Urban ecological resilience refers to the resistance, restoration, and adaptability urban ecosystems demonstrate to shocks and stresses (Dakos and Kéfi, 2022; Wang et al., 2022b). It emphasizes the necessity for urban areas to resist ecological disasters by establishing natural barriers and buffer potentials (Wu et al., 2020b), and their ability to repair ecological deficiencies by leveraging self-purification capability, clean technology, and policy regulation (Li and Wang, 2023), which serves as the foundation and guarantee for promoting high-quality urban development. However, rapid urbanization has swelled a substantial influx of rural populations into urban areas (Yin and Miao, 2024), resulting in severe ecological risks (Wang et al., 2024), including frequent extreme weather, intensified soil erosion, deteriorating environmental quality, and a sharp decline in biodiversity (Bautista-Puig et al., 2022). As major human activity carriers, urban areas are expected to accommodate 70% of the world’s population and consume 75% of global energy by 2030 (Li et al., 2024a), which may cause irreversible damage to urban ecosystems and become a pressing issue for countries worldwide (Feng et al., 2024; Zhao et al., 2024). For example, according to statistics from the Chinese Ministry of Emergency Management, 112 million people in China were affected by natural disasters in 2022, causing direct economic losses of 238.65 billion RMB yuan. Thus, it is necessary to explore how to enhance urban ecological resilience.

Environmental policy synergy is a governance mode that enhances compatibility, coordination, and support among policies through transboundary communication and interregional dialogue to address complicated issues and achieve common goals (Zheng and Xu, 2020; Ferry, 2021). It not only facilitates factor agglomeration and efficient utilization through resource integration and strategic synergy (Kosow et al., 2022; Vedeld et al., 2021) but also accelerates knowledge dissemination and technology diffusion via transboundary cooperation and information sharing (Liu et al., 2022b; Wang and Juo, 2021). This contributes to environmental governance systems and ecological governance efficiency, ultimately strengthening urban ecological resilience (Zhang et al., 2024). On the one hand, factor agglomerations can foster diverse eco-industries, such as environmental protection, clean energy, and eco-tourism, stimulating green transformation, facilitating coordinated development between regional economies and ecological environments (Yan et al., 2023), and enhancing ecosystem adaptability. On the other hand, enterprises can engage in innovative activities and technological upgrading by mutual learning under spatial spillovers and industrial linkages (Jiang et al., 2023), which can transform traditional production models by introducing low-carbon technologies and green products (Wang et al., 2023d). As a result, such transformations increase renewable resource usage and decrease fossil fuel consumption, ultimately reducing ecological damage and maintaining ecosystem stability. Thus, investigating how environmental policy synergy can support greater urban ecological resilience is important.

Existing studies on environmental policy synergy and urban ecological resilience focus on three aspects. Firstly, there is widespread concern regarding environmental policy synergy effectiveness. Most scholars contend that environmental policy synergy is beneficial and could significantly reduce pollution emissions (Li et al., 2023a). Nevertheless, several studies provided differing perspectives. They observed that various policies may duplicate incentives for specific industries or enterprises, and amplify uneven resource allocation and market distortion, making it impossible for them to achieve a synergistic effect (Hu et al., 2023). Secondly, some academics have explored the potential determinants of urban ecological resilience, including natural and social factors. Natural factors mostly focus on urban ecosystem inherent characteristics, such as climate, soil, hydrology, vegetation, and topography (Greaves and Parrott, 2024; Sánchez-Pinillos et al., 2024), whereas social factors mainly include urbanization, land development and technological innovation (Sharifi, 2023; Viñals et al., 2023). Notably, some studies concluded that urban ecological resilience was the result of joint interactions between social activities and natural habitats (Li et al., 2023b). Thirdly, several studies investigated indirectly the effectiveness of environmental policy synergy on urban ecological resilience (Lu et al., 2023). Li et al. (2021) argued that environmental co-governance could reduce enterprise migration, restrain pollution transfer, and improve ecological governance efficiency. By analyzing cross-regional environmental protection policies, Li and Ye (2021) concluded that policy synergy yields highly beneficial haze control outcomes.

Although environmental policy synergy and urban ecological resilience have received significant attention, little research focuses on the linkage between the two. Particularly, environmental policy synergy involves both actor and instrument synergies, and their role in urban ecological resilience needs to be examined in depth. Meanwhile, prior studies have revealed significant differences in urban ecological resilience between regions (Wang et al., 2023c; Shamsipour et al., 2024), and such differences are associated with regional heterogeneity in environmental policy effectiveness (Xu et al., 2024). However, little attention has been paid to the heterogeneous impact of environmental policy synergy on urban ecological resilience. Moreover, although established literature has revealed the channels through which regional policies affect urban ecological resilience, including environmental regulations, industrial upgrading, and green innovation (Jiang and Jiang, 2024; Xu and Song, 2024), it is still unknown how environmental policy synergy works. Thus, this paper examines the effects and mechanisms of environmental policy synergy on urban ecological resilience.

Based on environmental policies enacted in 285 Chinese cities from 2006 to 2022, this paper uses text mining analysis to calculate environmental policy synergy considering policy actor synergy and policy instrument synergy and further examines its impact on urban ecological resilience (Fig. 1). The results indicate that environmental policy synergy significantly enhances urban ecological resilience, and it remains reliable after robustness tests. Particularly, policy actor synergy and policy instrument synergy determine the direction and degree to which environmental policy synergy affects urban ecological resilience, respectively, and their joint enhancement generates a greater promoting effect, suggesting a complementary relationship between the two. Meanwhile, regional heterogeneity demonstrates that environmental policy synergy exhibits a stronger promoting effect in regions with strict environmental regulation intensity and high economic development levels. Mechanism analysis shows that factor agglomeration effects and green innovation effects serve as potential channels through which urban ecological resilience is enhanced by environmental policy synergy.

Distribution of environmental policy synergy in 2022.

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The contributions of this paper are as follows: First, this paper creatively uses text mining analysis to measure environmental policy synergy and examines its impact on urban ecological resilience, yielding valuable insights into how to deepen ecological environment reforms. Second, environmental policy synergy is further distinguished into policy actor synergy and policy instrument synergy to clarify their interaction in influencing urban ecological resilience, which offers a scientific basis for constructing urban ecosystem collaboration mechanisms. Third, to reveal the impact characteristics of environmental policy synergy, this paper investigates its differentiated effects based on environmental regulation intensity and economic development levels. It not only provides novel perspectives for understanding policy effectiveness in environmental pollution reduction but also provides empirical support for enhancing urban ecological resilience tailored to local conditions. Fourth, this paper identifies the mechanism through which environmental policy synergy contributes to urban ecological resilience, which helps strengthen environmental policies’ incentive effects and optimize feasible strategies for urban ecosystem enhancement.

The remainder of this paper is organized as follows. Section 2 is a theoretical analysis and research hypotheses. Section 3 describes the methodology and data. Section 4 presents and analyzes empirical results, and Section 5 summarizes the main conclusions and proposes policy implications.

Impact effects

According to synergetic theory, environmental policies across regions should cooperate and support one another to maximize effectiveness (Ferry, 2021), which plays an important role in enhancing urban ecological resilience. On the one hand, urban ecological resilience crosses multiple fields, such as environmental protection, energy, technology, and finance, which are interconnected and interdependent (Li et al., 2024a). Environmental policy synergy contributes to cross-departmental collaboration, resulting in an overall outcome. Meanwhile, it minimizes resource waste by reducing policy conflicts and maximizes resource utilization by integrating environmental protection efforts. On the other hand, environmental policy synergy contributes to policy flexibility (Vedeld et al., 2021) that can adapt to environmental pressures at various development stages and enhance urban ecosystem self-restoration. For example, flexible disaster management policies and long-term recovery plans can be developed jointly in response to extreme weather events, and environmental policy goals and specific governance measures can be adjusted in coordination based on real-time monitoring data. Thus, the following hypothesis is proposed:

H1a: Environmental policy synergy can enhance urban ecological resilience.

The key to environmental policy synergy lies in both policy actor synergy and policy instrument synergy (Davoudi and Johnson, 2022). On the one hand, policy implementation usually involves multiple actors, including various stakeholders such as governments, enterprises, and citizens in a broad sense, and multi-level departments such as the central, provincial, municipal, county, and township levels in a narrow sense. By sharing information and allocating resources, policy actor synergy can help departments move information and take consistent actions, preventing unmanned and multi-headed management during policy implementation and reducing insufficient or repeated incentives in policy resource allocation due to insufficient information (Kosow et al., 2022). On the other hand, policy goals require the combined efforts of various environmental policy instruments such as government command, market incentives, and public participation. By integrating high-quality factors and providing behavioral guidance, policy instrument synergy can encourage different actors to participate in governance practices and promote a cohesive connection between governance goals in various departments, contributing to improving urban ecological resilience by meeting the complex requirements. Thus, this paper proposes the following hypothesis:

H1b: Both policy actor synergy and policy instrument synergy are indispensable in enhancing urban ecological resilience.

Impact features

Environmental regulation intensity varies greatly among regions due to differences in environmental protection pressure, resource utilization efficiency, policy implementation efforts, and environmental governance systems, which may lead to heterogeneous effects of environmental policy synergy on urban ecological resilience. Firstly, regions with strict environmental regulation intensity commonly experience more complex ecological problems. It is difficult to develop comprehensive solutions through a single policy, but collaborative policies can achieve complementary advantages through multi-departmental collaboration, which will encourage them to form systemic solutions through environmental policy synergy. Secondly, resource constraints are tighter in regions with strict environmental regulation intensity, making resource integration more urgent. Through interregional cooperation, environmental policy synergy can improve resource utilization efficiency, resulting in increased transboundary collaborative tendencies and greater environmental governance effects. Thirdly, regions with strict environmental regulation intensity typically place an increased emphasis on protecting ecological ecosystems and implementing policies more effectively. As a result, environmental policy synergy is more likely to be adopted and produce more significant results. Finally, regions with strict environmental regulation intensity pose sound environmental governance systems, which not only facilitate actor participation in policy implementation but also achieve policy goals through resource integration. Thus, this article proposes the following hypothesis:

H2a: The promoting effect of environmental policy synergy on urban ecological resilience is stronger in regions with strict environmental regulation intensity.

Differentiating economic development levels between regions may also affect environmental policy synergy effectiveness. Firstly, regions with high economic development levels have abundant funds, advanced technology, and skilled manpower (Sun et al., 2023), meeting the investment and standard conditions necessary for environmental policy synergy. Secondly, regions with high economic development levels are more likely to adopt professional policy-making teams to quickly address environmental issues and will also be able to utilize outstanding social governance capabilities to smoothly implement policy synergy. Thirdly, regions with high economic development levels possess well-developed public infrastructure and institutional systems that can provide solid foundations for environmental policy synergy. For example, developed public transportation systems and environmental protection facilities can support more efficient resource utilization and ecological governance, while mature environmental regulation systems and ecological supervision mechanisms can provide legal protection for policy synergy. Additionally, regions with high economic development levels are often designated as pilot areas to implement environmental governance models, and their residents with higher educational attainment support environmental policies more readily. Thus, this paper proposes the following hypothesis:

H2b: The promoting effect of environmental policy synergy on urban ecological resilience is stronger in regions with high economic development levels.

Impact mechanisms

According to agglomeration theory, environmental policy synergy may enhance urban ecological resilience through factor agglomeration effects. Traditional environmental policies may constrain the transboundary movement of environmental resources because of mutual contradictions, whereas collaborative policy combinations can reduce institutional barriers to factor flow by ensuring environmental governance goals are consistent among regions. For example, they can reduce transboundary transfer resistance by establishing unified environmental standards and streamlining cross-departmental approval processes. Meanwhile, environmental policy synergy can not only focus environmental resources on specific environmental goals, but also optimize environmental resources distribution by supporting specific fields, thereby promoting factor aggregation and strengthening urban ecosystem diversity and sustainability. Additionally, environmental policy synergy can increase environmental resource supply and enhance factor aggregation stability when combined with fiscal and monetary policies such as fiscal subsidies, tax incentives, and green finance (Yan et al., 2023). This can form a systematic synergy effect and enhance the resistance, restoration, and adaptability of urban ecosystems.

Resource allocation theory holds that limited resources must be allocated reasonably to achieve optimal utilization. For environmental resources, various regions can collaborate to develop measures to attract capital, technology, and manpower into eco-friendly fields, enhancing urban ecosystem self-restoration. Meanwhile, under environmental policy synergy, various regions can utilize new generation information technologies like big data and cloud computing to achieve optimal allocation of environmental resources based on comparative advantages, thereby alleviating resource shortage pressure and strengthening risk resistance capabilities. Moreover, urban ecological resilience entails multiple complex and diverse fields and aspects. Environmental policy synergy can optimize resource allocation and enhance ecological governance comprehensiveness and systematicity by comprehensively considering multi-objective needs (Kosow et al., 2022). For example, pollution control policies and ecological restoration plans can be combined to reduce environmental resource waste and resource allocation inefficiencies.

Environmental policy synergy may also enhance urban ecological resilience through green innovation. Firstly, environmental policy synergy can be seen as a positive signal that the government is committed to reducing environmental pollution control, which will encourage innovation entities to conduct green innovation activities by reducing uncertainty in the innovation environment. Secondly, environmental policy synergy will attract a large amount of innovative resources through demand orientation to be invested in green technology research and development (Xu and Song, 2024), such as energy-saving and emission reduction laboratories and green technology verification platforms. This can provide a solid foundation for innovation activities and form collaborative innovation ecosystems. Finally, environmental policy synergy can promote the integration and development of cross-disciplinary green technologies through joint actions between departments including the environment, energy, and transportation. Through deep cooperation among institutions such as universities, research institutes, and businesses, green technologies can also be transformed and applied from laboratories to markets more quickly (Wang and Juo, 2021). In addition, joint innovation between departments and entities can also lower innovation costs and increase innovation efficiency (Jiang and Jiang, 2024), enhancing urban ecological resilience by promoting circular economies and improving sustainable development. Thus, the following hypothesis is proposed:

H3: Environmental policy synergy enhances urban ecological resilience through factor aggregation, allocation efficiency, and green innovation effects.

Model setting

Panel data is commonly used in empirical analysis (Chen et al., 2024). Panel data is a data type that contains cross-sectional and time series dimensions; therefore, they can observe both individual variances and dynamic changes (Cai et al., 2022; Xu and Yang, 2020). Considering the variations in resource endowment among cities unaffected by time factors may be overlooked, and there may exist time trends distinct from individual characteristics, we developed the following two-way fixed effect model (Xu and Chen, 2022) to examine the impact of environmental policy synergy on urban ecological resilience (Duan et al., 2024):

where subscripts i and t represent city and year, respectively, UER represents urban ecological resilience, EPS is environmental policy synergy, and Controls is a set of control variables, including economic level, industrial structure, government size, human capital, and economic openness. u_i and v_t are individual and time fixed effects, respectively; ε_it stands for the error term.

Variable selection

Explained variable

The explained variable is urban ecological resilience. To investigate urban ecological resilience, this paper draws on Wang et al. (2023a) and Zhang et al. (2023a) to establish a three-dimensional evaluation index system encompassing resistance, restoration, and adaptability, and further applies the entropy method to measure urban ecological resilience. Table 1 presents specific indicators that construct the urban ecological resilience index.

Explanatory variable

The explanatory variable is environmental policy synergy (EPS), represented by the product of policy actor synergy and policy instrument synergy. Policy actor synergy (PAS) refers primarily to the coordination degree among subjects involved in policy enactment. Following Peng et al. (2008), policy actor synergy is considered to exist if a policy is issued by multiple policymaking subjects. Since the policies in this paper are all at the prefecture-level, policy actor synergy is mainly determined by the number of jointly issued policy departments. The formula for calculating the annual policy actor synergy of each city is: ZT_it = (fracsum _n=1^NPZ_nN), where t represents the year, j denotes a prefecture-level city, N is the number of policies enacted in city j in year t, and n stands for the nth policy enacted in city j in year t. PZn quantifies the actor synergy of the nth policy, which is expressed by the number of issuance subjects. By summing up all policy actor synergies issued in city j in year t and taking their average value, annual policy actor synergy is obtained.

Policy instrument synergy (PIS) refers primarily to the coordination degree of policy content. China’s environmental policies encompass various aspects, such as the pollutant discharge permit system, sewage charges, clean production, and environmental protection technology upgrades. Policy instruments can be categorized into command-and-control, market-incentive, and public-participation types. This paper employs the content analysis method, and policy instrument synergy is considered if multiple instruments are involved in policy content. The formula for calculating policy instrument synergy in each prefecture-level city is: GJ_it = (fracsum _n=1^NPG_nN), where PG_n represents instrument synergy of the nth policy, n denotes the nth policy enacted in city j in year t, and N is the number of policies enacted in city j in year t. By adding all policy instrument synergies issued in city j in year t and taking their average value, the annual policy instrument synergy of city j in year t is obtained.

Control variables

Referring to Jiang and Jiang (2024) and Wang et al. (2023b), the control variables are as follows: Economic level (EL) is controlled based on the environmental Kuznets curve, expressed by the logarithm of per capita GDP. Industrial structure (IS) not only determines energy consumption and pollution emissions but also influences resource allocation and economic output. This study uses the ratio of value added of secondary and tertiary industries to GDP as a proxy variable. Government size (GS) has a dual impact on ecological resilience, demonstrated by the ratio of local budget expenditures to regional GDP. Human capital (HC) is crucial for improving environmental governance efficiency and is measured as the ratio of education funds to public budget expenditures. Economic openness (EO) is represented as the proportion of actual foreign capital utilized in the current year to regional GDP, to control the impact of foreign direct investment on urban ecological resilience.

Data sources

This paper uses panel data from 285 cities in China from 2006 to 2022 for empirical analysis. To ensure the representativeness and effectiveness of policy texts, we search the official websites of the State Council, the Ministry of Ecology and Environment, the Ministry of Natural Resources, and local governments’ official websites at all levels. Meanwhile, we use the “PKULAW� retrieval system to supplement and search keywords “atmosphere�, “air pollution�, “air prevention�, and “air control� in the full texts. Environmental policies less relevant to transregional synergy and air prevention and control are eliminated, and a total of 6531 policy texts including laws, rules and regulations, notices, opinions, and standards are finally selected; meanwhile, text mining analysis is conducted for air pollution prevention and control policies. The remaining data are from the China Research Data Service Platform and China Urban Statistical Yearbook. Some variables are logarithmically processed in this paper to avoid heteroscedasticity. Table 2 shows descriptive statistics of the main variables.

Baseline results

Table 3 reports the baseline results. Column (1) shows that the coefficient of policy actor synergy is 0.2254 and is significant at the 1% level, indicating that urban ecological resilience increases by 0.2254% for every 1% rise in policy actor synergy. This is consistent with our expectations that policy actor synergy can enhance urban ecological resilience. Firstly, policy actor synergy promotes rights diversity among governments (Margerum and Robinson, 2015), which can not only enhance central government environmental management by vertical governance mechanisms but also facilitate interactions between local governments by horizontal competition mechanisms (Huang and Zhou, 2023). Secondly, all subsystems are interconnected, coordinated, and collaborative, which helps to achieve unity between differences and consistency, thereby strengthening cooperation and establishing linkages between various entities, and promoting social resource optimization and integration. Lastly, policy actor synergy improves governance efficiency (Park et al., 2019). Local governments can express their interests equally, mobilize enthusiasm, and creativity effectively, and guide social entities in the right direction, thus achieving environmental governance goals and enhancing urban ecological resilience (Zhang et al., 2023c).

Column (2) of Table 3 presents the impact of policy instrument synergy on urban ecological resilience. The results suggest that the coefficient of policy instrument synergy is 0.1105 but is insignificant, which is contrary to the expectations. This may be attributed to vicious competition among local governments for environmental governance instruments (Bodin, 2017). On the one hand, local governments with limited resources strive to secure high-quality resources by leveraging local advantages to create favorable conditions. On the other hand, when environmental performance is integrated into official performance appraisal systems, local governments minimize environmental instrument outflow to improve governance effectiveness, which results in regional barriers, and ultimately hampers environmental governance efficiency and urban ecological resilience.

Column (3) of Table 3 shows that the coefficient of environmental policy synergy is 0.3098 and significant at the 1% level, indicating that a 0.3098% increase in urban ecological resilience increases for every 1% rise in environmental policy synergy. Policy actor synergy and policy instrument synergy can synergize to enhance environmental policies’ integration and comprehensiveness. Environmental policy synergy broadens and deepens the network connection of government policies, improves environmental policy formulation and implementation effectiveness by involving multiple subjects, strengthening inter-departmental coordination (Newig and Fritsch, 2009; Wu et al., 2020a). Meanwhile, effective inter-governmental relations can reduce policy fluctuations (Lapologang and Zhao, 2023) and foster a favorable environment, increasing environmental governance efficiency and urban ecological resilience. Furthermore, environmental policy synergy facilitates communication frequency and cooperation degree among local governments while improving independent innovation capacity by diversifying policy instruments and optimizing resource allocation, thereby enhancing urban ecological resilience. In summary, H1a is validated.

Grouping results

To test whether policy actor synergy and policy instrument synergy interact, this paper divides them into high policy actor synergy, low policy actor synergy, high policy instrument synergy, and low policy instrument synergy based on their median values, and records them as A₁, A₀, I₁, and I₀, respectively. Table 4 demonstrates the grouping results. When both policy actor synergy and policy instrument synergy are high, the coefficient is 0.0169 and passes the 10% significance level. In cases of high policy actor synergy and low policy instrument synergy, the coefficient remains significant but decreases in value; in opposite cases, the coefficient changes from positive to negative and is not significant. When both policy actor synergy and policy instrument synergy are low, the coefficient is −0.0213 and passes the 1% significance level. Undoubtedly, the coefficient is significantly positive only when there is high policy actor synergy, implying that policy actor synergy determines whether environmental policy synergy has a positive effect on urban ecological resilience. Furthermore, compared to low policy instrument synergy, high policy instrument synergy exhibits a larger coefficient, suggesting that policy instrument synergy determines the degree to which environmental policy synergy affects urban ecological resilience. In conclusion, policy actor synergy and policy instrument synergy are complementary, and their combined enhancement exerts more promoting effect, thereby verifying H1b.

Under China’s current system, local governments are responsible for environmental pollution control, and their competition fosters effective market mechanisms and leverages market functions (Zheng, 2023). Meanwhile, according to stakeholder theory, air pollution can be effectively reduced and urban ecological resilience can be significantly enhanced only by establishing multi-stakeholder joint pollution control forces (Liu et al., 2022a). Additionally, low policy actor synergy and low policy instrument synergy may cause policy conflict and disordered implementation, which exacerbate pollution transfer, hamper green innovation, and ultimately impede urban ecological resilience.

Robustness test

Changing explained variables

Given the lag characteristics of environmental policy formulation and the temporal evolution of policy effectiveness, we conduct robustness tests by adjusting the explained variables to their t + 1 and t + 2 periods. As shown in Columns (1)-(2) of Table 5, the coefficients of environmental policy synergy are significantly 0.2754 and 0.2819, respectively, which maintain baseline regression and indicate robust results.

Eliminating potential interference

The 19th National Congress of the Communist Party of China, held in October 2017, elevated ecological civilization construction to a new height and introduced comprehensive environmental governance measures. To eliminate interference from additional environmental policies, this paper excluded data after 2018 from empirical analysis. Column (3) of Table 5 demonstrates that the environmental policy synergy coefficient is 0.2667 and significant at the 1% level. Obviously, this is basically consistent with Table 3, verifying the baseline results.

Adjusting research samples

Since direct-administered municipalities have comparative advantages in economy and technology compared to prefecture-level cities, they are likely to be more inclined towards collaborative governance, resulting in greater governance effectiveness. Consequently, this paper conducts regression analysis again after excluding four direct-administered municipalities from the sample, with results presented in Column (4) of Table 5. The coefficient of environmental policy synergy is 0.3154 and significant at the 1% level, which does not differ significantly from Table 3, indicating the baseline results are reliable.

Heterogeneity analysis

Environmental regulation heterogeneity

To investigate the heterogeneity of environmental policy synergy affecting urban ecological resilience, this paper refers to Ge et al. (2023) utilized built-up area green coverage as environmental regulation intensity and divided the samples into two groups based on the median. As presented in Columns (1)-(2) of Table 6, environmental policy synergy coefficients are significantly 0.3722 and 0.3482, respectively, demonstrating that the promoting effect is more pronounced in regions with strict environmental regulation intensity than in those with loose environmental regulation intensity. On the one hand, regions with loose environmental regulation intensity encounter fewer ecological and environmental issues, which may lead to a reduction in their willingness to synergize environmental policies. On the other hand, environmental policy synergy necessitates a variety of supporting measures, such as policy coordination mechanisms, joint action plans, and information sharing platforms, to strengthen resource integration and promote policy implementation (Li and Lu, 2022). However, there are insufficient environmental information feedback and safeguard mechanisms and inadequate enforcement and accountability mechanisms in regions with loose environmental regulation intensity (Jiang et al., 2024), making it impossible to establish linkage effects. As a result, environmental policy synergy on urban ecological resilience is weaker than it could be. Obviously, H2a is verified.

Economic development heterogeneity

To further investigate the heterogeneity of environmental policy synergy affecting urban ecological resilience, this paper draws on Guo and Wang (2024) to use per capita GDP as economic development levels and divides the cities into two categories based on their median. As displayed in Columns (3)-(4) of Table 6, the coefficients of environmental policy synergy are significantly 0.5686 and 0.3176, respectively, suggesting that the promoting effect is considerably stronger in regions with high economic development levels than in those with low economic development levels. According to the environmental Kuznets curve theory, regions tend to shift from extensive growth to intensive development mode as economic development levels improve (Wang et al., 2022a), which can reduce damage to ecological resilience and achieve coordinated development between economic growth and ecological resilience (Yi et al., 2021). Meanwhile, regions with high economic development levels benefit from stable fiscal revenues, allowing them to design fiscal policy with increased flexibility. This means that they can not only restrain ecological destructive behaviors through regulatory approaches but also enhance enforcement efficiency and synergistic effects through fiscal instruments. Furthermore, they also have professional decision-making teams and excellent social governance capabilities, which can diagnose environmental issues accurately and implement collaborative policies effectively. In summary, H2b is validated.

Mechanism analysis

To explore the impact channels by which environmental policy synergy affects urban ecological resilience, this paper selects year-end total population as factor agglomeration effects (Wei et al., 2024; Zhu et al., 2024), energy consumption per ten thousand industrial production units as allocation efficiency effects (Xin et al., 2023; Wang and Hao, 2024), and green invention patent filings as green innovation effects (Li et al., 2024b). Table 7 presents mechanism analysis results. Columns (1)-(2) show the results of factor agglomeration effects (FA) as a mediating variable. It can be found that the environmental policy synergy coefficient in Column (1) is significantly 1.5450 and the factor agglomeration coefficient in Column (2) is significantly 0.0486. The results indicate that environmental policy synergy promotes factor agglomeration which contributes to urban ecological resilience, implying that factor agglomeration effects serve as a critical channel for environmental policy synergy to enhance urban ecological resilience. Columns (3)-(4) provide results related to allocation efficiency effects (AE). It can be noted that the coefficient of environmental policy synergy in Column (3) is not statistically significant, indicating environmental policy synergy cannot promote allocation efficiency. Thus, this paper adopts the Bootstrap method for further analysis, and the results show that the null hypothesis is accepted, indicating that environmental policy synergy cannot enhance urban ecological resilience through allocation efficiency effects. In addition to environmental governance, local governments have a variety of responsibilities, such as economic development, industrial transformation, and urban construction. This makes them compete for high-quality resources based on their own interests, leading to improper resource allocation and inefficient resource utilization (Ge et al., 2020). Columns (5)-(6) illustrate green innovation effects (GI) as a mediating variable. The results suggest that environmental policy synergy coefficient in Column (5) is statistically 0.1528 and green innovation coefficient in Column (6) is significantly 0.3991, implying environmental policy synergy stimulates green innovation that contributes to urban ecological resilience. Thus, green innovation effects serve as a crucial channel for environmental policy synergy to enhance urban ecological resilience. In conclusion, this paper confirms H3.

Notably, the mediating effects of factor agglomeration and green innovation account for 36.03% and 13.69% of the total effect, respectively, indicating that factor agglomeration effects play a more important role, although environmental policy synergy can enhance urban ecological resilience through both. Environmental policy synergy can break down administrative barriers and promote resource integration and factor agglomeration (Lin and Xie, 2023). Through resource integration and factor agglomeration, environmental policy implementation efficiency can be significantly improved, thus maximizing policy effectiveness and improving urban ecosystems (Zhang et al., 2023b). Meanwhile, by promoting differentiated factor agglomeration based on industrial characteristics, resource endowments, and development requirements, diverse regions can fully leverage factor synergetic effects and accelerate production factors convergence towards advanced productivity, ultimately enhancing ecosystem function.

Conclusions

Urban areas are becoming increasingly susceptible to catastrophic events that may exceed their defense capabilities as external threats increase, which makes enhancing urban resilience a global consensus. Based on 6531 environmental policies in China from 2006 to 2022, this paper uses text mining analysis to investigate environmental policy synergy and examine its impact on urban ecological resilience. The results indicate that environmental policy synergy significantly enhances urban ecological resilience, and such findings remain reliable after a series of robustness tests. The grouping results show that policy actor synergy and policy instrument synergy determine the direction and degree of environmental policy synergy affecting urban ecological resilience, respectively. Furthermore, the promoting effect demonstrates regional heterogeneity, which is stronger in regions with strict environmental regulation intensity and high economic development levels. Mechanism analysis reveals that environmental policy synergy enhances urban ecological resilience through factor agglomeration effects and green innovation effects rather than allocation efficiency effects.

Recommendations

In light of the aforementioned conclusions, we recommend the following policy implications: Firstly, the government should promote environmental policy synergy to enhance urban ecological resilience. Due to deep-rooted fragmentation and weak leadership institutions to coordinate various parties’ interests, environmental policy synergy in China is slow and inefficient. For example, Shanghai had the highest environmental policy synergy degree 1.2930 and the second highest urban ecological resilience 1.7762 in 2022, but only 17.65% and 22.92% higher than the annual average, respectively. Therefore, policymakers must develop transregional governance holistic plans and expand policy synergy implementation scope. For instance, they can establish joint operation command headquarters, comprehensive coordination centers, and transboundary deliberative coordination mechanisms. Moreover, they should focus on synchronizing policy actor and instrument synergy when implementing environmental policy synergy. For instance, Shanghai and Anhui cooperate on pollution emission reduction, but their governance policies are devoted to PM_2.5 and PM₁₀ respectively and adopt differentiated policy instruments, resulting in unsatisfactory governance effects. Thus, they should formulate environmental policies from an actor synergy perspective to reduce potential policy conflicts and improve policy implementation accuracy. They can also utilize digital technology to maintain policy instrument consistency and maximize instrument synergy effectiveness through tailored environmental policies, such as establishing institutionalized information platforms and big data coordination centers.

Secondly, local governments should implement regional classification management to optimize urban development modes. Considering regional heterogeneity in various factors affecting urban ecosystems, it is essential to implement tailored measures for urban ecological resilience protection. Economically developed areas can alleviate population pressure and land tension, invest more funds in environmental governance and ecological protection, and promote technology diffusion and talent movement by adopting industrial transformation and upgrading strategies. For example, urban clusters such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta should strengthen policy actor synergy by developing transregional ecosystem management organizations, collaborative ecological legislation systems, and coordinated governance pilot programs. They can also strengthen policy instrument synergy by creating transboundary ecological environment compensation mechanisms, collaborative disposal working groups, and green technology promotion catalogues. Economically underdeveloped areas possess lower capacities to address environmental ecological risks. Thus, they should enhance local ecological resilience and decrease regional inequality through diverse tactics, such as developing ecological industries, establishing ecological protection and restoration entities, and conducting market-oriented trade in ecological protection and restoration products, in order to reduce local risks and avoid chain effects.

Thirdly, the government can utilize factor agglomeration effects to improve green innovation capabilities. On the one hand, the government needs to conduct factor agglomeration and rational allocation based on ecological carrying capacity and industrial structure. For example, resource-abundant areas can attract a substantial influx of skilled labor and social capital to develop high-yield resource-intensive industries, while labor-rich areas can establish production lines to minimize labor costs. Meanwhile, the government should adopt development models with low energy consumption, low pollution emissions, and high economic output to enhance resource utilization, such as improving production processes, introducing advanced equipment, and elevating personnel quality. On the other hand, the government must consolidate institutional guarantees for green innovation, such as boosting intellectual property protection systems, enhancing innovative achievement transformation mechanisms, and establishing green technology evaluation systems. Furthermore, the government should strengthen financial support for green innovation, improve green industry systems, and promote clean energy usage through green finance, such as carbon credit trading, green stocks, and peer-to-peer energy trading.

Limitations and further research

Although this paper investigates the impact of environmental policy synergy on urban ecological resilience, there are still some limitations. Firstly, the empirical analysis in this paper is based on city-level data, and future research can extend to county-level and enterprise-level data to increase research reliability and applicability. Secondly, this paper reveals regional heterogeneity according to environmental regulation intensity and economic development levels, and future research can identify heterogeneity from a spatiotemporal perspective. Lastly, while this paper reveals the channels through which environmental policy synergy affects urban ecological resilience, further investigation should consider additional channels for achieving such a promoting effect, including whether it reduces environmental pollution costs and increases environmental governance benefits.