The Population Flow under Regional Cooperation of “City-Helps-City”: The Case of Mountain-Sea Project in Zhejiang

Abstract

Regional cooperation has been increasingly recognized as indispensable in promoting coordinated regional development in China’s new urbanization. The “city-helps-city” cooperation arises as an important type of regional approach to reduce regional inequalities. This study focuses on the “city-helps-city” cooperation of the Mountain-Sea Cooperation Project in Zhejiang province and aims to examine how this type of cooperation affects the interjurisdictional linkages of backward places. First, based on the cellphone signaling data from China Mobile and social network analysis, we capture the interjurisdictional linkages represented by the population flow between poverty counties and other municipalities as our dependent variables, which are expected to be stimulated by the regional cooperation of Mountain-Sea projects. Second, through text semantic analysis on the news data of Mountain-Sea cooperation, we further identify three measures of cooperation, including the diversity of cooperation fields, the intensity of different cooperation focuses, and the legitimacy of cooperation as our main explanatory variables. Last, we run regression models to show differentiated impacts of cooperation diversity, intensity, and legitimacy on the linkages between poverty counties and developed places. The findings interrogate whether and how Mountain-Sea cooperation effectively engages backward localities in the regional network of economic production, social affairs, and institutional arrangements to enhance their linkages with other places. This study not only contributes to theoretical and empirical understandings of the state-driven “city-helps-city” cooperation as the new regional institution in transitional China, but also attempts to provide policy implications on reducing regional inequalities from the perspective of intercity cooperation.

1. Introduction

The notion of regionalism has resurfaced in the transitional China [1,2,3,4]. The state-orchestrated regional approach has been recognized as one of the most salient features in China’s “new urbanization” [1,5]. While the formations of city-region [6] for urban agglomeration, urban unification (e.g., twin-city), metropolitanization, and urban clusters have enhanced regional competitiveness of China, the regionalization of economy did not effectively counter the fierce intercity competition, governance fragmentation, and rising regional inequalities [1,4,7,8,9,10,11]. According to the report of the State Council in 2020, it is essential to promote the coordinated development of large, medium, and small cities to resolve regional imbalances. This requires new regional efforts of provincial and local governments to formulate strategies of cooperation and build regional institutions of intercity cooperation, coordination, and integration, instead of competition [1].

The regional cooperation focusing on the theme of “city-helps-city” has emerged as a new type of spatial political economy in China and was actively promoted by the central government. In 2002, when President Xi worked as the governor in Zhejiang, the provincial government issued the Opinions on Implementing Mountain-Sea Project [12] to address the problems of rising regional disparities, insufficient linkages, and uncoordinated development between developed coastal areas and the poor mountainous region within Zhejiang. Municipalities in Zhejiang were paired by the provincial government for interjurisdictional cooperation. In 2021, the regional cooperation of “city-helps-city” was upscaled [13] to the Yangtze River Delta (YRD) region to stimulate cooperation crossing provincial boundaries. To advance regional integration, cities in the developed provinces of Zhejiang, Jiangsu and Shanghai, have been mandated to aid less-developed cities in Anhui province.

The cooperation of “city-helps-city” represents different theoretical understandings of city-regionalism [14,15], regional governance [1], and state rescaling [16]. City-regionalism sees intercity cooperation as the response to the enhancement of global competitiveness. It is formed through state spatial selectivity or (regional) scale-building for the purpose of enhancing regional competitiveness [17]. The famous notion of “new state space” [16,17] suggests the inevitable overconcentration of state resources (e.g., policies, authorities, funding, etc.) in large cities or city-regions and the consequent spatial divergence under this regional trend. Moreover, intercity cooperation is also regarded as the manifestation of “new regionalism” to engage different actors in a process of (regional) scale-building and solve the regulatory deficits in regional governance. Thus, cooperation is contingent upon the “distribution of politics” [18,19]. However, disadvantaged actors are often caught isolated or marginalized in these regional affairs, and a widened regional gap is assumed.

By contrast, the unique regional cooperation of “city-helps-city” represents the new norm of China’s regionalism. This type of cooperation is intrinsically associated with the redistributive regime of intergovernmental relations [20,21] and has been pursued for the coordination, integration, and regional equity rather than the competition under growth politics [3,4]. Different from the direct top–down spatial redistribution of higher-level government or the bottom–up collaboration based on local interests [5,22,23,24], “city-helps-city” is a mix of the vertical and horizontal regional governance approaches. State or provincial government promotes, orchestrates, and mandates the tasks of cooperation to ensure the legitimacy of this regional strategy [1,3]. The implementation still relies on local practices of interjurisdictional cooperation that might be geographically distant and not confined to the metropolitan areas among adjacent localities [22]. Therefore, this type of cooperation cannot only remedy the crisis of previous spatial development, but also broaden the scope of regional cooperation. It resonates with Brenner [17] and Wu [1] on the periodization of regionalism that more spatially redistributive focuses are expected in the later stages of state spatial rescaling.

In this study, we focus on the Mountain-Sea Cooperation Project in Zhejiang province, which is the national pilot region for “common prosperity” and the critical component of YRD integration. Our research question is to examine how regional cooperation of “city-helps-city” correlates with interjurisdictional linkages and reshapes city network structure of the region. We employed population flow to represent the interjurisdictional linkages of county pairs in Zhejiang to capture the regional centrality of localities. The flows of production factors such as labor, capital, information, and technology are considered as concomitants with population flow [25] to reflect the functional correlations among cities [26]. Our study assumes that regional cooperation of economic production, social affairs, and institutional arrangements under the Mountain-Sea projects are expected to enhance these linkages through various collaborative activities, especially for the backward places in the mountainous region of Zhejiang. To this end, we examine our hypothesis from three dimensions: the diversity of cooperation fields (scope), the intensity of different cooperation focuses (depth), and the legitimacy of cooperation.

Location-based big data [27,28] from China Mobile is employed to capture population flow in real-time. Our study comprehensively adopts methodologies of social network analysis, text semantic analysis, and regression models to identify the patterns of interjurisdictional linkages (dependent variables), the crucial information of Mountain-Sea cooperation (independent variables) and the correlations between these two. We propose the perspective of regional cooperation on “city-helps-city” to understand the intercity linkages that are not confined to geographic neighbors, especially for the less-developed areas. The aim is to contribute to the theoretical and empirical understanding of “city-helps-city” cooperation as the new spatial political economy in the transitional China and to provide implications on combating regional inequalities through intercity cooperation.

The remainder of the paper is organized as follows: Section 2 puts forward a conceptual framework about the dimensions of the Mountain-Sea Cooperation Project and possible mechanisms of the impacts on population flow. Section 3 introduces the research area, data source, and analytical methodologies. Section 4 demonstrates the spatial patterns of interjurisdictional population flow in Zhejiang, the Mountain-Sea Cooperation, and their correlations in the regression models. Section 5 discusses relevant cases to elaborate the empirical results of regression models. The last section summarizes the paper and draws conclusions.

2. Conceptual Framework

During the past century, the processes of capitalist globalization have provoked distortions in the urban and regional development to exacerbate inequalities not only within the city but also at the intercity scale. Within the city, gentrification, economic segregation, and corporate controls, etc. [29,30], have brought about eviction to working-class people and undermined social injustice. For example, the transnational capitalist class (TCC) can control where people live, consume, and think through “icon projects”, thereby solidifying their own interests [29]. Such inequalities are more pronounced in the “Alpha City”, such as London or New York City [30,31]. Moreover, the capitalist urbanization has promoted the winner-takes-all model that increased regional inequalities of winning and losing cities.

Therefore, it is crucial to address the increasing inequality under capitalist urbanization. Integrated planning should be prompted to engage a broader range of stakeholders representing the marginalized groups, facilitate capacity-building, and improve policy frameworks in the backward areas [32]. The cooperation of different stakeholders on mega-events can also bring about positive social impacts on promoting skill training in disadvantaged people, enhancing social inclusion, etc. [33]. Urban redevelopment, e.g., on brownfield infrastructure, needs to be cautious to the neoliberalism and its unequal and exclusionary impacts [34].

As for the spatial dimension of inequalities, regional cooperation has been largely promoted. Regional cooperation refers to the type of development that occurs when two or more jurisdictions cross boundaries to form collaborations for short-term or long-term benefits in economic, ecological, or social fields [35,36]. Generally, existing studies have divided regional cooperation into three types: economic, social, and institutional [10,36,37]. For example, Pan et al. explored the impacts of these three dimensions on urban land-use efficiency and indicated the potential mechanism behind them [36]. Interjurisdictional cooperation is an effective way to promote coordinated economic development, joint construction, and sharing of public services and facilities, and finally realize balanced regional development.

The “city-helps-city” [10], as an emerging mode of cooperation in China, vigorously bridges the regional gap and promotes the development of poorly developed cities under the assistance of their partners. Regional cooperation of the Mountain-Sea projects in Zhejiang can be conceptually separated into four dimensions of cooperation (see Figure 1), including industrial development, new economies, social affairs of public services and environment, and institutional arrangements.

Industrial cooperation is the most common type of collaboration [38,39]. It involves fields of manufacturing, agriculture, and forestry, etc. The shortage of land quotas incentivizes the developed localities to collaborate with less-developed places that can adequately supply physical space for development [36]. For example, developed and underdeveloped municipalities may jointly build industrial parks in underdeveloped regions, which can facilitate the economic development of disadvantaged places and accelerate the exchanges of production factors [8,9].

The cooperation on new economies is regarded as a more effective strategy. Less-developed counties can celebrate its endowed ecological and cultural resources. Transforming the value of the endowment into the value of development is one of the primary targets for interjurisdictional cooperation. This kind of cooperation brings about tourists and consumers to poor places, thereby stimulating ecological or cultural tourism and service industries. For example, the cooperation to organize mega-events can prominently brand the hosting city and promote its tourism and trade [33]. Additionally, cooperation on digital technology is also common. For manufacturers, the efficiency and quality of products would be highly enhanced by intelligent manufacturing technology. Retails, especially for the agricultural products in impoverished and less connected areas, can also benefit. For instance, digital platforms of e-commerce are useful in advertising agricultural products of poverty counties through online streaming and fill the information gap between suppliers and demanders.

Social service is intimately related to the daily lives of people, especially on healthcare, education, and social welfare [40]. The provisions of social services are dramatically uneven among counties with different capacities. Poor counties are more likely to suffer from the lack of social infrastructure, such as universities, research institutions, and healthcare facilities [41,42]. The collaboration on environmental protection and beautification are also pursued as complimentary tactics to prompt the influx of people and improve local living conditions. The cooperation on social services is expected to greatly equalize the levels of service provisions, increase human capital, and enable people in poor counties to have more opportunities in human development (e.g., jobs, education, health), thereby driving more population flow between developed and less-developed counties.

The institutional cooperation on spatial or regional planning enhances the stability and sustainability of socioeconomic interactions across regions [43,44]. Industrial and social cooperation is concomitant with regional or spatial planning of infrastructure construction (e.g., transportation planning) for people’s commuting across localities [43,45]. Institutionalized cooperation further legitimizes initiatives, programs, and proposals of collaboration, which can guarantee the implementation of projects. Under the guidance of planning, policy, or agreement, the patterns and measures of cooperation are clearly stipulated to support intercity connections and coordinated regional development [1].

We establish a framework that proposes the insightful perspective of regional cooperation, especially the “city-helps-city” scheme, to understand the intercity population flow and the linkages of backward places in Zhejiang. Moreover, this study sheds light on how regional cooperation of Mountain-Sea project reshapes the structure of the city network by engaging less-developed localities in regional production networks and service together with institutional arrangements. This type of cooperation differs from the conventional cooperation types in that it not only goes beyond the collaboration between geographic neighbors, but also has been orchestrated by the central and provincial government to have institutional legitimacy. Therefore, we suppose that the Mountain-Sea Cooperation Project has reconfigured the spatial structure of interjurisdictional networks in Zhejiang province and offers new insights on reducing spatial disparity in regional development.

3. Data and Methodology

3.1. Research Area

The analysis described in this paper takes Zhejiang province as the study area (see Figure 2). In Zhejiang province, the regional policies of the Mountain-Sea Cooperation Project represent the new spatial political economy in the transitional China. Zhejiang province, as one of the most developed provinces in China, lies in the eastern coast and is a part of the Yangtze River Delta region. It is composed of 11 prefecture-level cities (Hangzhou, Ningbo, Wenzhou, Shaoxing, Huzhou, Jiaxing, Jinhua, Quzhou, Taizhou, Lishui, and Zhoushan), which can be further divided into 37 municipal districts, 19 county-level cities, and 33 counties (including one autonomous county). In 2019, the gross domestic product (GDP) in Zhejiang reached RMB 6.23 trillion, ranking fourth in China. As a highly developed province, the intraregional inequality is still severe in Zhejiang. For example, the cities of Lishui, Quzhou, and Zhoushan take 27% of Zhejiang’s district area but only harness 10% of Zhejiang’s GDP. Many backward places are in the mountainous areas to the southwest of Zhejiang, which lack in connection with developed areas. Therefore, regional cooperation, especially the Mountain-Sea Cooperation Project, has played an indispensable role to reduce regional inequalities.

3.2. Data Sources

Multisourced data were retrieved from China Mobile, the Mountain-Sea Cooperation Project website, and the County Statistical Yearbook of Zhejiang Province. First, our dependent variable of interjurisdictional linkages between poor counties and the developed places under the Mountain-Sea Cooperation Project is measured by the population flow data. This location-based big data relies on cellphone signaling and use of APPs. In China, Tencent migration [25] data, Baidu migration [46] data, and Weibo check-in data [47] are all based on the location of users when using different APPs. In this study, mobile phone signaling big data from China Mobile, one of China’s three major communication operators, was employed to show spatial patterns of population flow in Zhejiang province. According to our partner, China Mobile, the market share of China Mobile in Zhejiang province is 81.9%, which indicates that the data can reflect most of the population flow in Zhejiang. Compared to the tracks of the usage of APPs, mobile phone data transmission is real-time, so it leaves records in the operator’s network whether the user created it intentionally or not. In this paper, we collected China Mobile phone signaling big data of 13,898,039 users during 2 December and 8 December 2019 (nonmajor event period to avoid national holidays), and the time interval of cross-county flow was set to one hour. The data include the frequency of population flow, making it possible to track the flowing direction of people in 87 county-level municipalities in Zhejiang province. Two municipalities, Jingning autonomous county and Nanhu District, were not included due to the lack of data.

Second, our independent variables of “city-helps-city” cooperation were captured by the news data of the Mountain-Sea Cooperation Project, retrieving from the official website of Zhejiang Provincial Development and Reform Commission. This dataset includes the collaboration of mandated county pairs under the Mountain-Sea Cooperation Project and the voluntary cooperation between poverty counties and other municipalities. This state-orchestrated regional approach of “city-helps-city” takes Zhejiang as the pilot to demonstrate how the balanced regional development and common prosperity can be achieved by interjurisdictional cooperation, through which the connectivity of poverty counties to other places would be strengthened. We collected 203 pieces of news data on cooperation available back to 2016. Third, other socioeconomic data for controls were collected from the 2019 Zhejiang District–County Statistical Yearbook.

3.3. Methodologies

3.3.1. Regression Models

The research question of this study is to examine how the “city-helps-city” cooperation impacts the regional population flow between poverty counties and other places in Zhejiang. Therefore, we built regression models to test the correlations between interjurisdictional cooperation and population flow. Our dependent variable is the population flow of each county pair while the main explanatory variable is the interjurisdictional cooperation under the Mountain-Sea Cooperation Project. Thus, the regression models only covered county pairs that had been recorded on the official website of the Mountain-Sea Cooperation Project. We calculated three measures of cooperation as independent variables, which are the diversity of cooperation fields, the intensity of cooperation focuses, and the legitimacy of cooperation. We expected that higher diversity and intensity along with the legitimacy of cooperation would be correlated with more population flow between poverty counties and nonpoverty places, to enhance the linkages of the backward areas with other places. Two cross-sectional regression models using OLS (ordinary least squares) were conducted for the collaborated county pairs.

$$PopFlo{w}_{ij}={\beta }_0+{\beta }_{1}CopDi{v}_{ij}+{\beta }_{2}CopFo{c}_{ij}+{\beta }_{3}CopMan{d}_i+{\beta }_{4}Control{s}_{ij}+e_{ij}$$

(1)

where $PopFlo{w}_{ij}$ represents population inflow and outflow in the logarithm format of county pair (i and j, either i or j is the poverty county). $CopDi{v}_{ij}$ is the diversity of cooperation fields between county i and j. $CopFo{c}_{ij}$ denotes the intensity of cooperation, which is measured by the word counts of different cooperation focuses according to the news. The cooperation focuses cover not only the conventional aspects of industrial development and capital investment, but also the emerging fields of cultural heritage and tourism, digital technology, public services, environmental governance, spatial planning, etc. The dummy variable $CopMan{d}_{ij}$ refers to the legitimacy of cooperation, describing whether the cooperation between county i and j is legitimated by the Mountain-Sea Cooperation Project. The $Control{s}_{ij}$ are control variables of geographical distance and economic gap between collaborated counties i and j. The error of the model estimations is represented by $e_{ij}$ for each county pair.

3.3.2. Constructing Dependent Variables

The dependent variable of population flow ($PopFlo{w}_{ij}$) between poverty counties and the developed municipalities is generated by using social network analysis (SNA). We constructed the weighted network of interjurisdictional population flow at county level based on the mobile phone signaling data from China Mobile. In our case, the nodes refer to the county-level municipalities, consisting of districts, county-level cities, and counties. Taking the number of flows between any two nodes, the matrix of 87 × 87 population flows was constructed. We further selected the population flow between the poor places and the developed municipalities to capture the type of “city-helps-city” cooperation as our dependent variables.

3.3.3. Constructing Independent Variables

Our main explanatory variables capture three dimensions of the interjurisdictional cooperation, including the diversity of cooperation fields ($CopDi{v}_{ij}$), the intensity of different cooperation focuses ($CopFo{c}_{ij}$), and the legitimacy of cooperation ($CopMan{d}_{ij}$). The construction of these explanatory variables requires the extraction of key information of the cooperation news and the calculation of the diversity ($CopDi{v}_{ij}$) and intensity ($CopFo{c}_{ij}$) of cooperation.

Text semantic analysis allows us to do the extraction. Semantic analysis embodies text mining, machine learning, natural language processing, and other technologies to disaggregate text files, e.g., policies, news, or comments, into phrases, sentences, or paragraphs, so as to deeply analyze and mine semantic information within the text. For example, through text mining and cluster analysis, Arenal et al. confirmed the cycle curves and the evolution of themes with respect to 576 entrepreneurship policies in the European Union [48]. Yang et al. used text semantic analysis to compare the contents of Urban and Rural Planning Regulations of three western cities in China [49]. In this study, we conducted semantic analysis in Python to decompose and analyze the news data from the Mountain-Sea Cooperation Project website.

$CopFo{c}_{ij}$: First, we used the Jieba package to segment the cooperation news into words and thus constructed text corpus. Second, we calculated the weight of TF–IDF (term frequency–inverse document frequency) through Scikit-learn (Sklearn) to extract the keywords of text corpus (Equations (2) and (3)). Third, we classified these keywords based on their semantics and identified nine Mountain-Sea cooperation fields of industrial development, capital investment, digital technology, cultural heritage, and tourism, medical and health service, educational service, social welfare service, environmental protection and resource conservation, and planning of urban construction and management. The frequencies of the relevant keywords were calculated as the proxies for each cooperation focus ($CopFo{c}_{ij}$). For a term i in document j:

$$TF-ID{F}_{i,j}=t{f}_{i,j }\times id{f}_i,$$

(2)

$$id{f}_{i }=\log \left(\frac{1+N}{1+d{f}_i}\right)+1$$

(3)

where N denotes the total number of documents in text corpus, tf_i,j refers to the number of occurrences of i in document j, and df_i refers to the number of documents containing i.

$CopDi{v}_{ij}$: We further calculated the diversity of interjurisdictional cooperation based on the results of semantic analysis on the news data of the Mountain-Sea Cooperation Project. We employed Shannon entropy to calculate the information entropy of the Mountain-Sea cooperation news. The Shannon entropy is widely used to measure biodiversity, land-use diversity, etc. [50], since this metric quantitatively describes the uncertainty or complexity of information. For the cooperation news in this study, the Shannon entropy method calculates the diversity (i.e., complexity) of cooperation fields for each county pair based on the website of the Mountain-Sea Cooperation Project. The diversity of pair i can be expressed as:

$$D\left(i\right)=-{\displaystyle \sum }_{j=1}^n{p}_{j}log{p}_j$$

(4)

where n denotes the total class number of different cooperation fields, and p_j refers to the ratio of keyword counts from jth cooperation field to the total keyword counts of all fields for the county pair i. For each county pair i, the larger entropy value represents greater complexity or diversity in terms of the cooperation fields.

$CopMan{d}_{ij}$: The legitimacy of cooperation was constructed by matching the county names with the official list of the Mountain-Sea Cooperation Project. The matched county pair was denoted as 1 with legitimacy by the central and provincial government, while the county pair in voluntary cooperation was denoted as 0. The mandated county pair was assumed to be more effective in stimulating more population flow and enhancing the linkages of the poverty counties with other places.

4. Results

This study examines the correlation between the population flow (dependent variables) at county-level and key dimensions of interjurisdictional cooperation (independent variables) for the Mountain-Sea Cooperation Project in Zhejiang. In Section 4.1, we start with the descriptive analysis on dependent variables of county population inflows and outflows that are generated by social network analysis. Section 4.2 further illustrates the features of independent variables of the intensity of different cooperation focuses, the diversity of cooperation fields, and the legitimacy of cooperation based on the text semantic analysis. Then, Section 4.3 reports the results of our regression models to demonstrate whether and how the three dimensions of cooperation affect population flows between the poverty counties and other places under the Mountain-Sea Project. More interpretation on the results is given in this section as well.

4.1. Dependent Variables: The Population Flow of Municipalities (County-Level) in Zhejiang

Figure 3 shows the uneven patterns of interjurisdictional linkages of population flow (boldness of the lines) in Zhejiang province. Stronger interjurisdictional linkages mainly concentrate in four metropolitan areas: Hangzhou metropolitan area in the north, Ningbo metropolitan area in the northeast, Jinhua-Yiwu metropolitan area in the central region, and Wenzhou metropolitan area in the south. Central cities in these four metropolitan regions are core areas for population agglomeration and function as major engines of city-regional development. The red lines in Figure 3 indicate that there are several subcenters that exhibit higher strengths of linkages to adjacent counties. Most of them are the hinterland of the metropolitan regions that enjoy the privileges of locations, such as “Changxing County–Wuxing District–Nanxun District”, “Linhai City–Jiaojiang District–Luqiao District–Wenling City”, “Cixi City–Yuyao City”, etc. The attractiveness of these functional nodes can be attributed to their comparative advantages in certain resource endowment and cultural heritage.

Although interjurisdictional linkages are not strong in the nonmetropolitan regions, there are still plenty of blue and yellow lines connecting mountainous counties to other municipalities. According to Figure 4, this type of linkages overcomes the geographical limitations and mainly arises in the northwestern poverty region, e.g., Jiangshan City, Changshan County, and Kaihua County. Most of these lines bridge poverty counties with the provincial capital Hangzhou or some municipalities (e.g., Jiashan County) in Jiaxing that are next to Shanghai. By contrast, some poverty counties are more connected to their adjacent metropolitan cities (Appendix A). For example, Cangnan County is well connected with its neighboring districts in the metropolitan city of Wenzhou. This pattern is also obvious for Jinhua-Yiwu metropolitan cities where short lines connect neighboring poverty counties (e.g., Wuyi County, Longyou County, and Yongkang County). Nevertheless, a large number of isolated poverty counties remain, which lack interactions with other municipalities. That is to say, these population flows are primarily intrajurisdictional within poverty counties. In this study, we expect the Mountain-Sea Cooperation Project ($CopDi{v}_{ij}$, $CopFo{c}_{ij}$, $CopMan{d}_{ij}$) to be correlated with interjurisdictional linkages between poverty counties and other municipalities ($PopFlo{w}_{ij})$.

4.2. Independent Variables: Mountain-Sea Cooperation in Zhejiang

We identified key information based on the news data from the website of the Mountain-Sea Cooperation Project as our main explanatory variables. Through text semantic analysis, we grouped similar information and identified nine important cooperation fields (

Table 1. Model variables for the county pair under the Mountain-Sea cooperation.

	Mean	Std. Dev.	Description
Outflow (ln)	5.8	1.9	Population flow from poverty counties to nonpoverty counties under Mountain-Sea cooperation
Inflow (ln)	5.7	1.9	Population flow from nonpoverty counties to poverty counties under Mountain-Sea cooperation
CopDiv	2.5	0.7	Shannon entropy value to measure the diversity of cooperation fields for each pair of counties
CopFoc			Word counts of cooperation fields to reflect cooperation policy focus for each pair of counties
CopInd	54.4	61.5	Industrial zones, enclave parks, retail and marketing
CopInves	26.8	44.8	Capital investment, business attraction
CopDigital	15.6	27.0	Digital Tech: platform, e-commerce, smart production
CopTour	21.6	37.6	Cultural and ecological tourism and recreation
CopEdu	11.0	14.4	Educational service: schools, curriculums, teachers
CopMed	2.4	7.2	Medical service: healthcare, hospitals, treatment
CopWel	10.1	13.8	Welfare: aging care, social security, employment
CopEnv	7.2	8.5	Environment protection and resource conservation
CopPlan	32.5	38.8	Planning of urban construction and management
CopMand	1 = mandated by the provincial government (65.1%); 0 = voluntary (34.9%)
Controls
Geodist	192.8	91.9	Geographical distance of county pair (km)
Econdist	826.3	557.9	Economic gap of county pair (RMB 1000)

). The focuses not only cover the economic cooperation in traditional fields but also embody collaborations on social affairs and institutional arrangements. The word counts for each cooperation field were calculated to represent the focuses of policies, programs, and projects under Mountain-Sea cooperation.

Word counts of the nine cooperation fields, shown in Figure 5, representing the intensity of cooperation focuses, were used as our independent variables ($CopFo{c}_{ij}$). The shares of each field are illustrated in Figure 5 for the top 20 well-connected county pairs (interjurisdictional population flow). Pairs are arranged along the x-axis as their population flows decrease. The size of circles denotes the intensity of different cooperation focuses ($CopFo{c}_{ij}$). It is assumed that higher intensity of cooperation fields is associated with more population inflow or less population outflow of poverty counties.

According to Figure 5, the economic cooperation (black and gray circles) is often promoted through the co-building of industrial or agricultural parks, enclaves of industrial parks, retail and marketing, and capital investment. Economic cooperation, as the foundation for other types of collaborations, tends to be evenly distributed across county pairs. In addition, the pink and blue circles indicate that the cultural heritage and tourism together with digital technology account for a fair amount in the cooperation. This result suggests a common collaborative trend in new economy, the essential role of cultural heritage in tourism development, and the impacts of digital technology (e.g., digital platform) in promoting e-commerce in poverty counties.

Additionally, Figure 5 also shows that cooperation on social affairs is one important component of the Mountain-Sea Cooperation Project. Medical, educational, and social welfare services are at the top of Figure 5, correlating with more population flow (top 20) and reinforced linkages of county pairs. However, cooperation on environmental governance is not strong in the displayed county pairs. The institutional cooperation, which is symbolized with yellow circles in the figure, is believed to offer the legitimacy through interjurisdictional agreement, regional or spatial plan, joint-issued policies, and forums of political leaders, etc., to ensure the implementation of projects.

Meanwhile, based on the news data from the official Mountain-Sea Cooperation Project website, we identified other model variables, as shown in Table 1. Our models also include the diversity of cooperation fields ($CopDi{v}_{ij}$), which is measured by Shannon entropy. The average diversity score for each county pair is 2.5 and a higher Shannon entropy value suggests that “city-helps-city” cooperation involves more fields. The diversity of cooperation may promote comprehensive development of poverty counties and avert population loss. The dummy variable of whether a county pair is mandated by the provincial government under the Mountain-Sea Cooperation Project is used as the proxy for the legitimacy of interjurisdictional collaboration ($CopMan{d}_{ij}$). The mandated county pair is expected to be more effective to conduct the mission, that is, to promote the development of poverty counties. We also control for the geographic ($Geodis{t}_{ij}$) and economic ($Econdis{t}_{ij}$) distance between counties in the cooperation pair. Greater distance is usually accompanied with higher cost of travel and communication that inhibits population flow, while the economic gap may drive people in poverty counties to flow into the developed places for better opportunities.

4.3. Model Results: The Impact of Mountain-Sea Cooperation on Population Flow in Zhejiang

Table 2. Regression results of population flow under the Mountain-Sea Cooperation Project.

	Outflow (ln)	Inflow (ln)
CopDiv	−2.065 * (1.085)	−0.130 (0.464)
CopFoc
CopInd	−0.017 * (0.009)	−0.011 (0.008)
CopInves	0.019 (0.014)	0.014 (0.010)
CopTour	0.015 ** (0.007)	0.011 * (0.006)
CopDigital	−0.015 (0.024)	−0.011 (0.018)
CopEdu	0.039 * (0.020)	0.025 (0.018)
CopMed	0.092 *** (0.027)	0.096 *** (0.027)
CopWel	2.29 × 10−4 (0.016)	−0.001 (0.016)
CopEnv	0.033 (0.049)	9.70 × 10−5 (0.044)
CopPlan	−0.004 (0.014)	0.002 (0.011)
CopMand	4.751 * (2.481)	4.881 ** (2.242)
Controls
Geodist	−0.014 *** (0.004)	−0.015 *** (0.004)
Econdist	0.002 *** (6.19 × 10−4)	0.002 *** (5.04 × 10−4)
Constant	5.638 *** (1.490)	6.229 *** (1.323)

Robust standard errors are in parentheses; * p < 0.10, ** p < 0.05, *** p < 0.01.

presents the cross-sectional regression model results on how the Mountain-Sea Cooperation Project shapes the patterns of population outflow (from the poverty counties) and inflow (to the poverty counties). The interjurisdictional linkages of population are expected to be contingent upon the diversity of cooperation fields, different focuses of cooperation, and the legitimacy of cooperation. As for the geographical distance and economic gap between paired counties, they either facilitate or hinder the connections between localities. The purpose is to interrogate whether poverty counties can improve their positions in the regional network through cooperation with other municipalities, stimulated by the Mountain-Sea Project. The R-squared of Outflow and Inflow models is, respectively, 0.5986 and 0.6375, suggesting our models have relatively high goodness of fit.

According to Table 2, the geographical distance ($Geodis{t}_{ij}$) hinders the commuting between poverty counties in the mountainous area and other developed places, while the economic gap of county pair ($Econdis{t}_{ij}$) accelerates both inflow and outflow of population. The diversity of cooperation fields ($CopDi{v}_{ij}$), measured by Shannon entropy values, which is one of the primary explanatory variables, proved to be useful in restraining the outflow of population from impoverished counties. However, the expanding scope of cooperation fields does not work in bringing more inflow to poverty counties.

The interjurisdictional linkages of population flow also depend on policy focuses in cooperation fields ($CopFo{c}_{ij}$). As for the economic collaborations $(CopIn{d}_{ij}$) in traditional fields, according to the news data, joint construction on industrial zones and enclave industrial parks can speed the industrial development and create more jobs in poverty counties, thereby reducing the outflow of population. However, despite the increased economic opportunities in poverty counties, this type of economic collaboration has little effect on attracting people to these counties based on our model results. Capital investment $(CopInve{s}_{ij}$) that has been brought into poverty counties by Mountain-Sea cooperation is neither correlated with the outflow nor the inflow of population.

Our models further captured the cooperation on new types of economy, including cultural heritage and tourism and digital technology. The cooperation examples of the news data highlight that cooperation on cultural heritage and tourism $(CopTou{r}_{ij}$) fits the development of some poverty counties, where the legacies of culture and history provide privileged resources for tourism. This type of cooperation is effective to drive both population outflow and inflow, and it can comprehensively improve the interjurisdictional linkages of poverty counties according to the results in Table 2. In addition, the news data also suggest that cooperation on digital technology $(CopDigita{l}_{ij}$) is pursued for the purpose of facilitating connections between poverty counties and other places via digital platforms for retailing and investment, technical assistance for manufacturing, etc. However, our model results indicate that this variable is not significant in influencing population flow. This leads us to further consider whether the promotion of digital technology in poverty counties under the guide of the Mountain-Sea Cooperation Project is working. These days, one significant phenomenon in China is that some residents (especially farmers) in backward areas have limited digital literacy and insufficient willingness to accept and take advantage of digital technology, which may diminish its effectiveness.

Moreover, the intermunicipal cooperation that goes beyond the economic affairs is found in our models to strongly promote the development of poverty places and increase their linkages with other parts of Zhejiang. Cooperation on educational ($CopEd{u}_{ij}$) and medical $(CopMe{d}_{ij}$) services is expected to bring about more equitable provision of critical public services, which can promote human well-being in backward counties. Table 2 indicates that cooperation on educational and medical services drives people in poverty counties to flow toward more-developed areas to seek for better opportunities. The cooperation examples in the news data illustrate how these cooperation programs contribute to the exchange of premium educational and medical resources (e.g., teachers, labor trainers, doctors, etc.) between poverty counties and the developed areas. Therefore, cooperation on educational and medical services empowers poverty counties with more human capital, which is equipped with competitive labor skills and improved health conditions. Additionally, our regression models display that cooperation on medical service is essential to driving a two-way linkage to have people commuting between county pairs. Nonetheless, collaborations on social welfare $(CopWe{l}_{ij}$) and environmental governance $(CopEn{v}_{ij}$) produce little effect on driving more population flow. These mixed results suggest that the focuses of current social cooperation between Mountain-Sea paired counties are limited to the educational and medical services, while cooperation in other fields have not yielded significant effects and should be given additional attention.

The institutional legitimacy of cooperation matters as well. Specifically, our models reveal that mandated cooperation ($CopMan{d}_{ij}$) under the Mountain-Sea Cooperation Project has positive effects on driving population flow and interjurisdictional linkages of poverty counties. Policy documents from the higher-level government endow cooperation with institutional legitimacy, to ensure that proposed initiatives are reasonable and the collaboration programs are effective. We also expect that intermunicipal agreement, regional or spatial planning, joint forums of political leaders, etc., $(CopPla{n}_{ij}$) can institutionalize the collaboration of county pairs to guarantee the legitimacy of projects and protect the benefits of poverty counties. However, it is not significant in our models. The fragmented institutional settings in China may undermine the implementation of regional or spatial plans, and the conflicts of interjurisdictional interests can also be detrimental to the linkages of counties.

5. Discussion

With the deepening of regional integration, cooperation fields have been extensively expanded from economic cooperation on industrial development to more comprehensive collaborations on social affairs and institutional arrangements. These types of cooperation exert influences on population flow and regional linkages of poverty counties.

5.1. Conventional Economic Cooperation

Economic cooperation has brought significant benefits to poverty counties [7,8,9,36]. Based on our descriptive and regression analysis, co-construction of industrial facilities, such as enclave industrial parks, is still the important form of Mountain-Sea cooperation. The enclave economy refers to developed municipalities that build industrial parks in underdeveloped municipalities with affluent land. It overcomes the geographical constraints and bridges two or more municipalities with different incentives and endowments [51]. In China, the unique land institutional system assigns very few land quotas to the developed localities, while the underdeveloped areas often possess excess land due to the lack of attractiveness to capital, industries, business, etc. [36]. This enclave cooperation not only resolves the problem of insufficient land for developed places, but also assists the less-developed localities through revenue sharing and industrial or business injections [7,8,9]. One example from Mountain-Sea cooperation is the cooperation between Kecheng District and Yuhang District, in which Yuhang District helped Kecheng District to develop industries of new material, intelligent manufacturing, and fashion clothing, via enclave parks. The R&D activities of these industries are based in Yuhang District where innovation resources are abundant, while manufacturing is based in Kecheng District to promote its industrial upgrading.

Recently, the reversed enclave industrial park arises as an innovative form of cooperation [51]. Rather than building joint industrial zones in the backward place, this mode allows the less-developed county to provide land quotas to establish the industrial zones/parks in developed areas with economic advantages. The economic agglomeration, high-quality public services, and premium human capital of developed localities provide a suitable environment for the incubation of new businesses, which are expected to contribute back to the economy of poverty counties [51]. For example, in the Mountain-Sea Cooperation Project, the reversed enclave park in Pinghu City reinforced the economic development of several poor villages in Qingtian County. The economic cooperation allows poverty counties to actively join in the regional production network and brings about more population flow for commercial and industrial development.

5.2. Emerging Cooperation on New Economy

Our models show that cooperation is also popular in the new economy on services. For counties that are abundant in ecological resources or cultural heritages, they should seek for cooperation to maximize the values of such endowments. Collaborations on cultural and ecological tourism along with recreational services can attract a large number of tourists to less-developed places and boost local economies [33]. One of the typical examples according to the news data is the pair Tiantai County–Luqiao District, whose cooperation mainly focuses on cultural tourism. These two municipalities have co-designed and co-constructed a series of distinctive sites based on the culture of the Tang Dynasty, including a poetry-themed park, cuisine street, venues, and homestays. Further, they collaborated to build the Greentown–Tiantai Mountain Snow Park for attracting enthusiasts of snow sports, which has become a strong development engine for Tiantai County.

Another focus of cooperation lies in the digital service based on our descriptive analysis shown in Figure 4. Digital technology rises as a new impetus for economic development, such as smart manufacturing and e-commerce of platform economy [52]. In the case of the Mountain-Sea agenda in Zhejiang, a digital platform was co-built by the Taishun County–Lucheng District pair for the display and sale of agricultural products. This platform allows merchants to advertise their products via live streaming, and it is convenient for customers to order items online. The digital platform is also supposed to attract more capital investment, population, and other opportunities to the poverty counties [53]. However, this effect has not been realized according to our model results. One possible reason is that the current Mountain-Sea Cooperation Project on digital service is narrowed to the retailing of agricultural products, so more online activities instead of population flow have been generated. Another explanation is that poor counties may not have sufficient capacity and lack of digital literacy to put these digital technologies in good use. This result suggests Mountain-Sea cooperation on digital technology has not generated considerable developmental effects for the backward areas. In the future, we expect digital technology to be more widely used, especially in terms of digital training and the cultivation of digital talents.

5.3. Emerging Cooperation on Social Affairs

Social cooperation is playing an increasingly significant role in the Mountain-Sea Cooperation Project, based on our descriptive and regression results. It has integrated multiple agents (e.g., the public, market, governments, etc.) to provide better social services to the less-developed counties, particularly medical health and educational service [40,41,42]. According to news data, all 26 mountainous counties have been equipped with high-quality medical resources in Zhejiang province. For example, an outstanding medical resource for both Cangnan County and Longwan District was assembled in Zhongkui community health service station, where residents can enjoy convenient healthcare services such as electronic health record query and the diagnostic data sharing. The forms of educational cooperation include joint building of schools, exchange of teachers, etc. At present, 1500 primary and secondary schools in Zhejiang have carried out interschool pairing assistance, which has greatly improved the teaching quality in mountainous counties. A typical example is the “Shanhai Class” in Longyou Middle School, in which famous teachers from Zhenhai Middle School came to Longyou to give lectures and teach classes for local students. In addition, Longyou Middle School also sends excellent students to Zhenhai Middle School for better training every year. Improved public service contributes much to local well-being by equipping people with more competitive labor skills, better health conditions, and abilities to pursue opportunities in other places. The cooperation on environmental protection and resource conservation is vital [37,41], but only a few county pairs give attention to these fields, which is reflected by the insignificant model results. The cooperation of Xianju County–Yuhuan City beautified and remolded villages, roads, and rivers, bringing about the improved living environment that corresponds with the slogan of “one village with one characteristic, one family with one beautiful scenery” in Xianju, and thereby its continuously increased attractiveness to populations.

5.4. Institutional Cooperation and Legitimacy-Building

The institutional cooperation carried out under regional or spatial planning, interjurisdictional agreement, or authorized documents by higher-level government can ensure the implementation of programs and policies [32,44]. Nevertheless, according to our models, institutional cooperation has no significant effect on population flow among counties. This might be partly attributed to the fragmented institutional settings of China’s regional government systems and the conflicts of local interests [1]. For example, there is a contradiction of interests between central government and local governments; that is, the former considers the jointly participated spatial planning as a method to realize the ideal spatial structure of regions, while the latter are more concerned with their own developmental goals [36]. In the Mountain-Sea Cooperation Project, our news data suggests some county pairs have jointly made comprehensive plans and interjurisdictional agreements. For instance, Pingyang County and Yueqing City signed three major agreements, including “2019 Yueqing-Pingyang Enclave Co-construction Agreement”, “2019 Yueqing-Pingyang Rural Revitalization Demonstration Site Co-construction Agreement”, and “Yueqing-Pingyang Joint Investment Cooperation Agreement”. Such integrated planning can contribute to the coherence of policy frameworks and effectively prevent fragmentation in regional governance [32]. Nevertheless, this effect is not reflected in our models for the major county pairs in the Mountain-Sea projects. Further, it is clear that not all county pairs were able to institutionalize their cooperation and formulate the agreements or spatial plans.

In contrast, the top–down building of institutional legitimacy by the central and provincial governments is more effective to affect population flow to enhance interjurisdictional linkages of poverty counties. According to our model results, the cooperation for the mandated county pairs by the Mountain-Sea Project is significant to increase both population inflow and outflow. Therefore, we call for the central or provincial government to strengthen the institutional legitimacy for cooperation by upscaling various programs and policies in the regional development.

6. Conclusions

This paper focuses on the regional cooperation of Mountain-Sea projects and how this “city-helps-city” scheme correlates with the interjurisdictional linkages (population flow) of poverty localities with other places in Zhejiang. Based on the location-based big data and the news data of Mountain-Sea cooperation, we are able to use social network analysis to measure interjurisdictional linkages between poverty counties and other places (dependent variable), and adopt text semantic analysis to identify the vital information of their collaborations (main explanatory variables) for our regression models.

The network structure of population flow is uneven in Zhejiang, that is, mainly concentrated in the four major metropolitan regions. However, the descriptive analysis of dependent variables shows that poverty counties in the mountainous regions also have connections with other municipalities of developed regions. These linkages are not confined to the geographic neighbors. The results of regression models further confirm that the linkages are associated with interjurisdictional cooperation that is stimulated by the Mountain-Sea projects. The diversity of cooperation fields, the intensity of different cooperation focuses, and the legitimacy of cooperation are found to have differentiated effects on the linkages of the backward counties with other developed places. Particularly, regional collaborations on cultural tourism and public services of education and health are essential to increasing the regional stances of poverty counties through the enhancement of their comparative advantages and human capital. Though we expect that the legitimacy matter for the effectiveness of cooperation, the institutional cooperation of spatial planning and regional agreement has not yielded significant impacts on stimulating more interjurisdictional population flow. Therefore, we suggest that regional cooperation, especially the “city-helps-city” projects, should take both the scope and depth of cooperation into account for future improvement and highlight the importance of institutional legitimacy-building, especially by the higher-level government to ensure the effectiveness of cooperation policies.

Emphasis on coordinated regional development is a prominent feature in the new urbanization of China. We see the rising importance of the state-orchestrated regional cooperation that focuses on “city-helps-city” for spatial redistribution and regional balance rather than simply fostering “new state space” for competitiveness. Our paper contributes to the empirical and theoretical understandings of this unique regional institution of “city-helps-city” in China. Through this study, we offer an important perspective of state-orchestrated regional cooperation to address spatial disparity and provide theoretical understanding on this spatial regime of “city-helps-city” cooperation. There are several policy implications that arise from the findings of our research.

First, the “city-helps-city” cooperation represents an attempt to combine the vertical and horizontal approaches in regional governance that can be learned by other countries and regions. It differs from the direct top–down spatial redistribution of higher-level government (the spatial Keynesianism) or the purely bottom–up collaboration based on local interests (the new regionalism). It is intrinsically redistributive for the coordination, integration, and regional equity, and often goes beyond the geographical adjacency. Within the context of capitalist globalization, we see the necessity for bridging the gap between global cities or metropolitan cities that are more favored by international capitals and the developing, declining, and backward places that are left behind in the intercity competition. Therefore, to involve stakeholders in less-developed areas, policymakers should develop the discourse and regime of development that consider space and emphasize the region. The spatial regime of “city-helps-city” cooperation represents this transformation and becomes an important reminder for other countries and regions when making policies to reduce regional inequality.

Second, when formulating regional cooperation policies, especially the “city-helps-city” projects, policymakers should give attention to legitimacy-building as well as scope and depth of cooperation. Our discussion highlights the significant role of higher-level government for promoting, orchestrating, and mandating cooperation to ensure the legitimacy of this regional strategy, thereby improving the scope and depth of cooperation. In this sense, the legal and regulatory system along with cooperation initiatives should be developed within a comprehensive realm of policymaking. The policy design needs to ensure the implementation of cooperation projects and provide guidelines of long-term evaluation on projects. For the developed municipalities, they should undertake more responsibilities in helping less-developed counterparts for more balanced regional development, particularly regarding improvement in education, healthcare, and technology development. On the other hand, backward places should be aware of their comparative advantages, e.g., cultural and natural resources, and strategically develop local industries and strengthen functions to complement other cities from a regional perspective. The collaborative localities should give full play to their comparative advantages for the effectiveness and the sustainability of interjurisdictional cooperation, thereby promoting more in-depth cooperation in broader fields.

Third, the “city-helps-city” cooperation aims to reach a “win-win” goal, rather than prompt one-way assistance from developed areas to poverty areas. Thus, it provides a new direction concerning co-benefits for future policy making. The formation of spatial regime on “city-helps-city” cooperation depends on the common interests of the poverty counties and the developed municipalities. Thus, it is crucial to provide incentives for both places and achieve the objectives of redistribution and development simultaneously. For example, in the economic cooperation of jointly building industrial parks, the developed municipalities can benefit from the sufficient land quotas provided by underdeveloped localities, and meanwhile, transferring industries and sharing revenues will promote the local conditions of poor places. This principle is universal and essential to fostering “city-helps-city” cooperation across various contexts. Different levels of government and multiple actors should collaborate on engaging less-developed localities in regional networks of production, service, and institutional arrangements, ensuring the legitimacy of cooperation and the realization of mutual benefits, and finally forming a more balanced and coordinated regional development.

There are some limitations in this study. First, for privacy concerns, the mobile phone signaling big data from China Mobile cannot show the social attributes of users in detail (such as their age, occupation, income level, etc.), so we are unable to conduct more in-depth analysis to explore the mechanisms of how intercity cooperation specifically drives population flows. Future research can analyze the cases of specific cooperation programs to reveal the mechanisms. Second, this paper takes the Mountain-Sea Cooperation Project in Zhejiang province as the case to examine whether the “city-helps-city” scheme of regional cooperation is correlated with the interjurisdictional linkages reflected by population flow, especially between the backward areas and other developed places. However, whether our conclusions and implications can be applied to other regions needs to be tested by taking various local contexts into account in future research. Third, this study only uses population flow to manifest the interjurisdictional linkages, while there are many types of urban networks that can represent the regional connections, such as the transportation network and the innovation network. Based on the analytical framework proposed in this study, we expect future works to explore the impacts of “city-helps-city” cooperation on other types of interjurisdictional networks and investigate their coupling degrees.