Can the Effectiveness of Urban Water Pollution Control Contribute to the Overall Development of the City? Evidence from 268 Cities in China

Abstract

The rapid growth of global urbanisation has resulted in significant environmental pollution, with urban water pollution emerging as a critical factor in comprehensive urban development. The present study employs panel data from 268 Chinese cities between 2013 and 2022, utilising entropy weighting and a two-effect fixed-effects model to empirically analyse how urban water pollution control promotes comprehensive urban development. The research findings reveal that water pollution control significantly promotes comprehensive urban development, but there are differences across urban regions and scales, with greater effectiveness observed in central and western regions and medium-sized and small cities. This paper also highlights that water pollution control can promote urban development by optimising industrial structure and proposes that governments should formulate regionally differentiated water pollution control policies, establish a ‘Regional Water Environment Governance and Industrial Transformation Coordination Centre,’ and implement the ‘River and Lake Chief System+’ policy.

1. Introduction

As the global urbanisation rate surged from 30% in 1950 to 57% in 2022, urban water pollution has become a core bottleneck in crossing the middle-income stage and moving towards sustainable urbanisation. In China, between 2013 and 2018, over 70% of prefecture-level cities and above experienced black and odorous water bodies, resulting in annual direct economic losses exceeding 150 billion yuan [1]. The phenomenon of industrial agglomeration, in conjunction with population expansion, has resulted in elevated levels of pollutants, which have surpassed the self-purification capacity of water bodies. This has precipitated a series of crises, encompassing drinking water safety, flood risks, and public health concerns. According to the World Bank, the economic cost of water pollution amounts to 1.5–2.5% of global GDP [2]. In the context of these developments, water pollution control has been elevated to a national strategy in China. The ‘River Chief System’ was fully implemented in 2016, the ‘Water Ecological Civilisation City Construction Pilot Programme’ was launched in 2020, and the ‘Water Pollution Prevention and Control Action Plan’ entered its final phase in 2022. Water pollution control can therefore be regarded not only as an environmental restoration project, but also as an ‘institutional lever’ driving comprehensive urban development. Firstly, investment in governance can directly stimulate green infrastructure, environmental protection equipment, and circular economy industries, creating new growth poles. Secondly, improved water quality enhances urban liveability and human capital quality, attracting the aggregation of high-end elements, thereby reversely promoting industrial structure upgrading and urban competitiveness enhancement [3].

International experience demonstrates that when cities adopt a systems-thinking approach to integrating water quality objectives into land use, transportation planning, and industrial access policies, they can achieve the Kuznets inflection point of ‘governance-development synergy’ within a period of 10–15 years. It is therefore vital to clarify the dynamic relationship between water pollution governance and comprehensive development in Chinese cities. This is not only in response to the ‘water-city’ interaction objectives outlined in the 2030 Agenda for Sustainable Development, but also to provide a replicable institutional design model for developing economies [4]. However, the potential of governance investments to function as a ‘catalyst’ for comprehensive urban development, that is to say, enhancing water quality whilst concurrently driving economic growth, industrial upgrading and social welfare enhancement, remains to be substantiated by large-sample, long-time-series empirical tests. The following three core questions have been identified as the focal point of research: A. To what extent does the governance of water pollution have a significant impact on a city’s economic, social, and ecological performance? B. By what mechanisms is this effect achieved? C. How do policy effects vary according to heterogeneity in economic development levels, urban scale, and watershed location?

In order to address the aforementioned questions, this paper employs panel data from 268 Chinese cities spanning 2013–2022 in order to construct an analytical framework for ‘water pollution control–comprehensive development’ from an environmental sociology perspective. This offers a new theoretical interpretation and refinement of existing urban sustainable development frameworks. The contributions of this study are as follows: This study utilises a decade of longitudinal data to substantiate the causal relationship between ‘environmental governance—comprehensive development’, thus addressing the limitations of previous case studies or short-term evaluations. Secondly, it employs quantitative methods to define the effectiveness of water pollution control within the theoretical framework of environmental sociology, conducting an empirical analysis to provide city-level evidence that supports the concept that ‘green mountains and clear waters are as valuable as mountains of gold and silver’. A comprehensive analysis of the mechanisms through which industrial structure upgrading and technological innovation influence water pollution control in comprehensive urban development is presented. This paper also tests the overall differences, thereby providing theoretical guidance for the Chinese government’s future environmental governance planning.

2. Theoretical Framework and Research Hypotheses

2.1. A Sociological Analysis Framework for Water Pollution Control and Comprehensive Urban Development

The issue of water pollution control and its impact on comprehensive urban development is a complex real-world problem that necessitates an understanding of the multifaceted interactions between urbanisation, environmental policies, and water quality dynamics. It has been demonstrated by certain scholars that there is a relationship between urbanisation levels and water quality. The findings of these scholars indicate that low urbanisation rates (below 25%) have minimal and irregular effects on water quality. By contrast, moderate urbanisation rates (25% to 40%) have been shown to lead to irreversible deterioration of stream water quality. This finding suggests a direct correlation between urban expansion and water pollution levels, underscoring the necessity for a sociological examination of urban development patterns to mitigate environmental impacts [5]. It has been demonstrated by other scholars that a strong correlation exists between a city’s socio-economic activities and the trajectory of water quality development [6].

Furthermore, a number of scholars have examined the relationship between the water environment and urban development from the perspectives of socio-economic indicators and environmental data, including energy structure [7], urban watershed management [8], and the impact of environmental policies [9]. In the context of social construction theory, the governance of water pollution control and comprehensive urban development is theorised [10]. Ecological modernisation theory and sustainable development theory posit that investments in governance are deemed ‘sustainable’ only when water quality improvements simultaneously lead to enhanced ecological capacity, industrial green upgrading, and improved resident health and welfare [11,12]. The present study proposes an environmental sociology-based framework for analysing the impacts of water pollution control (Figure 1).

As illustrated in Figure 1, from the perspective of social construction theory, the issue of water pollution control cannot be considered to be merely an engineering and technical problem. Instead, it is a ‘development narrative’ that is co-constructed by multiple actors. When the government, market, and civil society define ‘clean water’ as a core symbol of urban competitiveness, governance investments are transformed into attractions for talent, capital, and high-end resources. This, in turn, elevates the city’s overall comprehensive development status and supports H1. The theoretical framework of sustainable development further elucidates the synergistic relationship between water environment restoration, economic growth, and social inclusion, positing that these factors are not mutually exclusive. Governance has been demonstrated to enhance ecological carrying capacity, reduce long-term operational costs, and unlock green dividends, thereby facilitating the adoption of a ‘strongly sustainable’ trajectory by cities with diverse resource endowments. However, it is important to note that differences in technological absorption capacity and institutional response speed among cities inevitably lead to divergent governance outcomes. This provides theoretical support for H2. The present study sets out to explore the implications of ecological modernisation theory in the context of industrial upgrading and pollution control spillover into comprehensive development. The theoretical framework underpinning this study is that when industries with high water consumption and high emission levels are replaced by technology-intensive and service-oriented industries, industrial upgrading becomes the key transmission mechanism for pollution control spillover into comprehensive development. This theory is then used to explain the intermediary role of industrial structure optimisation in H3.

2.2. The Relationship Between Water Pollution Control and Comprehensive Urban Development

The impact of water pollution control on comprehensive urban development is a multifaceted issue that has attracted considerable attention from the academic community. Numerous studies have emphasised the critical role of implementing targeted water resource management measures in alleviating urban water-related issues and promoting sustainable urban development. As urban residents become more environmentally conscious, behaviours that protect the environment have become a key factor in urban development for governments, businesses, and individuals [13]. Rong (2021) [14] discovered that development measures such as vegetated ditches, green spaces, and rainwater collection barrels can significantly influence water resource management within university campuses. In a similar vein, other scholars have demonstrated that mitigating stormwater runoff issues can enhance the adaptability of urban water resources [15]. As Aivazidou (2021) [16] noted, digitalisation has the potential to facilitate more precise and efficient water resource management, a factor that is of crucial importance for sustainable urban development in the context of increasing population density. Yin (2024) [17] emphasised the importance of regional and multi-scale perspectives in water resource management. Their research indicated that the implementation of tailored strategies is more efficacious in the context of regional water pollution control. Wang (2023) [18] developed a quantitative assessment method for urban lakes, thereby revealing the impact of socioeconomic development on water quality. The findings of this study indicate that urbanisation exerts a significant impact on water bodies. To maintain ecological balance and promote urban development, it is necessary to control pollution sources. James (2024) [19] explored the broader context of urbanisation’s impact on environmental sustainability, discussing theoretical models such as urban ecology theory and the environmental Kuznets curve. These frameworks posit that effective water pollution control can promote the formation of resilient human settlements. The following hypothesis is thus proposed by this study:

H1. 

The hypothesis under investigation is that water pollution control can promote the overall level of urban development.

2.3. The Heterogeneous Impact and Mediating Role of Water Pollution Control on the Comprehensive Development of Cities

Significant disparities among cities in terms of fiscal resources, administrative efficiency, and institutional coordination give rise to markedly divergent economic and social benefits from water pollution control investments of equivalent intensity. For instance, the GDP gains from South Korea’s ‘Four Major Rivers Restoration Project’ in the capital region were approximately 1.8 times those in non-capital regions, due to heterogeneity in the allocation of control funds and local governance capabilities [20]. The enhancement of water quality in upstream cities engenders favourable externalities for downstream cities through river transport. Concurrently, downstream cities are obligated to incur supplementary expenses to achieve local benefits, thereby establishing a ‘pollution control benefit gradient’. As stated by Chen (2024) [21], scholars have indicated that untreated sewage has been identified as a contributing factor to the augmentation of the load on downstream areas. This, in turn, has been demonstrated to result in an escalation in governance costs and a reduction in development dividends in downstream cities. Consequently, this has been shown to exacerbate existing regional disparities. Large cities, with their economies of scale, industrial diversification, and high public service standards, are better positioned to convert pollution control into land value appreciation, industrial green upgrading, and resident health benefits. Conversely, medium and small cities, with their mono-industrial structure and limited fiscal capacity, have been found to exhibit comparatively diminished governance dividends. It has been established by scholars that the advantages inherent in environmental policies are subject to significant heterogeneity across eastern, central, and western Chinese cities [22]. Moreover, pollution control measures, including emission standards and water withdrawal permits, directly phase out or relocate high-water-consuming and high-polluting industries. Consequently, industrial structures are compelled to transition towards low-pollution, high-value-added modern services and technology-intensive industries. The optimisation of industrial structures, in turn, has been demonstrated to reduce pollution emission intensity, thus forming a positive feedback loop of ‘control—structural upgrading—comprehensive development’ [23]. It has been posited by certain scholars that the incentivisation of environmentally sustainable practices, concomitant with industrial upgrading requirements, has the capacity to promote high-quality economic development [24]. Therefore, this article puts forward the following hypotheses:

H2. 

The positive effects of water pollution control on the comprehensive development of cities vary significantly among different cities.

H3. 

Industrial structure optimization plays a mediating role in the impact of water pollution control on the comprehensive development of cities.

3. Model Design and Variable Selection

3.1. Model Design

To assess the impact of water pollution control on comprehensive urban development, this paper refers to Pan and Zhou [25,26] to construct the following panel regression model and performs stepwise regression according to the mediation model of Baron and Kenny [27]:

$${UCD}_{it}={\alpha }_0+{\alpha }_1{Wpc}_{it}+\sum {\alpha }_2{Control}_{it}+{Year}_{it}+{\mu }_{it}+{\epsilon }_{it}$$

(1)

$${IS}_{it}={\beta }_0+{\beta }_1{Wpc}_{it}+\sum {\beta }_2{Control}_{it}+{Year}_{it}+{\mu }_{it}+{\epsilon }_{it}$$

(2)

$${{UCD}^{\prime }}_{it}=b_0+b_1{Wpc}_{it}+b_2{IS}_{it}+\sum b_3{Control}_{it}+{Year}_{it}+{\mu }_{it}+{\epsilon }_{it}$$

(3)

In this model, α, β, and b represent the coefficients of the same variables in different equations; i represents the city; t represents the year; UCD represents the comprehensive urban development level; WPC is the water pollution control effect; control is a set of control variables affecting UCD; IS represents the industrial structure; year indicates the time fixed effect; μ represents the city fixed effect; and ε is the random disturbance term. In formula (1), UCD represents the direct effect of WPC. In formula (3), UCD′ represents the effect of WPC after the inclusion of the mediating variable.

3.2. Variable Definition

3.2.1. Dependent Variable

The dependent variable in this study is comprehensive urban development. The present study adopts the definition of sustainable urban development proposed by Zhou (2025) [28], which is employed to validate the four primary indicators and 16 specific indicators that were selected by Zhou. These indicators are subsequently utilised as the comprehensive urban development indicators for the analysis in this study (

Table 1. Comprehensive urban development index system.

Primary Indicator	Specific Indicators	Unit	Attribute	Weight
Economic development (ED)	Social retail goods consumption/GDP	%	+	0.031005
	Imports and exports/GDP	%	+	0.191400
	Proportion of the tertiary industry	%	+	0.036046
	Per capita disposable income of urban residents	Yuan	+	0.024093
	Number of students in higher education institutions	Person	+	0.199058
Social development (SD)	Total number of patent authorizations	%	+	0.193042
	End-of-year registered unemployed population	Person	+	0.215936
	Total social goods consumption	Ten thousand yuan	+	0.081652
	Industrial wastewater discharge volume/Industrial output value	tons per ten thousand yuan	−	0.000118
Environmental development (END)	Industrial sulphur dioxide discharge volume/Industrial output value	tons per ten thousand yuan	−	0.001653
	Industrial smoke (powder) dust discharge volume/Industrial output value	tons per ten thousand yuan %	−	0.003088
	Per capita industrial nitrogen oxide emissions	%	−	0.011618
	Harmless treatment rate of domestic waste	%	+	0.004934
Governance development (GD)	Green coverage rate of built-up areas	%	+	0.003237
	Comprehensive utilization rate of industrial solid waste	hectares per thousand people	+	0.003046
	Per capita park green space in urban districts	%	+	0.000075

Note: + indicates a positive effect, − indicates a negative effect.

).

3.2.2. Explanatory Variables, Mediating Variables, and Control Variables

The concept of water pollution control effectiveness (WPC) encompasses a multitude of factors. The implementation of rational water pollution control measures has been demonstrated to be an effective strategy for enhancing per capita water resource availability and reducing per capita wastewater discharge. Drawing upon empirical evidence regarding prevailing social development conditions and extant research, it is posited that elevated per capita wastewater discharge levels are indicative of heightened water resource pressures. Moreover, the expansion of urban areas has been demonstrated to have a detrimental effect on water resources [29]. This study integrates the experience of other scholars in constructing water pollution-related indicators [30] and uses an expert consultation method to allocate weights for WPC [31]. The indicators have been set at the following proportions: 0.40 for per capita water resources, 0.35 for wastewater discharge, and 0.25 for built-up area. The final weights have been calculated as a linear combination of the indicators and are thus used as the independent variables in this study. The present study employs the ratio of the added value of the tertiary industry to GDP as a metric for the degree of industrial structure upgrading (IS) in a given region. The present paper identifies characteristics that may influence comprehensive urban development as control variables. These include urbanisation level (Urban), government intervention level (Gov), population density (PD), economic development level (EDL), and foreign investment level (FIL). Specific definitions for these variables are provided in

Table 2. Definition of independent variables, mediating variables and control variables.

Variable	Name	Definition	Indicator Attribute
Independent variable	Effectiveness of water pollution control (WPC)	Per capita wastewater discharge volume	−
		Per capita water resource possession	+
		Per capita built-up area	−
Mediating variable	Industrial structure (IS)	Ratio of added value of the tertiary industry to GDP in the region	+
	Degree of urbanization level (Urban)	Ratio of urban population to permanent resident population	+
Control variable	Government intervention (Gov)	General government fiscal expenditure/Regional GDP	+
	Population density (PD)	Logarithm of the total population at the end of the year	+
	Economic development level (EDL)	Logarithm of per capita regional GDP	+
	Foreign investment level (FIL)	Actual foreign investment utilized per year/Regional GDP	+

Note: + indicates a positive effect, − indicates a negative effect.

.

3.3. Model Evaluation

As illustrated in

Table 3. Correlation analysis.

Variable	UCD	ED	SD	END	GD	WPC	Urban	Gov	PD	EDL	FIL
UCD	1.000
ED	0.738 ***	1.000
SD	0.888 ***	0.345 ***	1.000
END	0.051 ***	0.021	0.054 ***	1.000
GD	0.087 ***	0.082 ***	0.058 ***	−0.038 *	1.000
WPC	0.360 ***	0.170 ***	0.384 ***	0.043 **	0.076 ***	1.000
Urban	0.554 ***	0.535 ***	0.405 ***	−0.103 ***	0.193 ***	0.239 ***	1.000
Gov	−0.354 ***	−0.193 **	−0.360 ***	0.066 ***	−0.211 ***	−0.178 ***	−0.399 ***	1.000
PD	0.266 ***	−0.046 **	0.401 ***	0.228 ***	0.063 ***	0.222 ***	−0.217 ***	−0.243 ***	1.000
EDL	0.584 ***	0.538 ***	0.445 ***	−0.124 ***	0.273 ***	0.236 ***	0.740 ***	−0.664 ***	−0.060 ***	1.000
FIL	0.203 ***	0.112 ***	0.206 ***	0.115 ***	0.051 ***	0.172 ***	0.157 ***	−0.199 ***	0.111 ***	0.210 ***	1.000

Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

, a positive correlation is evident between WPC and UCD. The findings indicate that the correlation coefficient between WPC and UCD is 0.360, which is significant at the 1% level, thereby initially validating the effect of WPC on UCD. Concurrently, the present study utilised the VIF method to assess multicollinearity (see

Table 4. Results of multiple linear tests.

Variable	VIF	1/VIF
Urban	2.45	0.40
Gov	2.12	0.47
PD	1.34	0.74
EDL	3.5	0.28
FIL	1.09	0.92
WPC	1.08	0.85
Mean	1.94

), which revealed that the VIF values for all variables were less than 5, indicating a relatively minor multicollinearity issue.

3.4. Data Sources

In order to ensure the scientific validity of the data, the study sample does not include Hong Kong, Macau, and Taiwan. The present study selected data from 268 cities in China from 2013 to 2022, with the sample exhibiting satisfactory continuity. The relevant data were primarily derived from the annual China Urban Statistical Yearbook, the China Environmental Statistical Yearbook, and publicly available data from municipal statistics bureaus. Due to the absence of certain environmental pollution data, such as general solid waste emissions, officially released in 2022, regression analysis was employed to fill in the gaps. The results of the descriptive statistics are presented in

Table 5. Descriptive statistical results.

Variable	N	Mean	SD	Min	Max
UCD	2682	0.063	0.046	0.012	0.422
ED	2682	0.129	0.080	0.018	0.790
SD	2682	0.031	0.048	0.000	0.441
END	2682	0.108	0.006	0.015	0.140
GD	2682	0.292	0.022	0.151	0.421
WPC	2682	0.028	0.041	0.002	0.998
Urban	2682	0.582	0.140	0.181	1.001
Gov	2682	0.202	0.094	0.044	0.741
PD	2682	5.891	0.666	2.996	7.350
EDL	2682	10.846	0.533	9.084	13.056
FIL	2682	0.016	0.019	0.000	0.229

.

3.5. Limitations

The present study is based solely on data from 268 prefecture-level cities in China from 2013 to 2022, which limits the scope of the sample selection. The selection of indicators for the article may be more indicative of geographical and climatic conditions than the effects of policy interference. Concurrently, while the mediating effect is found to be statistically significant, it is imperative to interpret the findings within the context of social reality. It should be noted that the mediating model utilises stepwise regression, which is a key limitation of this study. It is recommended that future research consider the use of a more diverse range of mediating test methods for analysis. Such methods may include structural equation modelling.

4. Empirical Analysis

4.1. Basic Regression Analysis

As illustrated in

Table 6. Basic regression results.

Variable	(1)	(2)	(3)	(4)
	UCD	UCD	UCD	UCD
WPC	0.407 ***	0.127 ***	0.043 ***	0.041 ***
	(0.020)	(0.016)	(0.012)	(0.011)
Urban		0.119 ***		−0.031 ***
		(0.007)		(0.010)
Gov		0.103 ***		0.048 ***
		(0.009)		(0.013)
PD		0.027 ***		0.010
		(0.001)		(0.007)
EDL		0.039 ***		0.008 ***
		(0.002)		(0.003)
FIL		0.075 **		−0.050
		(0.033)		(0.032)
_cons	0.051 ***	−0.615 ***	0.054 ***	−0.077
	(0.001)	(0.024)	(0.001)	(0.058)
City	NO	NO	Yes	Yes
Year	NO	NO	Yes	Yes
N	2682.000	2682.000	2682.000	2682.000
R2	0.129	0.539	0.148	0.158

Note: Standard errors in parentheses. ** p < 0.05,*** p < 0.01.

, the regression results of Equation (1) demonstrate the impact of WPC on UCD. In the empirical analysis, to ensure the robustness of the model, this paper controls for both city and time fixed effects and gradually adds control variables. Specifically, Column (1) demonstrates the impact of WPC on UCD in the absence of supplementary controls. The regression coefficient is positive at the 1% level, indicating that WPC can effectively promote the development of UR. The second column presents the results of the regression analysis, incorporating control variables. The regression coefficients demonstrate a consistent and significant positive correlation with UCD at the 1% level, thereby indicating a robust positive correlation between WPC and UCD. Columns (3) and (4) present net regression and regression with control variables added, respectively, based on fixed city and year effects, with results that are also significantly positive. However, a significant decline can also be observed in WPC from column (1) to column (4), which may be attributed to the buffering effect of urbanisation. Regions exhibiting high levels of urbanisation (Urban) characteristically possess more advanced water infrastructure, thereby reducing the direct constraints imposed by water scarcity on urban development. The industrial structure upgrades that have accompanied urbanisation have reduced water consumption per unit of GDP, thereby alleviating the negative impacts of resource pressures on development. The first hypothesis is confirmed, thereby demonstrating that WPC exerts a favourable influence on UCD.

4.2. Robustness Tests

In consideration of potential lag effects in the benchmark analysis, this paper undertakes a re-run of the empirical regression, with the explanatory variables lagged for a designated period. The results can be observed in Column (1) of

Table 7. The results of the robustness analysis.

Variable	(1)	(2)
	UCD	UCD
Time lag	0.024 **
	(0.012)
Delete year		0.065 ***
		(0.014)
Urban	−0.024 **	−0.029 ***
	(0.011)	(0.011)
Gov	0.048 ***	0.050 ***
	(0.016)	(0.014)
PD	0.016 *	0.010
	(0.008)	(0.008)
EDL	0.005	0.007 **
	(0.004)	(0.003)
WPC	−0.060 *	−0.055 *
	(0.035)	(0.033)
_cons	−0.087	−0.072
	(0.071)	(0.062)
City	Yes	Yes
Year	Yes	Yes
N	2372.000	2410.000
R2	0.144	0.164

Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

. The findings suggest that the estimated regression coefficients maintain a positive sign at the 1% statistical significance level. Furthermore, this paper excludes certain years and re-runs the regression, with the results presented in Column (2). The reliability of the benchmark regression results is confirmed through the implementation of these two robustness tests. This approach establishes the foundation for subsequent mediation analysis and heterogeneity analysis. Furthermore, model (1) demonstrates, through the implementation of lag tests, that the reverse causality of the independent variable is weak. This is due to the fact that water pollution control in China is primarily driven by central environmental inspections and river basin assessments. Furthermore, funding allocation follows the administrative logic of ‘the worse the water quality, the more investment’, rather than the market logic of ‘the richer the city, the more investment’ [32,33]. This institutional characteristic inherently undermines the notion of reverse causality.

4.3. Mediation Effect Analysis

The findings of the mechanism test (

Table 8. Analysis results of the mechanism.

Variable	(1)	(2)	(3)
	UCD	IS	UCD
WPC	0.041 ***	0.059 ***	0.040 ***
	(0.011)	(0.023)	(0.012)
IS			0.018 *
			(0.010)
Urban	−0.031 ***	−0.036 *	−0.031 ***
	(0.010)	(0.020)	(0.010)
Gov	0.048 ***	0.053 **	0.047 ***
	(0.013)	(0.025)	(0.013)
PD	0.010	−0.077 ***	0.011
	(0.007)	(0.014)	(0.007)
EDL	0.008 ***	−0.019 ***	0.008 ***
	(0.003)	(0.005)	(0.003)
FIL	−0.050	0.228 ***	−0.055 *
	(0.032)	(0.062)	(0.032)
_cons	−0.077	1.014 ***	−0.095
	(0.058)	(0.115)	(0.059)
City	Yes	Yes	Yes
Year	Yes	Yes	Yes
N	2682.000	2682.000	2682.000
R2	0.158	0.667	0.159

Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

) demonstrate that the positive impact of water pollution control on comprehensive urban development is statistically significant. Furthermore, the results indicate that industrial structure optimisation plays a partial mediating role in this process. Specifically, for every one-standard-deviation increase in water pollution control, the industrial structure optimisation indicator increases by 0.059 standard deviations (p < 0.01). Additionally, industrial structure optimisation significantly promotes comprehensive urban development by 0.018 standard deviations (p < 0.1), satisfying the Baron–Kenny condition. The underlying cause of this phenomenon can be attributed to the implementation of increasingly stringent environmental standards, which have compelled high-water-consuming, high-emission, low-end manufacturing industries to either cease operations or relocate, thereby releasing land, capital, and labour resources. Concurrently, enterprises proactively adopt clean production technologies and circular economy models, driven by the dual incentives of reputation and market factors. This has resulted in synchronous green technology upgrades across the entire industrial chain. This dynamic process gives rise to a sequence of events characterised by the interplay of governance, innovation and industrial upgrading within the urban context. Consequently, the hypothesis H3 of this study has been validated.

4.4. Heterogeneity Analysis

4.4.1. Regional Heterogeneity

The empirical results presented above indicate that the implementation of water pollution control measures can have a positive effect on comprehensive urban development. However, due to China’s substantial geographical expanse, there are evident spatial and regional disparities in policy implementation. Consequently, the present study categorises the sample into eastern, central, and western cities based on geographical location, and conducts a re-regression of the data by sample. The results are presented in

Table 9. Regression results for urban area heterogeneity.

Variable	Region
	Eastern City	Central City	Western Cities
WPC	0.027	0.019 *	0.246 ***
	(0.029)	(0.011)	(0.039)
Urban	−0.033 *	−0.043 ***	0.061 **
	(0.018)	(0.013)	(0.024)
Gov	−0.024	0.041 **	0.040 **
	(0.036)	(0.017)	(0.019)
PD	0.081 ***	−0.026 ***	−0.084 ***
	(0.017)	(0.008)	(0.016)
EDL	0.013 ***	0.012 ***	0.005
	(0.005)	(0.004)	(0.006)
FIL	−0.052	0.005	−0.015
	(0.066)	(0.036)	(0.080)
_cons	−0.542 ***	0.084	0.429 ***
	0.081 ***	(0.071)	(0.116)
City	Yes	Yes	Yes
Year	Yes	Yes	Yes
N	939.000	1052.000	671.000
R2	0.302	0.153	0.195

Note: Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.

. It has been observed that the promotional effect on comprehensive urban development increases gradually from east to west. The following factors may be contributing to this phenomenon: Firstly, there are differences in marginal improvement potential. The industrialisation process was initiated at an earlier date in eastern cities. Following numerous cycles of governance, the quality of water resources has either been brought to or has reached a stage threshold. This has resulted in a sharp increase in marginal governance costs and a decrease in marginal benefits. In contrast, western cities have a lower baseline and a greater volume of pollution. Consequently, the same level of investment can achieve more significant emission reductions and water quality improvements. This, in turn, has a greater effect on the comprehensive driving factors of land appreciation, investment attraction and public health. Secondly, the ‘pollution dividend’ arises from industrial relocation. The implementation of stringent environmental regulations in the eastern region has prompted the relocation of high-water-consuming and high-emission industries to the western region. Western cities have been able to leverage their status as late-mover destinations for industrial relocation, thereby simultaneously adopting more advanced clean technologies and environmental standards. This has enabled pollution control and industrial upgrading to occur in tandem. Thirdly, the spillover of governance capabilities and policy bias is considered. It is evident that national ecological civilisation strategies and fiscal transfers have been oriented towards the west, with policy instruments such as central environmental inspections and the river chief system exhibiting dual functions of ‘catch-up’ and ‘demonstration’ upon implementation in the west. Furthermore, the exchange of talent between leading and lagging cities has been shown to have a significant impact on the environmental management knowledge base of western regions, resulting in enhanced governance outcomes. Consequently, H2 of this study has been validated.

4.4.2. Scale Heterogeneity

The categorisation of the samples is based on population size, with small and medium-sized cities and large cities forming the categories (according to the official announcement by the State Council of China, cities with a permanent urban population of over 1 million are considered large cities, while those with fewer than 1 million residents are considered medium-sized or small cities). The regression results are presented in

Table 10. Analysis of urban scale heterogeneity.

Variable	Scale
	Small and Medium-Sized Cities	Big Cities
WPC	0.099 ***	0.051 ***
	(0.021)	(0.015)
Urban	−0.011	−0.048 ***
	(0.010)	(0.018)
Gov	0.032 ***	0.070 **
	(0.011)	(0.029)
PD	0.020 ***	−0.013
	(0.008)	(0.018)
EDL	0.007 **	0.017 ***
	(0.003)	(0.005)
FIL	0.081 **	−0.145 ***
	(0.036)	(0.051)
_cons	−0.144 ***	−0.015
	(0.055)	(0.146)
City	Yes	Yes
Year	Yes	Yes
N	1308.000	1374.000
R2	0.237	0.191

Note: Standard errors in parentheses. ** p < 0.05, *** p < 0.01.

. The findings of this study indicate that both small and medium-sized cities, as well as large cities passed the significance test. However, it was observed that the effects in small and medium-sized cities were superior to those in large cities. The following reasons may be advanced: Firstly, the majority of small and medium-sized cities are still in the mid-to-late stages of industrialisation, with high levels of water pollution and a significant historical debt. It is evident that a constant investment in pollution control results in a considerable enhancement in water quality. In contrast, large cities have undergone multiple rounds of governance and are nearing a stage threshold, resulting in diminishing marginal returns. Secondly, large cities have relocated high-water-consuming and high-emission production processes to small and medium-sized cities through the ‘retreat from the second and advance to the third’ strategy. While small and medium-sized cities are assuming control of industrial transfers, they are concurrently adopting clean production technologies and circular processes. This enables synchronised pollution control and industrial structure upgrading, thereby amplifying comprehensive development dividends. Thirdly, medium-sized and small cities have smaller-scale pollution control facilities and lower pipeline density. This results in unit treatment costs that decrease rapidly with scale expansion and higher input–output elasticity. In contrast, large cities have already achieved economies of scale, and further emissions reductions require extremely high marginal costs, weakening the elasticity of pollution control with respect to comprehensive development. Consequently, H2 of this study has been further validated.

5. Discussion

The core findings of this study provide substantial support for the development philosophy that ‘green mountains and clear waters are as valuable as mountains of gold and silver,’ thereby confirming the importance of environmental quality improvement as a core element of a city’s comprehensive competitiveness [34,35]. This finding aligns with the Environmental Kuznets Curve (EKC) theory, which posits that environmental improvements accompany rising development levels after a specific stage of development [36]. However, this study places greater emphasis on the proactive role of governance initiatives rather than passively awaiting a turning point. The findings of this study are in alignment with the fundamental premise of the Porter hypothesis, which posits that the judicious formulation of environmental regulations can catalyse innovation and efficiency enhancements, consequently augmenting long-term competitiveness and ultimately precipitating a leap in overall development levels [37,38]. In the context of China’s pronounced commitment to the construction of an ecological civilisation and the objectives of achieving ‘dual carbon’ [39], this finding provides substantial empirical evidence in support of the pragmatic strategy of ‘using environmental governance to drive development transformation.’ The introduction of the ‘zero-waste city’ policy also provides policy support for the governance of urban water pollution [40].

The present study corroborates the hypothesis that water pollution control constitutes a policy practice of ecological modernisation, a finding that resonates with Mol’s (2003) [41]‘environmental reform’ mechanism: Chinese local governments utilise WPC as a mechanism for ecological modernisation, leveraging policy directives to catalyse technological innovation within enterprises, thereby generating impetus for urban development. The promotion of WPC for UCD essentially involves the synergistic evolution of environmental, economic, and social dimensions, aligning with Lehtonen’s (2004) [42] assertions regarding sustainable development theory. The present study reveals that industrial structure optimisation is the key transmission pathway through which WPC influences UCD. This finding is of profound practical and theoretical significance. On the one hand, it confirms that WPC effectively guides resources from high-pollution, low-value-added industries to clean technology, high-end manufacturing, and services by increasing pollution costs, forcing technological upgrades, eliminating outdated production capacity, and attracting green investment [43,44]. This structural transformation has been demonstrated to have a direct impact on the reduction of pollution emissions, whilst concurrently providing a comprehensive foundation for comprehensive urban development. This is achieved by enhancing total factor productivity, creating higher-quality employment opportunities, and improving urban liveability and attractiveness [45]. This is in close alignment with China’s core strategy of promoting high-quality economic development and constructing a modern industrial system [46]. In terms of heterogeneity analysis, the findings of this study align with those of numerous scholars. The impact is more pronounced in central and western regions, potentially attributable to their comparatively weaker development foundations, more evident ‘shortfall effects’ in environmental governance, and more substantial comprehensive benefits from marginal improvements [47,48]. Furthermore, the central and western regions may be in the middle stages of industrialisation, offering greater space and potential for industrial restructuring, making it easier to realise the transformation dividends brought by WPC [49]. This suggests to policymakers that the promotion of ecological civilisation in central and western regions is not only a necessity for ecological compensation but also an important strategic lever for driving regional coordinated development and narrowing the gap with eastern regions [50]. In comparison to large cities, which generally possess more advanced infrastructure, diversified industrial structures, and stronger environmental governance capabilities, medium and small cities often encounter heightened environmental governance pressures and capacity shortages [51]. The findings of this study indicate that once medium-sized and small cities effectively advance WPC, with support from the national and regional levels, its impact on improving the overall appearance of the city, promoting industrial upgrading, and enhancing population attractiveness may be more direct and rapid than in large cities [52]. This provides a foundation for the implementation of differentiated urban environmental governance strategies, emphasising the necessity of investing in and supporting environmental governance in medium-sized and small cities. Moreover, an analysis of European and American cases reveals a tendency for coastal or economic core areas to demonstrate greater benefits [53]. Conversely, this study uncovers a more pronounced positive impact on central and western cities, a phenomenon attributed to industrial transfer. This observation underscores the distinctive implications of China’s regional balance strategy. Secondly, from the perspective of city size, the latest report from the EU Green City Accord indicates that large cities achieve more pronounced green transition effects due to the concentration of technology, capital, and talent [54]. However, the findings of this study indicate that medium-sized and small cities benefit more significantly, attributable to lower marginal abatement costs and higher industrial transition flexibility. This finding is consistent with China’s gradient development characteristics, which are characterised by rapid urbanisation.

However, this study also posits that the implementation of more stringent pollution control measures may compel companies to reallocate capital from production processes to end-of-pipe treatment equipment, resulting in a substantial decline in total factor productivity in the short term. Drawing upon microdata from Chinese industrial enterprises emitting chemical oxygen demand (COD) between 2000 and 2007, several scholars have identified a correlation between the reduction targets for COD emissions and the decline in total factor productivity. The findings indicate that for every 10% increase in the reduction targets, there is an average decline of 2% to 3% in TFP, with enterprises that emit higher levels of pollutants experiencing more significant output losses [55]. Furthermore, the implementation of rigorous governance measures has the potential to impede employment prospects. Walker (2011) [56] utilised a quasi-experimental design, underpinned by the U.S. Clean Air Act, to demonstrate that the implementation of stringent environmental regulations resulted in a substantial curtailment of employment in industries, characterised by substantial pollution levels, particularly within the short term. The most pronounced impact was observed among low-skilled workers. The period of peak employment losses occurred 2–4 years subsequent to the implementation of the regulations, subsequently exhibiting a gradual recovery. However, it was observed that low-skilled positions failed to fully regain their pre-regulation levels.

6. Conclusions

The present study focuses on panel data from 268 cities in China from 2013 to 2022, constructs a concept and evaluation system for comprehensive urban development, and thoroughly explores the impact of WPC on UCD. The results of this study demonstrate the following:

(a)

WPC effectively promotes UCD, and this result remains valid after multiple robustness tests.

(b)

Mechanism analysis indicates that there is a direct impact of WPC on UCD through the process of industrial structure optimisation. This is due to the proactive introduction of clean production technologies and circular economy models by enterprises, which drives synchronous green technology upgrades across the entire industrial chain. Consequently, this promotes the development of UCD.

(c)

Heterogeneity analysis demonstrates that, from a geographical perspective, the effectiveness of UCD is more pronounced in central and western regions, and from a city size perspective, it is more evident in small and medium-sized cities. The resultant phenomenon is influenced by a number of factors, including urban marginal benefits, industrial transfer, and policy preferences.

7. Suggestions

Based on the core findings of this study, the following three specific and actionable policy recommendations are proposed, aiming to translate the research findings into actual improvements in governance effectiveness:

Firstly, the government should formulate regionally differentiated water pollution control policies. It is imperative that a mechanism is implemented, which will allow for the differentiation of pricing of sewage treatment fees, with the introduction of green financial incentives. Furthermore, the collection and use mechanism for urban sewage treatment fees must be reformed, and differentiated pricing must be implemented. The implementation of a tiered, differentiated sewage treatment fee collection standard should be informed by the industry sector, water usage efficiency, pollution intensity, and the adoption of advanced clean production technologies. For industries and enterprises with high water consumption and pollution levels, a significantly higher punitive rate than the benchmark should be applied. Conversely, enterprises that adopt internationally or domestically leading water-saving and pollution-reduction technologies, with water consumption per unit of product and pollution emissions far below industry standards, should be eligible for rate discounts or refunds. Concurrently, this should be linked to green finance. It is imperative that an environmental credit evaluation system be established for enterprises. It is imperative that the evaluation results be integrated into green finance policies in a profound manner. Enterprises that demonstrate excellence in environmental credit should be accorded priority when it comes to accessing green bank loans, local government green bond support projects, and green guarantee enhancement.

Secondly, the establishment of a ‘Regional Water Environment Management and Industrial Transformation Collaboration Centre’ is recommended. The establishment of physical or virtual ‘Regional Water Environment Management and Industrial Transformation Collaboration Centres’ in key central and western regions or typical small and medium-sized city clusters is to be initiated by provincial governments, with the objective of facilitating the implementation of WPC and enhancing UCD. The primary focus should be on the procurement or development of low-cost, high-efficiency wastewater treatment and monitoring technologies that are suitable for small and medium-sized cities and underdeveloped regions. The establishment of a technology database and an expert database is also essential, as is the provision of customised technical solutions, operational training, and diagnostic services to cities within the region, with the aim of addressing their technical capacity constraints. The combination of regional resource endowments and WPC requirements should result in the compilation of a ‘negative list’ and ‘encouragement directory’. The centre should be staffed with industrial analysts to provide local governments and enterprises with consulting services on transformation pathways, green project matching, and clean production technology transfer services. The primary focus should be on the guidance of traditional manufacturing industries towards the adoption of water-saving and clean production upgrades, while concomitantly fostering related green industries such as the manufacturing of environmental protection equipment, the provision of ecological tourism services, and the provision of water environment services. Digital technology can be employed to evaluate the effectiveness of environmental protection or development and reform platforms at the provincial level. The effectiveness of these platforms can be measured using metrics such as the number of cities served by the centre, project implementation rates, technology adoption rates, and final water-quality improvement and industrial-upgrading indicators.

Thirdly, the implementation of an upgraded version of the ‘River and Lake Chief System +’ is to be undertaken, with this system then being incorporated into the comprehensive assessment of UCD and interdepartmental coordination. It is imperative that the existing River and Lake Chief System framework be strategically upgraded in order to create a ‘River and Lake Chief System + UCD’ model. It is imperative to explicitly state that river and lake chiefs at all levels are not only accountable for maintaining the quality of the water environment within their respective jurisdictions, but also share the responsibility of promoting UCD. It is imperative that core indicators which reflect the effectiveness of WPC in promoting UCD are incorporated into the performance evaluation system for river and lake chiefs, with reasonable weightings. The establishment of a ‘UCD Water Environment Coordination Working Group’ is proposed, to be led by river and lake chiefs. Fixed member units must include departments such as ecology and environment, housing and urban–rural development, development and reform, natural resources, finance, investment promotion, and culture and tourism. The working group is to convene on a regular basis for the purpose of ensuring seamless integration between water pollution control plans and land use planning, industrial layout planning, waterfront urban design, and tourism planning. In the context of major projects involving water environment governance, the working group is responsible for coordinating the entire process, from planning, project approval, and funding procurement to post-construction operation and management. It is imperative that project designs incorporate the UCD objectives, including industrial integration, landscape enhancement, and public service infrastructure, thereby achieving the overarching objective of ‘treating one water body and enhancing one region’.