Collaborative Industrial Agglomeration and a Green Low-Carbon Circular Development Economy: A Study Based on Provincial Panel Data in China

Abstract

As an important direction in industrial evolution, the synergistic agglomeration of manufacturing and productive service industries has become a key path to promote the green transformation of the economy. Based on China’s provincial panel data, this study utilizes a variety of econometric methods to explore in depth the mechanisms, spatial effects and regional differences in the impact of the synergistic agglomeration of manufacturing and productive service industries on the green, low-carbon and recycling development of the economy. The empirical results show that the synergistic agglomeration of manufacturing and productive services not only directly promotes the green, low-carbon and recycling development of the economy, but also generates an indirect impact through the intermediary channel and exhibits significant spillover characteristics in the spatial dimension. This conclusion holds firm after a series of robustness tests. In addition, environmental regulations and the level of regional industrialization play a moderating role on the impact of industrial synergistic agglomeration and green, low-carbon and recycling development of the economy, and the effect of the role varies across regions and levels of economic development. This paper provides a decision-making reference for further optimizing the regional layout of China’s industries and enhancing the green, low-carbon and recycling development of the economy in each province.

1. Introduction

The Opinions on Accelerating the Overall Green Transformation of Economic and Social Development of the Central Committee of the Communist Party of China and the State Council explicitly proposed that an economic system of green, low-carbon and recycling development should be initially formed by 2035, which also provides a new way of thinking for the country to promote green, low-carbon transformation and high-quality development. As a special economic system, the green, low-carbon and circular development economic system can be understood as the concept and mode of green development, low-carbon development and circular development running through all links, levels and areas of economic development, and the formation of a resource-saving, environmentally friendly and low-carbon energy-based economic development model [1]. Since the mid-20th century, Western developed countries have reflected on the traditional development model in the process of industrialization and have gradually formed new development concepts such as the circular economy and low-carbon economy to promote the coordinated development of economic growth and resources and the environment. In this process, developed countries, through the adjustment of the global industrial division of labor, have shifted the middle and low-end segments of their manufacturing industries to developing countries, and transformed their own industrial structure to the service industry [2]. Data show that the proportion of the service industry in the United States, the United Kingdom, France, and other countries has exceeded 70 percent of the total national economy, and while realizing the decoupling of economic growth and resource consumption they have also basically solved local environmental problems. For example, European Union countries reached peak resource consumption and emissions in the early 1990s, while the United States peaked in carbon around 2005. In contrast, developing countries are facing more complex green development challenges in the course of rapid industrialization and urbanization [3]. Not only do they need to deal with local environmental pollution problems, but they also have to assume the same responsibility for global climate governance as developed countries. China’s practice in exploring green, low-carbon and recycling development paths was therefore particularly important as the largest developing country [4]. Its experience would not only contribute to the construction of a domestic resource-saving society and the realization of the goal of reducing pollution and carbon emissions but would also provide other developing countries with a model for industrial transformation that could be used as a reference.

At the same time, the successful experience of developed economies shows that in the process of economic structural transformation, the synergistic development of manufacturing and productive services has become an important direction of industrial evolution, and its theoretical connotation and practice mode have been deepening since Ellison and Glaeser (1997) [5] proposed it. Early research was based on a Marshallian economic efficiency orientation, which has evolved into a systemic paradigm incorporating eco-efficiency, with the European Union’s “industrial symbiosis” model combining the circular economy and industrial agglomeration through by-product exchange networks [6]. This model can effectively enhance resource utilization efficiency and reduce raw material consumption and environmental pollution. Its core lies in converting waste from one production chain into inputs for another, thus forming a closed-loop flow of materials similar to that of a natural ecosystem. Current research focuses on the two dimensions of formation drivers and effects, where regional differences such as the degree of economic development, human resources and other factors affect the synergistic agglomeration of manufacturing industries [7], while transaction costs constrain the synergy between manufacturing and productive services [8]. However, the academic community has not yet reached a consensus on the relationship between synergistic agglomeration and green development. Established studies identify nonlinear relationships such as the inverted U-shape [9] and U-shape [10], and specific regions such as the Yangtze River Economic Belt also show the evolutionary characteristics of inhibition followed by promotion [11].

The interaction mechanism between industrial synergistic agglomeration and the green, low-carbon and recycling economy has also become an emerging research frontier. However, there are still significant research gaps. On the one hand, the mechanism of synergistic agglomeration of manufacturing and productive service industries has not yet formed a systematic theoretical framework; on the other hand, there is a lack of sufficient theoretical and empirical evidence to support its regional green economy spillover effect. Existing studies have shown that industrial synergistic agglomeration has a multi-dimensional impact on the green economy, and Liu Jun et al. [8] found that industrial synergistic agglomeration has a facilitating effect on the efficiency of green innovation, and there is a regional heterogeneity characterized by “high in the east and low in the west”. However, the economic effects of synergistic agglomeration are complex in nature; Song [12] reveals that the impact of industrial synergistic agglomeration on the green efficiency of local industrial firms is characterized by an inverted U-shape, while Chen et al. (2022) [13] demonstrate that the intensity of environmental regulation is a key variable in moderating this nonlinear relationship. Notably, Jian D’s (2022) [14] study breaks through the local perspective and confirms that synergistic agglomeration not only enhances regional green performance but also has spatial spillover effects across regions, which provides an important direction for subsequent studies.

To summarize, this study systematically searched Chinese and English databases such as Web of Science, Scopus, CNKI and ScienceDirect, and adopted “green low-carbon circular economy” and its related terms (such as “sustainable development”, “circular development”, etc.) as the core keyword combinations. The term “green low-carbon circular economy” and its related terms (e.g., “sustainable development”, “circular development”, etc.) were used as the core keyword combinations. The timeframe of the literature was limited to 1997–2025, focusing on the research progress in the last five years (2019–2024). The inclusion criteria include (1) peer-reviewed academic papers; (2) studies directly exploring the theory or practice of the green, low-carbon, and circular economy; (3) clear methodological contributions or empirical findings. Finally, the study found that research on the mechanism of the role of the synergistic agglomeration of manufacturing and productive service industries on the green, low-carbon and circular development of the economy and its spatial effects is still insufficient, and the intrinsic connection has not yet been clarified. Incorporating the economic and spatial effects of synergistic industrial agglomeration and green, low-carbon and circular development into a unified analytical framework is rarely considered.

2. Theoretical Analysis and Research Hypotheses

Synergistic industrial agglomeration not only directly affects the regional green, low-carbon and circular development economy, but also strengthens its impact by mitigating resource mismatches and generating spatial spillover effects that narrow the development gap between regions. At the same time, environmental regulation and marketization level will regulate the strength of this effect. Based on this, this article will analyze from the following four dimensions, as visualized in Figure 1.

2.1. Direct Impact of Synergistic Industrial Agglomeration on a Green, Low-Carbon and Circular Development Economy

Industrial synergistic agglomeration has multiple positive effects on promoting China’s green, low-carbon and circular development. Firstly, through the “labor accumulation effect” [15], the centralized layout of manufacturing and production service industries forms an efficient talent market, enabling enterprises to flexibly adjust their employment structure according to actual demand and improve the fitness of talents, thus optimizing the combination of production factors and promoting the green transformation of the production process. Secondly, thanks to the “knowledge spillover effect”, geographic proximity accelerates technological exchanges and information sharing among enterprises, which not only promotes the dissemination of explicit knowledge [16] but also facilitates the transfer of tacit knowledge through frequent interactions, and this knowledge fusion stimulates innovative thinking, providing intellectual support for the research and development of cleaner production technologies and the recycling of resources. Finally, in terms of the “agglomeration scale effect” [17], the specialization division of labor enables the productive service industry to provide better intermediate services for manufacturing enterprises, while the common construction and sharing of infrastructure reduces resource consumption and pollution emissions per unit of output, thus realizing a win-win situation in terms of both economic and environmental benefits.

On the other hand, industrial cooperative agglomeration has a negative inhibitory effect on the green, low-carbon and recycling development economy. With the increasing scale of industrial agglomeration, the centripetal force will attract the agglomeration of more production factors [8]; however, the carrying capacity of a regional space is limited, and the agglomeration of too much of the labor force, population, and industries will exceed the load of the regional space, resulting in the over-exploitation of resources, energy consumption, increased pollution of the environmental problems, and congestion effect, which is not conducive to the green, low-carbon and recycling development of the economy.

In the light of the above analysis, the synergistic agglomeration of industries can promote the green, low-carbon and recycling development economy from three aspects, namely the following: the “labor pool” effect, the economy of scale effect, and the “knowledge reservoir” effect; and in the path of congestion effect, the over-agglomeration of industries is not conducive to the promotion of green, low-carbon and recycling development economy. Under the congestion effect path, excessive industrial agglomeration is unfavorable to the promotion of a green, low-carbon and recycling economy. Based on this, this paper puts forward hypothesis 1 as follows:

Hypothesis 1:

Synergistic industrial agglomeration promotes or inhibits the green, low-carbon and circular development of the economy.

2.2. Indirect Effects of Synergistic Industrial Agglomeration on Green, Low-Carbon and Circular Development of the Economy

Collaborative industrial agglomeration is essentially a process of the continuous convergence of factors in a specific spatial scope, which can promote the rapid flow of factors. As two different factors of production, the rational allocation of labor and capital has an important impact on the efficiency of resource allocation. The rational allocation of resources has a direct impact on production efficiency, technological innovation and economic growth, which are the key to realizing the green, low-carbon and circular development of the economy [18]. Reasonable labor relations can significantly improve the level of the green, low-carbon and circular development economy. Therefore, this paper analyzes the indirect effects of capital mismatch and labor mismatch from two aspects, respectively.

Firstly, industrial synergistic agglomeration–capital allocation–the green, low-carbon and circular development economy. Synergies among enterprises in industrial agglomerations have significantly improved the efficiency of resource utilization. Through cooperation in infrastructure construction and joint technology research and development, enterprises are able to share investment risks, prompting the transfer of capital to high-tech and digitalized areas [19]. This capital flow orientation accelerates the development and application of innovative technologies and effectively reduces resource consumption and pollution emissions in the production process. In terms of industry chain synergy, the close cooperation between upstream and downstream enterprises optimizes the efficiency of capital allocation. This synergistic mechanism reduces the capital mismatch problem brought about by information asymmetry, improves the return on investment, and accelerates the speed of capital turnover at the same time. In this process, capital flows more accurately to green emerging industries, promotes traditional industries to realize environmental upgrading through technological transformation, and ultimately promotes the transformation and development of the whole industrial system in the direction of being green and low-carbon [20]. Therefore, the following theoretical assumptions are proposed:

Hypothesis 2a:

Industry synergistic clustering promotes green, low-carbon and recycling economy through capital allocation.

Secondly, synergistic industrial agglomeration–labor allocation–the green, low-carbon and circular development of the economy. The clustering effect of the manufacturing industry and productive service industry has formed a large-scale labor market, which significantly reduces the cost of labor for enterprises through the optimization of supply and demand [21]. This agglomeration mode not only reduces the risk of job mismatch and recruitment costs, but also improves the overall labor productivity, which is especially conducive to the cultivation of high-quality talents adapted to the development of the green, low-carbon economy. In addition, the specialized division of labor and infrastructure sharing in the industrial chain generates a significant incremental effect of returns to scale. It improves the flexibility and efficiency of the labor market and promotes the research, development and application of green, low-carbon recycling technology [22], thus promoting the development of green, low-carbon recycling development economy. Therefore, the following theoretical hypotheses are proposed:

Hypothesis 2b:

Synergistic industrial clustering promotes a green, low-carbon and circular economy through labor allocation.

2.3. Spatial Spillover Effects of Synergistic Industrial Agglomeration on a Green, Low-Carbon and Circular Development Economy

Production services are derived from manufacturing and therefore have a high degree of relevance to the manufacturing industry; they are interdependent, synergistic and value-creating. Under the influence of industrial interdependence, the two industries tend to be geographically clustered together to gain the external advantages of agglomeration [23]. Specifically, the first is spatial spreading and depth enhancement. The joint agglomeration and close correlation of manufacturing and production services will constitute a complex social network and market network within the cluster. Through the increase in network density it will gradually increase from upstream to downstream, forming the spatial and temporal evolution trend of “multi-polarization and multi-threading” [24], which strengthens the correlation of the nodes of the inter-regional innovation network and promotes the integration and interaction of the industries between regions, making the industrial synergism more effective and more efficient. It strengthens the degree of association of innovation network nodes between regions, promotes the integration and interaction of industries between regions, and significantly improves the breadth and depth of the spatial spread of industrial synergistic agglomeration [25]; secondly, it is the formation of a technological knowledge pool. Based on production services, they promote the establishment of a common technical knowledge pool between manufacturing and production service industries. This knowledge pool can promote tacit knowledge spillover through geographic proximity and create a good external support environment for the income surplus generated by tacit knowledge sharing, which in turn improves the absorption capacity of the manufacturing industry for the knowledge spillover of productive services [26] and realizes the enhancement of the regional overall green, low-carbon and recycling development economy.

Therefore, collaborative industrial agglomeration can not only promote the industrial integration and interaction between the region and the neighboring regions and strengthen the correlation between the nodes of the innovation network in the region [27], but also realize the close and high-frequency exchanges and interactions and motivate the green R&D sector to accelerate the pace of innovation and promote the green, low-carbon and recycling development economy. Based on this, this paper proposes hypothesis 3 as follows:

Hypothesis 3:

The economic impact of synergistic industrial agglomeration on green, low-carbon and circular development has spatial spillover effects on neighboring regions.

2.4. The Moderating Role of Environmental Regulation and Regional Industrialization Levels in Synergistic Industrial Agglomeration and the Green, Low-Carbon, and Circular Development of the Economy

The phenomenon of synergistic industrial agglomeration is often the result of the joint action of the market and the government. Environmental regulation is a type of public intervention implemented by the government to protect the ecological environment through the establishment of institutional frameworks and behavioral norms, prompting market players to reduce negative environmental impacts in their business activities to achieve synergistic promotion of ecological protection and economic development. High environmental regulation will significantly increase the compliance cost of the manufacturing industry, forcing enterprises to reduce green technology R&D investment [28]. At this point, although the productive service industry can theoretically provide support for emission reduction, its service costs further increase the burden on enterprises. For example, in China’s iron and steel cluster, after environmental inspections were tightened, some firms had to reduce their R&D investment in low-carbon steelmaking processes in order to pay for the high costs of pollution control, leading to an increase in carbon emissions per ton of steel instead of a decrease [29]. In addition, manufacturing industries in high-regulation regions may migrate to low-regulation regions, destroying the original industrial synergy network. For example, the environmental protection policy in Beijing–Tianjin–Hebei has led to the relocation of some iron and steel enterprises to areas with less stringent policies, and the lack of supporting productive services in the new agglomeration area has reduced the efficiency of resource recycling by 30% [30].

From the perspective of economic structural adjustment, the increase in the level of regional industrialization has provided a structural impetus for the synergistic agglomeration of manufacturing and production service industries to promote green, low-carbon and circular development. As industrialization deepens, the accumulation of regional human capital and green technology reaches a threshold, shifting industrial synergy from traditional “production support” to “green innovation synergy” [31]. Specifically, the deep integration of the R&D-intensive service industry and the advanced manufacturing industry promotes the spillover of recycling technology. A typical example is of the Shenzhen electronic information industry cluster, in which the late stage of industrialization of the production service industry accounted for 42%, driven by the waste electronic products metal recycling rate which jumped from 60% to 92%. At the same time, when the regional industrialization enters the middle and advanced stages, the incremental scale compensation prompts enterprises to build green infrastructure together, and the specialized supply of the productive service industry significantly improves [32], forming a support network for green innovation in the manufacturing industry. Based on this, this paper proposes hypotheses 4 and 5 as follows:

Hypothesis 4:

Environmental regulations negatively inhibit the impact of industrial synergistic agglomeration on green, low-carbon and recycling economy.

Hypothesis 5:

Regional industrialization level positively moderates the impact of industrial synergistic agglomeration on green, low-carbon and circular development economy.

3. Research Design

3.1. Modeling

To test the above research hypotheses, the following basic model is first constructed for the direct transmission mechanism:

$${\mathrm{GLCE}}_{\mathrm{it}}=\alpha +{\alpha }_1{\mathrm{Caogglo}}_{\mathrm{it}}+\mathrm{\gamma }{\mathrm{control}}_{\mathrm{it}}+{\eta }_t+{\delta }_i+{\epsilon }_{\mathrm{it}}$$

(1)

where explanatory variable ${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$ represents the economic level of green, low-carbon and recycling development in province i in year t; explanatory variable ${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$ is the level of industrial synergistic agglomeration; ${\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ represents a series of control variables; ${\mathrm{\delta }}_{\mathrm{i}}$ is an individual fixed effect; ${\mathrm{\eta }}_{\mathrm{t}}$ is a year fixed effect; and ${\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$ represents a random error term.

According to the previous section, the test is conducted by considering whether it is a mediating variable between capital mismatch and labor mismatch from two perspectives. The specific testing steps are as follows:

$${\mathrm{A}\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\theta }+{\mathrm{\theta }}_1{\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}+\mathrm{\gamma }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\eta }}_{\mathrm{t}}+{\mathrm{\delta }}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(2)

$${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}={\mathrm{\gamma }}_0+{\mathrm{\gamma }}_1{\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}+\mathrm{\phi }{\mathrm{A}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+\mathrm{\gamma }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\eta }}_{\mathrm{t}}+{\mathrm{\delta }}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(3)

Finally, in order to better explore the spatial spillover effect of industrial synergistic agglomeration in green, low-carbon cycle development, which is not only affected by its own industrial synergistic agglomeration but also by the economic level of green, low-carbon cycle development in neighboring places, we thus introduce the spatial interaction terms of the variables on the basis of Equation (1) to extend it into a spatial measurement model, specifically as follows Equation (4):

$${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\alpha }+\mathrm{\rho }{\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\alpha }}_1{\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}{+\mathrm{\rho }}_1{\mathrm{W}\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}+\mathrm{\gamma }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\eta }}_{\mathrm{t}}+{\mathrm{\delta }}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(4)

where $\mathrm{\rho }$ represents the spatial autocorrelation coefficient, W represents the spatial weight matrix, and ${\mathrm{\rho }}_1$ represents the spatial spillover coefficient of industrial synergistic agglomeration. In order to improve the robustness of the empirical results, this paper constructs the economic–geographical nesting matrix and the spatial economic distance weighting matrix from the perspectives of geographic and socio-economic links between regions, respectively, to accurately grasp the spatial spillover effects of green, low-carbon and recycling development under the different matrices, with the formulas as follows:

$${\mathrm{W}}_{ij}=\left\{\begin{array}{c}{\displaystyle \frac{1}{Y_{ij}^2}},i\ne j\\ 0,i=j\end{array}\right.$$

(5)

That is, the inverse of the square of the difference in per capita GDP between the two provinces is used to construct the weights, and normalization is performed to obtain the final economic distance matrix. As such, the economic–geographic nested matrix is constructed with the following formula:

$${\mathrm{W}}_1=\left\{\begin{array}{c}{\displaystyle \frac{1}{d_{ij}^2}},i\ne j\\ 0,i=j\end{array}\right.{\mathrm{W}}_2=\left\{\begin{array}{c}1/|{\stackrel{\_}{\mathrm{y}}}_i-{\stackrel{\_}{\mathrm{y}}}_j|,i\ne j\\ 0,i=j\end{array}\right.$$

(6)

$${\mathrm{W}}_{ij}={\displaystyle \frac{1}{2}}{\mathrm{W}}_1+{\displaystyle \frac{1}{2}}{\mathrm{W}}_2$$

(7)

where ${\mathrm{W}}_1$ is the spatial geographic distance matrix, ${\mathrm{W}}_2$ is the spatial economic distance matrix, and the weights computed from latitude, longitude, and GDP differences are 0.5, normalized to obtain the final economic–geographic nested matrix.

3.2. Variable Measurement and Description

3.2.1. Economic Level of Green, Low-Carbon and Cyclical Development

The authors believe that a green, low-carbon and recycling economy is a new economic paradigm with sustainable development as its core concept, the essence of which lies in the realization of synergies between economic growth and ecological and environmental protection through systemic innovation. This development paradigm consists of the following three interrelated dimensions: the “green” dimension emphasizes the friendliness of economic activities to the ecological environment and the reduction in environmental loads through cleaner production and pollution prevention and control; the “low-carbon” dimension focuses on the optimization of the energy structure and the improvement of energy efficiency in order to reduce greenhouse gas emissions; and the “recycling” feature is reflected in the construction of a closed-loop system of “resources-products-reproduced resources” in order to maximize the efficiency of resource utilization. The organic unity of the three together promote the formation of resource-saving, environment-friendly, climate-adapted modern economic system, and ultimately achieve a win-win pattern of high-quality economic development and ecological civilization construction. On this basis, the following indicators for assessing the economic level of China’s green, low-carbon and recycling development have been established on the basis of data availability, comparability, validity, representativeness and comprehensiveness, drawing on the research ideas of Zhang Youguo (2020) [33].

The economic assessment system for green, low-carbon and recycling development contains the following four core dimensions: development dynamics, production system, living system and development effectiveness [34]. The development momentum dimension focuses on the two pillars of technological innovation and financial support, of which the green technological innovation system covers four assessment indicators, while green finance is systematically measured through seven tools, including credit, investment and insurance. The production system unfolds from two dimensions, namely the support of the living system and the transformation of production behavior, with special attention paid to five key indicators in the agricultural and rural areas. The living system is assessed in both directions through the lifestyle of the population (four behavioral indicators) and the construction of a livable environment (urban green space and rural sanitation facilities). Development benefits, as an integrated dimension, are comprehensively quantified from four perspectives, ecological benefits (six indicators), carbon emission benefits (degree of energy cleanliness), resource recycling benefits, and economic and social benefits (three indicators), forming an evaluation framework that takes into account both environmental friendliness and economic sustainability.

The indicator system transforms abstract concepts into operational standards through multi-level refinement and has the following three prominent features at the implementation level: first, it emphasizes urban–rural synergy, and sets up a single assessment indicator for agriculture and rural areas in order to make up for the shortcomings of development; second, it focuses on behavioral orientation, and measures the transformation practices of production and management subjects (such as industrial enterprises and agricultural subjects) as well as examines the transformation of residents’ lifestyles; third, it highlights systematic integration and comprehensively reflects the holistic nature of green, low-carbon and recycling development through technological innovation–financial support, production–life, environment–economy and other multi-group relationships to comprehensively reflect the wholeness of green, low-carbon and recycling development. In particular, key elements such as the decoupling status of carbon emissions and resource utilization efficiency are incorporated into the benefit assessment, so that the indicator system can both diagnose the current problems and guide the direction of future development. The specific indicator system is shown in

Table 1. Indicators for evaluating the economic level of green, low-carbon and recycling development.

Primary Index	Secondary Indicators	Tertiary Indicators	Number	Direction of Indicators
Development Dynamics	Green, low-carbon and recycling technology innovation system	Investment in research and experimental development	$X_1$	+
		Number of engineering research centers for green, low-carbon recycling technologies	$X_2$	+
		Technology market maturity	$X_3$	+
		Share of environmental protection expenditure in general public budget expenditure	$X_4$	+
	Green finance	Green finance index	$X_5$	+
Production System	Living systems	Gas penetration rate	$X_6$	+
		Road area per capita	$X_7$	+
		Number of beds in medical institutions per 10,000 population	$X_8$	+
		Local financial expenditure on education	$X_9$	+
	Productive behaviors	Water-saving irrigation area	$X_{10}$	+
		Pesticide use per unit of cultivated area	$X_{11}$	−
		Fertilizer use per unit of cultivated area	$X_{12}$	−
		Share of investment in industrial pollution control	$X_{13}$	+
Living System	Lifestyle	Per capita domestic energy consumption	$X_{14}$	−
		Per capita domestic water consumption	$X_{15}$	−
		Green travel	$X_{16}$	+
		Car ownership per 100 households	$X_{17}$	−
	Building a livable environment	Green space per capita in parks	$X_{18}$	+
		Rural sanitary latrine penetration rate	$X_{19}$	+
Development Effectiveness	Green benefits	Composite air pollution index	$X_{20}$	−
		Centralized treatment rate of sewage treatment plants	$X_{21}$	+
		Intensity of soil and water management	$X_{22}$	+
		Green space ratio in built-up areas	$X_{23}$	+
		Non-hazardous treatment rate of domestic waste	$X_{24}$	+
		Number of environmental emergencies	$X_{25}$	−
	Low-carbon benefits	Energy consumption per unit of GDP	$X_{26}$	−
		Carbon emissions per unit of GDP	$X_{27}$	−
		Share of non-fossil energy	$X_{28}$	+
	Circularity benefits	Duplication of surface and groundwater resources	$X_{29}$	+
		Water productivity	$X_{30}$	+
		General industrial solid waste utilization rate	$X_{31}$	+
		Urban reclaimed water utilization rate	$X_{32}$	+
	Economic and social benefits	Unemployment rate	$X_{33}$	−
		Per capita disposable income	$X_{34}$	+
		Ratio of disposable income of urban and rural residents	$X_{35}$	−

, where “+” denotes that higher values reflect positive development trends, while “−” indicates that lower values represent favorable developmental outcomes.

Since the indicators involve multiple dimensions, this paper uses the method of factor analysis, which is combined into one variable, in order to verify that the data can be applied to factor analysis. This paper first carried out the KMO and Bartlett’s spherical test on the data, and the test results are shown in

Table 2. Factorial test table.

	Project	Value
KMO Test		0.756
Bartlett’s Spherical Test	Approximate chi-square	14,658.213
	Degrees of freedom	595
	Significance	0.000

. According to the table, it can be seen that the approximate Chi-square is 14,658.213 and the p-value of 0.000 is less than 0.05; therefore it is considered that the correlation coefficient matrix is significantly different from the unit matrix and that it can be used to do the factor analysis, which also means that it can be used to rank the importance of the variables. At the same time, in order to more intuitively understand the number of factors selected, this paper draws a gravel diagram, as shown in Figure 2. According to the gravel diagram nine variables can be selected, after which the gravel diagram eigenvalue stops to decline significantly. Therefore, this paper believes that there are nine most important factors affecting the green, low-carbon recycling development economy.

3.2.2. Level of Industrial Synergistic Agglomeration

The synergistic agglomeration of manufacturing and productive services is an important feature of industrial spatial layout. The theoretical basis of this phenomenon is mainly derived from the theoretical framework of industrial agglomeration, covering such classical theories as location choice, innovation clustering and optimal scale [35]. In essence, industrial agglomeration manifests itself as the concentrated spatial distribution of specific industries, accompanied by the market-oriented allocation process of production factors. As far as these two types of industries are concerned, they show obvious symbiotic characteristics: the development of the productive service industry relies on the foundation of manufacturing industry while the manufacturing industry promotes the formation of a specialized division of labor and complementary agglomeration of the productive service industry through the locational locking effect [36]. This interactive mechanism based on industrial linkage ultimately contributes to the spatial synergistic agglomeration of the two types of industries.

At present there are many ways to measure industrial agglomeration, and in general, it can be divided into economic activity-based, distance-based, and spatial relevance-based metrics. Based on the research on the spatial distribution of manufacturing and productive service industries by Zhang Hu [37] and others, a spatial agglomeration index system for manufacturing and productive service industries based on location entropy is proposed. Secondly, based on the differences in industrial agglomeration, the synergistic agglomeration characteristics between manufacturing and productive service industries are analyzed, as shown in Equations (8) and (9). Equation (8) is as follows:

$${\mathrm{L}\mathrm{Q}}_{\mathrm{i}\mathrm{j}}={\displaystyle \frac{{\mathrm{\chi }}_{\mathrm{i}\mathrm{j}}}{{\mathrm{\chi }}_{\mathrm{i}}}}/{\displaystyle \frac{{\mathrm{\chi }}_{\mathrm{j}}}{\mathrm{\chi }}}$$

(8)

where ${\mathrm{L}\mathrm{Q}}_{\mathrm{i}\mathrm{j}}$ is the location entropy index of industry j in province i in the whole country, ${\mathrm{\chi }}_{\mathrm{i}\mathrm{j}}$ is the number of employment in industry j in province i, ${\mathrm{\chi }}_{\mathrm{i}}$ is the number of employment in manufacturing and productive services in province i, ${\mathrm{\chi }}_{\mathrm{j}}$ is the number of employment in industry j in the whole country, and $\mathrm{\chi }$ is the number of employment in manufacturing and productive services in the whole country. Equation (9) is as follows:

$${\mathrm{I}\mathrm{C}}_{\mathrm{c}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}=\left(1-{\displaystyle \frac{{|\mathrm{I}\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{u}}-{\mathrm{I}\mathrm{C}}_{\mathrm{s}\mathrm{e}\mathrm{r}}|}{{\mathrm{I}\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{u}}+{\mathrm{I}\mathrm{C}}_{\mathrm{s}\mathrm{e}\mathrm{r}}}}\right)+|{\mathrm{I}\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{u}}+{\mathrm{I}\mathrm{C}}_{\mathrm{s}\mathrm{e}\mathrm{r}}|$$

(9)

where the agglomeration index of the manufacturing industry is (${\mathrm{I}\mathrm{C}}_{\mathrm{m}\mathrm{a}\mathrm{n}\mathrm{u}}$), the agglomeration index of the productive service industry is (${\mathrm{I}\mathrm{C}}_{\mathrm{s}\mathrm{e}\mathrm{r}}$), and the synergistic agglomeration index of the manufacturing industry and productive service industry is (${\mathrm{I}\mathrm{C}}_{\mathrm{c}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}$), of which the former is used to characterize the degree of “synergistic agglomeration” and the latter is used to characterize the degree of “synergistic agglomeration”, the sum of which reflects the high degree of agglomeration of the manufacturing industry and productive service industry, and the higher and deeper degree of integration of both. On the whole, the higher the degree of industrial agglomeration, the higher the degree of synergistic agglomeration and the stronger the synergistic effect.

The synergistic agglomeration of productive services and manufacturing industries also shows very significant geographical differences between regions, as can be seen from Figure 3, and the degree of synergistic agglomeration of productive services and manufacturing industries is the highest in the east, followed by the central part, with it the lowest in the west. The synergistic agglomeration of productive services and manufacturing industries in the eastern and central regions exhibits a trend of rising and then falling, with inflection points in 2012 and 2017, whereas the degree of cooperative agglomeration in the western region continues to decrease, exhibiting a unidirectional decline.

3.2.3. Intermediary Variable

In this paper, resource allocation is considered in terms of capital and labor, and the capital mismatch and labor mismatch indices of each province for the period 2010–2022 are measured separately, drawing on the research results of Bai Junhong [38] and others. The larger its value, the more serious the resource mismatch situation is. When the coefficient is greater than 0, it indicates that the overall allocation of factors of production in the region is less than the theoretical one, which is due to the serious under-allocation of resources in the region; on the contrary, it indicates the over-allocation of resources.

3.2.4. Moderating Variables

This study adopts a green tax as a core measure of environmental regulation. As an important policy tool for the government to regulate the environmental behavior of enterprises, a green tax can effectively reflect the intensity and quality of regional environmental regulation. Specifically, this paper applies the entropy value method to comprehensively measure eight indicators, including resource tax, environmental protection tax, urban maintenance and construction tax, urban land use tax, vehicle and vessel tax, arable land occupation tax, consumption tax, and vehicle purchase tax; and the level of regional industrialization is measured by the proportion of the added value of the secondary industry to GDP.

3.2.5. Control Variable

In reference to other scholars, this paper sets the control variables as follows: social consumption level (${\mathrm{S}\mathrm{C}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$): total retail sales of consumer goods/gross regional product; urbanization level (${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$): urban population/total population; income level (${\mathrm{I}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$): ln (average wage of on-the-job workers in urban units); the level of development of the digital economy (${\mathrm{D}\mathrm{I}\mathrm{G}}_{\mathrm{i}\mathrm{t}}$): composite index of digital economy development; foreign direct investment (${\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}$): ln (total FDI).

3.3. Data Sources and Descriptive Statistics

Considering the availability and completeness of the data, this study is based on the panel data of 30 provinces (Tibet, Hong Kong, Macao and Taiwan are not included due to missing data) from 2010 to 2022, and the specific provinces are shown in Figure 4. The data used are from the China Statistical Yearbook, the China Energy Statistical Yearbook, the China Environmental Statistical Yearbook, and the China Statistical Yearbook of Regional Economy. At the same time, to eliminate the effect on extreme values, continuous variables were shrink-tailed at the top and bottom 1%. Descriptive statistics for each variable are shown in

Table 3. Lists the descriptive statistics.

Variables	N	Mean	SD	Median	Min	Max
${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$	390	0.000	0.352	−0.021	−1.101	1.283
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	390	2.618	0.455	2.615	1.776	3.975
${SCL}_{it}$	390	0.385	0.058	0.388	0.180	0.504
${\mathrm{I}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$	390	11.086	0.402	11.103	10.278	12.304
${UR}_{it}$	390	0.595	0.124	0.582	0.338	0.896
${DIG}_{it}$	390	−0.044	0.705	−0.271	−1.251	3.368
${\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}$	390	16.089	2.540	16.393	8.330	20.199
${CM}_{it}$	390	0.331	0.366	0.246	−0.035	2.744
${LM}_{it}$	390	0.324	0.261	0.264	0.001	1.684

.

4. Empirical Results and Analyses

4.1. Baseline Regression

Table 4. Benchmark regression results.

	(1)	(2)	(3)	(4)	(5)	(6)
Variables	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	0.151 **	0.132 **	0.184 ***	0.129 **	0.148 **	0.145 **
	(2.28)	(2.08)	(2.89)	(2.11)	(2.47)	(2.48)
${SCL}_{it}$		1.120 ***	1.093 ***	0.530 **	0.359 *	0.509 ***
		(5.72)	(5.69)	(2.58)	(1.75)	(2.81)
${IC}_{it}$			0.602 ***	0.494 ***	0.395 ***	0.299 *
			(3.94)	(3.37)	(2.71)	(1.96)
${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$				2.188 ***	2.902 ***	3.098 ***
				(6.07)	(7.31)	(5.93)
${Dig}_{it}$					0.127 ***	0.146 ***
					(3.92)	(3.26)
${lnFDI}_{it}$						−0.042 **
						(−2.18)
Constant	−0.333	−0.753 ***	−7.602 ***	−7.712 ***	−7.347 ***	−5.766 ***
	(−1.54)	(−3.43)	(−4.34)	(−4.62)	(−4.49)	(−3.27)
individual fixed	YES	YES	YES	YES	YES	YES
time fixed	YES	YES	YES	YES	YES	YES
Observations	390	390	390	390	390	390
R-squared	0.901	0.910	0.913	0.922	0.925	0.927
r2_a	0.889	0.898	0.902	0.912	0.915	0.917
F	5.178	19.17	82.76	90.16	92.23	125.5

Note: (1) Standard errors are in parentheses; (2) *, **, and *** represent that the effect of each explanatory variable is significant at the 10%, 5%, and 1% confidence levels, respectively.

presents the results of estimating the synergistic agglomeration effect on the green, low-carbon and recycling development economy. The results of the study show that the coefficient of industrial synergistic agglomeration has been significant at the 5% level of significance with gradual control of variables, which indicates that industrial synergistic agglomeration has a significant role in promoting the green, low-carbon and recycling development economy, and verifies hypothesis 1.

From the perspective of control variables, there is a significant positive correlation between the social consumption level (${\mathrm{S}\mathrm{C}\mathrm{L}}_{\mathrm{i}\mathrm{t}}$) and income level (${\mathrm{I}\mathrm{C}}_{\mathrm{i}\mathrm{t}}$) and the green, low-carbon and recycling development economy, indicating that as people’s consumption level and income level increase, consumers’ demands for product quality and environmental characteristics also increase, thus promoting the development of the green, low-carbon and recycling economy. The level of urbanization (${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$) and the level of the development of the digital economy (${\mathrm{D}\mathrm{i}\mathrm{g}}_{\mathrm{i}\mathrm{t}}$) similarly pass the 1% level significance test. Urbanization, through the spatial agglomeration of population and economic activities, can significantly improve resource efficiency (including energy efficiency) and thus reduce the overall resource consumption intensity per unit of output. In terms of smart manufacturing and the industrial Internet, the digital economy can make the production process more automated and intelligent and the efficiency and quality of production can be significantly improved, and at the same time it can also prevent the production of excess and inventory backlog and reduce resource consumption and waste [39]. Finally, foreign direct investment (${\mathrm{l}\mathrm{n}\mathrm{F}\mathrm{D}\mathrm{I}}_{\mathrm{i}\mathrm{t}}$) has a significant negative effect on the green, low-carbon and recycling economy, which is due to the fact that foreign-funded enterprises are usually characterized by high energy consumption and high emissions, and the expansion of the scale of foreign investment will lead to an increase in energy consumption and greenhouse gas emissions, which further aggravates the environmental problems [40].

4.2. Mechanism Analysis

In order to test the mechanism hypothesis in the previous section, we chose the mediation effect model to analyze it empirically, and the regression results are shown in

Table 5. Test results of the mechanism of industrial synergistic agglomeration affecting the economic role of green, low-carbon and recycling development.

	(1)	(2)	(3)	(4)
Variables	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathit{C}\mathit{M}}_{\mathit{i}\mathit{t}}$	${\mathit{L}\mathit{M}}_{\mathit{i}\mathit{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	0.145 **	−0.421 ***	−0.010	0.052	0.146 **
	(2.48)	(−6.21)	(−0.21)	(0.87)	(2.51)
${CM}_{it}$				−0.219 ***
				(−4.17)
${LM}_{it}$					0.118 *
					(1.76)
${SCL}_{it}$	0.509 ***	0.966 ***	−0.821 ***	0.721 ***	0.606 ***
	(2.81)	(3.68)	(−3.06)	(4.00)	(3.33)
${IC}_{it}$	0.299 *	0.276	−0.084	0.359 **	0.309 **
	(1.96)	(1.42)	(−0.50)	(2.42)	(2.05)
${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$	3.098 ***	−4.733 ***	1.444 ***	2.064 ***	2.929 ***
	(5.93)	(−5.70)	(2.91)	(4.06)	(6.10)
${Dig}_{it}$	0.146 ***	0.091 **	−0.079 **	0.166 ***	0.156 ***
	(3.26)	(2.44)	(−2.17)	(3.68)	(3.49)
${lnFDI}_{it}$	−0.042 **	0.015	0.004	−0.039 **	−0.043 **
	(−2.18)	(1.03)	(0.34)	(−2.13)	(−2.22)
Constant	−5.766 ***	1.513	0.810	−5.435 ***	−5.842 ***
	(−3.27)	(0.61)	(0.46)	(−3.22)	(−3.37)
Individual fixed effect	YES	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES	YES
Observations	390	390	390	390	390
R-squared	0.927	0.909	0.842	0.932	0.928
r2_a	0.917	0.896	0.820	0.922	0.918
F	125.5	59.01	153.8	128.4	123.6

Note: (1) Standard errors are in parentheses; (2) *, **, and *** represent that the effect of each explanatory variable is significant at the 10%, 5%, and 1% confidence levels, respectively.

. The regression coefficient of industrial synergistic agglomeration is negative for capital mismatch and is significant at the level of 1% in both; meanwhile, the capital mismatch is significantly negative for the green, low-carbon and recycling development economy at least at the level of 5%, which indicates that industrial synergistic agglomeration improves the resource allocation efficiency by improving capital mismatch and then promotes the green, low-carbon and recycling development economy. Thus the empirical results support H2a.

Secondly, from column (2) of Table 5, it can be seen that the regression coefficient of collaborative industrial agglomeration on labor mismatch is −0.010 but not significant and the regression coefficient of labor mismatch is 0.118, which is significant at the 10% level. Therefore, the effect of collaborative industrial agglomeration on labor mismatch is not obvious, and collaborative industrial agglomeration cannot realize the green, low-carbon and recycling development economy by changing the allocation of labor resources. Referring to the study and others [41], the possible reason is that in economically developed regions the phenomenon of talent agglomeration is widespread, and its siphoning effect will further exacerbate the brain drain from the surrounding regions with weaker economies. This inter-regional imbalance directly affects the overall distribution of labor resources, thus preventing the study results from passing the significance test. Hypothesis H2b does not pass the test.

4.3. Analysis of Spatial Spillover Effects

4.3.1. Moran’s Index Test

In order to explore the spatial correlation and interactive effect of the economic level of green, low-carbon and recycling development in each province, it is necessary to carry out the spatial autocorrelation test first. Currently, Moran’s I is a commonly used spatial autocorrelation test, which can effectively reveal the similarity of attribute values of spatially neighboring regional units. Therefore, this paper also calculates Moran’s I index based on the economic distance weights of the green, low-carbon and recycling economic development level of each province from 2010 to 2022, aiming to verify whether there is a spatial agglomeration phenomenon in the economic level of the green, low-carbon and recycling development of each province. The specific formulas as follows:

$$\mathrm{I}={\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{\omega }}_{\mathrm{i}\mathrm{j}}({\mathrm{\chi }}_{\mathrm{i}}-\stackrel{\_}{\mathrm{\chi }})({\mathrm{\chi }}_{\mathrm{j}}-\stackrel{\_}{\mathrm{\chi }})}{{\mathrm{P}}^2\sum _{\mathrm{i}=1}^{\mathrm{n}}\sum _{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{\omega }}_{\mathrm{i}\mathrm{j}}}}$$

(10)

where ${\mathrm{P}}^2$=$\sum _{\mathrm{i}=1}^{\mathrm{n}}{({\mathrm{\chi }}_{\mathrm{i}}-\stackrel{\_}{\mathrm{\chi }})}^2$/n, n is the total number of districts, ${\chi }_i$ is the observed value of $\mathrm{i}$ district, and ${\mathrm{\omega }}_{i\mathrm{j}}$ is an element in the spatial weight matrix. The results are shown in

Table 6. Moran’s index test.

Year	${\mathbf{C}\mathbf{a}\mathbf{o}\mathbf{g}\mathbf{g}\mathbf{l}\mathbf{o}}_{\mathbf{i}\mathbf{t}}$			${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$
	Moran’s I	Z	p-Value	Moran’s I	Z	p-Value
2010	0.1282	3.5395	0.0004	0.1003	2.9039	0.0037
2011	0.1134	3.2177	0.0013	0.1190	3.2675	0.0011
2012	0.1191	3.3456	0.0008	0.1044	2.9938	0.0028
2013	0.0886	2.6908	0.0071	0.0915	2.6882	0.0072
2014	0.0942	2.7978	0.0051	0.1000	2.8551	0.0043
2015	0.1016	2.9475	0.0032	0.0036	0.8156	0.4147
2016	0.1003	2.9111	0.0036	0.0723	2.2933	0.0218
2017	0.0918	2.7216	0.0065	0.0619	2.0815	0.0374
2018	0.0923	2.7403	0.0061	0.0334	1.4757	0.1400
2019	0.1146	3.1843	0.0015	0.0394	1.6178	0.1057
2020	0.1174	3.2437	0.0012	0.0732	2.3547	0.0185
2021	0.1195	3.2821	0.0010	0.0555	1.9876	0.0469
2022	0.1160	3.2116	0.0013	0.0630	2.1863	0.0288

. It can be seen that ${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$ is significantly positive in most years, indicating that there is spatial clustering in the economic level of green, low-carbon and recycling development in each province.

4.3.2. Analysis of Results

Before model estimation, this study screens the applicability of the spatial error model and the spatial lag model through the joint test of the Lagrange multiplier (LM) and its robust form (Robust LM). Further, with the help of Wald’s test with the likelihood ratio (LR) test, the likelihood of the spatial Durbin model simplifying to a spatial error or spatial lag model is assessed.

Table 7. Model type test results.

Type of Test		W1		W2
		t	p-Value	t	p-Value
LM	Moran’s I	182.761	0.000	1.300	0.000
	LM–error	13.090	0.000	64.910	0.000
	Robust LM–error	0.090	0.764	8.119	0.004
	LM–sar	15.860	0.000	87.975	0.000
	Robust LM–sar	2.860	0.091	31.184	0.000
LR	LR–spatial error	23.16	0.000	39.41	0.000
	LR–spatial sar	23.50	0.000	38.67	0.000
Wald	Wald–spatial error	23.90	0.000	41.29	0.000
	Wald–spatial sar	24.22	0.000	40.66	0.000

demonstrates the test results, in which the LM test shows that all of them are significant except Robust LM–error, which highlights the advantages of the spatial lag model. Meanwhile, the Wald and LR test results reject the original hypothesis that the spatial Durbin model is degenerate and support the alternative hypothesis that it exists independently, avoiding the simplification to a spatial lag or spatial error model.

Table 8. Results of economic spatial measurement of the impact of synergistic industrial agglomeration on green, low-carbon and circular development.

Variables	W1			W2
	SEM	SAR	SDM	SEM	SAR	SDM
$\mathrm{\rho }$	0.539 ***	0.290 ***	0.377 ***	0.168 ***	0.109 **	0.176 ***
	(6.00)	(2.76)	(3.48)	(2.99)	(2.21)	(3.58)
W × ${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$			0.569 **			0.246 ***
			(2.10)			(2.84)
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	0.107 *	0.137 **	0.161 ***	0.109 *	0.130 **	0.052
	(1.92)	(2.35)	(2.88)	(1.76)	(2.19)	(0.86)
${SCL}_{it}$	0.561 ***	0.268	0.560 ***	0.255	0.232	0.252
	(2.80)	(1.63)	(2.72)	(1.52)	(1.41)	(1.52)
${IC}_{it}$	−0.069	−0.142 *	0.379 **	−0.054	−0.067	0.454 ***
	(−0.99)	(−1.87)	(2.56)	(−0.79)	(−0.99)	(3.75)
${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$	2.691 ***	2.504 ***	3.045 ***	2.567 ***	2.441 ***	2.306 ***
	(6.94)	(6.63)	(7.36)	(6.57)	(6.36)	(5.23)
${ Dig}_{it}$	0.139 ***	0.112 ***	0.131 ***	0.130 ***	0.114 ***	0.177 ***
	(4.51)	(3.51)	(4.31)	(3.88)	(3.55)	(5.58)
${lnFDI}_{it}$	−0.041 ***	−0.027 **	−0.038 ***	−0.035 **	−0.025 *	−0.040 ***
	(−3.09)	(−2.08)	(−2.99)	(−2.54)	(−1.95)	(−3.09)
Observations	390	390	390	390	390	390
Log-Likelihood	347.8716	338.7393	363.3020	339.5388	337.5939	373.6316
R-squared	0.241	0.258	0.220	0.251	0.258	0.204

Note: (1) Standard errors are in parentheses; (2) *, **, and *** represent that the effect of each explanatory variable is significant at the 10%, 5%, and 1% confidence levels, respectively.

reports the results of the spatial regression modeling of industry synergistic agglomeration on the economic level of green, low-carbon and recycling development under the economic–geographical nesting matrix and the economic distance matrix. From the regression results in Table 8, it can be seen that the spatial autocorrelation coefficients of the spatial lag model, spatial error model, and spatial Durbin model passed the significance test of more than 5% regardless of whether the economic–geographical nested matrix or economic distance matrix was used for the spatial weight matrix, which also proves that there is a significant positive spatial correlation for the green, low-carbon and recycling development of each province.

Secondly, through the regression analysis of the spatial lag model, spatial error model, and spatial Durbin model, it is concluded that the coefficients of the influence of industrial cooperative agglomeration on the green, low-carbon and recycling development economy are all positive, which pass the significance level of 5%, 10%, and 1%, respectively. Additionally, the spatial interaction term is positive, which indicates that there is not only an exogenous interaction effect of the industrial cooperative agglomeration among the sample provinces and cities, but also an endogenous interaction of green, low-carbon and recycling development economy. Therefore, hypothesis H3 is verified.

Since the regression coefficient value of the spatial interaction term cannot be directly used to discuss the marginal impact of industrial cooperative agglomeration on the green, low-carbon and recycling development economy of each province, and since a single single-point regression method may have bias in the study of inter-regional spatial spillovers, it is necessary to decompose the changes in the variables with the partial differentiation method and explain the impact of the independent variables in the region on the local area and the other regions through the direct effects and the indirect effects, respectively, which are shown in

Table 9. Effect decomposition results under SDM.

Variables	W1			W2
	Direct Effect	Indirect Effect	Total Effect	Direct Effect	Indirect Effect	Total Effect
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	0.183 ***	1.028 **	1.211 ***	0.072	0.286 ***	0.358 ***
	(3.17)	(2.28)	(2.60)	(1.25)	(2.94)	(3.72)
${SCL}_{it}$	0.535 ***	−0.186	0.350	0.239 *	0.042	0.281
	(3.18)	(−0.27)	(0.56)	(1.74)	(0.20)	(1.22)
${ IC}_{it}$	0.388 **	−0.023	0.365	0.436 ***	−0.385 ***	0.051
	(2.45)	(−0.05)	(0.84)	(3.50)	(−2.75)	(0.49)
${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$	3.047 ***	−2.106	0.941	2.345 ***	−0.007	2.338 ***
	(7.20)	(−0.83)	(0.38)	(5.12)	(−0.01)	(3.88)
${Dig}_{it}$	0.124 ***	−0.155	−0.031	0.161 ***	−0.138 ***	0.023
	(3.48)	(−0.75)	(−0.15)	(4.63)	(−2.78)	(0.45)
${lnFDI}_{it}$	−0.036 ***	0.080	0.044	−0.032 ***	0.097 ***	0.065 ***
	(−2.75)	(0.89)	(0.47)	(−2.61)	(4.38)	(2.60)
Observations	390	390	390	390	390	390
R-squared	0.220	0.220	0.220	0.204	0.204	0.204

Note: (1) Standard errors are in parentheses; (2) *, **, and *** represent that the effect of each explanatory variable is significant at the 10%, 5%, and 1% confidence levels, respectively.

. From Table 9, it can be seen that the indirect effect of industrial synergistic agglomeration on the green, low-carbon and recycling development economy of each province exists significantly. For every 1% increase in the degree of collaborative industrial agglomeration in the region, the growth of the green, low-carbon and recycling development economy in the region is 0.183%, which will have a driving effect of 1.028% on the neighboring regions, which implies that the collaborative industrial agglomeration has a strong spatial network externality and its diffusion effect is larger than its return effect.

4.4. Further Analysis

4.4.1. Moderating Effects Test

Moderating Effects Modeling

To further investigate the effect of industrial cooperative agglomeration on the green, low-carbon and recycling economy under the constraint of environmental regulation, Equations (11) and (12) are constructed based on Equation (1) by introducing the interaction terms of environmental regulation, the level of regional industrialization, and industrial cooperative agglomeration, respectively. Equations (11) and (12) are as follows:

$${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\tau }+{\mathrm{\tau }}_1{\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\tau }}_2{\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\tau }}_3{\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}{\ast \mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\tau }}_4{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\eta }}_{\mathrm{t}}+{\mathrm{\delta }}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(11)

$${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\pi }+{\mathrm{\pi }}_1{\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_2{\mathrm{I}\mathrm{N}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_3{\mathrm{I}\mathrm{N}}_{\mathrm{i}\mathrm{t}}{\ast \mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\pi }}_4{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\eta }}_{\mathrm{t}}+{\mathrm{\delta }}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(12)

where ${\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ represents environmental regulation, ${\mathrm{I}\mathrm{N}}_{\mathrm{i}\mathrm{t}}$ represents the level of industrialization, and the rest of the variables are consistent with Equation (1).

Analysis of the Results of the Moderating Effect

The fixed-effects model is used to empirically analyze the moderating effects of environmental regulation and regional industrialization level, and the specific results are shown in

Table 10. Moderated effects test results.

	(1)	(3)
Variables	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	0.154 **	0.196 ***
	(2.42)	(3.27)
${\mathrm{E}\mathrm{N}}_{\mathrm{i}\mathrm{t}}{\times \mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	−0.000 ***
	(−3.31)
${\mathrm{I}\mathrm{N}}_{\mathrm{i}\mathrm{t}}{\times \mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$		1.007 ***
		(3.78)
Constant	−5.783 ***	−3.734 **
	(−3.44)	(−2.20)
Control Variables	YES	YES
Observations	390	390
R-squared	0.930	0.930

Note: (1) Standard errors are in parentheses; (2) ** and *** represent that the effect of each explanatory variable is significant at 5%, and 1% confidence levels, respectively.

. According to the results in column (1) of Table 10, the coefficient of the interaction term of environmental regulation is significantly negative at the 1% level; it indicates that environmental regulation has an inhibitory moderating effect on the relationship between industrial synergistic agglomeration and green, low-carbon and recycling development, and this inhibitory effect mainly originates from the pollution shelter effect and the cost crowding-out effect [42].

On the contrary, column (2) of Table 10 shows that the coefficient of the interaction term between the level of regional industrialization and industrial synergistic agglomeration is significantly positive, at least at the 1% level, and when regional industrialization enters into the middle and late stages the scale effect and technological spillover brought about by the agglomeration significantly promote the improvement of the economic level of the green, low-carbon and recycling development. For example, in the “green closed-loop” model of the Suzhou Industrial Park, where secondary industry accounts for 58% (2023) of the total and it is in the late stage of industrialization, the average annual growth rate of the park’s recycling economy output value will be 15% from 2020 to 2023.

4.4.2. Heterogeneity Analysis

In recent years, the north–south economic differences have been increasing, something which is especially reflected in the industrial layout of China, in which the south is mainly engaged in productive services and foreign trade while the north is mainly focused on agriculture and manufacturing: this gap is formed due to the endowments and needs of China at different stages of development. Therefore, it is typical to examine the synergistic agglomeration of manufacturing and productive services on the north–south regional differences in the green, low-carbon and recycling development economy with reference to others. In addition, it is examined from the level of economic development whether the impact of industrial synergistic agglomeration on the green, low-carbon and recycling development economy will show heterogeneous characteristics.

Geographical Position

Based on the regional division of traditional economic zones, the impact of industrial cooperative agglomeration on the high-quality development of the green, low-carbon and recycling economy in the north and the south is examined separately. Columns (1)~(2) of

Table 11. Results of heterogeneity analysis.

	Northern	Southern	High Economic Development	Low Economic Development
	(1)	(2)	(3)	(4)
VARIABLES	${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$	${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$	${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$	${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$	0.379 ***	0.113	−0.099	0.242 **
	(3.54)	(1.53)	(−1.22)	(2.09)
${SCL}_{it}$	1.581 ***	−0.236	0.003	0.535
	(5.32)	(−0.68)	(0.01)	(1.52)
${IC}_{it}$	0.783 ***	0.137	0.437 *	0.525 **
	(3.40)	(0.62)	(1.67)	(2.39)
${\mathrm{U}\mathrm{R}}_{\mathrm{i}\mathrm{t}}$	1.602 ***	4.449 ***	6.262 ***	2.036 **
	(2.85)	(7.99)	(8.59)	(2.29)
${Dig}_{it}$	0.193 ***	0.143 ***	0.222 ***	0.062
	(4.10)	(2.99)	(4.87)	(0.78)
${lnFDI}_{it}$	−0.022	−0.090 ***	−0.181 ***	−0.006
	(−1.23)	(−3.56)	(−3.78)	(−0.34)
Constant	−11.640 ***	−3.749	−6.537 **	−7.108 ***
	(−4.33)	(−1.51)	(−2.18)	(−2.90)
Individual fixed effect	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES
Observations	195	195	192	198
R-squared	0.927	0.938	0.951	0.900
r2_a	0.913	0.925	0.935	0.873
F	64.49	75.96	61.10	33.26

Note: (1) Standard errors are in parentheses; (2) *, **, and *** represent that the effect of each explanatory variable is significant at the 10%,5%, and 1% confidence levels, respectively; (3) southern region: Hubei, Chongqing, Guizhou, Hunan, Fujian, Jiangsu, Zhejiang, Shanghai, Sichuan, Yunnan, Jiangxi, Anhui, Guangxi, Guangdong, Hainan and other 15 provinces and cities; northern region: Beijing, Tianjin, Liaoning, Shaanxi, Inner Mongolia, Qinghai, Shandong, Hebei, Henan, Shanxi, Gansu, Heilongjiang, Xinjiang, Jilin, Ningxia and other 15 provinces and cities.

show that there is an obvious promotion effect of industrial cooperative agglomeration on the high-quality development of the green, low-carbon and recycling economy in the northern region, while the coefficient of influence in the southern region is positive and non-significant. The transformation and upgrading of the manufacturing industry in the northern region is faced with a double driving force: on the one hand, the traditional industries have a strong foundation but the transformation pressure is significant, the synergistic agglomeration of manufacturing industry and productive service industry accelerates the research and development and application of green technology, and the competitive relationship between enterprises effectively stimulates the innovation vitality [43]; on the other hand, the northern region actively participates in regional economic cooperation, for example, through the regional cooperation mechanism of the Beijing–Tianjin–Hebei Cooperative Development, industrial dismantling and restructuring have optimized the efficiency of resource allocation and provided institutional safeguards for green and low-carbon development [4].

Level of Economic Development

Using GDP per capita as an indicator to measure the degree of regional economic development, the average value is divided into two categories: one is high economic development areas, and the other is low economic development areas. Columns (3) and (4) of Table 11 show that there are regional differences in the impact of collaborative industrial agglomeration on the development of a green, low-carbon and recycling economy: there is a significant positive effect in less-developed regions. This difference may stem from the fact that, firstly, less-developed regions have a higher proportion of traditional industries and face greater pressure on resources and the environment, and industrial agglomeration has become an important way to promote green transformation; secondly, local governments tend to introduce more incentives to promote development and provide institutional safeguards for the application of green technologies and sustainable development models [44], thus strengthening the positive effect of industrial synergistic agglomeration.

5. Robustness Check

5.1. Endogeneity Test

In order to verify the correlation between collaborative industrial agglomeration and green, low-carbon and recycling development economy, this study deals with the possible endogeneity problems by constructing an alternative model and introducing the instrumental variable method and then developing an empirical test of the relationship.

5.1.1. Instrumental Variable Approach

In order to overcome the possible bidirectional causality problem between industrial synergistic agglomeration and the green, low-carbon and recycling development economy, this study adopts the instrumental variable method to test the robustness of the benchmark results through 2SLS. Topographic features have natural stability and are not disturbed by external factors such as the green, low-carbon and circular development economy, which meets the requirement of exogeneity; meanwhile, flat terrain is more conducive to the centralized layout of enterprises and urban spatial expansion, which is significantly associated with industrial synergistic agglomeration and meets the basic premise of instrumental variable correlation. Therefore, based on Lin Boqiang’s [45] research experience, this paper selects the degree of terrain undulation as an instrumental variable. Columns (1)–(2) of

Table 12. Robustness test results.

	Endogeneity Test			Excluding Systemic Changes in Macro Factors
	(1)	(2)	(3)	(4)	(5)
VARIABLES	Phase I	Phase II	${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$	${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$	${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$
${\mathrm{T}\mathrm{e}\mathrm{r}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}}_{\mathrm{i}\mathrm{t}}$	−0.005 ***
	(−3.15)
L.${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}$			0.363 ***
			(3.89)
${\mathrm{C}\mathrm{a}\mathrm{o}\mathrm{g}\mathrm{g}\mathrm{l}\mathrm{o}}_{\mathrm{i}\mathrm{t}}$		0.872 **	0.380 **	0.034 ***	0.139 *
		(2.07)	(2.28)	(2.62)	(1.71)
Constant	13.030 ***	−11.930 ***	−5.537 ***	0.060	−7.904 ***
	(6.34)	(−2.96)	(−4.30)	(0.16)	(−3.52)
Controls	YES	YES	YES	YES	YES
Kleibergen-Paap rk LM		10.962 [0.000]
Kleibergen-Paap rk Wald F		9.814 [8.96]
AR(1)			−2.06 [0.040]
AR(2)			−0.49 [0.622]
Hansen			11.54 [0.173]
Individual fixed effect	YES	YES		YES	YES
Time fixed effect	YES	YES		YES	YES
Observations	390	390	390	390	300
R-squared	0.9649	0.8952		0.930	0.918

Note: (1) Standard errors are in parentheses; (2) *, **, and *** represent that the effect of each explanatory variable is significant at the 10%, 5%, and 1% confidence levels, respectively, and p-values are in [ ].

show that the Kleibergen-Paap rk LM test is significant at the 1% level, which excludes the problem of under-identification; the Kleibergen-Paap rk Wald F-value is 9.814, which is more than the critical value of 8.96, indicating that there is no weak instrumental variable problem. The empirical results of the second stage also show that the coefficient of industrial synergistic agglomeration is significantly positive, which further confirms the robust positive relationship between it and the green, low-carbon and circular development economy.

5.1.2. GMM Model

In order to examine the dynamic characteristics of the green, low-carbon and recycling development economy, this paper incorporates the lagged terms of the explanatory variables in the model and adopts the GMM method to deal with the resulting endogeneity problem. The results in column 13(3) of Table 12 show that both Hansen test and AR(2) test are passed, indicating that the instrumental variables are reasonably selected and the model setting is valid. At the same time, this paper also finds that, after removing the first stage of the factors, industrial synergistic agglomeration still has a significant impact on the economic level of green, low-carbon and recycling development.

5.2. Substitution of Explanatory Variables

The entropy weight method is used to re-measure the explanatory variables for robustness testing. As shown in column (4) of Table 12, the coefficient of industrial synergistic agglomeration is 0.034, which reaches the 1% level of significance, indicating that the robustness of the benchmark regression results is proved.

5.3. Changing the Time Range

The sudden arrival of the New Crown virus in 2020 has brought some shocks to the economic and social development of the country. In order to mitigate the possible impact of the New Crown epidemic on the conclusions, this paper deletes the samples in the three periods of 2020, 2021 and 2022. The regression results are shown in column (5) of Table 12, and the regression coefficient of industrial synergistic agglomeration is 0.139, which is significant at 10% statistical level. Therefore, the conclusion of the study still holds.

5.4. Exogenous Shock Test

5.4.1. Policy Background and DID Modeling of “National High-Tech Zones”

The State Council issued the “13th Five-Year Plan for the Development of National High-Tech Industrial Development Zones” in 2016, which for the first time incorporated green development into the construction requirements of national high-tech zones and explicitly put forward the goals of building eco-industrial parks and cultivating green industries. At present, 178 national high-tech zones have become an important platform for innovation demonstration and industrial agglomeration. This study takes the establishment of high-tech zones as a quasi-natural experiment and analyzes its policy effects by using the double-difference method: firstly, it guides the synergistic agglomeration of manufacturing and productive service industries through preferential policies; secondly, the characteristic of the policy’s implementation in batches provides an ideal experimental scenario for the study.

The DID model in Equation (13) is set to test whether the pilot “national high-tech zones” have promoted the green, low-carbon and recycling development of the regional economy: where i denotes the province and t denotes the year; ${\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}}$ denotes whether there is a new national high-tech zone in that year, and if so it is taken as 1, otherwise it is 0; ${\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}$ represents a series of control variables;${\mathrm{\delta }}_{\mathrm{i}} \mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{\eta }}_{\mathrm{t}}$ denote individual fixed effects and year fixed effects respectively.; and ${\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$ denotes the random error term. Equation (13) is as follows:

$${\mathrm{G}\mathrm{L}\mathrm{C}\mathrm{E}}_{\mathrm{i}\mathrm{t}}=\mathrm{\varrho }+{\mathrm{\varrho }}_1{\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}}\times {\mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}}_{\mathrm{t}}+\mathrm{\gamma }{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}_{\mathrm{i}\mathrm{t}}+{\mathrm{\eta }}_{\mathrm{t}}+{\mathrm{\delta }}_{\mathrm{i}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{t}}$$

(13)

5.4.2. Benchmark Regression Results

Prior to the regression, the parallel trend assumption of this model is verified based on the event test, and the results show that this important premise assumption of the DID method is passed. The results of the regression analysis for the whole sample are shown in

Table 13. Examination of the economic effects of “national high-tech zones” on green, low-carbon and recycling development.

	(1)	(3)
Variables	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$	${\mathbf{G}\mathbf{L}\mathbf{C}\mathbf{E}}_{\mathbf{i}\mathbf{t}}$
${\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}}_{\mathrm{i}}\times {\mathrm{T}\mathrm{i}\mathrm{m}\mathrm{e}}_{\mathrm{t}}$	0.083 ***	0.062 ***
	(3.53)	(2.87)
Constant	−0.266 ***	−4.410 ***
	(−12.56)	(−2.84)
Control Variables	NO	YES
Observations	390	390
R-squared	0.584	0.690

Note: (1) Standard errors are in parentheses; (2) *** represents that the effect of each explanatory variable is significant at 1% confidence levels, respectively.

. Column (1) does not include control variables while column (2) includes control variables, and all the regression results show that the pilot program of “national high-tech zones” has a positive and significant impact on the green, low-carbon and recycling economy of the region, with all of them significant at the 1% level.

6. Conclusions and Policy Implications

This paper analyzes the impact mechanism of the synergistic agglomeration of the productive service industry and manufacturing industry on the green, low-carbon and recycling development economy through empirical research using 30 provinces and cities in China, as samples from 2010 to 2022. Secondly, the economic impact of industrial synergistic agglomeration on green, low-carbon and recycling development has spatial spillover effects on neighboring regions, and the diffusion effect is greater than the return effect. Thirdly, there is regional and economic development level heterogeneity in the economic impact of industrial synergistic agglomeration on green, low-carbon and recycling development. As far as regional heterogeneity is concerned, there is an obvious promotion effect of industrial cooperative agglomeration on the high-quality development of the green, low-carbon and recycling economy in the northern region; as far as the level of economic development is concerned, in the regions with a lower level of economic development the industrial cooperative agglomeration has a significant positive driving effect on the high-quality development of the green, low-carbon and recycling economy. Fourthly, environmental regulations negatively inhibit the impact of industrial synergistic agglomeration on the green, low-carbon and recycling economy, while the level of regional industrialization positively moderates the impact of industrial synergistic agglomeration on the green, low-carbon and recycling economy. Based on the above findings, this paper puts forward the following policy recommendations:

Firstly, it is necessary to enhance the spatial spillover effect of collaborative industrial aggregation in order to break through the spatial barriers of factors, to establish cross-regional industrial collaborative innovation centers, to set up shared R&D platforms in the junction zone, to implement the industrial chain digital twin system, and to realize the real-time matching and traceability of cross-regional production capacity, technology and human resources through blockchain technology.

Secondly, a “service-for-capacity” sharing model has been implemented to continuously promote the deep integration and mutual penetration of productive services and the manufacturing industry. Manufacturing enterprises are encouraged to divest non-core processes such as logistics and R&D to be undertaken by specialized service enterprises, and enterprises that have divested their businesses are given three-year income tax exemptions. (For example, the Hangzhou “Chain Factory” platform has led to a 32 percentage improvement in collaboration efficiency, and the Suzhou Industrial Park has established the country’s first “Manufacturing Service Innovation Centre”, which has promoted 300+ manufacturing enterprises to outsource 30 percent of their non-core business.).

Lastly, we should promote synergistic industrial agglomeration in accordance with local conditions. For cities in underdeveloped regions, this is the establishment of digital infrastructure facilities and productive service facilities supporting the realization of logistics, testing equipment, cross-enterprise deployment, and industrial development to create a favorable “hard” environment. For heavy polluters in the north, it is mandatory to install real-time monitoring equipment for pollution sources and to provide access to green financial credit systems, opening up “green credit fast-tracks” for enterprises that meet the standards and at the same time granting a 30 percent financial subsidy for outsourcing costs to heavy polluters that take the initiative to implement outsourcing.

It should be noted in particular that, limited to the availability of data, the research in this paper actually still relies on macro statistics, and it is difficult to obtain subsectors (e.g., high-end equipment manufacturing and scientific and technological services) or accurately matched data from the city level to examine the impact of the benefits of industrial synergistic agglomeration.