Environmental and Economic Sustainability of Urban Agglomeration Under Resource-Conserving and Environmentally Friendly Policy: Evidence from China

Abstract

Environmental policy helps policymakers and researchers understand the process and expected effects of policy before the policies are fully implemented. This study aims to estimate the effects of resource-conserving and environmentally friendly policy implemented in the Wuhan metropolitan area and Changsha–Zhuzhou–Xiangtan urban agglomeration. The synthetic control method is employed as an estimation method. The results show that policy has positive impacts on economic development and SO₂ emission reduction in the pilot regions but cannot improve wastewater treatment. Compared to large cities, medium-sized and small cities are more sensitive to policies since the large cities have transferred a large number of enterprises with high energy consumption and high emissions to the surrounding medium-sized and small cities. The study also finds that the Wuhan metropolitan area reduces pollution emissions through increasing environmental investment and the efficiency of resource allocation. In the Changsha–Zhuzhou–Xiangtan urban agglomeration, policy triggers green technology innovation to improve the environment and boost the economy.

1. Introduction

Since the dawn of the new century, Chinese cities have experienced significant economic growth. However, this development has concurrently led to severe environmental degradation, necessitating urgent attention from the Chinese government. To address this issue, the Chinese government has formulated a series of environmental policies to improve the environmental quality of cities. Hence, the increasing stringency of environmental regulations compels governments to address the interplay between environmental protection and economic development with greater seriousness, aiming to achieve sustainable growth. Environmental regulations, while essential, can elevate the costs associated with environmental protection, thereby diminishing productive inputs and stifling innovation [1]. Consequently, these regulations may hinder the transformation and upgrading of industries, ultimately having a detrimental effect on economic development [2]. In regions characterized by low economic or industrial activity, governments may find it necessary to relax environmental protection standards to facilitate economic growth. This decision often stems from the observation that the impact of environmental regulations on environmental protection can be minimal or even negligible in such areas [3,4].

However, Porter and van der Linde [5] find that stringent and flexible environmental regulations can trigger innovation, leading to innovation compensation effects that ultimately enhance competitiveness and economic performance. Some research also indicates that there is a positive relationship between environmental regulation and economic development [6,7]. Environmental regulations stimulate green technology innovation, optimize the energy consumption structure, improve resource allocation, and enhance environmental protection awareness, thereby improving economic performance and ecological efficiency [8,9,10]. There is still no concordant conclusion as to whether environmental policies can achieve the win–win goals of economic development and environmental protection.

The government usually selects several pilot cities to observe the policy implementation effect to reduce the uncertainty and identify the implementation cost of such policy. In 2007, the Chinese government chose the Wuhan metropolitan area (WMA) and Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration as pilot cities for resource-conserving and environmentally friendly (RCEF) society policies to explore the experience and mechanism of urban green transformation and sustainable development. After many years of policy implementation, policy effects (environmental and economic effects) and operational mechanisms need to be tested and revealed. This study collects city-level data and employs the synthetic control method (SCM) to estimate the effects of this policy on economic development and environmental governance. First, the results show that this policy improves economic development and decreases SO₂ emissions sharply and observably in pilot city clusters after policy implementation. However, this policy has no significant impact on wastewater treatment. Second, there are significant differences in policy effects on cities of different sizes. Small and medium-sized cities are more likely than large cities to be influenced by policy. Small and medium-sized cities have received many industrial transfers from large cities and have great emission reduction potential. Third, the effectiveness of the influencing mechanism is determined by the industrial structure. In the WMA, green innovation is not an effective mechanism because electronic information and automobile manufacturing are core industries. In contrast, industries in CZX focus mainly on new materials, new energy, and environmental protection technologies. Therefore, green innovation is the policy-driven mechanism of the Changsha-Zhuzhou-Xiangtan urban agglomeration. Furthermore, under this policy, urban construction and resource allocation also play a crucial role in driving the economic development and environmental protection of the pilot area’s urban agglomeration.

In 2007, the National Development and Reform Commission designated the Wuhan metropolitan area and the Changsha–Zhuzhou–Xiangtan urban agglomeration as pilot zones for the reform of resource-conserving and environmentally friendly (RCEF) policy. These regions are dedicated to maximizing economic and social benefits while minimizing resource consumption and environmental pollution. This initiative aims to ensure the sustainable development of both the economy and society. RCEF policy is a comprehensive environmental policy that incorporates both mandatory elements and market-oriented aspects. In the part related to mandatory environmental standards, this policy stipulates the proportion and trend of pollutant emission reduction within a certain period in the region, including sulfur dioxide, industrial wastewater, etc. It requires that during the period of the policy’s pilot implementation, the pollutant emissions of cities must meet the prescribed standards. In the part of the policy text related to marketization, the policy encourages the implementation of pollutant emission trading in the pilot areas and the establishment of a unified resource trading market. At the same time, the policy further encourages the pilot areas to carry out financial services, stimulating green innovation and industrial transformation of enterprises within the region.

The Wuhan metropolitan area (WMA), an ‘8+1’ networked urban circle centered on Wuhan, encompasses six prefecture-level cities. This region is the core area for manufacturing and technology in Hubei Province. Automobiles, steel and information technology are the main driving forces for the economic growth in this region. The WMA prioritizes several key aspects in its reform efforts: establishing effective mechanisms to constrain energy consumption and emissions, enhancing market-driven approaches to promote resource conservation, and expediting the reform of urban public utility services. After the construction of a resource-conserving and environment-friendly society, the Wuhan metropolitan area will be developed into a livable ecological urban agglomeration, an important advanced manufacturing base and a modern service industry center. The Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration, centered around the cities of Changsha, Zhuzhou, and Xiangtan, prioritizes the development of new materials, new energy, food and tobacco production, and construction machinery. The key components of the reform in the Changsha-Zhuzhou-Xiangtan urban agglomeration encompass price reform for resource products, enhancement of the regional innovation system and mechanisms, and active advancement of reforms in urban public utilities. Through the construction of this policy pilot, the CZX aims to become a green innovation hub, an advanced materials base, a center for green energy consumption, and a commanding height for industrial transformation.

This study contributes to the literature and practice as follows. First, Porter’s hypothesis is confirmed at the level of urban agglomeration. Most studies have tested the effects of Porter’s hypothesis at the firm or industry level, and there is a lack of relevant studies from both urban agglomerations and cities within urban agglomerations. At present, the development of Chinese cities has formed a trend of coordinated development on the basis of urban agglomerations. Studying the overall effect of policies on urban agglomerations is more in line with China’s development reality. Second, differences in policy effects across cities of different sizes are accurately identified. In existing studies on urban agglomerations, researchers usually focus on the overall effect of policies on urban agglomerations and ignore the comparison of cities within urban agglomerations. This situation makes the actual implementation effect deviate. The real assessment of the effect of the policy produces significant bias. Third, this study explores the discrepancy of influencing mechanisms under different industrial structures. This study will help the central government design diversified environmental policies and provide environmental governance and economic development experience that can be used in other cities in China.

The remainder of the paper is organized as follows. The literature review is presented in Section 2. The research design, which includes the sample, data, variables, and model, is discussed in Section 3. Section 4 includes the empirical results, robustness tests, heterogeneity analysis, and mechanism analysis. The conclusions, policy implications, and limitations are presented in Section 5.

2. Literature Review

2.1. Environmental Regulation and Pollution Emissions

Environmental regulation primarily focuses on controlling the total level and intensity of pollutant emissions. In the early stages, environmental regulation was mandatory and controlled pollutant emissions through administrative orders [11]. Environmental regulations play a large role in reducing manufacturing emissions, whereas productivity gains and trade costs play relatively small roles [12]. Since 2000, market-based environmental regulations have become the primary tool for controlling pollutant emissions. Market-based environmental regulations are more flexible, enabling enterprises to better control environmental costs and operating costs [13]. Market-based environmental regulation typically includes emissions trading, environmental fees, and environmental taxes [14,15]. For carbon emission trading, the pressure to reach peak carbon levels strengthens the inhibitory effect of environmental regulations on emissions [16]. As environmental regulations have improved, their impact on pollution reduction through effective governance has progressively increased. In comparison to less developed regions, areas experiencing significant economic growth have observed a more pronounced positive effect from enhanced environmental regulations in reducing pollutant levels [17]. In addition, Huang and Tian [18], Wang and Zhang [19], and Zhang, et al. [20] empirically report that the impact of environmental regulation on pollution emissions presents an inverted U-shaped curve. However, a “green paradox”, in which local officials often choose short-term economic growth over environmental protection, occurs due to political pressure for fiscal decentralization and fierce economic competition [21]. This emphasis on current economic needs diverts financial resources away from environmental investment, weakens the regulatory capacity of environmental regulations, and makes efforts to curb pollutant emissions ineffective [22]. In the last ten years, voluntary environmental regulation, which is seen as a kind of informal environmental regulation, has gradually been welcomed by governments of various countries [23]. Chen and Duan [24] suggest that informal environmental regulations positively influence air pollution reduction by reinforcing environmental regulation enforcement and increasing environmental accountability pressure.

2.2. Environmental Regulation and Economic Development

Environmental regulation constrains enterprises’ environmental negative externalities by internalizing environmental costs into their operating process. Environmental regulations, by increasing uncertainty and risk, negatively impact the efficiency of resource allocation [25]. However, strict but flexible environmental regulations stimulate technological innovation, yielding compensatory benefits that surpass the costs associated with compliance. These dynamics underscore the potential for regulatory frameworks to foster advancements that ultimately enhance economic and ecological outcomes [5]. Aydin, Degirmenci, Bozatli, Radulescu, and Balsalobre-Lorente [7] report that command-and-control environmental regulation contributes directly to industrial production and economic development. Command-and-control environmental regulations stipulate emission standards for pollutants, which must be strictly and unconditionally adhered to; violations result in severe administrative penalties [7]. The synergistic impact of three types of environmental regulations—command-and-control, market-based, and public participation—is significantly greater than that of any single regulation [26]. Command-and-control regulations establish strict guidelines and penalties to ensure compliance. Market-based regulations utilize economic incentives, such as trading permits, to achieve environmental goals cost-effectively. Public participation regulations engage citizens and stakeholders in decision-making processes, fostering transparency and accountability. Together, these approaches create a comprehensive framework that enhances environmental protection more effectively than any individual strategy [27]. Furthermore, certain studies have indicated a nonlinear correlation between regulatory intensity and economic development. Wang, et al. [28] indicate that the intensity of environmental regulation and environmental productivity has an inverted U-shaped relationship. Currently, environmental regulation has a positive relationship with sustainable development. As the intensity of environmental regulation continues to increase, its counterproductive effects on the environment also gradually become apparent. Chen, et al. [29] also find a nonlinear, U-shaped correlation between environmental regulations and sustainable development.

2.3. Influencing Mechanisms

Many existing studies have investigated how technological innovation plays a mediating role in the impact of environmental regulations on economic development and environmental protection [30]. Ren, et al. [31] find that investments in environmental technologies enable environmental regulations to achieve dual objectives: reducing pollutant emission intensities and enhancing production efficiency. However, strict environmental regulations may result in the compliance costs outweighing the benefits of innovation. Certain sectors may lack sufficient incentives to invest in green technological innovations, thereby impeding industrial progress. As environmental regulations become increasingly stringent, the financial penalties for violations escalate, thereby incentivizing various sectors to engage in environmentally sustainable production practices and invest in green technologies [32]. This transition not only ensures compliance but also stimulates industrial growth by promoting innovation and enhancing efficiency in environmentally friendly processes. The urban form and green space structure are correlated with the surface temperature of the city, which highlights the significance of integrating green infrastructure in spatial planning for sustainable development [33]. Reasonable urban construction and land utilization are conducive to promoting the sustainable development of cities [34,35]. When faced with stringent environmental regulation, local governments are inclined to reduce the land supply for polluting industries [36], enhancing urban land green use efficiency [37,38]. Environmental regulation contributes significantly to improving the efficiency of resource allocation, achieving consistency between the marginal cost and marginal price of factors [39]. Li, et al. [40] report that environmental regulation decreases the degree of resource misallocation and exerts an optimizing effect on resource allocation through the factor flow effect and survival of the fittest effect.

3. Research Design

3.1. Sample and Data

Nineteen urban agglomerations are selected as research samples—the WMA and CZX comprise the treatment group, and the other 17 urban agglomerations are assigned to the control group. Basic city information, such as economic performance, SO₂ emissions, and wastewater, is obtained from the China City Statistical Yearbook. Innovative information and data are from the National Intellectural Property Administration.

3.2. Variables and Model

Dependent Variables

In this study, per capita GDP (rgdp) is selected as a proxy of economic development. rgdp is an important indicator that measures economic development. Industrial sulfur dioxide emissions (SO₂) and industrial wastewater discharge are selected as proxies for environmental governance. The per capita GDP (rgdp), industrial sulfur dioxide emissions (SO₂) and industrial wastewater discharge up to the standard rate (wastewater) are selected as the dependent variables, whereas SO₂ and wastewater are two indicators that measure environmental pollution. The predictors of the dependent variables are total population (population), industrial scale (indus), industrial structure (stru), traffic conditions (trans), educational level (edu), and gross investment in fixed assets (gifa).

Table 1. Measurement of the variables.

Variable	Abbreviation	Measurement Methods
per capita GDP	rgdp	per capita gross domestic product (yuan/year)
industrial sulfur dioxide emissions	SO2	industrial sulfur dioxide emissions (ton/year)
industrial wastewater discharge up to the standard rate	wastewater	standardized discharge rate of industrial wastewater annually (%)
industrial scale	indus	number of industrial enterprises annually
traffic conditions	trans	volume of railway freight (ten thousand tons/year)
educational level	edu	number of colleges and universities annually
industrial structure	stru	proportion of secondary industry in GDP annually (%)
gross investment in fixed assets	gifa	gross investment in fixed assets (million yuan/year)
total population	population	total population at year end (ten thousand)
urban construction	uc	greenery coverage area of built-up area (hectare/year)
resource allocation	ra	proportion of resources allocated by the market annually (%)
green innovation	grn_inno	total number of green patent applications annually

and

Table 2. Statistical description of the variables.

Variable	Min	Max	Mean	Std. Dev.
rgdp	24,457	1,423,066	257,761.9	253,076.3
SO2	159,176	2,414,046	681,880.9	516,354.1
wastewater	61.35	99.62	91.73	7.17
indus	712	142,055	15,846.52	26,175
trans	597	39,179	9.0391	0.8989
edu	6	335	90.7456	73.3211
stru	35	59	49.89	5.53
gifa	0	4,506,011	643,424	773,456.8
population	440	12,993	4349.64	3636.89
uc	4460	198,717	46,750.13	44,781.18
ra	46.29094	93.69186	87.57	5.14
grn_inno	21	13,454	1127.763	1944.537

summarize the measures and statistical descriptions of these variables, respectively. The relationship among all the variables is shown in Figure 1.

In this study, only two urban agglomerations are affected by the policy, and it is difficult to find a suitable counterfactual control group from other regions where the policy has not been piloted. However, the SCM can build a “counterfactual” control group similar to the pilot area by determining the optimal weight of the control group on the basis of data characteristics. Therefore, this study uses the SCM proposed by Abadie, et al. [41] to evaluate the effects of RCEF society policies on the economy and environment.

4. Empirical Results

4.1. Synthetic Effect and Weight Portfolio

This study constructs a synthetic WMA and a synthetic CZX as the convex combination of city clusters in the donor pool that closely resemble the WMA and CZX in terms of pre-RCEF society policy values of dependent variable predictors, respectively. The results of the rgdp predictors are displayed in

Table 3. Per capita GDP (RGDP) of WMA predictor means.

Variable	Wuhan Metropolitan Area		Average of 17 Control City Clusters
	Treated	Synthetic
population	2686.0000	2228.1155	4623.742
indus	3536.5000	3969.9555	17,160.45
trans	9.4466	8.3052	9.10009
edu	65.0000	42.5095	93.30392
stru	42.5000	47.3810	50.16975
gifa	177,838.8000	164,872.9000	683,684.7
rgdp (2005)	73,504.0000	73,612.7920	183,057.3
rgdp (2006)	84,527.0000	84,672.3550	213,447.3

, which compares the pretreatment characteristics of the actual WMA with those of the synthetic WMA, as well as with an average of 17 control city clusters in the donor pool. We see that the average number of city clusters that did not implement RCEF society policy from 2005 to 2007 does not seem to provide a suitable control group for the WMA. Moreover, prior to the implementation of RCEF society policy, rgdp was substantially greater on average in the 17 control city clusters than in the WMA. In contrast, the synthetic WMA accurately reproduces the values that the rgdp and rgdp predictor variables had in the WMA prior to the RCEF society policy. The results for SO₂ and wastewater for the WMA and the dependent variables of the CZX predictors are displayed in Appendix A.

Table 4. Weight portfolio of city clusters in the synthetic WMA and CZX.

City Cluster	WMA			CZX
	RGDP	SO2	Wastewater	RGDP	SO2	Wastewater
Harbin–Changchun Urban Agglomeration	0.023	0.019	0.011	0.0130	0.001	0.045
Liaozhongnan Urban Agglomeration	0.019	0.011	0.011	0.0110	0.001	0.035
Beijing–Tianjin–Hebei Urban Agglomeration	0.019	0.010	0.002	0.0110	0	0.027
Shandong Peninsula Urban Agglomeration	0.016	0.011	0.015	0.0090	0.001	0.025
Yangtze River Delta Urban Agglomerations	0.004	0.003	0.001	0.0020	0	0.024
Western Taiwan Straits Urban Agglomeration	0.017	0.020	0.003	0.0100	0.001	0.039
Pearl River Delta urban agglomeration	0.019	0.010	0.003	0.0090	0.001	0.039
Beibu Gulf Urban Agglomeration	0.020	0.018	0.003	0.0140	0.002	0.024
Central Yunnan Urban Agglomeration	0.286	0.683	0.002	0.1060	0.756	0.011
Central Guizhou Urban Agglomeration	0.308	0.010	0.014	0.6220	0.209	0.137
Central Plains Urban Agglomeration	0.024	0.009	0.914	0.0130	0.001	0.436
Central Urban Agglomeration of Shanxi	0.036	0.013	0.002	0.0250	0.001	0.028
Guanzhong Plain Urban Agglomeration	0.032	0.098	0.002	0.0200	0.001	0.013
Chengdu–Chongqing Urban Agglomeration	0.022	0.005	0.003	0.0130	0	0.041
Baotou–Hohhot–Erdos–Yulin Urban Agglomeration	0.019	0.025	0.01	0.0130	0.001	0.032
Ningxia–Yellow River Urban Agglomeration	0.055	0.030	0.001	0.0410	0.021	0.01
Lanzhou–Xining Urban Agglomeration	0.08	0.025	0.003	0.0680	0.003	0.033

displays the weights of each control city cluster in the synthetic WMA and the synthetic CZX for different dependent variables. The weights reported in Table 4 indicate that rgdp, SO₂, and wastewater in the WMA and CZX urban agglomeration prior to the implementation of the RCEF society policy are best reproduced by a combination of 17 control city clusters.

4.2. Baseline Results Analysis

4.2.1. Impact of Policy on Economic Development

Figure 2a displays the GDP per capita for the WMA and its synthetic counterpart during the period 2005–2010. Figure 2b displays the GDP per capita for CZX and its synthetic counterpart during the period 2005–2010. Figure 2a shows that rgdp in the synthetic WMA very closely tracks the trajectory of this variable in the WMA for the entire pre-RCEF society policy period. Combined with the high degree of balance in rgdp (Table 2), this finding suggests that the synthetic WMA provides a sensible approximation of the rgdp that would have been produced in the WMA from 2007 to 2010 in the absence of the RCEF society policy. Our estimate of the effect of the RCEF society policy on rgdp in the WMA is the difference between rgdp in the WMA and in its synthetic version after policy implementation. Immediately after the policy’s implementation, the two lines begin to diverge noticeably. While rgdp in the synthetic WMA continued its moderate upward trend, the real WMA experienced a sharp increase. Note from Figure 2b that rgdp in the synthetic CZX and real CZX also showed the same trend. The discrepancy between the two lines suggests that the policy has achieved a significantly positive effect on GDP per capita in the WMA and CZX and effectively improved the economic development level of the two pilot urban agglomerations after policy implementation.

4.2.2. Impacts of Policy on SO₂ and Wastewater

Figure 3a displays the SO₂ emissions for the WMA and its synthetic counterpart during the period 2005–2010. Figure 3b displays the SO₂ emissions for CZX and its synthetic counterpart during the period 2005–2010. Figure 3a,b show that the actual SO₂ values of the WMA and CZX are similar to their synthetic values, thereby indicating that there was no significant difference between the SO₂ values of the pilot and synthetic city clusters before the implementation of the RCEF society policy. After policy implementation, the SO₂ values of the pilot city cluster decreased strikingly, whereas those of the synthetic cluster exhibited an increasing trend, which suggests that the policy effectively reduced SO₂ emissions in the pilot city clusters.

Figure 4a,b display the emission compliance rates of industrial wastewater for the WMA and CZX and their synthetic counterparts from 2005 to 2010, respectively. Figure 4a shows that wastewater in the synthetic WMA very closely tracks the trajectory of this variable in the WMA for the entire pre-RCEF society policy period, whereas the two lines do not significantly differ after policy implementation. Figure 4b shows that the actual wastewater in CZX is similar to its synthetic value before policy implementation, whereas the wastewater in CZX is lower than that in the synthetic CZX after policy implementation. The above results suggest that the policy cannot effectively improve the compliance rate of industrial wastewater emissions.

4.2.3. Results Analysis

Since 2007, China’s greatest environmental problem has resulted from the excessive emissions of a polluting gas, namely, sulfur dioxide. These emissions are mainly from the consumption of coal, especially from thermal power generation and urban residents. After the implementation of the RCEF society policy, the WMA and CZX changed their energy production and consumption methods. First, residents continued to optimize the urban energy consumption structure. Natural gas and hydropower account for an increasing proportion of urban energy consumption, thereby reducing sulfur dioxide emissions from residents in their daily lives. Second, the local government improved the urban energy production structure. In accordance with administrative regulations, the government closed low-technology and small-scale thermal power plants, thus reducing the proportion of thermal power generation in energy production. Third, the government required the industrial sector to transform and upgrade, improve production technology, and install equipment to remove sulfur and soot from terminal emissions, thereby reducing sulfur dioxide and soot emissions.

Prior to policy implementation, the Chinese government promulgated the Amendment to the Law of the People’s Republic of China on the Prevention and Control of Water Pollution in 2005, which strictly regulated the discharge of wastewater from industrial enterprises. The wastewater treatment of enterprises must meet discharge standards, and those that fail to meet such standards are subjected to severe administrative penalties or even forced to shut down. The governance of enterprise wastewater discharges up to the standard has achieved remarkable results. Therefore, the RCEF society policy has no significant effect on the rate of compliance with wastewater discharge standards. Moreover, after policy implementation, the government paid more attention to industrial transformation and upgrading. The scale of high-value-added and low-pollution industries gradually increased, which promoted the economic development of the city.

4.3. Robustness Test

4.3.1. Placebo Test

Similar to Abadie and Gardeazabal [42], this study runs placebo studies by applying the SCM to city clusters that did not implement the RCEF society policy during the sample period of the study. In this study, the Central Yunnan Urban Agglomeration (CYUA), which contributes more weight to the synthetic WMA, and the synthetic CZX are selected to construct a counterfactual control group and perform a placebo test. Figure 5a displays the GDP per capita for the CYUA and its synthetic counterparts during the period 2005–2010. Figure 5b displays the SO₂ emissions for the CYUA and its synthetic counterparts from 2005 to 2010. The CYUA, which was not included in the policy pilot, is not affected by the policy, which suggests that the empirical results pass the placebo test.

4.3.2. Changing the Portfolio of the Control Group

In this study, to test whether the assessment results will change due to the absence of city clusters in the control group, one city cluster with a positive contribution to the synthetic results is removed successively, and the model is re-estimated by the SCM. Figure 6a,b show the test results of the sensitivity analysis of the economic effects of the policy on the WMA and CZX. After any city cluster in the control group was removed, the per capita GDP of the real pilot area after the implementation of the policy still significantly differed from that of the synthetic group, which is consistent with the results before deletion. This finding indicates that the empirical results do not vary across the city clusters of the control group.

Figure 7a,b show the test results of the sensitivity analysis of the environmental effects of the policy on the WMA and CZX. The results show that after removing any city cluster in the control group, the industrial sulfur dioxide emissions of the synthetic city cluster after the implementation of the policy are still significantly different from the trajectory of this variable in the real pilot area, and those of the real pilot area are significantly reduced, which is consistent with the results before deletion. The empirical results do not vary across the different urban agglomerations of the control group, which proves the robustness of the above empirical conclusions.

4.3.3. Alternative Variables

In this study, gross industrial production (gip) is used to measure the economic level of urban agglomerations, and industrial dust emissions (dust) are used to measure the air pollution status of urban agglomerations. Figure 8a,b show the impacts of the policy on gip in the WMA and CZX, respectively. The results show that the policy has a significant positive effect on the gip of the pilot urban agglomeration.

Figure 9a,b show the impacts of the policy on dust in the WMA and CZX, respectively. The results show that before policy implementation, dust in the synthetic urban agglomeration is very close to the trajectory of this variable in the real pilot area. After the implementation of the policy, the two lines begin to diverge significantly, and dust in the real pilot area decreases rapidly, which indicates that the implementation of the policy effectively reduces industrial dust emissions.

4.4. Heterogeneity Analysis at the City Level

This section further analyzes the impact of the policy on the economy and environment at the city level, aiming to analyze the differences in the effects of the policy in different cities.

Table 5. Analysis of SO₂ heterogeneity at the city level in the WMA and CZX.

Time	Treatment Effect
	WMA						CZX
	Wuhan	Huangshi	Ezhou	Xiaogan	Huanggang	Xianning	Changsha	Zhuzhou	Xiangtan
2007	−3283.4375	8862.0078	−12,241.9219	−14,538.7031	−3376.0977	−8167.1211	−3043.1602	−14,298.0859	−4564.7266
2008	2012.7891	13,981.8594	−10,248.3125	−10,249.5000	−1988.7139	−10,602.5547	10,528.9492	−15,555.6250	−7575.8438
2009	2205.8125	12,548.1172	−7380.1797	−8267.5000	−1279.4609	−18,242.2285	5808.1367	−12,329.7266	577.8828
2010	−25,112.7813	3656.4297	2646.5703	−10,342.9336	581.9277	−17,364.6211	7905.3828	−19,738.8906	929.1094
ATE	−6044.4043	9762.1035	−6805.9609	−10,849.6592	−1515.5862	−13,594.1313	5299.8271	−15,480.5820	−2658.3945

presents the impact of various policies on sulfur dioxide emissions at the city level. The average treatment effect (ATE) is the ATE after policy implementation. In the WMA, the policy has a significant inhibitory effect on sulfur dioxide emissions in the five cities, and the effect of the policy in Wuhan city is lower than that in other cities. In CZX, the ATE of Changsha city is positive, whereas that of other cities is negative, indicating that the policy has no effect on the sulfur dioxide emissions for Changsha.

The results of the heterogeneity analysis reveal that the governance effect of this policy on small cities is much more obvious than that on large cities. Larger cities have stricter environmental standards, requirements, and regulations. Before policy implementation, the control effect of air pollution was fruitful. In addition, large cities perform mainly administrative functions, and high-polluting and energy-consuming industries have been transferred to surrounding small and medium-sized cities. Therefore, the policy has a more significant effect on the environmental governance of medium-sized cities than on that of larger cities.

Table 6. Analysis of wastewater heterogeneity at the city level in the WMA and CZX.

Time	Treatment Effect
	WMA						CZX
	Wuhan	Huangshi	Ezhou	Xiaogan	Huanggang	Xianning	Changsha	Zhuzhou	Xiangtan
2007	0.0272	0.0107	0.0910	−0.0201	0.1374	−0.0865	0.0137	0.0121	−0.0208
2008	0.0256	0.0167	0.0681	−0.0375	0.1217	−0.0900	0.0301	0.0226	−0.0109
2009	0.0227	0.0122	0.0267	0.0148	0.0952	−0.0655	0.0653	0.0399	−0.0213
2010	0.0159	0.0087	0.0144	0.0048	0.0169	−0.0867	0.0242	0.0210	0.0011
ATE	0.0229	0.0121	0.0500	−0.0095	0.0928	−0.0822	0.0333	0.0239	−0.0130

reports the impact of this policy on industrial wastewater at the city level. In the WMA, both Xiaogan and Xianning have negative ATEs. Other cities in the WMA have positive effects. The effect of the policy is very weak because the ATE of cities in the WMA is 1.21% to 9.28%. The same situation also occurs in CZX, where the ATE of two cities is 2.39% and 3.33%, respectively. This finding confirms the previous empirical results that the effect of policy on improving the standardized discharge rate of industrial wastewater has almost reached saturation, which means that the treatment of industrial wastewater in China has been widely covered and is very comprehensive.

4.5. Mechanisms Analysis

4.5.1. Urban Construction

Land is a limited natural resource for urban development [43]. Under environmental regulations, local governments can enhance the green utilization efficiency of land, thereby achieving sustainable development of the city [37]. In this study, the green coverage of built-up areas is used as a proxy variable to measure urban construction (uc). Figure 10a,b display the uc for the WMA and CZX and their synthetic counterparts during the period 2005–2010, respectively. After policy implementation, the two lines diverge significantly, and the green coverage area of the real pilot area significantly expands in size compared with that of the synthetic urban agglomeration, indicating that the policy encourages the local government to actively increase expenditure on urban construction. After the city’s environmental governance, the level of urban construction and investment significantly improved, which directly improved the atmospheric environment and the environmental protection awareness of residents, thus effectively promoting urban environmental governance and sustainable development [44].

4.5.2. Green Innovation

Under the pressure of environmental regulations, local governments usually choose to enhance the city’s green innovation capabilities in order to improve the environmental situation [45]. The enhancement of the innovation capabilities of urban agglomerations not only implies the upgrading of technologies, but also means the improvement of the regional capacity for sharing innovation resources, the collaborative ability of innovation elements, and the communication ability of innovation entities [46]. The enhancement of innovation capabilities is conducive to reducing pollutant emissions [47]. The study utilizes the number of green patents for application to measure green innovation (grn_inno). Figure 11a,b display grn_inno for the WMA and CZX and their synthetic counterparts from 2005 to 2010, respectively. The results show that the policy has a significant positive effect on the urban green innovation in CZX. However, this policy has a relatively weak effect on the green innovation in the WMA and its duration is also short. On the one hand, the economic development models of the two areas differ. The WMA focuses on the development of electronic information, automobile manufacturing, and other industries, which do not focus on green technology, and thus, these enterprises pay less attention to green innovation ability. In contrast, CZX pays more attention to the upgrading and transformation of new materials, new energy, and other industries in economic development, providing a good environment and conditions for scientific and technological green innovation. On the other hand, the implementation of environmental policy involves different strategies. The WMA is more focused on industrial structure adjustment and resource conservation. In contrast, CZX more actively explores the application and promotion of green technology and promotes the development of green technology innovation through policy guidance and technical support.

4.5.3. Resource Allocation

Increasing the intensity of environmental regulations can alleviate the degree of resource misallocation through the effect of factor mobility [40]. The market-oriented allocation of resources and environmental factors encourages all kinds of market players to take the initiative to improve technology and market transactions through energy conservation, carbon reduction, pollution reduction, and resource conservation, thus reducing the cost of green and circular development across society as a whole [48]. The study uses the proportion of market allocation resources to measure the efficiency of city resource allocation (ra). Figure 12a,b display the ra values for the WMA and CZX and their synthetic counterparts during the period 2005–2010, respectively. The results show that after policy implementation, the ra value of the real pilot area increased. This finding shows that policy implementation promotes the optimization of resource and environmental factors through market-oriented mechanisms, improves the efficiency of resource allocation, and is conducive to the sustainable development of the economy and society.

5. Conclusions, Policy Implications, and Limitations

5.1. Conclusions

This paper examines the effects of RCEF policies in pilot urban agglomerations. On the basis of the panel data of 19 urban agglomerations in China from 2005 to 2010, the WMA and CZX urban agglomeration are selected as the treatment group, and the other 17 urban agglomerations are taken as the control group. The SCM is employed to evaluate the impact of policies on the two urban agglomerations. The study reveals that the policy can effectively improve the economic development of these two urban agglomerations and reduce SO₂ emissions, but it cannot effectively improve the level of wastewater treatment. Furthermore, in each urban agglomeration, the impact of the policy on small and medium-sized cities is significantly greater than that on large cities. This policy can effectively control the emissions of SO₂ in small and medium-sized cities. Finally, mechanism analysis demonstrates that the WMA achieves a pollution reduction effect by increasing its level of investment in environmental governance and improving the efficiency of resource allocation under the influence of this policy. In addition, the CZX urban agglomeration has improved the situation of environmental pollution through green technology innovation.

5.2. Policy Implications

Based on our empirical findings, several policy recommendations are proposed. Firstly, local governments should establish more stringent emission standards for enterprises that have relocated to smaller cities. Unlike larger urban areas, small and medium-sized cities often have less rigorous environmental regulations and lower compliance costs. Consequently, businesses with significant pollution or high emissions tend to move to these regions, where they can expand operations more easily. To address this issue, local authorities must conduct thorough investigations and assessments of the overall emissions from these enterprises. Utilizing these findings, they should develop and implement new emission standards tailored to the specific needs of each city. Specifically, concerning wastewater discharge, local governments should raise wastewater discharge standards and enhance the treatment processes for industrial effluents to mitigate environmental impact.

Secondly, policymakers must develop more diversified and differentiated environmental policies. Compulsory environmental regulations should establish legally binding emission standards for enterprises. The government should consider variations in economic levels, industrial structures, and resource endowments across different regions to prevent the homogenization of policy design. Market-oriented, incentive-based environmental policies should be more flexible. Utilizing incentives or compensatory measures, such as green subsidies, green loans, and environmental rewards, can guide high-polluting or high-emission enterprises to upgrade their technologies or equipment, thereby reducing overall emissions and lowering environmental costs. Additionally, the government should design exemplary voluntary emission reduction projects to encourage key enterprises to engage in voluntary emission reduction initiatives. Insights and experience gained from these voluntary efforts can facilitate the dissemination of new technologies and equipment into small and medium-sized enterprises.

Third, a collaborative environmental governance framework among cities should be designed and established. This framework involves detailed protocols and shared responsibilities that can effectively restrain the transfer of pollution to nearby areas, ensuring cleaner air and water across regions. Collaborative environmental governance can significantly improve the efficiency of environmental governance by pooling resources and expertise, and it can reduce the cost of environmental governance through economies of scale and shared technological advancements. Large cities should design a comprehensive framework of environmental governance together with surrounding small and medium-sized cities, integrating local industries into sustainable practices. This collaboration ensures that smaller businesses adopt greener technologies and practices, reducing their overall environmental footprint. Finally, the government should increase its level of investment in city governance and market construction. Such investments can not only reduce air pollution by funding advanced filtration systems and renewable energy projects but also enhance market mechanisms to a certain extent. Optimizing the efficiency of resource allocation, decreasing the degree of distortion of resource allocation, and improving industrial production efficiency are key benefits of this approach. Enhanced market mechanisms ensure that resources are distributed more equitably and sustainably, fostering economic growth while protecting the environment.

5.3. Research Limitations

This study has several limitations. Firstly, the implementation of subsequent new policies has resulted in a relatively short study period, which restricts the analysis to the short-term effects of these policies. Secondly, there is insufficient verification and conclusive evidence at the enterprise level regarding the effectiveness of the policies. This study exclusively used urban data, limiting its ability to provide comprehensive guidance for corporate practices. Additionally, the investigation focused solely on formal institutions, while a country’s policy framework encompasses both formal and informal institutions. Therefore, further research is necessary to explore the impact of informal institutions and their interaction with formal institutions.