{{\mathrm{m}\mathrm{a}\mathrm{x}}_j\left(X_{ij}\right)-{\mathrm{m}\mathrm{i}\mathrm{n}}_j\left(X_{ij}\right)}}$$

(1)

For the inverse impact indicators, we used Equation (2):

$$R_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{m}\mathrm{a}\mathrm{x}}_j\left(X_{ij}\right)-X_{ij}}{{\mathrm{m}\mathrm{a}\mathrm{x}}_j\left(X_{ij}\right)-{\mathrm{m}\mathrm{i}\mathrm{n}}_j\left(X_{ij}\right)}}$$

(2)

where $R_{\mathrm{i}\mathrm{j}}$ is the standardized index value, $X_{ij}$ is the measured value of the j-th evaluation factor, and ${\mathrm{m}\mathrm{a}\mathrm{x}}_j\left(X_{ij}\right)$ denotes the maximum value of the j-th indicator of the j-th sample. ${\mathrm{m}\mathrm{i}\mathrm{n}}_j\left(X_{ij}\right)$ denotes the minimum value of the i-th indicator of the j-th sample.

The entropy combination weighting method is calculated as:

$$W_i={\left(W_{1i}\times W_{2i}\right)}^{{\displaystyle \frac{1}{2}}}/\sum {\left(W_{1i}\times W_{2i}\right)}^{{\displaystyle \frac{1}{2}}}$$

(3)

where $W_i$ is the combined weight of indicator i, $W_{1i}$ is the subjective weight of indicator i, and $W_{2i}$ is the objective weight of indicator i.

3.2. Evaluation Index Integration and Comprehensive Evaluation Model

In this study, the entropy value method was used to determine the evaluation object S. First, the entropy value was calculated based on the linear transformation of the original data after the standardization of the deviation to objectively assign weights, and the comprehensive evaluation model was constructed by combining the weighted summation method to derive the comprehensive score of the evaluation object in each region. The formula is as follows:

$$S={\displaystyle \sum _{i=1}^m}{w}_j\times x_{ij}^{\prime }$$

(4)

where $S$ is the comprehensive evaluation index, $w_j$ is the weight of the j-th index, and $x_{ij}^{\prime }$ is the index value after standardization. The measurements of the economic growth quality and resource and environment carrying capacity indices in this study are based on a comprehensive weighted evaluation model.

The final evaluation index system for the quality of non-timber forest-based economic growth, along with the combined weight of each indicator calculated by the entropy combination weighting method in Section 3.1, is shown in

Table 1. Evaluation index system and weight of non-timber forest-based economy growth quality.

Level 1 Indicators	Level 2 Indicators	Level 3 Indicators	Data Measurement	Weight
Quality of growth in the non-timber forest-based economy	Stable economic growth	Efficiency of economic returns	Ratio of total non-timber forest-based economy output to total non-timber forest-based economy investment (+)	0.0539
		Degree of industrial concentration	Entropy in the location of the non-timber forest-based economy industry (+)	0.0900
		Elasticity of economic growth	Ratio of incremental gross output value of the non-timber forest-based economy to incremental regional GDP (+)	0.1341
		Economic growth guaranteed inputs	Non-timber forest-based economy supports and safeguards investment (+)	0.0328
	Efficient economic growth	Contribution of primary industry	Ratio of incremental forest-related industries to incremental total forestry output in primary production (+)	0.0136
		Contribution of secondary industry	Ratio of incremental wood processing industry to incremental total forestry output (+)	0.0193
		Contribution of tertiary industry	Ratio of incremental tertiary sector to incremental total forestry output (+)	0.0511
	Sustainable economic growth	Science and technology innovation capacity	Number of patents granted for the year (+)	0.0184
		Economic growth efficiency	Labor productivity in the non-timber forest-based economy industry (+)	0.0531
		Degree of industrial structure optimization	Ratio of the output value of the three forest products to the total output value of the non-timber forest-based economy (+)	0.0051
		Industrial development inputs	Investment in forestry industry development (+)	0.0147
	Social welfare upgrades	Medical security benefits	Number of basic health insurance participants in the forest area at the end of the year (+)	0.0883
		Material standard of living	Per capita income in forest areas (+)	0.1025
		Infrastructure input	Investment in forestry livelihood projects (+)	0.0224
	Ecological improvements	Forest resources	Forest area (+)	0.0998
		Forest environment improvement	Area of planted forest (+)	0.0463
		Investment in ecological protection	Investment in ecological construction and conservation in forest areas (+)	0.1336
		Natural environment quality	Industrial exhaust emissions (−)	0.0210

Note: Indicators followed by “+” are positive indicators and “−” are negative indicators; raw data have been standardized.

.

3.3. Measurement of Ecological Carrying Capacity

The ecological carrying capacity refers to the area of real biologically productive space in a region, reflecting the size of the supply capacity of the ecosystem for human activities. In this study, the ecological carrying capacity of the study area to represent the carrying capacity of the “land” in the human–Earth system [47,48]. The formula for calculating the ecological carrying capacity is as follows.

$$EC=\left(1-12\%\right)\times N\times ec=\left(1-12\%\right)\times N\times {\displaystyle \sum _{t=1}^6}{r}_j\times r_k\times y_k$$

(5)

where $EC$ is the ecological carrying capacity; “$ec$” is the ecological carrying capacity per capita; N is the number of populations; $r_j$ is the area of consumption subjects; $r_k$ is the equilibrium factor of various land use types; and $y_k$ is the yield factor. It should be noted that the calculation results needed to be deducted by 12% to account for the biodiversity conservation land. Biologically productive land on the Earth’s surface is classified into six major types: arable, forest, grass, construction, fossil fuel-containing, and water, according to the ecological footprint theory.

In the ecological carrying capacity calculation model, the scientifically accurate yield and equilibrium factors are key parameters. The equilibrium factor is a coefficient by which different types of ecologically productive land are transformed into equivalents of ecological productivity, reflecting the differences in the average ecological productivity of different land types. The equilibrium factor changes accordingly with different years, land use, and technology, but these changes are small and do not significantly impact the calculation results. Therefore, globally consistent equilibrium factors were used in this study; these factors were 1.1 for forest and fossil fuel-containing land, 2.8 for arable and construction land, 0.5 for grassland, and 0.2 for watersheds. Because the productivity of similar ecologically productive land varies among countries and regions, the actual area of similar ecologically productive land in each country and region cannot be directly compared. The yield factor is a parameter that converts similar ecologically productive land in each country and region into a comparable area and is the ratio of the average productivity of a certain land type in a country or region to the average productivity of the same land type in any other area worldwide, so that the calculation results of different regions are comparable. The yield factors currently used in China are 1.66 for arable and construction land, 0.91 for forest land, 0.19 for grassland, 1.00 for waterbodies, and 0 for fossil fuel-containing land.

3.4. Coupling Coordination Degree Model

The coupled coordination model can be used to describe the correlation between the interaction and mutual influence of the elements in a system or systems [49,50]. Since 2000, the coupled coordination degree model has been widely used in the fields of economic development, urbanization, ecological environment, and tourism development [51,52]. This study used the coupling coordination degree model to assess the coupling degree between ecological and environmental carrying capacity and the quality of economic growth in the designated region. The formula for calculation is as follows:

$$C=2\times {\left[{\displaystyle \frac{E_i\times D_i}{{\left(E_i\times D_i\right)}^2}}\right]}^{{\displaystyle \frac{1}{2}}}$$

(6)

where $C$ is the coupling degree, which measures the intensity of interactive and mutual constraining effects between the economic growth and ecological carrying capacity systems; $E_i$ is the comprehensive ecological carrying capacity index of the study area, reflecting the ecosystem’s supply and support capacity for human economic activities in the study area; and $D_i$ is the economic development quality index.

The coupled coordination degree D of the carrying capacity and quality of development in the study area was calculated as follows:

$$D={\left(C\times T\right)}^{{\displaystyle \frac{1}{2}}};T={\alpha E}_i+{\beta D}_i$$

(7)

where $T$ is the comprehensive evaluation index; and $\alpha ,\beta$ are the parameters to be determined (equal to 1/2). According to the previous relevant research results and the actual situation of the study area, the magnitude of the coupling and coordination values can be divided into different types (

Table 2. Level of coupling coordination degree [53,54].

D-Value	Grade	Coupling Coordination Degree	D-Value	Grade	Coupling Coordination Degree
[0–0.1)	1	Extreme disorder	[0.5–0.6)	6	Slightly coordinated
[0.1–0.2)	2	Severe disorder	[0.6–0.7)	7	Primary coordination
[0.2–0.3)	3	Moderate disorder	[0.7–0.8)	8	Intermediate coordination
[0.3–0.4)	4	Mild disorder	[0.8–0.9)	9	Good coordination
[0.4–0.5)	5	Nearly dysfunctional	[0.9–1]	10	Quality coordination

).

4. Results

4.1. Characteristics of Spatial and Temporal Variation of Economic Growth Quality

During 2013–2023, the growth quality index of the non-timber forest-based economy in the Changbai Mountain region of Jilin Province showed a fluctuating upward trend (Figure 3). Fusong County and Dunhua City maintained consistently high levels of economic growth quality throughout the study period. Helong City and Wangqing County exhibited a significant upward trend in the growth quality index, while Linjiang City showed an upward trend from 2013 to 2018 and then remained stable with no further significant increase, with an overall low level of growth quality during the study period. Antu County and Hunchun City maintained a medium level of growth quality with slight fluctuations in individual years. Helong City was the region with the most significant decline in growth quality index during the study period. Overall, the regional average increased from 2013 to 2023, while the trajectories of individual counties were not completely consistent.

The spatial pattern of the growth quality of the non-timber forest-based economy in the Changbai Mountain region underwent significant changes during 2013–2023 (Figure 4). In 2013, the spatial distribution of growth quality was relatively scattered, without any obvious agglomeration effect. Jiaohe City in the western region was a high-quality growth area due to its early development of forest-related industries, while medium- and low-quality areas were scattered in a dotted pattern across the central and eastern regions. At this stage, the overall growth quality level was relatively low, and regional development was uncoordinated. Most areas still relied on traditional wood production, and the ecological economy had not yet taken shape. By 2018, there was a dramatic shift in the spatial pattern. High-quality growth areas moved from the western region (Jiaohe City) to the central region (Fusong County), forming a core–periphery structure. Medium-quality growth areas included Wangqing County and Dunhua City in the north, as well as Linjiang City in the south. This formed a circular spatial distribution where growth quality gradually decreased from the central core to the surrounding areas. By 2023, the spatial agglomeration of the growth quality of the non-timber forest-based economy had become increasingly prominent, with an overall improvement in the regional development level. High-quality growth areas expanded significantly, concentrating in the central region and forming a contiguous agglomeration that included Wangqing County, Dunhua City, Fusong County, and Helong City. Medium-quality growth areas are distributed around the high-quality core, forming a transitional zone that facilitates the diffusion of development dividends. Low-quality growth areas are mainly concentrated in the fringe regions, such as Huadian City and Hunchun City, where geographical constraints and insufficient industrial support hinder their development. It is worth noting that the spatial distribution pattern has evolved into a radial structure that weakens from the central contiguous high-quality area to the surrounding areas. High-quality areas are continuously distributed in the center, medium-quality areas are adjacent to them, and low-quality areas are scattered in the fringes. In summary, the temporal variation of growth quality is characterized by an overall upward trend with regional differences, while the spatial pattern has transformed from scattered distribution to centralized agglomeration. This reflects the profound impact of policy guidance and resource endowment on regional economic development.

4.2. Spatial and Temporal Variation Characteristics of Ecological Carrying Capacity

The per capita ecological carrying capacity of the study area shows a fluctuating upward trend from 2013 to 2023, with the most obvious upward trend in Helong City, Antu County, and Wangqing County (Figure 5). This indicates that the ecological carrying capacity of the study area has improved in the past ten years; thus, the measures implemented for ecological environmental protection and restoration in the study area have begun to yield positive results. The promotion and implementation of relevant policies have protected forest, grass, and arable land and limited the expansion rate of construction land; therefore, the overall ecological carrying capacity of the study area is maintained at a relatively stable level without significant changes, which, in turn, improves the ecosystem balance of the study area.

From the perspective of spatial distribution, the average ecological carrying capacity among the nine administrative units during 2013–2023 shows little difference, generally presenting a spatial pattern where the central region boasts high growth quality, gradually decreasing towards the eastern and western fringes (Figure 6). Wangqing County, Antu County, and Helong City, located in the central part of the study area, consistently achieve significantly higher growth quality in ecological carrying capacity compared to the eastern and western administrative units.

Throughout the 2013–2023 period, the ecological carrying capacity of the Changbai Mountain region in Jilin Province maintains a spatial distribution pattern of radial weakening from the center to the surrounding areas (Figure 6). High-ecological-carrying-capacity (ECC) areas are concentrated in the central region, showing a continuous distribution; medium-ECC areas are adjacent to the high-value areas, forming a transitional zone; and the only low-ECC area, mainly distributed in the southwestern part of the study area, corresponds to the river source region with fragile ecological characteristics. In 2013, the ECC of the study area decreased from the central area to both the east and west sides: Wangqing County and Antu County in the center were identified as high-ECC areas, while medium- and low-ECC areas were concentrated in the eastern and western edges, presenting obvious spatial clustering characteristics. By 2018, the scope of high-ECC areas in the central region had expanded. The number of high-ECC units increased from two counties in 2013 to four administrative units, namely Wangqing County, Antu County, Dunhua City, and Helong City, further consolidating the circular spatial structure of ECC decreasing from the center to the periphery. By 2023, high-ECC areas remain concentrated in the central region, including Wangqing County, Antu County, and Helong City, while the other six administrative units, all located in the eastern and western fringes, fall into the medium- or low-ECC categories, showing distinct spatial clustering. Overall, the ecological carrying capacity of the study area exhibits a slightly increasing trend during the 2013–2023 period.

4.3. Spatial and Temporal Variation Characteristics of Coupled Coordination Analysis of Economic Growth Quality Resource and Environmental Carrying Capacity

Ecological carrying capacity focuses on the change of “land” in the regional human–Earth system, while economic growth indicates the intensity of human activities in the human–Earth system. To comprehensively and systematically describe the spatial and temporal change characteristics of the human–Earth system in the study area, the coupling coordination degree of ecological carrying capacity and economic growth of nine counties in the study area from 2013 to 2023 were measured. This value was approximately between 0.3 and 0.6 during 2013–2023 (Figure 7), which shows a transitional state between disorder and basic coordination, and most areas were >0.5 in 2023, which shows that a generalized coordination state was reached. Among them, Wangqing City had the highest increase in ecological carrying capacity–economic growth coupling coordination degree, whereas that of Linjiang City decreased the most significantly. The coupling coordination degree of Fusong County showed a fluctuating recovery trend after a short-term decline, while Linjiang City, Huadian City, Helong City, Fusong County, and Dunhua City exhibited varying degrees of fluctuating changes. The coupling coordination degrees of Wangqing County in 2013, 2018, and 2023 were all >0.6 (a coordinated state), maintaining a stable and high level of coordination; the coupling coordination degrees of Linjiang City were <0.5 (a disorder state) in most years of the study period; the coupling coordination degrees of Hunchun City, Huadian City, Fusong County, Dunhua City, and Antu County shifted between coordinated and disordered states in different periods, showing obvious stage characteristics. The disparities in the coupling coordination degree among these regions can be attributed to a combination of factors, including local economic structure, natural resource availability, and regional policies.

As shown in Figure 8, most of the counties in the Changbai Mountain region in Jilin Province from 2013 to 2023 had coupling coordination degree levels concentrated between 0.4 and 0.6, among which the number of counties on each level of coupling coordination degree is relatively uniform in 2013; however, most of the cities and counties in 2018 were concentrated between 0.5 and 0.6, accounting for 55.6% of the number of study area units, and most of the cities and counties in 2023 were concentrated between 0.5 and 0.7, accounting for 77.8% of the number of study area units. There were no areas with coupling coordination degree values between 0 and 0.3 and 0.8 and 1, indicating that the coupling coordination degree of ecological carrying capacity and non-timber forest-based economy growth quality in Changbai Mountain region in Jilin Province has neither extreme disorder nor high-quality coordination. In 2023, the coupling coordination of ecological carrying capacity and non-timber forest-based economy growth quality in Wangqing County was in an intermediate coordination state (Figure 8).

In terms of the spatial sub-characteristics of the coupling coordination of ecological carrying capacity and economic growth, Moran’s I index of coupling coordination in the study area was 0.32, 0.21, and 0.09 in 2013, 2018, and 2023, respectively, which was significant (p < 0.05 and Z > 1.96 or Z < −1.96). This indicates that there is a certain global spatial clustering effect of the coupled coordination degree of the ecological carrying capacity and economic growth in the study area, but this clustering effect shows a weakening trend. From the spatial distribution characteristics of the coupling coordination degree (Figure 9), most of the dysfunctional areas are in the southwestern part of the study area, and most of the coordination areas are concentrated in the central region of the study area. In 2013, the dysfunctional areas in the study area were Hunchun and Huadian, and the remaining areas were in the coordination state, and Wangqing were in a primary coordinated state. In 2018, Linjiang City transitioned from a state of disorder to one of near dysfunction, aligning with the conditions observed in Huadian City and Hunchun City, and the remaining areas were in the coordination state, while Fusong, Dunhua, Helong, Antu, and Jiaohe were in a slightly coordinated state. In 2023, the areas in the dysfunctional state were Linjiang City, and the remaining regions were in the coordination state; Antu County, Dunhua City, and Fusong County were in a primary coordinated state. The regional disparities in coupling coordination degree also reflect underlying socio-economic structures, where areas with higher levels of poverty or limited economic development tend to have lower coupling coordination degrees due to a lack of resources for ecological investment. Meanwhile, regions that have more robust local economies and stronger policy frameworks are better able to achieve a balance between ecological protection and economic growth, as seen in Wangqing and Hunchun. This dynamic highlights the critical role that multifaceted local policy initiatives, including environmental regulations, sustainable development programs, and government incentives, play in influencing the coordination between ecological carrying capacity and economic growth.

4.4. The Operating Mechanism of Human–Land Relationship System

Additionally, this study reveals a clear temporal evolution in the coupling relationship between the non-timber forest-based economy and ecological environment carrying capacity, demonstrating that long-term monitoring and timely adjustments to management practices can progressively enhance coordination between the two. Based on empirical data from nine cities and counties in the Changbai Mountain region across three distinct time periods, the spatial dynamics of this coupling relationship were examined. The analysis captures macroscopic spatial distribution patterns while indicating that localized variations within each period merit further investigation. The interaction between economic and ecological systems fundamentally reflects the broader human–land relationship, wherein human activities and natural systems are interdependent. As illustrated in Figure 10, this bidirectional coupling embodies the theoretical concept of the “human–nature life community”, a holistic, dynamic framework that transcends the traditional dichotomy between humans and nature. In this framework, nature underpins human development, while human practical activities serve as the medium through which ecological resources are utilized and protected. Given the Changbai Mountain region’s rich forest resources and its relatively advanced non-timber forest-based economy, exploring the coupling between ecological carrying capacity and economic development quality in this context contributes both to theoretical refinement and practical policymaking. The findings provide scientific support for developing region specific ecological protection and economic strategies, thereby promoting the coordinated and sustainable development of human–land systems.

5. Discussion

5.1. Theoretical Insights and Empirical Findings of Coupling Coordination

The quality of non-timber forest-based economic development is essential to the sustainable development of forested regions, which function as unique human–environment territorial systems. This form of development not only utilizes understory land resources and biodiversity to enhance both economic and ecological outcomes but also plays a critical role in optimizing industrial structures, diversifying income sources, and improving the livelihoods of local communities. In ecologically complex regions such as the Changbai Mountain area, the promotion of the non-timber forest-based economy holds particular significance. Owing to its distinct geographical and climatic conditions, along with abundant forest resources, the Changbai Mountain region is well suited for a variety of forest-based business activities, including non-timber forest cultivation (e.g., medicinal herb farming), forest-based agriculture (e.g., livestock farming integrated within forest ecosystems), and ecotourism that depends on forest landscapes. When implemented within the framework of ecological protection, these initiatives support biodiversity conservation, ecosystem preservation, and the long-term ecological sustainability of the Changbai Mountain region in addition to the growth of a green economy and the revitalization of rural areas.

This study discovers that there are substantial regional and temporal variations in the coordination between the non-timber forest-based economy and ecological carrying capacity through the development of a coupling coordination degree model and subsequent empirical analysis. This variability is primarily driven by differences in local management strategies, technological advancement, and the level of emphasis placed on ecological conservation. Empirical cases show that achieving a harmonious coexistence between the non-timber forest-based economy and the ecological environment depends on strict adherence to ecological carrying capacity limits, the promotion of technological innovation, the optimization of industrial structures, and the adoption of green production practices. These findings offer important contributions to existing theoretical frameworks, particularly in advancing the understanding of the relationship between non-timber forest-based economic development and ecological carrying capacity. Future theoretical research should focus on refining and adapting these frameworks to better account for regional and ecological heterogeneity and on proposing context-specific policy recommendations that align with local environmental and socio-economic conditions. Moreover, further theoretical development should address the complex interplay of interests within the non-timber forest-based economy and explore how interdisciplinary approaches can support the realization of long-term sustainable development goals.

5.2. Practical Implications for Sustainable Forest Economic Development

The importance of institutional design and policy guidance in enhancing the ecological environment’s carrying capacity and promoting the high-quality development of the non-timber forest-based economy is immense. The following measures should be implemented by local administrations to facilitate the coordination of the non-timber forest-based economy and ecological environment: First, implement ecological compensation mechanisms to provide financial incentives to regions with strong ecological protection while encouraging farmers and businesses to engage in ecological conservation. Second, enhance technical support and financial assistance for industries linked to the non-timber forest-based economy, promoting green production and sustainable management practices. Finally, establish regional and sector-specific supervisory mechanisms to ensure effective policy implementation, while regularly evaluating the outcomes and making necessary adjustments to optimize the effectiveness of these policies.

In the context of sustainable development, numerous researchers have conducted in-depth research on the development of the non-timber forest-based economy, yielding fruitful results [55,56,57]. To promote its long-term development, it is essential to address the inherent tension between short-term gains and long-term sustainability. Short-term interests must not come at the expense of long-term ecological and economic viability. Therefore, greater attention should be paid to the sustainable development capacity of the non-timber forest-based economy. This includes considering the effects of scale, agglomeration, and branding, as well as dynamically monitoring and assessing changes in sustainability from the perspective of the entire industry chain of the understory economy. These measures are key to ensuring the scientific and sustainable direction of its development. Empirical studies have demonstrated that the relationship between the development quality of the non-timber forest-based economy and ecological environment carrying capacity is not linear but rather a complex, dynamic coupling process [58,59]. In some cases, appropriate increases in economic activity—when guided by scientific management and technological progress—can lead to an effective transformation of both economic and ecological benefits, thereby improving the degree of coupling coordination. However, over-exploitation or improper management may harm forest ecosystems and reduce ecological carrying capacity, ultimately weakening the coordination between the two systems. Moreover, due to regional differences in natural conditions, governance capacity, and policy orientation, the degree of coupling between the quality of non-timber forest-based economic development and ecological environment carrying capacity varies significantly. This highlights the need for region-specific strategies that consider local environmental and socio-economic conditions. Differentiated management models tailored to local circumstances are essential to achieving an optimal balance between economic development and ecological protection.

5.3. Limitations and Future Research Directions

It is important to acknowledge the following limitations of this study: (1) Due to limitations in data availability, the analysis is confined to the period from 2013 to 2023, which restricts the investigation of long-term evolutionary trends and limits the depth of temporal analysis. This gap will be addressed in future research. Nevertheless, the study focuses on the development characteristics of the non-timber forest-based economy and ecological environment carrying capacity in the Changbai Mountain area of Jilin Province, grounded in the region’s actual conditions. It systematically constructs an index system tailored to the human–environment system of the study area. The findings offer valuable insights into the coordinated coupling of non-timber forest-based economic development and ecological sustainability in the Changbai region. Future studies may yield more com-prehensive, theoretical, and practical results if improvements are made in the consistency of underlying statistical data. (2) The coupling coordination model used in this study is theoretically grounded in the interdependence of economic and ecological systems. While this model is widely adopted in sustainability research for evaluating balance and synergy between such systems, it may not fully capture the dynamic and nonlinear interactions that evolve over time. Future research could explore alternative approaches, such as system dynamics models or integrated assessment models, to provide a more nuanced understanding of these complex interactions. Comparative analyses among different models could further enhance the robustness and validity of the results. (3) Although this study assesses the interaction between the “economy” and “ecology” using coupling coordination degree modeling, future research will expand the scope to explore the broader dynamics of the “human–environment” system. Further efforts will also focus on quantifying the underlying drivers of these interactions, which remain insufficiently understood.

6. Conclusions

This paper employs the Changbai Mountain region of Jilin Province as its empirical target to investigate the spatial distribution characteristics of the coupling and coordination of non-timber forest-based economy and ecosystem. This coupling and coordination is beneficial for the preservation of the diversity, stability, and sustainability of the human–land system in the Changbai Mountain region, as well as for the promotion of high-quality construction and development in the region. Finally, the following conclusions are drawn from this study:

(1)

Improvement in Economic Growth Quality: Between 2013 and 2023, the economic growth quality of the non-timber forest-based economy in the Changbai Mountain region exhibited a fluctuating but overall upward trend. Fusong County, Wangqing County, and Dunhua City consistently maintained high growth quality levels, due to strong development in industries such as forest ginseng and ecotourism. In contrast, Helong City experienced the most significant decline, mainly due to industrial simplification and geographic marginalization. The 2015 implementation of a total logging ban in key state-owned forests marked a critical policy turning point. Following this, the region’s economic growth quality became more stable, particularly in central counties, indicating a successful shift toward sustainable and ecological development models.

(2)

Improvement in Ecological Carrying Capacity: The per capita ecological carrying capacity of the region increased between 2013 and 2023, with significant improvements in Dunhua, Helong City, and Antu County. This improvement is largely attributed to the effective implementation of ecological protection policies, such as forest preservation, arable land protection, and a ban on commercial logging. The ecological carrying capacity showed a spatial pattern of higher values in the central region, which further expanded to include additional areas by 2023, indicating successful environmental management and policy implementation.

(3)

Coupling Coordination of Ecological Capacity and Economic Growth: From 2013 to 2023, the coupling coordination degree between ecological carrying capacity and non-timber forest-based economic growth in the Changbai Mountain region fluctuated between 0.4 and 0.7 in most counties, reflecting persistent imbalance. Wangqing County and Dunhua City showed notable improvement, with Wangqing achieving an intermediate coordination state in 2023 (coordination degree > 0.7), supported by 88.79% forest coverage and a diversified ecological economy. In contrast, Linjiang County experienced the largest decline, primarily due to limited industrial diversification. Spatially, coordination was generally concentrated between 0.5 and 0.6 by 2018, while dysfunctional regions such as Linjiang City remained below 0.5. The spatial clustering of coordination weakened over time, as Moran’s I values declined from 0.32 in 2013 to 0.09 in 2023. These patterns indicate a spatial transmission effect: areas with stronger ecological investment and policy-driven diversification achieved higher coordination levels, whereas resource-constrained or industrially dependent regions continued to lag.