The Ecological–Economic Zoning Scheme and Coordinated Development of the China–Russia Northeast–Far East Transboundary Region

Abstract

The China–Russia northeast–far east transboundary region is ecologically complex and economically promising, but fragmented cross-border management poses challenges to ecological security and regional sustainable development. To scientifically reveal functional differentiation and support bilateral cooperation, this study established a comprehensive evaluation system comprising 21 indicators across five categories: natural, ecological, economic, social, and resource. Using the Partitioning Around Medoids (PAM) clustering algorithm at the grid scale, eight initial clusters with distinct eco-economic characteristics across administrative boundaries were identified. Based on these results, spatial patterns were refined using expert knowledge from both China and Russia, ultimately delineating ten core eco-economic functional zones. The study finds that (1) the results of the eco-economic zoning scheme reveal clear spatial functional differentiation, with the northern part of the region focusing on ecological conservation and resource development, and the southern part on agricultural and forestry production as well as port trade; and (2) China and Russia show significant differences in natural resource endowments, infrastructure levels, and population distribution, indicating strong potential for functional complementarity and coordinated development. Further, this study breaks through traditional administrative-unit-based zoning approaches and proposes a grid-scale eco-economic zoning scheme across administrative boundaries, providing spatial support for ecological protection, resource development, and regional governance in the border areas between China and Russia. The findings may also serve as a methodological reference and practical demonstration for eco-economic zoning scheme and coordinated management in other complex transboundary regions around the world.

1. Introduction

The China–Russia northeast–far east transboundary region is the area where the two countries share the longest border, with the most intensive economic and cultural exchanges, and a high concentration of transport ports and cross-border hubs. It has become a potentially important region shaping the geo-economic landscape of Northeast Asia [1].

This region exhibits pronounced spatial heterogeneity in terms of geographical structure, ecological background, and industrial composition [2,3,4]. At the same time, as a key component of the Heilongjiang–Amur River Basin, it encompasses several transboundary ecological units, including the Sanjiang Plain and the Sikhote-Alin Mountains. Valuable natural resources such as water and black soil are distributed across national borders, while ecosystems such as wetlands and lakes, as well as habitats for rare species like tigers and leopards, are ecologically continuous across the boundary [5,6,7]. However, under the dual pressures of global climate change and intensified human activity, stark differences between China and Russia in development speed and modes, resource exploitation intensity and efficiency, as well as governance systems and cultural frameworks, have led to long-term fragmented and disconnected management. This fragmentation threatens the sustainability of the shared ecological system and exacerbates the tension between ecological integrity and administrative divisions. As a result, a series of severe ecological issues have emerged, posing significant risks to ecological security, biodiversity conservation, and sustainable development in both countries [8,9]. Therefore, it is imperative to conduct a scientifically grounded and well-structured analysis of eco-economic zones for this important transboundary region. Clarifying the core functions of different sub-regions and identifying appropriate green development pathways are essential steps to enhancing bilateral cooperation and supporting the shared development goals of both nations [10].

Eco-economic zoning schemes serve as a key approach for identifying regional ecological characteristics, resource endowments, and differences in economic development. It provides an important technical foundation for optimizing functional regionalization and promoting coordinated governance. In recent years, extensive research has been conducted on the theoretical framework, indicator systems, and zoning methodologies of eco-economic zoning schemes. These approaches have been widely applied at various spatial scales, including provinces, counties, river basins, and natural regions. Early studies primarily focused on static zoning approaches based on natural resources and environmental carrying capacity. More recent efforts have shifted toward integrated functional zoning, which combines ecosystem services, environmental constraints, and socioeconomic potential [11,12]. For example, Li Bin et al. (2009) conducted cluster analysis using ecological and socioeconomic indicators for 181 counties in Sichuan Province, revealing the spatial differentiation of eco-economic patterns [13]. Qin Xiangdong et al. (2018) developed a three-tier indicator system for Zuoyun County and proposed industrial development pathways based on functional positioning [14]. At the basin scale, Zhang Yanfei et al. (2016) employed rough set theory to classify the eco-economic functions of the Poyang Lake region and suggested corresponding management strategies [15]. In terms of methodology, traditional approaches such as principal component analysis and hierarchical clustering have evolved toward the integration of RS-GIS spatial analysis, ecosystem function assessment models, composite index methods, and multi-model integration [11,16] (Bai et al., 2014; Xiao et al., 2021). For instance, Xu Chongqi et al. (2017) enhanced the dynamism of urban zoning by incorporating nighttime light data and a linear model of population density [17]. Wang Sen (2020) constructed a multidimensional indicator system encompassing ecological, resource, environmental, and socioeconomic factors, and developed a “location advantage index” model to improve the spatial precision and policy relevance of zoning outcomes [18]. Research on eco-economic zoning in transboundary regions remains limited. Dong Suocheng et al. (2021) developed a provincial-level eco-economic zoning scheme for the China–Mongolia–Russia transboundary transport corridor [19]. At the urban scale, Bárcena-Ruiz and Casado-Izaga (2017) examined the zoning of a cross-border linear city formed by two neighboring towns, analyzing the roles of local regulators within their respective jurisdictions [20].

In summary, most current studies divide spatial units based on administrative boundaries, while grid-based eco-economic zoning that transcends administrative borders remains limited. Moreover, for transboundary regions—particularly the China–Russia northeast–far east region, where ecological and economic heterogeneity is pronounced—there is still a lack of systematic and fine-scale eco-economic zoning research, making it difficult to support the needs of bilateral cooperation and governance under such complex contexts.

Therefore, this study focuses on the China–Russia northeast–far east transboundary region. We establish a zoning evaluation framework comprising 21 indicators across five dimensions—natural conditions, ecology, economy, society, and resources—and apply Gower distance with the Partitioning Around Medoids (PAM) algorithm at the grid scale across administrative boundaries. Building on preliminary clustering results, insights from Chinese and Russian scientists, and expert knowledge refinement, we identify ten representative eco-economic functional zones that define the region’s core development functions. Compared to traditional administrative-unit-based zoning, this grid-based approach emphasizes the spatial coupling of ecosystems and socio-economic elements, providing a foundation for differentiated management, green development modes, and enhanced China–Russia cooperation. Moreover, the methodology offers a transferable framework for other transboundary regions with complex ecological–economic structures worldwide.

2. Materials and Methods

2.1. Study Area

The China–Russia northeast–far east transboundary region serves as the core pilot zone of the China–Mongolia–Russia Economic Corridor. It constitutes the primary cross-border interface between China and Russia, covering four prefectures in eastern Inner Mongolia and the provinces of Heilongjiang and Jilin in Northeast China, as well as five federal subjects in Russia’s southern Far East: Primorsky Krai, Khabarovsk Krai, Jewish Autonomous Oblast, Amur Oblast, and Zabaykalsky Krai (Figure 1). The total area spans approximately 2.9 million km², with 1.78 million km² in Russia and 1.12 million km² in China. As of 2021, the region had a total population of 71 million—5 million in the Russian part and 65 million in the Chinese part. The region shares a 4273 km land boundary and hosts all 20 overland ports between China and Russia.

2.2. Construction of the Indicator System for Eco-Economic Zoning Scheme

By comprehensively considering five dimensions—natural conditions, ecology, economy, society, and resources—we developed an integrated evaluation indicator system for eco-economic zoning schemes in the China–Russia northeast–far east transboundary region. The data sources and classification criteria for the indicators are listed in

Table 1. Index System for Eco-Economic Zoning Scheme in the China–Russia Northeast–Far East Transboundary Region.

Goal	Illustrate	Code	Indicator	Classification Standard	Classification Standard Reference	Data Source	Year
Natural	Natural environment background	A1	Average annual temperature	≤−10 °C: very low; −10~−5 °C: low; −5~0 °C: relatively low; 0~5 °C: relatively high; 5~10 °C: high	[19]	Global Surface Summary of the Day—GSOD [21]	2022
		A2	Accumulated Growing Degree Days (GDD), defined as the annual sum of daily mean temperatures exceeding 10 °C	≥1700 °C: soybean, spring wheat, potato; ≥1900 °C: corn, glutinous rice, millet, beans, cabbage; ≥2100 °C: rice, sweet corn, silage corn, eggplant, beans, watermelon; ≥2300 °C: cabbage, pepper, green pepper, sharp pepper, tomato; ≥2500 °C: processed red pepper	Yearbook of the People’s Republic of China	Calculated based on Global Surface Summary of the Day—GSOD [21]	2022
		A3	Total annual precipitation	<200: arid area; 200~400: semi-arid area; 400~800: semi-humid area; >800: humid area	Yearbook of the People’s Republic of China	IDAHO_EPSCOR/TERRACLIMATE [22]	2022
		A4	Elevation	<200 m: plain; 200~500: hills; 500~1000: mountains; 1000~1500: plateau; >1500: plateau	[23]	USGS/GMTED2010 [24]	/
		A5	Slop	≤2: flat slope; 2~5: relatively flat slope; 5~15: gentle slope; 15~25: relatively gentle slope; 25~35: steep slope	Yearbook of the People’s Republic of China	Calculated based on elevation data	/
		A6	Permafrost	isolated (0–10%) sporadic (10–50%), discontinuous (50–90%) and continuous permafrost (90–100%)	ESA Permafrost Climate Change Initiative [25]	ESA Permafrost Climate Change Initiative [25]	2021
		A7	Heating duration	5, 6, 7, 8, 9 months	/	Calculated based on Global Surface Summary of the Day—GSOD [21]	2022
		A8	NDVI	≤0.1: low NDVI, barren rocky, sandy or snowy areas; 0.1~0.5: medium NDVI, sparse vegetation (such as shrubs and grasslands or aging crops); 0.5~1: high NDVI, dense vegetation, such as vegetation in temperate and tropical forests or crops at peak growth.	USGS, United States Geological Survey (https://www.usgs.gov/special-topics/remote-sensing-phenology/science/ndvi-foundation-remote-sensing-phenology, accessed on 8 September 2025)	MODIS/061/MOD13A2 [26]	2022
Ecological	Importance of ecosystem services	B1	Species richness	Divided into 5 categories by natural breakpoint method	IUCN, International Union for Conservation of Nature	IUCN, International Union for Conservation of Nature	2022
		B2	Endangered species richness	Divided into 5 categories by natural breakpoint method	IUCN, International Union for Conservation of Nature	IUCN, International Union for Conservation of Nature	2022
		B3	Importance of water conservation function	Divided into 5 categories by natural breakpoint method	[27]	NPP: MODIS/006/MOD17A3HGF [28]	2022
						Soil seepage factor: HWSD
		B4	Importance of soil and water conservation function	Divided into 5 categories by natural breakpoint method	[27]	Soil erodibility: ESDAC [29]	2022
Economy	GDP	C1	GDP per unit area	≤0.1: low economic development area; 0.1~1: medium-low economic development area; 1~10: medium-high economic development area; >10: high economic development area	Policies for the classified governance of China’s urban agglomerations during the 14th Five-Year Plan period [30]	[31]	2019
	Population	C2	Population Density	0~1: no-people area; 1~25: extremely sparse area; 25~50: absolutely sparse area; 50~100: relatively sparse area; 100~200: general transition area; 200~400: low concentration area; 400~500: moderate concentration area; 500~1000: highly concentrated area; >1000: concentrated core area	[32]	worldpop	2020
Social	Location advantage	D1	Road density	Divided into 5 categories by natural breakpoint method	/	Road, Railway: Open Street Map	2022
						Airport: Heywhale
Resources	Available resources	E1	Distribution of forest resources	/	/	ESA [33]	2022
		E2	Distribution of cropland resources	/	/	ESA [33]	2022
		E3	Mineral point kernel density	Divided into 5 categories by natural breakpoint method	/	USGS, United States Geological Survey [34]	2011
	Environmental capacity overload	E4	PM2.5 concentration	≤5: excellent; 5~10: good; 10~15: light pollution; 15~25: moderate pollution; 25~35: heavy pollution; >35: severe pollution	WHO Global Air Quality Guidelines: Global Air Quality Guidelines (2021) [35]	Washington University in St. Louis	2022
		E5	Soil erodibility	Divided into 5 categories by natural breakpoint method	/	European Soil Data Centre (ESDAC) [29]	/
		E6	Nuclear density of fire area	Divided into 5 categories by natural breakpoint method	/	NASA, earthdata, Global Fire Atlas with Characteristics of Individual Fires, 2003–2016 [36]	2003~2016

.

2.3. Partitioning Around Medoids (PAM)

Partitioning Around Medoids (PAM) is an iterative clustering algorithm based on the partitioning approach and belongs to the category of unsupervised learning methods. It is particularly well-suited for handling nonlinear and non-parametric datasets. PAM selects actual sample points from the dataset—referred to as medoids—as the representatives of each cluster. Through iterative optimization, the algorithm minimizes the total dissimilarity between each data point and its corresponding medoid. Similar to the widely used K-Means algorithm, PAM differs in that it selects existing data points as cluster centers, which makes it more robust to noise and outliers [37,38].

In this study, since all 21 indicators were reclassified into categorical variables, they are treated as nominal variables. Accordingly, Gower distance was adopted to measure the dissimilarity between samples. For any two observations $i$ and $j$, the dissimilarity $d_{ij}$ is defined as follows:

$$d_{ij}=d\left(i,j\right)={\displaystyle \frac{{\sum }_{k=1}^p{w}_k{\delta }_{ij}^{\left(k\right)}{d}_{ij}^{\left(k\right)}}{{\sum }_{k=1}^p{w}_k{\delta }_{ij}^{\left(k\right)}}}$$

(1)

where the dissimilarity $d_{ij}$ between two observations $i$ and $j$ is defined as the weighted average of the partial dissimilarities $d_{ij}^{\left(k\right)}$, where the weight is given by $w_k{\delta }_{ij}^{\left(k\right)}$. In this study, the weights $w_k$ are set to 1 for all variables. ${\delta }_{ij}^{\left(k\right)}$ equals 0 or 1, depending on the presence of missing values. Specifically, ${\delta }_{ij}^{\left(k\right)}$ is set to 0 if the variable $x\left[,k\right]$ is missing for either observation $i$ and $j$; otherwise, it is set to 1. As missing values were already preprocessed in this study, all ${\delta }_{ij}^{\left(k\right)}$ equal 1. For the partial dissimilarity $d_{ij}^{\left(k\right)}$ measures the distance between observations $i$ and $j$ for the $k-th$ variable. If $x\left[i,k\right]$ equals $x\left[j,k\right]$, then $d_{ij}^{\left(k\right)}$ = 0; otherwise, $d_{ij}^{\left(k\right)}$ = 1.

Since the essential goal of clustering is to make the total sum of squared errors (SSE) between each sample point and its nearest cluster center as small as possible [39], a suitable K value can be determined by finding the point at which, when K exceeds this value, the rate (or magnitude) of SSE reduction becomes significantly smaller. This indicates that increasing K beyond this point no longer leads to a significant improvement in cluster compactness, and this K value can be regarded as the optimal number of clusters. By plotting a line chart of different K values against their corresponding SSE values, if there is an “elbow point” (i.e., a turning point where the rate of SSE reduction drops sharply), the K value at that point is the appropriate number of clusters. The formula for calculating the total sum of squared errors (SSE) of the dataset is:

$$d(x,C_i)=\sqrt{{\sum }_{j=1}^m (x_j-C_{ij}{)}^2}$$

(2)

$$SSE={\sum }_{i=1}^k {\sum }_{x\in C_i} {|d(x,C_i)|}^2$$

(3)

where $d(x,C_i)$ represents the Euclidean distance between the data object and the cluster center in space, where $x$ is the data object, $C_i$ is the $i-th$ cluster center, $m$ is the number of dimensions of the data object, and $C_{ij}$ is the $j-th$ attribute value of $x$ and $C_i$. The value of SSE indicates the quality of the clustering result, and $k$ is the number of clusters.

According to the above method, the final number of clusters for eco-economic zoning scheme in the China–Russia northeast–far east transboundary region is determined to be eight (Supplementary Materials Figure S1).

3. Results

3.1. Spatial Distribution Characteristics of Key Zoning Elements in the China–Russia Northeast–Far East Transboundary Region

In terms of natural environment, the China–Russia northeast–far east transboundary region is located in the middle and lower reaches of the Heilongjiang (Amur) River Basin. The overall terrain slopes from the southwest to the northeast, with significant topographic variation, ranging from plains below 200 m to mountains exceeding 1500 m in elevation. Within the Chinese territory, the landscape is mainly composed of plains and hills below 500 m, especially in eastern Heilongjiang and central Jilin, which are suitable for agriculture and urban development. In contrast, eastern Inner Mongolia and the Russian Far East are dominated by mountains and plateaus, particularly in northern Zabaykalsky Krai and Khabarovsk Krai, where elevations mostly exceed 1500 m, with complex terrain and fragile ecosystems (Figure 2). In terms of thermal conditions, accumulated temperature decreases from south to north and from low to high elevations. Southern Heilongjiang, most of Jilin, and southeastern Inner Mongolia are the optimal zones for accumulated temperature and serve as core areas for agricultural and pastoral development. In contrast, the Russian Far East, especially the northern part of the frontier regions, suffers from limited heat resources and constrained agricultural conditions.

In terms of ecological conditions, as a transboundary river basin, the main stream and tributaries of the Heilongjiang River, together with the surrounding wetlands, play a crucial role in water conservation. Areas with high water conservation capacity are mainly concentrated near the border (along the main stream of the Heilongjiang River) (Figure 3). They are vital for regulating surface runoff, recharging groundwater, reducing seasonal fluctuations in river discharge, retaining floodwaters, maintaining dry-season flows, and ensuring water quality. Moreover, there are nine transboundary nature reserves distributed across the region, covering key ecosystems such as wetlands and forests, and jointly protecting multiple rare species. Large carnivores (such as tigers and leopards) and rare migratory birds are widely distributed in Khabarovsk Krai, the Jewish Autonomous Oblast, Primorsky Krai, and the border areas with China’s Heilongjiang Province (Figure 3). In particular, the border region between Primorsky Krai and Heilongjiang has the highest species richness and density of endangered species in the region (Figure 3), making it a key area for Sino-Russian cooperation in biodiversity conservation.

In terms of socio-economic conditions, there are significant differences in regional GDP, population, and transportation infrastructure between China and Russia. In 2022, the population on the Chinese side was approximately 13 times that of the Russian side, mainly concentrated in the Northeast Plain and the Sanjiang Plain (Figure 4). The overall level of economic development of this area is relatively low (based on statistics from the 2022 Statistical Yearbooks of China and Russia). In 2020, the GDP of the Chinese part was CNY 118.7 billion, while that of the Russian part was approximately CNY 55.7 billion, both below their respective national averages (based on statistics from the 2020 Statistical Yearbooks of China and Russia). Cities such as Harbin, Daqing, Changchun, Chita, Khabarovsk, and Vladivostok exhibit notable economic agglomeration (Figure 4). In terms of transportation, the road network density is 0.203 km/km² in China and 0.104 km/km² in Russia, both lower than the Chinese national average (0.56 km/km²). In the Chinese Northeast Plain, railway and road networks are densely developed, whereas in Russia, transportation lines are mainly distributed along the border, with relatively low density in the northern areas (Figure 4). The transboundary region is an important area where China and Russia actively promote the joint construction of transportation infrastructure, the development of port economy, and the shipping economy.

In terms of resources, the region has a forest cover of 1.821 million square kilometers, dominated by northern coniferous forests, which extend from southern Russia to China’s Greater Khingan Mountains, Lesser Khingan Mountains, and the Changbai Mountains, forming a continuous forest ecological corridor (Figure 5). The total arable land area is 518,000 square kilometers, but its distribution is uneven: in China, it is mainly concentrated in the Northeast Plain and the Sanjiang Plain (Figure 5), which are suitable for high-quality agriculture and animal husbandry; in Russia, it is mostly located near the border, serving as a key area for Sino-Russian arable land cooperation and development. The region is rich in mineral resources, with cross-border distributions of gold, limonite, and other minerals, mainly concentrated in Zabaykalsky Krai, Amur Oblast, and Primorsky Krai of Russia, as well as the border areas between China and Russia (Figure 5). Among them, the border area between Zabaykalsky Krai and Inner Mongolia is the core zone for mineral cooperation and development.

3.2. Clustering Results of Eco-Economic Elements in the China–Russia Northeast–Far East Transboundary Region

According to the above clustering method, the final clustering results of eco-economic zoning scheme in the China–Russia northeast–far east transboundary region are shown in Figure 6. To identify the key features that distinguish each cluster from the others, box plots of the 21 indicator values for the 8 clusters were generated (Figure 6). The eight cluster types identified in this study exhibit significant differences in terms of natural environmental characteristics, ecosystem functions, and human activity features:

Cluster AF (Agriculture and Forest): This cluster is mainly distributed in central Heilongjiang Province, China, and the border area with Hulunbuir City in Inner Mongolia Autonomous Region. The region has relatively high accumulated temperature, meeting the thermal requirements for nearly 20 types of crops, and thus possesses favorable conditions for agricultural cultivation. Vegetation coverage is high, providing good water conservation capacity. At the same time, the proportion of arable land is relatively large, the economy is relatively developed, the density of the transportation network is high, and species richness is high, although the need for endangered species protection is relatively low. Therefore, compared with other clusters, AF combines both agricultural–forestry and ecological functions. Under relatively low ecological protection pressure, it demonstrates notable economic vitality and high resource utilization intensity.

Cluster AH (Agriculture and Husbandry): This cluster is mainly distributed in Chifeng City, Tongliao City in Inner Mongolia Autonomous Region, and the western part of Jilin Province. The accumulated temperature in this area is higher than that in other clusters, providing favorable conditions for large-scale crop cultivation. Arable land is widely distributed, agricultural production intensity is high, the economy is developed, road network density is high, and the risk of soil erosion is relatively low. Compared with Cluster AF, Cluster AH has lower ecological protection value, with both species richness and endangered species density at lower levels. It is a typical region dominated by agricultural and animal husbandry production functions, with relatively weaker ecological functions.

Cluster EC1 (Ecological Conservation 1): This cluster is located in Zabaykalsky Krai, the northern part of Amur Oblast and their border areas, as well as the central part of Khabarovsk Krai in Russia. The region contains discontinuous and continuous permafrost, with extremely low accumulated temperature, making it unsuitable for meeting the thermal requirements of crops. The regional economy is underdeveloped, and the transportation network is sparse. However, the density of endangered species is relatively high, and the demand for ecological protection is significant. Compared with other clusters, this region is characterized by harsh natural conditions, low development intensity, and high biodiversity value, representing a typical ecological priority conservation area.

Cluster WF (Water and Forest): This cluster is mainly distributed in central Amur Oblast and along the Amur River to Khabarovsk Krai in Russia. The region has significant water conservation functions, high forest coverage, a relatively large number of endangered species, and strong ecological protection demand. The overall level of economic development and transportation infrastructure remains weak. Compared with Cluster AF, Cluster WF has weaker production functions but higher ecological protection value, exhibiting ecological dominance characterized by water conservation and biodiversity.

Cluster EC2 (Ecological Conservation 2): This cluster is mainly distributed in the northern part of Khabarovsk Krai, Russia. Similar to Cluster ECI, it is also widely covered by discontinuous and continuous permafrost. The accumulated temperature is even lower, making the area essentially unsuitable for crop cultivation. The level of economic development is low, the road network is sparse, and the density of endangered species is high, indicating a high ecological protection value.

Cluster ME (Mineral Exploitation): This cluster is mainly distributed in the western and southern parts of Zabaykalsky Krai, Russia, and the northern part of Hulunbuir City in Inner Mongolia Autonomous Region, China. Permafrost is widely distributed in the area, and mineral resources are abundant, but the level of economic development and transportation conditions are still relatively limited. Species richness is high, but the density of endangered species is low, and the demand for protection is not significant. Therefore, Cluster ME emphasizes the potential for resource exploitation, with relatively low ecological protection demand, representing a development-priority area dominated by mineral resource utilization.

Cluster WG (Wildlife and Grassland): This cluster is located in the western part of Hulunbuir City, Inner Mongolia Autonomous Region, China. Its most prominent features are extremely high species richness and endangered species density, with an urgent need for ecological protection. Compared with other clusters, Cluster WG places greater emphasis on maintaining the integrity of grassland ecosystems and biodiversity, representing a typical key area for the protection of wildlife habitats.

Cluster CE (Coastal Ecotourism): This cluster is mainly distributed in Primorsky Krai, Russia, the southeastern part of Jilin Province, and parts of Heilongjiang Province in China. The region has high forest coverage, relatively high economic development level, and a well-developed transportation network. Species richness is high, but the demand for endangered species protection is relatively low. Similar to Cluster AF, Cluster CE also demonstrates strong ecological and economic dual functions. However, it relies more on coastal resources and ecotourism potential, with ecological protection efforts focusing on maintaining landscape quality and tourism carrying capacity.

3.3. Eco-Economic Zoning Scheme for the China–Russia Northeast–Far East Transboundary Region

Using the PAM algorithm, we initially identified eight distinct eco-economic clusters as the basic framework for zoning. Yet, the results showed inevitable enclaves and fragmentation, which would cause spatial discontinuity if applied directly. To resolve this, we refined the clusters through expert evaluation and spatial adjustment, while preserving their main functions. This refinement considered regional development needs and the context of China–Russia cooperation, and was conducted under the China–Russia Intergovernmental Scientific and Technological Cooperation Project of the National Key R&D Program of China, incorporating insights from scientists of both countries. Ultimately, ten core functional zones were established (Figure 7): the Ecological Conservation Zone (mainly formed from cluster EC1 and EC2), Transboundary Development Cooperation Zone for Gold and Tungsten Mining in the Outer Khingan Mountains (mainly formed from cluster ME), Transboundary Agriculture and Forestry Development Cooperation Zone (mainly formed from cluster AF), Transboundary Conservation Cooperation Zone for Wildlife and Mountain Grassland Ecosystems (mainly formed from cluster WG), High-Quality Agriculture and Animal Husbandry Development Zone in the Northeast Plain (mainly formed from cluster AH), Transboundary Water Source Conservation and Forest Ecosystem Protection Zone (mainly formed from cluster WF), Transboundary Cooperation Zone of Inland River Shipping and Port Development (mainly formed from cluster WF), Coastal Ecological Conservation Zone (mainly formed from cluster EC2), Transboundary Conservation Cooperation Zone for the Endangered Species: Northeast Tiger and Leopard (mainly formed from cluster AF), and the Transboundary Coastal Ecotourism and Transportation Cooperation Zone (mainly formed from cluster CE).

The Ecological Conservation Zone is mainly distributed in the northern parts of Zabaykalsky Krai, Amur Oblast, and Khabarovsk Krai in Russia. The overall level of economic development in this area is very low, and the road network density is also low. However, there is a significant need for the protection of endangered species. Therefore, excessively pursuing economic development in this region may be neither appropriate nor efficient; instead, it is more important to protect the local ecological environment and biodiversity.

The Transboundary Development Cooperation Zone for Gold and Tungsten Mining in the Outer Khingan Mountains is mainly distributed in Zabaykalsky Krai of Russia and the northern part of Hulunbuir City in Inner Mongolia Autonomous Region of China. This region is a major distribution area of mineral resources, especially gold and tungsten, and can serve as a cooperative mining development zone between China and Russia. However, attention should be paid to the constraints of elevation, slope, and soil erosion. The region also has high species richness; therefore, while developing mineral resources, joint efforts are needed to protect regional biodiversity.

The Transboundary Agriculture and Forestry Development Cooperation Zone is mainly distributed in the border area between northeastern Heilongjiang Province, China, and the southeastern part of Amur Oblast, Russia. In this region, forests and arable land are interwoven, making it an important demonstration cooperation zone for agricultural and forestry collaboration between China and Russia.

The Transboundary Conservation Cooperation Zone for Wildlife and Mountain Grassland Ecosystems is mainly distributed in the southern part of Zabaykalsky Krai, Russia, and the western part of Hulunbuir City in the Inner Mongolia Autonomous Region, China. This region is mainly composed of mountain grassland ecosystems and is characterized by extremely high species richness and endangered species density, with a strong need for protection. It can serve as a transboundary cooperation zone for wildlife and ecosystem conservation between China and Russia.

The High-Quality Agriculture and Animal Husbandry Development Zone in the Northeast Plain is mainly distributed in Chifeng City and Tongliao City of Inner Mongolia Autonomous Region, as well as most areas of Jilin Province and Heilongjiang Province in China. This region has a wide distribution of high-quality arable land, with a solid foundation for economic development and well-developed transportation infrastructure. However, it also faces relatively high levels of air pollution and is under pressure to achieve high-quality and sustainable agricultural and animal husbandry development.

The Transboundary Water Source Conservation and Forest Ecosystem Protection Zone is mainly distributed in most parts of Amur Oblast and a small portion of western Khabarovsk Krai in Russia. This region has extensive forest coverage and plays an important role in water source conservation. It can serve as a cooperation zone between China and Russia for the protection of transboundary forest resources and aquatic environments.

The Transboundary Cooperation Zone of Inland River Shipping and Port Development is mainly distributed in Khabarovsk Krai, Russia. This region is located in the lower reaches of the Heilongjiang (Amur) River and includes major inland ports such as Khabarovsk, Komsomolsk-on-Amur, and Nikolayevsk-on-Amur (Miaojie). It has strong potential for the development of inland waterways. Moreover, the Nikolayevsk-on-Amur port provides convenient access to the Arctic shipping route, making this region a key area for China and Russia to jointly develop inland river navigation and port transportation.

The Coastal Ecological Conservation Zone is mainly distributed in the southern part of Khabarovsk Krai, Russia. Similar to the above-mentioned Ecological Conservation Zone, excessively pursuing economic development in this region may be neither appropriate nor efficient. It is more important to protect the region’s important forest ecosystems and coastline.

The Transboundary Conservation Cooperation Zone for the Endangered Species: Northeast Tiger and Leopard is mainly distributed in eastern Heilongjiang Province, China, and the border areas with the Jewish Autonomous Oblast and Primorsky Krai in Russia. This region has high species richness and a high density of endangered species, and it is the primary habitat for the Northeast tiger and leopard. Therefore, the top priority in this region is the joint protection by China and Russia of these rare and endangered species and their habitats.

The Transboundary Coastal Ecotourism and Transportation Cooperation Zone is mainly distributed in central and southern Heilongjiang Province, southeastern Jilin Province in China, and most parts of Primorsky Krai in Russia. This region has high vegetation coverage and species richness, along with high-quality coastal landscapes. With a high level of location advantage and close proximity to China, North Korea, South Korea, and Japan, it is well-suited for the joint development of tourism resources by China and Russia. In addition, this region includes Vladivostok, the starting point of the Trans-Siberian Railway and the largest port city in the Russian Far East, which supports cooperation between China and Russia in developing railways, ports, and other infrastructure. Such collaboration will jointly promote green and high-quality development in the region.

4. Discussion

This study focuses on the China–Russia northeast–far east transboundary region. In contrast to traditional zoning approaches centered on administrative units, it places greater emphasis on the goal-oriented cooperation between China and Russia and the natural continuity of ecosystems. Most existing eco-economic zoning studies focus on a single country or administrative unit [13,14] (e.g., county-level zoning in the six provinces of Northwest China [40]), while cross-border research remains limited. Only a few studies, such as those on the China–Mongolia–Russia Economic Corridor [19], have addressed transnational scales. However, transboundary regions often exhibit both the ecological integrity of natural systems and the institutional fragmentation of national governance. Their ecosystem types, resource distribution patterns, and human interventions are characterized by significant heterogeneity and institutional differences. Against this background, the eco-economic zoning method proposed in this study not only focuses on the regional functional divisions themselves, but also addresses shared concerns such as ecological conservation, resource utilization, and regional development, with the aim of promoting bilateral cooperation and serving the common development goals of both countries.

In terms of methodology, this study proposes an eco-economic zoning scheme constructed through the integration of clustering algorithms and multi-source data, which balances the boundary authenticity of natural systems with the operational feasibility of identifying development functions. It demonstrates strong innovation and practical value. Based on 21 integrated indicators covering natural, ecological, economic, social, and resource dimensions, we applied a “disaggregation followed by clustering” approach to identify eco-economic zones at the grid scale across administrative boundaries in the China–Russia northeast–far east transboundary region. Li Feng et al. (2017) [17] also pointed out that previous zoning studies usually relied on administrative statistical data, whereas shifting to a grid scale allows for a finer simulation of spatial patterns. Presenting zoning results at the grid level is more conducive to protecting key ecological processes.

In finalizing the zoning scheme, the initial clustering results (Figure 6) reflected the spatial differentiation patterns derived from objective data and algorithms. However, the spatial distribution of clusters inevitably showed some “enclaves” and fragmentation. For example, cluster AH was divided into two parts: one in the southwest and the other in the northeast of Heilongjiang Province. Directly applying such results to functional zoning would lead to spatial discontinuity and interpretation difficulties, which are not favorable for policy application. To address this, we refined the clusters through expert evaluation and adjustment, retaining their main functional characteristics while integrating fragmented enclaves. Taking cluster AH as an example, in defining Functional Zone V, we highlighted its agricultural development features and aligned it with the distribution of cropland to ensure a clear agricultural orientation. In contrast, in the northeast of Heilongjiang, clusters AF and AH were interlaced, with AF dominating and AH being more scattered. This area, however, is also an important habitat for endangered species. Therefore, in the adjustment process, it was incorporated into Functional Zone IX to emphasize its biodiversity conservation value. Through this refinement, the functional zoning better reflects the actual functional patterns of the region while avoiding the fragmentation and discontinuity of the initial clustering results.

The zoning results of this study can provide spatial support for the implementation of cooperative policies. On the one hand, in terms of transboundary natural resource protection, ecological buffer zones and cooperation conservation zones can be prioritized in areas with dense forests and high concentrations of endangered species, to enhance ecological connectivity. On the other hand, in border ports, mining belts, and regions rich in agricultural and forestry resources, efforts can be made to promote joint infrastructure development, information exchange, and institutional alignment within functional zones, in order to improve overall regional development coordination. Therefore, differentiated cross-border management based on eco-economic zoning scheme not only helps balance resource conservation and utilization, but also has the potential to promote more efficient bilateral cooperation and advance regional green development modes between China and Russia.

5. Conclusions

This study focuses on the China–Russia northeast–far east transboundary region, taking the natural continuity of ecosystems and the spatial heterogeneity of socio-economic elements as entry points, with the goal of promoting cooperation between China and Russia. A comprehensive evaluation system consisting of 21 indicators across five categories—natural, ecological, economic, social, and resource—was established. Based on the PAM clustering method and following the approach of “disaggregation followed by clustering,” eight initial cluster types with significantly different eco-economic characteristics across administrative boundaries were identified at the grid scale. On this basis, supported by the China–Russia Intergovernmental Scientific and Technological Cooperation Project under the National Key R&D Program of China for International Scientific and Technological Innovation Cooperation, spatial pattern recognition and expert-based corrections were carried out by integrating the experiences and suggestions of scientists from both China and Russia. Ultimately, ten core eco-economic functional zones were delineated, including Ecological Conservation Zones, Transboundary Resource Development Cooperation Zones, Agriculture and Forestry Synergistic Development Zones, Water Source Conservation Zones, and Joint Biodiversity Conservation Zones.

This study systematically reveals the spatial differences and coupling characteristics among natural resource endowments, ecological function distribution, and socio-economic development in the China–Russia transboundary region in Northeast Asia. It provides a spatial foundation and scientific support for formulating locally adapted, functionally clear, and operationally feasible regional cooperative development pathways. Different from traditional administrative-unit-based zoning approaches, this study conducts eco-economic zoning scheme across national and administrative boundaries at the grid scale, overcoming the problem of fragmented ecological management caused by jurisdictional divisions. It offers scientific support for policy coordination between China and Russia in areas such as resource protection, ecological governance, industrial collaboration, and port development. The findings can provide strategic guidance for promoting regional sustainable development and serve as a reference model for integrated eco-economic governance in other similar transboundary regions.