1. Introduction
As global urbanization intensifies, construction and demolition waste (CDW) has become one of the largest components of urban solid waste, reaching over 10 billion tons globally and accounting for nearly one-third of total solid waste [1,2]. In response, various regions have launched initiatives, such as Europe’s circular economy policies [3,4] and the recent adoption of zero-waste cities in various countries [5,6,7]. However, substantial variation exists in how cities manage CDW, especially between developed economies with high recycling rates (e.g., Germany and The Netherlands) and developing regions where untreated CDW is often landfilled [8,9,10]. These disparities highlight the importance of identifying the factors that drive differences in CDW management (CDWM) performance under varying local conditions.
In the context of China’s Guangdong–Hong Kong–Macao Greater Bay Area (GBA)—a rapidly urbanizing and economically diverse metropolitan cluster—recent studies have further underscored the heterogeneity of CDWM capabilities across cities. For instance, Yu, et al. [11] evaluated the self-fulfillment degree of CDW treatment capacity in 11 GBA cities and revealed stark contrasts: while some cities, like Guangzhou and Foshan, exhibit relatively high processing self-sufficiency, others, such as Hong Kong and Shenzhen, rely heavily on external treatment pathways. Similarly, Peng, et al. [12] quantified the carbon-saving potential of CDW recycling in the region and found that under enhanced recycling scenarios, the GBA could avoid up to 3.55 million tons of CO2 emissions annually—yet this potential remains unevenly distributed due to local infrastructure and policy gaps. These findings point to a pressing need for comparative, city-level analyses of CDWM performance within the GBA, not only to understand the determinants of current disparities but also to inform regionally adaptive strategies that align with international broader sustainability goals.
Previous studies have extensively investigated the determinants influencing CDWM performance, approaching the issue from various perspectives. For example, Ding, et al. [13] and Li, et al. [14] explored which policies can effectively enhance CDWM performance. Kabirifar, et al. [15] conducted a review to identify key factors contributing to effective CDWM and categorized them based on the reduce, reuse, and recycle strategies. Additionally, Begum, et al. [16] and Wu, et al. [17] examined the factors influencing stakeholder behavior and attitudes towards CDWM in Malaysia and China, respectively. Furthermore, some studies have explored the impact of economic penalties [15,18] and CDW governance capacity on performance [11,19]. While these studies have revealed several important determinants, they primarily focus on project-level performance [20,21] and are limited to developed countries in Europe and Oceania [8,22,23] or individual cities, such as Hong Kong and Mexico City [24,25].
In contrast, multi-city regions, such as the New York Bay Area, Tokyo Bay Area, and the GBA remain relatively underexplored, despite facing unique CDWM challenges due to high urban density, uneven development levels, and intense construction activity [11,26]. Research on the GBA has predominantly concentrated on Hong Kong [27,28] and Shenzhen [29,30], largely overlooking the other nine cities in the region, including Macao, Guangzhou, Zhuhai, Foshan, Zhaoqing, Jiangmen, Huizhou, Dongguan, and Zhongshan. This imbalance may obscure significant differences in CDWM effectiveness across the region. Given the economic and infrastructural diversity among GBA cities, it is unreasonable to assume that successful practices in more developed cities can be directly replicated elsewhere.
Therefore, this study aims to identify the key determinants that shape CDWM performance in this diverse and densely urbanized region. To achieve this goal, we have formulated the following three research hypotheses:
CDW governance capacity is positively correlated with urban population and economic level.
CDW recycling performance is positively correlated with governance capacity.
CDWM performance is positively correlated with urban population, economic development, and CDW generation volume.
The process of hypothesis testing is essentially the process of identifying the factors that affect CDWM performance. To this end, this study selects the 11 GBA cities as the research focus and employs a data-driven, exploratory analytical framework to systematically examine these determinants. This study provides valuable insights to help policymakers develop context-based strategies, and the findings may also inform similar efforts in other complex urban clusters, such as Tokyo Bay and the New York metropolitan area. To avoid any misunderstanding, it is noted that in this study, CDW refers to materials generated during construction, expansion, renovation, and demolition activities—including excavated earth, concrete, bricks, ceramics, metals, plastics, and related debris.
2. Methodology
The study aims to explore the driving factors behind CDWM performance across cities. To achieve this goal, a research framework was developed, as illustrated in Figure 1. Firstly, characteristic parameters related to CDWM performance, city attributes, and CDW governance capacity were identified based on a literature review and brainstorming sessions. Secondly, we classified GBA cities into different clusters according to CDWM performance. Thirdly, a correlation analysis was conducted to uncover the drivers of CDWM performance. Finally, valuable waste management insights and implications from GBA cities were proposed to inform CDWM practices in other regions.
2.1. Identifying Characteristic Parameters
Before exploring the driving factors of CDWM performance, it is crucial to establish the metrics by which to measure such performance. Drawing on the previous studies, three characteristic parameters related to CDWM performance were identified. Previous research suggests that the main disposal methods of CDW include recycling into construction materials comparable to natural aggregates [31], reusing as backfill in earthworks or land reclamation, and landfilling [11]. Accordingly, the rates of CDW recycling, reuse, and landfill are often used as indicators of a city’s CDWM efficiency, serving as widely recognized performance evaluation metrics in various studies [32,33,34]. Moreover, these three indicators are among the few CDWM metrics that are systematically reported at the city level in China. Their availability in official statistical sources ensures data consistency and comparability across cities, making them both practically accessible and suitable for regional-level analysis. Compared to other potential indicators, such as total CDW generation or disposal cost, these three rates directly reflect the efficiency and outcome of CDW handling processes. They capture the final distribution of CDW streams and are, thus, widely regarded as core indicators of CDWM performance.
Based on the research hypotheses and aforementioned three CDWM performance indicators, this study further identifies additional characteristic parameters for analysis. The first step involves determining the key factors that influence the rates of CDW recycling, reuse, and landfilling. Typically, population and economic development levels not only shape CDW disposal modes but also drive construction activities, which, in turn, generate varying amounts of CDW [1,35]. Accordingly, population, GDP, and CDW generation are selected as indicators representing a city’s structural attributes.
Secondly, variations in CDW governance capacity across cities inevitably lead to differing management outcomes. For example, cities with limited landfill capacity often need to transport their CDW to adjacent areas for disposal [11,36], while those lacking adequate recycling infrastructure tend to rely more heavily on landfilling [9,10]. Furthermore, CDWM policies and the strength of institutional systems play a critical role in achieving effective waste management outcomes [26]. Therefore, CDW governance capacity in this study is defined by two dimensions, namely disposal capacity and the strength of policy and institutional frameworks. CDW recycling capacity, landfill capacity, the number of CDWM-related policies, and the level of institutional development are employed as indicators to capture a city’s governance capacity in CDWM.
In the context of this study, “reuse” refers to the utilization of CDW in such activities as engineering backfill, land reclamation, and coastal land reclamation, while “recycling” refers to the use of CDW in the production of recycled building materials. “CDW recycling capacity” refers to the amount of CDW that a city’s operational recycling facilities can process in one year. Similarly, “landfill capacity” refers to the amount of CDW that landfills within a city’s jurisdiction can accommodate annually. “Institutional development level”, as defined in our previous study [26], reflects a city’s capacity in CDWM across the following four dimensions: institutional framework, market mechanisms, technological capacity, and regulatory oversight. Detailed descriptions and sources of the characteristic parameters can be found in Table 1.
2.2. Grouping GBA Cities Based on CDWM Performance
For the purpose of identifying the drivers behind the varying CDWM performance across cities, this study classified the GBA cities into several clusters based on their relative performance in recycling, reuse, and landfill rates. Specifically, the grouping was determined by comparing the values of these three indicators for each city. The city was assigned to the cluster corresponding to the highest of the three rates. For example, if a city’s recycling rate was higher than its reuse and landfill rates, it was categorized into Cluster 1: Recycling-Dominant Cities; if the reuse rate was the highest, the city was placed in Cluster 2: Reuse-Dominant Cities; and if the landfill rate was the highest, the city fell into Cluster 3: Landfill-Dominant Cities. For instance, Hong Kong, which exhibits the highest value in recycling among the three metrics, was classified under Cluster 1.
This classification approach provides an intuitive yet effective means to reveal performance tendencies and underlying policy orientations among cities. It enables the comparison of cities with similar CDWM priorities and facilitates the identification of shared characteristics. While more complex clustering methods (e.g., k-means) exist, the current method was preferred for its transparency and interpretability, especially under data constraints.
Subsequently, through an exploratory analysis, the shared characteristics of each cluster were investigated. For instance, for the clusters of cities that primarily use CDW for recycling, reusing or landfilling, relevant traits, such as population, gross domestic product (GDP), CDW generation, and governance capacity (e.g., recycling and landfill capacity, policies and institutional system), were further analyzed to uncover potential patterns. By doing so, the study can initially identify the critical characteristics influencing urban CDWM performance, which are likely the targeted determinants that this study seeks to uncover.
2.3. Revealing the Factors Affecting Urban CDWM Performance
Based on the previous findings on the shared characteristics of cities with similar CDWM performance, the study aims to adopt a data-driven approach by utilizing a correlation analysis to thoroughly explore the relationships between these characteristic parameters, which cover three dimensions, namely city attributes, CDW governance capabilities, and CDWM performance. By analyzing the correlations among these characteristics, particularly between city attributes and CDWM performance as well as between CDW governance capacity and CDWM performance, the study further identifies the key factors influencing waste management performance.
The advantage of the correlation analysis is that it can quantify the strength of relationships between these parameters and determine which ones exert the greatest impact on CDWM performance [37]. Given that most of the data for these parameters do not follow a normal distribution and the relationships may be nonlinear, Spearman’s rank correlation is well-suited for capturing monotonic trends [38]. Moreover, as the study adopts an exploratory design with a relatively small sample size (11 cities), Spearman’s method offers robust performance against outliers and does not rely on strict assumptions about data distribution or linearity. All analyses were conducted using Python (version 3.12).
2.4. Empirical Study and Data Collection
As one of the most economically active regions in China, the GBA experiences frequent construction activities, generating nearly 300 million cubic meters of CDW annually [39], accounting for approximately 10% of the national total. This significant volume highlights the urgent need to address waste disposal issues in the region. The GBA comprises 11 cities with varying population sizes and economic development levels, including Hong Kong (HK), Macao (MA), Shenzhen (SZ), Guangzhou (GZ), Zhuhai (ZH), Foshan (FS), Zhaoqing (ZQ), Jiangmen (JM), Huizhou (HZ), Dongguan (DG), and Zhongshan (ZS) [40]. These cities exhibit distinct differences in CDWM effectiveness [26]. Analyzing the factors leading to varying CDWM performance could contribute to the construction of “Zero Waste City” in the region, promoting sustainable urban development. Accordingly, cities in this region were selected for empirical investigation in this study.
Given the wide geographic distribution of these cities and the improvements in official statistical reporting driven by the national “Zero Waste City” initiative, this study adopted a desktop research method to collect data efficiently and systematically. This method has been widely applied in similar studies involving large metropolitan regions [36,39]. It is worth noting that the nine mainland GBA cities (excluding Hong Kong and Macao) are all administratively within Guangdong Province and, thus, follow unified provincial CDWM regulations. As a result, the statistical scope, definitions, and calculation methods of CDW indicators are highly consistent across these cities, ensuring data comparability and consistency. In exceptional cases where data anomalies were identified—such as in Zhaoqing—telephone verification was conducted with senior officials from the local Urban Management Bureau to cross-check and correct the reported figures. Specifically, data on CDW generation, recycling, reuse, and landfill rates, as well as CDW disposal capacity, were obtained from publicly available documents and reports released by the environmental management departments of each city. These sources include the Environmental Protection Departments of Hong Kong and Macao, as well as the Ecology and Environment Bureaus of Guangzhou, Shenzhen, and other mainland cities.
Additional data were collected on the number of CDWM-related policies and regulations issued by each city between 1999 and July 2022, including drafts, tender documents, and official approvals. This information was compiled through extensive online surveys and policy archives. Socioeconomic indicators, such as population and GDP, were sourced from the official statistical yearbooks of each city. A detailed list of sources and policies is available in the Supplementary Materials. The data on “institutional development level” used in this study were derived from our previous study [26], which developed a weighted scoring system to evaluate the institutional development level of CDWM in GBA cities. This system assessed the following four key dimensions: institutional framework, market mechanisms, technological capacity, and regulatory oversight. While the score incorporates some degree of subjectivity, it is grounded in a systematic and reproducible evaluation process based on publicly available data and established policy framework. Given its strong relevance to the objectives of the present study, the accessed score was incorporated into our analysis. A higher score reflects stronger institutional support for CDWM. The relevant data for these characteristic parameters are illustrated in Figure 2.
3. Results and Discussion
3.1. Clusters of GBA Cities Based on CDWM Performance
Based on CDWM performance in recycling, reuse, and landfill rates, GBA cities were categorized into three clusters (Figure 3). Cities in Cluster 1 are inclined to use CDW for recycling, cities in Cluster 2 primarily reuse CDW for backfilling and similar activities, while cities in Cluster 3 focus on landfill. The following sections describe the common characteristics of cities in each performance group, with a focus on both city attributes and CDW governance capacity. Additionally, the most prominent feature of each cluster is highlighted. These findings inform the subsequent correlation analysis.
3.1.1. Cluster 1: Recycling-Dominant Cities
Both HK and ZQ, with higher recycling rate than reuse rate and landfill rate, were assigned to Cluster 1, which is characterized by recycling predominance (Figure 3a). Specifically, more than 60% of CDW is used to produce recycled building materials in these cities. However, there are significant gaps in their city attributes and CDW governance capacity—such as the fact that HK’s GDP (over 2.5 trillion yuan) is nearly nine times that of ZQ, its population (over 7 million) is almost twice as large, and its CDW generation (over 12 million cubic meters) is eight times higher—which make the similar CDW recycling outcome seem unreasonable. As a result, the research team reached out to Zhaoqing’s CDWM department for clarification. The head of the department informed us that, in recent years, the city has been receiving waste from GZ and SZ, primarily for recycling and landfilling [41]. However, the official data mainly come from waste disposal statistics (e.g., waste recycling enterprises, landfills, etc.), including the total waste disposed of both within and outside the city, but it does not specify how much of the waste from other cities was recycled or landfilled, which leads to Zhaoqing’s high recycling rate and landfill rate. This might also explain why Zhaoqing’s recycling and landfill rates are higher than those of other GBA cities at the same level.
3.1.2. Cluster 2: Reuse-Dominant Cities
Cities with reuse rate exceeding recycling rate and landfill rate were grouped into Cluster 2, including the eight cities of SZ, GZ, DG, FS, ZH, JM, ZS, and HZ (Figure 3b). A key feature distinguishing this cluster from the other two is that cities in this cluster are more inclined to use CDW for such activities as backfilling and land reclamation.
Compared to SZ and GZ, the other six cities in Cluster 2 rely more heavily on reuse, as evidenced by their reuse rates exceeding recycling rates by over 50%. More specifically, except for DG, these cities generally have reuse rates above 90%, recycling rates below 10%, and landfill rates of less than 1%. These six cities share several common traits, for instance, small populations and economies, as well as CDW generation below 25 million cubic meters. Accordingly, the CDW governance capacity of these cities is also very limited, with annual CDW recycling capacity generally below 5 million cubic meters and landfill capacity under 0.25 million cubic meters. Moreover, these cities have introduced fewer than 7 management policies, and their institutional scores vary widely, ranging from 5 to 32 points. This significant variation reflects that the development level of CDWM institutions does not necessarily lead to consistently positive outcomes, which also validates the findings of [26]. For example, DG and FS have similar institutional scores (31.53), yet their disposal structures differ substantially—DG’s recycling rate is 76%, while FS’s reuse rate reaches 91%. This contrast highlights that similar institutional development may lead to divergent CDWM outcomes, influenced by local implementation strategies or industrial conditions.
Notably, SZ and GZ prefer to adopt more comprehensive strategies: SZ primarily combines reuse and recycling, while GZ integrates reuse, recycling, and landfill. Specifically, their waste reuse rates are around 60%, and their recycling rates range between 15% and 38%. These two cities share common traits, such as being large metropolitan areas with dense populations (more than 17 million people), highly developed economies (GDP above 3 trillion yuan), and significant CDW generation (over 60 million cubic meters). They often have strong governance capacity, enabling them to recycle an average of about 50 million cubic meters of CDW per year. However, there is a serious disparity in landfill capacity, likely due to the tight land resources in certain cities. These first-tier cities also set exemplary standards in CDWM system development, with an average of over 35 policies and institutional scores exceeding 40 points.
3.1.3. Cluster 3: Landfill-Dominant Cities
MA, characterized by a higher landfill rate compared to its recycling and landfill rates, was categorized into Cluster 3. As shown in Figure 3c, its landfill rate is around 51%, while its reuse rate is about 49%. Since data on its waste recycling rate are unavailable, this study excludes that component from the statistics. However, according to a previous study [42], it is evident that the recycling rate in MA is relatively low. In terms of city attributes, MA is a small-scale GBA city with a population of 0.68 million, a GDP of 338.5 billion yuan, and a CDW generation of 3.14 million cubic meters. Its CDW landfill capacity is highly constrained, with a capacity below 1.6 million cubic meters, and it also exhibits an institutional score (32 points) that is inconsistent with its CDWM performance.
The above results indicate the current patterns of CDWM performance in GBA cities, particularly in terms of recycling, reuse, and landfill performance. Specifically, most GBA cities with smaller populations and economies (such as DG, FS, ZH, JM, ZS, and HZ) tend to generate less CDW, and their CDW recycling and landfill capacities are correspondingly limited. Their CDWM system development is relatively underdeveloped, and they are more inclined to adopt simpler disposal methods, such as reusing CDW, rather than recycling it. Conversely, cities with larger populations and higher GDP, such as GZ and SZ, tend to generate more CDW, possess stronger waste recycling capacity, and have more robust CDWM policies and institutional system. Although they currently prioritize reuse, they are increasingly emphasizing the adoption of comprehensive waste disposal strategies, which has led to gradual improvements in their CDWM performance. While HK and MA primarily prioritize waste recycling and landfill, respectively, this may be related to their significant differences in urban attributes and waste governance capacity.
3.2. Correlation Analysis of CDWM Performance Determinants
Based on the three clusters of GBA cities and their traits found in the previous section, this section seeks to further verify whether the relationships between city attributes, CDW governance capacity, and CDWM performance align with the research hypotheses, thereby identifying which factors play a key role in the CDWM performance.
Figure 4a illustrates the correlations between different city attributes and CDW governance capacity. Consistent with the findings of [43], there is a strong correlation between population, GDP, and CDW generation, as cities with higher population densities typically experience greater economic and construction activities, leading to higher waste generation. Furthermore, the three aforementioned city attributes—population, GDP, and CDW generation—show significant positive correlations with CDW recycling capacity (correlation coefficients: 0.88, 0.75, and 0.64, respectively), but negative correlations with landfill capacity. This suggests that city size, economic scale, and waste generation intensity partially influence the capacity of waste recycling and landfill. In other words, this shows a potential trend where more developed cities tend to have sufficient funding to enhance CDW recycling technologies and thereby strengthen their recycling capacity. However, their limited and highly valuable land resources are typically not allocated for landfill development, as exemplified by cities, like Shenzhen and Hong Kong, which are compelled to transport large volumes of waste to other cities for disposal [11,26]. The strong correlation (0.76) between institutional score and GDP indicates that more economically developed cities tend to prioritize improving CDWM institutional system. These findings provide strong support for Hypothesis 1, confirming that CDW governance capacity is positively correlated with both urban population and economic development. Additionally, the high correlations among recycling capacity, institutional score, and the number of policies highlight the pivotal role of CDWM policies and institutional system in strengthening recycling capacity. This aligns with previous studies [13,35], which similarly conclude that effective CDWM policies and institutional system are likely key drivers behind enhanced recycling performance.
Figure 4b demonstrates the correlation between CDW governance capacity and performance. It is evident that a city’s CDW recycling capacity strongly correlates with its recycling rate (0.77), and landfill capacity shows a similar strong correlation with its landfill rate (0.87). This indicates that cities with larger recycling capacity tend to achieve higher recycling rates, while those with more landfill capacity often rely more on landfilling strategies. Notably, the reuse rate is negatively correlated with both landfill rate (−0.67) and recycling rate (−0.63), suggesting that some cities may make trade-offs between reuse, landfill, and recycling strategies. More specifically, most cities in GBA are more likely to reuse waste for backfilling rather than recycling or landfilling, particularly in areas undergoing extensive underground construction activities, which create a higher demand for backfill material, such as in Shenzhen and Guangzhou [44]. Additionally, the positive correlation between the recycling rate and the number of policies (0.57), as well as the institutional score (0.37), further reinforces the importance of CDWM policies and institutional systems in improving CDW recycling performance [35]. These results provide strong empirical support for Hypothesis 2, confirming that CDW recycling performance is positively associated with governance capacity, both in terms of waste disposal capacity and institutional development.
Compared to the correlation strength between CDW governance capacity and performance, the relationship between city attributes and CDWM performance appears to be much weaker (Figure 4c). This reflects that a city’s attributes, including population, GDP and CDW generation, do not have a direct influence on its CDWM performance in terms of recycling, reuse and landfill rates. Instead, as illustrated in Figure 4d, these city attributes indirectly affect waste management performance through their impact on CDW governance capacity, highlighting the mediating role of governance capacity in this relationship. In other words, there is no direct, one-size-fits-all relationship between a city’s attributes (such as population, GDP, and CDW generation) and its CDWM performance. Rather, CDWM performance is more influenced by the city’s CDW governance capacity, including its CDW disposal capacity (e.g., recycling and landfill capacity) and its CDWM policies and institutional system. This result suggests that Hypothesis 3—which posits a positive correlation between CDWM performance and urban population, economic development, and CDW generation volume—is not supported. Instead, the analysis highlights the mediating role of governance capacity in shaping CDWM performance.
It is found that city attribute—represented by characteristic parameters, such as population, GDP, and CDW generation—has a clearly significant positive impact on CDW governance capacity, which is shaped by both the “hard” infrastructure’s disposal capacity and the “soft” governance capabilities of CDWM policies and institutional system [26]. Meanwhile, CDW governance capacity, especially recycling capacity and CDWM policies and institutional system, positively influences waste recycling performance. Furthermore, the relationship between city attributes, CDW governance capacity, and CDWM performance is progressive, with CDW governance capacity acting as a mediator to transmit the impact from city attribute to CDWM performance. Therefore, to achieve excellent CDWM performance, particularly outstanding recycling outcomes, improving a city’s capacity for CDW recycling and its CDWM system is pivotal. In other words, a city’s recycling capacity and its CDWM policies and institutional system are significant factors for enhancing its CDW recycling performance.
3.3. Insights on CDWM from GBA Cities
This section further reveals the reasons and factors that contribute to the difference in CDWM performance across the GBA cities, building on the previous analysis. It delves into the issue from a more granular level, examining the specific characteristics of the cities themselves to understand why they have achieved such waste management outcomes and what measures they have implemented to attain these results.
3.3.1. CDWM Insights from Hong Kong
Hong Kong, as a Special Administrative Region of China with a high degree of autonomy and a rapidly developing international metropolis, has encountered CDWM challenges earlier than other GBA cities. As a result, it has been proactive in addressing these issues and implementing corresponding solutions, placing it ahead of other cities in the region. Consequently, Hong Kong has developed more mature and effective waste management practices. In addition to this unique background, which has enabled Hong Kong to achieve the highest CDW recycling rate in the GBA, several other factors have contributed to its success.
Firstly, a set of closed-loop CDWM policy systems has had a positive impact. Since the enactment of the Waste Disposal Ordinance in 1980, Hong Kong has embarked on its journey toward standardized CDWM. Over the years, the city has gradually refined its life-cycle CDWM system, covering all stages from generation to disposal and recycling, by establishing robust legal frameworks. Key elements of this system include the following: (a) long-term waste reduction planning (e.g., the 10-Year Plan launched in 1989 and the Waste Reduction Framework Plan in 1998); (b) the establishment of disposal charging schemes (e.g., the Landfill Charging Scheme in 1999 and the Construction Waste Disposal Charging Scheme in 2005); (c) the supervision of waste transportation (e.g., the Trip Ticket System in 2004); and (d) incentives for waste recycling (e.g., the practice note on “Use of Recycled Aggregates in Concrete” in 2003) [45].
Secondly, rational planning of CDW disposal facilities and a supportive waste disposal system have led to positive progress. Hong Kong currently has two off-site sorting facilities, seven refuse transfer stations, four public fill reception facilities, and three landfills [46]. Public fill reception facilities are designated for construction waste that is entirely inert (e.g., concrete, rubble, sand, and earth), whereas landfills accept a broader range of waste and typically contain no more than 50% by weight of inert construction waste. This distinction reflects the different roles that these facilities play in the city’s CDWM strategy. Notably, the two off-site sorting facilities are strategically located next to landfills, facilitating the direct transportation of non-inert construction waste to landfills after sorting, thereby reducing transportation costs and minimizing environmental pollution. Additionally, with the support of the Construction Waste Disposal Charging Scheme and the off-site CWS program, these off-site sorting facilities classified a total of 5.11 million tons of CDW between 2006 and 2012, maximizing waste reduction and recycling efforts [47].
Thirdly, attractive waste reduction incentives and high disposal charges have had a positive impact. To effectively promote a source reduction in waste, Joint Practice Note No. 1 (JPN1) and No. 2 (JPN2) were introduced in 2001 and 2002, respectively. These notes provided incentives for builders by offering exemptions on a certain percentage of site coverage and/or gross floor area (GFA) calculations, encouraging the use of prefabricated external walls to reduce the generation of waste, such as discarded concrete, bricks, and other materials. Furthermore, in the 2011 update of JPNs 1 and 2, the exemption cap for GFA was raised to 10% [45]. On the other hand, under the recent Construction Waste Disposal Charging Scheme, the charge for waste disposed of at landfills and transfer stations is both HKD 200 per ton, which is HKD 129 and HKD 25 higher than the fees for waste sent to public fill reception facilities and off-site sorting facilities, respectively [46]. This creates a financial incentive for waste producers to prioritize on-site recycling or transportation to sorting facilities, while landfilling becomes a less attractive option due to the higher cost [48,49].
3.3.2. CDWM Insights from Shenzhen and Guangzhou
Shenzhen and Guangzhou, as two of the most economically dynamic cities in mainland China, have undertaken numerous underground construction projects and urban village renovation projects in recent years. These projects have increased the burden of CDW disposal in these cities, but they have also accelerated their efforts to implement specialized CDWM, leading to gradual improvements in waste recycling performance. These achievements can be attributed to several factors.
Firstly, the gradual improvement of CDWM standards and codes has had a positive impact. Similar to Hong Kong, Shenzhen is also in the process of developing a set of standards and regulations tailored to the CDW sector. For example, two standards were issued in 2019, namely the Standard for CDW Discharge Quota of Construction Engineering [50] and the Technical Standard for CDW Reduction and Comprehensive Utilization of Construction Engineering [51]. In addition, a revised version of the Technical Standard for CDW Reduction [52] is soon to be released. These three standards form an interlocking system that addresses the entire process of “CDW generation—discharge—comprehensive utilization,” providing effective guidance for the standardized management of in the CDW industry.
Secondly, the booming development of high-profit-driven CDW resource utilization enterprises has had a positive impact. With the widespread underground engineering projects in these two cities, a large amount of excavated soil and rock has been generated, accounting for 90% of all CDW [44]. After being processed for recycling, these excavated soil and rock can serve as raw materials for the production of recycled aggregates. For market-driven resource utilization enterprises, the cost of obtaining these raw materials is very low, and in some cases, even zero-cost (depending on the composition and sand content of the excavated waste), making it a highly profitable business. As a result, a large number of enterprises focusing mainly on the disposal of excavated soil and rock have emerged, with more than 50 such enterprises now operating in both cities. This not only provides substantial waste disposal capacity but also greatly promotes CDW recycling.
Thirdly, the connecting role of third-party associations has had a positive impact. Guangzhou and Shenzhen established separate associations for CDWM, namely the Guangzhou Construction Waste Disposal Association in 2016 and the Shenzhen Construction Waste Recycling Association in 2018 [52,53]. By regularly organizing seminars and training sessions on CDW treatment technologies and educating the public on CDW-related knowledge, these platforms have not only promoted communication and collaboration among government departments, industry players, and academic circles, but also increased public awareness of the environmental benefits of waste recycling. The presence of these associations has accelerated the development of the industry, making them an important factor in the improvement of CDWM performance in both cities in recent years.
3.3.3. CDWM Insights from Other GBA Cities
Other GBA cities (excluding Zhaoqing and Macao) mainly focus on the reuse of CDW, especially the reuse of excavated soil and rock, as these materials constitute the primary CDW in these cities. Unlike Shenzhen and Guangzhou, which recycle a large portion of excavated soil and rock, other cities in the Pearl River Delta primarily use this waste for engineering backfilling, resulting in a high reuse rate. From the strong correlation between city attributes and CDW governance capacity, these cities tend to have medium- to small-scale populations, GDP, and CDW generation, along with weaker recycling and landfill capacities and less comprehensive CDWM systems compared to Hong Kong, Shenzhen, and Guangzhou. However, this has not affected their internal balance of CDW disposal, indicating that their current waste disposal model, which prioritizes reuse, is appropriate. In other words, for these cities, the urgency of waste disposal issues is not as high as in Hong Kong, Shenzhen, and Guangzhou, and their existing infrastructure is sufficient to handle current waste disposal needs. Therefore, fully leveraging their existing advantages may be a reasonable choice for these cities.
Macao, as another Special Administrative Region of China, currently relies mainly on engineering backfilling, land reclamation, landfilling, and cross-regional balanced disposal to manage its CDW. This article does not disclose the specific volume of waste disposed of through cross-regional disposal due to the lack of accessible data. However, according to previous official reports, Macao has long relied on transporting waste to nearby cities for disposal, thereby handling the majority of its waste [11,26]. Therefore, for cities with similar conditions to Macao, it may be beneficial to consider regional collaboration. Under the protection of an ecological compensation mechanism, excess waste can be transported to other cities with surplus disposal capacity for treatment [54]. This approach would promote a win–win situation and contribute to the sustainable development of waste management at the regional level [55].
3.4. Research Contributions and Limitations
This study offers a novel, data-informed comparative analysis of CDWM performance across all 11 cities in the GBA, shedding light on regional disparities and the underlying governance and urban attributes that drive them. It contributes to the underexplored field of city-level CDWM research in densely urbanized regions and provides actionable insights for policymakers seeking to strengthen institutional systems and infrastructure in the context of rapid urbanization. However, the analysis is constrained by the cross-sectional nature of the data and the absence of primary or longitudinal data, which limits our ability to capture dynamic policy effects and informal governance mechanisms. Future research could address these limitations by incorporating temporal datasets, conducting in-depth case studies, and expanding the research scope to other metropolitan clusters, such as the Yangtze River Delta, Tokyo Bay, and New York metropolitan area. Additionally, exploring the role of emerging technologies and sustainable building practices may offer a more comprehensive understanding of the key drivers behind improved CDWM outcomes [56,57].
4. Conclusions
To explore the determinants influencing CDWM performance, this study examined the hypothesized relationships among CDWM performance, city attributes, and governance capacity. Using 11 GBA cities as the analytical sample, clustering and correlation analyses were employed to test these hypotheses. The findings support Hypotheses 1 and 2 but not Hypothesis 3. Specifically, CDW governance capacity is positively correlated with urban population and economic level (Hypothesis 1), and CDW recycling performance is positively correlated with governance capacity (Hypothesis 2). However, no direct relationship was found between CDWM performance and city attributes, such as population, GDP, or CDW generation volume (Hypothesis 3). These results underscore that a city’s recycling capacity, along with its CDWM policies and institutional framework, are critical drivers of improved waste management outcomes—particularly in terms of recycling. Governance capacity serves as a mediating factor that channels the effects of city attributes into actual performance outcomes.
Based on this understanding, the study further identified effective strategies and institutional measures adopted by successful GBA cities. For instance, Hong Kong’s exceptional recycling outcomes are largely due to its closed-loop policy framework and strong institutional mechanisms, including the Construction Waste Disposal Charging Scheme. Shenzhen and Guangzhou have demonstrated steady progress through refined CDWM standards, active development of resource recovery industries, and support from third-party associations. These examples highlight the importance of context-based policy design and institutional innovation.
However, given the cross-sectional nature of the data and the relatively small sample size, the generalizability of the findings is inherently constrained. Future research incorporating longitudinal data and broader comparative samples is necessary to deepen understanding and validate these insights in other urban contexts.
R.C.: Investigation, Methodology, Formal Analysis, Visualization, Writing—Original Draft. H.W.: Conceptualization, Methodology, Supervision, Writing—Review and Editing. H.Y.: Supervision, Writing—Review and Editing. Q.Y.: Investigation. D.O.: Writing—Review and Editing. All authors have read and agreed to the published version of the manuscript.
The original contributions presented in the study are included in the article and
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Footnotes
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Figure 1 Research framework.
Figure 2 Characteristic data on city attributes, construction and demolition waste management (CDWM) capacity, and CDWM performance in Guangdong–Hong Kong–Macao Greater Bay Area (GBA) cities. Notes: (a) ➀~➄, ➇~➉: Hong Kong and Foshan for 2022; other cities for 2023. ➅: issued up to July 2022. ➆: from [
Figure 3 Distribution of GBA cities in clusters 1, 2, and 3 based on CDWM performance. Notes: ➀ CDWM = construction and demolition waste management; ➁ GBA = Guangdong–Hong Kong–Macao Greater Bay Area.
Figure 4 Correlation between city attributes, CDW governance capacity, and CDWM performance of GBA cities. Notes: ➀ CDW = construction and demolition waste; ➁ CDWM = construction and demolition waste management.
Descriptions and sources of the characteristic parameters.
| Dimensions | Characteristic Parameters | Descriptions | Reference |
|---|---|---|---|
| City attributes | Population | The total resident population of a city at the end of the year. | [ |
| GDP | Gross domestic product, the total market value of all final goods and services produced within a city during a year. | ||
| CDW generation | The total amount of CDW generated within a city in a year, including waste produced from new construction, renovation, and expansion activities. | ||
| CDW governance capacity | Recycling capacity | The amount of CDW that a city’s operational recycling facilities are capable of processing annually. | [ |
| Landfill capacity | The amount of CDW that landfills within a city’s jurisdiction can accommodate annually. | ||
| Number of policies | The number of CDWM policies and regulations issued by cities over a certain period. | ||
| Institutional development level | The level of a city’s CDWM institutional development across four dimensions, namely institutional framework, market mechanisms, technological capacity, and regulatory oversight. | ||
| CDWM performance | Recycling rate | The proportion of CDW used to produce recycled building materials. | [ |
| Reuse rate | The proportion of CDW used for backfilling, land reclamation, and sea reclamation. | ||
| Landfill rate | The proportion of CDW disposed of in landfills. |
Notes: ➀ CDW = construction and demolition waste; ➁ CDWM = construction and demolition waste management.
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; Yuan Hongping 2
; Qiaoqiao, Yong 1 ; Oteng, Daniel 3 1 College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China; [email protected] (R.C.); [email protected] (Q.Y.)
2 School of Management, Guangzhou University, Guangzhou 510006, China; [email protected]
3 School of Architecture and Civil Engineering, The University of Adelaide, Adelaide, SA 5005, Australia; [email protected]