Can Industrial Spatial Configuration Catalyze the Transition and Advancement of Resource-Dependent Regions? An Empirical Analysis from Heilongjiang Province, China

Abstract

Resource-based regions are built upon the endowment of abundant natural resources; however, they often fall into development dilemmas due to the depletion of natural resources and ecological environmental regulations. How to achieve transformative development relying on the original industrial base is an important choice for the sustainable development of resource-based regions. This paper takes Heilongjiang Province, a resource-based province in China, as the research area and analyzes its process and strategies of transformative development from the perspective of industrial spatial patterns. The results show that: (1) There is spatial convergence in the development of secondary industry and industry in Heilongjiang Province from 2011 to 2020. The construction industry does not have spatial convergence, and the development of tertiary industry and its sub-industry does not have spatial convergence on the whole. (2) From 2011 to 2022, the development of secondary and tertiary industries in Heilongjiang Province formed a relatively stable spatial correlation network with good accessibility, but the hierarchy of network structure is not obvious, and the correlation strength and stability of the network need to be improved. (3) Harbin, Hegang, Qitaihe and other regions occupy a relatively central position in the spatial association network of the secondary industry; Harbin, Jiamusi, Suihua and other regions are in a leading position in the spatial association network of the tertiary industry which plays an important role as an intermediary bridge; other regions are in a relatively marginal position in the spatial association network of the industrial industry. (4) The increase in network density can effectively promote the development of the secondary and tertiary industries, and the network level and network efficiency will inhibit the development of the secondary and tertiary industries. The increase in network density will narrow the spatial difference of the secondary and tertiary industries, and the decrease in network level and network efficiency can effectively promote the spatial balance of the development of the secondary and tertiary industries. (5) The closer the spatial correlation between each region and other regions, the more benefits from the overall network, the more conducive to the development of local secondary and tertiary industries. The aforementioned results indicate that Heilongjiang Province is constructing a spatial pattern characterized by the complementarity of the primary, secondary and tertiary industries, which serves as a strategy for the transformative development of resource-based regions.

1. Introduction

It has been shown that the spatial distribution of industries has far-reaching significance in determining the potential and speed of economic development of a region (Martin et al., 1996) [1]. Reasonable industrial layout is conducive to the efficient use of production factors, minimization of costs, inter-regional exchanges and cooperation, thus promoting the harmonious and effective development of regional industries (Zhang et al., 2023) [2]. With the development of secondary and tertiary industries, production factors and product outputs have strong regional mobility, which makes the spatial correlation between regions even closer (North et al., 1955) [3], showing a continuous transformation of industrial agglomeration or industrial diffusion. Existing studies mainly analyze the evolution of industrial spatial patterns from the perspective of industrial agglomeration (Fan et al., 2003; Li et al., 2020) [4,5]. For example, some scholars have analyzed the spatial distribution of the U.S. manufacturing industry and introduced the EG index based on enterprise-level analysis (Krugman et al., 1991; Ellison et al., 1997) [6,7]. Scholar Jungyul developed the MS index to characterize the spatial pattern of the French manufacturing industry based on the EG index (Sohn, 2004) [8]. Korean scholars Kim et al. used location entropy and county data to study the spatial pattern characteristics of creative industries (Kim et al., 2015) [9], revealing the different distribution characteristics of each county and emphasizing the need to develop differentiated industrial development strategies for specific regions. Chinese scholars Dai et al. took resource-based industries as the research object and observed the similarities and differences in the spatial distribution of each resource-based industry by using the Pierce coefficient and the entropy of transfer of production factors (Dai et al., 2019) [10]. Zheng et al. analyzed the evolution of the spatial pattern of different industries in Mianyang City by using methods such as kernel density estimation (Zheng et al., 2020) [11], and found that the spatial agglomeration patterns of different industries were different.

In summary, most of the existing studies use a single statistical method to elaborate the complex spatial characteristics of the development of a certain type of industry, often ignoring the spatial correlation between different industries and the development of industries in different regions. In addition, there is a gap in the research on the impact of industrial spatial configuration on the transformation and development of resource-based regions. This paper attempts to promote resource-dependent regions to optimize their industrial layout and promote the coordinated development of regional industries by analyzing the spatial pattern of resource-based industries and their driving effects.

The transformation and development path of resource-based regions has always been the focus of attention of experts and scholars at home and abroad, and as one of the key points to guarantee global sustainable development, the sustainable development of resource-based regions has been generally valued in both developed and developing countries (Onifade, 2022) [12]. Resource-based regions are characterized by an overdependence on abundant natural resources, but with the process of the late stage of industrialization, their insufficient power for subsequent development has also become a major challenge for resource-based regions (Yang et al., 2012) [13].

Resource-based cities are a typical city type in China’s urban system (Gu et al., 2022) [14], abundant natural resources, while generating considerable income, may also lead to problems such as homogeneous industrial structure, lack of economic resilience and ecological degradation, thus constraining sustainable economic and social development (Mollick et al., 2020; Kotsadam et al., 2016) [15,16]. A representative example is Northeast China, which experienced massive layoffs and economic stagnation in the early 21st century. Despite attempts to explore transformational development paths, the resource-based region remains bound by its original industrial base (Onifade, 2022) [12]. As in Heilongjiang Province of China, a typical resource-based region in northeastern China with vast fertile black soil resources, it became an area of net population inflow during the period of agrarian society, which laid the foundation for the initial economic prosperity (Li et al., 2024) [17]. At the beginning of the industrial society, its abundant natural resources such as forests, coal and oil became an important supporter of the People’s Republic of China (Wan et al., 2024; Zhang et al., 2021) [18,19]. However, by the late 20th century, the long-term overexploitation of natural resources led to a bottleneck of economic growth in many resource-based cities in Heilongjiang Province, resulting in a wave of worker layoffs (Li et al., 2007) [20]. Overall, it has experienced a journey from economic prosperity to resource curse and finally to industrial revitalization. Since 2003, the Chinese government has proposed the revitalization of the old industrial base in Northeast China, which has facilitated the transformational development of Heilongjiang (Tang et al., 2004) [21]. As the core area of the aging industrial base in Northeast China, Heilongjiang province has long been committed to industrial development (Li, 2016) [22]; especially in recent years, the service sector has grown significantly (Gao et al., 2010) [23]. Currently, Heilongjiang Province is vigorously developing new economic growth engines such as foreign trade, ecological economy and tourism (Cui et al., 2023; Xia et al., 2024) [24,25].

The current successful cases of industrial transformation in resource-based regions are generally of the following two kinds: transforming from heavy industry to high-end service economy, and adjusting the pattern of industrial structure with industry chain extension. However, most resource-based regions have difficulties in detaching from the industrial base and overcoming economic inertia and the corresponding institutional framework (Wiig et al., 2012; Carmignani et al., 2014) [26,27]. Although studies have shown that industrial diversification can enhance economic resilience and promote transformational development by creating new economic growth points to mitigate economic shocks caused by price and market volatility in a single industry (Breul et al., 2022) [28]. However, the successful transformation of industries in resource-based regions has not yet been realized.

This study examines how resource-based regions utilize their industrial base and layout design to formulate transformative development strategies. Heilongjiang Province was selected as the study area and as the data-collection boundary. The spatial layout of industries in Heilongjiang Province was investigated through a spatial convergence model, a modified gravity model and a social network analysis. The double driving effect of spatial correlation on the development level and spatial differences of the second and third industries in Heilongjiang Province was examined by using the cross-sectional regression model and panel data model, and it was evaluated whether the second and third industries in Heilongjiang Province had formed a spatial pattern of industrial complementarity. Finally, industrial strategies for the transformation and development of resource-based regions are summarized. The marginal contribution of this study is to scrutinize the process of transformation and development of resource-based regions from the perspective of the complementary spatial pattern of industries and to propose industrial layout strategies for the transformation and development of resource-based regions.

2. Materials and Methods

2.1. Research Area

Heilongjiang Province is located in the northeast of China, ranging from 121°11′ to 135°05′ east longitude and 43°26′ to 53°33′ north latitude. It covers a total area of 473,000 square kilometers, ranking sixth in China, with a border line of 2981.26 km.

Heilongjiang Province’s abundant natural resources have supported its early economic development. The province has about 10.4 million hectares of black soil, 21 million hectares of forest resources, as well as mineral resources such as oil and coal. At the beginning of the establishment of the People’s Republic of China, a large number of resource-based cities were constructed. These include forest cities like Yichun City and the Greater Khingan Range area, oil cities like Daqing and coal cities such as Jixi, Hegang and Qitaihe. Additionally, China’s largest grain company, Beidahuang Group, is primarily based in Heilongjiang Province. These cities and large state-owned enterprises have all encountered severe economic problems due to long-term overdevelopment.

To revitalize the economy of Heilongjiang Province, the Chinese government has introduced a series of strategic plans and measures for industrial transformation. Among the most notable is the strategy proposed in 2003 to revitalize the old industrial bases in Northeast China. In recent years, the Heilongjiang Provincial Government has deepened the reform of resource-based state-owned enterprises and cities, and based on the agricultural and industrial foundations, it has launched characteristic ecological industries and tourism industries. In 2023, the income from characteristic tourism in Heilongjiang Province reached 2215.3 billion yuan (approximately USD 308.8 billion).

Heilongjiang Province is a typical area for the transformation of resource-based regions in China, providing experience for observing how industrial spatial layout supports the transformation of resource-based regions. This study takes 13 prefecture-level cities in Heilongjiang Province as research objects, covering a period of 2011–2020. The distance data is the straight-line distance between prefecture-level cities, while the original data of all other indicators come from the fourth National Economic Census of Heilongjiang Province and the Heilongjiang Statistical Yearbook.

2.2. Spatial Convergence Model

Solow (1956) and Swan (1956) [29,30] believed that when the marginal returns of various production factors decreased and the overall returns to scale remained unchanged, the economic development would gradually converge to a certain stable level in a certain way. Therefore, in order to test the evolution trend of regional differences in industrial development in Heilongjiang Province, this study will test the spatial convergence of per capita output value of secondary and tertiary industries in Heilongjiang Province from 2011 to 2020.

The spatial convergence method can quantify the rationality of the spatial layout of industries and assess the development potential and competitive advantages of different regions and industries. By comparing the degree of spatial agglomeration and diffusion trend of different industries, advantageous industries and potential regions can be identified, which can provide decision-making basis for policy makers. Through the spatial convergence method, the historical evolution process of the industrial spatial pattern in Heilongjiang Province can be systematically analyzed to reveal its intrinsic laws and characteristics. This helps to understand the formation mechanism of industrial spatial layout and provides theoretical support for future optimization of industrial layout. At present, the analysis methods of spatial convergence mainly include convergence, absolute convergence and conditional convergence (Zhou et al., 2020) [31].

The convergence model is mainly used to represent the evolution trend of divergence of industrial development level in different regions (Yao et al., 2020) [32]. If the value decreases year by year, it indicates that the degree of dispersion of industrial development in Heilongjiang Province is decreasing. The formula is as follows:

$$\sigma =\sqrt{{\displaystyle \frac{1}{n-1}}{\displaystyle \sum _{i=1}^n{\left(f_i-\overline{f}\right)}^2}}/\overline{f}$$

(1)

In the formula, $n$ is the number of prefecture-level cities, $f_i$ is the per capita industrial output value of the $i$ prefecture-level city and $\overline{f}$ is the average industrial output value per capita. If ${\sigma }_{t+1}<{\sigma }_t$, it means that the industrial development of Heilongjiang Province has $\sigma$ convergence.

The $\beta$ convergence model is divided into the absolute $\beta$ convergence model and the conditional $\beta$ convergence model. The absolute $\beta$ convergence model is mainly used to characterize whether the industrial development speed among different regions exhibits convergence; that is, whether the regions with lower industrial development level have the tendency to catch up with the regions with higher industrial development level (Yuan et al., 2019) [33]. The formula is as follows:

$$[\ln (f_{i,t})-\ln (f_{i,0})]/T=\alpha +\beta \ln (f_{i,0})+\epsilon$$

(2)

In the formula, $f_{i0}$ and $f_{it}$ are the per capita industrial output value of prefecture-level cities in Heilongjiang Province at the beginning and end of the study, respectively, $T$ is the length of the study period, $\alpha$ is the constant and $\beta$ is the absolute convergence coefficient. If $\beta <0$, it indicates that the industrial development of Heilongjiang Province has absolute $\beta$ convergence; that is, the regions with low industrial development level tend to catch up with the regions with high industrial development level. Otherwise, it means that the industrial development of Heilongjiang Province is relatively scattered and there is no absolute $\beta$ convergence.

The conditional $\beta$ convergence model is mainly used to characterize whether the industrial development in different regions converges to its own stable state (Liu, 2019) [34]. The formula is as follows:

$$\ln (f_{i,t})-\ln (f_{i,t-1})=\alpha +\beta \ln (f_{i,t-1})+\epsilon$$

(3)

In the formula, $f_{it}$ and $f_{i(t-1)}$ represent the per capita industrial output value of prefecture-level cities in Heilongjiang Province in two adjacent periods, $\alpha$ is a constant and $\beta$ is the conditional convergence coefficient. If $\beta <0$, it indicates that conditional $\beta$ convergence exists in the industrial development of Heilongjiang Province; that is, the industrial development level of each prefecture-level city converges to its own stable state; otherwise, it indicates that there is no conditional $\beta$ convergence.

2.3. Modified Gravity Model

Before analyzing the characteristics of the spatial correlation network, it is necessary to test whether there is spatial correlation between regional industrial development. Existing studies mainly use VAR model and gravity model to explore spatial correlation (Li et al., 2014; Lu et al., 2017 [35,36]). Since the VAR model is sensitive to the selection of lagged order, it cannot describe the evolution process of spatial correlation network. Therefore, the gravity model is selected and modified by referring to related studies (Liu et al., 2015; Shao et al., 2018; Liu et al., 2018 [37,38,39]) so as to determine the spatial correlation of industrial development in Heilongjiang Province. The formula is:

$$G_{ij}={\lambda }_{ij}{\displaystyle \frac{{(P_i{O}_i)}^{1/3}(P_j{O}_j{)}^{1/3}}{{d_{ij}}^2}},{\lambda }_{ij}={\displaystyle \frac{O_i}{O_i+O_j}}$$

(4)

In the formula, $G_{ij}$ represents the attraction of industrial development between regions, $P$ and $O$ represent the number of employees and per capita industrial output value, respectively, $d_{ij}$ represents the distance between different prefecture-level cities in Heilongjiang Province, ${\lambda }_{ij}$ is the adjustment coefficient of spatial correlation of industrial development between regions, and is calculated by per capita industrial output value. The gravity matrix of the industrial development of Heilongjiang Province is calculated according to Equation (4). The mean value of each row and column in the matrix is taken as the critical value, and the value of each unit greater than the critical value is denoted as 1, indicating that the industrial development of the corresponding two regions has a spatial correlation; otherwise, it is denoted as 0, indicating that there is no correlation. Therefore, the spatial correlation matrix of the industrial development of Heilongjiang Province is obtained.

2.4. Social Network Analysis

Social network analysis is an analytical method that uses relational data to study spatial correlation networks. This method breaks through the limitations of geographical location, and is therefore widely used in research into economies (Borg et al., 2009) [40], management (Cassi, et al., 2012) [41], the environment (Tong et al., 2020) [42] and other fields. In this paper, social network analysis is used to study the overall characteristics and node characteristics of industrial the spatial correlation network in Heilongjiang Province.

• Overall network characteristics

In this paper, network density D (Equation (5)), network correlation degree C (Equation (6)), network rank degree H (Equation (7)) and network efficiency E (Equation (8) are used to describe the overall network structure characteristics. Among them, network density reflects the tightness degree of industrial spatial correlation network in Heilongjiang Province, network correlation degree describes the stability or vulnerability of network structure, network level describes the status difference of different regions in the spatial correlation network and network efficiency reflects the degree of redundant correlation in the spatial correlation network.

$$D={\displaystyle \frac{I}{N(N-1)}}$$

(5)

In the formula, I is the actual number of relationships contained in the association network; N indicates the number of nodes in the network.

$$C=1-{\displaystyle \frac{V}{[N(N-1)/2]}}$$

(6)

In the formula, V is the number of unconnected nodes in the network.

$$H=1-{\displaystyle \frac{S}{\max (S)}}$$

(7)

In the formula, S is the logarithm of the symmetrically reachable relation in the network, and max(S) is the maximum possible value of the logarithm of the symmetrically reachable relation.

$$E=1-{\displaystyle \frac{K}{\max (K)}}$$

(8)

In the formula, K is the number of redundant association relationships in the network, and max(K) is the maximum possible number of redundant association relationships.

• Node network characteristics

In this paper, Degree centrality DC_i (Formula (9)), Closeness centrality (Formula (10)) and Betweenness centrality (Equation (11)) are used to describe the network structure characteristics of each node. Point degree centrality measures the position of each node in the overall network. The higher the point degree centrality of a node, the more prominent the centrality of the node in the network. Proximity centrality describes the degree of direct association between a single node and other nodes in the overall network. The higher the proximity centrality of a node, the more direct relations it has, and the more obvious the position of “central actor” in the network. Intermediate centrality reflects the degree to which a node plays an intermediary role in the overall network. The greater the intermediate centrality of a node, the more elements passing through the node, the more obvious the intermediary role.

$$D{C}_i={\displaystyle \frac{K_i}{N-1}}$$

(9)

In the formula, K_i is the number of edges connected to node i; N − 1 is the maximum number of possible connected edges of node i.

$$C{C}_i=1/({\displaystyle \frac{1}{N-1}}{\displaystyle \sum _{j=1}^N{d}_{ij})}$$

(10)

In the formula, d is the shortest path distance between two nodes.

$$B{C}_i={\displaystyle \frac{2}{(N-1)(N-2)}}{\displaystyle \sum _{s=1}^N{\displaystyle \sum _{t=1}^N\frac{{\delta }_{st}(i)}{{\delta }_{st}}}}$$

(11)

In the formula, ${\delta }_{st}$ is the number of shortest paths from node s to node t; ${\delta }_{st}(i)$ is the number of shortest paths from node s to node t through node i.

3. Spatial Convergence Characteristics of Industrial Development in Heilongjiang Province

3.1. Characteristics of σ Convergence in Industrial Development

According to the σ convergence results of the secondary industry and its sub-industries (

Table 1. σ convergence results of the secondary industry and its sub-industries in Heilongjiang Province.

Time	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Secondary industry	1.34	1.42	1.48	1.47	1.52	1.71	1.32	1.11	1.06	1.07
industry	1.43	1.50	1.56	1.56	1.61	1.83	1.44	1.21	1.16	1.17
Construction industry	0.90	0.87	0.89	0.91	0.96	0.99	1.04	1.08	1.13	1.10

), from 2011 to 2020, the σ values of the secondary industry and industry in Heilongjiang Province both rose first and then declined, indicating a trend of σ convergence during the study period, while that of the construction industry fluctuated and increased during the study period, indicating that there was no σ convergence in the development of the construction industry in Heilongjiang Province. Specifically, from 2011 to 2016, the σ value of the secondary industry and industry increased, indicating that the development of the secondary industry and industry in Heilongjiang Province was diverging during this period, and from 2016 to 2020, the σ value of the two decreased continuously, indicating that the development of the secondary industry and industry was converging during this period. Meanwhile, the σ value of the two in 2020 was lower than that in 2011. The results show that both of them show a trend of σ convergence in the whole study period. From 2011 to 2020, the value of σ in Heilongjiang Province’s construction industry fluctuated and increased, and only slightly decreased in 2012 and 2020. Therefore, in general, there was no convergence of σ in the development of Heilongjiang Province’s construction industry during the study period.

From the results of σ convergence of the tertiary industry and its sub-industries (

Table 2. σ convergence results of tertiary industry and its sub-industries in Heilongjiang Province.

Time	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Tertiary industry	0.49	0.48	0.50	0.49	0.51	0.56	0.56	0.57	0.57	0.57
Transportation, warehousing and postal services	0.43	0.43	0.42	0.40	0.41	0.51	0.50	0.50	0.52	0.53
Wholesale and retail, accommodation and catering	0.56	0.58	0.60	0.59	0.58	0.67	0.67	0.66	0.66	0.67
Other services	0.52	0.49	0.51	0.51	0.55	0.57	0.55	0.57	0.58	0.58

), it can be seen that the value of σ of the tertiary industry and its sub-industries in Heilongjiang Province fluctuates and increases during 2011–2020, and only decreases slightly in some years. Therefore, the development of the tertiary industry and its sub-industries in Heilongjiang Province does not show a trend of σ convergence in general during the study period.

The aforementioned findings reveal a gradual narrowing of the disparities in secondary industry output values among cities in Heilongjiang Province subsequent to 2016, indicative of the profound impact of industrial restructuring. This trend underscores the migration of traditional industries from developed to less-developed regions within the province. Several cities in Heilongjiang have capitalized on this shift by successfully accommodating industrial transfers, thereby fostering rapid growth in their secondary sectors. This process of industrial relocation and reception has contributed significantly to bridging the industrial output gaps between cities.

Furthermore, heightened regional cooperation among cities in Heilongjiang, characterized by resource sharing and complementary advantages, has facilitated their collective development. Such collaboration not only enhances the overall competitiveness of the regional economy but also effectively diminishes the disparities in industrial output values among cities, fostering a more balanced and integrated growth trajectory across the province.

3.2. β Convergence Characteristics of Industrial Development

• Absolute β convergence

According to the absolute β convergence results of the secondary industry and its sub-industries in Heilongjiang Province (

Table 3. Absolute β convergence results of the secondary industry and its sub-industries in Heilongjiang Province.

Parameter	Secondary Industry	Industry	Construction Industry
β value	−0.03207	−0.03245	−0.00057
β value	0.026123	0.03968	0.96384

), the absolute β values of the secondary industry and industry in Heilongjiang Province are both less than 0 and pass the test at the significant level of 5%, which indicates that the development of the secondary industry and industry in Heilongjiang Province has a significant absolute β convergence. This means that the areas in Heilongjiang province that are relatively backward in terms of secondary industry and industrial development may have the ability to narrow the gap with areas with higher levels of development at a relatively fast pace in the future. Dating back to the per capita output value of the secondary industry and industry in prefecture-level cities of Heilongjiang Province, it can be found that the development level of the secondary industry and industry in Daqing, Harbin and Qitaihe, which are in the lead, has been declining year by year in recent years, while the development level of the secondary industry and industry in Heihe and Suihua, which are relatively backward, has been steadily improving. Therefore, the above analysis results are better verified; although the absolute β value of Heilongjiang Province’s construction industry is also less than 0, it has not passed the test. Therefore, there is no absolute β convergence in the development of Heilongjiang Province’s construction industry.

According to the absolute β convergence results of the tertiary industry and its sub-industries in Heilongjiang Province (

Table 4. Absolute β convergence results of tertiary industry and its sub-industries in Heilongjiang Province.

Parameter	Tertiary Industry Transportation	Warehousing and Postal Industry	Wholesale and Retail, Accommodation and Catering	Other Services
β value	0.012107	0.008943	0.005897	0.009813
β value	0.200813	0.724886	0.680856	0.344173

), the absolute β values of the tertiary industry and its sub-industries are all greater than 0, which indicates that there is no absolute β convergence in the development of the tertiary industry and its sub-industries in Heilongjiang Province; that is, the regions with lower development level of the tertiary industry and its sub-industries do not catch up with the regions with higher development level.

• Conditional β convergence analysis

According to the conditional β convergence results of the secondary industry and its sub-industries in Heilongjiang Province (

Table 5. Conditional β convergence results of the secondary industry and its sub-industries in Heilongjiang Province.

Parameter	Secondary Industry	Industry	Construction Industry
β value	−0.05774	−0.0548	−0.02219
β value	0.007699	0.012915	0.183092

), the conditional β convergence values of the secondary industry and industry are all less than 0 and pass the test at the significant level of 5%, which indicates that conditional β convergence exists in the development of the secondary industry and industry in Heilongjiang Province. That is, the development of the secondary industry and its sub-industry in prefecture-level cities is gradually converging to its own stable state. The conditional β convergence of the construction industry has not passed the test, so there is no conditional β convergence in the development of the construction industry in Heilongjiang Province.

According to the conditional β convergence results of the tertiary industry and its sub-industries in Heilongjiang Province (

Table 6. Conditional β convergence results of tertiary industry and its sub-industries in Heilongjiang Province.

Parameter	Tertiary Industry Transportation	Warehousing and Postal Industry	Wholesale and Retail, Accommodation and Catering	Other Services
β value	−0.02	−0.01213	−0.03294	−0.02
β value	0.046171	0.581857	0.060976	0.046171

), the conditional β convergence values of the tertiary industry and its sub-industries are all less than 0, the conditional β values of the transportation, warehousing, postal industry, wholesale and retail industry, accommodation and catering industry did not pass the test, and the conditional β values of the tertiary industry and other services passed the test at a significant level of 5%. The results show that there is no conditional β convergence in the development of transportation, warehousing, postal industry, wholesale and retail industry, accommodation and catering industry in Heilongjiang Province, while there is conditional β convergence in the development of the tertiary industry and other service industries; that is, the development of the tertiary industry and other service industries in prefecture-level cities gradually converges to their own stable state.

In a comprehensive perspective, the transition of resource-intensive municipalities within Heilongjiang Province appears to confront formidable challenges. As a pivotal constituent of the historic industrial heartland in Northeast China, Heilongjiang’s economic trajectory has been inherently intertwined with the conventional framework of heavy industries, perpetuating a structural inertia. Lately, amidst the national endeavor to restructure and elevate the economic landscape, Heilongjiang Province too finds itself under the exigency of economic metamorphosis and modernization.

In comparison to advanced regions, Heilongjiang lags in technological ingenuity and R&D investments, a deficiency that undermines its capability for industrial escalation and novelty creation. This predicament is exemplified in cities like Daqing, long sustained by oil and mineral extraction, now confronting monumental hurdles in economic redirection as their resource reserves dwindle.

Convergent analyses further underscore that even erstwhile frontrunners, including Daqing, Harbin and Qitaihe, encounter developmental impasses. Despite their former prominence, these cities are now ensnared by a conglomeration of factors: resource exhaustion, industrial aging and inadequate technological progress. Consequently, the development indices of their secondary and tertiary sectors have experienced a progressive decline, necessitating an accelerated push towards transformation and identification of novel economic drivers.

Notwithstanding the generalized economic deceleration, pockets of positivity persist. Cities that were initially less developed, such as Heihe and Suihua, exhibit signs of belated yet robust progress. These municipalities, armed with modest beginnings, have progressively fortified their industrial backbone and fostered industrial development through strategic initiatives like industrial relocation, infrastructure augmentation and business environment optimization.

Concurrently, Heilongjiang Provincial Government has embarked on an array of industrial reconfiguration policies, aimed at catalyzing and sustaining the emergence of nascent industries, high-tech sectors and modern service industries. These measures have been instrumental in catalyzing economic transformation, structural refinement and upgrading, thereby reshaping Heilongjiang’s economy.

4. Network Characteristics of Industrial Spatial Correlation in Heilongjiang Province

4.1. Overall Characteristics of Industrial Spatial Correlation Network

Through the modified gravity model, the spatial correlation matrix of the secondary and tertiary industries in Heilongjiang Province was calculated, respectively. Four time nodes in 2011, 2014, 2017 and 2020 were selected and Ucinet 6 software was used to draw the network topology diagram of the spatial correlation of the secondary and tertiary industries, respectively. As shown in Figure 1 and Figure 2, the nodes in the spatial association network represent 13 prefecture-level cities in Heilongjiang Province, the lines between nodes represent the spatial correlation of industrial development among prefecture-level cities, and the arrow direction represents the direction of industrial development spillover.

The spatial correlation network of the secondary and tertiary industries in Heilongjiang Province shows that the spatial correlation of the secondary and tertiary industries among prefecture-level cities is no longer limited to spatial geographical restrictions. The development of the secondary and tertiary industries in each region not only produces correlation effects on its neighboring regions, but also has spatial correlation relations with non-neighboring regions, thus presenting complex spatial correlation network characteristics. In addition, Figure 1 and Figure 2 show that the secondary and tertiary industries in Heilongjiang Province have formed relatively stable spatial correlation networks, with no obvious changes during the study period. From the comparison of the spatial correlation network of secondary and tertiary industries, it is not difficult to find that the spatial correlation of tertiary industry among prefecture-level cities in Heilongjiang Province is closer than that of the secondary industry. The reason is that transportation, tourism and financial industry in the tertiary industry all involve a large amount of trans-regional flow of capital and personnel, thus promoting the spatial correlation of the tertiary industry in different regions.

Based on the social network analysis method, Ucinet software is used to calculate the network density, network correlation degree, network level and network efficiency of the spatial associated network of the secondary and tertiary industries so as to describe the overall characteristics of the spatial associated network quantitatively.

Figure 3a shows that the correlation strength of the spatial correlation network of the secondary industry in Heilongjiang Province fluctuates and increases during 2011–2020. From 2011 to 2013, the number of correlation relationships and network density increased rapidly from 52 and 0.33 to 55 and 0.35 respectively, and then fluctuated slowly to 56 and 0.36, respectively, in 2020. Figure 3b shows that the correlation strength of the spatial correlation network of the tertiary industry in Heilongjiang Province during 2011–2020 fluctuates first and then becomes stable. The number of correlation relationships and the network density fluctuated from 56 and 0.36 in 2011 to 54 and 0.35 in 2015, and both remained unchanged from 2015 to 2020. In contrast, the correlation strength of spatial association network of the tertiary industry is stronger than that of the secondary industry. In addition, compared with the maximum correlation number (156 = 13 × 12), the actual correlation number of the two has a large room for improvement, indicating that the spatial correlation strength of the secondary and tertiary industries in Heilongjiang Province needs to be strengthened.

The network correlation degree of spatial correlation network of secondary and tertiary industries in Heilongjiang Province from 2011 to 2020 is all 1, indicating that the secondary and tertiary industries in any two prefecture-level cities are accessible and the network accessibility is good. Figure 4a shows that the network hierarchy of the secondary industry is mostly between 0.5 and 0.6, indicating that the spatial correlation network structure of the secondary industry in Heilongjiang Province is not obvious, and there is no significant difference in the role of prefecture-level cities in the network. Figure 4b shows that the tertiary industry’s network hierarchy is mostly between 0.55 and 0.65, indicating that the spatial correlation network structure of the tertiary industry in Heilongjiang Province is more significant than that of the secondary industry, and different regions play a greater role in the spatial correlation network of the tertiary industry than that of the secondary industry. At the same time, the network efficiency of the spatial correlation network of the secondary and tertiary industries is stable at about 0.7, and there are relatively few redundant connections in the spatial correlation network structure, indicating that the stability of the spatial correlation network of the secondary and tertiary industries in Heilongjiang Province needs to be improved.

4.2. Individual Characteristics of Industrial Spatial Correlation Network

In order to reveal the position of prefecture-level cities in the industrial spatial association network, four nodes in 2011, 2014, 2017 and 2020 are selected to analyze the individual characteristics of the spatial association network of secondary and tertiary industries in Heilongjiang Province, respectively, with the degree of utilization centrality, proximity centrality and intermediate centrality (

Table 7. Analysis results of individual characteristics of spatial correlation network of secondary industry in Heilongjiang Province.

Area	Degree Center Degree				Degree of Proximity to Center				Intermediate Centrality
	2011	2014	2017	2020	2011	2014	2017	2020	2011	2014	2017	2020
Harbin	0.67	0.63	0.67	0.63	100	100	100	100	35.25	14.27	32.64	12.88
Qiqihar	0.33	0.33	0.33	0.29	63.16	63.16	63.16	63.16	0.88	0.25	0.51	0.00
Jixi	0.33	0.38	0.33	0.42	63.16	66.67	63.16	66.67	3.66	2.90	4.92	6.82
Hegang	0.42	0.46	0.38	0.46	66.67	70.59	66.67	70.59	13.08	12.75	13.32	13.13
Shuangyashan	0.38	0.38	0.42	0.42	63.16	63.16	66.67	66.67	4.82	2.40	8.78	2.53
Daqing	0.21	0.29	0.29	0.38	57.14	60.00	60.00	66.67	0.00	0.00	0.76	1.14
Yichun	0.25	0.25	0.25	0.25	63.16	63.16	63.16	63.16	1.29	1.14	0.38	0.38
Jiamusi	0.33	0.33	0.38	0.42	66.67	66.67	66.67	66.67	2.07	1.52	4.36	2.53
Qitaihe	0.42	0.42	0.42	0.42	66.67	66.67	66.67	66.67	14.04	5.43	12.44	3.03
Mudanjiang	0.25	0.21	0.29	0.21	57.14	57.14	60.00	60.00	19.47	0.00	20.27	0.38
Heihe	0.25	0.25	0.21	0.25	66.67	66.67	63.16	66.67	3.41	5.30	3.41	5.30
Suihua	0.33	0.38	0.38	0.33	66.67	66.67	66.67	66.67	1.26	1.01	1.26	0.38
Daxinganling	0.17	0.21	0.17	0.21	60.00	63.16	60.00	63.16	0.00	0.00	0.00	0.00

and

Table 8. Analysis results of individual characteristics of spatial correlation network of tertiary industry in Heilongjiang Province.

Area	Degree Center Degree				Degree of Proximity to Center				Intermediate Centrality
	2011	2014	2017	2020	2011	2014	2017	2020	2011	2014	2017	2020
Harbin	0.67	0.67	0.67	0.67	100	100	100	100	32.32	34.80	33.97	33.21
Qiqihar	0.33	0.33	0.33	0.33	63.16	63.16	63.16	63.16	0.00	0.00	0.00	0.00
Jixi	0.33	0.33	0.33	0.33	63.16	63.16	63.16	63.16	1.52	1.67	7.13	6.31
Hegang	0.29	0.29	0.29	0.29	60.00	60.00	60.00	60.00	9.22	8.23	8.65	8.33
Shuangyashan	0.38	0.38	0.38	0.42	63.16	63.16	63.16	66.67	6.31	5.56	6.31	7.58
Daqing	0.42	0.38	0.38	0.33	70.59	66.67	66.67	63.16	0.51	0.25	0.25	0.00
Yichun	0.25	0.25	0.25	0.25	63.16	63.16	63.16	63.16	0.76	4.09	4.67	0.38
Jiamusi	0.42	0.38	0.38	0.42	75.00	70.59	70.59	75.00	16.54	11.92	13.01	17.05
Qitaihe	0.33	0.33	0.29	0.29	63.16	63.16	63.16	63.16	1.52	1.67	0.25	0.25
Mudanjiang	0.38	0.38	0.33	0.29	66.67	66.67	66.67	63.16	22.48	24.07	23.23	20.46
Heihe	0.25	0.25	0.25	0.25	66.67	66.67	66.67	66.67	3.03	2.20	2.27	3.41
Suihua	0.42	0.42	0.42	0.42	70.59	70.59	70.59	70.59	0.51	1.01	1.01	1.52
Daxinganling	0.21	0.21	0.21	0.21	63.16	63.16	63.16	63.16	0.00	0.00	0.00	0.00

).

• Degree center degree

The degree centrality is used to determine the position of prefecture-level cities in the industrial spatial correlation network. In terms of degree centrality of prefecture-level cities in the spatial correlation network of the secondary industry, Harbin has the largest degree centrality and is directly correlated with all other regions. Therefore, it occupies the absolute core position in the spatial correlation network of the secondary industry in Heilongjiang Province. The second place is Hegang, Shuangyashan and Qitaihe, which are at the core of the spatial correlation network of the secondary industry and are closely related to other regions. The degree centrality of the remaining regions is small, and the development of the secondary industry is less related to other regions, so it is in the marginal position in the spatial correlation network. From the time dimension, the degree centrality of Harbin, Qiqihar and Mudanjiang decreased from 2011 to 2020, indicating that the positions of the above regions in the spatial correlation network of the secondary industry gradually tended to the edge. However, due to the obvious center advantage of Harbin, its core position will not change in the short term. The degree centrality of Yichun, Qitaihe, Heihe and Suihua remained basically unchanged, while the degree centrality of the other regions fluctuated and increased, indicating that with the continuous development of industry and construction, the secondary industry in the above regions had a close relationship with other regions in the process of development, making it close to the core position in the spatial correlation network (Table 7).

In terms of degree centrality of prefecture-level cities in the spatial correlation network of the tertiary industry, Harbin has the largest degree centrality and is in a central position in the network. As a provincial capital city, Harbin is an important transportation hub, so its transportation industry has developed rapidly. Secondly, as a city of ice and snow, ice and snow projects effectively promote the development of local tourism, accommodation and the catering industry, resulting in a close relationship between Harbin and the other 12 regions in the process of rapid development of the tertiary industry. Therefore, it occupies a significant central position in the spatial correlation network. Shuangyashan, Daqing, Jiamusi and Suihua are next; the degree of centrality of the above regions is second only to Harbin, in the tertiary industry spatial correlation network in a relatively core position. The rest of the region has less connection with other regions in the process of tertiary industry development, so it is in the edge of the spatial network. From the time dimension, the degree centrality of Shuangyashan increased during the study period, while that of Daqing, Qitaihe and Mudanjiang decreased, while that of other regions remained stable, indicating that most regions formed a relatively stable correlation with other regions in the process of tertiary industry development (Table 8).

• Proximity to the center

The degree of proximity to the center is used to determine the degree of correlation between industrial development and other regions. The results of proximity centrality of prefecture-level cities in the spatial correlation network of secondary and tertiary industries in Heilongjiang Province show that Harbin has the highest proximity to the center of both secondary and tertiary industries, indicating that the development of secondary and tertiary industries in Harbin is more likely to generate spatial correlation with other regions and play a core role in the spatial correlation network. There is little difference in the degree of proximity to the center in other regions, indicating that the degree of difficulty of spatial correlation between these regions and other regions is similar in the process of the development of secondary and tertiary industries. In addition, from the perspective of time dimension, in the spatial correlation network of the secondary industry, the proximity degree of Jixi, Hegang, Shuangyashan, Daqing, Mudanjiang and Greater Khingan Mountains is increasing, indicating that the difficulty of spatial correlation between the development of the secondary industry in these regions and other regions is decreasing, while the proximity degree of other regions is unchanged. In the spatial correlation network of the tertiary industry, the degree of proximity to the center of Shuangyashan is increasing, that of Daqing and Mudanjiang is decreasing and that of other regions is unchanged, indicating that the degree of accessibility of spatial correlation between most regions and other regions in the development of the tertiary industry is basically unchanged during the study period (Table 7 and Table 8).

• Intermediate centrality

Similar to the calculation results of degree centrality and proximity centrality, in the spatial correlation network of the secondary industry, Harbin has the largest degree of middle centrality, followed by Hegang and Qitaihe. In the spatial correlation network of the tertiary industry, the intermediate centrality of Harbin, Mudanjiang and Jiamusi ranks the top three, followed by Hegang, Shuangyashan and Jixi. This shows that the above regions are in a dominant position in their respective spatial correlation networks and play an important role as intermediary bridges. The intermediate centrality of other regions is small, and these regions are at the edge of the industrial spatial correlation network (Table 7 and Table 8).

4.3. Driving Effect of Industrial Spatial Correlation Network

Based on a detailed analysis of the overall characteristics and individual characteristics of the spatial correlation network of the secondary and tertiary industries in Heilongjiang Province, the influence of the spatial correlation network of the industrial development on the development level and spatial difference of the secondary and tertiary industries in Heilongjiang Province is explored from the overall and individual dimensions.

• Overall driving effect

The mean and standard deviation of per capita output value of secondary and tertiary industries in Heilongjiang Province were taken as explained variables, and the overall characteristic indicators of the spatial correlation network (network density, network level, network efficiency) were taken as explanatory variables for regression analysis (

Table 9. Overall driving effect of spatial correlation network of secondary industry in Heilongjiang Province.

Explained Variable	Average per Capita Output Value of Secondary Industry			Standard Deviation of per Capita Output Value of Secondary Industry
Model	(1)	(2)	(3)	(4)	(5)	(6)
Constant term	−0.78 ***	2.75 **	5.71 ***	2.34 ***	5.92 ***	1.52 **
Network density	6.49 **	—	—	−0.83 ***	—	—
Network level	—	−2.28 ***	—	—	7.01 **	—
Network efficiency	—	—	−5.99 ***	—	—	0.76 ***
R2	0.72	0.75	0.81	0.78	0.73	0.82

Note: ** means pass the test at the significance level of 5%, *** means pass the test at the significance level of 1%.

and

Table 10. Overall driving effect of spatial correlation network of tertiary industry in Heilongjiang Province.

Explained Variable	Average per Capita Output Value of Primary Industry			Standard Deviation of per Capita Output Value of Tertiary Industry
Model	(1)	(2)	(3)	(4)	(5)	(6)
Constant term	22.76 ***	−3.67 **	−38.50 ***	14.53 ***	−2.67 **	−24.96 ***
Network density	61.26 ***	—	—	−39.48 ***	—	—
Network level	—	−8.42 **	—	—	5.70 **	—
Network efficiency	—	—	−56.55 ***	—	—	36.45 ***
R2	0.87	0.69	0.87	0.86	0.72	0.86

Note: ** means pass the test at the significance level of 5%, *** means pass the test at the significance level of 1%.

). The results showed that all regression coefficients passed the significance test, and the R2 of most regression coefficients was greater than 0.7, so the fitting effect of the model was good.

In terms of the influence of the overall network structure on the development level of the secondary and tertiary industries in Heilongjiang Province, the regression coefficients of network density in the spatial correlation network of the secondary and tertiary industries are 6.49 and 61.26, respectively, indicating that the increase in network density can effectively promote the development of the secondary and tertiary industries. Because the increase in network density represents the strengthening of spatial correlation between regions during the development of secondary and tertiary industries, the core position of the central region is gradually weakened, and the spatial spillover effect between different regions is strengthened, thus promoting the overall development level of the secondary and tertiary industries in Heilongjiang Province. The regression coefficients of network level and network efficiency are both negative, indicating that the decrease in network level and network efficiency will promote the development of secondary and tertiary industries. This is because the reduction in network level represents that the region in the core dominant position gradually changes from single correlation to bidirectional correlation; that is, it will also generate space overflow to other regions while receiving space overflow from other regions. Moreover, the marginal regions will increase their influence on other regions in the process of the development of secondary and tertiary industries. The decrease in network efficiency indicates the increase in correlation in the network, the closer correlation between the development of the secondary and tertiary industries between regions and the gradual enhancement of the mobility of human resources, capital and other factors, thus promoting the significant improvement of the overall development level of the secondary and tertiary industries.

In terms of the impact of the overall network structure on the spatial difference of the secondary and tertiary industries in Heilongjiang Province, the regression coefficients of network density are −0.83 and −39.48, respectively, indicating that the increase in network density will reduce the spatial difference of the secondary and tertiary industries. Because the increase in network density represents an increase in inter-regional correlation, the flow of inter-regional economic, technological, information and other factors is gradually strengthened, which effectively restrains the polarization trend of the development of secondary and tertiary industries caused by inter-regional factor differences. The regression coefficients of network level and network efficiency are both positive, indicating that the reduction in network level and network efficiency can effectively promote the spatial balance of the development of the secondary and tertiary industries. Specifically, the reduction of network hierarchy means that the dominant position of the region at the core of the network is weakened, while the influence of the region at the edge of the network is gradually strengthened, thus providing more channels and possibilities for the strengthening of inter-regional correlation. The decrease in network efficiency indicates that the stability of the network is gradually strengthened and the correlation between regions is increasingly close, which effectively narrows the development difference between the secondary and tertiary industries.

In addition, it can be seen from Table 9 and Table 10 that the absolute values of the regression coefficients of network density, network rank and network efficiency of the tertiary industry are basically greater than those of the secondary industry, indicating that the overall network structure has a far greater impact on the tertiary industry than the secondary industry. The reason is that the development of the tertiary industry involves greater flow of economic, personnel and other factors, such as tourism, transportation, accommodation and catering industry and financial industry, while the development of the secondary industry involves relatively weak mobility of factors.

• Individual-driven effect

The per capita output value of secondary and tertiary industries of prefecture-level cities was taken as the explained variable, and the individual network characteristic index (degree centrality, proximity centrality, intermediate centrality) was taken as the explanatory variable to build a panel model to explore the influence of individual network structure on the development level of secondary and tertiary industries in each region. Through a Hausman test, it is found that all models support the fixed-effect model, and all coefficients pass the significance test; R2 is greater than 0.7, indicating that the model has a good fitting effect, and the results are shown in

Table 11. Individual driving effect of spatial correlation network of secondary industry in Heilongjiang Province.

Explained Variable	Per capita Output Value of Secondary Industry in Each Region
Model	(1)	(2)	(3)
Constant term	3.46 ***	11.16 ***	1.58 ***
Degree center degree	5.63 ***	—	—
Degree of proximity to center	—	6.43 ***	—
Intermediate centrality	—	—	0.01 ***
R2	0.89	0.90	0.88
Hausman Statistic	8.58 ***	6.68 **	10.53 ***
FE/RE	FE	FE	FE

Note: ** means pass the test at the significance level of 5%, *** means pass the test at the significance level of 1%.

and

Table 12. Individual driving effect of spatial correlation network of tertiary industry in Heilongjiang Province.

Explained Variable	Per capita Output Value of Tertiary Industry in Each Region
Model	(1)	(2)	(3)
Constant term	3.95 ***	4.73 **	1.62 ***
Degree center degree	6.52 ***	—	—
Degree of proximity to center	—	7.05 **	—
Intermediate centrality	—	—	0.04 **
R2	0.75	0.73	0.73
Hausman Statistic	20.17 ***	5.58 **	7.89 ***
FE/RE	FE	FE	FE

Note: ** means pass the test at the significance level of 5%, *** means pass the test at the significance level of 1%.

.

The results showed that the per capita output value of secondary and tertiary industries would increase by CNY 563 yuan and CNY 65,200, respectively, with a 1% increase in the degree centrality of each region. That is to say, the more direct connections each region has with other regions, the more benefits it will gain from the overall network, and the more beneficial it will be to the development of local secondary and tertiary industries. The regression coefficients of proximity to centrality of secondary and tertiary industries in each region are 6.43 and 7.05, respectively, indicating that the proximity to centrality of each region is positively correlated with the per capita output value of secondary and tertiary industries. The increase in proximity to the center makes it easier for each region to generate spatial correlation in the process of the development of secondary and tertiary industries, which makes the correlation between regions closer, thus reducing the trans-regional flow cost of resources and technologies, and promoting the rapid development of the secondary and tertiary industries in each region. The regression coefficients of intermediate centrality of secondary and tertiary industries are 0.01 and 0.04, respectively, indicating that the per capita output value of secondary and tertiary industries will increase by CNY 0.01 and 0.04 with an increase of 1% in the intermediate centrality of each region. Regions with a high degree of intermediate centrality can effectively control the correlation between other regions in the spatial correlation network, and have a significant impact on the development of secondary and tertiary industries in other regions. In the process of industrial development, they can receive more spatial spillovers from other regions, thus promoting the development of their own secondary and tertiary industries.

5. Conclusions and Suggestions

5.1. Conclusions

By studying the spatial pattern of industrial development in Heilongjiang Province and its dual driving effects, the following conclusions are drawn:

(1)

Significant convergence in secondary industry space, more unified development trajectory

Firstly, we observed notable spatial convergence characteristics within the secondary industry, whereas the construction industry and the tertiary industry, along with its sub-sectors, did not exhibit such a pattern. This finding suggests that the secondary industry in Heilongjiang has experienced a more cohesive development trajectory compared to other sectors.

(2)

The industrial network is stable, and the structure level needs to be improved

Secondly, our investigation into the spatial correlation network characteristics revealed a stable network for both secondary and tertiary industries during the study period. Notably, the network accessibility was good, indicating that any two prefecture-level cities in Heilongjiang Province were interconnected. However, the hierarchy of the network structure was not pronounced, and there is room for improvement in terms of correlation strength and stability. Comparatively, the tertiary industry displayed higher network intensity and structure hierarchy than the secondary industry. Regions such as Harbin, Hegang and Qitaihe occupied central positions in the secondary industry’s spatial association network, while Harbin, Jimusi and Suihua led in the tertiary industry’s network, serving as crucial intermediary bridges.

(3)

Network density facilitates development, hierarchy and efficiency pose constraints

Regarding the dual driving effect, our results indicated that an increase in network density positively influenced the development of both secondary and tertiary industries. Conversely, network level and network efficiency exerted inhibitory effects. The increase in network density contributed to narrowing the spatial disparities within these industries, while a decrease in network level and efficiency promoted spatial balance. Notably, the overall network structure had a more profound impact on the tertiary industry than on the secondary industry. At the individual level, regions with stronger direct correlations with others benefited more from the overall network, facilitating local industrial development. The spatial correlation between regions was conducive to reducing inter-regional flow costs of resources and technologies, thereby accelerating regional industrial growth.

Our findings suggest that enhancing inter-regional correlations and promoting network density can effectively drive the overall development of Heilongjiang’s secondary and tertiary industries. Policies aimed at strengthening regional connectivity and cooperation, such as improving transportation infrastructure and facilitating the flow of economic, technological, and informational resources, could yield substantial benefits. The observed β convergence trends indicate that targeted support for relatively underdeveloped regions could accelerate their catch-up with more advanced areas, thereby promoting balanced regional development.

5.2. Suggestions

• Optimize the industrial structure and promote industrial development

The reform of the traditional resource-dependent industrial development mode is the core link to promote the highly developed secondary industry of Heilongjiang Province, which can be started from the following aspects: (1) actively reform the traditional industry, strengthen the important role of information technology, and give full play to the driving effect of information technology industry on industrial development; (2) actively promote supply-side structural reform, optimize the supply structure and avoid overcapacity; (3) focus on developing new energy sources, actively cultivate emerging industries and accelerate the innovation of traditional industries through the introduction of high-tech talents. As far as the tertiary industry is concerned, Heilongjiang Province is rich in ice and snow resources and forest resources, so the development of the tertiary industry such as tourism occupies a dominant position in the three industries. In order to further optimize the development of tertiary industry in Heilongjiang Province, we can start from the following aspects: (1) gradually enrich industrial categories, consolidate the pillar position of the tertiary industry, better integrate information technology into tourism, catering, finance and other services, and promote the development of new modern service industry; (2) promote the vigorous development of the life service industry for the convenience of the people, integrate information technology with transportation, storage and postal industry properly, and improve the convenience of the elderly care service industry by using information technology; (3) have the courage to innovate, break traditional barriers for institutional reform, reduce the entry threshold of the tertiary industry, actively attract talents, introduce foreign investment, increase publicity and relax the market entry of the cultural industry and the Internet industry.

• Give play to regional characteristics and develop advantageous industries

The capital city of Heilongjiang Province, Harbin has superior geographical location and transportation conditions, as well as abundant snow and ice resources. Therefore, it can not only provide perfect elements for the development of the secondary and tertiary industries, but also provide effective guarantee for the marketization of its related products. In addition, Harbin is in the absolute core position in the spatial correlation network of industrial development in Heilongjiang Province. It can also exert a great influence on the industrial development of other regions while receiving a large number of spatial spillovers from other regions. Therefore, Harbin should fully develop knowledge and technology-intensive industries and new tourism with the help of good local factor supply markets. Strengthen the transformation and upgrading of the manufacturing industry, promote the development of new energy, increase the promotion of ice and snow tourism, give full play to the positive driving role of Harbin on the surrounding cities so as to promote the construction of urban agglomeration with Harbin as the center. In addition, forest resource-based cities such as Yichun and Greater Khingan Mountains can make full use of their rich forest resources to vigorously develop forest tourism, increase investment in forest tourism publicity and construction, improve forest tourism service quality, promote the development of local tourism and drive the development of local catering, accommodation, transportation and other related industries. In addition, we should actively promote the integrated development of forestry-related industries such as the understory planting industry and the forest product-processing industry. For Hegang, Jixi, Shuangyashan and Qitaihe, which are mainly coal resources, we should actively get rid of resource dependence and gradually realize industrial and urban transformation. We will increase the introduction of human resources and technology, fully develop new forms of energy and explore the development of emerging industries.

• Improve the transportation network and guide the flow of factors

Emphasis should be placed on the construction of railway, highway and other transportation networks with Harbin as the core and Daqing as the secondary core so as to strengthen the accessibility between regions. On the one hand, this can promote the fluidity of production factors of industrial development between different regions and realize the reasonable distribution of resources and technologies among regions. On the other hand, it can reduce the flow cost of various products between different regions and promote economic and trade exchanges between regions. In addition, it can promote population flow between different regions and drive the development of regional tourism, accommodation and catering industry, transportation industry, etc., so as to give full play to the radiation and driving role of the central city, strengthen the spatial correlation of industrial development and realize the coordinated development of industries between different regions. In addition, it focuses on the development of transportation network construction in remote areas such as Greater Khingan Mountains and Yichun. On the one hand, it can promote the spatial spillover of various elements in the areas at the edge of the spatial correlation network of industrial development to other areas. On the other hand, it can effectively absorb the spatial spillover of other areas so as to promote the spatial correlation of industrial development between regions and drive the development of local industries.

• Strengthen regional cooperation and strengthen correlation effect

To create a good market environment, this is an important basis for the orderly industrial division of labor and cooperation between different regions. Heilongjiang Province can establish a community through economic relations, such as a community with Harbin Economic Zone as the core, to radiate the common development of its surrounding cities, so as to form a market-based resource-allocation system. An orderly market environment needs to give full play to the leading role of the market, reduce government intervention and create a good competitive atmosphere for enterprises. In order to promote the optimization of industrial division of labor and cooperation, it is necessary to build a community of industrial exchange and investment, in which an orderly financial market is essential. Only a good financial market can promote mutual investment and loans between regions, so as to provide capital flow for industrial development in underdeveloped regions. Use the leading role of economically developed areas to promote the industrial development of backward areas. In addition, the efficient regional cooperation platform is also the basis of industrial division of labor and cooperation between regions. Information technology is used to realize the information sharing of product trading and goods circulation among regions, so as to ensure the smooth transferal of information of industrial development among prefecture-level cities in Heilongjiang Province and realize the networked development of regional industrial cooperation.

Further research could delve into the specific mechanisms underlying the observed spatial convergence and network characteristics, examining the roles of technological innovation, resource allocation and policy interventions.

A more granular analysis of the individual characteristics of prefecture-level cities within the spatial correlation networks could elucidate the factors contributing to their positions and roles within these networks.

The study could be expanded to investigate long-term trends and potential future changes in the spatial correlation networks of Heilongjiang’s secondary and tertiary industries, taking into account the impacts of economic, social and technological advancements.

In conclusion, this study provides valuable insights into the spatial convergence and network characteristics of industrial development in Heilongjiang Province, laying a foundation for future research and policy formulation aimed at fostering balanced and sustainable regional development.