Spatial Pattern of Ecosystem Services and the Mechanism of Eco-Industry Formation in South China Karst Nature Reserves

Abstract

Karst nature reserve (NR) ecosystems are vulnerable, and the development of regulatory and cultural services industries is still in its infancy. Realization of ecological product value (EPV) is a crucial way to promote the integration of eco-protection and industrial development in nature reserves (NRs). This study calculates EPV using a modified equivalent factor, analyzes the spatial pattern of EPV using the fishnet tool in Arc GIS, and constructs a model for realizing EPV. Finally, the driving factors for the formation of eco-industry are identified using the grey correlation analysis (GCA) method. The results show that (1) the regulatory service products in karst NRs play a decisive role in EPV, and forest ecosystems have significant EPV potential; (2) high-value grids are concentrated in the core and buffer zones of karst NRs and the spatial distribution of EPV in the experimental zone is highly heterogeneous, with high and low-value grids in mixed distribution; (3) the realization model of EPV in karst NRs follows the logic of “preservation, transformation, and appreciation”; (4) human resources (0.813), ecology (0.798), industry (0.693), policy (0.783), the market (0.778), and economy (0.715) are essential drivers for the formation of eco-industry in karst NRs. Overall, karst NRs can promote the realization of EPV through planning and regulation of land use, based on the interaction between internal and external factors, to promote the coordinated development of eco-industry in karst NRs. This study provides a reference for the scientific management of eco-resources and the sustainable development of eco-industry in karst NRs.

1. Introduction

Ecological services (ESs) are the foundation of human survival and development [1,2] and an important component of ensuring the connection between ecological, social, and economic health development and human well-being [3,4]. Eco-products are the final products or services provided to human society by the ecosystem under the dual production function of natural ecology and human society. They manifest the realization of ecological environmental value [5,6], which is significant for solving the contradiction between economic development and environmental protection [7]. The main difference between eco-products and ESs lies in their conceptual attributes. ESs focus on the natural attributes of ecosystems, while eco-products emphasize ecosystems’ social and market attributes. The value of eco-products comes from the value of ESs [8]. When calculating value, ecological product value (EPV) can be considered ecosystem service value (ESV) [9]. The early concept of eco-industry originated from the “ecological industry system” and “industrial ecology” [10]. Eco-industry refers to an industrial form that takes natural resources and ESs as its foundation, uses economic activities as a means, and achieves coordinated development of ecology, economy, and society [11]. In the 21st century, the continuous impact of humans has become a key force affecting ecosystem changes [12]. Currently, natural resources are facing the danger of widespread degradation [13], and 60% of the global ecosystems are in a state of continuous degradation [14]. Therefore, enhancing the scientific coordination of ESs and human well-being to achieve harmonious coexistence between humans and nature [15] has become the core theme of current research on human–land relationships and sustainability.

For the realization of EPV and the development of eco-industry, it is necessary to evaluate EPV, convert it into economic value in a specific way, and then obtain specific economic benefits in practical application [16]. The value realization of eco-products in China is still in its infancy [17], and the research on the ecosystem of karst NRs is not comprehensive. Approximately 30% of China’s national-level NRs have conducted ecological value assessments, and there are no established unified standards for selecting assessment indicators and methods [18]. With the construction and advancement of China’s ecological civilization and the in-depth development of ecological construction, scientific monitoring and valuation of ecological value have become increasingly important. However, there is currently a lack of research on the accounting of karst NRs, and this gap in research has become a constraint on scientific understanding of ecological issues in NRs and the protection and restoration of the ecological systems within these areas. At present, a considerable amount of research has been conducted on the valuation of ESs both domestically and internationally [19,20]. The academic community has widely applied the ESV accounting method based on the equivalent factor per unit area. This method is characterized by easy access to data and low demand, offering strong operability, and is suitable for macro-scale regional accounting [21]. Additionally, it can account for regional differences in equivalent factors [22]. Therefore, this study selects the equivalent factor approach and revises the indicators for the valuation of eco-products based on the scale of the study area.

The karst distribution area across the world is nearly 22 million km², accounting for about 15% of the Earth’s land area [23]. The geographical features of karst areas result in environmental conditions that differ from those in non-karst areas [24]. Karst resources and environment have four typical characteristics: fragility, susceptibility, vulnerability, and low natural regeneration capacity [25]. The ESV of these regions is prone to change [26], especially the karst regions in southern China, where the conflicting demands between the fragile ecological environment protection and local development are prominent [27]. NRs play an essential and irreplaceable role as critical areas of significant ecological security barriers [28]. By April 2022, karst NRs accounted for 27.45% of NRs globally [18]. The ecological systems in an NR provide a wide range of direct and indirect services to humans [29], including regulatory services, provisioning services, and cultural services [19,30], especially in maintaining carbon sequestration, water balance, and economic security [31,32]. The southern China karst NR combines high-quality landscape resources with a fragile ecological environment, and its eco-products have significant uniqueness and irreplaceable value [33,34]. It is an important carrier for protecting and maintaining the karst ecological environment and biodiversity. Only 25% of NRs are effectively managed, and it is recognized that NRs around the world are in different stages of deterioration [35]. As an important guarantee for national ecological security [36], an NR provides high ESV and affects regional ESV. Land-use and land-cover changes (LUCCs) result from coupling socio-economic activities and nature and are the main driving forces of Ess’ functions [37]. Therefore, scientifically quantifying the value of regional eco-products and quantitatively describing their spatial pattern characteristics is of great significance for optimizing land use types, formulating differentiated models for realizing the value of eco-products, and promoting the sustainable development of eco-industry.

The Sustainable Development Goals of the United Nations aim to comprehensively and thoroughly solve social, economic, and environmental development issues from 2015 to 2030 and promote a shift towards sustainable development [38]. As an indispensable part of the global ecosystem, the karst region ecosystems are significant for sustainable global development [39]. Study on the realization of EPV and the formation mechanism of eco-industry in karst NRs is an important measure to protect fragile karst ecosystems and enhance the well-being of karst communities [18]. It is of great significance for promoting the coordinated development of the regional “population–resources–environment” and achieving a virtuous cycle of “society–ecology–economy” [40]. However, compared with other regions, the mechanisms for building EPV realization and eco-industry formation in karst areas are influenced by the fragile and complex social-ecological system of the natural ecosystem, which makes it more difficult to carry out ecological protection and restoration [41]. The communities influenced by karst culture have a low acceptance of new things. Additionally, the theoretical research and practical application of the realization of EPV and the formation mechanism of eco-industry in karst areas have yet to become very close [42], due to the lack of a unified research framework and quantitative methods. As a result, there are significant knowledge gaps regarding the ESs of karst NRs. Therefore, how to scientifically promote the coordination of the fragile ecological environment and economic development needs in karst NRs is particularly urgent.

Based on the actual problems of fragile ecological environment, low self-repair ability, poor stability, and easy rebound in karst NRs, this paper aims to address the frontier scientific problems of EPV accounting and the eco-industry formation mechanism in karst NRs. Combining the technological needs of eco-protection and economic development in karst NRs, adopting the technical route of “EPV evaluation, EPV realization, and eco-industry formation” and a research method of “equivalent factor, grey correlation analysis (GCA), GIS spatial analysis” to explain the spatial pattern of the EPV, it reveals the mode of EPV realization and identifies the driving factors of eco-industry formation. The main objectives include (1) evaluating and quantifying the EPV of karst NRs, (2) analyzing the spatial distribution trends of EPV in karst NRs, (3) identifying the models for realizing EPV under functional zoning in karst NRs, (4) constructing the driving mechanisms for the formation of eco-industries in karst NRs.

2. Materials and Methods

2.1. Study Area

The karst region in southwestern China is one of the largest continuous karst areas in the world [43]. The karst landforms in southern China are the most concentrated and largest area among the three major karst concentrations in the world, with diverse landscape types [44,45]. The region is the most typical of tropical and subtropical karst development in China and even globally [46]. It represents the structural system and evolutionary sequence of tropical and subtropical karst uplift development, and its geomorphic value is unparalleled in other regions [47]. The integrity of the natural landscape, geological and geomorphic relics, forest ecosystems, and biodiversity in the South China karst NR has been well preserved, and it has high ecological value due to its unique natural landscape resources [48]. According to the principles of typicality, representativeness, and demonstration, we selected two adjacent karst forest NRs, Guizhou Maolan National NR and Guangxi Mulun National NR, which represent the overall eco-environment structure of the South China Karst Plateau Mountains and Guangxi low hill slopes (Figure 1). These two NRs, at the same latitude, jointly constitute the largest continuous area, most primitive, and best-preserved karst forest ecosystems in the world.

2.1.1. Guizhou Maolan National Nature Reserve

Maolan NR is located in the southern part of Libo County, Qiannan Buyi and Miao Autonomous Prefecture, Guizhou Province, China (107°52′10″~108°05′40″ E, 25°09′20″~25°20′50″ N). The reserve’s total area is 21,285 hm², with a forest coverage rate of 88.61%. The reserve’s main protection focus is the subtropical karst forest ecosystem and its rare wildlife and plant resources. The karst landforms in the area are well developed, with the main types being Fengcong funnel and Fengcong depression.

2.1.2. Guangxi Mulun National Nature Reserve

Mulun NR is located in the northwestern part of Huanjiang Maonan Autonomous County, Guangxi Zhuang Autonomous Region, China (107°53′29″~108°05′45″ E, 25°06′09″~25°12′25″ N). The reserve’s total area is 8969 hm², with a forest coverage of 94.7%. The main protected objects are the forest ecosystem of subtropical karst evergreen and deciduous broad-leaved mixed forest, and nationally protected wild animal plants. The karst topography is highly developed, mainly consisting of Fengcong funnel and Fengcong depression.

2.2. Data Sources

The data used in this study mainly include the grid data of LUCC, socioeconomic data, and questionnaire survey data. (1) The LUCC data were derived from the 2022 land satellite remote sensing images with a spatial resolution of 30 m, divided into eight land use categories: forest land, shrub land, grassland, paddy field, dry land, water area, bare land, and construction land. (2) The socioeconomic statistical data mainly consisted of statistical yearbooks supplemented by bulletins on national economic and social development statistics [49,50,51]. (3) To further ensure the study’s authenticity, credibility, and comprehensiveness, a field questionnaire survey was conducted among the village staff, government officials, and tourism industry practitioners in Maolan and Mulun NRs. The questionnaire mainly included basic demographic information and factors influencing the development of eco-industry. Of these, the factors influencing the development of the eco-industry were the questionnaire’s core content. They were mainly addressed from six aspects: ecological resources, basic industry development, human resources, market demand, policy regulations, and socio-economic environment.

2.3. Methods

2.3.1. Value Accounting of Eco-Products

(1) Revision of equivalent factor table

The value accounting of eco-products in this paper is mainly based on the equivalent factor table proposed by Costanza [19] and Xie [20]. However, ESs’ functions vary regionally and exhibit dynamic changes in time and space. Therefore, based on the actual situation of the study area, we divided the eco-products of Maolan and Mulun NR into three major categories: supply services, regulation services, and cultural services, as well as eight minor categories including food production, gas regulation, aesthetic landscaping, etc. The current equivalent factor table is based on research into the value of ESs conducted nationwide or at a large regional scale [52]. However, because karst NRs have a smaller ecosystem scale, directly applying the equivalent factor for calculation would result in significant errors [8] and fail to reflect the study area’s actual situation. Therefore, scientific and reasonable revision of the equivalent factor is an inevitable requirement for assessing the value of ESs in karst NRs, and equivalent revision is the foundation for accurately evaluating regional EPV.

Considering the study area’s local socio-economic reality, this article adopts the “regional adjustment based on farmland as the benchmark” method [53] to modify the equivalent factor table at the study area scale. The calculation formula is:

$$\mathsf{\beta }=\frac{A}{A_i}$$

(1)

In Equation (1), $\mathsf{\beta }$ represents the revision coefficient of ESV in the study area, and $A$ and $A_i$, respectively, represent the unit yield of the main grain crops in the research area and the unit yield of the main grain crops in the country. According to the national, Libo, and Huanjiang statistical yearbooks [49,50,51], the grain yields per unit area in Libo, Huanjiang, and China in 2022 were 309.94 kg/mu, 358.83 kg/mu, and 387.04 kg/mu, respectively. According to Formula (1), the revised coefficients of ESV in Maolan and Mulun NR were 0.80 and 0.93, respectively. Therefore, an equivalent table of ESV in the study area can be established (

Table 1. Revised Table of Equivalent Factors for Ecosystem Service Value (ESV) in Karst Nature Reserve (NR).

Ecosystem Classification	Primary Classification	Farmland		Forests		Grassland	Desert	Water
	Secondary Classification	Dry Land	Paddy Field	Mixed Needle and Broadleaf	Shrub	Meadow	Bare Land	Water System
Maolan Nature Reserve
Supply services	Food production	0.68	1.09	0.25	0.15	0.18	0.00	0.64
	Raw material production	0.32	0.07	0.57	0.34	0.26	0.00	0.18
	Water supply	0.02	−2.10	0.30	0.18	0.14	0.00	6.62
Regulation services	Gas regulation	0.53	0.89	1.88	1.13	0.91	0.02	0.61
	Climate regulation	0.29	0.45	5.61	3.38	2.41	0.00	1.83
	Purifying environment	0.08	0.14	1.59	1.02	0.80	0.08	4.43
	Hydrological regulation	0.22	2.17	2.80	2.67	1.76	0.02	81.61
Cultural services	Aesthetic landscape	0.05	0.07	0.91	0.55	0.45	0.01	1.51
Mulun Nature Reserve
Supply services	Food production	0.79	1.26	0.29	0.18	0.20	0.00	0.74
	Raw material production	0.37	0.08	0.66	0.40	0.31	0.00	0.21
	Water supply	0.02	−2.44	0.34	0.20	0.17	0.00	7.69
Regulation services	Gas regulation	0.62	1.03	2.18	1.31	1.06	0.02	0.71
	Climate regulation	0.33	0.53	6.52	3.92	2.80	0.00	2.12
	Purifying environment	0.09	0.16	1.84	1.19	0.93	0.09	5.15
	Hydrological regulation	0.25	2.52	3.25	3.11	2.05	0.03	94.79
Cultural services	Aesthetic landscape	0.06	0.08	1.06	0.64	0.52	0.01	1.75

).

(2) Economic value of ecosystem service functions

Based on the socioeconomic conditions of Libo County in Guizhou Province and Huanjiang County in Guangxi Province, the ESV of one standard equivalent factor was calculated using the following formula:

$$E_a=\frac{1}{7}{\sum }_{i=1}^n{m}_i{n}_i/{\sum }_{i=1}^n{A}_i$$

(2)

In Equation (2), $E_a$ represents the ESV of one standard equivalent factor, $m_i$ represents the yield of the $i$-th crop, $n_i$ represents the average unit price of the $i$-th crop, $A_i$ represents the planting area of the $i$-th crop. Water rice and corn were selected as the main crops to calculate the economic value of the ESV equivalent factors [49,50]. According to Equation (2), the economic values of one standard equivalent factor in Maolan and Mulun NR were CNY 1604.89/hm² and CNY 1373.58/hm², respectively. Combined with the revised equivalent factor table, the unit area ESVs of different land use types in the study area were estimated (

Table 2. ESV per Unit Area of Maolan NR (CNY/hm²).

Ecosystem Classification		Supply Services			Regulation Services				Cultural Services
Primary Classification	Secondary Classification	Food Production	Raw Material Production	Water Supply	Gas Regulation	Climate Regulation	Purifying Environment	Hydrological Regulation	Aesthetic Landscape
Farmland	Dry land	1088.87	512.41	25.62	858.29	461.17	128.10	345.88	76.86
	Paddy field	1742.19	115.29	−3369.09	1421.94	730.18	217.77	3484.39	115.29
Forests	Mixed needle and broadleaf	397.12	909.53	473.98	3010.41	9005.60	2549.24	4496.40	1460.37
	Shrub	243.39	550.84	281.83	1806.24	5418.73	1639.71	4291.43	883.91
Grassland	Meadow	281.83	422.74	230.58	1460.37	3868.69	1281.02	2831.06	717.37
Desert	Bare land	0.00	0.00	0.00	25.62	0.00	128.10	38.43	12.81
Water	Water system	1024.82	294.64	10619.69	986.39	2933.55	7109.68	130971.92	2421.14

and

Table 3. ESV per Unit Area of Mulun NR (CNY/hm²).

Ecosystem Classification		Supply Services			Regulation Services				Cultural Services
Primary Classification	Secondary Classification	Food Production	Raw Material Production	Water Supply	Gas Regulation	Climate Regulation	Purifying Environment	Hydrological Regulation	Aesthetic Landscape
Farmland	Dry land	1082.45	509.39	25.47	853.22	458.45	127.35	343.84	76.41
	Paddy field	1731.92	114.61	−3349.22	1413.55	725.88	216.49	3463.83	114.61
Forests	Mixed needle and broadleaf	394.77	904.16	471.18	2992.65	8952.48	2534.20	4469.87	1451.75
	Shrub	241.96	547.59	280.16	1795.59	5386.77	1630.04	4266.12	878.69
Grassland	Meadow	280.16	420.24	229.22	1451.75	3845.87	1273.47	2814.36	713.14
Desert	Bare land	0.00	0.00	0.00	25.47	0.00	127.35	38.20	12.73
Water	Water system	1018.77	292.90	10557.04	980.57	2916.24	7067.74	130199.30	2406.85

).

(3) Value evaluation of ecological products per unit area

According to Table 2 and Table 3, the EPV in the study area was calculated using the following formula:

$$\mathrm{EPV}={\sum }_{i=1}^n{\sum }_{i=1}^m{A}_j{E}_{ij}$$

(3)

In Equation (3), EPV is the total value of ecosystem services (CNY), $A_j$ is the area of the $j$-th type of ecosystem service (hm²), $E_{ij}$ is the unit area value of the $i$-class ecosystem service function of the $j$-th type of ecosystem (CNY/hm²).

2.3.2. Spatial Analysis of Ecological Product Value

Using the Create Fishnet tool in Arc GIS 10.2 software, a grid of the study area was prepared. Based on the reference literature [9,54,55] and repeated experiments, the study area was divided into square grid cells with a size of 250 m × 250 m. The spatial distribution pattern of the EPV of LUCC in Maolan and Mulun NRs in 2022 was visually analyzed.

In Arc GIS 10.2, the natural breakpoint method was used to classify the total value of LUCC eco-products in the study area, which were classified as low (I), low-medium (II), medium (III), medium-high (IV), and high (V) values (

Table 4. Grid Unit EPV Classification.

Serial Number	EPV Classification	Legend
1	High value (V)
2	Medium-high value (IV)
3	Medium value (III)
4	Low-medium value (II)
5	Low value (I)

). Considering the integrity of geomorphic units and the degree of fragmentation of land types, the quantity of EPV at each level was counted and the dominant development model was divided into zones.

2.3.3. Grey Correlation Analysis

The grey relational analysis method has made up for the shortcomings of mathematical statistical methods and has been widely applied in fields such as agricultural science, economic management, and environmental science. The data obtained through the questionnaire survey in this study had the characteristics of insufficient information and small sample size, making it suitable to use the GCA method to extract key factors affecting the development of eco-industries in karst NRs. The calculation steps of the GCA method for extracting key factors from survey questionnaires are as follows:

(1) The evaluation index system is determined based on the survey purpose, and the calculation formula is:

$$X_i^T=\left[x_i\right(1),x_i(2),x_i(3),...,x_i(m\left)\right]$$

(4)

In the formula, $X_i$ represents the score set of the $i$-th survey object, (k) represents the score of the $i$-th survey subject on the k-th factor, $i$ = 1, 2, 3, ..., n, K = 1, 2, 3, ..., $m$, and $T$ represents the transpose matrix.

(2) The reference sequence is an ideal comparison standard, which can be composed of the optimal (or worst) values of each indicator to form a reference data column, or other reference values can be selected according to the survey purpose. The calculation formula is as follows:

$$X_0^T=\left[x_0\right(1),x_0(2),x_0(3),..., x_0\left(m\right)]$$

(5)

(3) To calculate the difference sequence, the absolute difference is calculated between the corresponding elements of each evaluated object indicator sequence (comparison sequence) and the reference sequence, the calculation formula is as follows:

$$\left|X_0\right(K)-X_i(K\left)\right|, \mathrm{i} = 1, 2, 3, \dots ,\mathrm{n}; \mathrm{k} = 1, 2, 3, \dots ,\mathrm{m}$$

(6)

(4) The formula for determining the minimum difference and maximum difference between two levels is as follows:

$$\underset{i=1}{\overset{n}{\min }} \underset{k=1}{\overset{m}{\min }}\left|X_0\right(K)-X_i(K)|\hspace{1em}\underset{i=1}{\overset{n}{Max}} \underset{k=1}{\overset{m}{Max}}\left|X_0\right(K)-X_i(K)|$$

(7)

(5) To calculate the correlation coefficients between the corresponding elements of each comparison sequence and the reference sequence separately, use the formula:

$$\mathrm{R}\left[X_0\right(K),X_i(K)]=[\underset{i=1}{\overset{n}{\min }} \underset{k=1}{\overset{m}{\min }}\left|X_0\right(K)-X_i(K)|+\mathsf{\mu }\underset{i=1}{\overset{n}{Max}} \underset{k=1}{\overset{m}{Max}}\left|X_0\right(K)-X_i(K)\left|\right]/\left[\right|X_0\left(K\right)-X_i\left(K\right)|+\mathsf{\mu }\underset{i=1}{\overset{n}{Max}} \underset{k=1}{\overset{m}{Max}}\left|X_0\right(K)-X_i(K\left)\right|]$$

(8)

In the formula, R[$X_0\left(K\right),X_i\left(K\right)$] is the correlation coefficient of the $i$-th observation object at the $K$-th factor. If the optimal value is used as a reference sequence, the larger the R[$X_0\left(K\right),X_i\left(K\right)$], the stronger the correlation degree. μ is the resolution coefficient, which is within the interval (0, 1). The smaller the μ, the greater the difference between the correlation coefficients and the stronger the discrimination ability, usually taken as 0.5.

(6) The score of each factor is calculated based on the weighted average of the correlation coefficient, to reflect the correlation between each evaluation object and the reference sequence; this is called the correlation degree. The higher the score, the stronger the correlation degree, and the more important the factor is. The calculation formula is as follows:

$$\mathrm{R}(\mathrm{K})={\sum }_{i=1}^n{W}_i\mathrm{R}\left[X_0\right(K),X_i(K)]$$

(9)

In the formula, R(K) is the correlation degree of the $K$-th factor and $W_i$ is the weight of the $i$-th observation object.

2.3.4. Construction of Indicator System

The development of eco-industry in the karst NR is influenced by many factors, which may result from the combined action of internal and external factors. However, the mechanism of action and the impact of these influencing factors need further in-depth research and empirical testing. Considering the accessibility and representativeness of indicator data and referring to relevant research results [27,56,57], combined with the actual situation of the research area, a total of 40 factors were selected from six aspects: eco-resources, basic industry development, human resources, market demand, policies and regulations, and socioeconomic environment, as comparative sequence indicators (

Table 5. Selection of Indicators for Factors Influencing the Formation of Eco-industries.

Influence Factor	Dimension of Influencing Factors	Influencing Factor Indicators
Internal factors	Ecological resources (A)	The richness/diversity of ecological resources (A1)
		Unique geological and natural landscape resources (A2)
		Ethnic minority cultural resources (A3)
		Featured tourism resources (A4)
		The development value of ecological resources (A5)
		Planting/breeding of special ecological products (A6)
		Good ecological environmental conditions (A7)
	Development level of basic industries (B)	Industrial organization situation (B1)
		Agricultural or forestry cooperative organizations (B2)
		Enterprise organization (B3)
		Investment promotion project (B4)
		Industrial structure development (B5)
		Primary industry (B6)
		Secondary industry (B7)
		Third industry (B8)
		Development of industrial scale (B9)
	Human resources (C)	Comprehensive quality of farmers (C1)
		The skill level of farmers (C2)
		Protected area management personnel (C3)
		Scenic area service personnel (C4)
		Technical talents related to the development of ecological industries (C5)
		Leadership ability of village committee (C6)
External factors	Market demand (D)	Consumer demand differences (D1)
		Consumer willingness and ability to consume (D2)
		Market sales of ecological products (D3)
		Brand building in the ecological industry (D4)
		Economic benefits of ecological industries (D5)
	Policies and regulations (E)	Government encouragement and support (E1)
		Green channel for loans (E2)
		Tax incentives (E3)
		Skills training (E4)
		Subsidies and rewards (E5)
		Support for protected area management (E6)
		Industrial policy support (E7)
		Support for collective forest rights reform (E8)
		Support for reform of protected area management (E9)
	Socioeconomic environment (F)	Location conditions (F1)
		Infrastructure construction (F2)
		Residents’ income level (F3)
		Economic development level of surrounding areas (F4)

), and a scoring questionnaire was designed, with critical factors extracted based on the GCA formula.

3. Results

3.1. Analysis of EPV Evaluation Results

The EPVs of Maolan and Mulun NR are CNY 5.87 × 10⁴ million and CNY 2.38 × 10⁴ million, respectively. The values of different types of eco-products are ranked as follows: regulating services > supply services > cultural services. Regulating services dominate the ecosystems in both study areas, accounting for over 85% of the total value. Among them, climate regulation and hydrological regulation functions have the highest value, accounting for over 60% of the total value (Figure 2).

LUCC exhibits significant spatial heterogeneity, with forests being the main land use type in the two study areas, while other types of land use and cover are relatively few. Considerable differences exist in the EPV among different types of land use, with forest eco-products contributing 98.57% and 99.76% to the total value, respectively (Figure 3), fully reflecting the strong eco-function of forests in NRs. However, the lowest proportion of cultural services indicates the enormous potential for the development of the forest industry in the region.

3.2. Analysis of Spatial Characteristics of EPV

The EPV distribution in Maolan NR shows a layered structure with the core zone (CNY 3.24 × 10⁴ million) > buffer zone (CNY 1.74 × 10⁴ million) > experimental zone (CNY 8.95 × 10³ million). It shows a trend of attenuation from the core zone to the buffer zone and experimental zone and a spatial distribution pattern with lower values in the north and higher values in the south. The low-value and medium-low-value areas of EPV are widely distributed in the experimental area in a concentrated and contiguous manner, while the medium-high-value and high-value areas are mainly distributed in the core area and buffer zone (Figure 4). This is highly consistent with the distribution of land-use types in the study area, with the core area and buffer zone having a high rate of forest coverage at 90%, consistent with the distribution of medium-high-value areas of EPV. The experimental area is population-dense, mainly featuring paddy fields. However, due to the fragmented surface caused by karst landforms, the spatial distribution of agricultural and other land uses is scattered, resulting in deteriorating ecological stability in the study area. The values of different eco-products are scattered and interwoven, leading to a decrease in the EPV of buffer zones.

The EPV distribution in Mulun NR shows a layered structure with the core area (CNY 1.21 × 10⁴ million) > experimental area (CNY 8.09 × 10³ million) > buffer zone (CNY 3.61 × 10³ million). From the perspective of the core area and the buffer zone, the spatial distribution pattern shows lower values in the north and higher values in the south. From the perspective of the experimental zone, the spatial distribution pattern includes higher values in the north and lower in the south. The low-value and medium-low-value areas of eco-products are sporadically distributed along the edge of the entire reserve, while the medium-high-value and high-value areas are distributed across the whole reserve (Figure 5). This is highly consistent with the distribution of types of land use in the study area. The forest coverage within the NR is over 95%, while there are shrubs and bare land in the core zone, resulting in the distribution of low-value zones for some eco-products. The population has increased in the experimental area, but the tourism industry has not been fully developed. There is a significant lag in landscape planning and construction, transportation infrastructure construction, ecotourism integration, and ecological branding construction. Economic activities have relatively limited development and utilization of natural resources, leading to the intersecting distribution of different EPV.

3.3. Analysis of the Dominant Model for Realizing the EPV

The high-value forest eco-product areas within the Maolan and Mulun NRs are the most extensive in the study area, mainly concentrated in the core and buffer zones. The high-value areas of eco-products in farmlands, grasslands, deserts, and water bodies are mainly concentrated in the experimental zones and are scattered across the region (Figure 6 and Figure 7). In conclusion, the spatial distribution of EPV in the Maolan and Mulun NRs is closely related to the distribution of land use. The distribution of forest is significantly correlated with areas of high EPV, while construction land is associated with areas of low EPV. Based on this, it is proposed to prioritize forest-based eco-products as the main eco-products for realizing EPV in the Maolan and Mulun NRs.

Under the logic of protection and development, based on the spatial zoning data of EPV and the limitations of strict protection of NRs, the value realization models are divided into three types: “transformation”, “preservation”, and “appreciation” (

Table 6. Classification of EPV Realization Models.

Value Realization Model	Dominant Model	Areas with High Suitability	Forest Eco-Products with High Suitability	Characteristics and Realization Paths
Transformation	Market trading model	Experimental area	Supply service products	Promoting the realization of the value of material eco-products through the eco-industrialization management and development model. Operating tourism, catering, and shopping products, realizing value through direct market transactions.
			Cultural service products	Promoting the realization of the value of cultural service eco-products through the industrialized operation model of ecotourism. Gaining profits through market transactions, such as operating ecotourism products, designing and selling ecological cultural and creative products, etc.
			Regulatory service products	Promoting quantifiable value realization of regulatory service eco-products through market-oriented property rights trading models. The regulatory service products in the experimental area are products that can be traded in the market. Based on market mechanisms, property rights trading methods are used, such as trading usage rights and income rights between community residents and tourism enterprises and carbon sink trading between government departments represented by protected area management agencies and tourism enterprises.
Preservation	Government compensation model	Core zone, buffer zone	Regulatory service products	Promoting the value realization of difficult-to-quantify regulatory service eco-products through eco-compensation models, following user payment and beneficiary compensation principles.
Appreciation	Product premium model	Core zone, buffer zone	Regulatory service products	Promote the indirect realization of the value of difficult-to-quantify regulatory service eco-products through the eco-product premium model. The core area and buffer zone of the karst NR are strictly protected areas, with the highest proportion of regulatory service products and the lowest rate of value realization. In addition to relying on eco-compensation models, it is also necessary to try to attach EPV to industrial, agricultural, or service products and achieve EPV through market premium sales.

). The value realization models of supply service products, regulatory service products, and cultural service products in the experimental area are classed as “transformation” models, and value realization models of regulatory service products in the core and buffer areas are categorized into “preservation” and “appreciation” models.

3.4. Analysis of Driving Factors for the Formation of Eco-Industries

According to the calculation steps of GCA 1–6, the scores of 40 influencing factors were obtained, and the scores of each influencing factor were in the range [0.4~1.0]. The minimum value was 0.477, and the maximum value was 1.0 (

Table 7. Scores of influencing factors.

Dimension of Influencing Factors	Evaluating Indicator	Score	Order
A	A1	1.000	1
	A2	1.000	2
	A3	0.477	40
	A4	0.653	33
	A5	0.988	3
	A6	0.499	39
	A7	0.968	6
B	B1	0.731	18
	B2	0.740	17
	B3	0.727	19
	B4	0.574	36
	B5	0.725	20
	B6	0.712	23
	B7	0.577	35
	B8	0.687	27
	B9	0.765	14
C	C1	0.908	13
	C2	0.528	37
	C3	0.756	15
	C4	0.740	16
	C5	0.961	8
	C6	0.988	5
D	D1	0.663	32
	D2	0.653	34
	D3	0.966	7
	D4	0.666	31
	D5	0.941	10
E	E1	0.988	4
	E2	0.685	28
	E3	0.713	22
	E4	0.922	12
	E5	0.703	24
	E6	0.950	9
	E7	0.685	29
	E8	0.703	25
	E9	0.700	26
F	F1	0.941	11
	F2	0.685	30
	F3	0.513	38
	F4	0.722	21

). Among them, the top ten factors influencing the formation of eco-industry in the NR are A1 (1.000) > A2 (1.000) > A5 (0.988) > E1 (0.988) > C6 (0.988) > A7 (0.968) > D3 (0.966) > C5 (0.961) > E6 (0.950) > D5 (0.941) (Figure 8).

Based on the extraction of key factors for eco-industry, the driving forces behind the formation of eco-industry were divided into internal and external entities. The driving mechanisms for developing the eco-industry in karst NRs are analyzed from six dimensions: human resources, ecology, industry, policy, market, and economy. Among the internal driving forces (Figure 9), the scores of various influencing factors of human resources range from 0.5 to 1.0, with a minimum value of 0.528 and a maximum value of 0.988. Among them, the leadership ability of the village committee (0.988) and the relevant technical talents for the development of ecological industries (0.961) are the main driving factors. The scores of various factors influencing ecological resources range from 0.4 to 1.0, with a minimum value of 0.477 and a maximum value of 1.0. Among them, the richness/diversity of ecological resources (1.000), unique geological and natural landscape resources (1.000), the development value of ecological resources (0.988), and good ecological environmental conditions (0.968) are the main driving factors. The scores of various factors influencing the development level of basic industries range from 0.5 to 0.8, with a minimum value of 0.574 and a maximum value of 0.765. Among them, the development of industrial scale (0.765) and agricultural or forestry cooperative organizations (0.740) are the main driving factors.

In the external driving force (Figure 10), the scores of various influencing factors of policies and regulations range from 0.7 to 1.0, with a minimum value of 0.685 and a maximum value of 0.988. Among them, the main driving factors are government encouragement and support (0.988) and support for protected area management (0.950). The scores of various influencing factors on market demand range from 0.7 to 1.0, with a minimum value of 0.653 and a maximum value of 0.966. Among them, the main driving factors are market sales of ecological products (0.966) and the economic benefits of ecological industries (0.941). The scores of various influencing factors in the socioeconomic environment range from 0.5 to 1.0, with a minimum value of 0.513 and a maximum value of 0.941. Among them, location conditions (0.941) and the level of economic development in the surrounding areas (0.722) are the main driving factors.

4. Discussion

4.1. Total Value of Ecological Products

As national-level NRs in China, Maolan and Mulun NRs are rich in natural resources and have a good eco-background. The research results show that the eco-environment of the two is highly similar, with forests as the main body and other types of land use coexisting. The regulating service products have the optimal EPV, and forests are an important factor affecting the EPV, consistent with the conclusions of similar studies [58,59,60]. This indicates that regulating service products is the focus of EPV realization in the two study areas. Furthermore, innovative breakthroughs are needed to realize material supply products and cultural service product value. This demonstrates the strong eco-function of the forests in the reserve, indicating that the development of forest health care in the reserve is feasible and has enormous development potential.

4.2. Spatial Pattern of Ecological Product Value

LUCC as a critical factor affecting the temporal and spatial variation of ESs directly or indirectly affects the patterns and processes of ecosystems and changes the EPV [61,62]. Based on the analysis of land use classification and the grid-scale spatial pattern of EPV, it can be seen that the spatial distribution of EPV has apparent differences between the core area, buffer zone, and experimental area. In particular, the strict eco-protection and restricted development measures in the core and buffer zones have led to significant differences in regional EPV. The high-value and middle-high-value areas are mainly located in the core and buffer zones, which are mainly forest land. The superior resource endowment of eco-products in this area indicates a positive correlation between EPV and forest land. The middle-value and low-value areas are interlaced, and the low-value areas are mainly located in the experimental zone, which is mainly agricultural land and construction land, in the area of concentrated human activity. This indicates that EPV has a negative correlation with agricultural land and construction land, which is similar to the findings of related studies [37,63].

This phenomenon also reflects that the experimental zone of the karst NR plays a good buffer role, effectively alleviating the negative impact of peripheral human activities on the EPV of the core and buffer zones; due to the increasing demand for natural resource development and utilization for economic development and human activities in the experimental zone, ecological stability decreases and the EPV in the experimental zone is relatively low. Therefore, the spatial distribution of EPV is closely related to LUCC, and the karst NR can improve its EPV through the planning and regulation of land use.

4.3. Implementation Models and Paths of Ecological Product Value

The implementation model of EPV is not static, and there is a certain degree of cross-integration between various models [64]. Many scholars focus on discussing macro eco-products in their research on EPV implementation models. Based on different understandings of eco-products and their value connotations, there are generally two logics: “protection” and “transformation” [65]. Based on LUCC, the spatial pattern of EPV, and the restrictive factors of NRs, further research reveals that the experimental areas in the two study regions are the most densely populated areas, with the richest eco-product types in comparison to the core and buffer zones, but the lowest EPV. This reflects that in areas of high demand for eco-products, it is often challenging to provide ecological functions that match the high demand due to population concentration and industrial development. In this study, the implementation model of EPV in the experimental area is divided into the “transformation” model, which is applied to supply service, regulatory service, and cultural service eco-products. Among these, the main path of EPV realization involves direct and indirect market transactions.

Secondly, the core and buffer zones of the two study regions are occupied mainly by regulatory service products, as the gathering areas for forest eco-products. Among them, climate regulation value accounts for the largest proportion, exceeding 39%, followed by hydrological regulation value, which accounts for more than 20%. They bear important eco-functions, such as conservation of water sources, and are related to the eco-security of the region. Such key natural capital eco-fragile and unique value, artificial capital can not be replaced or replaced will pay a high price of artificial capital, therefore, limiting or prohibiting the development, strengthening protection to improve its eco-product supply capacity is the primary EPV implementation model [66]. Based on the principle of “protection first, reasonable utilization”, the implementation model of EPV in the core and buffer zones is divided into an “appreciation” mode and a “preservation” mode, which are applied to regulatory service eco-products. Among them, the realization of EPV is first carried out through eco-compensation from a government perspective, with compensation standards from high to low corresponding to the core zone, buffer zone, and experimental zone, respectively. The compensation should also be tilted towards the core and buffer zones, similar to relevant research results [67]. Secondly, a combination of government and market contributes to carrying out transactions based on ecological resource rights and interests, building protective market-coordination mechanisms in areas such as water-rights trading and carbon-sink trading. In summary, the future spatial pattern of EPV realization in the karst NR needs to be tailored to local conditions [59] to maximize eco-products’ advantages.

4.4. Ecologically Driven Ecological Industrial Value

As a typical eco-fragile area in southern China, the karst region has long faced dual pressures of eco-governance and economic development. Achieving coordinated development between the economy and the environment is the foundation for regional development [68]. The formation and development of eco-industries are crucial to reversing the trend of eco-deterioration in ecologically fragile areas [69]. Clarifying the driving mechanism of eco-industry in karst NRs is of great significance for the protection of eco-conservation and the sustainable development of the industry.

The formation and development of eco-industries are not simply the result of one factor but include the comprehensive effects of multiple internal and external factors [70]. Based on the previous analysis of influencing factors, these factors have different impact strengths on the development of eco-industries in NRs, and they further constitute the driving forces for the development of eco-industries. From the perspective of internal influencing factors, the main driving forces are human resources, ecological resources, and the development level of basic industries. From the perspective of external influencing factors, the main driving forces are policy regulations, market demand, and the social and economic environment. The above driving mechanisms are interconnected and interact, forming a diverse, integrated, and complex driving system for industrial development. This study analyzes the driving mechanisms for developing eco-industries in karst NRs from six dimensions: human resources, ecology, industry, policy, market, and economy. The interactions among the six driving forces of eco-industries form a model of industrial development dynamics (Figure 11).

Among them, improving the leadership capabilities of the village committee and cultivating technical talents related to the development of eco-industries are mechanisms for a system of leadership through internal talent and the introduction of external talent that drives industrial development. The NR has a good eco-environment and abundant and unique eco-resources, which provide high-quality “raw materials” for developing eco-industries [71], and it forms a mechanism for the protection and development of eco-resources, which drives industrial development. The expanding scale of industry and good local industrial organization will help drive the continuous improvement and optimization of related eco-industries. This forms a mechanism for driving industrial development by integrating multiple industries. The government’s policy support for NRs directly relates to the strategies and paths for developing eco-industries [56], which form a management and guidance mechanism that drives industrial development. Widening the market distribution of eco-products, giving full play to the economic benefits of eco-industry, and promoting strong external market demand are the driving mechanisms for forming an eco-market demand that propels industrial development. Good location and transportation conditions coupled with growing economic development in the surrounding areas form external economic radiation and a driving mechanism that promotes industrial development.

4.5. Research Limitations and Prospects

When studying the conservation of the karst NR ecosystem and the development of eco-industries, it is necessary to consider both the temporal and spatial heterogeneity and the natural and socioeconomic driving forces. (1) This study employed a “regionally adjusted based on farmland” approach to modify the equivalent factor table at the research area scale to calculate the EPV. However, this approach may need to pay more attention to the relevance of its internal functional services. Additionally, subjective factors in the selection of ESs’ functions and the need for more data in some aspects may result in potential errors in estimating economic value. Future research can consider incorporating indicators such as crop yield, socio-economics, biomass, and vegetation coverage to quantify the equivalent factor system more reasonably and effectively. (2) This study qualitatively analyzed the factors influencing eco-industry formation using a comprehensive analysis method from multiple perspectives and factors. It quantitatively analyzed the degree of influence and operating mechanisms of each factor. A qualitative and quantitative combination was used to construct the driving mechanism of the formation of eco-industries through the joint effect of internal and external factors. However, the trade-offs/synergy between each factor and eco-industry have yet to be thoroughly analyzed. Key factors constraining the development of eco-industries may vary by region. Additionally, comparative analysis has yet to be conducted due to the small sample size, which may have introduced bias to the research findings. In future research, the degree of coupling coordination can be used as a theoretical method to evaluate the degree of coordination between the two, and a quantitative model can be established for evaluation. The degree of coupling coordination can serve as the basis for establishing a circular eco-industry chain.

5. Conclusions

From the perspective of LUCC, this study first evaluates the EPV of karst NRs using equivalent factor indexes that were adjusted based on the scale of the study area. Secondly, based on the visualization analysis of LUCC in the reserve, the spatial distribution pattern of EPV is examined. The contribution value of different types of land use to EPV and the limiting factors of strict protection in the NRs are considered. This study proposes that forest eco-products should be the dominant products for value realization, and the value realization mode is divided into three types. Finally, the driving factors involved in developing eco-industry are verified using grey relational theory, and the main driving mechanisms are summarized and refined. The conclusions are as follows:

(1) The potential of ESs’ function in karst NRs is enormous, and the contribution rate of regulating service products to EPV is above 85%, while the forest ecosystem contributes significantly to EPV, with a contribution rate of over 98%.

(2) There are spatial differences in the EPV of karst NRs, and the spatial distribution of EPV is closely related to LUCC. High-value and medium-high-value areas are mainly located in the core and buffer zones, medium-value areas are interlaced with low-value areas, and low-value areas are mainly located in the experimental zone. EPV is positively correlated with forest eco-products and negatively correlated with human activities.

(3) Based on the abundant forest eco-resources and high environmental quality in the core and buffer zones of the karst NRs, the “preservation” mode of eco-compensation and the “appreciation” mode of carrier premium are adopted. The experimental zone serves as a gathering area for diversified eco-products and adopts the “transformation” mode of market transactions.

(4) The formation of eco-industry in karst NRs results from the comprehensive interaction of internal and external factors. Six dimensions, including human resources, ecological resources, development level of basic industries, policies and regulations, market demand, and socioeconomic environment, are crucial for driving the realization of EPV and the development of eco-industry.