1. Introduction
Amid rapid urbanization and industrialization, the environmental challenges posed by solid waste have attracted increasing attention. The Global Waste Management Outlook (2024) projects alarming trends: without immediate intervention, global municipal solid waste generation is expected to increase by 1.5 billion tons by 2050 compared to 2023 levels [
1]. Concurrently, the direct costs associated with waste management are projected to rise from
$252.0 billion in 2020 to
$640.3 billion by 2050 [
1], underscoring the urgent need for effective waste management strategies. Beyond environmental pollution and rising management costs, solid waste treatment contributes significantly to greenhouse gas (GHG) emissions. Landfill biogas, for example, generally consists of 45–76% CH
4 and 30–45% CO
2 [
2,
3,
4,
5]. While gas collection systems help capture GHG emissions, approximately 10–50% of landfill GHG emissions still release into the atmosphere [
6,
7,
8]. Overall, direct carbon emissions generated during solid waste management account for approximately 3.2% of global GHG emissions [
9,
10,
11]. Data from Climate Watch reveal that solid waste management in China generated over 216.23 million tons of CO
2 in 2021 [
12]. Exploring sustainable solid waste management practices is therefore essential to achieving pollution reduction and carbon mitigation (PRCM). This serves a dual purpose: alleviating environmental pressure while making substantive contributions to global climate change mitigation. Furthermore, in-depth research and implementation of innovative waste management strategies can catalyze synergistic economic, social, and environmental progress, serving as a key driver in global transition toward a sustainable, green and low-carbon future.
In response to the global PRCM imperative, international organizations and countries have actively pursued strategic initiatives. “The European Green Deal” announced by European Union and Japan’s “The Green Growth Strategy for Carbon Neutrality by 2050” exemplify this trend, demonstrating how sustainable development with green and low-carbon practices has become a global consensus [
13]. As a major contributor to global solid waste and carbon emissions, China has been proactively exploring pathways for green, low-carbon development. A pivotal initiative toward this goal is the zero-waste city pilot (ZWCP). This concept was first introduced in China through a 2017 proposal by Academician Du Xiangwan, titled “On Promoting the Resource Utilization of Solid Waste and Building a Zero-Waste Society through the Zero-Waste City Pilot”. The Chinese government prioritized this recommendation, leading to the 2018 release of the “Pilot Work Plan for the Construction of Zero-Waste City”. By April 2019, the Ministry of Ecology and Environment of China (MEEC) had designated 16 cities and regions as zero-waste pilot sites. These pilots aim not only to achieve ecological goals thought efficient waste management but also to enhance the synergistic relationship between reducing pollution and mitigating carbon emissions [
14,
15]. The program’s expansion to 108 cities in 2022 underscores China’s strong commitment to the ZWCP and its crucial role in advancing sustainable waste management. China’s pioneering zero-waste city (ZWC) development provides both domestic implementation models and valuable global insights for environmental governance and climate change mitigation. By examining the ecological effects of China’s ZWCP policy, this study can facilitate significant advancements in solid waste management, thereby generating new momentum for achieving global sustainable development goals.
Numerous studies have demonstrated that optimized solid waste management can minimize solid waste emissions, enhance energy efficiency, and lower carbon emissions [
16]. As such, China’s ZWCP policy holds significant potential to improve environmental quality and support the nation’s “dual carbon” goals of carbon peaking and carbon neutrality. Despite these theoretical expectations, a critical research question remains unexplored: does the ZWCP policy effectively generate synergistic improvement in PRCM? The current literature offers limited insights, with the most relevant study by Liu et al. (2024), which evaluated the ZWCP’s influence on urban total factor carbon emission efficiency (TFCEE) without a comprehensive PRCM assessment [
17]. To answer this question, this paper employed panel data encompassing 158 cities in China from 2011 to 2021 to systematically investigate the mechanism of the ZWCP policy in achieving PRCM, with a focus on green technology innovation and environmental regulatory intensity. This study further examined the heterogeneity of the ZWCP policy from the perspectives of urban location, resource endowment, and urban scale, while exploring its exit effect and spatial spillover effect. The findings contribute significantly to enriching the ZWCP policy framework construction while offering empirically grounded insights to policymakers. Moreover, this study provides actionable experience and a reference for global urban sustainability transitions, advancing progress toward global sustainable development goals (SDG), particularly SDG 11 (Sustainable Cities) and SDG 13 (Climate Action).
Compared to previous studies, this study makes several novel contributions. First, by focusing on the PRCM effects of the ZWCP policy, this study provided direct empirical evidence linking ZWCP implementation to measurable environmental improvements, advancing the discourse on zero-waste urban governance. Second, this study uncovered the dual transmission mechanism of green technological innovation and environmental regulation in achieving the PRCM effects of the ZWCP policy, thus providing a fresh perspective for optimizing ZWCP policy and enhancing the depth of the research. Third, this study provided empirical evidence for tailored ZWCP policies by discussing the heterogeneity of the policy’s effects. Fourth, this paper evaluated the enterprise exit effect and spatial spillover effect, providing empirical support for enhancing regional collaboration in ecological policy.
The rest of this research is structured as follows.
Section 2 presents a comprehensive literature review.
Section 3 conducts the theoretical analysis and proposes the research hypotheses.
Section 4 explains the research design.
Section 5 presents the estimation results, robustness tests, and mechanism examinations.
Section 6 makes an in-depth analysis from three aspects: heterogeneity, enterprise exit effect and spatial spillover effect.
Section 7 summarizes the findings and provides recommendations.
4. Research Design
4.1. Data and Sample Selection
Considering the completeness of the indicators, this study defined the research period from 2011 to 2021. The treatment group comprises 16 cities and regions designated as ZWCP zones in 2019, while all non-pilot cities are the control group. The dependent variables in this analysis are general industrial solid waste and CO2. However, significant absence was identified in city-level panel data for these indicators. To address this, we established the following data screening criteria: (1) Cities missing data for both key indicators for three or more consecutive years were excluded from the sample; (2) For cities with isolated missing data in a specific year, linear interpolation was applied to impute values. Following this processing, the final sample consisted of 158 cities, 14 pilot cities in the treatment group (Baoding, Baotou, Beijing, Nanping, Tianjin, Weihai, Xuzhou, Shenzhen, Panjin, Shaoxing, Xining, Ganzhou, Chongqing, and Tongling) and 144 non-pilot cities in the control group. The data sources are the China City Statistical Yearbook, China provincial statistical yearbooks, the EPS database, and the China Emission Accounts and Datasets (CEADs). All economic data were adjusted for inflation using the Consumer Price Index (CPI) with 2011 as the base year. Missing data points were estimated through linear interpolation.
4.2. Model Construction
As a regional pilot initiative, the ZWCP policy can be likened to a quasi-natural experiment. Therefore, this study used the DID method to examine the PRCM effects of the ZWCP policy. Following the framework established by Zhao et al. (2023), two DID models were set [
60]:
where the subscripts
i and
t in the two equations denote the city and year;
lnwasteit and
lnCO2it are dependent variables;
zwcit is the core explanatory variable, indicating whether city
i adopted the ZWCP policy in year
t;
Xit represents the set of control variables that influence pollution emissions and CO
2 emissions; the city fixed effect and year fixed effect are expressed by
μi and
τt, respectively;
εit is the random error term of the models;
α,
β1,
β2 and
δ are estimated parameters; α is the intercept term of the equation; and
β1 and
β2 reflect the effect of pollution reduction and carbon reduction of the ZWCP policy, respectively. If
β1 < 0, this suggests that the ZWCP policy can effectively reduce pollution emissions. If
β2 < 0, this implies that the ZWCP policy can effectively decrease CO
2 emissions.
δ denotes the estimated coefficient vector for each control variable, quantifying how other observed factors influence the pollution emissions and CO
2 emissions.
To ensure the validity of the DID estimates, we constructed a systematic test process. First, we verified the parallel trends assumption by examining whether the treatment and control groups followed similar pre-treatment trajectories in the outcome variables—a critical requirement for causal inference in DID models. Second, a placebo test was carried out to eliminate the interference of random factors on the estimation results. Finally, robustness tests were carried out from multiple perspectives, including policy contamination control (controlling for similar environmental policies interference), administrative hierarchy adjustment (excluding cities with high administrative levels, e.g., municipalities/provincial capitals), and control group refinement (refining the selection of control groups) to verify the robustness of the results.
4.3. Variable Definitions
Dependent variable. Solid waste reduction and carbon mitigation are two kinds of environmental benefits examined in this paper. Therefore, this research used city-level general industrial solid waste emissions (lnwaste) and CO2 emissions (lnCO2) as two dependent variables. To mitigate potential heteroscedasticity, both variables underwent log-transformation in this analysis.
Core explanatory variable. The ZWCP policy (zwc) is a dummy variable. If city i participates in ZWCP in year t, it takes the value of 1 in that year and subsequent ones and 0 otherwise.
Control variables. Economic development level (
lnpgdp) is indicated by the natural logarithm of per capita GDP. According to the Environmental Kuznets hypothesis [
61], pollution initially rises with economic growth but declines beyond a certain income threshold. Industrial structure (
struc) is defined by the proportion of the secondary industry in GDP. Sun et al. (2022) showed that a high proportion of industrial output value is a factor exacerbating pollution emissions in China [
62]. Government intervention intensity (
lngov) is quantified by the logarithm of the fiscal expenditure intensity. The existing literature presents divergent perspectives on the environmental impact of government fiscal expenditure. Some scholars argue that excessive government intervention through fiscal spending may distort resource allocation efficiency, potentially exacerbating environmental degradation [
63]. Conversely, other researchers contend that properly designed fiscal expenditures can generate pollution reduction co-benefits by enhancing human capital development and facilitating emissions-reduction technological innovation [
64]. Foreign direct investment (
lnfdi) is measured by the logarithm of foreign direct investment (FDI) to GDP. The environmental impact of FDI manifests through two competing theoretical lenses: the “pollution haven effect” indicates that FDI facilitates the agglomeration of pollution-intensive industries in host countries, thereby intensifying local environmental degradation [
65], while the “pollution halo effect” suggests that FDI can generate positive environmental externalities through the transfer of advanced production technologies, implementation of superior management practices, and systemic improvements in energy efficiency [
66]. Financial development level (
lnfin) is quantified by the logarithm of the ratio between the annual loan volume of financial institutions and GDP. Robust financial systems enable firms to adopt cleaner technologies, supporting pollution reduction [
67].
7. Conclusions and Suggestions
Taking the ZWCP policy as a quasi-experiment, this paper used a DID model to examine its PRCM effects. The main conclusions are as follows. First, the ZWCP policy demonstrated significant efficacy in reducing pollutants and CO2 emissions, confirming a dual environmental effect. Second, mechanism testing showed that the PRCM effects in the ZWCP policy are primarily driven by accelerated green technology innovation and strengthened environmental regulation. Third, there is urban heterogeneity in the PRCM effects of the ZWCP policy. Specifically, more pronounced PRCM effects were observed in central region cities, non-resource-based cities and large cities. Finally, the ZWCP policy showed no significant enterprise exit effects or spatial spillover effects. This indicates that the ZWCP policy maintains economic sustainability by avoiding disruptive market exits, yet the absence of spatial spillover potentially constrains its broader environmental impacts. Considering these research results, this paper puts forward the following suggestions.
- (1)
Constructing a differentiated policy pilot promotion framework. Priority should be given to expanding ZWCP coverage in central region cities, non-resource-dependent cities, and large cities. The central region, serving as a critical nexus between eastern and western China, possesses both industrial absorption capacity and ecological buffering functions. Its substantial population density and economic scale enable the efficient deployment of waste classification and resource circulation infrastructure, maximizing economies of scale. Meanwhile, megacities (urban population ≥ 5 million) should be granted greater regulatory autonomy in environmental governance, permitting the adoption of stricter waste management standards than national baselines—exemplified by Shanghai’s “No sorting, No collection” policy under its municipal solid waste regulations. Concurrently, a dedicated technical assistance mechanism should be established for resource-dependent cities. For resource-oriented cities such as Daqing and Hulunbuir, a specialized circular economy technology transformation fund should be introduced to support critical technologies, including tailings recycling and industrial equipment remanufacturing, thereby fostering sustainable economic restructuring.
- (2)
Innovating green technology incentive mechanisms and facilitating technology diffusion pathways. A centralized environmental technology sharing platform should be implemented between pilot cities and non- pilot cities, encouraging participating enterprises and regions to disclose green technology patents between pilot zones and neighboring areas. For instance, in 2022, Hangzhou pioneered a “Green Technology Market” aggregating 530 energy-saving and environmental protection technologies, which accelerated broader societal decarbonization. A dedicated “Dual Carbon Technology” subsidy fund should be established, implementing a tiered incentive structure. Tianjin’s “Notice on the Establishment of Zero-Waste Enterprises” provides a valuable template.
- (3)
Establishing a dual-dimensional dynamic supervision framework integrating pollution and carbon emissions to enhance policy enforcement efficacy. At the technical level, a “Pollution-Carbon Emission Synergistic Monitoring Platform” should be developed, leveraging IoT-enabled sensor networks to capture real-time data on industrial solid waste generation, logistical pathways, and end-treatment facility operations. This will create a closed loop tracking system spanning source generation, transportation, and final disposal, ensuring comprehensive oversight. At the mechanism level, a tiered response mechanism should be implemented. Pilot cities failing to meet baseline performance thresholds will trigger a three-colored warning system (yellow, orange, red). The yellow-warning pilot cities need to submit a corrective action plan coupled with technical support interventions; the orange-warning pilot cities face temporary suspension of approvals for new waste treatment projects; and the red-warning pilot cities suspend their pilot qualification and conduct an investigation under comprehensive supervision.
- (4)
Strengthening public environmental education to cultivate conservation awareness and ecological consciousness. An immersive environmental education system should be established through the dissemination of “Zero-Waste Lifestyle Guidelines” and the promotion of the whole life cycle environmental protection concept. Digital transformation serves as both a strategic priority for ecological civilization advancement during the 14th Five-Year Plan period and as a critical enabler for sustainable development education. A digitally-driven incentive framework should be implemented to raise awareness about the impact of a single person on pollution reduction and carbon mitigation. Another important measure is to establish a national environmental governance framework that institutionalizes public participation and oversight. To strengthen transparency and civic engagement, the government should mandate open-access policies for environmental protection facilities, ensuring the public’s right to access information, participate in decision-making, and exercise supervision. This approach aligns with international best practices in environmental democracy.
This study still has the following limitations. First, due to the availability of data, the study period of this research only spans from 2011 to 2021. Therefore, this study only included cities that qualified for the ZWCP in 2019 as the treatment group. The MEEC announced a second batch of pilot cities in 2022, but the lack of key city-level data (e.g., CO2, pollution metrics) hindered their inclusion in the empirical analysis. In the future, once more comprehensive city-level data become available, the second batch of pilot cities can be included as the treatment group, employing a progressive DID approach to further investigate the environmental effects of the ZWCP policy. Second, this study examined the impact of the ZWCP policy on solid waste and CO2 emissions at the city level, providing reliable macro-level evidence. However, it lacks empirical evidence at the micro level of individual enterprises. Future research could further explore the pollution reduction and carbon mitigation effects of the ZWCP from a micro-level enterprise perspective.