Ecological Zoning Based on Suitability Evaluation of Ecological Product Development from the Value-Risk-Cost-Demand Perspective

Abstract

Current ecological value assessment models predominantly emphasize the potential value of ecological resources, neglecting the crucial aspect of value realization processes. Analyzing the value of ecological resources from the perspective of ecological products (EPs) is more instructive in realizing ecological values. The key factors controlling the realization of ecological product value are potential value, ecological risk, development costs, and human demand. Previous research has rarely integrated these four factors within the ecological zoning framework. This study proposes a suitability evaluation and zoning framework for ecological product development based on the “value-risk-cost-demand” perspective. First, an evaluation index system for the potential value of ecological products was developed, dividing EPs into ecological agriculture (EA), ecological industry (EI), and ecological tourism (ET), and assessing them using 13 indicators. Ecological risks were modeled using multi-scale patch analysis (MSPA) and other models. Development costs were estimated using cost entropy. The impact of population dynamics on EP demand was quantified using population density, night-time light data, and average land GDP, along with stacked buffer analysis. Next, an improved TOPSIS method was applied to integrate these four dimensions, producing a comprehensive suitability assessment for EP development. Finally, EP zoning was determined by overlaying the comprehensive evaluation results. This framework was used to identify the dominant mode zones of EPs within the region of Jintan District, Jiangsu Province, China. The findings suggest that the integrated assessment model proposed in this study has produced more reasonable outcomes in terms of spatial layout, land use area, reduction of fragmentation and ecological risk. This conclusion is supported by spatial distribution comparisons, optimal area deviation analyses, landscape index calculations and multi-model driven future simulations. This model effectively resolves the spatial mismatch present in the traditional approach, which solely focuses on the potential value of EPs. This study can be applied to other regions with developed economies and rich ecological resources, providing an effective reference for the choice of paths to realize the value of EPs.

1. Introduction

With the rapid pace of urbanization and the abuse of resources, global ecological issues have gained prominence [1]. The prior adverse impacts of solely prioritizing economic development while disregarding ecological damage have prompted scholars to reconsider the traditional regional development model [2]. Ecological zoning, an integral aspect of territorial spatial planning, plays a crucial role in ecological protection efforts. Ecological zoning is designed to preserve natural resources and safeguard biodiversity by delineating various ecological functional zones [3]. The prevailing scheme supporting ecological zoning is the quantitative assessment from the perspective of ecosystem services (ESs) value [4]. However, the evaluation of ecosystem service value only reflects its intrinsic potential value and lacks market-based transactional attributes, thus limiting its practical applicability. This has led to insufficient understanding of ecosystem service value among the public and government departments, resulting in a lack of enthusiasm for ecological protection when implementing planning schemes [5].

The concept of ecological products (EPs) can be divided into broad and narrow definitions. The concept of a narrow EP was proposed as early as 1985 [6] and has since been widely adopted by researchers and government departments [7,8]. It is commonly defined as a natural element that maintains ecological security, ensures ecological regulation functions, and provides a favorable living environment and includes things like clean air, fresh water, and a pleasant climate. Research on narrow EPs primarily focuses on the selection of dominant ecological products in specific regions and the siting of particular types of ecological products, lacking a top-level design for overall regional spatial planning [9,10]. The introduction of the gross ecosystem product (GEP), which monetizes the value of ecological products and services, has enabled a broader perspective on utilizing these products for ecological zoning [11]. Since then, researchers have widely accepted the concept of a broad EP [12,13] and have defined it as representing the goods and services that ecosystems provide through biological processes and interactions with human activities, ultimately enhancing human well-being. The concept of a broad EP emphasizes the dual natural and economic attributes of EPs and their value transformation through institutional design [14]. Broad EPs primarily emphasize the market-oriented economic characteristics of ecosystems and focus more on the development, utilization, and transformation of ecosystem services. Current ecological value assessment models predominantly emphasize the potential value of ecological resources, neglecting the crucial aspect of value realization processes. Analyzing the value of ecological resources from the perspective of ecological products (EPs) is more instructive in realizing ecological values. Thus, ecological zoning based on broad EPs could be more conducive to achieving objectives such as ecological protection and sustainable development [15,16].

Although ecological zoning studies based on ecosystem services (ESs) primarily highlight variations in the capacity of different zones to provide these services, in recent years, there has also been a significant amount of research focusing on the realization of ESs’ value. (1) Ecological value: Many studies focus on the assessment of eco-system services and the quantification of environmental value. For instance, quantitative evaluation models of ecosystem services are widely applied in ecological zoning research, with an emphasis on measuring the ecological value of different regions [17,18]. (2) Risk assessment: Other studies prioritize the analysis of ecological risks, particularly the impacts of climate change, land use change, and other risk factors on the ecological environment. Research methods in this area often employ risk assessment models to evaluate the vulnerability of ecosystems [19,20]. (3) Cost–benefit analysis: Research on ecological costs typically centers on cost–benefit analyses of conservation measures, examining the economic feasibility and effectiveness of different protection strategies to assist policymakers in rational resource allocation [21,22]. (4) Demand factors: Regarding demand factors, studies often focus on human activities’ demands on ecosystems, such as land demand and urbanization needs, exploring how human demands influence ecological zoning and conservation strategies [23,24]. However, although these studies individually address the important factors of value, risk, cost, and demand, most research primarily focuses on single dimensions or dual-factor trade-offs [22,25]. Few studies have comprehensively integrated these four factors within the framework of ecological zoning. Consequently, existing ecological zoning methods still face theoretical and practical challenges in simultaneously balancing value protection, risk mitigation, cost constraints, and demand matching. There is an urgent need to develop a comprehensive zoning model that integrates multiple factors to enhance the scientific rigor and operational feasibility of decision-making.

At the same time, with the introduction of the concept of EPs, scholars have also explored the primary approaches to realizing EPs. This mainly involved ecological equity trading [26], financial transfer payment [27], material raw material production [28], tourism and cultural service provision [29], resource quota trading [30], ecological carrier spillover value [31] and so on. These researches were the synthesis and summary of numerous practical cases of EP value realization, examining them from the perspectives of exchange subjects, exchange carriers, and exchange mechanisms related to the utility value of EPs using economic principles. However, the zoning research of EPs is actually more concerned with the suitability of EP development. Existing studies on the suitability of EPs development can be broadly categorized into two main types: (1) Studies focusing on the selection of dominant EPs in specific regions. For instance, research has been conducted on the identification of the Tea Culture Heritage Site in Chaoshan, Guangdong Province, as a model for development that is dominated by ecological tourism products [32]. Similarly, a portion of the Agusan Marsh Wildlife Sanctuary in Mindanao Island, Philippines, has been established using an ecological compensation model involving EP trading [33]. Furthermore, studies have been conducted on forest land in Shunchang County, Fujian Province, primarily supplying material types of EPs [34]. (2) Research focusing on the selection of locations for specific types of EPs. For example, priority eco-logical wetlands for waterbird conservation have been identified in Wuhan, China [35]. Additionally, an artificial island in Shenzhen Bay has been earmarked for development as an ecological habitat. Similarly, an ecological compensation area has been selected in the forests of southern Finland to conserve biodiversity values [36]. However, most of these studies have focused on the specific practice of EPs (specific localized scenarios or specific types of EPs). This narrow focus hinders the achievement of rational allocation and orderly utilization of regional overall spatial resources. There is a lack of top-level territorial spatial planning in the region as a whole.

To address the aforementioned issues, this study aims to explore and propose an evaluation method that includes potential value analysis of EPs, ecological risk pattern analysis, and cost analysis of ecological zoning transformation. This evaluation method is approached from the perspective of assessing the suitability of EP development in different regions. Furthermore, the study constructed the ecological zoning model based on the value-risk-cost-demand perspective. The objectives of this paper are as follows: (1) To construct an evaluation framework for assessing the suitability of EP development from the perspective of value-risk-cost-demand. (2) To establish regional quantitative solution methods and dominant mode zoning methods for EP value, risk, cost and demand. (3) To verify the feasibility of the zoning method by taking the Jintan District of Changzhou City as an example. The research outcomes aim to carry out ecological zoning from the perspective of realizing the value of EPs. Additionally, these outcomes provide policy guidance for implementing ecological zoning and delineating the rights and responsibilities of each participant.

2. Study Area and Datasets

2.1. Study Area

Jintan, situated in the southern region of Jiangsu Province within Changzhou City, spans a longitude range of 119°17′45′′ to 119°44′59′′ and a latitude range of 31°33′42″ to 31°53′22″. Administratively, it comprises three subdistricts and six towns (Figure 1). The district experiences four distinct seasons characterized by concurrent rainfall and high temperatures. The terrain primarily consists of low hills, alluvial lakes, and plains. It features extensive water resources, woodland, and renowned scenic attractions (e.g., Maoshan Mountain and Changdang Lake). It is recognized as one of the nation’s top 100 counties and cities in terms of comprehensive strength and is designated as a national ecological protection demonstration area. With its exceptional ecological environment and abundant natural resources, Jintan holds significant research value concerning the pathway of EP value realization.

2.2. Datasets

The data were collected in 2020, mainly including land use data, basic geographic data, meteorological data and socio-economic data. Among these, the land use data are from the Database of the Third Land Survey of Jintan District; digital elevation model (DEM) data are taken from the Geospatial Data Cloud (https://www.gscloud.cn/, accessed on 13 January 2024) with a spatial resolution of 30 m × 30 m; Soil data were obtained from the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn, accessed on 13 January 2024). Meteorological data, such as temperature and rainfall, were obtained from the National Meteorological Science Data Center (https://data.cma.cn/, accessed on 13 January 2024). Socio-economic data, such as agricultural production and prices, were obtained from the Jintan District Statistical Yearbook (https://www.jintan.gov.cn/, accessed on 13 January 2024); POI data were extracted from the Open Street Map Platform. Other relevant data, such as the ecological red line, special plan for woodland, and road network data, were provided by Changzhou Natural Resources and Planning Bureau. All data were processed to a 100 m resolution.

3. Methods

3.1. Basic Idea

Ecological zoning based on the realization of ecological product value serves two primary objectives: first, it provides support for various pathways to realize ecological products, fostering a conducive environment; second, it ensures the fulfillment of macro-level ecological service values. Therefore, the core aim of this paper is to develop an assessment and zoning methodology that effectively bridges these two aspects.

As illustrated in Figure 2, the key controlling factors for the realization paths of EPs are potential value, ecological risk, development cost, and human demand. In the ecological resource property rights transfer path, market mechanisms such as carbon trading and water rights trading monetize the value of ecological products, primarily reflecting the superposition of ecological potential value [37]. In mixed pathways involving both government and market participation, diversified green financial support can reduce the development costs of ecological products and accelerate project progress [38]. In the ecological industrialization path, the sustained popularity and significant economic benefits of eco-tourism products demonstrate the high demand for ecological products [39]. To achieve these goals, this study proposes a “Value-Risk-Cost-Demand” framework. This framework systematically connects macro-planning with micro-operation for EP value realization by integrating the four critical dimensions of ecological product development. The framework is embedded in both the process and outcomes of ecological product value realization. By adopting the “Value-Risk-Cost-Demand” perspective, this study conducts suitability assessments and zoning for ecological product development. This approach promises to address the limitations of traditional single-dimensional ecosystem service assessments or risk-value two-dimensional models. It tackles the issue of the “spatial mismatch of ecological resources” through multi-dimensional coupling, providing a more comprehensive and integrated solution for ecological product value realization.

3.2. Methodology for EP Development Suitability Evaluation and Ecological Zoning

The workflow of the development suitability and zoning of EP from the perspective of value-risk-cost-demand is illustrated in Figure 3. Firstly, an evaluation indicator system for assessing the potential value of EPs is constructed. We referenced the 13 value assessment indicators for the value of ES, which include material supply, cultural support, and environmental regulation. EPs are categorized into three types: ecological agriculture (EA), ecological industry (EI), and ecological tourism (ET) and are evaluated separately. Subsequently, we developed an ecological risk pattern analysis model to assess development risks for ecological products. Using a multi-scale patch analysis (MSPA) and hotspot analysis (Getis–Ord Gi*), we identified key ecological sources. After constructing a comprehensive resistance surface, the minimum cumulative resistance (MCR) model pinpointed ecological corridors. The gravity model determined the importance levels of these corridors. Together, ecological sources and corridors form the regional ecological risk pattern. Thirdly, from the perspective of the difficulty of ecological product development, we calculated the cost entropy values for each evaluation unit based on land use proportion and road network impact. These values represent the development cost of ecological products. For calculating human demand for ecological products, we considered the impact of population dynamics using population density, nighttime light data, and average land GDP as indicators. Using stacked buffer analysis, we determined the radiative effect of population dynamics to indicate human demand for ecological products.

To achieve a comprehensive evaluation of development suitability, we integrated the four dimensions—value, risk, cost, and demand—and applied the technique for order preference by similarity to ideal solution (TOPSIS) method for comprehensive decision-making. This approach enabled us to evaluate the suitability of EP development while simultaneously considering value, risk, cost, and demand. Finally, through a superimposed analysis of the evaluation results, we achieved the delineation of ecological product zones (EPZs) in the study area.

3.2.1. Quantifying the Potential Value of EPs

In this study, we utilized international mainstream ecological value assessment indicator systems such as the Millennium Ecosystem Assessment, the Economics of Ecosystems and Biodiversity, and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services as references. Additionally, we considered Zhang et al.’s research [40], which compiled value indicators related to EPs from governmental documents worldwide. Combining these sources, a total of 13 types of indicators were selected for this paper, including food provision, water yield, daily recreation, tourism aesthetics, air purification, climate regulation, water conservation, carbon sequestration, oxygen release, biodiversity, negative ion supply, soil conservation, and flood storage. Quantitative calculations were performed for these value indicators [41,42,43,44,45,46]. The specific calculation methods for these indicators are provided in Table S1.

For the aforementioned 13 indicators, a high-dimensional ranking method based on dyadic linear programming was introduced to determine the weights of each indicator after experts ranked their importance [47]. This is a high-dimensional extension of the traditional ranking method so that it is no longer limited by the number of indicators like the ranking method coupled with the entropy weight method [48]. In the high-dimensional ranking weighting method, it is first assumed that the weight ranking of all $J$ dimensions is $k$. At this point, the composite index ($C_i^k$) of any evaluation unit i can be expressed by the arithmetic mean of the optimal ($U{C}_i^k$) and the worst ($L{C}_i^k$) values of the weighted sums of the scores for each of its dimensions. Additionally, when the ranking weights of each dimension are known (assumed to be $k$), $U{C}_i^k$ and $L{C}_i^k$ can be derived by dyadic linear programming. The aforementioned calculation process can be described as follows:

$$C_i^k=(U{C}_i^k+L{C}_i^k)/2$$

(1)

$$U{C}_i^k=\max \left\{\sum _{p=1}^1{z}_{i,k_p},\sum _{i=1}^2{z}_{i,k_p}/2,\cdots ,\sum _{p=1}^n{z}_{i,k_p}/n\right\}$$

(2)

$$L{C}_i^k=\min \left\{\sum _{p=1}^1{z}_{i,k_p},\sum _{i=1}^2{z}_{i,k_p}/2,\cdots ,\sum _{p=1}^n{z}_{i,k_p}/n\right\}$$

(3)

where $k_p$ denotes the dimension with ranking weight $p$ (descending order of weight values) when the dimension ranking case is $k$ and $z_{i,k_p}$ denotes the score of evaluation unit $i$ in the $k_p$ dimension.

3.2.2. Quantifying the Ecological Risk Pattern of EPs

In the field of spatial planning research, studies that take ecological risk into account primarily focus on the connectivity of spatial units [49]. It is essential to consider the preservation of landscape integrity and the transmission of ecological functions [50]. The ecological risk pattern considers the fundamental threshold for preventing ecological risks during EP development. This represents a comprehensive approach to ensuring the integrity and connectivity of regional ecosystems, incorporating key landscape elements and their spatial structure to effectively coordinate functions [51]. For the development of EPs, the analysis of ecological risk patterns can be employed to identify ecologically sensitive areas and corridors. This analysis helps constrain the development of ecological products and guides the implementation of risk mitigation strategies. As ecological risks function solely as spatial constraints in this context, the study adopts the following analytical paradigm: “identifying ecological sources—constructing resistance surfaces—extracting ecological corridors—forming ecological risk patterns” [52].

In the identification of ecological source sites, the study area underwent ecological sensitivity and disturbance evaluations, integrating ecological land patches using morphological spatial pattern analysis. Subsequently, clustering analysis, employing the hotspot analysis method (Getis–Ord Gi*) and area scale screening, was conducted to determine the final ecological source sites.

$${Gi}^{\ast }=\frac{{\displaystyle \sum _{i=1}^n}{w}_{ij}{x}_i}{\sqrt{\frac{{\displaystyle \sum _{i=1}^n}{w}_{ij}{\displaystyle \sum _{i=1}^n}{w}_{ij}{x}_i^2}{{\displaystyle \sum _{i=1}^n}{w}_{ij}{\displaystyle \sum _{i=1}^n}{w}_{ij}}-{\left(\frac{{\displaystyle \sum _{i=1}^n}{w}_{ij}{x}_i}{{\displaystyle \sum _{i=1}^n}{w}_{ij}{\displaystyle \sum _{i=1}^n}{w}_{ij}}\right)}^2}}$$

(4)

where ${Gi}^{\ast }$ represents the Getis–Ord Gi* statistic, $w_{ij}$ denotes the weight of the spatial adjacency between the $i$th and $j$th elements, and $x_i$ stands for the value of the $i$th element.

For the construction of resistance surfaces, three sub-resistance surfaces were established based on land use types, natural environmental attributes, and human construction activities, drawing from existing studies. A composite resistance surface was derived through comprehensive superposition. In the extraction of ecological corridors, the minimum cumulative resistance model was applied, utilizing the identified ecological source points and composite resistance surfaces. Cost–distance analysis was performed, treating all ecological source sites as both starting and ending points, and employing cycle calculation to delineate all ecological corridors.

$$MCR=f_{\min }\sum _{j=n}^{i=m}{D}_{ij}\ast r_{ij}$$

(5)

where $D_{ij}$ denotes the distance between connecting neighboring points $i$ and $j$, $r_{ij}$ denotes the resistance coefficient between neighboring points $i$ and $j$, $n$ is the number of ecological sources, and $m$ is the number of resistance surface grids.

Subsequently, the gravity model was employed to assess the interaction strength between ecological source sites and grade the importance of different corridors.

$$G_{ij}=\frac{N_i\times N_j}{M_{ij}^2}=\frac{\left[\frac{1}{R_i}\times \ln \left(S_i\right)\right]\left[\frac{1}{R_j}\times \ln \left(S_j\right)\right]}{{\left(\frac{Y_{ij}}{Y_{\max }}\right)}^2}={\displaystyle \frac{Y_{\max }^2\times \ln \left(S_i\right)\times \ln \left(S_j\right)}{Y_{ij}^2\times R_i\times R_j}}$$

(6)

where $G_{ij}$ is the strength of interaction between ecological source sites; $N_i$ and $N_j$ are the values of source sites $i$ and $j$, respectively; $M_{ij}$ denotes the value of resistance between two spots; $R_i$ and $R_j$ denote the cumulative resistance values of patches $i$ and $j$, respectively; $S_i$ and $S_j$ denote the areas of source sites $i$ and $j$, respectively; $Y_{ij}$ denotes the cumulative resistance value of the corridor which is between source sites $i$ and $j$, and $Y_{\max }$ denotes the minimum resistance value of all corridors in the region.

3.2.3. Quantifying the Development Costs of EPs

In EP development, assessing development costs in monetary terms is challenging due to the lack of relevant data, encompassing both direct costs such as capital investment in infrastructure construction and indirect costs such as opportunity costs resulting from changes in land use types. Therefore, this paper characterizes the development cost of EP based on the degree of difficulty of EP development, considering two key aspects:

1.

The degree of difficulty of land development and utilization: This factor is evaluated based on indicators such as “the proportion of arable land” [53], “the proportion of construction land” [54], and “the proportion of city greenland” [55]. Regions with a high proportion of arable land are more suitable for developing EA products, while it is more challenging to develop other types of EP. EI products typically require a high proportion of construction land and are often developed in regions with such land availability. Areas with a high proportion of city green space are also typically more amenable to ET-type products. Conversely, it is more challenging to develop EA products in areas with a high proportion of construction land.

$$P_i=S_i/S_{unit}$$

(7)

where $P_i$ is the proportion of agricultural land, built-up land, or urban green space; $S_i$ is the area of agricultural land, built-up land, or urban green space within the unit; and $S_{unit}$ is the total area of the unit.

2.

Road network impact. The road network plays a crucial role in facilitating the transportation of regional EA products, fostering the industrial agglomeration of EI products, and enhancing the accessibility of ET products [56]. In this paper, the influence of the road network on EP development is characterized by “road network density” ($D_r$) and quantified through kernel density analysis [57].

At the same time, the four considerations regarding development costs—‘proportion of arable land’, ‘proportion of construction land’, ‘proportion of city greenland’, and ‘impact of road network’—are not entirely independent of each other. They also exhibit a degree of causal relationships; for instance, a high proportion of construction land often correlates with a decrease in the proportion of arable land and an increase in road network density. Accordingly, we referred to the treatment of such causality in AQUCROSS et al. [58,59]. The generated formula for calculating cost entropy is as follows:

$$V{C}_i={\left({\displaystyle \frac{u_i{D}_r\times P_i}{{\displaystyle \prod _1^n\left(1-P_i\right)}}}\right)}^{-1}$$

(8)

where $V{C}_i$ represents the development cost of EP, $P_i$ denotes the proportion of site types, and $u_i$ stands for the constant correction factor for such EP.

3.2.4. Quantifying the Product Demand of EPs

Generally, the demand for EPs is closely related to population dynamics. Specifically, higher population density corresponds to increased levels of human activity, greater economic development, and a higher demand for ecological products in the region. In this study, population density, night lighting index, and average land GDP were selected as indicators to calculate the demand for ecological products across different regions. The specific formula is as follows:

$$\left\{\begin{array}{l}E{D}_{i,j}={\displaystyle \frac{E{R}_{i,j}-E{R}_{j\min }}{E{R}_{j\max }-E{R}_{j\min }}}\\ E{R}_{i,j}=k_j\times x_{1,i}\times \lg \left(x_{2,i}\right)\times \lg \left(x_{3,i}\right)\end{array}\right.$$

(9)

where $E{D}_{i,j}$ denotes the demand for EP, $E{R}_{j\min }$ represents the minimum demand value for EP of category $j$ in the study area, and $E{R}_{j\max }$ represents the maximum demand value for EP category $j$ in the study area. $k_j$ is the adjustment coefficient, $x_{1,i}$ is the population density, $x_{2,i}$ is the night lighting index, and $x_{3,i}$ is the average GDP of the land.

3.2.5. Suitability Calculating and EP Zoning

The parcel unit with higher product value, lower ecological risk, smaller development cost and greater product demand is more suitable for EP development, and vice versa. TOPSIS is a multi-objective decision analysis method. Its basic principle is to calculate the distance between the evaluation object and the optimal solution and the worst solution to calculate or sort the comprehensive score [60]. In this paper, the TOPSIS method, coupled with the entropy weight method, is utilized to comprehensively evaluate the value, ecological risk, and development cost of EPs. This paper uses the Canberra distance in TOPSIS evaluations instead of the Euclidean distance. Subsequently, a comprehensive evaluation coupling model of EP development suitability based on value-risk-cost-demand is established.

There are a few points that need to be clarified in response to the improved TOPSIS approach below, as follows:

1.

The multi-objective is co-trended (this paper turned the ecological risk and development cost into a positive), and the standardized decision matrix $X$ is constructed.

$$X=\left[\begin{array}{cccc}{X}_{11}& X_{12}& \dots & X_{1n}\\ X_{21}& X_{22}& \dots & X_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ X_{m1}& X_{m2}& \dots & X_{mn}\end{array}\right]$$

(10)

where $X_{ij}$ denotes the score of the $i$th evaluation unit under the $j$th decision goal.

2.

The entropy weight method is employed to determine the weight of each decision objective’s influence on the outcome. The entropy weight method is used for matrix transformation. Its advantage lies in determining weights by measuring the information entropy of indicators, which reduces subjective judgment and ensures the objectivity of weights and the relative importance among indicators. This involves calculating the product of the standardized decision matrix and the weight vector, which results in the creation of the weighted standardized decision matrix Y.

$$\left\{\begin{array}{l}{E}_j=-{\displaystyle \frac{1}{2\ln (2)}}{\displaystyle \sum _{i=1}^4}\left({\displaystyle \frac{X_{ij}}{{\displaystyle \sum _{k=1}^4}{X}_{kj}}}\ln {\displaystyle \frac{X_{ij}}{{\displaystyle \sum _{k=1}^4}{X}_{kj}}}\right)\\ w_j={\displaystyle \frac{1-E_j}{4-{\displaystyle \sum _{j=1}^4{E}_j}}}\end{array}\right.$$

(11)

where $E_j$ represents the information entropy and $w_j$ denotes the influence weight on the outcome under the $j$th decision objective.

$$Y_{ij}=w_j\times X_{ij}$$

(12)

$$Y=\left[\begin{array}{cccc}{Y}_{11}& Y_{12}& \dots & Y_{1n}\\ Y_{21}& Y_{22}& \dots & Y_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ Y_{m1}& Y_{m2}& \dots & Y_{mn}\end{array}\right]$$

(13)

where $Y_{ij}$ denotes the weighted score of the $i$th evaluation unit under the $j$th decision objective.

3.

In traditional TOPSIS evaluations, the Euclidean distance is frequently utilized to quantify the distance between an object and an ideal solution. However, the Euclidean distance has certain limitations—it is less sensitive to smaller values and disregards the importance of dimensionality. In contrast, the Canberra distance exhibits greater sensitivity to values close to zero and remains unaffected by the dimensionality of the data [61]. It can address these limitations more effectively. Therefore, this paper opts to employ the Canberra distance.

During the dominant mode zoning process, taking the 100 m × 100 m grid as the basic unit and using the results of EA, EI, and ET development suitability evaluation based on the “value-risk-cost-demand” perspective with a superimposed analysis, the highest score method is used to determine the mode corresponding to the highest suitability evaluation score of each grid cell, which is determined to be the dominant realization mode. The formula is as follows:

$$EPZ=\max \left(S_{agri},S_{inds},S_{\mathrm{tou}r}\right)$$

(14)

where $EPZ$ is the dominant mode zoning, $S_{agri}$ is the suitability evaluation score of EA, $S_{inds}$ is the suitability evaluation score of EI, and $S_{\mathrm{tou}r}$ is the suitability evaluation score of ET.

It should be noted that, if the development suitability scores of EA, EI, and ET for an evaluation unit are all lower than 0.3, then this signifies that none of the three EPs is suitable for development in that area. In such cases, the dominant mode of this unit is changed to ecological compensation (EC).

4. Results

4.1. Indicator System and Similarity Test

The value indicator system of EPs is calculated by the high-dimensional ranking weighting method (

Table 1. Indicator system for the value of EPs.

Indicator	Ecological Agriculture	Ecological Industry	Ecological Tourism
Food provision	0.089	0.015	0.020
Water yield	0.069	0.009	0.018
Air purification	0.017	0.027	0.056
Climate regulation	0.014	0.030	0.052
Water conservation	0.048	0.025	0.010
Carbon sequestration	0.021	0.015	0.029
Oxygen release	0.019	0.018	0.060
Biodiversity	0.008	0.026	0.002
Negative ions supply	0.028	0.024	0.070
Soil conservation	0.041	0.022	0.013
Flood storage	0.003	0.006	0.002
Daily recreation	0.005	0.004	0.034
Tourism aesthetics	0.001	0.012	0.038

). It can be seen that food provision, water yield, and soil conservation mainly focus on EA, which is due to their reliance on high-quality arable land resources, characteristic agricultural products, and freshwater resources. Air purification, climate regulation, oxygen release, negative ion supply, and tourism aesthetics are mainly focused on ET because these functions have a positive impact on human health and tourism development. Climate regulation and biodiversity have a higher value in EI, which is due to the need to develop environmentally sensitive biomedicine and storage of electronic components.

Meanwhile, this paper carried out a similarity test for 13 indicators by using Pearson correlation analysis (Figure 4). The result shows that the absolute value of the correlation coefficient R between different indicators is less than 0.8, which indicates that there are no highly consistent redundant indicators, and that each indicator plays a role in the value evaluation.

4.2. EP Development Suitability Evaluation

We used the TOPSIS method to conduct a comprehensive assessment of the suitability of the three EPs for development. Figure 5 shows that (1) the high suitability areas for EA are predominantly situated in the eastern half of Jintan and along the lake, particularly concentrated in Xuebu Town, Rulin Town, and Zhiqian Town. These regions boast abundant arable land resources and ample surface water, making them favorable for agricultural activities. Conversely, the low suitability areas are mainly located within urban development zones and ESP areas. (2) High suitability areas for EI are primarily found in the built-up areas of towns and cities within the Dongcheng and Xicheng subdistricts, as well as in the northwestern region characterized by superior biodiversity and strong temperature regulation capabilities. This includes locations such as Jintan High-tech Park and Changzhou Jintan Economic Development Zone, which exhibit high development potential. Conversely, suitability in all other areas is low. (3) The ET zone primarily relies on positive ESs that are beneficial to human health, such as air purification, climate regulation, provision of negative ions, and oxygen release, as well as ET services like tourism aesthetics and daily recreation. The more suitable areas are mainly distributed in Xuebu Town and Rulin Town, encompassing destinations such as the Jintan Maoshan tourism resort and Changdang Lake Water Street.

4.3. EPZ Result

After conducting a comprehensive suitability analysis, we utilized the highest score method to determine the EPZ (Figure 6). The region’s EA dominant mode area occupies the largest proportion, mainly concentrated in Zhixi Town, Zhulin Town, and Zhizian Town. Arable land, breeding pits, and ponds cover the most extensive area, followed by tea gardens and water bodies. On the other hand, the dominant mode area in EI has the smallest proportion, mainly found in the built-up areas of the Dongcheng and Xicheng subdistricts, with sporadic distribution in other subdistricts, primarily near the old city center and surrounding townships. Construction land comprises the highest proportion of land use in these areas. ET dominant mode areas are dispersed among attractions with high tourist aesthetic value, service areas of farmhouses with excellent ecological environments, and areas providing significant ecosystem regulating services such as the release of negative ions and climate regulation. Examples include Changdang Lake scenic area, Maoshan National Forest Park, and Rulin characteristic countryside. The EC dominant mode area is notably influenced by the ESP construction results, encompassing forested land with robust vegetation cover and larger river and lake areas, displaying spatial clustering characteristics.

5. Discussion

5.1. Comparison of Zoning Results of Different Methods

To validate the effectiveness of the zoning method proposed in this paper, we compare the EPZ results with those obtained using the ecological regulation (ER)-based method and the ecological potential value (EPV)-based method. The ER method calculates the suitability of ecological products for development [62], while the EPV method, which is a special case of the approach presented in this paper, focuses solely on the value of EPs. Compared with the ER method, the EPV method incorporates both the supply and cultural values of ecological products. This section compares the integrated value-risk-cost-demand evaluation method proposed in this paper with the two aforementioned methods. The experiment was conducted at the same 100 m resolution. As shown in Figure 7, the zoning results display significant differences, which are illustrated using four typical regions.

(1) Region 1: Jintan Economic Development Zone. This region hosts numerous industrial industries and is situated in the city’s built-up area. Under the ER and EPV methods, a larger proportion of the space is allocated to EA or EC. However, under the integrated assessment method, EI dominates. This shift occurs because the integrated method considers development costs, and the costs of EA or EC in this region are too high for these activities to be feasible. (2) Region 2: Important grain-producing area. In this region, the ER method emphasizes space for EI and EC. In contrast, the EPV method, and integrated assessment methods allocate significantly more space to EA. This change arises from the integrated consideration of the supply value of agro-ecological products. (3) Region 3: Qianzihu Park. Located near the urban built-up area, this region sees less space allocated to recreational tourism under the ER method due to its failure to account for the cultural value of ecotourism products. However, the integrated assessment method assigns more space for ET compared with the EPV method. This is driven by the high population density and extensive urban green space surrounding Qianzihu, which create a strong demand for cultural tourism. Additionally, the area’s accessibility facilitates tourism project development, further enhancing its suitability for recreational tourism under the integrated assessment method. (4) Region 4: Important ecological source. This region is a key area for ecological protection and Taoist cultural heritage. Considering its ecological risks and high cultural value, the integrated assessment method prioritizes ET and EC, limiting EA and EI.

5.2. Quantitative Comparison of Zoning Results

This section quantitatively compares the results of different models based on the optimal area deviation rate and landscape index calculations to demonstrate the rationality and advantages of the zoning results.

The optimal area deviation rate measures the difference between the optimal quantity structure of various land use types and the actual land use distribution in the zoning results of different models. The optimal quantity structure is determined using the optimal land use allocation model. Drawing upon the research conducted by [63,64], we identified three landscape indices to quantify the extent of landscape fragmentation. The Fragstats 4.3 software was utilized to compute these landscape indices. The indicators are described and formulated as follows in

Table 2. Calculation formula for the quantitative analysis of zoning results.

Indicator	Introduction	Formula	Formula Interpretation
Optimal area deviation rate (δArea)	The δArea measures the discrepancy between actual and optimal land use areas, with higher values indicating greater deviation and less rational planning.	$\delta Areaij=\left|{\displaystyle \frac{ Are{a}_{ijModel}-Are{a}_{jOptimal}}{ Are{a}_{jOptimal}}}\right|$	$Are{a}_{ijModel}$ is the area of land use type $j$ in model type $i$ and $Are{a}_{jOptimal}$ is the optimal area of land use type $j$.
Patch density (PD)	PD quantifies the patch count per unit area, with higher values denoting increased fragmentation.	$\mathrm{PD}={\displaystyle \frac{N{P}_i}{Are{a}_i}}$	$N{P}_i$ denotes the number of patches in the landscape of category $i$ and $Are{a}_i$ denotes the total area of the landscape of category $i$.
Cohesion index (COHESION)	The COHESION index, ranging from 0 to 100%, evaluates internal patch connectivity, with values near 100% indicating high aggregation and connectivity.	$\mathrm{COHESION}=\left[1-{\displaystyle \frac{{\displaystyle \sum _{j=1}^m}{p}_{ij}}{{\displaystyle \sum _{j=1}^m}{p}_{ij}\sqrt{a_{ij}}}}\right]{\left[1-{\displaystyle \frac{1}{\sqrt{A}}}\right]}^{-1}\times 100$	Where $a_{ij}$ refers to the area of patch $j$ in the landscape of category $i$; $p_{ij}$ represents the perimeter of patch $j$ in the landscape of category $i$; and $A$ is the total area of the landscape.
Contagion index (CONTAG)	The CONTAG index assesses the distribution pattern of a patch type, with higher values suggesting a more clustered arrangement in the landscape.	$\mathrm{CONTAG}=\left[1+{\displaystyle \frac{{\displaystyle \sum _{i=1}^m}{\displaystyle \sum _{k=1}^m}\left[(pi)\left(\frac{\mathrm{g}ik}{{\displaystyle \sum _{k=1}^m}\mathrm{g}ik}\right)\ln (pi)\left(\frac{\mathrm{g}ik}{{\displaystyle \sum _{k=1}^m}\mathrm{g}ik}\right)\right.}{2\ln (m)}}\right]\times 100$	Where $pi$ is the percentage of area occupied by type patches; $\mathrm{g}ik$ denotes the number of type $i$ and $k$ patches in close proximity; and $m$ is the total number.

.

There are no ready-made data for the optimal land use area ($Are{a}_{jOptimal}$); the calculation formula is as follows:

$$Z=\max \sum _{k=1}^n{c}_{k}f\left(x_k\right)$$

(15)

$$\mathrm{s}.\mathrm{t}.\left\{\begin{array}{l}{\displaystyle \sum _{j=1}^n}{a}_{ik}{x}_k=(\ge ,\le )b_k,(i=1,2,\cdots ,m)\hfill \\ x_k\ge 0,(k=1,2,\cdots ,n)\hfill \end{array}\right.$$

(16)

$$Are{a}_{jOptimal}=\sum _{k=1}^{n}Gj\left(f\left(x_k\right)\right)$$

(17)

where $Z$ represents the land-use efficiency of all evaluation units, $f\left(x_k\right)$ denotes a set of land types within the evaluation unit, and $c_k$ signifies the coefficient of different land-use efficiencies per unit area. Equation (16) represents the constraint, where $a_{ik}$ represents the coefficient corresponding to the $k$th variable in the $i$th constraint, and $b_k$ represents the constraint value. $Gj\left(f\left(x_k\right)\right)$ represents the area of land category $j$ in the evaluation unit.

Figure 8 shows that, in terms of land area, the integrated evaluation model proposed in this paper has the smallest degree of discrepancy with the optimal quantitative structure. This consistency confirms the rationality of the research evaluation results. Furthermore, it is noteworthy that 93.1% of the industrial zoning results from our study are situated within the urban development boundary. This observation further corroborates the alignment of our zoning outcomes with the development objectives of the regional government.

From the perspective of landscape indices, the integrated model exhibits a lower patch density compared with the other two models. Additionally, the values for COHESION and CONTAG are higher for the integrated model. This suggests that the integrated assessment model leads to reduced patch fragmentation, enhanced internal connectivity, and greater cohesion among various land types, thereby delineating distinct functional areas. This methodology encourages contiguous land development, which is conducive to efficient and sustainable land use practices.

5.3. Comparison of Ecological Development Simulations Driven by EP Zoning

As a critical tool for the optimal allocation of land resources, ecospatial zoning results have diverse application potential. One significant application is using ecological zoning results to simulate future land use patterns, which helps assess the scientific validity and practical applicability of zoning.

In this study, four potential scenarios of future regional development were constructed by combining ecological zoning results from different methods with land use simulations to validate the practical efficacy and advantages of the integrated assessment model. The four scenarios are (1) Scenario consistent with previous development (S-NONZ), which represents historical development trends without EPZ; (2) Scenario of EPZ development based on the ER model (S-ERZ); (3) Scenario of EPZ development based on the integrated model (S-EVZ); and (4) Scenario of EPZ development based on the integrated assessment model (S-IZ).

In this paper, the patch-generating land use simulation (PLUS) model was used to simulate the land use structure in 2036 across four scenarios by adjusting the land transfer probabilities of Markov chains based on land use data and 14 driving factors from 2008, 2015, and 2022. The 14 driving factors include elevation, slope, slope aspect, soil type, annual mean precipitation, annual mean temperature, population density, GDP, nighttime light data, distance to urban centers, distance to major roads, distance to rivers, distance to transportation hubs, and the normalized difference vegetation index (NDVI). The PLUS model incorporates a rule detection technique derived from the land expansion analysis strategy along with a cellular automaton framework that utilizes a multi-type stochastic seeding approach [65]. Initially, the expansion of land use across two timeframes was examined using the random forest classification algorithm to determine the likelihood of various landscape types. Subsequently, the integrated probabilities of land use change were computed using a roulette wheel adaptive inertia competition method. In the final step, the definitive land use categories were established through the implementation of random patch generation, a transition transfer matrix, and a threshold-decreasing mechanism. In line with related studies, the transfer probability and neighborhood weight parameters were adjusted and set.

In land use simulation, addressing land use conflict areas remains a significant challenge. As shown in Figure 9, the zoning method based on the integrated assessment model improves simulation accuracy in conflict areas to a certain extent. This paper illustrates the resolution of different types of land use conflicts, as follows: (1) conflict between construction land and forest land. Region 1 represents the confluence of Hengshan Forest Farm and construction areas. In the first three simulation results, construction land encroaches significantly on forest land. However, the S-IZ simulation results better protect the forest land area. As Hengshan Forest is classified as a provincially important forest protection area, its preservation is a priority. (2) Conflict between woodland and cropland. Region 2 encompasses two mountainous areas predominantly covered by woodland. The first three simulations show the presence of arable land in the mountainous areas, while the S-IZ simulation preserves the integrity of the woodland. This outcome aligns with Jintan District’s policy of returning cropland to forests to create high-quality, contiguous woodland areas. (3) Conflict between cultivated land and construction land. Region 3 is situated adjacent to urban residential zones. In the first three simulation results, construction land encroaches on cultivated land to varying degrees. The S-IZ simulation, however, ensures that cultivated land remains intact. With high soil quality and complete irrigation infrastructure, this area is part of the core high-standard farmland demonstration area and the grain-green, high-efficiency creation project. Protecting this land is vital for maintaining the arable land red line and ensuring food security. (4) Conflict between construction land and other land. Region 4 constitutes a segment of the Maoshan–Qianzihu ecological corridor, extending east–west across the city. In the first three simulations, construction land obstructs the corridor, disrupting ecological connectivity. The S-IZ simulation preserves the corridor, ensuring the exchange of materials and energy between ecological sources and maintaining the integrity of this crucial ecological link.

Figure 10 illustrates the projected areas and landscape indices of different land use types under four development scenarios in 2036. The results reveal significant differences in spatial patterns and ecological impacts across the scenarios. The S-NONZ exhibits the most extensive expansion of construction land and wasteland, accompanied by increased landscape fragmentation (higher PD and lower COHESION/CONTAG). In contrast, the S-IZ demonstrates optimized land allocation, significantly protecting forest and grassland ecosystems, enhancing landscape connectivity (higher COHESION and CONTAG), and reducing landscape fragmentation (lower PD). The S-ERZ and S-EVZ show intermediate outcomes, performing better than the S-NONZ but less effectively than the S-IZ, highlighting the trade-offs between ecological conservation and development pressures. These findings underscore the superiority of the integrated zoning approach in balancing sustainable land use and ecological integrity under future development pressures.

6. Conclusions

This study proposed a comprehensive framework for ecological zoning based on the suitability evaluation of ecological product development from the value-risk-cost-demand perspective. This paper has successfully achieved the following specific objectives.

6.1. Construction of an Evaluation Framework for EP Development Suitability

A multidimensional evaluation framework integrating potential value, ecological risk, development cost, and product demand was established. The value dimension incorporated 13 indicators (e.g., food provision, climate regulation, biodiversity) through a high-dimensional ranking method based on the binary linear programming method, which objectively determined weights while mitigating subjective bias. Ecological risk was quantified by constructing an ecological risk pattern using MSPA, MCR, and gravity models, prioritizing source areas and corridors. Development costs were characterized by land-use compatibility and road network impacts, while product demand was assessed via population density, night lighting, and GDP. The TOPSIS method integrated these dimensions, resolving spatial mismatches caused by traditional single-value approaches. This framework provides a systematic and quantitative basis for EP suitability assessment.

6.2. Development of Regional Quantitative Solutions and Dominant Mode Zoning Methods

The study introduced a grid-based (100 m × 100 m) zoning approach, where the dominant EP mode for each unit was determined by the highest suitability score among ecological agriculture, ecological industry, and ecological tourism. Areas with scores below 0.3 were designated for ecological compensation. In Jintan District, EA dominated in eastern plains and lakeside regions (e.g., Xuebu Town), aligning with fertile farmland and water resources. EI clustered in urbanized zones (e.g., the Dongcheng subdistrict), reflecting industrial agglomeration potential. ET prevailed in scenic areas (e.g., Maoshan Mountain), driven by tourism aesthetics and health benefits. EC zones matched ESP-protected forests and water bodies. The method demonstrated high spatial accuracy, with 93.1% of industrial zones conforming to urban boundaries, validating its alignment with regional planning objectives.

6.3. Verification of Feasibility Through a Case Study in Jintan District

The integrated model outperformed traditional ER and EPV models in spatial rationality and practicality. Quantitative comparisons revealed its optimal area deviation rate was 12.3% lower than ER and 8.7% lower than EPV, indicating closer alignment with ideal land-use structures. Landscape indices (e.g., lower patch density, higher cohesion) confirmed reduced fragmentation and enhanced connectivity. Future simulations using the PLUS model further validated its efficacy in resolving land-use conflicts (e.g., forest encroachment, ecological corridor preservation). These results underscore the model’s capability to balance ecological protection with socio-economic development.

This study advances ecological zoning by integrating multi-dimensional drivers of EP development, addressing gaps in traditional value-centric approaches. The comprehensive framework offers methodological insights and the zoning outcomes provide actionable insights for territorial spatial planning. The approach is transferable to regions facing similar ecological–economic trade-offs, fostering sustainable resource utilization and value realization of ecosystem services. This contribution is meaningful in enhancing our comprehension of mechanisms for realizing the value of ecological products and enhancing ecosystem management and protection strategies.

7. Limitations

This study has several limitations that warrant discussion. Firstly, while we provided a preliminary assessment of development costs, our analysis did not fully account for indirect economic costs (e.g., long-term infrastructure maintenance, labor displacement) or ecological trade-offs (e.g., biodiversity loss impacting ecosystem services). The omission of region-specific economic conditions (e.g., income disparities, subsidy policies) and dynamic land-use regulations (e.g., zoning restrictions, conservation mandates) may lead to an underestimation of spatial heterogeneity in cost–benefit outcomes. Future cost frameworks should integrate spatially explicit socioeconomic datasets and policy scenario modeling to enhance practical relevance.

Secondly, the assumption of strictly localized demand for ecological products requires refinement. By neglecting cross-regional demand–supply interactions (e.g., resource teleconnections, inter-basin transfers), our demand quantification approach may oversimplify real-world spatial dynamics. This limitation could affect the accuracy of resource allocation predictions, particularly in regions with high external dependency. Adopting methods such as multi-regional input–output analysis or spatially coupled agent-based models could better capture demand leakage and interjurisdictional resource competition.

This study acknowledges the underuse of the input–output framework for assessing land-use efficiency at the planning scale, primarily due to methodological challenges in quantifying eco-output rate indicators. Unlike well-established economic indicators (e.g., GDP), the standardized output capacity of ecological products has not yet been fully developed in spatial planning. Developing methods to forecast output capacity will be a key focus for future research.