How Policy Misalignment Shapes the Municipal Solid Waste Disposal Capacity: A Multi-Level Governance Analysis

Abstract

Policy misalignment is a key factor affecting the implementation of solid waste management policies and resolving such a misalignment is critical to advancing the solid waste disposal capacity (SWDC) and supporting the goal of a “zero-waste city”. This policy misalignment indicator provides a measurable tool to track progress toward Sustainable Cities and Communities. This study used panel data from 281 cities at the prefecture level and above from 2018 to 2022. The study involved constructing an original database of central and provincial policy documents on urban waste governance and transforming the policy documents into an indicator to capture the degree of policy misalignment, which serves as the key explanatory variable in a fixed-effects model. The study further examined how fiscal decentralization, the digital economy, and regional and administrative characteristics influence cities’ responses to policy misalignments. These factors serve a vital function in moderating the effects of misalignment and explaining heterogeneity across cities. The empirical results show that a vertical policy misalignment significantly reduced the solid waste disposal capacity, while fiscal decentralization and digital economy development mitigated its negative effects. The adverse impacts were particularly pronounced in non-key cities, eastern regions, and cities with low government attention, highlighting the role of local capacity and administrative focus in mediating cross-level policy impacts. The heterogeneous effects observed across city types further offer targeted insights for designing sustainability-oriented waste management policies, enabling regions to tailor interventions based on their administrative capacity and development context.

1. Introduction

As a “misplaced resource”, municipal waste has a huge potential for resource and energy utilization, and the use of composting, incineration, and landfills to dispose of waste exerts a key role in the realization of the “dual-carbon” goal. However, it is difficult to achieve this goal by relying solely on technological innovation or economic incentives. As the pressure on waste governance continues to intensify, the function of policy factors has become increasingly prominent. Existing studies have shown that improvements in the solid waste disposal capacity (SWDC) do not rely solely on technological innovation or financial investment. Instead, their effectiveness largely depends on the combination of policy tools, the clarity of policy signals, and the strength of policy enforcement [1]. Effective policy implementation, especially policy coordination in a multi-level governance system, is key to guaranteeing the implementation of waste governance policies and capacity enhancement.

In a multi-level governance system, a policy misalignment may occur. Policy misalignments, broadly understood as inconsistencies or divergences between policies issued by different levels of government, have been widely recognized as a critical challenge to effective governance. In this study, we focused on a specific dimension of misalignment: differences in the policy constraint intensity, which reflects variations in the degree of control, enforcement mechanisms, and regulatory mandates embedded within policy texts at the central and provincial levels. In reality, a policy misalignment not only impedes the comprehension and acceptance of policy objectives by policy implementers, but they also may result in issues such as policy distortion or an imbalance in resource allocation, which further restrict the effectiveness of local government policy implementation.

Such limitations are especially salient in the context of environmental governance, where the complexity and urgency of waste management call for well-aligned policy guidance across government levels. Policy misalignments are directly related to the ability of local governments to treat waste. Through clear policy signals and standard-setting, the central government establishes national targets and implementation frameworks for environmentally sound waste governance, emphasizing its strategic significance for carbon emission reduction. However, these strategically oriented policies are often distorted during their transmission to local levels due to inconsistencies in policy intensity, vague implementation standards, and uneven resource allocation. Provincial governments, as the key link in transit and coordination, need to formulate refined programs considering the actual situation in their regions; coordinate resource allocation; and promote the shifting of funds, technology, and human resources toward key areas of waste governance. It is precisely because provincial governments have a certain degree of flexibility in formulating policies that their policy transmission process may still result in signal alienation due to local interest games. Municipal governments, as the final link in policy implementation, not only need to directly undertake the specific task of environmentally sound waste governance but also need to deal with both policies from the central and provincial levels. The sensitivity of municipal governments to signal strengths and differences will directly affect their understanding of policy, which will have a far-reaching impact on enhancing the SWDC.

Thus, it is evident that policy misalignments deserve focused attention in policy design. Against this backdrop, the core objective of this study was to examine the impact of policy misalignment on the SWDC within the central–provincial–municipal multi-level policy transmission process. This investigation aimed not only to clarify the direction and magnitude of a policy misalignment’s influence on the SWDC, but also to uncover the interactive dynamics among multi-level governments during policy implementation. The rest of our paper is structured as follows: Section 2 presents the literature review; Section 3 elaborates on the theoretical analysis and research hypotheses; Section 4 introduces the methodology and data; Section 5 contains an analysis of the results; and Section 6 concludes with conclusions and policy implications.

2. Literature Review

2.1. Policy Effectiveness and Environmental Governance

In recent years, scholars have systematically explored the impact of policy effectiveness on environmental governance performance from the perspectives of policy texts and implementation. Yu et al. assessed the degree of policy constraints by examining a government work report for the inclusion of key performance indicators and accountability mechanisms [2]. Xu et al. developed a vocabulary database to identify the features of policy constraints in environmental governance goals [3]. Subsequently, Xu et al. analyzed the frequency of keywords such as “regulate”, “assess”, and “supervise” to evaluate the degree of policy constraints and infer government preferences in achieving environmental goals [4]. Meanwhile, from the policy goal–subject–instrument framework, Zheng et al. found that the policy goal clarity, subject participation, and instrument effectiveness exerted a significant positive effect on environmental governance performance [5]. Using firm-level data, Hu et al. revealed the inhibitory effect of policy uncertainty on green investments, noting that a stable and predictable policy environment is an important prerequisite for ensuring policy implementation effectiveness [6]. Furthermore, Pan et al. found through an analysis of cross-country samples that policy stringency can improve governance performance, though its effectiveness depends on the degree of alignment between environmental policies and economic policies [7].

Overall, the existing studies generally agree that the effectiveness of policy elements is a key characteristic determining the policy implementation performance and environmental governance effectiveness. These policy elements not only reflect the strength of the government’s policy signals but also shape the implementation incentives and behavioral patterns of local governments and enterprises, thereby collectively influencing the overall performance of environmental governance.

2.2. Practical Characteristics of Policy Implementation Under Multi-Level Governance

The central government often formulates public policy tools that combine coercive and incentive-based measures. Local governments will include flexible policy implementation under “institutional resilience” [8]. That is, when local governments are faced with multi-tasking situations, they will rationally take policy implementation strategies [9]. China’s multi-level governance system enhances local governments’ autonomy while bringing about differences in policy implementation effects. Studies have shown that higher levels of government have formed a principal–agent relationship of “task assignment–execution” by indexing, quantifying, and decomposing tasks to lower levels of government [10]. This system requires lower levels of government to fulfill tasks within a prescribed framework, but lower levels of government usually flexibly adjust their policy implementation strategies according to their own circumstances in response to pressure from higher levels [11]. Under this model, lower-level governments have autonomy and can formulate specific implementation programs while ensuring the completion of tasks. It is also possible that the requirements of higher-level governments are too specific and stringent, leading to strategic adjustments of local governments in the process of implementation, which can result in policy implementation bias [12]. A vertical power structure exerts policy implementation pressure on lower-level governments through the task allocation and assessment mechanism of higher-level governments, which profoundly affects the autonomy, strategy adjustment, and implementation effect of local governments.

2.3. Policy Coordination and Implementation Effectiveness Among Multi-Level Governments

The research on policy coordination and implementation across multi-level governments has primarily focused on three areas. First, some studies have investigated policy implementation within a single government at a specific administrative level. These studies examined the continuity and stability of policy objectives, measures, and orientations over time [13]. Second, horizontal comparative studies have analyzed horizontal policy coordination between governments at the same level [14], which is often assessed through the similarity of policy document content [15]. Third, vertical comparative studies, which are the focus of this paper, have explored vertical policy misalignments between multiple levels of government. Much of the existing literature has examined policy misalignments across different levels of government through the lens of principal–agent theory, particularly focusing on the relationship between multiple levels of government. These studies highlight that policy deviation and discrepancies in formulation and implementation often arise due to significant information asymmetry between levels of government [16]. Central governments typically convey policy goals to local governments through formal guidelines and plans, while local governments adjust their responses and implementation strategies accordingly [17]. When policy transmission among administrative levels deviates, it will affect the policy implementation outcomes of local governments [18].

To summarize, policy consistency across different levels does play a critical role in shaping policy effectiveness. In particular, the policy consistency between the central and provincial governments exerts a significant impact on policy outcomes. However, there are two competing hypotheses regarding the policy relationship between the central and provincial governments. One is the complementary relationship hypothesis: provincial policies continue the policy goals and intentions of the central government. When supportive signals from both the central and provincial governments are simultaneously strengthened, municipal governments tend to achieve positive policy innovation performance in the process of policy diffusion [19]. The other is the competitive relationship hypothesis: policy conflicts exist between the central and provincial governments, and such conflicts will reduce policy implementation outcomes [20]. This study focuses on exploring how the policy outcomes of municipal governments behave when there are conflicts and misalignments between central and provincial policy signals.

2.4. Research Gaps and Design

To systematically sort out the core literature context of this study,

Table 1. Summary Table of Literature Themes, Research Perspectives and Gaps.

Literature Theme Section	Research Theme Focus	Research Gaps and Research Perspectives
Policy Effectiveness and Environmental Governance	What are the evaluation criteria for policy effectiveness? How does policy effectiveness affect environmental governance outcomes?	This study adopts the policy signal transmission perspective, expands the analysis of policy effectiveness from a single level to a multi-level governance structure, and examines the transmission mechanism of policy signals among cross-level governments.
Practical Characteristics of Policy Implementation Under Multi-Level Governance	Why may policy implementation outcomes of local governments deviate under multi-level governance?	Most existing studies focus more on the interaction between two levels of governments, while few have systematically analyzed the central-provincial-municipal three-level government relations. This study simultaneously analyzes the interactive relations among the central, provincial, and municipal governments.
Policy Coordination and Implementation Outcomes Among Multi-Level Governments	What are the research perspectives on policy consistency? How does policy consistency affect policy implementation outcomes? How do policy relations among multi-level governments affect policy implementation outcomes?	This study argues that the relationship between policy signals issued from the upper-level and middle-level governments tends to be competitive rather than complementary. Based on this hypothesis, this study examines how the central-provincial policy relationship affects the policy implementation outcomes of municipal governments.

summarizes the core research questions, existing research gaps, and the research perspective adopted by this study corresponding to the three major literature themes (see Table 1). Most prior studies on policy alignment have centered on horizontal consistency within a single governance level. While another subset of studies has adopted generalized multi-level governance frameworks, they have often failed to highlight policy signal transmission across the central–provincial–municipal three-tier hierarchy; this transmission process is a key link that influences the effectiveness of municipal policy implementation.

Against the above research gaps, this study aimed to fill the void through two targeted innovations, with its specific contributions outlined as follows: First, it integrated the “control rights” theory into the analytical framework to examine policy interactions across the central–provincial–municipal three-tier hierarchy, directly addressing the gap that “existing multi-level governance studies rarely highlight three-tier policy signal transmission”. By analyzing the characteristics of central and provincial policy texts, this study identified variations in the intensity of policy misalignment and clarified how central–provincial policy misalignment affects the implementation effectiveness of municipal governments.

Based on relevant research ideas, the present research drew on panel data encompassing 281 Chinese cities, covering the years 2018–2022. An empirical model of policy misalignment and the SWDC was constructed for cities at the prefecture level and above. The study focused on the impact of central–provincial policy misalignments on the policy implementation effect in these cities. It further explored the moderating roles of fiscal decentralization and the development of the digital economy in this relationship. A heterogeneity analysis was conducted from three perspectives: regional location, municipal government attention, and the pilot policy status. Through these analyses, the research provides a new perspective on the behavioral mechanism of grassroots governments’ policy implementation in the context of multi-level governance. It also reveals the interaction between both levels of government in policy implementation. The study then provides important references for optimizing policy design, rationally allocating resources, and enhancing the performance of grassroots governance.

3. Theoretical Analysis and Research Hypotheses

By integrating the control rights theory and the signaling theory, and embedding them into an analytical framework for the misalignment of multi-level policy signals, this study involved conducting an analysis from the perspective of a three-tier government structure (Figure 1). This theoretical integration employed the control rights theory to define the powers and responsibilities of governments at all levels, while utilizing the signaling theory to describe the dynamic process of policy transmission between these levels.

To explain Figure 1 in alignment with the integrated theoretical framework, the figure can be broken down into two core dimensions: the policy signal transmission process and intergovernmental roles and tasks. Regarding the process of signal transmission, the central government issues policy signals to provincial governments while directly sending policy signals to local governments, forming a cross-level information interaction pattern. After making adaptive adjustments to the signals from the central government, provincial governments transmit them downward to local governments, and this entire flow embodies the dynamic signal issuance–signal transmission–signal feedback mechanism of the signaling theory, where each level of government interprets and relays policy intentions in sequence. Regarding the roles and tasks, the central government (principal) sets overarching policy objectives; the provincial government (manager) undertakes tasks assigned by the central government and supervises local governments’ behaviors; and the local government (agent) executes policies from higher levels, translating the policy signals into concrete actions.

3.1. Direct Effect of Policy Misalignment on SWDC

The fundamental hypothesis underpinning this study is that the relationship between policy signals issued by central and provincial governments tends to be competitive rather than complementary [18]. It appears that municipal governments need to receive dual signals from different levels of government. Combined with signaling theory, municipal governments, as signal receivers, need to consider the consistency of the two types of policy senders [21]. Municipal governments have limited time, resources, and attention. As a result, they may not have sufficient capacity to fully and simultaneously respond to policy from different levels of government. When dealing with the dual pressures of central and provincial government policy implementation, they may face conflicting demands. As a result, municipal governments are usually less flexible in responding to policy from both. This may lead to less effective policy implementation. In particular, the municipal SWDC tends to be weakened to a greater extent when the difference between the central and provincial policy is high. When the policy gap between different levels of government increases, municipal governments must respond to more regulatory requirements from these different levels. This not only increases the administrative burden but may also lead to conflicts between municipal governments regarding resource allocation and policy prioritization judgments. Such conflicts may undermine their effective governance regarding environmental protection.

Therefore,

Hypothesis 1.

Proposes that when the degree of policy misalignment between the different levels of government (central–provincial) is high, the effectiveness of policy implementation—namely, the SWDC—will be weakened.

3.2. Theoretical Analysis of Moderating Effects

3.2.1. Fiscal Decentralization

Having established that policy misalignments in multi-level governments affect municipal governments’ SWDC, we further propose that local fiscal decentralization plays an important moderating role. This moderating role influences the relationship between policy misalignments and the SWDC. Fiscal decentralization affects the relationship between the central and local governments. It has important implications for local government policy formulation and implementation [22]. Existing studies have found that local fiscal conditions significantly impact the effectiveness of policy implementation. These findings draw attention to the role of fiscal decentralization in environmental governance [23]. Specifically, local governments with sound fiscal conditions tend to have a stronger resource mobilization capacity and greater policy autonomy, which may weaken the central government’s actual control [24]. In studies on the relationship between fiscal decentralization and local governance, researchers commonly advance two competing hypotheses: the “race to the top” and the “race to the bottom”. The “race to the top” hypothesis argues that greater fiscal autonomy can motivate local governments to improve their public service provision and environmental governance [25]. Conversely, according to the “race to the bottom” hypothesis, fiscal decentralization may prompt local governments to give precedence to near-term economic outcomes over long-term public good, thereby compromising governance benchmarks and initiating a competitive race to lower regulatory standards [26].

These two hypotheses highlight how fiscal decentralization may affect governance outcomes. This study is based on the “race to the top” hypothesis. It argues that local governments with greater fiscal autonomy have a stronger resource capacity and more policy discretion. These features encourage the more active implementation of environmental policies [27]. This study examined whether fiscal decentralization affects policy implementation at the municipal level, especially when the policy signals from the central and provincial governments diverge. Specifically, in the context of fiscal decentralization, local governments, especially municipal governments, have significantly increased their autonomy as the degree of fiscal decentralization increases. This fiscal autonomy means that local governments are less financially dependent on higher levels of government [28]. They are also able to deploy local resources more autonomously for policy implementation and local development [29]. This change allows local governments to be more flexible in making decisions based on local realities when responding to the policies of higher levels of government. This flexibility helps reduce the adverse effects of a policy misalignment between higher levels of government. In this case, local governments that are financially richer can respond better to the control of higher governments through their own resources, showing more flexibility [30]. Thus, the increased financial autonomy of local governments allows them to better balance the contradiction between the policy requirements of higher levels of government and local realities when implementing policies.

Taken together, the autonomy and flexibility of local governments in policy implementation have been enhanced through a higher degree of fiscal decentralization. This can promote the buffering role of local governments in responding to policy misalignments. A high degree of fiscal decentralization may help municipal governments respond more flexibly to differences between central and provincial policies. It may also help mitigate the negative effects of policy misalignments through resource allocation or policy adjustments. Ultimately, this mechanism can help reduce the adverse effects of policy misalignments between the central and provincial levels. This, in turn, improves the coordination and cooperation between the central and local governments.

This leads to Hypothesis 2:

Hypothesis 2.

Fiscal decentralization has a moderating effect on the relationship between policy misalignments and the municipal SWDC, and the higher the degree of fiscal decentralization, the weaker the negative effect of policy misalignment on the SWDC.

3.2.2. Digital Economy

Based on the existing literature, the digital economy is anticipated to exert a moderating effect on the association between policy misalignments and the SWDC. The emergence of the digital economy has promoted the uptake of advanced technologies within the realm of public governance. These tools enhance the efficiency, accuracy, and responsiveness of subnational governments in implementing policies. In contexts where policy misalignments exist between different levels of government, digital technologies can reduce information distortion and asymmetry while strengthening intergovernmental communication channels [31]. Therefore, in regions with more developed digital economies, subnational governments can better utilize digital tools to overcome communication barriers and mitigate the negative effects of policy misalignments on the governance effectiveness. On one hand, digital technologies improve the top-down communication by enabling higher-level governments to convey policy goals and intentions more clearly and promptly. On the other hand, they enhance performance monitoring, allowing higher-level governments to more accurately track local implementation, reinforce policy accountability, and ultimately improve the local governance capacity.

Hypothesis 3.

Digital economic development moderates the relationship between intergovernmental policy misalignments and the municipal SWDC, such that higher levels of digital economy attenuate the negative impact of these misalignments.

4. Methodology and Data

4.1. Empirical Model Setting

On the basis of theoretical analysis, this study focuses on the empirical relationship between policy misalignment and SWDC. Let $Y_{it}$ represent the SWDC of city $i$ in year $t$, and $PM{A}_{j,t-1}$ represent the policy misalignment between the central and provincial governments in year $t-1$ for province $j$. This variable is at the provincial level, and cities adopt the policy misalignment from the province $j$ they belong to. The empirical model is as follows:

$$Y_{it}={\gamma }_0+{\gamma }_{1}PM{A}_{j,t-1}+{\gamma }_2{X}_{it}+{\varphi }_i+{\psi }_t+{\epsilon }_{it}$$

(1)

4.2. Variable Definitions and Model Specification

In Equation (1), $Y_{it}$ represents the SWDC(Capacity) of city $i$ in year $t$. $PM{A}_{j,t-1}$ denotes the central-provincial policy misalignment (policy misalignment, PMA) for the province $j$ where city $i$ is located in year $i$. $X_{it}$ represents city-level control variables, control variables include GDP per capita (GDP), Population Density (Population), Waste Treatment Facilities (Facilities), Technological Level (tech), Industrial Structure (Structure), Energy Consumption (Consumption), Media Advocacy (Media), Public Environmental Attention (PEA). And ${\varphi }_i$ denote city fixed effects, and ${\psi }_t$ denote year fixed effects, and ${\epsilon }_{it}$ is the random error term. The Hausman test rejected the null hypothesis (Chi-square statistic = 96.60, p < 0.001), so the fixed effects model was used. To mitigate potential reverse causality, the policy misalignment variable is lagged by one period.

4.3. Endogeneity Considerations

The empirical model of this study may suffer from omitted variable and reverse causation problems. Specifically, omitted variable bias may occur when certain uncontrolled variables affect both the core explanatory variable, policy misalignment, and the explanatory variable, SWDC. This can lead to biased estimates. In addition, reverse causation problems may also exist. Municipal SWDC may, in turn, affect policy formulation, resulting in bidirectional causation. All these issues may affect the validity of the model. They may also impact the accuracy of causal inference.

Therefore, to address the problem of reverse causation, the policy misalignments are treated with a one-period lag in the measurement of the independent variables. This helps mitigate the possible reverse causality between policy misalignment and SWDC. In addition, to overcome omitted variable bias as much as possible, this study adopts the instrumental variable estimation method. The selected variable is a distance-type variable. Specifically, it is the distance from the provincial capital of the province where the city is located to Beijing (Distance). This type of instrumental variable is correlated with policy misalignment. The distance from the provincial capital to Beijing significantly affects central-provincial policy misalignment. Some scholars point out that geographically distant local governments may have poorer compliance in responding to the central government’s policy signals. This is due to factors such as lags in information transmission [32]. Remote provinces may only respond mechanically to central government policies. Even though some local governments adopt certain policies, the content of those policies may not correspond to the actual situation in that place [33]. In this way, the relatively low policy misalignment between the central government and the provinces can have a negative effect. These provincial governments may reduce policy adjustments and innovations when transmitting the central government’s policies “as is.” They may also enact policies only to cope with higher levels of government [34]. As a result, provinces that are far away have less flexibility and initiative in their policy response. They often can only mechanically “copy” policies rather than make proactive adjustments. This makes local policies inconsistent with local realities. Consequently, even though the difference between local and central policy is reduced, policies with a high degree of similarity may still be enacted only to respond to central policies. This may reduce the effect of policy implementation rather than increase it.

In addition, the exogeneity of this distance variable needs to be clarified. Specifically, the instrumental variable should not affect SWDC. Firstly, the distance from the provincial capital to Beijing is a spatial variable. It mainly affects the efficiency of policy transmission from the central government to the provincial government. This spatial distance does not directly change the city’s economic level or the condition of public service facilities. These factors are the key determinants of SWDC. Therefore, the exogeneity condition is approximately satisfied. Secondly, cities closer to provincial capitals are generally also cities with stronger economic levels or better public service facilities. To address this issue, the following control variables are introduced. First, the city’s level of economic development (such as GDP). Second, the city’s infrastructure situation (such as the condition of waste treatment facilities).

4.4. Variable Definition

4.4.1. Key Independent Variable

The independent variable in this study was the degree of central–provincial policy misalignment, whose concept consists of two core connotations. First, the policy in this concept specifically refers to the policy constraint characteristics. These characteristics are reflected in policy texts that convey governmental objectives and implementation intentions, including the assertiveness of policy intent, the clarity and intensity of policy signals, and the rigidity of policy targets, all of which essentially reflect the constraint intensity of policies. Second, the misalignment draws on the concept of institutional misalignment between governance levels: it refers to the state of misalignment between different levels of government regarding formal institutions, implementation capabilities, and goal orientations [35]. In the context of this study, this misalignment was concentrated on the policy constraint characteristics mentioned above. To summarize, a central–provincial policy misalignment is defined as a deviation in the intensity of policy constraints expressed by provincial governments relative to the central government on the same policy issue. To capture the level of this misalignment in an empirical operation, this study drew on existing measurement approaches for policy constraints in prior research [4], which quantify such constraints through text analyses of policy documents to reflect the binding nature of policy objectives and implementation requirements. Given that the constraint intensity is the core embodiment of policy constraint characteristics, we focused on analyzing the differences in the constraint intensity articulated in official policy documents (central and provincial) to quantify the degree of policy misalignment.

The policy misalignment was measured through an analysis of policy texts. A combination of manual coding and computer-assisted text analyses was used in the measurement process, as shown in Figure 2.

First, policy texts were collected and screened. The policy theme of the study, namely, “household waste”, was identified. Using official government websites and the Beida Fabao Database (https://www.pkulaw.com/, accessed on 10 August 2025), policy documents issued by the central and provincial governments from 2017 to 2021 were searched for using the keyword “household waste.” For this study, we collected 50 central policy documents and 412 provincial policy documents. We excluded other detailed documents such as specific working arrangements for individual meetings, targeted replies to local governments’ specific inquiries, and criteria for single-issue matters. The included policies comprised both comprehensive and specialized solid waste governance documents, covering the entire process of municipal solid waste classification, collection, transportation, and disposal.

Second, a vocabulary database of constraint-related words was constructed. Using Python (version 3.10), a list of the 1700 most frequently occurring words in the central government policy texts was generated. An evaluation team of four members was then formed, consisting of two doctoral students, one policy researcher (university faculty member), and one government official. Each member independently screened the list to identify words that reflect the control intent in the policy texts. The content of the questionnaire is provided in the Supplementary Material S1. Ultimately, 34 constraint-related words were agreed upon, with the top 25 most frequent words selected as characteristic terms for subsequent text analyses; please refer to Appendix A for the constraint-related words. We compared it with the keywords mentioned in existing literature [2,3], and it is basically consistent with the meanings intended by “constraints” and “bureaucratic control”.

Next, Python and other tools were used to count the occurrences of constraint-related words in each policy text. Following the measurement approach of Xiao et al., the frequency of constraint-related words was calculated as the total number of occurrences of these words in a single policy text divided by the total number of words in that text [36]. This calculation served to standardize the intensity of the policy constraints.

Third, the variable for policy misalignment was constructed. The constraint word frequency was aggregated by year to calculate the “policy constraint intensity of the central government in a given year”. Similarly, the frequency was aggregated by province and year to calculate the “policy constraint intensity of a specific province in a given year”.

Next, the policy misalignment index is calculated, and the measurement process is as follows: First, the policy constraint intensity at the central and provincial levels is standardized by year, respectively, with the standardization formula shown below:

$${{\displaystyle Z}}_t^C={\displaystyle \frac{C_t-\stackrel{\_\_}{\mathrm{C}}}{SD(C)}}$$

(2)

$${{\displaystyle Z}}_{jt}^P={\displaystyle \frac{P_{it}-\stackrel{\_\_}{P}}{SD(P)}}$$

(3)

Among them, $C_t$ represents the policy constraint intensity of the central government in year, $P_{jt}$ represents the policy constraint intensity of province $j$ in year $t$, $\stackrel{\_\_}{\mathrm{C}}$ and $\stackrel{\_\_}{P}$ are the annual averages of the policy constraint intensity at the central and provincial levels, respectively, $SD(C)$ and $SD(P)$ are the standard deviations of the corresponding years.

Second, the standardized difference between the two is calculated, with the formula as follows:

$${{\displaystyle PM{A}_{jt}=Z}}_t^C{{\displaystyle -Z}}_{jt}^P$$

(4)

Among them, $PM{A}_{jt}$ represents the degree of policy misalignment of province $j$ in year $t$. If $PM{A}_{jt}>0$, it indicates that the provincial policy constraint is weaker than that of the central government; If $PM{A}_{jt}<0$, it indicates that the provincial policy constraint is stronger than that of the central government.

4.4.2. Dependent Variable

The dependent variable, the SWDC (capacity), can be broadly categorized into the designed and actual SWDCs. This study utilized the designed capacity to measure the SWDC. The designed SWDC refers to the theoretical or expected waste treatment capability provided by a city’s existing infrastructure and waste treatment facilities. For example, indicators such as the “non-hazardous SWDC”, commonly reported in statistical yearbooks [37], reflect this dimension. Tang Ying et al. further refined this by using the “per capita non-hazardous SWDC” as a normalized measure across cities [38]. In contrast, the actual SWDC refers to the real volume of waste that is treated by a city. For instance, Lu et al. adopted the “daily average amount of non-hazardous waste treated” to represent the actual operational performance of a city’s waste governance system [39].

In international research, such as a study on the SWDC of municipal governments in Peru, the capacity for safe disposal is defined as the “appropriateness of waste treatment”. It is measured using the proportion of waste that is disposed of through approved methods such as landfilling, recycling, or composting on an annual basis [39].

Therefore, the municipal SWDC can be assessed through two main dimensions: the designed SWDC, which is based on the existing infrastructure capability, and the actual SWDC, which is based on a city’s historical waste treatment performance. The designed SWDC reflects a government’s policy responsiveness to higher-level policy guidance. In contrast, the actual SWDC captures the operational effectiveness and outcomes of waste treatment efforts. Since the core focus of this research was to investigate the policy implementation and responsiveness of municipal governments, the designed SWDC was employed as the primary measure for the dependent variable. This choice emphasizes the municipal government’s infrastructural response to policy directives, which is a more accurate reflection of their strategic and long-term alignment with higher-level policies.

Accordingly, this study measured the SWDC using the designed “non-hazardous SWDC”. This SWDC indicator was further standardized on a per capita basis, with the final metric expressed as tons per year per 1000 people. The data were sourced from the Urban Construction Statistical Yearbook, which reports statistics on the urban household waste treatment facility capacity.

4.4.3. Moderating Variables

The measurement of Fiscal Decentralization (FD) is based on the relative share of fiscal revenue and expenditure at the city level, specifically the proportion of a city’s fiscal revenue in the total national fiscal revenue [40]. This indicator reflects the fiscal autonomy and resource mobilization capacity of subnational governments, which is crucial for the provision of public services such as environmental governance. A higher degree of fiscal decentralization indicates that subnational governments have greater control over financial resources, thereby enhancing their capacity to invest in infrastructure and deliver services like municipal waste treatment (measured as the ratio of a city’s SWDC to its population). Referring to the variable construction method proposed by Zhan Xinyu [41], the formula used to calculate fiscal decentralization is as follows: FD = City Fiscal Revenue/(City Fiscal Revenue + Provincial Fiscal Revenue + Central Fiscal Revenue). Second, to objectively measure the digital economy level (Digital)at the prefecture-level city scale, this study constructs a composite index using the Entropy Weight Method. Drawing on existing literature and considering data availability at the city level, this study selects five positive indicators to construct the index: as shown in

Table 2. Composite index for the Evaluation of the Level of Digital Economy.

Primary Indicator	Secondary Indicator	Indicator Attributes
Number of people working in ICT industry	Tens of thousands of people employed in the ICT-related industries.	positive
Inclusive Development of Digital Finance	the Digital Inclusive Finance Index	positive
Internet penetration rate	Number of Internet users per 100 people	positive
Internet-related output	Total telecommunications business volume per capita (ten thousand yuan per person)	positive
Number of mobile internet users	Number of mobile phone users per 100 people	positive

, (1) the Digital Inclusive Finance Index, (2) the number of internet users per 100 people, (3) the proportion of the workforce employed in information, computer, and telecommunications (ICT)-related industries, (4) total telecommunications business volume per capita (10,000 yuan/person), and (5) the number of mobile phone users per 100 people [42]. Higher values of these indicators collectively reflect a higher level of digital economic development in the respective cities.

4.4.4. Control Variable

The GDP per capita was expressed as the city GDP per capita. The population density was expressed as the city population density [37]. Waste treatment facilities were expressed as the number of harmless treatment sites. The technological level was measured by scientific expenditure, which captures a city’s fiscal investment in scientific research, technological advancement, and innovation. Given that the treatment of non-hazardous municipal solid waste relies heavily on advanced technological support, this indicator serves as an important proxy for the city’s capacity to adopt and implement modern waste treatment technologies. The industrial structure was measured by the share of tertiary industry value added in the GDP (%), which reflects the developmental stage of a city’s industrial structure [38]. The energy consumption was proxied by the annual electricity consumption, which served as a representative indicator of a city’s overall energy usage and, by extension, its potential carbon emissions and environmental footprint [43]. The media advocacy was measured by the number of media reports on the keyword “solid waste”. It can increase public visibility and pressure local governments to enhance their waste management performance. Public environmental attention was controlled for to reflect the general level of public concern about environmental problems, as a higher awareness—proxied by the Baidu search index for “haze”—may motivate local authorities to respond more actively to environmental governance challenges.

At the same time, this study preprocessed the data by first filling in the missing values of the variables using interpolation. Then, the SWDC, GDP per capita, population density, treatment facilities, fiscal decentralization, removal volume, and instrumental variables were all logarithmic with a plus one. Next, for the possible extreme values, the upper and lower 1% deciles were used for the reduction in the tails. Finally, all the variables were standardized.

4.4.5. Other Variables

Firstly, this study uses key cities (key) as a dummy variable in grouped regression analysis. In line with the central government’s pilot city initiative, 46 key cities were designated to establish comprehensive systems for household waste sorting, including centrally administered municipalities, provincial capitals, as well as cities holding independent planning status. These systems are required to achieve full coverage across all stages of the waste governance chain, including disposal, collection, transportation, and treatment [44]. This designation reflects the central government’s strategic emphasis on strengthening the overall urban SWDC by directly prioritizing these key cities in national policy deployment. In addition, key cities often receive Supplementary Support from central government infrastructure investment initiatives [45]. Therefore, the representative variable for key city status is constructed as a dummy variable, taking the value of 1 if the city is among the 46 designated key cities, and 0 otherwise. Secondly, regional location (location) is a critical dimension in the study of urban development and spatial disparities. Within the Chinese context, cities are generally categorized into three primary regions—eastern, central, and western—according to their geographical location and economic development level. Thirdly, this paper constructs the municipal government’s attention allocation variable (Attention) as a binary dummy variable based on the presence of the term “hazard-free treatment of household waste” in municipal government work reports. If the term is explicitly mentioned, the variable is assigned a value of 1, indicating a higher level of policy attention; otherwise, it is assigned a value of 0.

4.5. Data Description

During the data preprocessing phase of this study, missing values were first imputed using interpolation methods. To ensure consistency and reduce skewness, variables such as SWDC, per capita GDP, population density, treatment facilities, fiscal decentralization, waste collection and transportation volume, technological level, human capital, industrial structure, energy consumption, and instrumental variables were all transformed using natural logarithms after adding one to each value. In order to address potential outliers, a Winsorization process was applied at the 1st and 99th percentiles. Descriptive statistics pertaining to the variables can be found in

Table 3. Descriptive Statistics.

Variable	Mean	Std. Dev.	Min	Max	Obs
Capacity	3.133415	0.4197401	0.90309	4.338351	1365
PMA	−0.0001484	1.225957	−3.429345	2.47914	1365
GDP	4.771704	0.2108229	4.271809	5.275168	1365
Population	2.496248	0.4182578	1.067041	3.414171	1365
Facilities	0.4958799	0.2054919	0.30103	1.252844	1365
Structure	2.350777	0.6950564	0.076961	3.906005	1365
tech	10.72436	1.533589	6.663133	15.52928	1365
Consumption	14.30893	0.8249554	11.79163	16.63427	1365
Media	0.3919414	1.112878	0	14	1365
PEA	33.71875	33.88851	0.4739726	272.2959	1365

. Meanwhile, the overall data patterns of the main variables are presented in Appendix C Figure A1, Figure A2 and Figure A3.

With regard to data coverage, due to issues such as missing data and changes in administrative divisions—such as the absence of data for regions including Ningxia, Tibet, Hong Kong, Macao, Taiwan, and Sansha, as well as the administrative withdrawal of Laiwu and Chaohu as prefecture-level cities and the exclusion of four centrally administered municipalities—this study focuses on a balanced panel dataset comprising 281 prefecture-level and above cities from 2018 to 2022. This dataset is used to examine the impact of provincial-level policy responsiveness on municipal SWDC. The sources of the data are as follows: policy-related variables are collected from Beida Fabao and official government websites; distance variables are calculated using ArcGIS geographic information system software (https://www.esri.com/en-us/arcgis/products/arcgis-online/overview, accessed on 25 November 2025). Data on SWDC, per capita GDP, population density, treatment infrastructure, technological level, human capital, transportation volume, tertiary industry proportion and electricity consumption are drawn from the China Urban Construction Statistical Yearbook, China Urban Statistical Yearbook, and China Statistical Yearbook. Data on central government taxation are sourced from the China Statistical Yearbook, while municipal-level fiscal data are obtained from the National City and County Financial Statistics. Data on Media Advocacy sources from the China National Knowledge Infrastructure Newspaper Database (https://www.cnki.net) (accessed on 10 August 2025). And municipal government attention is derived from official municipal government work reports. Indicators of the digital economy are compiled from the China Urban Statistical Yearbook, statistical yearbooks of select prefecture-level cities, and the Wind Information Database.

5. Analysis of Results

5.1. Benchmark Regression Results

First, this study used a fixed-effects panel regression model. It controls for city fixed effects and year fixed effects. In Model (1) of

Table 4. Policy misalignment and SWDC: Baseline Regression and Robustness Tests.

Variable	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)	Model (6)	Model (7)
	Capacity	Capacity	Total Capacity	Capacity 25%	Capacity 50%	Capacity 75%	Capacity
PMA	−0.0274 *** (0.0105)		−0.0284 ** (0.0110)	−0.0487 ** (0.0192)	−0.0411 ** (0.0165)	−0.0277 (0.0206)	−0.0351 ** (0.0143)
PMA 2							−0.0069 (0.0055)
Count		−0.0229 ** (0.0102)
GDP	0.5533 * (0.3272)	0.5481 * (0.3284)	0.5265 (0.3657)	1.4350 *** (0.1503)	1.4848 *** (0.1295)	1.6806 *** (0.1611)	0.5149 (0.3130)
Population	−1.8390 *** (0.4937)	−1.8135 *** (0.4999)	0.3532 (0.4976)	0.1296 ** (0.0590)	0.0551 (0.0508)	0.1185 * (0.0633)	−1.8120 *** (0.4935)
Facilities	2.1445 *** (0.1816)	2.1430 *** (0.1831)	2.1611 *** (0.1824)	2.0946 *** (0.1195)	2.0254 *** (0.1029)	1.9089 *** (0.1281)	2.1424 *** (0.1821)
Structure	−0.1199 (0.1393)	−0.1052 (0.1426)	−0.1577 (0.1461)	−0.3153 *** (0.0475)	−0.3185 *** (0.0409)	−0.3248 *** (0.0509)	−0.1249 (0.1374)
tech	0.0244 (0.0260)	0.0235 (0.0263)	0.0227 (0.0265)	−0.0782 *** (0.0201)	−0.1368 *** (0.0173)	−0.1836 *** (0.0216)	0.0289 (0.0253)
Consumption	0.0982 (0.2043)	0.1108 (0.2074)	0.0917 (0.2096)	−0.2883 *** (0.0343)	−0.3391 *** (0.0295)	−0.3333 *** (0.0368)	0.0988 (0.2059)
Media	0.0034 (0.0065)	0.0035 (0.0065)	0.0048 (0.0063)	0.0130 (0.0176)	0.0484 *** (0.0152)	0.0496 *** (0.0189)	0.0025 (0.0064)
PEA	0.0013 (0.0008)	0.0012 (0.0009)	0.0013 (0.0009)	−0.0004 (0.0009)	0.0007 (0.0008)	0.0003 (0.0010)	0.0013 (0.0008)
Cons	4.3278 (3.0690)	4.0941 (3.1389)	7.3377 ** (3.0866)	2.0715 ** (0.8631)	3.6332 *** (0.7433)	3.3749 *** (0.9251)	4.4312 (3.0756)
Control variable	Y	Y	Y	Y	Y	Y	Y
Year fixed effects	Y	Y	Y	Y	Y	Y	Y
city fixed effect	Y	Y	Y	Y	Y	Y	Y
Obs	1365	1365	1365	1365	1365	1365	1365
R2	0.4212	0.4196	0.3934	0.3681	0.3919	0.4017	0.4222

Notes: Standard errors of (robustness) are in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1, “²” denotes the squared term of the corresponding variable.

, the core explanatory variable was the policy misalignment, and the dependent variable was the SWDC. When controlling for other variables, a one-unit increase in the policy misalignment was associated with an average decrease of approximately 2.74% in the SWDC at the 1% significance level. Specifically, a one-unit increase in central-provincial policy misalignment reduces municipal solid waste disposal capacity by an average of about 3 tons per year per 1000 people. In other words, greater policy misalignment may reflect inconsistencies or confusion between higher- and lower-level policies, which can undermine policy coordination and effectiveness, ultimately hindering the SWDC.

From the perspective of multi-level policy interaction, central and provincial policies exerted simultaneous impacts on the implementation effectiveness of municipal policies, and the interactions between central and provincial policy interventions exhibited a competitive rather than complementary nature. This aligns with Zhu and Zhang’s research conclusion that “the competitiveness of central-provincial policy interventions undermines the implementation efficiency of local policies” [18]. Based on China’s central–provincial–municipal three-tier administrative system, their study found that when the central and provincial governments impose policy interventions on municipal governments, divergences in their development goals (e.g., the central government focusing on advancing nationwide reforms and provincial governments prioritizing local interest coordination) tend to result in a competitive interactive relationship. This leaves municipal governments facing conflicting dual mandates, ultimately undermining the policy implementation effectiveness.

This mechanism can be directly applied to the context of solid waste governance: central–provincial policy misalignment essentially reflects discrepancies in the policy constraint intensity and goal prioritization regarding solid waste governance. Such discrepancies cause municipal governments to encounter dilemmas in resource allocation for solid waste governance, leading to dispersed implementation resources and chaotic processes, which, in turn, hinder the improvement of the local solid waste governance capacity. Meanwhile, Fan et al. quantified the horizontal consistency of China’s solid waste policies from the dimensions of policy goals, instruments, and issuing agencies [46]. Their findings revealed that none of the solid waste policies across 26 municipalities achieved a “perfect consistency”, and an insufficient consistency emerged as a key factor constraining the policy’s role in guiding solid waste governance practices. This provides direct corroboration for the conclusion of this study within the same governance context: central–provincial policy misalignments affect the municipal solid waste governance capacity. It demonstrates that consistency issues in solid waste policies, whether horizontal or vertical, are closely linked to the effectiveness of governance practices, which further enhances the robustness of the conclusions drawn in this study.

5.2. Endogeneity Analysis

To overcome the omitted variable bias, the instrumental variable estimation method was adopted to conduct the endogeneity analysis. The instrumental variable selected for this study was the distance from Beijing to the capital of the province in which the city is located. It is also argued in the previous section that this type of instrumental variable has the characteristics of relevance and exclusivity and is a suitable instrumental variable. However, it should be noted that since the instrumental variables selected in this study do not vary over time, this invalidates the usual second-stage estimation. Therefore, we interacted the instrumental variable with the national average level of provincial policy responsiveness (excluding the city itself) to construct a new instrumental variable that captures time-varying effects [47].

In Models (1) and (2) of

Table 5. Policy misalignment and SWDC: Instrumental Variables.

Variable	Model (1)	Model (2)
	Capacity	Capacity
PMA	−0.0393 *** (0.0119)	−0.0300 ** (0.0143)
Year fixed effects	Y	Y
City fixed effect	Y	Y
Obs	1365	1365
R2	0.4203	0.1809
Control variable	Y	N
Under identification test	118.785 ***	110.466 ***
Weak identification test	2077.512	2352.908

Notes: *** p < 0.01, ** p < 0.05.

, the estimated coefficients for policy misalignment were significantly negative at the 1% level. After including control variables, the negative effect of policy misalignments on the SWDC remained significant. This finding aligns with the baseline regression results and strengthens the robustness of the conclusion. With respect to the validity tests for the instrumental variables, the outcomes of the under-identification tests consistently rejected the null hypothesis, which demonstrates that the selected instrumental variables were identifiable. In addition, the weak instrument tests showed that the test statistics were all well above the critical value of 16.38 at the 5% significance level, suggesting that the weak instrument problem does not exist. These two tests jointly indicate that the instrumental variables are valid. The empirical findings further confirm that policy misalignments have a significant impact on the SWDC.

5.3. Robustness Test

To verify the robustness of the model results, we first used the policy quantity differences (count) as a proxy variable for the regression analysis. The number of policies can reflect the strength and direction of policy signals to a certain extent. In Model (2) of Table 4, we found that the coefficient of policy quantity differences was significantly negative, which is consistent with the regression result when the policy misalignment is used as the independent variable. The results show that the central–provincial policy misalignment significantly affects the SWDC of cities, from the differences in both the policy constraint intensity and the policy quantity. This further validates the robustness of the main regression model results.

Second, to verify the robustness of the main findings, regression analyses were conducted in this study by substituting the explanatory variable with an alternative dependent variable—the total capacity (tons/year) of each city. In Model (3) of Table 4, the regression coefficient of the provincial policy responsiveness with respect to the per capita capacity was significantly positive, which is consistent with the baseline regression results using the aggregate waste capacity as the explanatory variable. This confirms the robustness of the core regression outcomes.

Third, to further explore the heterogeneity in the effects of a policy misalignment on the SWDC, this study employed quantile regression at the 25th, 50th, and 75th percentiles. The corresponding results are reported in Models (4) to (6) of Table 4. Quantile regression enables the investigation of whether the influence of explanatory variables varies across different levels of the dependent variable. The results show that the estimated coefficients for the policy misalignment were significantly negative at the 5% level for both the 25th and 50th percentiles, while they were not significant for the 75th percentile. This suggests that cities with a high governance capacity may have more robust resource reserves or implementation mechanisms, enabling them to be more resilient to the interference of a “central–provincial policy misalignment”. In contrast, cities with a low or medium governance capacity are more sensitive to such policy misalignments.

Fourth, to investigate the potential presence of a non-linear relationship, the squared term of the policy misalignment variable was incorporated into the regression model. Model (7) of Table 4 shows that the linear term remained significantly negative at the 5% level, reaffirming a negative linear relationship between the policy misalignment and the SWDC. However, the coefficient of the squared term was statistically insignificant, providing no substantial evidence to support the existence of a non-linear effect.

Fifth, a multicollinearity test was conducted, with results presented in Appendix B 

Table A1. Multicollinearity Test.

Variable	VIF	1/VIF
Structure	3.21	0.311162
GDP	2.92	0.342826
tech	2.88	0.347219
PEA	2.71	0.368608
Consumption	2.40	0.417305
Population	1.90	0.526352
Facilities	1.82	0.550923
PMA	1.21	0.828512
Media	1.15	0.867727
Mean VIF	2.24

. All the variables had VIF values below 3.17 (with a mean VIF of 2.08), indicating no severe multicollinearity. Then, to test for serial correlation, the Jochmans portmanteau test was employed to examine the intra-group serial correlation in the panel data; the results showed a chi-square statistic of 29.905 with a corresponding p-value of 0.0005, rejecting the null hypothesis at the 1% significance level and indicating significant intra-group serial correlation in the model. Finally, for heteroskedasticity testing, the modified Wald test was conducted; the results revealed a chi-square statistic of 940,000 and a p-value of 0.0000, rejecting the null hypothesis at the 1% significance level and demonstrating significant group-wise heteroskedasticity in the model. In response to the test results, this study employed clustered robust standard errors in the regression analysis to ensure the reliability of the estimation results.

5.4. Moderating Effect Analysis

5.4.1. Analysis Results of the Fiscal Decentralization

To examine how varying levels of fiscal decentralization between local governments affect the SWDC, this study investigated its moderating effect on the relationship between policy misalignments and the SWDC. Models (1) and (2) in

Table 6. Moderation effect and grouped regression.

Variable	Model (1)	Model (2)	Model (3)	Model (4)
	Capacity	Capacity	Capacity	Capacity
PMA	−0.0250 ** (0.0103)	−0.0418 *** (0.0125)	−0.0271 ** (0.0105)	−0.0574 *** (0.0176)
FD	57.6668 (36.7395)	81.1960 ** (38.1446)
PMA*FD		6.5534 *** (1.5330)
Digital			0.3020 (0.2789)	0.1036 (0.2733)
PMA*Digital				0.2199 ** (0.0873)
cons	4.3317 (3.1018)	5.3325 * (3.0052)	4.4077 (3.0500)	5.0074 * (3.0095)
Control variable	Y	Y	Y	Y
Year fixed effects	Y	Y	Y	Y
city fixed effect	Y	Y	Y	Y
Obs	1365	1365	1365	1365
R2	0.4230	0.4273	0.4216	0.4233

Notes: Standard errors of (robustness) are in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

add the interaction term between the policy misalignment and fiscal decentralization. This was implemented on the basis of the benchmark regression to test the moderating effect of fiscal decentralization on the relationship between the policy misalignment and the SWDC.

In Models (1) and (2) of Table 6, the estimation results show a significantly negative coefficient for the policy misalignment at the 5% significance level, suggesting that a greater misalignment corresponds to a reduced SWDC. In the interaction model with the moderating variable, the regression results indicate that the interaction term of fiscal decentralization with policy misalignment yielded a significantly positive effect at the 1% level. This suggests that, in regions with a higher degree of fiscal decentralization, the negative impact of a policy misalignment on the SWDC is mitigated. In other words, fiscal decentralization helps alleviate the governance barriers caused by a policy misalignment across different levels of government.

According to the estimation results, the interaction term of policy misalignment and fiscal decentralization yields a positive and significant coefficient. This indicates that the negative impact of a policy misalignment on the SWDC is weakened when the local government has a higher degree of fiscal decentralization. In other words, the negative impact of a policy misalignment on the SWDC decreases as the degree of fiscal decentralization increases. A high degree of fiscal decentralization may help local governments respond more flexibly to signaling differences between central and provincial policies. It can also mitigate the negative impacts of a policy misalignment by improving the efficiency of resource allocation.

The underlying mechanism can be explained from the research perspective of local autonomous governance. Fiscal decentralization enhances the financial autonomy and discretion of local governments, allowing them to allocate budgetary resources more efficiently in response to local environmental needs. This flexibility enables them to buffer against inconsistencies between higher-level policy signals and local implementation realities [28]. Specifically, as a type of public service that is highly technical, requires long investment cycles, and yields insignificant short-term performance, the effective advancement of urban waste governance is highly dependent on local resource support. Moreover, there is no obvious conflict between the supply of waste governance public services and local economic development. Thus, driven by the public’s demand for a clean living environment, local governments have strong incentives to proactively enhance urban waste harmless treatment capacity. When municipal governments have a high degree of fiscal decentralization, they often possess greater fiscal autonomy and operational flexibility. Faced with misalignment in multi-level policy signals, local governments can make responsive choices based on their own resource endowments and governance needs, and have the capacity to independently determine the level and plan of investment in local public services, thereby improving the feasibility and alignment of policy implementation [48]. This helps local governments formulate policy responses and allocate resources based on local realities, reducing their tendency to frequently adjust implementation strategies due to performance assessment pressures. Consequently, it enhances the stability and adaptability of the level of local public service provision [49].

Therefore, fiscal decentralization plays a positive moderating role between policy misalignments and the SWDC. This may be because an increased fiscal autonomy improves local governments’ resource allocation efficiency, thereby reducing the interference caused by policy misalignments from different government levels on policy implementation.

5.4.2. Analysis Results of the Digital Economy

To further examine the moderating role of the digital economy in the relationship between policy misalignments and the SWDC, this study introduced an interaction term between the two variables. Models (3) and (4) of Table 6 show that a policy misalignment continued to exhibit a significantly negative effect on the SWDC, while its interaction with the digital economy was positive and significant at the 5% level. This indicates that the development of the digital economy mitigates the adverse impact of a policy misalignment on municipal waste governance.

The moderating effect of the digital economy operates two interrelated mechanisms. On the one hand, the digital economy exerts its moderating effect from the perspective of improving regulatory efficiency. Waste management and other services of general economic interest are typically non-market in nature, requiring public regulation to ensure sustainable service provision at fair and affordable prices [50]. The digital economy enhances this regulatory capacity by introducing intelligent monitoring, big data analytics, and online reporting systems that strengthen oversight and accountability. Through its core feature of information connectivity, digital infrastructure facilitates real-time communication and data sharing across administrative levels, thereby reducing information asymmetry and enabling more rapid policy feedback and adjustments [31].

On the other hand, the digital economy exerts its moderating effect from the perspective of strengthening resource integration. Vertical policy misalignment often leads to imbalanced resource allocation and fragmented implementation in municipal solid waste disposal, as local governments may face mismatches between policy requirements and available resources due to inconsistent upper-level policy signals. The digital economy addresses this bottleneck. Through technologies such as big data mining and algorithmic analysis, digital tools can accurately predict regional waste generation trends, assess the operational load of disposal facilities, and match supply-demand of waste treatment resources. For instance, digital platforms enable real-time sharing of waste classification data, facility operation status, and policy implementation progress among relevant parties, realizing synergistic governance in waste collection, transportation, and treatment [17]. This enhances the flexibility and adaptability of local implementation—local governments can rely on digital-driven resource optimization to make up for the implementation gaps brought by inconsistent cross-level policies.

5.5. Group Regression Analysis

5.5.1. Analysis Results of the Pilot Policy

To investigate whether the impact of a policy misalignment on the SWDC varies according to the characteristics of city pilot policies, a subgroup regression analysis was conducted based on whether a city was designated as a key city.

This study examined the effect of a policy misalignment on the municipal SWDC in different city types. In Models (1) and (2) of

Table 7. Grouped regression analysis.

Variable	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)	Model (6)
	Non-Key	Key	East	Midwest	Low	High
	Capacity	Capacity	Capacity	Capacity	Capacity	Capacity
PMA	−0.0262 ** (0.0121)	−0.0200 (0.0139)	−0.0328 * (0.0166)	−0.0248 (0.0167)	−0.0236 * (0.0124)	−0.0250 (0.0319)
cons	4.4479 (3.2064)	7.6207 * (3.8553)	2.0054 (5.3339)	5.2233 (3.6312)	4.4399 (3.3473)	−0.8231 (10.4578)
Control variable	Y	Y	Y	Y	Y	Y
Year fixed effects	Y	Y	Y	Y	Y	Y
city fixed effect	Y	Y	Y	Y	Y	Y
Obs	1171	194	476	889	1126	239
R2	0.4214	0.6047	0.5124	0.3839	0.4367	0.3876

Notes: Standard errors of (robustness) are in parentheses, ** p < 0.05, * p < 0.1.

, the estimated coefficient of the policy misalignment was significantly negative at the 5% level. This indicates that, compared with the key city group, policy misalignments have a stronger negative impact on the SWDC in the non-key city sample. It was found that policy misalignments had a significant negative effect only on non-focus cities. However, the coefficients of policy misalignment were not significant in the sample of key cities. This indicates that the role of policy misalignments is weakened for cities that are supported or supervised by the central government’s focus. Specifically, key cities tend to be at the forefront of policy experiments. Key cities are directly affected by central support. They may enjoy more centralized financial support as well as additional policy resources. Therefore, the effect of policy misalignments on the SWDC of key cities may be relatively small or insignificant. Policy misalignments cannot explain the changes in the SWDC.

In addition, Model (1) in Table 7 captures how policy misalignments affect the SWDC in non-focus cities. In non-focus cities, policy misalignments have a significant negative effect on the SWDC. Non-focus cities may be more susceptible to policy misalignments than focus cities. Policy misalignments can prevent municipal governments from clearly and accurately identifying the policy objectives of higher levels of government. In the absence of direct policy support from the central government, local governments are more likely to face additional challenges. They may need to simultaneously weigh the policy objectives of both the central and provincial levels of government. In this case, policy misalignments become an important factor that affects local governments’ policy implementation.

5.5.2. Analysis Results of the City Locations

To explore whether differences in locational characteristics lead to variation in the impact of policy misalignments on the SWDC, subgroup regression analyses were conducted based on regional location variables.

In Models (3) and (4) of Table 7, the estimated coefficients for policy misalignment were significantly negative at the 10% level, indicating that such differences have a significant inhibitory effect on the SWDC in eastern cities. In contrast, the coefficients for policy misalignment in the central–western regions were statistically insignificant, suggesting that policy misalignments do not meaningfully affect the SWDC in those areas. This heterogeneity in the results may be attributed to the relatively weaker governance foundations in the central–western regions, where economic and social constraints limit the effectiveness of policy implementation, thereby reducing the marginal impact of a policy misalignment. Overall, the heterogeneity analysis revealed marked regional variation, with the economically advanced eastern region exhibiting a greater sensitivity to policy coordination issues.

5.5.3. Analysis Results of the Municipal Government Attention Allocation

To investigate whether the impact of policy misalignments on the SWDC varied with the level of municipal government attention, subgroup regression analyses were conducted based on the municipal government attention variable.

In Models (5) and (6) of Table 7, the estimated coefficients for policy misalignment were significantly negative at the 10% level in cities with low municipal government attention, indicating a substantial adverse impact on the SWDC. Conversely, in cities with high municipal government attention, the coefficients were statistically insignificant, indicating that the adverse effects of policy misalignments on the governance performance are mitigated when municipal governments prioritize waste governance. Cities with higher levels of attention proactively invest more resources in waste governance, and thus, are less sensitive to policy fluctuations at upper government levels. As a result, the marginal impact of policy misalignments is attenuated, diminishing their negative influence on the SWDC in these cities. In contrast, cities with lower municipal attention allocate fewer governance resources to waste governance, possess weaker governance capacities, and lack clear policy direction. Consequently, these cities rely more heavily on external policy guidance and are more vulnerable to shifts in higher-level policies, causing policy misalignments between central and provincial governments, which exert a significant negative effect on their SWDC.

6. Conclusions and Policy Implications

6.1. Conclusions

This study examined the impact of central–provincial policy misalignments on the SWDC and explored their moderating and heterogeneous effects. The key findings are as follows: First, policy misalignments exert a significant negative impact on the SWDC, and this result remained robust after instrumental variable endogeneity tests and robustness checks. Second, fiscal decentralization and digital economy development play positive moderating roles, effectively mitigating the adverse effects of policy misalignments. Third, a heterogeneity analysis showed that the negative impact of policy misalignments is more pronounced in non-key cities, eastern regions, and cities with low government attention, reflecting the critical role of the local governance capacity and administrative focus.

From a sustainability standpoint, these findings hold profound implications for promoting sustainable urban development and aligning with global sustainability agendas—most notably Sustainable Development Goal (SDG) 11 (Sustainable Cities and Communities). By identifying policy misalignment as a key barrier to solid waste disposal capacity, this study highlights a critical pathway to enhance the sustainability of urban waste governance—strengthening cross-level policy coordination directly contributes to reducing landfill pressure, minimizing environmental pollution, and promoting resource recycling in line with the circular economy principles advocated in global sustainability frameworks. The moderating roles of fiscal decentralization and digital economy further uncover synergistic mechanisms for sustainable waste management. Specifically, fiscal autonomy confers local governments enhanced decision-making discretion with respect to green infrastructure investments. It empowers local governments to tailor resource allocation to local sustainability needs rather than being constrained by uniform central mandates. Meanwhile, digitalization optimizes resource utilization and monitoring efficiency, supporting low-carbon urban transitions. Collectively, this research enriches the integrated socio-economic and environmental approaches to sustainable development, providing actionable insights for building zero-waste cities and advancing the global agenda of carbon neutrality.

6.2. Policy Implications

Drawing on the results of this study, we propose the following policy recommendations:

• The coordination between central and local policies should be strengthened. This study found that the policy misalignment between the central and provincial levels negatively affected the capacity of municipal waste governance. Therefore, central and provincial governments should strengthen their communication and collaboration when formulating policies. The central government should keep abreast of the actual situation at the local level and listen to the difficulties and needs encountered by local governments in the process of implementing policies. This can be achieved through regular talks and discussions with local governments, as well as a feedback mechanism. The aim is to make policy coordination more effective. Meanwhile, local governments should actively communicate with their superiors. This would ensure that a consensus is reached on policy objectives and implementation details and help avoid increased implementation difficulties due to asymmetric information. It would also ensure consistency in policy content and implementation standards and minimize the confusion and waste of resources that local governments may face during the implementation process.
• The digital economy should be leveraged to strengthen waste governance and address information asymmetries. First, the integration of digital technologies into municipal services can substantially enhance the SWDC. By utilizing intelligent platforms and real-time monitoring systems, local governments can optimize their waste classification and treatment efficiency, thus achieving more precise and responsive service delivery. Second, digital development plays a critical role in mitigating information asymmetry during policy implementation. Open data platforms enable more transparent communication between different levels of government, reducing misunderstandings and aligning policy actions. As a whole, the expansion of the digital economy not only supports the technical implementation of environmental policies but also promotes a more open and transparent governance environment.
• The central government could consider developing a more flexible policy support mechanism for localities. Differentiated support policies need to be developed in light of the specific attributes of local governments, including their geographical location, the allocation of municipal government attention, and the status of pilot policies. This could include financial subsidies, technical assistance, and policy guidance. These measures would help local governments overcome the challenges of policy implementation. Through these measures, the SWDC of cities can be effectively enhanced and the “dual-carbon” goal can be promoted, thereby improving the overall level of environmental governance.